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Speaker 1: This is Masters in Business with Barry Ridholds on Bloomberg Radio.

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Speaker 1: This week, we have a very special guest. And I

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Speaker 1: know I say that every week, but really, this week

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Speaker 1: we have a very special guest. You know him from

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Speaker 1: the five thirty eight blog, formerly of The New York

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Speaker 1: Times and now with ESPN, Nate Silver. We had a

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Speaker 1: wide ranging and lengthy conversation about everything from sports to

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Speaker 1: statistical analysis, to politics and campaigns, and given the current

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Speaker 1: madness in the um campaign season, we spent a lot

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Speaker 1: of time talking about what's going on, much of which

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Speaker 1: is really really fascinating. We we went a little long,

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Speaker 1: and normally I like to give you a little more

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Speaker 1: details in these intros, but I think this, uh, this

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Speaker 1: podcast stands on its own. We covered everything, so rather

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Speaker 1: than me continually babbling about how fast and in this is,

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Speaker 1: let's just jump right to it. Here's my conversation with

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Speaker 1: five thirty eight Nate Silver. This is Masters in Business

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Speaker 1: with Barry Ridholts on Bloomberg Radio. This week on Masters

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Speaker 1: in Business, my special guest is one Nate Silver. You

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Speaker 1: probably know him from five thirty eight. A quick bit

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Speaker 1: of background about Mr Silver graduated in two thousand from

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Speaker 1: the University of Chicago bachelor's degree in economics, became frustrated

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Speaker 1: by what he was seeing both in the world of

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Speaker 1: sports statistics and politics. Created we'll talk a little bit

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Speaker 1: about Pacoda, which was the baseball statistical system you had

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Speaker 1: set up, and basically said, while stranded in an airport

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Speaker 1: in New Orleans, the idea for five thirty eight popped

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Speaker 1: fully formed into your head. Is that true? I'm not

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Speaker 1: sure fully formed right. There was a certain amount of

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Speaker 1: you know, gumbo and stuff have been consumed, and yeah,

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Speaker 1: and on a slash slash thought lots of other things

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Speaker 1: in New Orleans to but the analysis about about elections

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Speaker 1: was frustrating you. And and obviously five thirty eight is

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Speaker 1: a number of total votes in the US Electoral College,

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Speaker 1: of which you need to sixty nine or two and

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Speaker 1: um and created five thirty eight. Just a quick background

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Speaker 1: about five thirty eight. In two thousand and eight, it

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Speaker 1: correctly predicted the winner in forty nine of fifty states

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Speaker 1: for the presidential election. Uh. It gets licensed by the

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Speaker 1: New York Times in two thousand ten in anticipation of

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Speaker 1: the two thousand twelve election, and he basically Nate runs

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Speaker 1: the table in two thousand twelve predicts every state and

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Speaker 1: the district of Columbia correctly as to the winner of

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Speaker 1: Barack Obama versus Mitt Romney, as well as all thirty

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Speaker 1: five UM senatorial race. I should be correct. The Senate

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Speaker 1: was in two thousand and eight, we had thirty thirty five.

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Speaker 1: We missed think two in how dare you sir? Well

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Speaker 1: the irony and we'll talk about this morning ironies and

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Speaker 1: like the fact that you know, uh, because we got lucky. Basically,

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Speaker 1: you know, there's some skill, but there's a lot of lucky,

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Speaker 1: and so let's jump right into that. So normally at

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Speaker 1: this point I say, welcome to Boolberg, but I know

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Speaker 1: you've been here before, and I'm I'm really thank you

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Speaker 1: so much for doing this. I'm really excited. We'll talk

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Speaker 1: a little bit about your relationship UM with various uh

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Speaker 1: statistical approaches, how five thirty eight just blew up at

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Speaker 1: the New York Times, and how life is currently at ESPN,

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Speaker 1: and then we'll take a really close look at the elections.

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Speaker 1: So I'm sure there's a ton of stuff. I would

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Speaker 1: be remiss if I failed to mention that you were

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Speaker 1: named by Time magazine is one of the hundred most

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Speaker 1: influential people in the world, and that your book, The

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Speaker 1: Signal in the Noise was a New York Times best

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Speaker 1: seller and an Amazon number one pick for Best our

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Speaker 1: Nonfiction in two thousand twelve. Well, thank you. So so

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Speaker 1: that's really um, we're out of time now, but let's

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Speaker 1: let's let's start with a little bit of of baseball.

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Speaker 1: So you develop something called Pakoda. Tell the listen one,

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Speaker 1: it's exactly what that. So Pakoda is a terrible acronymic. Yeah,

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Speaker 1: it's like picture empirical comparison and optimization tests algorithm. But

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Speaker 1: the idea is that couldn't you've come up with something

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Speaker 1: wonkier than that? Yeah, but Pakoda built Pacoda was a

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Speaker 1: baseball player. I played for the Royals, like in the eighties,

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Speaker 1: the Tigers fan and so it was a thorn in

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Speaker 1: the side of the Royals, right, So it was meant

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Speaker 1: to be kind of intentionally self parrying and giffy a

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Speaker 1: little a little um part of the punt inside baseball,

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Speaker 1: a little inside baseball. Yeah, And that's the thing I

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Speaker 1: think people don't realize about, you know, kind of what

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Speaker 1: I do. It's always a little bit very serious work.

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Speaker 1: But we're not taking ourselves too seriously. But anyway, the

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Speaker 1: idea of Pakoda was to use baseball is very rich

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Speaker 1: history to project the future, which is really all you

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Speaker 1: know of Cisco models are really using this history protict

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Speaker 1: the future, right, an extrapolation based on what's the highest

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Speaker 1: probability relative to what's happened in the past. But in

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Speaker 1: baseball you have so many good years of data and

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Speaker 1: so many players every season where you can say, you know, uh, taked,

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Speaker 1: you know current player, right, Curtis Grander cent or whatnot,

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Speaker 1: age thirty three whatever is for the Mets, and go

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Speaker 1: back in history and see who were the guys who

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Speaker 1: were like Curtis Granderson at age thirty three and similar attributes,

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Speaker 1: similar history, similar attributes, right, and you can say, okay,

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Speaker 1: so now we can say kind of, here are a

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Speaker 1: hundred different career paths for um, what's the risk of

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Speaker 1: a you very easily could have ended up in a

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Speaker 1: career path where you would have either been in Wall

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Speaker 1: Street analyst or economists because essentially the better ones. That's

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Speaker 1: what they do recognizing the limitations of pretty similar right.

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Speaker 1: And we're spoiled in sports in the sense that, um,

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Speaker 1: so much data is so much data. The data is

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Speaker 1: so reliable, you know, there's not really a lot of

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Speaker 1: uh uncertainty there in terms of measurement error. Right. Um,

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Speaker 1: so you get really spoiled in sport and you spend

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Speaker 1: a lot of your time actually testing hypotheses, whereas in

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Speaker 1: most fields, you know, to some extent, including politics, you're

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Speaker 1: spending nine saying your time cleaning up the data and

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Speaker 1: scratching your head and saying, you know, does this tell

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Speaker 1: us anything at all? We we look at non farm payrolls,

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Speaker 1: the way it's measured each month has changed and evolved

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Speaker 1: over time. Everybody forgets, you know, this data doesn't go

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Speaker 1: back forever. Was only after the Great Depression, and FDR

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Speaker 1: essentially created the Commerce and Bureau of Labor Statistics Department

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Speaker 1: in order to assemble this data so we can actually

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Speaker 1: so so let's get into a little more details and

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Speaker 1: and we'll we'll come back to Pacoda because it's it's fascinating.

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Speaker 1: In two thousand and seven, you're still working for you

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Speaker 1: sell Pacoda to Baseball Perspectives, you're writing for them, and

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Speaker 1: you start posting at the Daily Coast under the name

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Speaker 1: Poblano looking at various um polling data in a way

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Speaker 1: to to, well, why don't I let you describe, yeah,

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Speaker 1: what you're doing? An oh seven? So uh, So, I

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Speaker 1: had worked for Baseball Perspectives for for several years that point,

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Speaker 1: and this was kind of in an era when Moneyball

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Speaker 1: came out in two thousand three, right, fantastic book, the

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Speaker 1: movie was great, great book, and you kind of that

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Speaker 1: triggered a lot of interests and things that Bill James

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Speaker 1: have running about for decades. Actually, metrics has been around

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Speaker 1: for yeah, it seems like forever. But you saw how

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Speaker 1: much of an impact that had both on the game itself,

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Speaker 1: the way the game was played, and the way the

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Speaker 1: game was covered by the media, and it seemed like

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Speaker 1: there was very little of that in campaign coverage at all. Um.

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Speaker 1: So I kind of started anonymously a daily coast, uh,

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Speaker 1: kind of writing these little things about about the two

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Speaker 1: thousand and seven primary between Clinton and Obama. Um and

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Speaker 1: eventually said, you know, I kind of want to be

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Speaker 1: on my own here, and so I launched five dot

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Speaker 1: com in March two eight. You're listening to Masters in

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Speaker 1: Business on Bloomberg Radio. My special guest today Nate Silver

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Speaker 1: of five thirty eight, the man who correctly predicted just

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Speaker 1: about every race that mattered in two thousand and twelve

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Speaker 1: and came pretty darn close, and in two thousand and

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Speaker 1: eight got just about everything right then. So you launched

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Speaker 1: the side in o A. You do a great job

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Speaker 1: with the election in oh eight, uh, not only determining

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Speaker 1: who's going to win the Democratic primary. So a lot

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Speaker 1: of your insights turned out to be very prescient on that.

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Speaker 1: But then you do the head to head between Obama

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Speaker 1: and McCain and Hillary McCain and tell us what you found. Well,

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Speaker 1: I mean, two thousand and eight was not really among

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Speaker 1: them more suspenseful general elections. Um, I guess early on

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Speaker 1: in March when I launched, uh, it was a little

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Speaker 1: bit closer. But historically polls in March don't tell you

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Speaker 1: very much, right, And that's one of the things you

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Speaker 1: talk about very often, which I think a lot of

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Speaker 1: people don't really pay attention to us, which is, hey,

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Speaker 1: here's the polls, but at this stage don't give us

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Speaker 1: at election cycles are really really long thing, right, And

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Speaker 1: we spend this country, in this country, the UK, they

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Speaker 1: don't understand it's five and a half weeks and they

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Speaker 1: spend nine million dollars and They look at us like

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Speaker 1: what crazy? But here we have it's a two year process, right, um,

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Speaker 1: and we probably spend eighty percent of that time at

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Speaker 1: eight saying you know what, these polls are not very meaningful.

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Speaker 1: You should not take them to literally interpret them with

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Speaker 1: a lot of caution. Right. Then at the end we say, actually,

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Speaker 1: when you get after Labor Day, polls do a pretty

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Speaker 1: good track record, right, and pay more attention to them.

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Speaker 1: And so but people people don't do that, right, the

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Speaker 1: kind of are too distrustful of the polls in November

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Speaker 1: and much too serious about the polls like now in

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Speaker 1: August of all things, a year before the election. Um,

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Speaker 1: you know, you could go back and look at who

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Speaker 1: was ahead in August of the past nomination campaigns. Well,

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Speaker 1: in two thousand twelve, Rick Perry at this point in

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Speaker 1: time was surging past Mitt Romney. In two thousand and eight,

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Speaker 1: it was Rudy Giuliani and Hillary Clinton. Two thousand and

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Speaker 1: four it was Joe Lieberman and Howard Dean on the

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Speaker 1: Democratic side. Right, Um, so kind of four in a row.

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Speaker 1: I'm before that two thousand was a more predictable year,

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Speaker 1: right u. Um, but you know ninety two, Bill Clinton

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Speaker 1: hadn't even entered the race yet. Um, So you know,

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Speaker 1: people kind of ignore history at their at their peril.

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Speaker 1: That's fascinating. We'll get to that more in in a

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Speaker 1: later segment when we talk about this carent election. So, so,

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Speaker 1: oh eight comes and goes and you do a you know,

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Speaker 1: killer job. Who else besides The Times approached you? How

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Speaker 1: did that come about? We had four or five or

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Speaker 1: six conversations with different people, and you know, I try

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Speaker 1: and be respectful of those conversations, but there was there

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Speaker 1: was a lot of interest. I mean, I think people

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Speaker 1: knew how big a story two thousand and eight had been.

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Speaker 1: You know, for me, it was kind of like it

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Speaker 1: would be useful to have. I mean, I was basically

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Speaker 1: running eight on my own. We had some other part

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Speaker 1: time contributors. I was doing everything from doing the graphics

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Speaker 1: to writing the stories, trying to promote the blog, and

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Speaker 1: it was just a lot of work. And I think, um,

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Speaker 1: that's a nuver used word, but there has to be

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Speaker 1: some synergy between what a contributor might have to say

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Speaker 1: and a big media company like The Times. For example, Um,

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Speaker 1: was that the M I. T. Sloan Business Conference, which

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Speaker 1: is a sports business conference every year and ran into

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Speaker 1: an editor from The Times on a train platform and

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Speaker 1: we really and we talked, and so it's kind of spontaneous, right,

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Speaker 1: a little serendipity and uh yeah, a little serendipity, and

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Speaker 1: it wound up being a really good fit for a

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Speaker 1: couple of years. It was a tremendous fit. And if

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Speaker 1: memory serves something like in the height of the election

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Speaker 1: the I'm sure I'm destroying the statistic, but you were

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Speaker 1: twenty or of the total New York Times web traffic.

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Speaker 1: I think, you know a second, it's a little at

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Speaker 1: a peak. At a peak, yeah, um, you know, I

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Speaker 1: think on election week something like twenty percent of unique

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Speaker 1: and users at the Times went by and viewed eight Um.

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Speaker 1: It was really nice, and it definitely got really crazy.

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Speaker 1: I can imagine towards the end of I mean, it's

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Speaker 1: a little bit like this is really self agruandizing, right,

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Speaker 1: but it's a little bit like, um, I feel some

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Speaker 1: sympathy for like Olympic athletes, where you know that every

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Speaker 1: four years their life becomes like a total crazed swamp

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Speaker 1: of although the difference between you and an Olympic athlete,

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Speaker 1: um is that they're training the entire for you. They

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Speaker 1: train the entire for you. You have I need a

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Speaker 1: lot better than I do. But uh so let me

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Speaker 1: ask you about training. Food doesn't matter as much as so.

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Speaker 1: You have an undergraduate degree from University of Chicago, great

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Speaker 1: school in economics. I would have assumed it was applied mathematics, statistics,

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Speaker 1: and probability. I know those can be concentrations within economics.

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Speaker 1: How did you find your way into this form of

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Speaker 1: statistical analysis? I mean, you have c is a pretty

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Speaker 1: quantitative school in general, but it was really kind of

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Speaker 1: stuff I did outside of of school and outside of work.

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Speaker 1: So sports in particular was kind of like a lot

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Speaker 1: of applied statistics. You want to win your fantasy baseball league,

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Speaker 1: you want to win your instate tournament, Brackett. And you know,

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Speaker 1: like I said earlier, the data and sports is so

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Speaker 1: good that it's a good way to train your hypothesis

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Speaker 1: testing skills and logical inference skills and stuff like that.

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Speaker 1: But it's all, you know, it's all kind of a

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Speaker 1: passion jacked and also you know, I'm motivated by um

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Speaker 1: there's that cartoon about oh someone's wrong on the internet.

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Speaker 1: You can't get to sleep right like I'm motivated by that. Yeah,

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Speaker 1: x K c D one of my favorite come to

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Speaker 1: bed somebody on the internet. But I'm a little bit

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Speaker 1: like the person in that cartoon, right, he's not going

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Speaker 1: to bed because I'm like, boy, you know, you go

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Speaker 1: and read the campaign coverage and in the mainstream press,

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Speaker 1: and it's blatantly wrong think that because somebody saw some

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Speaker 1: one signs and palm beach, that didn't determine who was No.

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Speaker 1: I mean they rely on an anecdotal evidence or or

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Speaker 1: the you know what's funny, which to an article about

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Speaker 1: how how Hally Clinton's problems are at least as far

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Speaker 1: as the Democratic primary concerned, a little bit overrated. But

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Speaker 1: I went back and looked at the kind of coverage

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Speaker 1: of past campaigns like two thousand, Al Gore versus Bill

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Speaker 1: Bradley and theme for themes, sometimes almost word for a word.

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Speaker 1: You can see the same stories written, and the fact

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Speaker 1: that people kind of forget that history, right. It's not

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Speaker 1: it's not forgetting right. I mean, they're really smart people

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Speaker 1: working on on campaign coverage, but there's an insane have

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Speaker 1: to to tell the story and to sell the story

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Speaker 1: in a narrative format, and a narrative format, and that's

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Speaker 1: kind of for me. Campaign journalism's original sin, right, UM

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Speaker 1: is it's like a baseball season where things play out

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Speaker 1: really slowly. It's a really long season. Not that much

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Speaker 1: happens from day to day. Maybe you'll have a few

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Speaker 1: genuine events that are um, upset the apple cart, but

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Speaker 1: not many. But it's hard to write every day of

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Speaker 1: the story. Like you know, today people campaign and nothing

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Speaker 1: of importance happened. Right. So, in the last minute we

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Speaker 1: have in this segment, how did you end up finding ESPN?

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Speaker 1: I'm going to assume your love of sports has made

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Speaker 1: that a really strong fit. Yeah. Well, there were a

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Speaker 1: couple of reasons. I mean, afterlve the contract with the

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Speaker 1: New York Times, UM expired and we again UM we

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Speaker 1: the Royal We meaning me and my lawyer and whatnot,

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Speaker 1: talked to six or seven different companies, um, you know,

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Speaker 1: but ESPN offered a couple of things that were unique.

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Speaker 1: One is that they realize that I'm not just about

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Speaker 1: politics that to some extent, I want to hedge and

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Speaker 1: diversify what I'm doing because we did get lucky in um.

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Speaker 1: Another thing is that you know, they have the resources

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Speaker 1: to invest in a company that can grow at a

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Speaker 1: sustainable rate and UM and evolve a little bit. Right.

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Speaker 1: We have twenty five or thirty people now working for

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Speaker 1: us at at UM. You know it's sustainable. We we think,

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Speaker 1: we hope, I mean, you know, we say, hey, look,

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Speaker 1: our traffic is growing. Therefore can we hire this person

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Speaker 1: who will help us to keep that growing? Um. But

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Speaker 1: you know, that kind of more entrepreneurial attitude, I think

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Speaker 1: is is a good fit at ESPN. You're listening to

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Speaker 1: Masters in Business on Bloomberg Radio. My special guest this

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Speaker 1: week is statistical wizard Nate Silver On, the man who

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Speaker 1: correctly forecast the O eight and twelve elections. Let's talk

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Speaker 1: a little bit about baseball. So here we are. This

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Speaker 1: is being recorded in middle August. What stands out this

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Speaker 1: year is statistically aberrant or interesting or unusual. I think

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Speaker 1: you actually look at the preseason predictions and thank I

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Speaker 1: don't have to make these anymore. They've had one of

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Speaker 1: the more inaccurate years in a long time. A lot

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Speaker 1: of the set systems said the Royals um were likely

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Speaker 1: to regress heavily to the mean not so much, and

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Speaker 1: they haven't. Right. Um. You know, the Nationals, I think

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Speaker 1: we're supposed to win nine games and they're struggling, uh

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Speaker 1: to break five d Right now, you've seen the Mets

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Speaker 1: to pretty well, which is um, maybe not that's shocking,

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Speaker 1: given how it is New York. You're waiting for the

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Speaker 1: late season collapse. But given how much talent they had

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Speaker 1: in their farm system, maybe not that shocking. But it's

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Speaker 1: come maybe maybe the first time that's look at all

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Speaker 1: this talent. Well, look, we'll see how it goes. And

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Speaker 1: you know, finally teams have learned how to manage their

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Speaker 1: young pictures a little bit better and a little bit

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Speaker 1: more cautiously. Maybe, and we'll see if this is sustainable

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Speaker 1: for the Mets or not. But that's a little bit different.

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Speaker 1: You mean, not ruining them early on. It's not ruining uh,

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Speaker 1: not ruining them early on like the famous like Triple,

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Speaker 1: the van Popol years or the Jason if Ringhausen crop

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Speaker 1: and you know, um, so so far it's gone well

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Speaker 1: for the Mets with their with their young pictures famous

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Speaker 1: last words. I guess we'll we'll see how long that

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Speaker 1: that stands. Earlier, we were talking about Pakoda. Um. I

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Speaker 1: didn't realize that was just pictures. I thought that was players.

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Speaker 1: Is so start out with pictures, the idea being that UM,

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Speaker 1: pictures are much harder to predict than hitters. UM. And

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Speaker 1: so I thought this is later more. Yeah, what why

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Speaker 1: is that? Uh? For a lot of reasons. One is

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Speaker 1: that kind of measures of pitching are more indirect, Right,

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Speaker 1: like if you look at wins and losses, well, it's

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Speaker 1: conditional on how many runs you give up and how

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Speaker 1: many runs give up? Is it conditional on how many

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Speaker 1: hits you give up and how many walks you give up?

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Speaker 1: So now we know a lot. But you would think

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Speaker 1: you would think pitch is thrown and strikes versus balls.

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Speaker 1: And but ten years, ten years ago, this is not

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Speaker 1: where the state of the art of thinking was. And

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Speaker 1: also we didn't actually have data like now we can say, oh,

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Speaker 1: you know what, um, Jason Berlander, he's not the picture

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Speaker 1: he winch was. We can actually say, well, now he

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Speaker 1: doesn't throw as hard as he wins did write you

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Speaker 1: have data on every pitch and what the velocity is,

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Speaker 1: what the speed is, what a picture is doing on

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Speaker 1: different counts. Right. UM, So now the pitching UH, forecasts

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Speaker 1: have come a long way past Pakoda. But you know

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Speaker 1: the other thing too, is that pitching is kind of

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Speaker 1: an inherently unnatural act on a on a picture's arm. Right, Um, Okay,

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Speaker 1: I I pitched in high school, so i'll uh and

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Speaker 1: you know I have the torn real rotated to prove it.

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Speaker 1: But I'm not gonna argue with you whipping a ball,

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Speaker 1: whipping your shoulder around that way for ninety or so

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Speaker 1: throws a game is is not what normal you could

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Speaker 1: You could throw a spear if you're out in the

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Speaker 1: on the savannah hunting mammoths, but you're not gonna throw

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Speaker 1: a spear nineties seven times. The pictures, Yeah, pictures. And

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Speaker 1: you know, even though now pitch counts are monitored more.

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Speaker 1: Now every pitch, every at bat is contested so much, right,

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Speaker 1: and the strikeout rates are so high. There are a

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Speaker 1: lot of pitches per plate appearance. Obviously, there's a lot

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Speaker 1: of focus on taking walks and played discipline. Um. The

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Speaker 1: thing about pitching that people realize now, maybe not ten

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Speaker 1: years ago, is that a lot of what people think

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Speaker 1: of as pitching is really positional defense. And you've seen

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Speaker 1: phases evolved. Kind of in the early moneyball days, people

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Speaker 1: were like, well, it's hard to measure defense, Therefore, let's

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Speaker 1: just kind of ignore it and put a big lot

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Speaker 1: of big, clunky, chunky out fielders out there, and they'll

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Speaker 1: hit a lot of home runs, right, And then teams realized, um,

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Speaker 1: you know, actually a lot of this is positional defense,

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Speaker 1: Like really the impact of having a good center fielder,

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Speaker 1: a good tes not necessarily the slugging sort of batter

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Speaker 1: as they used to be, but the difference between a

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Speaker 1: hundred yard blooper over the UH over the second baseman's head,

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Speaker 1: depending on where it goes, is either an out or

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Speaker 1: a double. And the funny thing sasonally the same bad

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Speaker 1: contact with the ball. I mean, if you save uh,

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Speaker 1: you know, twenty hits a year with your defense, I mean,

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Speaker 1: you know, if you've got treen or hits a year

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Speaker 1: with your batting average, and that translates to what forty

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Speaker 1: points of betting average, right, So it's really pretty significant,

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Speaker 1: and the differences can be that large. But you know,

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Speaker 1: one one thing to remember here is that there's always

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Speaker 1: the peril that people think, oh, if you can't measure

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Speaker 1: something there, for it's not Wharton. And once we got

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Speaker 1: better locational data, we can actually say, oh, now we

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Speaker 1: can actually physically measure how much range uh Lorenzo Caine

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Speaker 1: covers or whatever. Right, we're no longer guessing, and it's like, boy,

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Speaker 1: those are some pretty big differences. Actually. So recently on

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Speaker 1: there was an article about tennis gets essentially the Moneyball treatment.

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Speaker 1: What other sports will benefit from this sort of statistical

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Speaker 1: analysis that currently are are escaping its gaze? I mean

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Speaker 1: in terms of kind of what sports are on that

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Speaker 1: nice part of the learning curve. Um, the NBA is

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Speaker 1: one of them, certainly. I mean, you know, teams like

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Speaker 1: the US TO Rockets are run by Darryl Moore, isn't

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Speaker 1: m I T guy? Right? Um, you know, we're actually

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Speaker 1: introducing a kind of basketball version of pakoda um called Carmelo,

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Speaker 1: which will debut at some point the next Oh gosh,

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Speaker 1: it's a very clever acronym. It's like career arc regression

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Speaker 1: estimate something like that. But a lot of layers in

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Speaker 1: Carmelo were good with very geeky words. Um. You know,

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Speaker 1: soccer is a sport where there's been very little data collected.

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Speaker 1: But now you see teams in the Premiership have their

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Speaker 1: their stet heads and whatnot. And obviously the magnitude of

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Speaker 1: the economy of soccer is so enormous right where um

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Speaker 1: where you know that might be the next frontier. You're

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Speaker 1: listening to Masters in Business on Bloomberg Radio. My special

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Speaker 1: guest this week is statistical wizard Nate Silver. And here

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Speaker 1: we are towards the end of the summer in and

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Speaker 1: we just had the Republican first debate, and we're looking

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Speaker 1: at Iowa and New Hampshire coming up. This is quite

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Speaker 1: a fascinating electoral season, isn't it. Yeah, we've never seen

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Speaker 1: so I know, Um, I'm being a little bit anecdotal here,

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Speaker 1: but the type of um traffic that we're getting on

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Speaker 1: our politics articles in August, which is historically this August

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Speaker 1: year before, Yeah, a year before this is historically about

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Speaker 1: the slowest time of the year for politics news or

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Speaker 1: hard news in general. Right. And it's it's kind of

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Speaker 1: like it's September of the election year, right, it's not

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Speaker 1: the November peak. But like you know, um, people are

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Speaker 1: fascinated by Donald Trump, by Bernie Sanders, um in a

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Speaker 1: way that's way ahead of where things were at twelve.

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Speaker 1: So I kind of conflicted here. On the one hand,

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Speaker 1: it's probably good for business. On their hand, it's a

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Speaker 1: little maddening, right, And I think, you know, so much

427
00:22:19,359 --> 00:22:22,959
Speaker 1: of what we say at is slow down, right, this

428
00:22:23,000 --> 00:22:26,440
Speaker 1: is not gonna unfold today or next week or this month.

429
00:22:26,440 --> 00:22:29,840
Speaker 1: It's gonna take a while. Slow down, take the long view,

430
00:22:30,000 --> 00:22:33,400
Speaker 1: don't hyperventilate about this stuff. And so they're conflicting impulses here.

431
00:22:33,680 --> 00:22:36,119
Speaker 1: I say that about stock markets all the time. It

432
00:22:36,240 --> 00:22:38,679
Speaker 1: is it is people wanted, like the hairs on fly

433
00:22:38,800 --> 00:22:41,040
Speaker 1: Apple is down. Part of this that you have this

434
00:22:41,080 --> 00:22:45,400
Speaker 1: whole industry of commentators who are asked to say, weigh

435
00:22:45,440 --> 00:22:48,560
Speaker 1: in on what happened today, right, and are very close

436
00:22:48,600 --> 00:22:52,320
Speaker 1: to the subject matter. Um, and you know, so to

437
00:22:52,359 --> 00:22:54,680
Speaker 1: a first approximation, you might learn more about the campaign

438
00:22:54,840 --> 00:22:58,000
Speaker 1: if you went on vacation for for a month right now.

439
00:22:58,000 --> 00:22:59,639
Speaker 1: We just love when reports. We'll get back on Twitter

440
00:22:59,680 --> 00:23:01,360
Speaker 1: and they say, oh my gosh, you know we're still

441
00:23:01,359 --> 00:23:04,040
Speaker 1: talking about Trump now. It's like, well, yeah, let's let's

442
00:23:04,040 --> 00:23:07,600
Speaker 1: talk about Trump for first. What does he do to

443
00:23:07,640 --> 00:23:11,359
Speaker 1: the calculate? Really, the first question is how unusual of

444
00:23:11,359 --> 00:23:15,159
Speaker 1: a candidate is he? So I'd say, right now, he's

445
00:23:15,280 --> 00:23:18,160
Speaker 1: not quite as unusual as people would say, at least

446
00:23:18,160 --> 00:23:20,280
Speaker 1: if you kind of made him a ciscal data point

447
00:23:20,359 --> 00:23:22,320
Speaker 1: right in the sense that first of all, we had

448
00:23:22,400 --> 00:23:26,600
Speaker 1: in UM four or five different Republican candidates from Michelle

449
00:23:26,640 --> 00:23:30,240
Speaker 1: Bachman to New ging Rich, who surged Herman Kaine in

450
00:23:30,280 --> 00:23:34,440
Speaker 1: the polls to about where Trump is UM and then

451
00:23:34,520 --> 00:23:38,120
Speaker 1: faded UM, sometimes rapidly sometimes slowly after that. But there's

452
00:23:38,119 --> 00:23:42,040
Speaker 1: also another tradition of kind of gadfly entire sufficient candidates

453
00:23:42,080 --> 00:23:46,000
Speaker 1: like Pat Buchanan which Trump belongs to, to UM, you know,

454
00:23:46,119 --> 00:23:47,600
Speaker 1: and some of them were able to hold on to

455
00:23:47,680 --> 00:23:52,080
Speaker 1: a segment of the Republican base UM through Iowa and

456
00:23:52,119 --> 00:23:55,480
Speaker 1: New Hampshire. So I don't discount the possibility that Trump

457
00:23:55,520 --> 00:23:57,960
Speaker 1: could be with us for for a long time. I

458
00:23:58,040 --> 00:24:01,040
Speaker 1: do discount the idea that he could ever become the nominee.

459
00:24:01,760 --> 00:24:04,400
Speaker 1: Or you think it's highly unlikely that Donald Trump will

460
00:24:04,400 --> 00:24:06,480
Speaker 1: be the Republican nominee. Yeah, I mean I put the

461
00:24:06,520 --> 00:24:09,560
Speaker 1: chances recently at which is when you kind of go

462
00:24:09,640 --> 00:24:13,359
Speaker 1: through and say, well, they're basically six different hurdles that

463
00:24:13,400 --> 00:24:16,160
Speaker 1: he faces, and say he has shot of clearing a hurtle.

464
00:24:16,200 --> 00:24:18,720
Speaker 1: Then you get to you know, one and sixty four chance,

465
00:24:18,760 --> 00:24:22,840
Speaker 1: which is, you know, right, that's that's pretty fascinating. So

466
00:24:22,880 --> 00:24:24,959
Speaker 1: what about on the other side, Bernie Sanders, what does

467
00:24:25,000 --> 00:24:28,280
Speaker 1: he do with this calculus? So socialist running for press,

468
00:24:28,280 --> 00:24:31,439
Speaker 1: socialis running first time in half a century. I mean,

469
00:24:31,480 --> 00:24:33,919
Speaker 1: the funny thing is, you have all the media hype

470
00:24:34,000 --> 00:24:37,200
Speaker 1: is about um a Democrat who's not even officially Democrat

471
00:24:37,200 --> 00:24:39,080
Speaker 1: and Republican who, let's be honest, it's not really even

472
00:24:39,080 --> 00:24:41,720
Speaker 1: a Republican, right, you know, I never thought that, but

473
00:24:41,760 --> 00:24:45,520
Speaker 1: that's absolutely true. Um, I think the Bernie Hillary race

474
00:24:45,720 --> 00:24:49,560
Speaker 1: is a lot more typical in some ways, where you

475
00:24:49,640 --> 00:24:53,360
Speaker 1: have someone to the far left of a Democratic candidate. Yeah,

476
00:24:53,440 --> 00:24:55,199
Speaker 1: and if you kind of mapped out and kind of

477
00:24:55,680 --> 00:24:58,320
Speaker 1: um actually mapped out in a very mathematical way, kind

478
00:24:58,320 --> 00:25:01,639
Speaker 1: of where our Democrats preferences, then you know about a

479
00:25:01,760 --> 00:25:05,080
Speaker 1: third of the electorate would say we're closer to Bernie Sanders.

480
00:25:05,080 --> 00:25:07,600
Speaker 1: He's not quite there yet in the polls, but it

481
00:25:07,640 --> 00:25:09,600
Speaker 1: would not be surprising if he got a third of

482
00:25:09,640 --> 00:25:13,280
Speaker 1: the vote nationally. And the thing is, though, Um, Iowa,

483
00:25:13,560 --> 00:25:15,800
Speaker 1: New Hampshire, you have a lot of liberals, and you

484
00:25:15,880 --> 00:25:17,320
Speaker 1: had a lot of a lot of white liberals, and

485
00:25:17,320 --> 00:25:19,199
Speaker 1: Bernie Sanders is doing pretty well with white voters, not

486
00:25:19,240 --> 00:25:22,800
Speaker 1: so well with his manox African Americans. So um, so

487
00:25:22,840 --> 00:25:26,920
Speaker 1: it's entirely possible he could win, Um, New Hampshire, maybe

488
00:25:26,920 --> 00:25:29,119
Speaker 1: Iowa and a few other states could win, Wisconsin and

489
00:25:29,160 --> 00:25:32,840
Speaker 1: Oregon and states like that Massachusetts. Um, you know, but

490
00:25:33,240 --> 00:25:36,240
Speaker 1: there was a poll out recently in Alabama which is

491
00:25:36,280 --> 00:25:38,639
Speaker 1: where Hilly was beating him eighty one to twelve or

492
00:25:38,680 --> 00:25:42,840
Speaker 1: something like that, like an Alabama football score or something almost. Um,

493
00:25:42,880 --> 00:25:46,960
Speaker 1: you know. And um, look, what you don't get from

494
00:25:47,000 --> 00:25:48,880
Speaker 1: kind of reading the press coverage every day is that

495
00:25:49,280 --> 00:25:52,000
Speaker 1: by every metric she's doing as well as any non

496
00:25:52,000 --> 00:25:55,000
Speaker 1: incumbent has the stage of the primary. Ever, whether she's

497
00:25:55,000 --> 00:25:58,960
Speaker 1: beating Sanders by twenty points or thirty points nationally, she's

498
00:25:59,000 --> 00:26:01,520
Speaker 1: still a way ahead. He's already been endorsed by half

499
00:26:01,520 --> 00:26:04,639
Speaker 1: of the Democratic Congress, basically. But also that you know,

500
00:26:04,960 --> 00:26:06,560
Speaker 1: these things tend to tighten if you go back and

501
00:26:06,560 --> 00:26:09,120
Speaker 1: look at candidates like Clinton in the past. So al

502
00:26:09,240 --> 00:26:11,880
Speaker 1: Gore UM came within a couple of points of losing

503
00:26:11,920 --> 00:26:15,399
Speaker 1: to Bill Bradley, right. Um, you know, George Bush and

504
00:26:16,320 --> 00:26:18,240
Speaker 1: was a sitting vice president, which is kind of analogous

505
00:26:18,280 --> 00:26:23,040
Speaker 1: to Clinton's position. He finished third in Iowa, lost like

506
00:26:23,040 --> 00:26:26,680
Speaker 1: seven or eight states. Um, George W. Bush in two

507
00:26:26,720 --> 00:26:28,720
Speaker 1: thousand was as a much of a juggernaut as you

508
00:26:28,720 --> 00:26:31,359
Speaker 1: could be. But McCain caught him one a few states.

509
00:26:31,400 --> 00:26:33,800
Speaker 1: So that kind of modal outcome was that is that

510
00:26:33,840 --> 00:26:37,200
Speaker 1: she does lose a few states, New Hampshire being one

511
00:26:37,200 --> 00:26:41,200
Speaker 1: of the better candidates, right um, and then by um

512
00:26:41,240 --> 00:26:43,199
Speaker 1: by February March of next year, we're like, you know,

513
00:26:43,240 --> 00:26:45,399
Speaker 1: what was all the what was all the fuss about it?

514
00:26:45,480 --> 00:26:49,199
Speaker 1: She's also out raising him monetarily and the money and

515
00:26:49,240 --> 00:26:51,280
Speaker 1: the money helps too. It might stay with Jeb. Right,

516
00:26:51,359 --> 00:26:53,479
Speaker 1: Jeb is the big winner on the Republicans. Jeb has

517
00:26:53,480 --> 00:26:56,400
Speaker 1: although on the GOP side, Uh, everyone has so much

518
00:26:56,440 --> 00:26:58,320
Speaker 1: access to capital that that's one of the things that is,

519
00:26:58,320 --> 00:27:00,440
Speaker 1: I think a little bit different now it used to

520
00:27:00,480 --> 00:27:04,679
Speaker 1: be that. So look at um Tim plenty uh in

521
00:27:04,720 --> 00:27:09,360
Speaker 1: two thousand and eleven, right, perfectly plausible Candy Midwestern governor

522
00:27:09,440 --> 00:27:13,600
Speaker 1: right um, kind of down the middle positions for Republican UM,

523
00:27:13,640 --> 00:27:16,160
Speaker 1: but he ran out of money, uh, you know, even

524
00:27:16,200 --> 00:27:18,119
Speaker 1: four years ago, and so he's like, well, I have

525
00:27:18,160 --> 00:27:21,040
Speaker 1: to put the plug. Um. You know. Now a candidate

526
00:27:21,080 --> 00:27:23,800
Speaker 1: like Rick Perry who's raised almost no public funds, like

527
00:27:23,840 --> 00:27:27,080
Speaker 1: a million dollars, which is Um, you know Donald Trumper

528
00:27:27,200 --> 00:27:29,880
Speaker 1: called that pathetic. I think, uh, you can, you can

529
00:27:29,920 --> 00:27:33,600
Speaker 1: tap into a superpack. The superpacks no longer are maintaining

530
00:27:33,640 --> 00:27:37,280
Speaker 1: any pretense of separating their operations from the campaigns. I

531
00:27:37,320 --> 00:27:40,840
Speaker 1: think there's no um enforceable legal risk there and so

532
00:27:41,240 --> 00:27:43,560
Speaker 1: his campaign can can continue. But that's why I think,

533
00:27:43,640 --> 00:27:45,760
Speaker 1: you know, we are a little bit in New Territory

534
00:27:45,760 --> 00:27:49,440
Speaker 1: with a goop slide, just because you have seventeen candidates running.

535
00:27:49,440 --> 00:27:51,240
Speaker 1: And as much as Democrats might not want to admit it,

536
00:27:51,440 --> 00:27:53,480
Speaker 1: these are seventeen candidates, a lot of whom have some

537
00:27:53,960 --> 00:27:57,960
Speaker 1: really impressive credentials, right like governors and centers from big

538
00:27:58,080 --> 00:28:02,240
Speaker 1: large swing states as our typically pretty strong candidates. Um,

539
00:28:02,359 --> 00:28:06,840
Speaker 1: of course, like let's stall go Hio then yeah, uh well,

540
00:28:07,320 --> 00:28:09,800
Speaker 1: um Ka Sick is a candidate who maybe is a

541
00:28:09,880 --> 00:28:13,240
Speaker 1: little bit of a of a dark horse. Um, he's

542
00:28:13,280 --> 00:28:15,159
Speaker 1: done something interesting, which is he's invested a lot of

543
00:28:15,200 --> 00:28:19,600
Speaker 1: money in advertising in New Hampshire. Now, um, the reason

544
00:28:19,600 --> 00:28:22,680
Speaker 1: interesting forging Iowa and going straight to New Hampshire. Well,

545
00:28:22,680 --> 00:28:25,760
Speaker 1: I think the goal is actually to get some media buzz, right,

546
00:28:25,800 --> 00:28:29,200
Speaker 1: because the advertising says that hey, um or the empirical

547
00:28:29,280 --> 00:28:33,320
Speaker 1: literature says advertising has really short lived effects. Right, you

548
00:28:33,320 --> 00:28:35,440
Speaker 1: see an ad, you might remember it for a week

549
00:28:35,560 --> 00:28:37,879
Speaker 1: or two, So you should save your money towards the

550
00:28:38,000 --> 00:28:40,440
Speaker 1: end and then advertise. But I think he wants to say, look,

551
00:28:40,720 --> 00:28:42,480
Speaker 1: it's a field of seventeen candidates. I want to stay

552
00:28:42,520 --> 00:28:45,080
Speaker 1: on the first debate stage, so to speak, get some

553
00:28:45,120 --> 00:28:48,040
Speaker 1: positive attention, build some momentum. And so he's kind of

554
00:28:48,040 --> 00:28:51,120
Speaker 1: investing in trying to make a name for himself now.

555
00:28:51,160 --> 00:28:52,880
Speaker 1: But yeah, he is a case and he can say, look,

556
00:28:53,120 --> 00:28:56,200
Speaker 1: I'm really popular in Ohio. Boy, that happens to be

557
00:28:56,240 --> 00:28:59,720
Speaker 1: a pretty important state in the general election. Um, And

558
00:28:59,760 --> 00:29:01,760
Speaker 1: I'll what it indicates that I can I can win

559
00:29:01,840 --> 00:29:06,440
Speaker 1: over abroad and diverse coalition of voters to um. So

560
00:29:06,560 --> 00:29:09,440
Speaker 1: you know, I mean there are questions about uh, you know,

561
00:29:09,480 --> 00:29:13,480
Speaker 1: so I'm skeptical about Trump's chances. There's no one Republican

562
00:29:13,560 --> 00:29:15,600
Speaker 1: you can point to though and say, boy, they've had

563
00:29:15,600 --> 00:29:19,320
Speaker 1: a great start to their campaign so far either right, Um,

564
00:29:19,760 --> 00:29:22,240
Speaker 1: Jeb has kind of faltered on the Iraq question a

565
00:29:22,240 --> 00:29:25,680
Speaker 1: few other things. I think people were generally unimpressed with

566
00:29:25,760 --> 00:29:30,160
Speaker 1: Walker's performance. Walker, although I would say, um, Rubio didn't

567
00:29:30,240 --> 00:29:33,200
Speaker 1: hurt himself yeah, Rubio is Rubio was in win candid

568
00:29:33,360 --> 00:29:35,400
Speaker 1: where you know I talked about on the Democratic side,

569
00:29:35,840 --> 00:29:37,719
Speaker 1: come March, we might be saying, yeah, that was kind

570
00:29:37,720 --> 00:29:39,520
Speaker 1: of obviously I was going to play out right. I

571
00:29:39,560 --> 00:29:43,120
Speaker 1: think we could say that March of next year. You know, boy,

572
00:29:43,160 --> 00:29:47,680
Speaker 1: it was clear that Rubio was a really smart consensus choice. Right.

573
00:29:47,720 --> 00:29:50,040
Speaker 1: He's a little bit more relatable than Bush. He's a

574
00:29:50,040 --> 00:29:53,520
Speaker 1: little bit more electable than Walker. Um, you know, he

575
00:29:53,560 --> 00:29:56,400
Speaker 1: wasn't panicked about trying to boost his poll numbers in August.

576
00:29:56,400 --> 00:30:01,080
Speaker 1: He waited till towards the end. Um you know, Ruby O,

577
00:30:01,240 --> 00:30:02,920
Speaker 1: I think I don't know. I mean, you know, Bush

578
00:30:02,920 --> 00:30:04,680
Speaker 1: has the money, he's a little bit ahead of Ruody

579
00:30:04,760 --> 00:30:06,440
Speaker 1: on the polls. And no, one can't have a Bush.

580
00:30:06,520 --> 00:30:09,680
Speaker 1: You can't have a Bush Rubio ticket. You can't. Well,

581
00:30:09,720 --> 00:30:12,680
Speaker 1: there's some machinations about I guess there are some end

582
00:30:12,720 --> 00:30:14,840
Speaker 1: arounds maybe, but we you know, I don't know all

583
00:30:14,840 --> 00:30:16,760
Speaker 1: the constitution of details there. But basically the answer is

584
00:30:16,800 --> 00:30:18,760
Speaker 1: basically no, I think you would not have you could

585
00:30:18,800 --> 00:30:22,520
Speaker 1: have you know, Rubio and Walker, or Rubio and Kasick.

586
00:30:22,600 --> 00:30:23,800
Speaker 1: You do have a lot of all the way around

587
00:30:23,880 --> 00:30:26,440
Speaker 1: Kasi Rubio, the senior potentially. So one thing that happened,

588
00:30:26,440 --> 00:30:29,400
Speaker 1: by the way, is that the GOP had UM a

589
00:30:29,560 --> 00:30:33,880
Speaker 1: very good election in and then another one in UM,

590
00:30:33,960 --> 00:30:36,040
Speaker 1: and they won lots of Senate and governor seats and

591
00:30:36,080 --> 00:30:39,040
Speaker 1: swing states. So you know, that's the benefit now as

592
00:30:39,080 --> 00:30:41,000
Speaker 1: they have a whole suite of caanics pick for them

593
00:30:41,000 --> 00:30:43,920
Speaker 1: that are are popular or at least somewhat popular in

594
00:30:43,920 --> 00:30:46,719
Speaker 1: in big swing states like Florida and Ohio and whatnot.

595
00:30:47,200 --> 00:30:51,400
Speaker 1: Quite quite fascinating. So I just said, Kasi Rubio, how

596
00:30:51,440 --> 00:30:55,960
Speaker 1: important are the VP choices to the electability or the

597
00:30:56,000 --> 00:31:01,040
Speaker 1: polling note numbers of either candidate? So history really, VP

598
00:31:01,200 --> 00:31:05,280
Speaker 1: choices are are not all that important. UM. As best

599
00:31:05,280 --> 00:31:06,880
Speaker 1: as we can tell, you get about a two or

600
00:31:06,920 --> 00:31:10,200
Speaker 1: three percent boost in the state where the VP candidate

601
00:31:10,640 --> 00:31:13,160
Speaker 1: is from sometimes, so even that seems to be fading.

602
00:31:13,160 --> 00:31:18,160
Speaker 1: Paul Ryan didn't help hit Romney very much in Wisconsin apparently, UM.

603
00:31:18,200 --> 00:31:20,400
Speaker 1: But there's downside of the VP choice, you know, I

604
00:31:20,440 --> 00:31:23,560
Speaker 1: think UM, although she was heralded at first, Sarah Palin

605
00:31:23,640 --> 00:31:26,880
Speaker 1: probably UM wound up further injuring any chance, but came

606
00:31:26,960 --> 00:31:29,760
Speaker 1: muld have had in two thousand and eight. Uh, you know,

607
00:31:29,920 --> 00:31:32,920
Speaker 1: we go back to seventy two and Eagleton and Shriver

608
00:31:33,120 --> 00:31:35,880
Speaker 1: and that whole mess. Um, so you know, some extent

609
00:31:36,080 --> 00:31:39,200
Speaker 1: making the um the way Obama played it, where you

610
00:31:39,200 --> 00:31:43,640
Speaker 1: picked this boring, old, safe, safe white guy basically right, Um,

611
00:31:43,720 --> 00:31:45,480
Speaker 1: but I know there are different ways to do it.

612
00:31:45,760 --> 00:31:49,480
Speaker 1: We've been speaking with Nate Silver. If you enjoy this conversation,

613
00:31:49,520 --> 00:31:52,160
Speaker 1: be sure to check out our podcast, where the tapes

614
00:31:52,240 --> 00:31:55,960
Speaker 1: keep rolling and we keep recording until the guests pass

615
00:31:56,000 --> 00:32:00,400
Speaker 1: out from exhaustion. If you enjoy this conversation, be sure

616
00:32:00,440 --> 00:32:03,720
Speaker 1: and check out all of our other interviews. Follow my

617
00:32:04,320 --> 00:32:07,480
Speaker 1: daily column on Bloomberg View dot com, or follow me

618
00:32:07,560 --> 00:32:11,160
Speaker 1: on Twitter at Ridhults. Your your Twitter handle is at

619
00:32:11,240 --> 00:32:14,520
Speaker 1: Nate Silver Silver, Nate Silver Fight. What did somebody else

620
00:32:14,560 --> 00:32:18,200
Speaker 1: grib Nates at Nate Silver? I think so, yeah, that's

621
00:32:17,920 --> 00:32:21,400
Speaker 1: that's a shame. I'm sure you're you have the blue checkmark,

622
00:32:21,480 --> 00:32:24,000
Speaker 1: so you've very got the blue check mark and um,

623
00:32:24,040 --> 00:32:26,920
Speaker 1: but you know so our website is actually five spells

624
00:32:26,960 --> 00:32:30,320
Speaker 1: spilled out, not the number. I'm Barry, Ridhults. You're listening

625
00:32:30,360 --> 00:32:34,120
Speaker 1: to Masters in Business on Bloomberg Radio. All right, welcome

626
00:32:34,160 --> 00:32:36,800
Speaker 1: to the podcast portion of our show. Nates, thank you

627
00:32:36,840 --> 00:32:39,600
Speaker 1: so much for doing this. I'm really I'm excited about

628
00:32:39,680 --> 00:32:42,400
Speaker 1: this and I've been looking forward to for a while

629
00:32:42,480 --> 00:32:46,280
Speaker 1: for having me my pleasure. So there's so many different

630
00:32:46,360 --> 00:32:49,400
Speaker 1: questions we we didn't get to that I really want

631
00:32:49,400 --> 00:32:51,640
Speaker 1: to talk to you. This is the big, big one

632
00:32:51,640 --> 00:32:55,960
Speaker 1: will save. So the one quiet Mike bat Nick in

633
00:32:56,000 --> 00:32:58,960
Speaker 1: my office is, uh said you have to ask him

634
00:32:59,000 --> 00:33:03,640
Speaker 1: this question, and it's how important are managers to team's

635
00:33:03,680 --> 00:33:07,920
Speaker 1: successful failure? And how do we measure that statistically? So

636
00:33:08,000 --> 00:33:11,720
Speaker 1: in in baseball in particular, I think you can talk

637
00:33:11,760 --> 00:33:14,480
Speaker 1: football also because that that that comes up quite often.

638
00:33:14,640 --> 00:33:18,360
Speaker 1: I think in baseball, First of all, the strategic choices

639
00:33:18,360 --> 00:33:22,040
Speaker 1: of manager makes are not very consequential, right. Really you

640
00:33:22,080 --> 00:33:24,200
Speaker 1: can debate like, oh, how MU should you sack bunt

641
00:33:24,360 --> 00:33:27,320
Speaker 1: or you know, um, some guys now have the picture

642
00:33:27,360 --> 00:33:29,320
Speaker 1: hitting a The impact of that stuff is probably not

643
00:33:29,400 --> 00:33:31,880
Speaker 1: more than a couple of wins a year over a

644
00:33:31,960 --> 00:33:36,680
Speaker 1: hundred two games season. Um. What I think is understudied is, uh,

645
00:33:36,720 --> 00:33:40,680
Speaker 1: how much influence a manager has on getting the best

646
00:33:40,680 --> 00:33:43,400
Speaker 1: out of his players? As simple as that sounds, right, Um,

647
00:33:43,440 --> 00:33:45,600
Speaker 1: you know there's been some studies done in the NBA.

648
00:33:45,760 --> 00:33:49,040
Speaker 1: When you say take a preseason prediction about a guy's

649
00:33:49,080 --> 00:33:51,520
Speaker 1: ciscal line that doesn't know who the manager is. And

650
00:33:51,520 --> 00:33:55,200
Speaker 1: then how often does uh, Gregg Popovitch, for example, get

651
00:33:55,240 --> 00:33:58,640
Speaker 1: his players to outperform that prediction, either by by improving

652
00:33:58,640 --> 00:34:00,400
Speaker 1: their skills or I fit up feeling a all better

653
00:34:00,400 --> 00:34:02,960
Speaker 1: than the answer is, you know, routinely the Spurs and

654
00:34:03,000 --> 00:34:07,440
Speaker 1: way that's disically significant beat their kind of raw um

655
00:34:07,440 --> 00:34:10,240
Speaker 1: projections from name, not scars from Vegas. Vegas can account

656
00:34:10,239 --> 00:34:13,399
Speaker 1: for that um, but from like a naive algorithm. Right,

657
00:34:13,520 --> 00:34:15,960
Speaker 1: and so I've not seen that done for baseball, and

658
00:34:16,040 --> 00:34:18,560
Speaker 1: I'm sure someone has, but kind of saying, you know,

659
00:34:18,600 --> 00:34:21,920
Speaker 1: who really motivates their guys to do the best, that's

660
00:34:21,920 --> 00:34:24,960
Speaker 1: still kind of a black box a little bit. That's fascinating.

661
00:34:25,200 --> 00:34:30,759
Speaker 1: I have a mixed um relationship with sports books. Some

662
00:34:30,800 --> 00:34:34,720
Speaker 1: are better, some are worse. The one that I really liked,

663
00:34:34,840 --> 00:34:39,120
Speaker 1: which is right up your alley. Did you read Tom

664
00:34:39,239 --> 00:34:43,360
Speaker 1: Coughlin's book on the Right to Win? Former coach? I

665
00:34:43,400 --> 00:34:46,840
Speaker 1: did not know. So the one statistical thing that stayed

666
00:34:46,880 --> 00:34:49,160
Speaker 1: with me from that book that was so fascinating. I

667
00:34:49,200 --> 00:34:54,000
Speaker 1: don't remember which player was complaining about it, um, uh

668
00:34:54,080 --> 00:34:59,040
Speaker 1: center linebacker. That was you know, they would run a

669
00:34:59,040 --> 00:35:02,640
Speaker 1: whole bunch of numbers for every opponent on second and long.

670
00:35:02,680 --> 00:35:04,359
Speaker 1: Here's what they like to do on third and one,

671
00:35:04,400 --> 00:35:07,479
Speaker 1: here's what I like to do on in the red

672
00:35:07,600 --> 00:35:09,960
Speaker 1: zone late in the game. Here's what they like to do.

673
00:35:10,000 --> 00:35:12,400
Speaker 1: And they they would just bury these guys who just

674
00:35:12,480 --> 00:35:15,640
Speaker 1: weren't used to this sort of mathematical stuff. And he always,

675
00:35:15,760 --> 00:35:19,080
Speaker 1: I wish I can remember who it was, which giant

676
00:35:19,719 --> 00:35:24,000
Speaker 1: um that one a championship with him, and complained about

677
00:35:24,040 --> 00:35:28,080
Speaker 1: this constantly, and then gets traded somewhere else and it's

678
00:35:28,360 --> 00:35:29,880
Speaker 1: you know, third and one. He goes, all, right, what

679
00:35:29,920 --> 00:35:32,440
Speaker 1: are they gonna do? And I don't know? And it

680
00:35:32,800 --> 00:35:36,839
Speaker 1: dawns on him that, oh, this conkling cockling guy was fantastic,

681
00:35:36,920 --> 00:35:40,400
Speaker 1: that that data was really helpful to know what the

682
00:35:41,280 --> 00:35:44,000
Speaker 1: Certainly it's not a sure thing, but hey, they like

683
00:35:44,200 --> 00:35:46,920
Speaker 1: to do this at at third and one. And I

684
00:35:46,960 --> 00:35:49,239
Speaker 1: got to think that that how do you measure? Yeah?

685
00:35:49,440 --> 00:35:51,919
Speaker 1: I mean football is a different story, right. I think

686
00:35:51,960 --> 00:35:55,959
Speaker 1: everyone from stat heads to UM coaches themselves would say

687
00:35:56,000 --> 00:35:59,759
Speaker 1: that NFL coaches are pretty darn significant. Yeah, look at

688
00:36:00,040 --> 00:36:04,719
Speaker 1: get the Seattle UM the Super Bowl. This year. Yeah,

689
00:36:04,840 --> 00:36:07,760
Speaker 1: you know, well, so this is you know, we would

690
00:36:07,760 --> 00:36:09,919
Speaker 1: say that there was some very long announced we did

691
00:36:09,920 --> 00:36:12,600
Speaker 1: that the choice that Pete Carroll may was fine, you

692
00:36:12,680 --> 00:36:16,160
Speaker 1: defend that, right, I see, I looked at it as

693
00:36:16,760 --> 00:36:23,759
Speaker 1: he was playing a U a high risk, lower probability game. Consistently,

694
00:36:23,800 --> 00:36:25,239
Speaker 1: if you look at a lot of the choices that

695
00:36:25,280 --> 00:36:27,920
Speaker 1: were made, you start to veer into the anecdotal Well

696
00:36:27,920 --> 00:36:30,040
Speaker 1: what about this and why did they run here? And

697
00:36:30,080 --> 00:36:33,120
Speaker 1: why did they But you know, something like one percent

698
00:36:33,239 --> 00:36:36,160
Speaker 1: of passes get intercepted in this situation, so it's kind

699
00:36:36,200 --> 00:36:38,919
Speaker 1: of a a rounding error really, and there's a lot

700
00:36:38,960 --> 00:36:42,560
Speaker 1: of kind of you know, post facto sure analysis. Right,

701
00:36:42,560 --> 00:36:44,680
Speaker 1: no one would ever have complained about that play call

702
00:36:44,840 --> 00:36:47,360
Speaker 1: for a second, if he caught the past, if it

703
00:36:47,440 --> 00:36:50,879
Speaker 1: was if it wasn't intercepted, it's a whole different Well,

704
00:36:50,960 --> 00:36:54,320
Speaker 1: to the to the victors not only goes to spoils,

705
00:36:54,360 --> 00:36:59,520
Speaker 1: but the opportunity to write. Um, So we we mentioned intangibles,

706
00:37:00,000 --> 00:37:02,880
Speaker 1: how do you measure the intangibles of a player? And

707
00:37:02,880 --> 00:37:08,080
Speaker 1: and again mentorship, attitude, locker room behavior. Can you ignore

708
00:37:08,160 --> 00:37:12,400
Speaker 1: these things? What do they actually actually mean? So I've

709
00:37:12,440 --> 00:37:14,400
Speaker 1: been thinking about ways to kind of set up a

710
00:37:14,920 --> 00:37:19,640
Speaker 1: um uh verifiable experiment for this, and one might be

711
00:37:20,760 --> 00:37:23,839
Speaker 1: if people identify in advance, say twenty baseball players they

712
00:37:23,840 --> 00:37:26,560
Speaker 1: think have strong or weak leadership, and then see what

713
00:37:26,640 --> 00:37:29,160
Speaker 1: happens when they change teams and as there's some residual

714
00:37:29,760 --> 00:37:33,600
Speaker 1: value you can identify, but you know sort of that

715
00:37:33,719 --> 00:37:36,160
Speaker 1: can you really another ways unless you really run a

716
00:37:36,160 --> 00:37:40,120
Speaker 1: control group, it's pretty hard to. In baseball performance is

717
00:37:40,160 --> 00:37:43,640
Speaker 1: pretty individualized. Like the NBA is different the NBA. The

718
00:37:43,640 --> 00:37:46,120
Speaker 1: whole challenge the NBA is you have um you know,

719
00:37:46,440 --> 00:37:49,880
Speaker 1: one possession per possession, right, one shot per possession. You

720
00:37:49,920 --> 00:37:52,400
Speaker 1: have to get the guys to cooperate. They have selfish

721
00:37:52,400 --> 00:37:55,240
Speaker 1: incentives to each take the shot and boost their stats

722
00:37:55,280 --> 00:37:57,319
Speaker 1: or whatnot, right, And so that makes coaching important, It

723
00:37:57,320 --> 00:38:00,600
Speaker 1: makes um chemistry important, and you can to measure this.

724
00:38:00,640 --> 00:38:03,759
Speaker 1: You can kind of say, uh, what residual value is

725
00:38:03,800 --> 00:38:05,520
Speaker 1: there when a guy is on the court or off

726
00:38:05,560 --> 00:38:07,480
Speaker 1: the court. And there's some better and better methods for

727
00:38:07,520 --> 00:38:10,960
Speaker 1: doing that. Now it's some of the guys who UM

728
00:38:11,000 --> 00:38:13,200
Speaker 1: who play twenty five minutes a game, they're like, oh,

729
00:38:13,200 --> 00:38:15,040
Speaker 1: they're defensive players. It might have been ten years ago

730
00:38:15,040 --> 00:38:17,319
Speaker 1: that the stats were like, oh, this guy can't shoot right,

731
00:38:17,320 --> 00:38:18,959
Speaker 1: you can't really do anything. And now we're seeing actually

732
00:38:19,000 --> 00:38:21,680
Speaker 1: they often do have a big impact on the game.

733
00:38:21,840 --> 00:38:24,960
Speaker 1: But baseball I'm a little bit more more skeptical, in

734
00:38:25,040 --> 00:38:29,080
Speaker 1: part because I think sometimes, um, sometimes the guys who

735
00:38:29,200 --> 00:38:32,200
Speaker 1: um have a rep for being good clubhouse guys are

736
00:38:32,200 --> 00:38:33,759
Speaker 1: just guys who are friendly to the media. And that's

737
00:38:33,760 --> 00:38:37,840
Speaker 1: a slightly different thing necessarily. Sure. So in basketball, you know,

738
00:38:37,880 --> 00:38:41,400
Speaker 1: I always as a nick fan who always felt thwarted

739
00:38:41,440 --> 00:38:46,520
Speaker 1: by the bulls under Michael Jordan's I always a picture

740
00:38:46,640 --> 00:38:48,640
Speaker 1: him as a guy who doesn't put up with any

741
00:38:48,680 --> 00:38:52,200
Speaker 1: nonsense in the locker room, and he demands top performance,

742
00:38:52,200 --> 00:38:55,680
Speaker 1: and he drives the rest of his team the way

743
00:38:55,760 --> 00:39:00,799
Speaker 1: that Patrick Ewing never did. It's arguable whether anybody since

744
00:39:00,840 --> 00:39:05,640
Speaker 1: Michael Um, since um, anybody in the Lakers ever did this,

745
00:39:05,719 --> 00:39:09,279
Speaker 1: since Magic Johnson. Um. You know, you don't really know,

746
00:39:09,560 --> 00:39:12,360
Speaker 1: you don't really see Kobe Bryant is that sort of player?

747
00:39:12,719 --> 00:39:15,759
Speaker 1: Is that just anecdote? And and after the fact, or

748
00:39:16,480 --> 00:39:18,280
Speaker 1: did he? Is he the sort of guy that really

749
00:39:18,320 --> 00:39:21,719
Speaker 1: had that impact? And is there any way after the

750
00:39:21,760 --> 00:39:24,239
Speaker 1: fact we can we can determine that. I mean, one

751
00:39:24,400 --> 00:39:29,120
Speaker 1: weird thing about and I grew up a Pistons fans, so, um,

752
00:39:29,200 --> 00:39:31,759
Speaker 1: you know, everyone hated Michael Jordan for reason or not.

753
00:39:31,800 --> 00:39:33,640
Speaker 1: I remember in the in the final Championship E one

754
00:39:33,719 --> 00:39:38,400
Speaker 1: was it I kind of involuntarily found myself rooting for him, right,

755
00:39:38,480 --> 00:39:41,759
Speaker 1: And I'm like, I can't root for Utah, come on, right, Um?

756
00:39:41,880 --> 00:39:46,560
Speaker 1: But those contents and letters to Nate Silver at ESPN

757
00:39:46,640 --> 00:39:49,840
Speaker 1: dot Com. One thing that I think helped Jordan's reputation,

758
00:39:49,880 --> 00:39:52,799
Speaker 1: apart from being maybe the best best player of all time,

759
00:39:53,040 --> 00:39:55,319
Speaker 1: that he didn't have a lot of near misses, right

760
00:39:55,400 --> 00:39:59,440
Speaker 1: you know, Uh, the Bulls were probably the favorites and

761
00:39:59,520 --> 00:40:02,600
Speaker 1: five out of the six UH championship seasons where they

762
00:40:02,640 --> 00:40:05,759
Speaker 1: won the championship, right, um, but they were blown up

763
00:40:05,760 --> 00:40:08,239
Speaker 1: in a hurry, right Um. The year before they won

764
00:40:08,280 --> 00:40:10,640
Speaker 1: their first one one, it was a really strong time

765
00:40:11,040 --> 00:40:13,960
Speaker 1: for Eastern Conference kind of sat out the year and

766
00:40:14,000 --> 00:40:16,920
Speaker 1: a half, right Um. So for some reason, it almost

767
00:40:16,920 --> 00:40:19,960
Speaker 1: feels to me like if Jordan had UH won five

768
00:40:20,040 --> 00:40:23,359
Speaker 1: championships and ten tries, he would look worse than six

769
00:40:23,360 --> 00:40:25,839
Speaker 1: out of six. But that's a pretty petty complaint. I mean,

770
00:40:25,880 --> 00:40:28,880
Speaker 1: you know, Jordan had an amazing career. One reason I

771
00:40:28,960 --> 00:40:31,520
Speaker 1: kind of like the NBA and contrast to other sports

772
00:40:31,520 --> 00:40:33,880
Speaker 1: where there's so much randomness, is that in the n

773
00:40:33,920 --> 00:40:39,359
Speaker 1: b A there are not very many undeserving NBA champions right,

774
00:40:39,640 --> 00:40:41,799
Speaker 1: we saw in the not a lucky call or a

775
00:40:41,800 --> 00:40:45,640
Speaker 1: bad bounce or something like that in tennis. Really, if

776
00:40:45,680 --> 00:40:48,279
Speaker 1: you want us opens coming right, um, but you have

777
00:40:48,360 --> 00:40:51,080
Speaker 1: to earn the NBA championship. We saw in the NBA

778
00:40:51,120 --> 00:40:54,400
Speaker 1: Finals last year the Calves playing the absolute best of

779
00:40:54,440 --> 00:40:57,319
Speaker 1: their ability and they can win. They can win two

780
00:40:57,360 --> 00:41:01,200
Speaker 1: close games, right, It's really hard to win a seven

781
00:41:01,200 --> 00:41:03,880
Speaker 1: game series against the team when you know your second

782
00:41:03,880 --> 00:41:07,520
Speaker 1: base player, best players j R. Smith or something more

783
00:41:07,520 --> 00:41:10,280
Speaker 1: power to j R. Smith. But but so in other words,

784
00:41:11,040 --> 00:41:14,560
Speaker 1: Lebron doesn't have the same supporting cast that Jordan's did,

785
00:41:15,160 --> 00:41:18,279
Speaker 1: and the Calves need to do some offseason. Well they've

786
00:41:18,280 --> 00:41:20,719
Speaker 1: done some stuff, they need to do some more. I

787
00:41:20,719 --> 00:41:23,040
Speaker 1: mean everyone's if everyone's healthy, they're a good team. I

788
00:41:23,040 --> 00:41:24,719
Speaker 1: mean it should be really fanned. That's a great era

789
00:41:24,840 --> 00:41:27,600
Speaker 1: for the NBA. By the way, between the Spurs and

790
00:41:27,600 --> 00:41:30,560
Speaker 1: the Warriers and the Calves, um, you're gonna have three

791
00:41:30,600 --> 00:41:34,440
Speaker 1: great teams are all very different, looks different stylistically, so

792
00:41:34,640 --> 00:41:37,440
Speaker 1: you know, Um, you know, my boss just signed this

793
00:41:37,480 --> 00:41:42,080
Speaker 1: big new NBA contract. Should have a great season coming ahead. Um,

794
00:41:42,120 --> 00:41:45,160
Speaker 1: and you said you mentioned tennis. Let's talk a little

795
00:41:45,160 --> 00:41:49,160
Speaker 1: bit about some of the the male players who I think.

796
00:41:49,200 --> 00:41:53,120
Speaker 1: It's been fascinating watching this go back and forth between

797
00:41:54,320 --> 00:41:57,799
Speaker 1: uh Na Doll and you know, go down the list

798
00:41:57,800 --> 00:42:00,120
Speaker 1: of the top five players. There's been this sort of

799
00:42:00,280 --> 00:42:05,280
Speaker 1: rotating how fast do the skills deteriorate? At age thirty?

800
00:42:05,320 --> 00:42:09,720
Speaker 1: It seems like these guys peak nine and then they start,

801
00:42:10,280 --> 00:42:12,439
Speaker 1: you know, just losing it a little bit. I think

802
00:42:12,440 --> 00:42:14,479
Speaker 1: twenty eight or twenty I might even be a little

803
00:42:14,480 --> 00:42:16,960
Speaker 1: bit early. Obviously, you have guys like Samprus that held

804
00:42:17,000 --> 00:42:19,880
Speaker 1: an agacy that held on for for longer, but maybe

805
00:42:19,880 --> 00:42:23,120
Speaker 1: more like like twenty five. I mean, I go to

806
00:42:23,160 --> 00:42:25,000
Speaker 1: the US Open every year. I'm not a huge tennis fan,

807
00:42:25,080 --> 00:42:28,279
Speaker 1: but I think people who are watching on TV don't

808
00:42:28,320 --> 00:42:32,440
Speaker 1: realize how physically demanding it is. A five set match,

809
00:42:32,600 --> 00:42:37,360
Speaker 1: right Um, in the heat in in New York in September,

810
00:42:37,520 --> 00:42:40,400
Speaker 1: or in Australia and in the you know what summer

811
00:42:40,440 --> 00:42:43,120
Speaker 1: down there, right Um, these guys are hitting at a

812
00:42:43,160 --> 00:42:48,000
Speaker 1: hundred thirty d and it's insane. And where that there

813
00:42:48,160 --> 00:42:51,120
Speaker 1: the guys now actually play a you know, really good

814
00:42:51,160 --> 00:42:56,160
Speaker 1: defensive game. Um, you know, very very challenging if you're

815
00:42:56,200 --> 00:42:58,759
Speaker 1: not absolutely in the top of shape. Right, And so

816
00:42:58,840 --> 00:43:01,840
Speaker 1: in general happens is that for you look at baseball

817
00:43:01,880 --> 00:43:04,120
Speaker 1: where the data is good or something, Um, you know

818
00:43:04,160 --> 00:43:06,719
Speaker 1: the average player peaks at twenty seven. What that really

819
00:43:06,880 --> 00:43:09,480
Speaker 1: is is at your fiscal attributes probably peak actually at

820
00:43:09,840 --> 00:43:14,359
Speaker 1: at age twenty three or something. Your mental attitudes keep growing. Um,

821
00:43:14,640 --> 00:43:17,760
Speaker 1: that makes up for the physics you have the best

822
00:43:18,200 --> 00:43:21,359
Speaker 1: overall combination for And if three of these guys peak

823
00:43:21,400 --> 00:43:23,759
Speaker 1: at twenty if you look at like an NFL running back,

824
00:43:23,880 --> 00:43:27,320
Speaker 1: and um, guys were just kind of purely about brute

825
00:43:27,400 --> 00:43:30,160
Speaker 1: strength and how beat up or not your body is. Right,

826
00:43:30,520 --> 00:43:32,440
Speaker 1: Very often in NFL running back is as best as

827
00:43:32,480 --> 00:43:35,680
Speaker 1: we'll ever be in his rookie season. Right. There's a

828
00:43:35,840 --> 00:43:39,160
Speaker 1: quarterback conversely, where obviously the arm streak matters, but the

829
00:43:39,200 --> 00:43:42,160
Speaker 1: mental parts important to a quarterback. Of course, those guys

830
00:43:42,440 --> 00:43:45,600
Speaker 1: can be perfectly good, sometimes even better in their thirties.

831
00:43:45,640 --> 00:43:48,320
Speaker 1: You could, you could deflate football's way into your thirties.

832
00:43:48,360 --> 00:43:51,080
Speaker 1: You can keep it. You could. I don't think there's

833
00:43:51,080 --> 00:43:54,040
Speaker 1: in any age limitation. But I love the stories about

834
00:43:54,120 --> 00:43:57,279
Speaker 1: Jerry Rice about how he used to train in San Francisco,

835
00:43:57,880 --> 00:44:00,719
Speaker 1: and you talk about the mental out of it. He

836
00:44:00,800 --> 00:44:04,520
Speaker 1: used to invite people from the opposing teams to train

837
00:44:04,560 --> 00:44:06,000
Speaker 1: with him, and he would just run up and down

838
00:44:06,040 --> 00:44:09,160
Speaker 1: the hills of San Francisco and he would destroy these guys.

839
00:44:09,520 --> 00:44:11,200
Speaker 1: They like could not keep up with him, and then

840
00:44:11,200 --> 00:44:14,160
Speaker 1: they seem on the field and they would be horrified.

841
00:44:14,520 --> 00:44:16,400
Speaker 1: Stop and think about that sort of head game. I

842
00:44:16,880 --> 00:44:19,880
Speaker 1: just find that, uh, fascinating. But he's a guy whose

843
00:44:19,920 --> 00:44:24,360
Speaker 1: career went you know, quite quite deep into his thirties.

844
00:44:24,400 --> 00:44:29,200
Speaker 1: That's um. Is that unusual for a receiver for receiver is? Yeah,

845
00:44:29,320 --> 00:44:33,120
Speaker 1: receivers are not quite as early peaked as running backs.

846
00:44:33,160 --> 00:44:35,919
Speaker 1: But um, but you know they're definitely mid to late

847
00:44:36,400 --> 00:44:40,840
Speaker 1: twenties for for the most part. Well, that that's amazing. Um.

848
00:44:40,880 --> 00:44:43,000
Speaker 1: So tennis we mentioned is the is one of the

849
00:44:43,040 --> 00:44:46,360
Speaker 1: next things. And soccer, how are they going to change?

850
00:44:46,480 --> 00:44:50,839
Speaker 1: How are you going to change the statistical analysis of

851
00:44:50,840 --> 00:44:54,160
Speaker 1: of World Cup football, of of soccer? Well, how do

852
00:44:54,200 --> 00:44:56,160
Speaker 1: you how do you look at that? Um? I mean

853
00:44:56,239 --> 00:44:59,120
Speaker 1: historically the only data that was collected, we're just goals

854
00:44:59,160 --> 00:45:02,359
Speaker 1: and bookings and yellow cards and and red cards. Right,

855
00:45:02,520 --> 00:45:06,480
Speaker 1: that's it. That's changing now though, Right So now, um,

856
00:45:06,560 --> 00:45:09,000
Speaker 1: you know in the big four or five leagues in Europe,

857
00:45:09,000 --> 00:45:11,440
Speaker 1: paying what you think of the French League? Um, you

858
00:45:11,480 --> 00:45:13,600
Speaker 1: know you have lots of real time data collectives. And

859
00:45:13,640 --> 00:45:15,280
Speaker 1: now we ran a big article and we know Messy

860
00:45:16,200 --> 00:45:19,200
Speaker 1: I remember that where it's like he actually is saying, now, okay,

861
00:45:19,200 --> 00:45:20,800
Speaker 1: now we can actually measure all these things that we

862
00:45:20,880 --> 00:45:24,520
Speaker 1: thought of as intangibles before, right, like how well does

863
00:45:24,520 --> 00:45:27,080
Speaker 1: he set up his teammates, how much space does he cover?

864
00:45:27,560 --> 00:45:29,160
Speaker 1: Some things are actually a little bit kinter intuitive, like

865
00:45:29,160 --> 00:45:31,239
Speaker 1: I think Messi doesn't cover that much territory. He's very

866
00:45:31,280 --> 00:45:32,880
Speaker 1: much kind of thinking about how can I have the

867
00:45:32,920 --> 00:45:35,200
Speaker 1: most impact and being in the center of of the

868
00:45:35,200 --> 00:45:37,120
Speaker 1: pitch more or less to kind of be the focal

869
00:45:37,160 --> 00:45:40,400
Speaker 1: point for for the offense. Um. But yeah, I didn't

870
00:45:40,400 --> 00:45:42,880
Speaker 1: play in the World today Messi for sure. Yeah. I mean,

871
00:45:42,920 --> 00:45:47,120
Speaker 1: and people people don't realize how good he's amazing he is. Yeah.

872
00:45:47,160 --> 00:45:48,960
Speaker 1: I'm a World Cup fan for a long time and

873
00:45:49,000 --> 00:45:50,640
Speaker 1: I find it fascinating and I wish we had the

874
00:45:50,680 --> 00:45:53,600
Speaker 1: World Cup every uh every two years. I actually went

875
00:45:53,960 --> 00:45:57,120
Speaker 1: uh to the Women's World Cup and in Canada, which

876
00:45:57,160 --> 00:46:00,800
Speaker 1: was a ton of fun. Right, Um, to see Americans

877
00:46:00,800 --> 00:46:04,840
Speaker 1: actually win something at soccer was spectacular. So, UM, all right,

878
00:46:04,880 --> 00:46:08,080
Speaker 1: so let's see what else we've forgotten or I skipped

879
00:46:08,120 --> 00:46:12,800
Speaker 1: over in the prior segment. Um, So we'll save the

880
00:46:12,880 --> 00:46:17,080
Speaker 1: election stuff for a little later. And I'm gonna I'm

881
00:46:17,080 --> 00:46:19,760
Speaker 1: gonna touch that. We did that, we did this. Wow,

882
00:46:20,480 --> 00:46:22,200
Speaker 1: there's so much stuff I could I could stay on

883
00:46:22,239 --> 00:46:24,279
Speaker 1: sports for a long time, but I want to. I

884
00:46:24,280 --> 00:46:28,239
Speaker 1: want to bring it back to um, bring it back

885
00:46:28,280 --> 00:46:30,359
Speaker 1: to to what you're doing with five thirty eight now

886
00:46:30,400 --> 00:46:32,399
Speaker 1: and a little bit of the history with that. So

887
00:46:32,680 --> 00:46:37,480
Speaker 1: your name to one of Time magazines most influential people

888
00:46:37,680 --> 00:46:41,120
Speaker 1: in two thousand and nine. Then comes the Times, then

889
00:46:41,160 --> 00:46:45,200
Speaker 1: comes the the election. Personally, what's it like blowing up

890
00:46:45,239 --> 00:46:48,440
Speaker 1: like that? I mean, I used to be disorienting and

891
00:46:49,520 --> 00:46:52,399
Speaker 1: kind of like what's going on? So one thing is that,

892
00:46:52,719 --> 00:46:56,279
Speaker 1: um two twelve, you're so busy that you kind of

893
00:46:56,320 --> 00:46:59,400
Speaker 1: don't have time to process it almost all really, so

894
00:46:59,560 --> 00:47:03,080
Speaker 1: he doesn't, Well, my book came out in September two

895
00:47:03,160 --> 00:47:06,759
Speaker 1: thousand twelve. Um, and so you know the combination of that,

896
00:47:06,920 --> 00:47:09,719
Speaker 1: and of course the you know Penguin. My PR people

897
00:47:09,760 --> 00:47:12,399
Speaker 1: had me doing shows like this like like every day

898
00:47:12,440 --> 00:47:15,600
Speaker 1: pretty much, right, and then there was random stuff. Hurricane

899
00:47:16,440 --> 00:47:19,960
Speaker 1: Uh Sandy hit at that time, so also's going on.

900
00:47:20,000 --> 00:47:23,200
Speaker 1: I was like kind of commuting in the dark from

901
00:47:23,320 --> 00:47:26,400
Speaker 1: my uh apartment then in Brooklyn to to the New

902
00:47:26,440 --> 00:47:28,680
Speaker 1: York Times office sort of do a media hit or whatnot.

903
00:47:28,800 --> 00:47:31,360
Speaker 1: It was just kind of crazy time and then you

904
00:47:31,400 --> 00:47:33,520
Speaker 1: emerge from it. And I was like at some conference

905
00:47:33,520 --> 00:47:35,840
Speaker 1: in Chicago the weekend after the election, and it was

906
00:47:35,920 --> 00:47:37,960
Speaker 1: like like every time I got the elevator, someone would

907
00:47:38,000 --> 00:47:40,120
Speaker 1: like recognize me and be like, hey, are you Nate Silver? Right?

908
00:47:40,120 --> 00:47:42,920
Speaker 1: And that was that was that's pretty weird. Yeah, so

909
00:47:43,000 --> 00:47:45,080
Speaker 1: I do less TV than I used to. But the

910
00:47:45,120 --> 00:47:47,680
Speaker 1: one place that I got to ask, if you have

911
00:47:47,800 --> 00:47:50,480
Speaker 1: this experience, if you ever check a bag when you

912
00:47:50,520 --> 00:47:54,640
Speaker 1: fly and you're waiting by the carousel, you're just fair game.

913
00:47:54,960 --> 00:47:56,880
Speaker 1: You're you're fair game. For some reason, it happens a

914
00:47:56,920 --> 00:47:58,480
Speaker 1: lot in airports. I think a lot of people watch

915
00:47:58,560 --> 00:48:01,720
Speaker 1: serious quote unquote TV and and airfats on the planes.

916
00:48:01,840 --> 00:48:04,439
Speaker 1: But um, but it definitely there's a half life based

917
00:48:04,480 --> 00:48:07,120
Speaker 1: on how recently you've been on TV. It probably happens,

918
00:48:07,239 --> 00:48:09,319
Speaker 1: you know, a couple of times a month still, but

919
00:48:09,440 --> 00:48:12,759
Speaker 1: not daily it used to be used to be. Yeah, yeah,

920
00:48:13,120 --> 00:48:15,839
Speaker 1: and certainly we may we may see that pick up

921
00:48:15,880 --> 00:48:19,560
Speaker 1: again towards next year, towards we'll see that although I'm

922
00:48:19,600 --> 00:48:22,319
Speaker 1: trying not to be uh, I don't know, I'm trying

923
00:48:22,360 --> 00:48:25,160
Speaker 1: not to overexpose myself too much. That makes sense, And

924
00:48:25,239 --> 00:48:26,960
Speaker 1: part of it is that you know, you've probably learned

925
00:48:26,960 --> 00:48:29,719
Speaker 1: this too. But if um, if you have your own

926
00:48:29,760 --> 00:48:32,680
Speaker 1: platform right and we have the opportunity to reach a

927
00:48:32,760 --> 00:48:35,640
Speaker 1: very large audience at five eight every day with the

928
00:48:35,719 --> 00:48:39,919
Speaker 1: writing and the stuff we produce in house podcasts and videos, um,

929
00:48:40,160 --> 00:48:43,560
Speaker 1: you get very finicky about how you're presented. You know.

930
00:48:43,680 --> 00:48:45,640
Speaker 1: It's really easy to kind of come across as oh,

931
00:48:45,719 --> 00:48:48,400
Speaker 1: here's this kind of you know, NERD is a know

932
00:48:48,480 --> 00:48:50,960
Speaker 1: it all and you know, and we trying to have

933
00:48:50,960 --> 00:48:53,600
Speaker 1: a little bit more subtlety and how we're presenting our

934
00:48:53,719 --> 00:48:57,640
Speaker 1: view of how campaigns and elections work and so you know, um,

935
00:48:57,719 --> 00:49:01,080
Speaker 1: so I'm trying not to uh go to overboard with

936
00:49:01,120 --> 00:49:04,720
Speaker 1: the with the media stuff. So what I certainly wouldn't

937
00:49:04,800 --> 00:49:09,399
Speaker 1: argue with Nerd. However, you never struck me as someone

938
00:49:09,400 --> 00:49:12,680
Speaker 1: who presents himself as a know it all. In fact,

939
00:49:13,440 --> 00:49:17,120
Speaker 1: your whole approaches. We don't know what the future can bring.

940
00:49:17,600 --> 00:49:21,040
Speaker 1: We don't understand which of these polls is gonna actually

941
00:49:21,080 --> 00:49:24,040
Speaker 1: be right. However, we can look at statistically taking the

942
00:49:24,080 --> 00:49:25,759
Speaker 1: average of all. Do you see how that didn't did

943
00:49:25,760 --> 00:49:30,000
Speaker 1: in the best adjusted for some minor modifications, and come

944
00:49:30,040 --> 00:49:33,280
Speaker 1: fairly close to the actual outcome. That's a humble approach,

945
00:49:33,480 --> 00:49:36,960
Speaker 1: not an arrogant. Appreciate that, thank you, But right, was

946
00:49:37,000 --> 00:49:38,920
Speaker 1: that part of the plan? So I think it's you know,

947
00:49:39,000 --> 00:49:41,400
Speaker 1: it's kind of how do you wait, what's your baseline?

948
00:49:41,880 --> 00:49:45,759
Speaker 1: Prior's right? Um? You know in a general election, the

949
00:49:45,840 --> 00:49:48,719
Speaker 1: prior in a campaign where the incumbents running is that

950
00:49:49,120 --> 00:49:51,439
Speaker 1: the incumbent will probably win unless things are pretty bad.

951
00:49:52,040 --> 00:49:54,440
Speaker 1: In an election with the incumbent, it's probably this is

952
00:49:54,440 --> 00:49:57,239
Speaker 1: going to be really close, right, Um. You know in

953
00:49:57,280 --> 00:50:00,239
Speaker 1: a primary it's maybe a little bit more difficult, but

954
00:50:00,320 --> 00:50:03,960
Speaker 1: you know, the history is that the establishment usually wins

955
00:50:04,400 --> 00:50:08,040
Speaker 1: the primary. In the primary, Um, you know, the candid

956
00:50:08,080 --> 00:50:11,840
Speaker 1: who it's a consensus building process, a nomination process. Literally,

957
00:50:11,840 --> 00:50:15,080
Speaker 1: it is a democratic party's nomination and the gops to

958
00:50:15,280 --> 00:50:19,759
Speaker 1: bestow they kind of set the rules for how delicates

959
00:50:19,760 --> 00:50:22,560
Speaker 1: are allegated and what happens. Right, So they have a

960
00:50:22,560 --> 00:50:24,839
Speaker 1: lot of influence on the a lot of influence, right.

961
00:50:24,960 --> 00:50:28,000
Speaker 1: You know, if the GOP doesn't want to nominate Donald Trump,

962
00:50:28,120 --> 00:50:31,000
Speaker 1: it's actually not a popularity contest, Right, they can make

963
00:50:31,000 --> 00:50:32,680
Speaker 1: it very difficult for Now and Crump to do it.

964
00:50:33,000 --> 00:50:35,400
Speaker 1: And there are maybe one or two cases. So McGovern

965
00:50:36,000 --> 00:50:40,719
Speaker 1: in seventy two, UM was very smart about getting UM

966
00:50:40,760 --> 00:50:42,800
Speaker 1: a lot of grassroots support from people who would be

967
00:50:42,800 --> 00:50:45,680
Speaker 1: delegate at the Democratic National Convention and kind of UM

968
00:50:45,760 --> 00:50:48,720
Speaker 1: he had designed the system for how delicates re alligated

969
00:50:48,760 --> 00:50:50,799
Speaker 1: and so he really knew allocated, so he knew how

970
00:50:50,840 --> 00:50:53,440
Speaker 1: to UM how to work that system really well. But

971
00:50:53,600 --> 00:50:59,120
Speaker 1: seventy two is kind of the only example of where, uh,

972
00:50:59,200 --> 00:51:02,920
Speaker 1: the inmates started burning the asylum. What about oh eight, Um,

973
00:51:03,120 --> 00:51:08,720
Speaker 1: wasn't Hillary sort of ordained in advance and sort of that?

974
00:51:09,000 --> 00:51:11,399
Speaker 1: So I'm upset the apple card a little bit. So

975
00:51:11,560 --> 00:51:14,440
Speaker 1: Clinton in two thousand and eight had ah, you know,

976
00:51:14,440 --> 00:51:16,239
Speaker 1: at this point in the race, like a ten or

977
00:51:16,239 --> 00:51:19,680
Speaker 1: fifteen point lead over Obama. This year it's kind of

978
00:51:19,719 --> 00:51:21,960
Speaker 1: a thirty point lead over Bernie Sanders. But it has

979
00:51:22,000 --> 00:51:23,719
Speaker 1: more let's do with Hillary and more to do with

980
00:51:23,920 --> 00:51:28,200
Speaker 1: uh with Barack Obama versus versus Bernie senators where Barack

981
00:51:28,200 --> 00:51:32,800
Speaker 1: Obama represented a whole, huge, kind of mainstream part of

982
00:51:32,840 --> 00:51:36,680
Speaker 1: the Democratic coalition. Um, obviously he did very well with

983
00:51:36,920 --> 00:51:39,200
Speaker 1: African American voters, which is a huge voting block in

984
00:51:39,239 --> 00:51:42,600
Speaker 1: the coalition. So you know, um, you can basically split

985
00:51:42,640 --> 00:51:45,760
Speaker 1: the Democratic Coalition into the third so you can have uh,

986
00:51:45,840 --> 00:51:49,560
Speaker 1: white liberals, white moderates, and non white voters. Actually it's

987
00:51:49,560 --> 00:51:53,160
Speaker 1: more of it really, right, and um, you know he

988
00:51:53,200 --> 00:51:56,000
Speaker 1: had better support across coalitions. But also he about the

989
00:51:56,040 --> 00:51:59,680
Speaker 1: support from Harry Reid and whatnot. Right, lots of influential

990
00:51:59,680 --> 00:52:02,040
Speaker 1: people in the dipretic party said said, we have an

991
00:52:02,040 --> 00:52:05,399
Speaker 1: amazing cadate in Obama, right, and we want to make

992
00:52:05,400 --> 00:52:07,759
Speaker 1: it a fair fight. Whereas this year there is not

993
00:52:07,920 --> 00:52:13,719
Speaker 1: a single sitting Democratic legislator excuse me, senator or representative

994
00:52:13,760 --> 00:52:16,640
Speaker 1: or governor who has endorsed Bernie Sanders, in part because

995
00:52:16,760 --> 00:52:20,960
Speaker 1: you know, he's not a Democrat. Um, he's seventy four

996
00:52:21,000 --> 00:52:26,080
Speaker 1: years old. He probably lose general election by by ten points. Right, Um,

997
00:52:26,120 --> 00:52:29,040
Speaker 1: you know, uh, what about Buden, what about al Gore.

998
00:52:29,160 --> 00:52:31,560
Speaker 1: Is anybody else got a credible gonna throw that hat

999
00:52:31,600 --> 00:52:34,120
Speaker 1: in the ring? Or is that just early chatter? I

1000
00:52:34,160 --> 00:52:37,680
Speaker 1: mean I think, you know, let's say that Hillary Clinton,

1001
00:52:37,719 --> 00:52:39,920
Speaker 1: I think it's not likely. Maybe it's a ten percent chance. Right,

1002
00:52:40,000 --> 00:52:42,640
Speaker 1: Let's say these scandals are more serious than they appear

1003
00:52:42,719 --> 00:52:45,799
Speaker 1: to be the email or something else, maybe something else.

1004
00:52:45,840 --> 00:52:48,760
Speaker 1: I'm not I'm not totally persuaded. So you know, baseball,

1005
00:52:48,760 --> 00:52:52,160
Speaker 1: there's just set value over replacement player. Right, It's like

1006
00:52:52,200 --> 00:52:54,640
Speaker 1: for me, it's like valuable replacement scandal. Right, We're gonna

1007
00:52:55,640 --> 00:52:57,680
Speaker 1: you see over the Clinton's that there's something like slightly

1008
00:52:57,719 --> 00:53:00,320
Speaker 1: funny going on, Right, So maybe something that's a given

1009
00:53:00,320 --> 00:53:04,520
Speaker 1: in Otherwise, Yeah, the placement scandal, I'm not sure that

1010
00:53:04,960 --> 00:53:08,000
Speaker 1: this is that remarkable proved to be, you know, the

1011
00:53:08,000 --> 00:53:11,680
Speaker 1: Republican Control Committee. But I have Republican friends who are

1012
00:53:11,680 --> 00:53:15,600
Speaker 1: in d C who insist the email scandal is totally

1013
00:53:15,600 --> 00:53:18,799
Speaker 1: different than it's. Sorry, this is more serious. You've seen

1014
00:53:18,840 --> 00:53:22,040
Speaker 1: a long decline in Clinton favorability ratings. It's really hard

1015
00:53:22,080 --> 00:53:25,000
Speaker 1: to determine cause and effect and say has that been

1016
00:53:25,040 --> 00:53:29,560
Speaker 1: accelerated by the email scandal or it certainly isn't helping

1017
00:53:29,960 --> 00:53:33,200
Speaker 1: um but overall, you look at markets and their betting markets,

1018
00:53:33,280 --> 00:53:36,640
Speaker 1: their perception of where Hilly Clinton stands hasn't changed. They've

1019
00:53:36,640 --> 00:53:38,560
Speaker 1: had her with about an eight chance of winning the

1020
00:53:38,600 --> 00:53:41,680
Speaker 1: nomination for a long time. It's not been affected except

1021
00:53:41,719 --> 00:53:45,600
Speaker 1: at the margin by by Bernie or by the scandals. UM.

1022
00:53:45,640 --> 00:53:49,440
Speaker 1: You know, Biden, UM or someone like Biden would at

1023
00:53:49,520 --> 00:53:52,400
Speaker 1: least be a little bit different in that UM in

1024
00:53:52,480 --> 00:53:55,320
Speaker 1: that let's say that Clinton is not doing well, Democrats

1025
00:53:55,360 --> 00:53:58,760
Speaker 1: would still, i think, want to intervene to not nominate

1026
00:53:58,760 --> 00:54:02,480
Speaker 1: Bernie Sanders. Whether they could say, well, Biden, he's fine, right,

1027
00:54:02,880 --> 00:54:05,480
Speaker 1: you know, Biden will give us a shot at least,

1028
00:54:05,520 --> 00:54:07,000
Speaker 1: and so you know, that would be one of the

1029
00:54:07,040 --> 00:54:10,200
Speaker 1: more significant developments so far. From what I've read, it

1030
00:54:10,200 --> 00:54:12,560
Speaker 1: all seems pretty speculative. I think part of what Biden

1031
00:54:12,560 --> 00:54:15,520
Speaker 1: wants to do is say, you know what, UM, it's

1032
00:54:15,520 --> 00:54:17,520
Speaker 1: tricky because it's August right now. It takes a long

1033
00:54:17,560 --> 00:54:20,480
Speaker 1: time to mount presentational campaign. What happens if there is

1034
00:54:20,520 --> 00:54:22,920
Speaker 1: maybe it's the email thing, maybe something new, maybe it's

1035
00:54:22,920 --> 00:54:25,160
Speaker 1: a health problem. What happens if Hiller Clinton, if that

1036
00:54:25,200 --> 00:54:29,040
Speaker 1: happens in November, and then UM and then only Bernie

1037
00:54:29,080 --> 00:54:32,440
Speaker 1: Sanders is on on the ballot, right, then you have

1038
00:54:32,600 --> 00:54:35,920
Speaker 1: a deeply damaged Democratic nominee in Clinton, or someone who

1039
00:54:35,920 --> 00:54:38,759
Speaker 1: the party will not want to nominate in Sanders. That's

1040
00:54:38,760 --> 00:54:41,920
Speaker 1: where you have the kind of break glass for emergency Biden, Gore,

1041
00:54:42,160 --> 00:54:45,920
Speaker 1: John Kerry uh type candidates. Right. Um, And so I

1042
00:54:45,920 --> 00:54:48,080
Speaker 1: think part of what Briden's trying to signal is to say, look,

1043
00:54:48,440 --> 00:54:51,880
Speaker 1: I'm here, I'm around if everything else is going wrong.

1044
00:54:52,480 --> 00:54:54,640
Speaker 1: You know, I'm at least thinking about this. I have

1045
00:54:54,640 --> 00:54:56,200
Speaker 1: a few people who are loyal to me. I can

1046
00:54:56,200 --> 00:54:59,400
Speaker 1: mount a campaign on the fly that's very different from

1047
00:54:59,440 --> 00:55:02,200
Speaker 1: actually run. And the way he sends signals it's very

1048
00:55:02,280 --> 00:55:05,080
Speaker 1: much like kind of his inside baseball you know, Marine

1049
00:55:05,080 --> 00:55:08,279
Speaker 1: Dowds Sunday column Right, those are kind of dog whistles too.

1050
00:55:08,280 --> 00:55:12,719
Speaker 1: I think other Democrats, more than to like mainstream voters, like, hey,

1051
00:55:12,840 --> 00:55:16,719
Speaker 1: if something really goes wrong with Clinton campaign, then then

1052
00:55:16,719 --> 00:55:19,279
Speaker 1: I'm here. But I would say, you know, um, we

1053
00:55:19,360 --> 00:55:22,520
Speaker 1: are speculative or dismissive. A lot of speculation about Clinton

1054
00:55:22,560 --> 00:55:24,640
Speaker 1: is struggling. If Biden ran, that would at least be

1055
00:55:24,680 --> 00:55:28,160
Speaker 1: a more tangible sign that that some influential Democrats are

1056
00:55:28,160 --> 00:55:32,200
Speaker 1: worried about Clinton. So right now. Statistically, if we had

1057
00:55:32,200 --> 00:55:36,399
Speaker 1: a guess, you're guessing Hillary Clinton is the Democratic Yeah,

1058
00:55:36,440 --> 00:55:38,080
Speaker 1: and you know, like I said, betting markets put the

1059
00:55:38,120 --> 00:55:41,560
Speaker 1: chances at eight percent, I put them marginally higher. I think,

1060
00:55:41,840 --> 00:55:46,040
Speaker 1: um you know, and I think of the of the percent,

1061
00:55:46,840 --> 00:55:48,799
Speaker 1: most of it's not Bernie Sanders. Most of it is

1062
00:55:48,840 --> 00:55:54,000
Speaker 1: that um Al Gore, Biden, Biden carry Gore right are

1063
00:55:54,000 --> 00:55:55,640
Speaker 1: the kind of the three people who could step in

1064
00:55:55,680 --> 00:56:00,839
Speaker 1: and in an emergency. So it's fascinating that you say

1065
00:56:00,880 --> 00:56:04,880
Speaker 1: that when we were looking at the O eight election,

1066
00:56:05,120 --> 00:56:10,200
Speaker 1: And I'm curious that you of your perspective. My thesis

1067
00:56:10,440 --> 00:56:14,040
Speaker 1: was at the time, so the Iraq War by then

1068
00:56:14,120 --> 00:56:17,000
Speaker 1: had turned sour, the economy was in a free fall,

1069
00:56:17,040 --> 00:56:21,319
Speaker 1: we were in the midst of a financial crisis. The

1070
00:56:21,560 --> 00:56:26,360
Speaker 1: running on the incumbents track record, I said, no matter

1071
00:56:26,440 --> 00:56:30,000
Speaker 1: who the GOP put up, they're willing to get destroyed

1072
00:56:30,040 --> 00:56:33,480
Speaker 1: by whoever the Democrats put up. But people have argued

1073
00:56:33,520 --> 00:56:35,640
Speaker 1: with me, well, if they would have nominated somebody instead

1074
00:56:35,640 --> 00:56:39,640
Speaker 1: of McCain, and someone less charismatic than Obama, perhaps it

1075
00:56:39,680 --> 00:56:41,719
Speaker 1: would have been a very different outcome. But I mean

1076
00:56:41,760 --> 00:56:45,840
Speaker 1: it was about the most difficult imaginable set of circumstances

1077
00:56:45,920 --> 00:56:47,880
Speaker 1: for the Republicans to win. I mean not that I

1078
00:56:47,880 --> 00:56:49,600
Speaker 1: feel sympathetic for them, because you know, they had a

1079
00:56:49,640 --> 00:56:52,319
Speaker 1: president who made some mistakes, shall we say, but you know,

1080
00:56:52,400 --> 00:56:56,680
Speaker 1: you had the economy collapsing, a very unpopular war, the

1081
00:56:56,840 --> 00:57:01,280
Speaker 1: sitting president had approval rating. Right, it wasn't a hugely

1082
00:57:01,360 --> 00:57:03,400
Speaker 1: low number for a sitting president. You can you know,

1083
00:57:03,400 --> 00:57:05,359
Speaker 1: one could argue about my ship won by twelve points

1084
00:57:05,400 --> 00:57:07,480
Speaker 1: instead of seven or something like that. Right, But that

1085
00:57:07,560 --> 00:57:11,760
Speaker 1: was about you know, there are some years that political

1086
00:57:11,800 --> 00:57:15,400
Speaker 1: scientists debate and say, you know, uh, he kind of

1087
00:57:15,480 --> 00:57:17,520
Speaker 1: was perceived to be kind of still in recession if

1088
00:57:17,520 --> 00:57:20,000
Speaker 1: you look back on revised cistics. Now it was actually doing.

1089
00:57:20,520 --> 00:57:23,480
Speaker 1: The recession was over before anybody realized. But oh way,

1090
00:57:23,520 --> 00:57:26,520
Speaker 1: it's one of those years where I don't know, Democrats

1091
00:57:26,560 --> 00:57:29,520
Speaker 1: would have had to make a huge error. Uh. And

1092
00:57:29,560 --> 00:57:33,480
Speaker 1: in some ways McCain wasn't a bad candidate. He was premoderate.

1093
00:57:33,600 --> 00:57:35,880
Speaker 1: He broke from the GOP in a lot of ways

1094
00:57:35,960 --> 00:57:38,760
Speaker 1: of the the time when that party was really unpopular. Right.

1095
00:57:39,040 --> 00:57:41,600
Speaker 1: You know, um, I thought he was a better candidate

1096
00:57:41,600 --> 00:57:45,320
Speaker 1: in two thousand. He seemed to be less captured and

1097
00:57:45,360 --> 00:57:48,360
Speaker 1: therefore less well, that's part of people are are kind

1098
00:57:48,360 --> 00:57:50,720
Speaker 1: of concerned about which of these seventeen Republicans will get

1099
00:57:50,720 --> 00:57:53,120
Speaker 1: nominated to some except that all kind of get except

1100
00:57:53,160 --> 00:57:55,880
Speaker 1: if it is a Trump or something. Right, Um, they're

1101
00:57:55,880 --> 00:57:57,280
Speaker 1: all going to kind of have the stamp of the

1102
00:57:57,320 --> 00:57:59,600
Speaker 1: party on them anyway, Right, And the ones who are

1103
00:57:59,640 --> 00:58:02,400
Speaker 1: more moderate Jeb Bush at least by the times you

1104
00:58:02,400 --> 00:58:05,800
Speaker 1: gets nominate, would be pulled more toward UM, toward the right.

1105
00:58:05,880 --> 00:58:08,800
Speaker 1: Scott Walker might be pulled more towards not the absolute left,

1106
00:58:08,840 --> 00:58:11,640
Speaker 1: but you know, become a little bit less conservative on

1107
00:58:11,680 --> 00:58:13,800
Speaker 1: some issues, and then they'll kind of go and do

1108
00:58:13,800 --> 00:58:16,120
Speaker 1: what they need to do for the general election. But um,

1109
00:58:16,160 --> 00:58:19,000
Speaker 1: but to some extent um, you know, it is a

1110
00:58:19,040 --> 00:58:22,960
Speaker 1: party driven process, and the candiates own policy positions might

1111
00:58:23,000 --> 00:58:26,040
Speaker 1: be swamped by what the consensus is among Is it

1112
00:58:26,160 --> 00:58:29,600
Speaker 1: still true you you um run the primary to the

1113
00:58:29,720 --> 00:58:33,760
Speaker 1: right and then um run the main election to the center,

1114
00:58:33,880 --> 00:58:36,400
Speaker 1: or for the Democrats, run the primary to the left

1115
00:58:36,440 --> 00:58:37,920
Speaker 1: and then tack to the center. For this, I mean

1116
00:58:37,960 --> 00:58:42,000
Speaker 1: that's the default, right, I mean I think, uh, you know,

1117
00:58:42,120 --> 00:58:44,880
Speaker 1: I think some partisans would say, well, it's all about

1118
00:58:44,880 --> 00:58:48,080
Speaker 1: turnout now, so you want to motivate your base, right.

1119
00:58:48,160 --> 00:58:51,120
Speaker 1: I'm always a little bit suspicious of that kind of

1120
00:58:51,120 --> 00:58:53,320
Speaker 1: the notion that where you can kind of have your

1121
00:58:53,360 --> 00:58:57,160
Speaker 1: cake and eat it too. Right. Um, you know, it

1122
00:58:57,160 --> 00:58:59,160
Speaker 1: maybe works a little bit better for Democrats in the

1123
00:58:59,240 --> 00:59:02,000
Speaker 1: sense that there are just a few more Democrats and

1124
00:59:02,280 --> 00:59:05,560
Speaker 1: the electorate and Republicans, so if everyone turns out, Um,

1125
00:59:05,720 --> 00:59:08,360
Speaker 1: they might have an edge out of those numbers. Different

1126
00:59:08,440 --> 00:59:12,520
Speaker 1: what what's the GOP percentage? And how accurate is is

1127
00:59:12,560 --> 00:59:15,640
Speaker 1: that about independence? I always thought people just angry at

1128
00:59:15,680 --> 00:59:17,560
Speaker 1: their party. A lot of people say their independence aren't

1129
00:59:17,560 --> 00:59:20,320
Speaker 1: really independence, right, but you know Democrats have right now

1130
00:59:20,320 --> 00:59:23,400
Speaker 1: about it five or six point edge and party identification,

1131
00:59:23,800 --> 00:59:26,120
Speaker 1: but it doesn't give them any well, els would be

1132
00:59:26,120 --> 00:59:31,200
Speaker 1: a whole another hour long segment right that time. Um,

1133
00:59:31,240 --> 00:59:33,480
Speaker 1: you know, but there are a lot of qualifications to that,

1134
00:59:33,520 --> 00:59:36,360
Speaker 1: one being that they Democrats get a lot of people

1135
00:59:36,360 --> 00:59:40,680
Speaker 1: who are only marginally likely to vote. Um. You know,

1136
00:59:40,720 --> 00:59:42,480
Speaker 1: if if we had mandatory voting in this country, a

1137
00:59:42,520 --> 00:59:45,360
Speaker 1: lot of things would be different. That's weird hypothetical, right, Um.

1138
00:59:45,400 --> 00:59:50,800
Speaker 1: Without mandatory voting benefit who would benefit Democrats? The Republicans

1139
00:59:51,200 --> 00:59:54,200
Speaker 1: on the other hand, you know a lot of times um,

1140
00:59:54,280 --> 00:59:56,600
Speaker 1: people won't turn out necessarily. Also, one thing that's a

1141
00:59:56,640 --> 01:00:00,200
Speaker 1: little bit tricky is that, um, because the GOP has

1142
01:00:00,240 --> 01:00:03,720
Speaker 1: been unpopular, Um, a lot of people that formally would say,

1143
01:00:03,720 --> 01:00:06,800
Speaker 1: you know, I'm a Republican, now they'll say I'm an independent,

1144
01:00:07,400 --> 01:00:10,240
Speaker 1: but I lean Republicans look at leaners and it gets

1145
01:00:10,240 --> 01:00:14,040
Speaker 1: a little bit closer still. Um. So I couldn't completely

1146
01:00:14,080 --> 01:00:16,880
Speaker 1: relate to that because we were talking before the show.

1147
01:00:16,920 --> 01:00:20,040
Speaker 1: I grew up. Jacob Javits was our senator, and that

1148
01:00:20,240 --> 01:00:24,840
Speaker 1: sort of center right republican you Kasik is probably the

1149
01:00:24,840 --> 01:00:28,040
Speaker 1: closest thing we mentioned. You know, Bush has actually surprised

1150
01:00:28,040 --> 01:00:31,120
Speaker 1: me to the extent that, um, he has run pretty

1151
01:00:31,160 --> 01:00:35,960
Speaker 1: explicitly as as a moderate right, which is an interesting approach.

1152
01:00:36,000 --> 01:00:37,840
Speaker 1: I think maybe he figures that, look, I have a

1153
01:00:37,920 --> 01:00:42,720
Speaker 1: very long track record in public life, right, Um, I'm

1154
01:00:42,720 --> 01:00:46,480
Speaker 1: not going to fool anyone like Romney tried to do

1155
01:00:46,640 --> 01:00:50,160
Speaker 1: in two thousand and twelve, right, Um, and then looked

1156
01:00:50,160 --> 01:00:52,760
Speaker 1: like a flip flopper in the general election. You know,

1157
01:00:53,080 --> 01:00:54,840
Speaker 1: they're always cases like The one thing that amazed me

1158
01:00:54,920 --> 01:00:57,280
Speaker 1: is that in two thousand and eight, all three of

1159
01:00:57,280 --> 01:01:01,760
Speaker 1: the major Democratic candidates were against gay marriage. Right officially

1160
01:01:01,840 --> 01:01:04,360
Speaker 1: even though Obama had said, like in two thousand two

1161
01:01:04,640 --> 01:01:07,280
Speaker 1: that he was pro gay marriage. Right. So I'm like,

1162
01:01:07,320 --> 01:01:10,600
Speaker 1: you know, are you really fooling anyone? I'm not. I'm

1163
01:01:10,640 --> 01:01:12,800
Speaker 1: not totally sure. And so I think Bush is saying,

1164
01:01:12,800 --> 01:01:15,200
Speaker 1: you know what I am? Who I am? I'm a

1165
01:01:15,280 --> 01:01:20,040
Speaker 1: moderate conservative, right, Um, you know, I'd be very electable.

1166
01:01:20,120 --> 01:01:21,800
Speaker 1: Although this is the thing that's tricky for him is

1167
01:01:21,840 --> 01:01:24,280
Speaker 1: usually you'd have a candidate like that, um, like the

1168
01:01:24,360 --> 01:01:26,800
Speaker 1: McCain type candid who's a moderate conservative and they have

1169
01:01:26,920 --> 01:01:31,720
Speaker 1: good UM electability numbers, and Bush because the Bush family

1170
01:01:31,800 --> 01:01:34,280
Speaker 1: name is still not that popular, maybe he's not seen

1171
01:01:34,320 --> 01:01:36,960
Speaker 1: as that relatable. You know, He's head to head numbers

1172
01:01:36,960 --> 01:01:39,240
Speaker 1: are not any better than like the more conservative candidates

1173
01:01:39,240 --> 01:01:41,800
Speaker 1: against Clinton. Right, So let's talk about the head to

1174
01:01:41,880 --> 01:01:45,520
Speaker 1: head numbers. So when we look at Clinton versus fill

1175
01:01:45,560 --> 01:01:50,320
Speaker 1: in the blank, Hillary versus Jeb Hillary versus Rubio, Hillary

1176
01:01:50,520 --> 01:01:57,080
Speaker 1: versus Kasik, who stands the best shot at at winning? Well,

1177
01:01:57,120 --> 01:01:58,360
Speaker 1: this is what we talked about. You know, I probably

1178
01:01:58,400 --> 01:02:00,000
Speaker 1: would not look at those head to head numbers very

1179
01:02:00,120 --> 01:02:03,439
Speaker 1: much right now. They're almost meaningless. Yeah, if you're gonna

1180
01:02:03,440 --> 01:02:06,480
Speaker 1: look at them, you know, candidates who have comfortable name

1181
01:02:06,480 --> 01:02:08,920
Speaker 1: recognition to Hillary, which is kind of almost no one.

1182
01:02:09,000 --> 01:02:12,920
Speaker 1: But you know, for that reason, the Bush number, you know,

1183
01:02:13,040 --> 01:02:15,760
Speaker 1: I would discount almost all of it instead of literally

1184
01:02:15,800 --> 01:02:17,600
Speaker 1: all of it as I might for like Hillary versus

1185
01:02:17,680 --> 01:02:21,520
Speaker 1: Rubio or whatnot. Um, but usually there's correlation between how

1186
01:02:21,600 --> 01:02:25,680
Speaker 1: moderate a guy is and how well he does among independents. Um.

1187
01:02:25,720 --> 01:02:29,520
Speaker 1: You know, because Bush isn't personally seeing that favorably, he

1188
01:02:29,600 --> 01:02:31,240
Speaker 1: might not have that edge this year, which is kind

1189
01:02:31,280 --> 01:02:35,960
Speaker 1: of why I say, if you're Republican, you might say, well, um,

1190
01:02:36,080 --> 01:02:38,560
Speaker 1: you know what, if we look at Rubio versus Bush,

1191
01:02:38,600 --> 01:02:41,880
Speaker 1: then we get a guy number one who's more conservative, right,

1192
01:02:42,400 --> 01:02:44,520
Speaker 1: number two who is actually a little bit more popular

1193
01:02:44,680 --> 01:02:48,360
Speaker 1: with independence, and number three avoids this whole voice versus

1194
01:02:48,360 --> 01:02:51,960
Speaker 1: Clinton dynasty angle. Again, it's not my job to advise parties,

1195
01:02:52,000 --> 01:02:53,840
Speaker 1: but that's what Rubio is one candidate where that you

1196
01:02:53,880 --> 01:02:56,760
Speaker 1: could kind of go back and say, I totally get

1197
01:02:56,800 --> 01:03:00,000
Speaker 1: why they nominated him and what we're all discounting somebody.

1198
01:03:00,000 --> 01:03:03,280
Speaker 1: I guess I'm you know, I'm bullish on Rubio's You know,

1199
01:03:03,520 --> 01:03:09,000
Speaker 1: here's the thing that I find fascinating. Bright, Hispanic articulate,

1200
01:03:09,480 --> 01:03:13,240
Speaker 1: like a really like and you know, has almost a

1201
01:03:13,440 --> 01:03:20,080
Speaker 1: Kennedy esk photo genetic photogenic sort of thing. But when

1202
01:03:20,080 --> 01:03:23,080
Speaker 1: you look at his policies and I know the Republicans

1203
01:03:23,120 --> 01:03:26,760
Speaker 1: are I don't remember was you or wrote about the

1204
01:03:26,840 --> 01:03:31,760
Speaker 1: gender gap on the GOP versus Democrats, he is surprisingly

1205
01:03:31,840 --> 01:03:36,560
Speaker 1: hard right. If he was a more centrist, moderate conservative,

1206
01:03:37,000 --> 01:03:39,840
Speaker 1: I would think that he would be the front runner

1207
01:03:40,000 --> 01:03:43,240
Speaker 1: for for getting elected in the general election. Well, and

1208
01:03:43,280 --> 01:03:45,240
Speaker 1: we'll and we'll see, right. You know, one way this

1209
01:03:45,280 --> 01:03:49,120
Speaker 1: could play out, um, is that people sour on Bush

1210
01:03:49,160 --> 01:03:52,240
Speaker 1: and then you'll see Rubio maybe move a little bit

1211
01:03:52,280 --> 01:03:56,920
Speaker 1: to the the center. Despite what he said recently about abortion,

1212
01:03:57,080 --> 01:04:00,439
Speaker 1: no exceptions for rape, incest, life of the mother, Like

1213
01:04:00,640 --> 01:04:04,560
Speaker 1: you don't hear that from mainstream candidates the past twenty years.

1214
01:04:04,800 --> 01:04:06,440
Speaker 1: When he is pretty and there are fiscal systems to

1215
01:04:06,480 --> 01:04:09,560
Speaker 1: the try and quantify how conservative people are, and Rubio

1216
01:04:09,680 --> 01:04:12,920
Speaker 1: is very conservative. I mean, the GOP is a very

1217
01:04:12,920 --> 01:04:18,240
Speaker 1: conservative party. So relative to the other seventeen people, he's

1218
01:04:18,240 --> 01:04:20,560
Speaker 1: about in the middle of the GOP field. But he

1219
01:04:20,560 --> 01:04:25,200
Speaker 1: would be, you know, twenty years ago considered very conservative. Um.

1220
01:04:25,280 --> 01:04:29,600
Speaker 1: But you know, um who's more to the right of Rubio,

1221
01:04:30,320 --> 01:04:36,040
Speaker 1: So Walker probably, Um, you know Walker is UM far

1222
01:04:36,120 --> 01:04:38,480
Speaker 1: enough to the right where he might kind of compete

1223
01:04:38,480 --> 01:04:42,160
Speaker 1: for votes with UM with Ted Cruz a right and

1224
01:04:42,480 --> 01:04:46,080
Speaker 1: Carson for that matter, right, UM, which you know, maybe

1225
01:04:46,160 --> 01:04:47,720
Speaker 1: isn't a good place to be. There is gonna be

1226
01:04:47,760 --> 01:04:51,120
Speaker 1: some support for candidates like those, I think throughout the race.

1227
01:04:51,320 --> 01:04:54,640
Speaker 1: What's interesting about Trump is that, UM, he's really difficult

1228
01:04:54,640 --> 01:04:58,080
Speaker 1: to peg Ideologically, He's kind of all over the place.

1229
01:04:58,360 --> 01:05:00,280
Speaker 1: And his support too, was all over the place. Right,

1230
01:05:00,280 --> 01:05:04,560
Speaker 1: people with somoa's capturing Tea Party voters not true? Right.

1231
01:05:04,560 --> 01:05:09,880
Speaker 1: That coalition he has is drawn from all different kind

1232
01:05:09,920 --> 01:05:14,160
Speaker 1: of ideological parts the GOP in. What's one reason I'm

1233
01:05:14,160 --> 01:05:15,800
Speaker 1: scipticle by him is that it's also drawn from people

1234
01:05:15,800 --> 01:05:19,600
Speaker 1: who don't traditionally turn out and vote in primaries. Right.

1235
01:05:19,760 --> 01:05:21,720
Speaker 1: It's a lot of people would say, you know, I

1236
01:05:21,800 --> 01:05:23,760
Speaker 1: like that kind of six it to the establishment and

1237
01:05:23,800 --> 01:05:27,800
Speaker 1: six it to the media. Right, they're fascinated by him.

1238
01:05:27,840 --> 01:05:31,160
Speaker 1: You know, does that translate into driving to the Iowa

1239
01:05:31,240 --> 01:05:34,680
Speaker 1: caucus in the snow when you've never voted before. You know,

1240
01:05:35,520 --> 01:05:38,520
Speaker 1: we don't know, there's some reason to be skeptical of that. UM,

1241
01:05:38,560 --> 01:05:41,080
Speaker 1: but Trump is you know, if you kind of if

1242
01:05:41,120 --> 01:05:44,480
Speaker 1: you average out his policy positions, then they're kind of,

1243
01:05:44,960 --> 01:05:48,680
Speaker 1: you know, fairly typical right of center. But that means

1244
01:05:48,680 --> 01:05:50,880
Speaker 1: you have some things that are like radical Tea Party

1245
01:05:50,920 --> 01:05:54,120
Speaker 1: and some things that are like practically like socialists. Yeah,

1246
01:05:54,280 --> 01:05:58,440
Speaker 1: and it kind of averages out um and that is interesting.

1247
01:05:58,440 --> 01:06:00,000
Speaker 1: I mean, you know, there were some article right today

1248
01:06:00,080 --> 01:06:04,840
Speaker 1: about how um so that was smart, uh, about how

1249
01:06:05,080 --> 01:06:09,160
Speaker 1: usually a candidate is so constrained right, and these disagreements

1250
01:06:09,200 --> 01:06:11,480
Speaker 1: remember in tuth as an eight the Democrats on on

1251
01:06:11,640 --> 01:06:15,040
Speaker 1: healthcare right, Democrats had huge fights about you know, did

1252
01:06:15,080 --> 01:06:19,400
Speaker 1: Obama have a employee mandate in his healthcare bill or

1253
01:06:19,440 --> 01:06:22,760
Speaker 1: a mandate and Hillary didn't. I mean, these are you know,

1254
01:06:23,160 --> 01:06:25,960
Speaker 1: pretty minor differences, whereas Trump is saying, I'm not going

1255
01:06:26,000 --> 01:06:28,560
Speaker 1: to play by those constraints where you can be you know,

1256
01:06:28,640 --> 01:06:30,480
Speaker 1: an eight point two or an eight point five on

1257
01:06:30,600 --> 01:06:32,280
Speaker 1: something right, I'm just gonna be all over the place.

1258
01:06:32,760 --> 01:06:34,920
Speaker 1: And that's how a lot of real people I think

1259
01:06:35,000 --> 01:06:38,880
Speaker 1: to write, um right there, their views aren't necessarily in

1260
01:06:38,920 --> 01:06:41,440
Speaker 1: tonally consistent, So so I would I would dispute that,

1261
01:06:41,520 --> 01:06:43,680
Speaker 1: like I think, you know, I don't think the party's

1262
01:06:43,720 --> 01:06:47,000
Speaker 1: views are that internally consistent either, Like I'm not sure why,

1263
01:06:47,160 --> 01:06:51,080
Speaker 1: you know, policy your views on taxation and gay marriage

1264
01:06:51,160 --> 01:06:55,400
Speaker 1: and um and uber and the war and Iraq should

1265
01:06:55,440 --> 01:06:59,080
Speaker 1: be correlate with one another that much. But you know, certainly, um,

1266
01:06:59,080 --> 01:07:03,240
Speaker 1: you know, these views coalescent to parties and people often. Um,

1267
01:07:03,280 --> 01:07:05,280
Speaker 1: you know, our Democrats are Helbicans for like one or

1268
01:07:05,320 --> 01:07:06,680
Speaker 1: two big issues, and they kind of say, you know what,

1269
01:07:06,720 --> 01:07:08,280
Speaker 1: it's a lot easier to kind of agree with the

1270
01:07:08,320 --> 01:07:11,120
Speaker 1: party on everything. Right, So I kind of disagree with

1271
01:07:11,160 --> 01:07:14,960
Speaker 1: the idea that, oh, um, trumps use are more incoherent,

1272
01:07:15,000 --> 01:07:17,360
Speaker 1: because I don't think anyone's views are all that coherent

1273
01:07:17,800 --> 01:07:22,040
Speaker 1: necessarily right, but definitely it's it's maps differently than any

1274
01:07:22,080 --> 01:07:25,880
Speaker 1: other candidates would. So at this point, given all the changes,

1275
01:07:26,160 --> 01:07:29,080
Speaker 1: well let's let's use gay marriage as an example. Yeah,

1276
01:07:29,200 --> 01:07:32,640
Speaker 1: the Supreme Court has ruled. I thought Case six. Answer was,

1277
01:07:33,000 --> 01:07:34,600
Speaker 1: and by the way, this is a kiss of death

1278
01:07:35,080 --> 01:07:38,360
Speaker 1: if I like you when a Republican primary, you're done

1279
01:07:38,680 --> 01:07:40,960
Speaker 1: last year. I thought, last last election, I'm like this

1280
01:07:41,040 --> 01:07:45,120
Speaker 1: huntsman guy seems pretty yeah, toast, that's the kisses I

1281
01:07:45,160 --> 01:07:48,160
Speaker 1: thought Case six answer was, Oh, that's who the responsible

1282
01:07:48,200 --> 01:07:51,440
Speaker 1: adults on the stages. It's him, So you know, he's not.

1283
01:07:51,680 --> 01:07:53,560
Speaker 1: And a lot of people who are running cases campaign

1284
01:07:53,880 --> 01:07:57,240
Speaker 1: ran Huntsman's campaign. Um, same people. It's a lot of

1285
01:07:57,320 --> 01:07:59,600
Speaker 1: same people. And their strategy is, you know, you want

1286
01:07:59,600 --> 01:08:02,400
Speaker 1: to kind of appealed to to the media. So the

1287
01:08:02,400 --> 01:08:04,920
Speaker 1: first debate was interesting in that, um, you know, the

1288
01:08:04,960 --> 01:08:08,200
Speaker 1: media spam was that case that had done really well, right,

1289
01:08:08,280 --> 01:08:10,880
Speaker 1: And I thought he did pretty well. I personally liked him,

1290
01:08:10,880 --> 01:08:13,120
Speaker 1: But I'm somewhere probably near you want to play the spectrum,

1291
01:08:13,120 --> 01:08:16,000
Speaker 1: and I know, right, um, socially progressive, a little, a

1292
01:08:16,040 --> 01:08:20,240
Speaker 1: little fiscally conservative and and to me, I I've assumed

1293
01:08:20,280 --> 01:08:23,080
Speaker 1: the abortion issue has been settled. I can't believe that's

1294
01:08:23,120 --> 01:08:26,920
Speaker 1: still ongoing. And and now it looks like the question

1295
01:08:27,000 --> 01:08:29,720
Speaker 1: of of nwage equality is settled. And the only one

1296
01:08:29,720 --> 01:08:32,000
Speaker 1: on the stage who seemed willing to say that was Casing.

1297
01:08:32,320 --> 01:08:35,280
Speaker 1: But if you looked at Google searches, which they now

1298
01:08:35,280 --> 01:08:38,720
Speaker 1: releas stayed in real time, Ben Carson was doing really well.

1299
01:08:38,720 --> 01:08:43,240
Speaker 1: People were interested in him. Cross is much less crazy

1300
01:08:43,360 --> 01:08:45,840
Speaker 1: than I expected, not that that's saying any He was

1301
01:08:45,840 --> 01:08:49,240
Speaker 1: pretty he was pretty down, toned down and mellow, and

1302
01:08:49,360 --> 01:08:52,559
Speaker 1: I know I thought that were okay, right, Um, yeah,

1303
01:08:52,600 --> 01:08:54,519
Speaker 1: for sure, when he's gaining the polls a little bit.

1304
01:08:54,560 --> 01:08:57,080
Speaker 1: So let's talk about Carly Fianna, who did really well

1305
01:08:57,120 --> 01:09:01,040
Speaker 1: at the children's tables. People have derisively called it, but

1306
01:09:01,720 --> 01:09:06,000
Speaker 1: I know her as a horrible CEO oversaw one of

1307
01:09:06,040 --> 01:09:09,800
Speaker 1: the worst mergers in technology history, you know, HP and

1308
01:09:09,880 --> 01:09:13,120
Speaker 1: Compact is described like a O L Time Warner. It

1309
01:09:13,160 --> 01:09:16,639
Speaker 1: was just a disaster, and she's laid off. I don't

1310
01:09:16,680 --> 01:09:20,599
Speaker 1: remember the number, a hundred thousand, some huge number. Can

1311
01:09:20,680 --> 01:09:24,840
Speaker 1: she really be a credible candidate given that background, she

1312
01:09:24,840 --> 01:09:28,639
Speaker 1: she's managed to fail And again send your hate email

1313
01:09:28,680 --> 01:09:33,160
Speaker 1: to Nate Silvertie, but I think she's failed upwards and

1314
01:09:33,200 --> 01:09:39,280
Speaker 1: I'm wholly unimpressed with her as a candidate UM or

1315
01:09:39,439 --> 01:09:42,519
Speaker 1: as a as a CEO, as a corporate I thought

1316
01:09:42,520 --> 01:09:45,800
Speaker 1: she did well in UM in the debate and the

1317
01:09:45,880 --> 01:09:48,519
Speaker 1: JV debate. It was very self possessed and kind of

1318
01:09:48,760 --> 01:09:51,639
Speaker 1: right understood the balance between you don't want to sound

1319
01:09:52,360 --> 01:09:53,960
Speaker 1: like a wing nut, but you want to make sure

1320
01:09:54,040 --> 01:09:56,200
Speaker 1: that you're memorable on that stage. People are kind of

1321
01:09:56,200 --> 01:09:58,320
Speaker 1: hap paying attention, you know. I know, I think it's

1322
01:09:58,360 --> 01:10:01,240
Speaker 1: a little bit premature. I think she's um she's not

1323
01:10:01,439 --> 01:10:03,120
Speaker 1: quite yet at the point where you're gonna see a

1324
01:10:03,120 --> 01:10:08,559
Speaker 1: lot of scrutiny. Um. But yeah, there's uh, you know,

1325
01:10:08,560 --> 01:10:11,360
Speaker 1: this is one of the things about comparing like Hillary

1326
01:10:11,400 --> 01:10:14,559
Speaker 1: and Bernie Sanders, right. You know, if you compare a

1327
01:10:14,640 --> 01:10:17,439
Speaker 1: candid who has um received a lot of scrutiny from

1328
01:10:17,439 --> 01:10:19,240
Speaker 1: the media, from other members of their party, from the

1329
01:10:19,280 --> 01:10:22,280
Speaker 1: other party, versus one who hasn't, it's a really apples

1330
01:10:22,280 --> 01:10:25,679
Speaker 1: to or or just comparison. Right. Um. You know, Sofia

1331
01:10:25,680 --> 01:10:29,759
Speaker 1: a Mina has not yet been through that scrutiny phase. Right. Um.

1332
01:10:29,960 --> 01:10:32,760
Speaker 1: If she keeps doing well, then her reward is that

1333
01:10:32,800 --> 01:10:36,960
Speaker 1: she'll she'll then endure that, right Um. But you know

1334
01:10:37,000 --> 01:10:39,439
Speaker 1: it's not I don't know. I would think she would

1335
01:10:39,479 --> 01:10:42,360
Speaker 1: have more chance me the nominee than Trump. Maybe you'd

1336
01:10:42,360 --> 01:10:44,599
Speaker 1: have to think about what about her is the vice

1337
01:10:44,600 --> 01:10:48,160
Speaker 1: presidential candidate. If Hillary is the nominee, put a woman

1338
01:10:48,200 --> 01:10:51,400
Speaker 1: on the ticket, I would imagine that would offset, um,

1339
01:10:51,479 --> 01:10:55,760
Speaker 1: some of the gender gap that is inherent with Hillary running. Sure,

1340
01:10:55,800 --> 01:11:00,679
Speaker 1: although women don't necessarily um support women who aren't good

1341
01:11:00,720 --> 01:11:04,120
Speaker 1: on on quote unquote women's issues, right, I mean, certainly

1342
01:11:04,120 --> 01:11:06,559
Speaker 1: not every woman is pro choice, right, but you know

1343
01:11:06,600 --> 01:11:09,800
Speaker 1: what many are but but many are, right, Um, and

1344
01:11:09,880 --> 01:11:12,960
Speaker 1: so you know, Republican women might have a more difficult

1345
01:11:12,960 --> 01:11:17,559
Speaker 1: time of it than than Democratic women. And what since,

1346
01:11:17,960 --> 01:11:22,519
Speaker 1: uh we're talking about Carly she kind of um caught

1347
01:11:22,640 --> 01:11:26,800
Speaker 1: some positive attention for her comments about the whole Megan

1348
01:11:26,920 --> 01:11:30,519
Speaker 1: Kelly Fox thing. Yea, how does that play into this? Uh?

1349
01:11:30,680 --> 01:11:33,120
Speaker 1: This is one thing that I, uh, one bad prediction

1350
01:11:33,120 --> 01:11:35,880
Speaker 1: we've made, right, I kind of thought that, boy when um,

1351
01:11:35,960 --> 01:11:39,360
Speaker 1: when Fox News started taking on Trump, that Fox were

1352
01:11:39,400 --> 01:11:42,160
Speaker 1: hammering and that Fox News wouldn't back down, and they

1353
01:11:42,160 --> 01:11:46,080
Speaker 1: did back down really really quickly to I mean, so

1354
01:11:46,160 --> 01:11:48,320
Speaker 1: you sound as surprised as I was. Yeah, I was

1355
01:11:48,360 --> 01:11:51,719
Speaker 1: surprised because I kind of thought that. But look, Fox News,

1356
01:11:51,760 --> 01:11:55,560
Speaker 1: like any major media organizations, a complicated place, right. Um.

1357
01:11:55,600 --> 01:11:58,759
Speaker 1: You know, they want ratings and attention. On the one hand,

1358
01:11:59,080 --> 01:12:03,120
Speaker 1: the their hand, you know, um have some influence in

1359
01:12:03,160 --> 01:12:07,760
Speaker 1: Republican politics, so on the some Yeah, on the third hand,

1360
01:12:07,880 --> 01:12:10,559
Speaker 1: you know, you have lots of individual producers, some of

1361
01:12:10,560 --> 01:12:12,920
Speaker 1: whom are awesome journalists and some of them are not.

1362
01:12:13,040 --> 01:12:16,400
Speaker 1: And you know it's a complicated place. But um, but

1363
01:12:16,479 --> 01:12:19,439
Speaker 1: you know, it was funny how explicit Trump was about

1364
01:12:19,479 --> 01:12:22,360
Speaker 1: the quid pro quel like, I got you terrific ratings

1365
01:12:22,479 --> 01:12:24,280
Speaker 1: right and made you all this money, and you're giving

1366
01:12:24,280 --> 01:12:27,280
Speaker 1: me this right and I'm like, yeah, you have a point,

1367
01:12:27,360 --> 01:12:30,160
Speaker 1: you know, And um, you know, it was amazing. I

1368
01:12:30,200 --> 01:12:34,000
Speaker 1: thought Fox did a great job on the on the debate.

1369
01:12:34,600 --> 01:12:37,719
Speaker 1: I watched about an hour of it, maybe a little more,

1370
01:12:38,000 --> 01:12:40,920
Speaker 1: and at that point, you know it started. You know,

1371
01:12:41,200 --> 01:12:43,000
Speaker 1: it's when you're watching a game and it looks like

1372
01:12:43,040 --> 01:12:45,519
Speaker 1: a blowout and all right, I don't need to watch

1373
01:12:45,560 --> 01:12:47,680
Speaker 1: the stay for the fourth quarter. That's kind of but

1374
01:12:47,960 --> 01:12:50,360
Speaker 1: it seemed really interesting. The one name we didn't mention,

1375
01:12:51,080 --> 01:12:53,920
Speaker 1: um who I could give you a whole laundry list

1376
01:12:54,000 --> 01:12:56,839
Speaker 1: of reasons why he's terrible and I don't like him.

1377
01:12:56,880 --> 01:12:59,320
Speaker 1: But I thought Chris Christie did a really good job

1378
01:12:59,360 --> 01:13:02,040
Speaker 1: at the debate. So the Christie story is kind of fascinating.

1379
01:13:02,040 --> 01:13:05,840
Speaker 1: We've been on um on the Chris Christie's toast man wagon,

1380
01:13:06,000 --> 01:13:09,120
Speaker 1: I recall, for a long time before it was cool, um,

1381
01:13:09,680 --> 01:13:11,720
Speaker 1: you know, in part because he's a guy who the

1382
01:13:11,720 --> 01:13:15,320
Speaker 1: party wants, Kenny, who's conservative but also reliable, right, And

1383
01:13:15,400 --> 01:13:17,680
Speaker 1: I think Christie is he is a guy who's not

1384
01:13:17,720 --> 01:13:20,759
Speaker 1: that reliable. He's no longer very popular with independent voters.

1385
01:13:20,840 --> 01:13:25,439
Speaker 1: But but who knows. I I almost think everyone at

1386
01:13:25,479 --> 01:13:26,840
Speaker 1: the office dis agrees with this, right. I think it

1387
01:13:26,840 --> 01:13:29,720
Speaker 1: would almost help Christie if um if he dropped out

1388
01:13:29,720 --> 01:13:31,960
Speaker 1: of the top ten and then had the stage to

1389
01:13:32,120 --> 01:13:35,439
Speaker 1: himself at the next JV debate, right, and could be

1390
01:13:35,560 --> 01:13:39,280
Speaker 1: alpha male and totally dominant. I don't think Christy has

1391
01:13:39,439 --> 01:13:42,000
Speaker 1: is very likely at all to win the nomination, but

1392
01:13:42,080 --> 01:13:45,240
Speaker 1: he's a guy who could have a surge right in

1393
01:13:45,280 --> 01:13:48,000
Speaker 1: the media. The media is fascinated by Christie. He's a

1394
01:13:48,040 --> 01:13:51,080
Speaker 1: fascinating guy. He's great on his feet. I thought he

1395
01:13:51,240 --> 01:13:54,479
Speaker 1: I thought he hurt Ran Paul. I mean, he really

1396
01:13:54,520 --> 01:13:57,720
Speaker 1: came at him hard. And then so there's guys who

1397
01:13:57,800 --> 01:14:02,000
Speaker 1: you know, there's Christie, Ben Carson, Ted Cruz, the three season.

1398
01:14:02,240 --> 01:14:03,800
Speaker 1: You know, they're all in the category of candidates I

1399
01:14:03,840 --> 01:14:06,000
Speaker 1: think could be the next candidate who searches but probably

1400
01:14:06,000 --> 01:14:11,519
Speaker 1: couldn't win the nomination, whereas Rubio Kik kind of slower

1401
01:14:11,520 --> 01:14:13,800
Speaker 1: and steadier. I think Rubio is a one candidate where

1402
01:14:13,840 --> 01:14:15,960
Speaker 1: you get the sense that he's not trying to win

1403
01:14:16,000 --> 01:14:18,759
Speaker 1: the nomination in August, which is probably a really smart

1404
01:14:18,840 --> 01:14:22,360
Speaker 1: thing to do. But my general impression of him is

1405
01:14:22,920 --> 01:14:26,040
Speaker 1: that he's a little young, he's a little green. Some

1406
01:14:26,080 --> 01:14:29,120
Speaker 1: of the other candidates that compared him to some guy

1407
01:14:29,640 --> 01:14:34,040
Speaker 1: at this term in his first Senate term named Barack Obama,

1408
01:14:34,520 --> 01:14:37,519
Speaker 1: which I find am using. But you could see there's

1409
01:14:37,560 --> 01:14:43,080
Speaker 1: a political um uh, there's a wind at his back

1410
01:14:43,240 --> 01:14:46,280
Speaker 1: that that could help him. If not this, uh not,

1411
01:14:47,120 --> 01:14:50,040
Speaker 1: then certainly. Yeah. Though I think one thing a lot

1412
01:14:50,080 --> 01:14:53,519
Speaker 1: of UM Republicans have learned is that UM and a

1413
01:14:53,520 --> 01:14:55,639
Speaker 1: lot of cands have learned is that you know, there's

1414
01:14:55,680 --> 01:14:58,360
Speaker 1: no time like today, right, you know, I'm sure are

1415
01:14:58,400 --> 01:15:01,760
Speaker 1: a lot of candidates this year, UM where you know

1416
01:15:01,960 --> 01:15:05,040
Speaker 1: Rick Perry, who was someone who had one of the

1417
01:15:05,040 --> 01:15:07,840
Speaker 1: better chances to beat Romney until he kind of imploded, right,

1418
01:15:08,160 --> 01:15:10,720
Speaker 1: you know, he was not in the top ten. Rick Santorum,

1419
01:15:10,720 --> 01:15:13,400
Speaker 1: who kind of sensibly finished in second place, was not

1420
01:15:13,479 --> 01:15:14,600
Speaker 1: in the top ten. So there has to be a

1421
01:15:14,600 --> 01:15:16,639
Speaker 1: lot of these candidates now we're kicking themselves and saying,

1422
01:15:16,920 --> 01:15:19,960
Speaker 1: you know what, Um, Romney a little bit underrated as

1423
01:15:19,960 --> 01:15:23,120
Speaker 1: candidate maybe, but boy, you know it was a much

1424
01:15:23,160 --> 01:15:27,200
Speaker 1: easier nomination to win in twelve and probably kicking themselves

1425
01:15:27,200 --> 01:15:29,040
Speaker 1: for not having run four years. Why do you say

1426
01:15:29,120 --> 01:15:33,280
Speaker 1: undernominated as a candidate. I'm curious about that because in

1427
01:15:33,360 --> 01:15:37,200
Speaker 1: the end, it's hard to beat an incumbent president. Um,

1428
01:15:37,320 --> 01:15:41,520
Speaker 1: the economy improved to the point where it was okay.

1429
01:15:42,040 --> 01:15:44,280
Speaker 1: I would take the other start of the arguments, say,

1430
01:15:44,439 --> 01:15:48,160
Speaker 1: when Romney was running against Obama, you had a very

1431
01:15:48,320 --> 01:15:53,120
Speaker 1: unpopular or at least it appeared to be unpopular. Um, Obamacare.

1432
01:15:53,160 --> 01:15:56,040
Speaker 1: It's since turned out to be quite successful. But here

1433
01:15:56,080 --> 01:15:58,400
Speaker 1: was a guy who put Obamacare only was Romney care

1434
01:15:58,439 --> 01:16:03,200
Speaker 1: in his state and the weakest economy, the weakest recovery

1435
01:16:03,240 --> 01:16:06,360
Speaker 1: you've seen in half a century. Certain, it was a gimme,

1436
01:16:06,479 --> 01:16:10,320
Speaker 1: but like like the average elected in president wins the

1437
01:16:10,400 --> 01:16:13,880
Speaker 1: second term by by eight percentage points in the popular vote,

1438
01:16:13,880 --> 01:16:16,880
Speaker 1: No Ababa went by four three point eight or something. Right,

1439
01:16:16,920 --> 01:16:20,640
Speaker 1: So Obama kind of did underperform, But it was a

1440
01:16:20,720 --> 01:16:24,719
Speaker 1: huge electoral college blowout. It was so the electoral college

1441
01:16:24,760 --> 01:16:26,559
Speaker 1: can make It's kind of designed that way to make

1442
01:16:26,600 --> 01:16:29,479
Speaker 1: a relatively small edge and the popular vote be bigger.

1443
01:16:29,800 --> 01:16:32,800
Speaker 1: UM in the electoral college, right, Um, And probably I

1444
01:16:32,800 --> 01:16:35,439
Speaker 1: think his campaign helped at the margin and some of

1445
01:16:35,439 --> 01:16:40,000
Speaker 1: the swing states to UM. But I don't know, I mean, UM,

1446
01:16:40,040 --> 01:16:43,200
Speaker 1: you know, I don't think Romney ran a terrific campaign.

1447
01:16:43,280 --> 01:16:45,680
Speaker 1: I think he was not a disaster though, either. And

1448
01:16:45,720 --> 01:16:48,120
Speaker 1: I think people, um, you know, again, you go back

1449
01:16:48,120 --> 01:16:52,000
Speaker 1: to the fact that historically incumbent presidents are re elected

1450
01:16:52,400 --> 01:16:55,599
Speaker 1: seventy percent of time UM and this was just one

1451
01:16:55,600 --> 01:16:57,240
Speaker 1: of those. And a lot of subtle ways, like going

1452
01:16:57,280 --> 01:17:00,599
Speaker 1: into like UM a college football stadium or something, right,

1453
01:17:00,960 --> 01:17:05,639
Speaker 1: UM Michigan Stadium where UM US, at least it used

1454
01:17:05,640 --> 01:17:07,760
Speaker 1: to be University Michigan was tough to to beat there,

1455
01:17:07,800 --> 01:17:09,920
Speaker 1: and it kind of manifests self in all sorts of

1456
01:17:10,400 --> 01:17:12,640
Speaker 1: subtle way us to get better officiating, you know, the

1457
01:17:12,680 --> 01:17:15,200
Speaker 1: playing surface a little bit better, a little bit better

1458
01:17:15,600 --> 01:17:19,000
Speaker 1: under pressure. It's tough to beat an incumbent president. And

1459
01:17:19,040 --> 01:17:21,800
Speaker 1: I'm not saying Romney was the most horrific nominee, but

1460
01:17:21,840 --> 01:17:24,599
Speaker 1: these guys get kind of tarnished with the loser brush

1461
01:17:24,720 --> 01:17:27,000
Speaker 1: or John Kerry is another one where he lost by

1462
01:17:27,040 --> 01:17:29,360
Speaker 1: by two points. You know, people are right in the

1463
01:17:29,400 --> 01:17:34,080
Speaker 1: middle of an active war, which at the time UM

1464
01:17:34,120 --> 01:17:37,880
Speaker 1: that war was somewhat popular, right where the plurality of

1465
01:17:37,880 --> 01:17:42,320
Speaker 1: Americans in the four right six months later, different six

1466
01:17:42,360 --> 01:17:44,680
Speaker 1: months later, I think um Bush might have lost that

1467
01:17:44,720 --> 01:17:47,559
Speaker 1: election was really close. But um, but you know, at

1468
01:17:47,600 --> 01:17:52,200
Speaker 1: the time, the economy was had recovered from a mild recession,

1469
01:17:52,800 --> 01:17:56,400
Speaker 1: um and looked not bad, right, um, you know, and

1470
01:17:56,439 --> 01:17:58,559
Speaker 1: you had a war that was becoming less and less popular.

1471
01:17:58,720 --> 01:18:02,400
Speaker 1: But um it it's easy to have hindsight bias now right.

1472
01:18:02,720 --> 01:18:04,640
Speaker 1: And their models part of the two their models that

1473
01:18:04,720 --> 01:18:07,839
Speaker 1: look at factors like the economy measured in various ways,

1474
01:18:08,400 --> 01:18:11,680
Speaker 1: you know, war measured in various ways. Incumbent see and

1475
01:18:11,760 --> 01:18:13,599
Speaker 1: most of them said, oh, you know, Carrie should lose

1476
01:18:13,640 --> 01:18:16,719
Speaker 1: by by several points, and you know he did about

1477
01:18:16,760 --> 01:18:19,040
Speaker 1: as well. I didn't realize it was that close. I

1478
01:18:19,080 --> 01:18:23,559
Speaker 1: thought carry had lost much more substantially Democrats, because you

1479
01:18:23,560 --> 01:18:25,519
Speaker 1: do have this kind of tied shifting in how Iraq

1480
01:18:25,640 --> 01:18:28,360
Speaker 1: was perceived and the Bush presidency was perceived, right, um,

1481
01:18:29,040 --> 01:18:31,599
Speaker 1: And I think you had Democrats kind of saying, boy,

1482
01:18:31,600 --> 01:18:34,439
Speaker 1: how can we not win this election? And that's kind

1483
01:18:34,439 --> 01:18:37,519
Speaker 1: of parallel to two thousand. I didn't hear a lot

1484
01:18:37,560 --> 01:18:39,720
Speaker 1: of Democrats saying that to me. I thought it was

1485
01:18:40,280 --> 01:18:44,080
Speaker 1: sitting President nine eleven, still very fresh in memory. You're

1486
01:18:44,160 --> 01:18:46,760
Speaker 1: you're the old expression I kept hearing was you don't

1487
01:18:46,840 --> 01:18:49,000
Speaker 1: change horses mid stream. You're in the middle of an

1488
01:18:49,000 --> 01:18:52,200
Speaker 1: active war, and the tendency is to reelect the income

1489
01:18:52,240 --> 01:18:54,360
Speaker 1: in in the middle of an active war. Is that

1490
01:18:54,479 --> 01:18:57,400
Speaker 1: is that overstating the circumstances. Well, war, it depends on

1491
01:18:57,439 --> 01:18:59,600
Speaker 1: it's a popular war and an unpopular war. Right in

1492
01:18:59,680 --> 01:19:03,040
Speaker 1: Iraq was going from a war that was initially very popular, uh,

1493
01:19:03,160 --> 01:19:05,479
Speaker 1: to one that wasn't And as easy as amazing as

1494
01:19:05,479 --> 01:19:08,120
Speaker 1: it sounds like, it's easy to forget the impact of

1495
01:19:08,120 --> 01:19:12,760
Speaker 1: September eleven, um, you know, and that still cast a

1496
01:19:12,840 --> 01:19:16,479
Speaker 1: real shadow on the way the race was contested to

1497
01:19:16,760 --> 01:19:19,000
Speaker 1: so things about Vietnam and seventy two and next we

1498
01:19:19,080 --> 01:19:22,479
Speaker 1: got reelected. That was a terribly unpopular war by seventy two,

1499
01:19:22,760 --> 01:19:25,400
Speaker 1: by the late sixties it was unpopular. But it just

1500
01:19:25,439 --> 01:19:27,519
Speaker 1: seems to be a sense that's not well. S two

1501
01:19:27,640 --> 01:19:29,639
Speaker 1: is one of those cases where the kindo was actually

1502
01:19:29,680 --> 01:19:32,280
Speaker 1: not so bad and seventy to um. But that is

1503
01:19:32,320 --> 01:19:35,800
Speaker 1: a case where the Democrats blew any chance they had

1504
01:19:35,840 --> 01:19:39,080
Speaker 1: by nominating McGovern And in this convention where it went

1505
01:19:39,160 --> 01:19:40,800
Speaker 1: until three in the morning that the world gave his

1506
01:19:40,880 --> 01:19:43,439
Speaker 1: speech and they picked a VP who they had to

1507
01:19:43,479 --> 01:19:47,559
Speaker 1: then veto. Right, I mean that was you know. So, um,

1508
01:19:47,880 --> 01:19:49,760
Speaker 1: so people should remember that SENTI Too campaign because it

1509
01:19:49,800 --> 01:19:53,320
Speaker 1: means like you know, this app doesn't always win, right. Um,

1510
01:19:53,360 --> 01:19:57,120
Speaker 1: there is this kind of you know five chance empirically

1511
01:19:57,280 --> 01:20:01,760
Speaker 1: that um that they don't um. But the odds are

1512
01:20:01,800 --> 01:20:05,799
Speaker 1: are you know, certainly in Hillary Clinton's favor uh, certainly

1513
01:20:05,840 --> 01:20:09,439
Speaker 1: against someone like like Donald Trump right where you know,

1514
01:20:09,479 --> 01:20:11,479
Speaker 1: and maybe at the party the GOP is weaker than

1515
01:20:11,479 --> 01:20:14,000
Speaker 1: it once was, but they're going to fight until they're

1516
01:20:14,040 --> 01:20:17,320
Speaker 1: dying embers to not nominate Donald Trump. He's kind of

1517
01:20:17,360 --> 01:20:20,479
Speaker 1: a trifecta of things that um that they wouldn't like.

1518
01:20:20,560 --> 01:20:24,439
Speaker 1: Number one, he's not very popular with independent voters. Um.

1519
01:20:24,479 --> 01:20:26,880
Speaker 1: People talk about how you know kind of Trump is

1520
01:20:26,920 --> 01:20:29,920
Speaker 1: extremely he's a new populism take of the country. Well,

1521
01:20:30,000 --> 01:20:32,960
Speaker 1: you know he is really awful. Favorability ratings with the

1522
01:20:33,040 --> 01:20:38,439
Speaker 1: electorate as a whole about negative positive right um. Number

1523
01:20:38,439 --> 01:20:40,880
Speaker 1: two he's now that conservative. And number three, you don't

1524
01:20:40,880 --> 01:20:43,320
Speaker 1: know we're gonna get with him, right. Um. So you

1525
01:20:43,320 --> 01:20:45,960
Speaker 1: know there's kind of the three criteria that the party

1526
01:20:45,960 --> 01:20:49,280
Speaker 1: would say, boy, you know it could this could be

1527
01:20:49,320 --> 01:20:52,120
Speaker 1: a real disaster. And and yeah, so gun to the

1528
01:20:52,120 --> 01:20:55,920
Speaker 1: head today you're are you looking at Hillary Bush or

1529
01:20:56,360 --> 01:20:59,920
Speaker 1: Clinton Bush or is it Clinton somebody else? I mean,

1530
01:21:00,120 --> 01:21:02,840
Speaker 1: you know, I kind of think, um, on the GP side,

1531
01:21:02,840 --> 01:21:10,520
Speaker 1: it's about Rubio Walker and other Right, Really, that's fascinating.

1532
01:21:10,520 --> 01:21:13,639
Speaker 1: I wouldn't have thought maybe I'd diminished Walker's chances a bit.

1533
01:21:13,680 --> 01:21:16,719
Speaker 1: He struggled in the polls after the debate somewhat unless

1534
01:21:16,720 --> 01:21:20,280
Speaker 1: he really comes back in the next debate. He seemed

1535
01:21:20,360 --> 01:21:23,120
Speaker 1: kind of soften, not ready for prime And that was

1536
01:21:23,200 --> 01:21:26,120
Speaker 1: some reputation he had, is that this guy is does

1537
01:21:26,240 --> 01:21:28,760
Speaker 1: find one on one but maybe his trouble standing out

1538
01:21:28,800 --> 01:21:31,760
Speaker 1: a little bit. At the same time, you know, um,

1539
01:21:31,840 --> 01:21:33,760
Speaker 1: he's leading the Iowa polls or at least as a

1540
01:21:33,840 --> 01:21:37,000
Speaker 1: leading non Trump candidate, depending on what poll you look at, right,

1541
01:21:37,080 --> 01:21:39,439
Speaker 1: and he kind of is that's a new category. Leading

1542
01:21:39,560 --> 01:21:42,320
Speaker 1: non Trump can today. But you do almost have to

1543
01:21:42,360 --> 01:21:46,439
Speaker 1: pull Trump. It's a little bit how uh, in a

1544
01:21:46,520 --> 01:21:50,679
Speaker 1: weird way with um Ron Paul in two thousand twelves

1545
01:21:51,439 --> 01:21:54,040
Speaker 1: where Ron Paul was going to get of the vote

1546
01:21:55,240 --> 01:21:58,160
Speaker 1: and if the vote were divided evenly enough among other candidates,

1547
01:21:58,160 --> 01:22:01,240
Speaker 1: then um, then that my have been enough to win.

1548
01:22:01,280 --> 01:22:04,200
Speaker 1: He came pretty close, right, you know. You know, but

1549
01:22:04,320 --> 01:22:06,680
Speaker 1: being the leading non Trump Candida. I mean the term

1550
01:22:06,720 --> 01:22:10,559
Speaker 1: front runners used um sometimes in horse racing for a

1551
01:22:10,600 --> 01:22:13,800
Speaker 1: horse that uh jumps out to really quickly, but it's

1552
01:22:13,840 --> 01:22:17,000
Speaker 1: not going to have the stamina stamina to go the distance. Right.

1553
01:22:17,080 --> 01:22:19,120
Speaker 1: And so you know, if you call Trump for front runner,

1554
01:22:19,120 --> 01:22:20,680
Speaker 1: I kind of think in that more ibronic sense, it

1555
01:22:20,760 --> 01:22:23,439
Speaker 1: might be more worthwhile. But you could also hold on

1556
01:22:23,479 --> 01:22:26,640
Speaker 1: to and then not really grow from there. There are

1557
01:22:26,640 --> 01:22:30,160
Speaker 1: a lot of guys that, um, you know again, Pat Robertson,

1558
01:22:30,160 --> 01:22:35,680
Speaker 1: Pat Buchanan, Paul Huckabee, Santaurum. Right, Um, you know you

1559
01:22:35,760 --> 01:22:38,640
Speaker 1: can win Iowa in a field with seventeen candidates, with

1560
01:22:38,760 --> 01:22:43,960
Speaker 1: twenty doesn't mean a lot, you know. One poster recently said, Um, okay,

1561
01:22:44,040 --> 01:22:48,559
Speaker 1: let's just take this down to three candidates. Um, you know, Trump, Walker,

1562
01:22:48,640 --> 01:22:52,799
Speaker 1: and Bush. I think it was and Trump barely gained anything,

1563
01:22:52,960 --> 01:22:55,720
Speaker 1: whereas Walker and Rubio pick up support from the sense

1564
01:22:55,800 --> 01:23:00,120
Speaker 1: Christie's and and the fi Arenas and the whatnot right. Um,

1565
01:23:00,160 --> 01:23:03,680
Speaker 1: And so as that field consolidates, right, then Trump will

1566
01:23:03,680 --> 01:23:05,880
Speaker 1: have trouble or maybe it won't consolidate, but then you're

1567
01:23:05,880 --> 01:23:09,960
Speaker 1: gonna have like know him with a plurality of delegates. Um,

1568
01:23:10,000 --> 01:23:14,640
Speaker 1: this is a scenario that journalists dream of, um, brokered convention, madness,

1569
01:23:14,680 --> 01:23:18,160
Speaker 1: brookered convention or at least, you know, maybe resolved before

1570
01:23:18,160 --> 01:23:20,400
Speaker 1: the convention. But and there's a lot of backroom dealing.

1571
01:23:20,640 --> 01:23:22,400
Speaker 1: Trump is not going to benefit from that because the

1572
01:23:22,400 --> 01:23:25,960
Speaker 1: Party of Salesman faciates his guts. Right. So, um, you know.

1573
01:23:26,080 --> 01:23:28,160
Speaker 1: But the one thing that would make me more bullish

1574
01:23:28,200 --> 01:23:30,639
Speaker 1: about Trump's chances. You hear he's actually investing in staff

1575
01:23:31,120 --> 01:23:33,760
Speaker 1: in Iowa, right, is he or is he not? You

1576
01:23:33,760 --> 01:23:36,519
Speaker 1: never know. It's there's a lot of smoke mirrors with

1577
01:23:36,560 --> 01:23:39,120
Speaker 1: any candy at the stage the race. Um. And also

1578
01:23:39,200 --> 01:23:40,960
Speaker 1: you can invest in something and not do a good

1579
01:23:41,040 --> 01:23:43,880
Speaker 1: job of it. Right. But you know, um, you know

1580
01:23:44,439 --> 01:23:47,920
Speaker 1: again the one candidate who defied the establishment and one

1581
01:23:48,040 --> 01:23:51,000
Speaker 1: was McGovern seventy two a long time ago. Very different

1582
01:23:51,040 --> 01:23:54,360
Speaker 1: cannate than Trump, very different. Um. But you know it

1583
01:23:54,439 --> 01:23:58,160
Speaker 1: wasn't just uh he got lucky. He had lots of

1584
01:23:58,200 --> 01:24:01,160
Speaker 1: grass roots support. He understood how to in caucuses, He

1585
01:24:01,200 --> 01:24:05,400
Speaker 1: understood the delegate rules. He understood you know, party conventions

1586
01:24:05,400 --> 01:24:07,639
Speaker 1: and how the delegates are allocated that the caucus can

1587
01:24:07,720 --> 01:24:11,920
Speaker 1: change later on. Right. Um, you know, so if he

1588
01:24:12,040 --> 01:24:14,840
Speaker 1: if Trump has that side of the operation, really good logistics,

1589
01:24:15,200 --> 01:24:16,920
Speaker 1: and understands the rules and has had a lot has

1590
01:24:16,960 --> 01:24:19,280
Speaker 1: a lot of lawyers willing to litigate when the GOP

1591
01:24:19,400 --> 01:24:21,920
Speaker 1: tries to change the rules. Then then you know, maybe

1592
01:24:21,920 --> 01:24:24,960
Speaker 1: i'd make his odds six percent instead of two percent.

1593
01:24:25,520 --> 01:24:30,600
Speaker 1: But still, yeah, I think so, Because I was the

1594
01:24:31,080 --> 01:24:33,680
Speaker 1: last political question I wanted to ask you was what

1595
01:24:33,840 --> 01:24:39,000
Speaker 1: happens in the head to head between Trump and Clinton? UM,

1596
01:24:39,120 --> 01:24:43,439
Speaker 1: I think Clinton wins thirty eight states or something. It's

1597
01:24:43,479 --> 01:24:47,360
Speaker 1: that so so Trump's core audience is his core audience.

1598
01:24:47,640 --> 01:24:51,479
Speaker 1: He's got those folks, but as other candidates drop out

1599
01:24:51,479 --> 01:24:55,639
Speaker 1: of the race, he's not gonna necessarily attract those those voters.

1600
01:24:55,760 --> 01:24:58,479
Speaker 1: Now he's not that popular, right, I mean, you know,

1601
01:24:58,520 --> 01:25:01,000
Speaker 1: again his kind of unfavorable reading ORG about Hillary because

1602
01:25:01,000 --> 01:25:03,639
Speaker 1: she's now like five points it's like, you know, forty

1603
01:25:03,920 --> 01:25:06,400
Speaker 1: to forty two or something. Now right with Trump, it's

1604
01:25:06,439 --> 01:25:09,879
Speaker 1: like sixty five to thirty. He is not very popular

1605
01:25:09,960 --> 01:25:13,519
Speaker 1: apart from UM, apart from you know, a certain number

1606
01:25:13,600 --> 01:25:16,640
Speaker 1: of Republicans. Um, of course you could also run as

1607
01:25:16,640 --> 01:25:19,360
Speaker 1: an independent. Is that likely to happen or is that

1608
01:25:20,439 --> 01:25:23,200
Speaker 1: a lot of noise? I mean, ordinarily you'd say no,

1609
01:25:23,439 --> 01:25:25,280
Speaker 1: but I think Donald Trump doesn't like to hear no

1610
01:25:25,439 --> 01:25:29,160
Speaker 1: for an answer, right, And you can certainly decide. Um,

1611
01:25:29,200 --> 01:25:32,320
Speaker 1: you know, I'm having too much fun, right, right, And

1612
01:25:32,400 --> 01:25:35,160
Speaker 1: it's gonna be grateful whatever my next show is, when

1613
01:25:35,200 --> 01:25:37,080
Speaker 1: it's gonna be great. Right. And he seems like he's

1614
01:25:37,080 --> 01:25:40,639
Speaker 1: a guy he wouldn't mind spying people. You know, It's again,

1615
01:25:40,760 --> 01:25:43,679
Speaker 1: it's not clearly he's really a Republican. Rights Like, Ordinarily

1616
01:25:43,680 --> 01:25:45,840
Speaker 1: a Republican would say, you know, at the end of

1617
01:25:45,840 --> 01:25:48,240
Speaker 1: the day, I owe an oath of loyalty to my party. Right,

1618
01:25:48,439 --> 01:25:50,080
Speaker 1: Trump might say, you know, I don't care if it's

1619
01:25:50,160 --> 01:25:53,800
Speaker 1: Jeff Bush or Clinton, right, or Rubio or Clinton. You know,

1620
01:25:53,840 --> 01:25:57,160
Speaker 1: it's all the same difference to me. Right, I'm so

1621
01:25:57,280 --> 01:26:01,000
Speaker 1: all over the place, So so I don't know. You know, Um,

1622
01:26:01,040 --> 01:26:04,160
Speaker 1: there was a story out some time ago that last

1623
01:26:04,240 --> 01:26:08,000
Speaker 1: year Trump spoke to Clinton and um, yeah, he was

1624
01:26:08,120 --> 01:26:12,080
Speaker 1: encouraged to run or or he wasn't discharged from running

1625
01:26:12,160 --> 01:26:15,200
Speaker 1: or something like that, and the conspiracy nuts all uncrazy

1626
01:26:15,200 --> 01:26:17,400
Speaker 1: about it. Yeah, I think it's quite a conspiracy. But

1627
01:26:17,439 --> 01:26:19,120
Speaker 1: you you clearly you have a guy who's running as

1628
01:26:19,120 --> 01:26:22,280
Speaker 1: a Republican who does not care about the long term

1629
01:26:22,280 --> 01:26:26,240
Speaker 1: misinterest of the Republican Party at all. Right, Um, you

1630
01:26:26,280 --> 01:26:30,599
Speaker 1: know he's a potential spoiler. That's very different. And and

1631
01:26:30,680 --> 01:26:34,240
Speaker 1: even though I think he's very unluckly to win the nomination,

1632
01:26:34,880 --> 01:26:36,840
Speaker 1: the chance that can make it difficult for the GEOP

1633
01:26:36,960 --> 01:26:40,839
Speaker 1: to nominate to candidate, difficult for them to coordinate a message.

1634
01:26:41,120 --> 01:26:43,320
Speaker 1: And the chance I still think is low but not

1635
01:26:43,400 --> 01:26:46,920
Speaker 1: negligible that he would run as independent. Right, So I

1636
01:26:46,920 --> 01:26:51,240
Speaker 1: think he's um mostly bad news for the GP. I

1637
01:26:51,280 --> 01:26:52,400
Speaker 1: was trying to make. I always want to make my

1638
01:26:52,400 --> 01:26:55,479
Speaker 1: own contrarian case, gendering a lot of enthusiasm where to

1639
01:26:55,520 --> 01:26:58,320
Speaker 1: look reasonable as compared to him. But you know, he

1640
01:26:58,400 --> 01:27:01,280
Speaker 1: certainly brought a lot of excitement so race that probably

1641
01:27:01,280 --> 01:27:03,639
Speaker 1: wouldn't be here August. But what I say is Trump

1642
01:27:03,680 --> 01:27:06,160
Speaker 1: can be a problem for the GOP, even if he's

1643
01:27:06,240 --> 01:27:09,640
Speaker 1: very unluckly to win. Right. Quite fascinating. So let me

1644
01:27:09,640 --> 01:27:13,040
Speaker 1: shift gears on you and talk a little bit about

1645
01:27:13,080 --> 01:27:16,120
Speaker 1: you and some other aspects. You know. We we quickly

1646
01:27:16,160 --> 01:27:21,200
Speaker 1: went over your background on the on the radio portion. Um,

1647
01:27:21,360 --> 01:27:23,960
Speaker 1: prior to Pakoda, there was a quote of yours that

1648
01:27:24,000 --> 01:27:29,360
Speaker 1: I really liked. You. You were working at KPMG in Chicago,

1649
01:27:29,840 --> 01:27:32,519
Speaker 1: and someone had once asked you what's your biggest regret

1650
01:27:32,520 --> 01:27:35,439
Speaker 1: in life? And you said, spending four years of my

1651
01:27:35,520 --> 01:27:38,599
Speaker 1: life in a job I didn't like? Yeah? Is that accurate?

1652
01:27:38,640 --> 01:27:42,000
Speaker 1: Is that a true? Uh? I've been true, true quote.

1653
01:27:42,080 --> 01:27:45,080
Speaker 1: I've been lucky enough. And so whether you work, it's

1654
01:27:45,120 --> 01:27:48,479
Speaker 1: been half your time working or you know, man with

1655
01:27:48,560 --> 01:27:50,240
Speaker 1: onum work and I we bet listeners of the show

1656
01:27:50,280 --> 01:27:53,719
Speaker 1: are somewhere higher on the spectrum of how much energy

1657
01:27:53,720 --> 01:27:56,639
Speaker 1: they to vote to work. Um, you know it's time.

1658
01:27:56,680 --> 01:27:58,880
Speaker 1: You can't get back really, and even though the day

1659
01:27:58,920 --> 01:28:00,840
Speaker 1: to day can be a grind any job, just to

1660
01:28:00,880 --> 01:28:05,680
Speaker 1: have a job where you fundamentally our challenged intellectually, were you? Uh,

1661
01:28:05,840 --> 01:28:08,519
Speaker 1: where you enjoy yourself, where you have ownership of the work,

1662
01:28:08,880 --> 01:28:10,600
Speaker 1: where you like the people I work, you work with?

1663
01:28:10,640 --> 01:28:12,240
Speaker 1: I did like my colleagues ATKPMG. That was not the

1664
01:28:12,240 --> 01:28:14,639
Speaker 1: issue at all, right, But um, but a really great

1665
01:28:14,680 --> 01:28:16,360
Speaker 1: team I work with now, I mean that's you know,

1666
01:28:16,800 --> 01:28:21,040
Speaker 1: that's really important to say the least. And then and

1667
01:28:21,080 --> 01:28:24,559
Speaker 1: then you quit KPMG and you start playing poker. Yeah,

1668
01:28:24,760 --> 01:28:27,439
Speaker 1: how did how did that work out? Um? So there

1669
01:28:27,479 --> 01:28:29,280
Speaker 1: was kind of a poker boom, which I would call

1670
01:28:29,360 --> 01:28:33,400
Speaker 1: more of a poker bubble really in the mid two thousands. So,

1671
01:28:33,760 --> 01:28:36,479
Speaker 1: uh so my buddy at KPMG is like, Hey, we're

1672
01:28:36,479 --> 01:28:38,479
Speaker 1: gonna get a game going. I'm like, I'm really competitive.

1673
01:28:38,479 --> 01:28:41,640
Speaker 1: So I started practicing online at like Yahoo where you

1674
01:28:41,680 --> 01:28:44,160
Speaker 1: play for free, and I'm like, you can't really play

1675
01:28:44,200 --> 01:28:47,519
Speaker 1: for poker without without real money, right, So some online

1676
01:28:47,560 --> 01:28:51,360
Speaker 1: site was like deposit Bucks and you can withdraw it

1677
01:28:51,400 --> 01:28:53,400
Speaker 1: and it's like basically free money. Right. Of course, I

1678
01:28:53,439 --> 01:28:56,120
Speaker 1: got kind of hooked and started staying up all night

1679
01:28:56,160 --> 01:28:58,320
Speaker 1: and playing poker. And at the time, you know, I

1680
01:28:58,320 --> 01:29:00,519
Speaker 1: played a little bit, and Kyle just a couple of

1681
01:29:00,560 --> 01:29:02,760
Speaker 1: years out of colle at that point, and you know,

1682
01:29:02,840 --> 01:29:05,160
Speaker 1: the quality of play was really poor and just kind

1683
01:29:05,160 --> 01:29:08,160
Speaker 1: of using a very basic ABC strategy. You can make

1684
01:29:08,160 --> 01:29:10,040
Speaker 1: a little bit of money if you started to, you know,

1685
01:29:10,720 --> 01:29:13,360
Speaker 1: um bluff a little bit and be a little more aggressive.

1686
01:29:13,360 --> 01:29:14,760
Speaker 1: Could do even better than that. So but yeah, I

1687
01:29:14,840 --> 01:29:17,320
Speaker 1: kind of made my living mostly playing poker for a

1688
01:29:17,320 --> 01:29:19,479
Speaker 1: couple of years, so you did pretty well and otherwise

1689
01:29:19,760 --> 01:29:23,000
Speaker 1: I made a couple hundred thousand and then lost some

1690
01:29:23,080 --> 01:29:24,760
Speaker 1: of it, but enough that, you know, it was a

1691
01:29:24,840 --> 01:29:27,880
Speaker 1: very cool experience. But ultimately it was like, you know,

1692
01:29:27,880 --> 01:29:30,439
Speaker 1: it's kind of like the proverbial hundred dollar bills sitting

1693
01:29:30,439 --> 01:29:32,240
Speaker 1: on the ground. It kind of dried up once people

1694
01:29:32,240 --> 01:29:36,000
Speaker 1: realize that that's money to be made. It got arbitraged away.

1695
01:29:36,200 --> 01:29:39,040
Speaker 1: That's the famous quote, if if there ever was a

1696
01:29:39,080 --> 01:29:42,559
Speaker 1: magic formula, it would eventually be whittled away as everybody

1697
01:29:42,600 --> 01:29:45,240
Speaker 1: started using the formula, at least in the stock market.

1698
01:29:45,320 --> 01:29:48,160
Speaker 1: That's uh, that's that's how it works. Let's talk a

1699
01:29:48,200 --> 01:29:52,840
Speaker 1: little bit about hunches, right, And I don't know if

1700
01:29:52,880 --> 01:29:55,320
Speaker 1: if Malcolm Gladwell is the best person to use as

1701
01:29:55,360 --> 01:29:58,000
Speaker 1: an example for that, and I think he's sort of

1702
01:29:58,040 --> 01:30:02,240
Speaker 1: backtracked on on some of his early earlier statements. But

1703
01:30:02,320 --> 01:30:05,040
Speaker 1: when you look at the work he's done with outliers

1704
01:30:05,680 --> 01:30:09,920
Speaker 1: that after you've done something for ten thousand hours or

1705
01:30:09,960 --> 01:30:14,639
Speaker 1: some ungodly decade amount of time, you know enough of

1706
01:30:14,720 --> 01:30:21,240
Speaker 1: your you subconsciously recognize the statistical spread at the the

1707
01:30:22,080 --> 01:30:24,800
Speaker 1: probable outcomes that you could select the best just out

1708
01:30:24,800 --> 01:30:28,120
Speaker 1: of out of habit. How do you, um, how do

1709
01:30:28,120 --> 01:30:31,400
Speaker 1: you look at that sort of approach of intuition or

1710
01:30:31,439 --> 01:30:33,840
Speaker 1: haunches or what have you. I mean, I'm suspicious of

1711
01:30:33,840 --> 01:30:37,760
Speaker 1: the ten hour hypothesis. Right. Um, you know, but one

1712
01:30:37,760 --> 01:30:39,760
Speaker 1: experience is pretty new for me now is that now

1713
01:30:39,840 --> 01:30:42,000
Speaker 1: we have a whole bunch of employees, a whole bunch,

1714
01:30:42,080 --> 01:30:44,599
Speaker 1: but um, you know, two dozen at five thirty eight,

1715
01:30:44,600 --> 01:30:47,800
Speaker 1: and I often work with our our younger writers and

1716
01:30:47,880 --> 01:30:53,160
Speaker 1: analysts son on the problem. And um, you do see

1717
01:30:53,200 --> 01:30:56,280
Speaker 1: the benefit of experience there, right, where you're developing some

1718
01:30:57,080 --> 01:31:01,160
Speaker 1: little model or formulate to address like a question in baseball. Right,

1719
01:31:01,520 --> 01:31:03,400
Speaker 1: And even though the younger writer I might work with

1720
01:31:03,479 --> 01:31:06,080
Speaker 1: is like super smart, as smart as me. Um, I've

1721
01:31:06,080 --> 01:31:09,240
Speaker 1: just been doing this now for for ten or fifteen years, right,

1722
01:31:09,280 --> 01:31:12,120
Speaker 1: so I can say, you know what, you're unnecessarily complicating

1723
01:31:12,160 --> 01:31:14,080
Speaker 1: the problem. They're right, And that's just gonna make it

1724
01:31:14,120 --> 01:31:17,040
Speaker 1: hard to explain and make the model. Overfit is technical

1725
01:31:17,120 --> 01:31:19,439
Speaker 1: term or you know what oh here in other words,

1726
01:31:19,479 --> 01:31:22,760
Speaker 1: overfit meaning it's geared towards what happened previously, and you're

1727
01:31:22,800 --> 01:31:25,519
Speaker 1: making it. Yeah, it's like too rigid mask right or

1728
01:31:25,520 --> 01:31:28,679
Speaker 1: commercially like, you know what, you're making an approximation here

1729
01:31:29,520 --> 01:31:31,800
Speaker 1: that's just way too clumsy, right, and you're missing the

1730
01:31:31,840 --> 01:31:34,240
Speaker 1: whole gist of the problem. We're trying to solve here, right,

1731
01:31:34,520 --> 01:31:39,160
Speaker 1: Like that intuition for kind of which method works. Um,

1732
01:31:39,200 --> 01:31:42,360
Speaker 1: you know that's built from experience, I think, but you

1733
01:31:42,400 --> 01:31:44,640
Speaker 1: know what I mean, it's you know again, intuition kind

1734
01:31:44,640 --> 01:31:46,120
Speaker 1: of makes it seem like you're not spending much time

1735
01:31:46,120 --> 01:31:48,200
Speaker 1: thinking about the problem. It's kinda like you've invested that

1736
01:31:48,320 --> 01:31:52,800
Speaker 1: time before and developing some some expertise. Right. Um, So

1737
01:31:52,840 --> 01:31:56,080
Speaker 1: I softened on that a little bit. Um. But the

1738
01:31:56,120 --> 01:32:00,679
Speaker 1: problem is that you know, um, they're very any systems

1739
01:32:00,760 --> 01:32:03,240
Speaker 1: like politics, for example, where kind of it's are all

1740
01:32:03,280 --> 01:32:06,960
Speaker 1: the Daniel Kneman system one versus system two type of thinking,

1741
01:32:07,520 --> 01:32:12,040
Speaker 1: where the instinctual reaction is to overreact to things, right,

1742
01:32:12,080 --> 01:32:13,760
Speaker 1: and to say, oh, here's a new poll that came

1743
01:32:13,760 --> 01:32:16,799
Speaker 1: out that shows um, Bernie Sanders ahead of Hiller Clinton

1744
01:32:17,280 --> 01:32:20,200
Speaker 1: in New Hampshire. This is really dramatic, and all my

1745
01:32:20,240 --> 01:32:22,000
Speaker 1: friends are talking about this poll, right, and you kind

1746
01:32:22,000 --> 01:32:23,680
Speaker 1: of ignore the fact that there have been file other

1747
01:32:23,720 --> 01:32:25,920
Speaker 1: polls of New Hampshire in the past two weeks that

1748
01:32:26,000 --> 01:32:27,920
Speaker 1: show Clinton ahead, and also that New Hampshire is one

1749
01:32:28,000 --> 01:32:31,000
Speaker 1: state and pulls every other states so show Clinton ahead, right,

1750
01:32:31,000 --> 01:32:33,840
Speaker 1: And that this happens kind of in every election. Cycle. Um,

1751
01:32:33,880 --> 01:32:35,559
Speaker 1: so it's very useful to kind of, I think, slow

1752
01:32:35,640 --> 01:32:40,000
Speaker 1: down and and not overreacting in the market. We call

1753
01:32:40,120 --> 01:32:43,799
Speaker 1: that the recency effect, where you have this long series

1754
01:32:43,840 --> 01:32:47,920
Speaker 1: of of of long data series and a trend supporting it,

1755
01:32:48,000 --> 01:32:52,600
Speaker 1: and then you'll get something within a statistical range of possibilities.

1756
01:32:52,880 --> 01:32:55,040
Speaker 1: But that's off trends, and that's so much of you know,

1757
01:32:55,080 --> 01:33:00,320
Speaker 1: we aren't trying to predict what the polls will say tomorrow, right.

1758
01:33:00,360 --> 01:33:01,439
Speaker 1: You know, there's a lot of people who want to

1759
01:33:01,439 --> 01:33:03,160
Speaker 1: know kind of what's the mark are gonna do today? Right,

1760
01:33:03,240 --> 01:33:05,280
Speaker 1: We're kind of like, well, you know, here's your kind

1761
01:33:05,320 --> 01:33:08,360
Speaker 1: of not ten year time horizon, right, but here's your

1762
01:33:08,439 --> 01:33:12,240
Speaker 1: six month time horizon. Right. That we still think Hillary

1763
01:33:12,240 --> 01:33:15,040
Speaker 1: Clinton is very likely to be the Democratic nominee. That

1764
01:33:15,120 --> 01:33:17,960
Speaker 1: we're not sure who the GOP nominee will be, but

1765
01:33:18,080 --> 01:33:23,240
Speaker 1: we'd be short Trump stock as it were, right. Um.

1766
01:33:23,360 --> 01:33:26,040
Speaker 1: It also helps to quantify these things to some extent too.

1767
01:33:26,040 --> 01:33:29,040
Speaker 1: So in some sense it's kind of like, um, if

1768
01:33:29,040 --> 01:33:31,519
Speaker 1: you ask me what's your chance of Trump winning? We

1769
01:33:31,560 --> 01:33:34,479
Speaker 1: don't have a model yet, we probably will at some point, right,

1770
01:33:34,840 --> 01:33:39,400
Speaker 1: but you know the nomination I say it's two percent.

1771
01:33:39,560 --> 01:33:41,400
Speaker 1: I mean, it's kind of a spit balled estimate, but

1772
01:33:41,439 --> 01:33:43,760
Speaker 1: it's useful to have to put at least the order

1773
01:33:43,760 --> 01:33:48,200
Speaker 1: of magnitude on the table, you know, a small, non

1774
01:33:48,320 --> 01:33:52,120
Speaker 1: zero chance, because there are journalists who will say, you know, right,

1775
01:33:52,160 --> 01:33:54,000
Speaker 1: an article and say, oh, of course Trump is unluckily

1776
01:33:54,000 --> 01:33:55,200
Speaker 1: to win, but here are all the reasons he has

1777
01:33:55,200 --> 01:33:57,640
Speaker 1: momentum blah blah blah. Right, you know, and if you

1778
01:33:57,640 --> 01:33:59,479
Speaker 1: read that article, the sense you might get is that,

1779
01:33:59,680 --> 01:34:01,920
Speaker 1: you know, kind a pretty decent shot, right, you know.

1780
01:34:01,920 --> 01:34:04,640
Speaker 1: But if you actually kind of actually had them say

1781
01:34:05,080 --> 01:34:07,320
Speaker 1: we think the chances about two percent or say zero

1782
01:34:07,360 --> 01:34:09,240
Speaker 1: to five percent, at least the order of magnitude, right,

1783
01:34:09,280 --> 01:34:11,840
Speaker 1: and that would be useful, right, you know, because in

1784
01:34:11,880 --> 01:34:13,679
Speaker 1: some sense, if we say a lot of our calls

1785
01:34:13,680 --> 01:34:16,120
Speaker 1: that are like, oh, you know, no one gives Bernie

1786
01:34:16,200 --> 01:34:18,280
Speaker 1: a chance, but but he could win. It's like I

1787
01:34:18,320 --> 01:34:23,519
Speaker 1: give a chance, you know, five percent or whatever. But

1788
01:34:23,600 --> 01:34:26,519
Speaker 1: the journalists or Pundy who writes a column saying, you know,

1789
01:34:26,600 --> 01:34:29,160
Speaker 1: Hillary is not inevitable. I mean, in a literal sense,

1790
01:34:29,240 --> 01:34:31,200
Speaker 1: I agree with that. It's not a percent. It's not

1791
01:34:31,280 --> 01:34:34,479
Speaker 1: quite close to say it's a rounding error, right, But

1792
01:34:34,760 --> 01:34:37,439
Speaker 1: you know, it's pretty low and to actually say is

1793
01:34:37,439 --> 01:34:40,360
Speaker 1: there a real beef here or not? You know, my

1794
01:34:40,439 --> 01:34:42,960
Speaker 1: view is at five eight, if we think a candidate

1795
01:34:42,960 --> 01:34:47,040
Speaker 1: has a chance of winning, then most of our coverage

1796
01:34:47,080 --> 01:34:49,519
Speaker 1: should reflect the reasons why she probably will win, and

1797
01:34:49,560 --> 01:34:52,680
Speaker 1: occasionally we would have a piece saying that here's something

1798
01:34:52,720 --> 01:34:55,439
Speaker 1: of her to be really worried about. Right. The ratio

1799
01:34:55,520 --> 01:34:59,120
Speaker 1: in the mainstream media is like almost the reverse, right,

1800
01:34:59,200 --> 01:35:01,320
Speaker 1: where it's like they're so many reasons why the percent

1801
01:35:01,400 --> 01:35:04,080
Speaker 1: might come through and there's very little, just a perfunctory

1802
01:35:04,120 --> 01:35:06,680
Speaker 1: reminder that, oh, by the way, she's leading in the

1803
01:35:06,720 --> 01:35:08,720
Speaker 1: national polls by thirty points and no kind has ever

1804
01:35:08,720 --> 01:35:11,680
Speaker 1: done that before, right, you know, without being nominated. Right,

1805
01:35:11,760 --> 01:35:13,920
Speaker 1: you know, so the ratio is a little bit askew,

1806
01:35:14,040 --> 01:35:17,479
Speaker 1: even though I'm not sure there would really be a beef.

1807
01:35:17,520 --> 01:35:20,080
Speaker 1: If I if I, you know, sat down with the

1808
01:35:20,200 --> 01:35:22,880
Speaker 1: calumnists that I read a sarchastic tweet about, right, you know,

1809
01:35:23,000 --> 01:35:25,759
Speaker 1: he or she might agree that, yeah, Bernie Sander's chances

1810
01:35:25,800 --> 01:35:28,559
Speaker 1: are you know, five percent or ten percent just kind

1811
01:35:28,600 --> 01:35:31,400
Speaker 1: of in you know, I want the thrust and the

1812
01:35:31,439 --> 01:35:33,960
Speaker 1: kind of tone of the articles, you're right to kind

1813
01:35:34,000 --> 01:35:37,400
Speaker 1: of reflect that reality, not just as a caveat in

1814
01:35:37,439 --> 01:35:40,639
Speaker 1: the fifth paragraph that the that's the problem with narrative

1815
01:35:40,880 --> 01:35:44,439
Speaker 1: and the problem with human speech, which developed around a

1816
01:35:44,520 --> 01:35:48,800
Speaker 1: time when there was no written language, that narratives are

1817
01:35:48,800 --> 01:35:52,679
Speaker 1: more memorable, the more exciting, and the greater the horse race.

1818
01:35:53,439 --> 01:35:57,479
Speaker 1: What you give up in accuracy, you gain in page views.

1819
01:35:57,479 --> 01:36:01,640
Speaker 1: And that seems to be my gross It seems like

1820
01:36:01,640 --> 01:36:05,479
Speaker 1: there's some reduction that can occur. You know, we're lucky

1821
01:36:05,479 --> 01:36:07,760
Speaker 1: in the sense that kind of because everyone else is

1822
01:36:07,760 --> 01:36:09,000
Speaker 1: saying one thing that we can get a lot of

1823
01:36:09,640 --> 01:36:12,519
Speaker 1: attention by kind of saying no, this is probably pretty wrong,

1824
01:36:12,560 --> 01:36:14,439
Speaker 1: you know. But you know, I think there's also something

1825
01:36:14,479 --> 01:36:17,880
Speaker 1: mathematically accurate, right, um, But I think there is also

1826
01:36:18,320 --> 01:36:21,639
Speaker 1: there is also a group think that sets in apart

1827
01:36:21,760 --> 01:36:25,439
Speaker 1: from just a page viewing everybody on the same bus

1828
01:36:25,439 --> 01:36:27,599
Speaker 1: here in the same nonsense and having that's and that's

1829
01:36:27,640 --> 01:36:30,559
Speaker 1: somewhat literally true. So I went to literally Yeah, So

1830
01:36:30,600 --> 01:36:33,799
Speaker 1: I went to New Hampshire for a week or so

1831
01:36:33,920 --> 01:36:36,240
Speaker 1: in two thousand and twelve, right, and for the first

1832
01:36:36,240 --> 01:36:39,200
Speaker 1: time in my life, I went to um a presidential debate,

1833
01:36:39,280 --> 01:36:41,840
Speaker 1: one of the primary debates, right, And I kind of thought,

1834
01:36:41,960 --> 01:36:43,640
Speaker 1: you go to the debate, and you have credential and

1835
01:36:43,680 --> 01:36:45,840
Speaker 1: you're in the debating hall, you kind of see how

1836
01:36:45,920 --> 01:36:48,880
Speaker 1: things play in the room. No, it says you're heard

1837
01:36:48,920 --> 01:36:52,520
Speaker 1: it in this giant gymnasium with two thousand other journalists

1838
01:36:52,600 --> 01:36:55,280
Speaker 1: all checking one other Twitter feeds in real time. It's

1839
01:36:55,280 --> 01:37:00,240
Speaker 1: like the literal definition of group think being being manufactured. UM,

1840
01:37:00,320 --> 01:37:03,559
Speaker 1: And so I want to emphasize that line, the literal

1841
01:37:03,680 --> 01:37:06,800
Speaker 1: definition of group think being manual. And so we're talking

1842
01:37:06,800 --> 01:37:09,080
Speaker 1: about kind of the conventional wisdom. You know, sometimes in

1843
01:37:09,160 --> 01:37:13,160
Speaker 1: markets you embody the convention wisdom with what the market prices, right,

1844
01:37:13,200 --> 01:37:16,679
Speaker 1: but there's some separation. You know, if you look at UM,

1845
01:37:16,720 --> 01:37:18,840
Speaker 1: I mention there are betting markets where you can go

1846
01:37:19,439 --> 01:37:23,240
Speaker 1: and UM by Stock so to speaking Bernie Sanders Hiller Clinton,

1847
01:37:23,560 --> 01:37:26,880
Speaker 1: and they have not seen Clinton's odds changed very much, right,

1848
01:37:26,920 --> 01:37:30,200
Speaker 1: whereas the tenor of the news coverage has changed quite

1849
01:37:30,240 --> 01:37:32,519
Speaker 1: a bit. That that's fascinating. I'm gonna shift gears on

1850
01:37:32,560 --> 01:37:36,000
Speaker 1: you again. UM. So, who are your early mentors that

1851
01:37:36,240 --> 01:37:39,919
Speaker 1: you kind of come out of Chicago and you create

1852
01:37:40,200 --> 01:37:45,200
Speaker 1: or at least amplify a form of statistical analysis. Apply

1853
01:37:45,320 --> 01:37:49,760
Speaker 1: it to baseball, Apply it to UM campaigns, and elections.

1854
01:37:50,240 --> 01:37:53,320
Speaker 1: Who who motivated this? I mean, you know, Bill James,

1855
01:37:53,439 --> 01:37:56,800
Speaker 1: kind of the guyfathers stuff and in terms of not

1856
01:37:56,960 --> 01:37:59,360
Speaker 1: just having the satistical child to being able to communicate

1857
01:37:59,400 --> 01:38:01,920
Speaker 1: it to white all agins and kind of ask essential

1858
01:38:02,040 --> 01:38:06,320
Speaker 1: questions about about baseball. Um, you know, I looked up

1859
01:38:06,320 --> 01:38:08,080
Speaker 1: to some of the guys that work with a baseball perspectives,

1860
01:38:08,080 --> 01:38:12,439
Speaker 1: but I'm pretty self motivated on the whole. You found

1861
01:38:12,439 --> 01:38:15,800
Speaker 1: your way into this basically, Hey, here's something that's not

1862
01:38:15,920 --> 01:38:17,360
Speaker 1: being done right, and here's a bit of way to

1863
01:38:17,360 --> 01:38:19,800
Speaker 1: do Yeah. Like, boy, I think, you know, I think

1864
01:38:19,840 --> 01:38:22,120
Speaker 1: the coverage I watched on TV during two thousand and

1865
01:38:22,120 --> 01:38:25,479
Speaker 1: seven is very frustrating and so um so boy, I'm

1866
01:38:25,479 --> 01:38:27,920
Speaker 1: gonna have to do it myself, right, you know I

1867
01:38:27,960 --> 01:38:29,880
Speaker 1: kind of if no one else is gonna do it right,

1868
01:38:29,920 --> 01:38:31,599
Speaker 1: you might as well step up. That's challenge me now,

1869
01:38:31,680 --> 01:38:33,360
Speaker 1: right where I have a bunch of great people I'm

1870
01:38:33,400 --> 01:38:35,120
Speaker 1: working with, right, and it's kind of like, you know,

1871
01:38:35,160 --> 01:38:37,400
Speaker 1: how can I figure out not to do everything well?

1872
01:38:37,840 --> 01:38:40,280
Speaker 1: It's a challenge learning how to delegate that stuff. Let's

1873
01:38:40,360 --> 01:38:43,840
Speaker 1: let's you mentioned, um, Bill James, Let's talk about books.

1874
01:38:44,200 --> 01:38:49,479
Speaker 1: So obviously Kamon's book very influential to thinking about thinking.

1875
01:38:49,800 --> 01:38:54,800
Speaker 1: What other books have really uh, either inspired you or

1876
01:38:54,840 --> 01:38:57,920
Speaker 1: you've really enjoyed fiction non fiction that doesn't matter. I mean,

1877
01:38:57,960 --> 01:39:02,479
Speaker 1: there's a very wonky book called UH by Phil Tetlock

1878
01:39:02,640 --> 01:39:05,720
Speaker 1: was press new book coming out and new becoming out soon, right.

1879
01:39:05,800 --> 01:39:09,320
Speaker 1: It was called Expert Political Judgment explic Forecast. So that's

1880
01:39:09,320 --> 01:39:11,960
Speaker 1: a good kind of te temic book about about forecasting.

1881
01:39:12,040 --> 01:39:16,640
Speaker 1: But um, and how the experts are indistinguishable from a

1882
01:39:16,760 --> 01:39:20,280
Speaker 1: random person in public when it comes to making these

1883
01:39:20,439 --> 01:39:23,439
Speaker 1: predictions and forecasts a year out. Um, they're like barely

1884
01:39:23,439 --> 01:39:27,040
Speaker 1: even better than undergraduates. I think was the most amazing Um,

1885
01:39:27,080 --> 01:39:28,640
Speaker 1: you know, I mean, I don't know. In terms of

1886
01:39:29,200 --> 01:39:33,120
Speaker 1: UH storytelling, I think Michael Lewis is a terrific storyteller.

1887
01:39:33,120 --> 01:39:36,559
Speaker 1: I guess my answers are quite conventional and boring. Here.

1888
01:39:36,640 --> 01:39:40,920
Speaker 1: Did you ever read James Glick's The Information? I've read

1889
01:39:41,040 --> 01:39:44,000
Speaker 1: parts of it? Yeah, dude, that is so up your alley.

1890
01:39:44,080 --> 01:39:46,120
Speaker 1: I will check. I you know, I love his stuff.

1891
01:39:46,360 --> 01:39:49,560
Speaker 1: Do you want to talk wonky? So my undergraduate was

1892
01:39:49,600 --> 01:39:52,600
Speaker 1: applied math and physics before I switched to Polly say

1893
01:39:52,680 --> 01:39:54,840
Speaker 1: and so his book. I found him because of the

1894
01:39:54,880 --> 01:39:58,439
Speaker 1: book Chaos Making of a Science, and it's you know,

1895
01:39:59,040 --> 01:40:02,799
Speaker 1: way into the weeds on physics. But few people can

1896
01:40:02,840 --> 01:40:07,280
Speaker 1: take hard science and make it a narrative. That's interesting.

1897
01:40:07,840 --> 01:40:09,800
Speaker 1: I read that on a beach one summer and found

1898
01:40:09,800 --> 01:40:15,120
Speaker 1: it absolutely information just absolute fascinating. Diet Tribe on the

1899
01:40:15,200 --> 01:40:18,519
Speaker 1: history of of information science. Yeah, I mean, I think

1900
01:40:18,560 --> 01:40:20,680
Speaker 1: all these things are you know, kind of history of

1901
01:40:20,800 --> 01:40:23,000
Speaker 1: nala tripe. But I know, you know, to be honest,

1902
01:40:24,320 --> 01:40:26,320
Speaker 1: I traveled quite a bit and I used to catch

1903
01:40:26,400 --> 01:40:30,920
Speaker 1: up reading on planes, and now that I have people

1904
01:40:30,920 --> 01:40:33,080
Speaker 1: to manage and there's Internet connections on the planes and

1905
01:40:33,080 --> 01:40:37,880
Speaker 1: stuff like that, reading for pleasure has has declined. Unfortunately.

1906
01:40:38,800 --> 01:40:43,120
Speaker 1: Um so the net. A related question, what other mathematicians

1907
01:40:43,200 --> 01:40:47,479
Speaker 1: or statisticians either influenced you or influenced your approach to

1908
01:40:48,400 --> 01:40:52,839
Speaker 1: looking at at what you do? You mentioned Tetlock. Anybody

1909
01:40:52,960 --> 01:40:56,599
Speaker 1: who's really a political scientist, anybody else really leap out.

1910
01:40:57,240 --> 01:40:59,439
Speaker 1: I mean, there a couple of guys like Colombia whose

1911
01:40:59,479 --> 01:41:02,120
Speaker 1: name is and be familiar, but you know, Andrew Gelman

1912
01:41:02,200 --> 01:41:05,040
Speaker 1: is a guy there, Bob Arricks, and who are political scientists,

1913
01:41:05,520 --> 01:41:07,960
Speaker 1: um who have still at elections for a long time,

1914
01:41:08,080 --> 01:41:12,960
Speaker 1: and and we're helpful. But you know, um, you know, I,

1915
01:41:13,120 --> 01:41:15,720
Speaker 1: like I said, I'm pretty self taught for for the

1916
01:41:15,760 --> 01:41:18,840
Speaker 1: most part. I picture you as a lonely mathematician, light

1917
01:41:18,920 --> 01:41:21,960
Speaker 1: burning in the window, and uh, working by yourself. Is

1918
01:41:22,280 --> 01:41:24,720
Speaker 1: that kind of how this all came about? Sort of?

1919
01:41:24,800 --> 01:41:26,439
Speaker 1: It's different. I mean, I live with a partner and

1920
01:41:26,479 --> 01:41:28,280
Speaker 1: I manage people at work, right, so I don't have

1921
01:41:28,320 --> 01:41:35,040
Speaker 1: as much, um much alone time as as the old days. Right. Um,

1922
01:41:35,120 --> 01:41:36,479
Speaker 1: you know, I'm one of those people who's kind of

1923
01:41:36,600 --> 01:41:40,320
Speaker 1: very much in the middle of the extroversion introversion curd.

1924
01:41:40,400 --> 01:41:45,360
Speaker 1: I can go, I can go crazy on either extreme. Um,

1925
01:41:45,400 --> 01:41:48,439
Speaker 1: but um, but you know, yeah, some of this pursuit

1926
01:41:48,520 --> 01:41:53,280
Speaker 1: is pretty pretty solitary inherently, I think, right, uh, sure,

1927
01:41:53,360 --> 01:41:59,320
Speaker 1: you're thinking about what the status quo is doing wrong, right,

1928
01:41:59,400 --> 01:42:02,000
Speaker 1: and trying to come up with ways of thinking about

1929
01:42:02,000 --> 01:42:05,719
Speaker 1: it that makes more sense and as more probitive value,

1930
01:42:05,760 --> 01:42:08,920
Speaker 1: more statistics. It seems like if you kind of start,

1931
01:42:10,120 --> 01:42:14,680
Speaker 1: you know, so I think about this Clinton versus Sanders thing, right, Um,

1932
01:42:14,720 --> 01:42:17,479
Speaker 1: you know, before the campaign began, if you kind of

1933
01:42:17,560 --> 01:42:21,040
Speaker 1: had told yourself, well, you know what, no matter what happens,

1934
01:42:21,080 --> 01:42:25,320
Speaker 1: there's gonna be a strong incentive for for the media

1935
01:42:25,479 --> 01:42:27,640
Speaker 1: to make it seem like the race is really competitive

1936
01:42:27,720 --> 01:42:31,880
Speaker 1: and it will be exceptionally clever in UM. In ways,

1937
01:42:31,920 --> 01:42:34,000
Speaker 1: almost all this is unconscious, by the way, Right, I'm

1938
01:42:34,000 --> 01:42:36,400
Speaker 1: not saying they're deliberately putting their finger on scale. Right. Well,

1939
01:42:36,439 --> 01:42:39,240
Speaker 1: you're boards are doing the same thing every day. You're boring.

1940
01:42:39,560 --> 01:42:41,800
Speaker 1: There's so many polls, so many indicators to look at,

1941
01:42:41,840 --> 01:42:44,639
Speaker 1: so many ways you can Um, it's a very large

1942
01:42:44,680 --> 01:42:48,320
Speaker 1: constellation of information, right, so many ways you can draw

1943
01:42:48,920 --> 01:42:52,559
Speaker 1: threads together that inevertly. You'll see some stories written about

1944
01:42:52,560 --> 01:42:56,679
Speaker 1: how quote unquote unexpectedly you know, Bernie Sanders is doing

1945
01:42:57,200 --> 01:42:59,880
Speaker 1: is doing well, right, and you can kind of brace

1946
01:43:00,000 --> 01:43:04,439
Speaker 1: yourself for that in in the abstract, UM, but it's

1947
01:43:04,479 --> 01:43:06,559
Speaker 1: kind of hard to do that when you're confronted with it.

1948
01:43:06,600 --> 01:43:11,280
Speaker 1: Another example is that almost every year, there are some exceptions. UM,

1949
01:43:11,439 --> 01:43:14,559
Speaker 1: a candidate gets a boost in the polls after the convention. Right,

1950
01:43:15,080 --> 01:43:20,320
Speaker 1: so the bump Rubio, Bush Walker, whomever, Trump, Um, we'll

1951
01:43:20,320 --> 01:43:23,160
Speaker 1: have his convention, Uh, get a five point bump in

1952
01:43:23,200 --> 01:43:26,760
Speaker 1: the polls and it'll fade, right, And this is you know,

1953
01:43:27,320 --> 01:43:31,640
Speaker 1: pretty predictable. Um, it's gonna be really hard when a

1954
01:43:31,720 --> 01:43:35,200
Speaker 1: Democrats experiencing that not to think it's something new and

1955
01:43:35,280 --> 01:43:38,280
Speaker 1: different this time, and the press will event all types

1956
01:43:38,280 --> 01:43:41,400
Speaker 1: of reasons why you know this time is different and

1957
01:43:41,400 --> 01:43:44,559
Speaker 1: it it probably won't be. You know, debates can produce

1958
01:43:44,600 --> 01:43:46,400
Speaker 1: little bumps too, but it's kind of like you're about

1959
01:43:46,400 --> 01:43:50,000
Speaker 1: to go on on a roller coaster. You can kind of, um,

1960
01:43:50,080 --> 01:43:51,559
Speaker 1: you can say, well, I know we're gonna go up

1961
01:43:51,600 --> 01:43:54,000
Speaker 1: this hill and then come down it really fast, right,

1962
01:43:55,479 --> 01:43:57,200
Speaker 1: and there's and I'd relate to it, but you know,

1963
01:43:57,439 --> 01:43:59,560
Speaker 1: it doesn't. You can still kind of suspend your disbelief,

1964
01:43:59,720 --> 01:44:02,639
Speaker 1: and of course, if you're an roller coaster, you want

1965
01:44:02,640 --> 01:44:04,400
Speaker 1: to suspend your disbelief and have a lot of fun

1966
01:44:04,439 --> 01:44:07,000
Speaker 1: with it. Right. But you know, my job as someone

1967
01:44:07,040 --> 01:44:10,519
Speaker 1: who covers campaigns, someone who's more empirical but also critical,

1968
01:44:10,600 --> 01:44:13,040
Speaker 1: is to kind of stay at a distance and say,

1969
01:44:13,160 --> 01:44:15,600
Speaker 1: you know what, I know, you guys think that this

1970
01:44:15,680 --> 01:44:18,160
Speaker 1: Trump thing is new and different, and by the way,

1971
01:44:18,240 --> 01:44:21,960
Speaker 1: it probably won't evaporate tomorrow. Right, it might take some time,

1972
01:44:22,280 --> 01:44:25,639
Speaker 1: but um, the odds of a guy who basically isn't

1973
01:44:25,640 --> 01:44:28,559
Speaker 1: a Republican being dominated by the Frolican party is pretty

1974
01:44:28,640 --> 01:44:32,080
Speaker 1: darned low. So on a related note, I have to

1975
01:44:32,120 --> 01:44:34,680
Speaker 1: share a funny story with you. So we just this

1976
01:44:34,800 --> 01:44:37,880
Speaker 1: past weekend. We we had family members in from Chicago

1977
01:44:38,400 --> 01:44:41,760
Speaker 1: and I remember No. Eight them saying, I know you

1978
01:44:41,880 --> 01:44:45,719
Speaker 1: think Hillary is going to be coronated, but this Barack guy,

1979
01:44:45,880 --> 01:44:49,800
Speaker 1: who we've seen in town for years, don't underestimate him.

1980
01:44:49,840 --> 01:44:54,639
Speaker 1: He's he's got something and he surprises Hillary. He gets

1981
01:44:54,640 --> 01:44:58,880
Speaker 1: the nomination and then Lader in the year. As as

1982
01:44:58,960 --> 01:45:03,000
Speaker 1: the election move forward, I want to say August September,

1983
01:45:03,640 --> 01:45:08,160
Speaker 1: October of eight. Um, my sister in law, we we

1984
01:45:08,280 --> 01:45:11,320
Speaker 1: just had this conversation this weekend. I mentioned I'm gonna

1985
01:45:11,439 --> 01:45:13,800
Speaker 1: be seeing Nate Silver on Monday. She goes, oh, he

1986
01:45:13,920 --> 01:45:17,719
Speaker 1: made my oh eight so much better? Why she goes, well,

1987
01:45:17,800 --> 01:45:22,639
Speaker 1: I really thought there were big Obama supporters. I really

1988
01:45:22,640 --> 01:45:25,920
Speaker 1: thought he was gonna lose. But every time I'm sorry,

1989
01:45:25,920 --> 01:45:31,840
Speaker 1: in every time they went to they said, you were

1990
01:45:31,880 --> 01:45:35,800
Speaker 1: reassuring that here's what we are statistically, and while it's

1991
01:45:35,840 --> 01:45:40,120
Speaker 1: not impossible for him to lose, you know, the electoral

1992
01:45:40,120 --> 01:45:43,439
Speaker 1: College makes it really hard for Mitt Romney to garner

1993
01:45:43,439 --> 01:45:46,519
Speaker 1: two seventy votes and and here's the likely and she

1994
01:45:46,560 --> 01:45:50,439
Speaker 1: said it was a despite the media drumbeat despite how

1995
01:45:50,800 --> 01:45:53,920
Speaker 1: how close this was. So so she credits you for

1996
01:45:54,040 --> 01:45:56,960
Speaker 1: making her two thousand twelve much better, and I get

1997
01:45:57,000 --> 01:46:01,599
Speaker 1: that coming a lot. Yeah, that exact kind of sentiment.

1998
01:46:01,720 --> 01:46:03,880
Speaker 1: That because you can kind of lose yourself in the

1999
01:46:03,920 --> 01:46:09,040
Speaker 1: media coverage and because yeah it's weird, like I, um,

2000
01:46:09,080 --> 01:46:13,160
Speaker 1: in our election coverage, we kind of always wind up um.

2001
01:46:13,280 --> 01:46:16,639
Speaker 1: Incentive wise, We're always kind of rooting for the favorite,

2002
01:46:17,240 --> 01:46:18,840
Speaker 1: which is I'm not that kind of guy, right, I'm

2003
01:46:18,840 --> 01:46:23,120
Speaker 1: like an underdog, unpredictable kind of guy. Um. But you know,

2004
01:46:23,640 --> 01:46:27,479
Speaker 1: the media will take a race where one can it

2005
01:46:27,640 --> 01:46:30,000
Speaker 1: kind of objectively quote unquote has a nine chance of

2006
01:46:30,000 --> 01:46:33,160
Speaker 1: winning and really really sell the case that it's neck

2007
01:46:33,200 --> 01:46:37,799
Speaker 1: and neck. Right. Um. Papers, Yeah, it's how you sell papers.

2008
01:46:37,800 --> 01:46:40,840
Speaker 1: And so I kind of always wind up um, you know,

2009
01:46:41,560 --> 01:46:44,000
Speaker 1: being the person who says, you know what, this is

2010
01:46:44,040 --> 01:46:47,120
Speaker 1: not that schucking and prediction. But Donald Trump probably not

2011
01:46:47,160 --> 01:46:49,720
Speaker 1: going to be Republican nominee, Bernie Sanders probably will not

2012
01:46:49,720 --> 01:46:53,040
Speaker 1: be the Democratic nominee. And we will, believe me, one year,

2013
01:46:53,080 --> 01:46:54,599
Speaker 1: we'll get one of those things wrong. I mean we've

2014
01:46:54,640 --> 01:46:58,120
Speaker 1: had cases where um, where another context sports and whatnot,

2015
01:46:58,160 --> 01:47:02,840
Speaker 1: where you know, unluckily things have happened. But um, but

2016
01:47:03,560 --> 01:47:06,240
Speaker 1: you know, it can really throw people for for a loop.

2017
01:47:06,520 --> 01:47:09,000
Speaker 1: Um but you know, the fact is also in fourteen

2018
01:47:10,600 --> 01:47:12,400
Speaker 1: we had from the very first time a couple shift

2019
01:47:12,400 --> 01:47:16,080
Speaker 1: forecast the GOPS favorites to pick up the Senate from

2020
01:47:16,320 --> 01:47:19,960
Speaker 1: from Democrats, and a lot of people were not remotely

2021
01:47:20,000 --> 01:47:22,720
Speaker 1: close to seeing that. No. No, it really depends on

2022
01:47:22,760 --> 01:47:25,400
Speaker 1: the presidential coattails, and it depends on this Yeah, and

2023
01:47:25,439 --> 01:47:27,880
Speaker 1: people you know, and you saw it quite a bit

2024
01:47:27,880 --> 01:47:30,559
Speaker 1: of it in in reverse. I think like Harry Reid,

2025
01:47:30,800 --> 01:47:32,960
Speaker 1: you know, he's like, this guy's an idiot. He always

2026
01:47:33,160 --> 01:47:35,720
Speaker 1: under said, well democrats would do see, you do get

2027
01:47:35,760 --> 01:47:39,840
Speaker 1: it from from both from both sides a little bit, right.

2028
01:47:39,880 --> 01:47:41,680
Speaker 1: I'm not saying this is you know, we're not going

2029
01:47:41,720 --> 01:47:43,439
Speaker 1: to burn another hour here. I'm not saying the parties

2030
01:47:43,439 --> 01:47:47,760
Speaker 1: are exactly identical. But um but you know you would

2031
01:47:47,760 --> 01:47:51,640
Speaker 1: get comments like how did Nate Silver turn into a Republican? Right,

2032
01:47:51,680 --> 01:47:53,960
Speaker 1: It's like my views haven't really changed that much, right,

2033
01:47:54,040 --> 01:47:57,160
Speaker 1: just like I think, you know, here's where the polls

2034
01:47:57,200 --> 01:47:59,080
Speaker 1: and the fundamentals point is that you have, you know,

2035
01:47:59,520 --> 01:48:03,080
Speaker 1: last year's mid terms, howquckly we forget right that, you know,

2036
01:48:03,400 --> 01:48:07,640
Speaker 1: with this big blowout for the GOP just last year. Right, Um,

2037
01:48:07,680 --> 01:48:09,560
Speaker 1: but you know, all these states are being fought for

2038
01:48:09,840 --> 01:48:13,880
Speaker 1: in red states, and Obama's unpopular and democratic turnout looks

2039
01:48:13,880 --> 01:48:16,360
Speaker 1: like it might be bad. Right, It's not that complicated necessarily,

2040
01:48:16,520 --> 01:48:19,640
Speaker 1: So I would be remiss if I didn't mention unskewed

2041
01:48:19,680 --> 01:48:25,640
Speaker 1: polls dot com. Yeah, I found that horribly, horribly amusing. Basically,

2042
01:48:26,240 --> 01:48:29,200
Speaker 1: the media is biased. Nate Silver doesn't know what he's

2043
01:48:29,240 --> 01:48:33,040
Speaker 1: talking about. We're gonna skew the polls or we're gonna

2044
01:48:33,120 --> 01:48:36,400
Speaker 1: unskew the polls, and Romney's gonna you know, he's gonna

2045
01:48:36,479 --> 01:48:43,120
Speaker 1: run the table. How often does something that blatant obvious

2046
01:48:43,920 --> 01:48:47,800
Speaker 1: concerted that was really like a How often does that

2047
01:48:47,840 --> 01:48:50,679
Speaker 1: sort of stuff pop up? Oh? No, I mean it'll

2048
01:48:50,720 --> 01:48:54,040
Speaker 1: be there'll be versions of that, probably on both sides

2049
01:48:54,040 --> 01:48:56,559
Speaker 1: by the time we get to next year, Right, and

2050
01:48:56,600 --> 01:48:59,519
Speaker 1: the more dangerous versions of versions where like, you know

2051
01:49:01,040 --> 01:49:02,920
Speaker 1: that guy, I've never met him personally, Maybe he's a

2052
01:49:02,960 --> 01:49:04,559
Speaker 1: nice guy to have a beer with, right, he seemed

2053
01:49:04,560 --> 01:49:07,439
Speaker 1: a little bit kind of screwy in the head, right,

2054
01:49:07,960 --> 01:49:11,880
Speaker 1: just just enumeroate, But it will not be that guy.

2055
01:49:12,040 --> 01:49:17,480
Speaker 1: It'll be someone who, um has sort of a sophomore

2056
01:49:17,520 --> 01:49:22,200
Speaker 1: acknowledge of statistics, right, who can actually kind of make

2057
01:49:22,479 --> 01:49:28,920
Speaker 1: superficially persuasive arguments for why, um, why my hypothesis is true,

2058
01:49:29,040 --> 01:49:31,400
Speaker 1: and you absolutely will see that, and I know there's

2059
01:49:31,439 --> 01:49:34,000
Speaker 1: a market for you know, one of people I'm realized

2060
01:49:34,000 --> 01:49:36,519
Speaker 1: about five thirty eight is there are now six or

2061
01:49:36,520 --> 01:49:39,800
Speaker 1: seven websites that do a forecast similar to rors, right,

2062
01:49:40,160 --> 01:49:42,760
Speaker 1: and we're always kind of somewhere in the middle of

2063
01:49:42,760 --> 01:49:44,719
Speaker 1: the consensus, which I think is usually a good place

2064
01:49:44,760 --> 01:49:50,240
Speaker 1: to be consensus of consensus analysis. Yeah right, Um, but

2065
01:49:50,280 --> 01:49:52,599
Speaker 1: there will be someone let's say we have Rubia with

2066
01:49:52,680 --> 01:49:56,639
Speaker 1: a seventy chance to win the GP nomination, and that's

2067
01:49:56,640 --> 01:49:58,920
Speaker 1: where the average is, right roughly, I can guarantee you

2068
01:49:58,960 --> 01:50:03,519
Speaker 1: le'll be someone who says, no, it's s Hillary, Um, no,

2069
01:50:03,680 --> 01:50:09,160
Speaker 1: it's Rubio right, right, Um, so they look like a

2070
01:50:09,200 --> 01:50:12,880
Speaker 1: genius even though statistically their their work is so that

2071
01:50:13,000 --> 01:50:14,800
Speaker 1: be someone who looks like yeah, right. And if there's

2072
01:50:14,840 --> 01:50:18,200
Speaker 1: someone who um, you know, it's the same problem with

2073
01:50:18,200 --> 01:50:22,000
Speaker 1: with Wall Street analysts, I guess right, you know, on average,

2074
01:50:22,040 --> 01:50:24,519
Speaker 1: the person someone who makes an outlandish forecast will be

2075
01:50:24,880 --> 01:50:28,479
Speaker 1: in first place, even if their meetian outcome is worse

2076
01:50:28,560 --> 01:50:30,960
Speaker 1: than the person who makes like a more kind of

2077
01:50:31,000 --> 01:50:34,559
Speaker 1: lower case could see conservative going back to Tetlock and others,

2078
01:50:34,600 --> 01:50:38,120
Speaker 1: when you look at punditry and people go on TV.

2079
01:50:38,840 --> 01:50:41,960
Speaker 1: Someone who goes on television and says, you know, the

2080
01:50:41,960 --> 01:50:44,720
Speaker 1: future is inherently unknowable. I don't. I can't tell you

2081
01:50:44,720 --> 01:50:46,960
Speaker 1: whether dow will be in one year and someone else

2082
01:50:47,000 --> 01:50:50,200
Speaker 1: says it will be at nineteen thousand seven fifty. Not

2083
01:50:50,320 --> 01:50:54,040
Speaker 1: only is that person less likely to be accurate, but

2084
01:50:54,160 --> 01:50:56,960
Speaker 1: they're more likely to be believed and and so that's

2085
01:50:57,040 --> 01:51:00,400
Speaker 1: the enhancement I did. I did a program for the

2086
01:51:00,439 --> 01:51:03,479
Speaker 1: BBC on the UK election and we had some guys

2087
01:51:03,479 --> 01:51:07,480
Speaker 1: we hire really smart guys and like everyone, they way underestimated,

2088
01:51:07,800 --> 01:51:11,160
Speaker 1: uh how well Tories would do. Right, But this BBC

2089
01:51:11,200 --> 01:51:14,160
Speaker 1: program was there for a week and my personal take

2090
01:51:14,280 --> 01:51:16,599
Speaker 1: was like, you know what, this is a really weird election.

2091
01:51:16,640 --> 01:51:20,040
Speaker 1: There are four parties or five parties that are assailiant

2092
01:51:20,200 --> 01:51:22,840
Speaker 1: and and um, there's not a lot of agreement in

2093
01:51:22,880 --> 01:51:25,280
Speaker 1: the polls, and like so I was fighting so hard

2094
01:51:25,320 --> 01:51:28,680
Speaker 1: to avoid how we go to commit to anything, And

2095
01:51:28,720 --> 01:51:32,639
Speaker 1: of course their whole stick was brilliant genius stistition comes

2096
01:51:32,640 --> 01:51:35,000
Speaker 1: from the United States, and was your you know, like

2097
01:51:35,040 --> 01:51:36,880
Speaker 1: tells us exactly what's gonna happen the elections. There was

2098
01:51:37,080 --> 01:51:39,600
Speaker 1: there was tension there, I think, right, it was like

2099
01:51:39,640 --> 01:51:42,800
Speaker 1: a week of trying not to answer this question. Um, well,

2100
01:51:42,880 --> 01:51:45,920
Speaker 1: the data is uncertain. Using a methodology, that data is certain.

2101
01:51:45,960 --> 01:51:47,320
Speaker 1: But in the end we published a model on five

2102
01:51:47,360 --> 01:51:49,880
Speaker 1: thirty eight and like everyone else, it blew it. So

2103
01:51:50,400 --> 01:51:53,519
Speaker 1: but you know, um, but yeah, it's a tricky thing

2104
01:51:53,600 --> 01:51:56,400
Speaker 1: to do. And that's a much shorter election, and that's

2105
01:51:56,439 --> 01:52:00,200
Speaker 1: a a very different process than in the United States.

2106
01:52:00,240 --> 01:52:02,960
Speaker 1: It's a much shorter election. It's only it's only six weeks,

2107
01:52:02,960 --> 01:52:06,040
Speaker 1: but when you add multiple parties to the race, it

2108
01:52:06,080 --> 01:52:09,760
Speaker 1: gets much more complex, like really fast. Um. But you know,

2109
01:52:09,760 --> 01:52:11,120
Speaker 1: we haven't talked about this. I know, we don't have

2110
01:52:11,320 --> 01:52:13,280
Speaker 1: too much more time. But the fact that, you know,

2111
01:52:13,640 --> 01:52:16,680
Speaker 1: the polls aren't as reliable as they used to be,

2112
01:52:16,800 --> 01:52:20,160
Speaker 1: at least in some non US context. So more cell phones,

2113
01:52:20,240 --> 01:52:24,439
Speaker 1: less reliable polls. Yeah, you know, Israel and in Greece

2114
01:52:24,520 --> 01:52:26,280
Speaker 1: and whatnot. We have a Canadian election. We'll see how

2115
01:52:26,280 --> 01:52:28,960
Speaker 1: the polls do there. They've had trouble in some Canadian

2116
01:52:28,960 --> 01:52:35,080
Speaker 1: parliamentary elections and whatnot, or provincial elections. Um. But you know, UM,

2117
01:52:35,320 --> 01:52:39,280
Speaker 1: polling is not fool proof either, and that's why it's like,

2118
01:52:39,840 --> 01:52:42,360
Speaker 1: you know, that's why I say we got lucky in

2119
01:52:42,400 --> 01:52:45,680
Speaker 1: part in two thousand and eight. The polls in two

2120
01:52:45,680 --> 01:52:50,639
Speaker 1: thousand twelve were okay but not great. Actually, Obama beat

2121
01:52:50,680 --> 01:52:53,439
Speaker 1: his polls by an average of three points? Is that

2122
01:52:53,520 --> 01:52:55,880
Speaker 1: unusual for the victors who have won by that? It's

2123
01:52:55,880 --> 01:52:58,599
Speaker 1: about It's about average. You know, a three point miss

2124
01:52:58,600 --> 01:53:00,640
Speaker 1: is about average. The thing is, if you'd had that

2125
01:53:00,640 --> 01:53:04,040
Speaker 1: three point miss any other direction, then Romney would have

2126
01:53:04,040 --> 01:53:06,800
Speaker 1: won the election, or at least made it really really close.

2127
01:53:08,000 --> 01:53:09,280
Speaker 1: All Right, I know we only have you for a

2128
01:53:09,320 --> 01:53:11,519
Speaker 1: few more minutes, so let me get to my last

2129
01:53:12,080 --> 01:53:16,120
Speaker 1: two questions. Um. This is the millennial question that that

2130
01:53:16,160 --> 01:53:19,960
Speaker 1: I we talked about earlier. So you've carved out your

2131
01:53:20,000 --> 01:53:24,120
Speaker 1: own kind of unique career by taking a couple of

2132
01:53:24,120 --> 01:53:27,559
Speaker 1: subjects you really liked and applying it in a in

2133
01:53:27,600 --> 01:53:31,840
Speaker 1: a unique and useful way. What would you what sort

2134
01:53:31,840 --> 01:53:35,760
Speaker 1: of advice would you give to millennials who are just

2135
01:53:35,840 --> 01:53:38,120
Speaker 1: coming out of school now or whatever we want to

2136
01:53:38,160 --> 01:53:41,280
Speaker 1: call this generation? What sort of career advice would you

2137
01:53:41,320 --> 01:53:45,920
Speaker 1: give the recent graduating class. I mean, you know, learn

2138
01:53:45,960 --> 01:53:48,599
Speaker 1: how to code if you want to become a journalist. Right,

2139
01:53:48,680 --> 01:53:54,280
Speaker 1: that's pretty more more important. Um. But you know, I mean, uh,

2140
01:53:54,479 --> 01:53:57,800
Speaker 1: it's a combination I think of of working really hard,

2141
01:53:57,920 --> 01:54:03,320
Speaker 1: but um, but not tolerating yourself being bored at work. Right,

2142
01:54:03,400 --> 01:54:08,000
Speaker 1: I think that's pretty that's pretty important. Um, it makes

2143
01:54:08,000 --> 01:54:12,680
Speaker 1: a lot of sense. Um, I mean, you know, uh,

2144
01:54:14,280 --> 01:54:18,400
Speaker 1: learning to critique the conventional wisdom, including the wisdom that

2145
01:54:18,439 --> 01:54:22,280
Speaker 1: your friends might have. Right. Um, did you get a

2146
01:54:22,280 --> 01:54:24,639
Speaker 1: lot of pushback on on what you were doing from

2147
01:54:24,680 --> 01:54:26,840
Speaker 1: from friends and colleagues. No, I mean they always know

2148
01:54:26,840 --> 01:54:28,559
Speaker 1: me as someone who did different things, so I don't

2149
01:54:28,600 --> 01:54:32,080
Speaker 1: think there was ever that much pushed back from it, exactly.

2150
01:54:32,120 --> 01:54:33,640
Speaker 1: But I do worry that we kind of enter a

2151
01:54:33,720 --> 01:54:37,000
Speaker 1: universe now where, um, where it's kind of so easy

2152
01:54:37,120 --> 01:54:40,880
Speaker 1: for an opinion to form on social media or whatnot.

2153
01:54:40,920 --> 01:54:43,760
Speaker 1: There's so much kind of communication and sharing that I

2154
01:54:43,800 --> 01:54:47,800
Speaker 1: think that can be a little bit dangerous, right. You know,

2155
01:54:47,880 --> 01:54:50,920
Speaker 1: I don't think I'm thirty seven. I don't think any

2156
01:54:50,960 --> 01:54:53,040
Speaker 1: thirty seven year old should say I have the whole

2157
01:54:53,040 --> 01:54:55,800
Speaker 1: world figure out, you know, certainly not any twenty seven

2158
01:54:55,840 --> 01:54:59,040
Speaker 1: year old or seventeen year old should say, you know, um,

2159
01:54:59,080 --> 01:55:02,000
Speaker 1: you know, my beliefs about the world are just the

2160
01:55:02,040 --> 01:55:04,880
Speaker 1: way things are, you know. I think I think there's

2161
01:55:05,360 --> 01:55:09,400
Speaker 1: there's that tendency a little bit um, not just with millennials,

2162
01:55:09,440 --> 01:55:11,840
Speaker 1: but with everyone now kind of the age of of

2163
01:55:11,880 --> 01:55:15,440
Speaker 1: social media. You can kind of feel for rocket bear

2164
01:55:15,520 --> 01:55:18,480
Speaker 1: term kind of smug about your view in the world. Um,

2165
01:55:18,480 --> 01:55:20,800
Speaker 1: and that can be a dangerous thing. Enough people, there's

2166
01:55:20,920 --> 01:55:25,360
Speaker 1: enough selective perception and enough things around that reinforces your views,

2167
01:55:25,840 --> 01:55:29,040
Speaker 1: especially with the Balkanization of the Internet that yeah, you

2168
01:55:29,080 --> 01:55:32,440
Speaker 1: could you could find applause for just about any opinion

2169
01:55:32,440 --> 01:55:34,480
Speaker 1: on the spectrum. Yeah, it's really easy to you know,

2170
01:55:34,480 --> 01:55:37,080
Speaker 1: it's part of part of what Trump's doing is you know,

2171
01:55:37,120 --> 01:55:39,400
Speaker 1: I sold Julier. He's not actually that popular, but the

2172
01:55:39,920 --> 01:55:42,160
Speaker 1: twenty five or the country that really likes him, he

2173
01:55:42,200 --> 01:55:44,880
Speaker 1: can kind of wallow in their world, right, I Mean,

2174
01:55:44,880 --> 01:55:47,040
Speaker 1: Trump is I'm kind of going on attention here. He

2175
01:55:47,200 --> 01:55:49,960
Speaker 1: is incredibly entertaining for sure to write like so one

2176
01:55:50,000 --> 01:55:53,040
Speaker 1: thing that I think does separate him out from um,

2177
01:55:53,080 --> 01:55:56,240
Speaker 1: from say, the harmon kines the Michelle Bachman's of four

2178
01:55:56,280 --> 01:56:00,560
Speaker 1: years ago. Was the guy has real talent for or something?

2179
01:56:01,360 --> 01:56:06,760
Speaker 1: Is it politics exactly? Your showmanship or demagoguery or somewhere

2180
01:56:06,760 --> 01:56:10,200
Speaker 1: in between those three things. I'm not. I'm not quite sure, right,

2181
01:56:10,320 --> 01:56:13,760
Speaker 1: but you know there's a charisma there. He's hosted or

2182
01:56:13,800 --> 01:56:19,560
Speaker 1: at least he's been significant in a television show very

2183
01:56:19,640 --> 01:56:22,400
Speaker 1: very fast. He has a sense for for theater. He

2184
01:56:22,440 --> 01:56:25,800
Speaker 1: can poke the eye. I mean, you know, um, I'm

2185
01:56:25,840 --> 01:56:29,360
Speaker 1: not that's a he's got a natural gift for that.

2186
01:56:29,440 --> 01:56:31,720
Speaker 1: I'm not saying I like Trump. You know, I wouldn't

2187
01:56:31,760 --> 01:56:33,040
Speaker 1: vote for him, but I love the fact that he

2188
01:56:33,080 --> 01:56:35,760
Speaker 1: had his helicopter around the Iowa State Fair because it's

2189
01:56:35,880 --> 01:56:39,520
Speaker 1: all these rituals that are silly in some ways. Right,

2190
01:56:39,520 --> 01:56:40,640
Speaker 1: He's gonna be like, I want to be the evil

2191
01:56:40,680 --> 01:56:42,920
Speaker 1: villain and just kind of circle my helicopter around the

2192
01:56:42,920 --> 01:56:46,520
Speaker 1: state fair grounds and like driver one's attention, right, And like, um,

2193
01:56:46,760 --> 01:56:51,400
Speaker 1: was he giving kids, healthy kids helicopter riots and telling

2194
01:56:51,440 --> 01:56:56,360
Speaker 1: them he's he's Batman? Literally? Right? Um? That's great. So

2195
01:56:56,400 --> 01:57:00,000
Speaker 1: I like people are willing to defy connection a little

2196
01:57:00,000 --> 01:57:03,160
Speaker 1: little bit, right, and he certainly he certainly has done that.

2197
01:57:03,360 --> 01:57:05,040
Speaker 1: But you know the reason why we kind of get

2198
01:57:05,680 --> 01:57:07,520
Speaker 1: end up as being perceived a very anti trumpet just

2199
01:57:07,560 --> 01:57:10,760
Speaker 1: because you know, we think the media takes this fascinating

2200
01:57:10,760 --> 01:57:13,280
Speaker 1: story and trumps up so to speak, the chance will

2201
01:57:13,280 --> 01:57:17,280
Speaker 1: actually be nominated by the GOP right and also great narrative,

2202
01:57:17,320 --> 01:57:21,920
Speaker 1: but also the fact that it is August, right, the

2203
01:57:21,960 --> 01:57:25,440
Speaker 1: polls you see in August aren't measuring anything real in

2204
01:57:25,480 --> 01:57:28,800
Speaker 1: a sense, people are hypothetically thinking about a vote they're

2205
01:57:28,800 --> 01:57:31,160
Speaker 1: going to make in six months when half the candidates

2206
01:57:31,200 --> 01:57:34,840
Speaker 1: will have dropped out, right, Um, when they'll have four

2207
01:57:35,080 --> 01:57:37,960
Speaker 1: times as much information. The idea of all you have

2208
01:57:38,000 --> 01:57:40,720
Speaker 1: a national primary, you don't actually vote kind of one

2209
01:57:40,800 --> 01:57:44,280
Speaker 1: state at a time, and so you know, um, some

2210
01:57:44,320 --> 01:57:47,920
Speaker 1: people kind of point, um, the Trump's polls as kind

2211
01:57:47,960 --> 01:57:50,800
Speaker 1: of self evident evidence that he's succeeding. Then I'm a

2212
01:57:50,880 --> 01:57:56,040
Speaker 1: little bit suspicious of that. But he certainly is ah

2213
01:57:56,280 --> 01:57:59,920
Speaker 1: entertaining in a way that I think raises some questions

2214
01:58:00,000 --> 01:58:04,600
Speaker 1: about the way campaigns are are covered. Right well, there

2215
01:58:04,640 --> 01:58:07,680
Speaker 1: certainly raises questions about the way they're run. But the

2216
01:58:07,760 --> 01:58:10,720
Speaker 1: coverage is part and parcel of that. The coverage is

2217
01:58:11,960 --> 01:58:15,840
Speaker 1: not not necessarily doing democracy any uh any favors. I

2218
01:58:15,880 --> 01:58:18,360
Speaker 1: think I mean, you know, kind of I guess the

2219
01:58:18,440 --> 01:58:20,960
Speaker 1: two grand thesis about Trump are that, you know, kind

2220
01:58:20,960 --> 01:58:23,440
Speaker 1: of Trump reveals what's wrong with the GOP, or that

2221
01:58:23,480 --> 01:58:26,480
Speaker 1: he reveals what's wrong with the media, right and kind

2222
01:58:26,520 --> 01:58:29,240
Speaker 1: of being Yeah, there some combination thereof I mean kind

2223
01:58:29,240 --> 01:58:31,120
Speaker 1: of you know, kind of an amateur media critic, I

2224
01:58:31,200 --> 01:58:35,000
Speaker 1: kind of lean towards that explanation more. But there's you know,

2225
01:58:35,680 --> 01:58:39,520
Speaker 1: there's something he's tapping into. I think I would resist

2226
01:58:39,520 --> 01:58:42,840
Speaker 1: the implication that he's all that popular or that people

2227
01:58:42,840 --> 01:58:46,800
Speaker 1: are responding, um, all that literally to the substance of

2228
01:58:46,840 --> 01:58:51,360
Speaker 1: his policy proposals. I think there responding to the affect

2229
01:58:51,520 --> 01:58:55,680
Speaker 1: to the you know, kind of he to the nerve. Yeah,

2230
01:58:55,760 --> 01:58:57,880
Speaker 1: I think that's I think. And certainly if you look

2231
01:58:57,920 --> 01:58:59,520
Speaker 1: beyond the poll's terms of is he popular or not,

2232
01:58:59,600 --> 01:59:02,480
Speaker 1: people like him or not, there's no doubt that there

2233
01:59:02,520 --> 01:59:06,920
Speaker 1: is enormous interest in Donald Trump, right. Um. You know,

2234
01:59:07,000 --> 01:59:09,600
Speaker 1: I think we did read some metrics from Google, like

2235
01:59:09,680 --> 01:59:12,320
Speaker 1: six of the news coverage of the GP campaign has

2236
01:59:12,320 --> 01:59:15,760
Speaker 1: been about Donald Trump. That's amazing, But the Google searches

2237
01:59:15,760 --> 01:59:17,360
Speaker 1: for the campaign have been about him. So the public

2238
01:59:17,440 --> 01:59:21,680
Speaker 1: is even more obsessed with So you know, you know

2239
01:59:21,840 --> 01:59:23,920
Speaker 1: that won't translate into support. Yeah, I want you to

2240
01:59:23,960 --> 01:59:26,840
Speaker 1: be president, but there certainly is a fixation with him

2241
01:59:26,840 --> 01:59:30,320
Speaker 1: that is not purely a media creation. So last question,

2242
01:59:30,560 --> 01:59:32,400
Speaker 1: because I know you have other places to be and

2243
01:59:32,480 --> 01:59:35,080
Speaker 1: I've kept you here for a long time. Um, what

2244
01:59:35,160 --> 01:59:41,600
Speaker 1: do you know about politics, campaign, sports statistics today that

2245
01:59:41,680 --> 01:59:44,080
Speaker 1: you wish you knew fifteen years ago when you were

2246
01:59:44,160 --> 01:59:46,440
Speaker 1: starting and you could pick any of those fields to

2247
01:59:46,520 --> 01:59:52,040
Speaker 1: work with? Oh gosh, um, I mean there are technical

2248
01:59:52,080 --> 01:59:54,760
Speaker 1: answers to this, right, kind of wish I wish I

2249
01:59:54,840 --> 01:59:57,360
Speaker 1: had when they took me a while to understand it's

2250
01:59:57,400 --> 01:59:59,000
Speaker 1: like really fundamental, but I think it takes other people

2251
01:59:59,000 --> 02:00:02,560
Speaker 1: a while too. Is that um, is that a Cisco

2252
02:00:02,680 --> 02:00:06,880
Speaker 1: model is built on past data and you're making a

2253
02:00:06,880 --> 02:00:10,720
Speaker 1: big assumption to therefore say the model is predictive? Right?

2254
02:00:11,320 --> 02:00:15,280
Speaker 1: Sometimes that's true. In baseball, the conditions are so stable

2255
02:00:15,680 --> 02:00:17,960
Speaker 1: that if you build a modle that explains the past, well,

2256
02:00:18,560 --> 02:00:21,680
Speaker 1: you're kind of, by definition usually predicting the future. Well, right,

2257
02:00:21,840 --> 02:00:24,920
Speaker 1: you can extrapolate better with that stable set up than

2258
02:00:25,000 --> 02:00:28,200
Speaker 1: you can perhaps with politics. But in politics, you know,

2259
02:00:28,560 --> 02:00:32,320
Speaker 1: not always true, and you get into areas like finance, right,

2260
02:00:32,360 --> 02:00:34,760
Speaker 1: and there used to be a correlation between which conference

2261
02:00:34,760 --> 02:00:37,120
Speaker 1: on the Super Bowl and how the stock market did. Right, Yeah,

2262
02:00:37,160 --> 02:00:40,440
Speaker 1: but that was always so I understand. My job has

2263
02:00:40,480 --> 02:00:44,240
Speaker 1: always been to point out that's a correlation without a causation,

2264
02:00:44,360 --> 02:00:48,000
Speaker 1: and whether it's crazy things like the Hindenburg almen or

2265
02:00:48,040 --> 02:00:50,520
Speaker 1: the death Cross, my job is to go back and

2266
02:00:50,560 --> 02:00:53,400
Speaker 1: look at these things historically and say, well, here's the

2267
02:00:53,440 --> 02:00:57,200
Speaker 1: net result of it, and this doesn't work this, this

2268
02:00:57,280 --> 02:01:00,920
Speaker 1: has a strong correlation, but it's a random accident. And

2269
02:01:00,960 --> 02:01:04,400
Speaker 1: here's what we get out of this. Um. But the

2270
02:01:04,440 --> 02:01:07,240
Speaker 1: leap of you are making a leap of inference. I

2271
02:01:07,280 --> 02:01:09,440
Speaker 1: was gonna say leap of faith. That's not really about faith, right,

2272
02:01:09,800 --> 02:01:12,600
Speaker 1: but a leap of inference where you say, okay, I

2273
02:01:12,720 --> 02:01:15,520
Speaker 1: fit a model the past data, therefore I'm predicting things

2274
02:01:15,560 --> 02:01:17,440
Speaker 1: like no, you fit a model of the past data.

2275
02:01:17,560 --> 02:01:19,640
Speaker 1: And then there are a lot of factors you have

2276
02:01:19,680 --> 02:01:22,480
Speaker 1: to consider in terms of how well that model will

2277
02:01:22,520 --> 02:01:27,040
Speaker 1: predict going forward. Right. Um, So you know, whenever I

2278
02:01:27,080 --> 02:01:30,400
Speaker 1: see you know, I think a lot of academics make

2279
02:01:30,440 --> 02:01:33,040
Speaker 1: this mistake, just like anyone else does. Right, But they'll say,

2280
02:01:33,080 --> 02:01:37,080
Speaker 1: you know, I've developed a model too to predict campaigns

2281
02:01:37,120 --> 02:01:39,360
Speaker 1: right and perfectly predicts every presidential election. It's like, no,

2282
02:01:39,400 --> 02:01:41,920
Speaker 1: you didn't predict anything right. You know, if you publish

2283
02:01:42,000 --> 02:01:44,760
Speaker 1: a mouth for the first time in and you say,

2284
02:01:44,880 --> 02:01:48,080
Speaker 1: perfectly predicted all fifty two presidential elections, however many there

2285
02:01:48,120 --> 02:01:50,480
Speaker 1: are in the past, Like, no, your record is zero

2286
02:01:50,560 --> 02:01:54,720
Speaker 1: for zero forfeited, what not? Not fifty two? That that

2287
02:01:54,840 --> 02:01:59,040
Speaker 1: was might be with Anthony Robbins model that was heavy

2288
02:01:59,080 --> 02:02:02,360
Speaker 1: on gold, have on bonds after a ten year gold

2289
02:02:02,400 --> 02:02:05,160
Speaker 1: bull market and a thirty year bond will market, both

2290
02:02:05,160 --> 02:02:08,080
Speaker 1: of which were unlikely to continue going forward. It was

2291
02:02:08,120 --> 02:02:13,200
Speaker 1: that overfitting predicting the past as opposed to looking forward. Um.

2292
02:02:13,240 --> 02:02:15,160
Speaker 1: You know, in the whole question of fascinated by is

2293
02:02:15,240 --> 02:02:18,800
Speaker 1: kind of you have this um tension between the fact

2294
02:02:18,880 --> 02:02:23,760
Speaker 1: that on the one hand, um, you know, the consensus

2295
02:02:23,840 --> 02:02:27,840
Speaker 1: is usually better than an individual forecast. It's very hard

2296
02:02:27,880 --> 02:02:30,720
Speaker 1: to beat the market when there's a robust market, you know.

2297
02:02:31,480 --> 02:02:35,080
Speaker 1: On the other hand, you know, understanding that there's group

2298
02:02:35,160 --> 02:02:38,560
Speaker 1: think and that everyone can kind of be delusional together,

2299
02:02:38,800 --> 02:02:41,880
Speaker 1: and that pension I'm kind of I'm kind of fascinated

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Speaker 1: by as as we all are. Nate, thank you so

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Speaker 1: much for being so generous with your time. Definitely this

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Speaker 1: This has been really just an absolutely fascinating conversation. We've

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Speaker 1: been speaking with Nate Silver of five thirty eight. If

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Speaker 1: you enjoyed this conversation, look up an Inch or Down

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Speaker 1: an Inch on iTunes and you can see all of

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Speaker 1: the rest of our conversations. If people want to find

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Speaker 1: your work, you're at Nate Silver five thirty eight and

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Speaker 1: at spelled out five eight dot com. I want to

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Speaker 1: thank Mike Batnick, who did a yeoman's job as our

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Speaker 1: head of research, and Charlie Bohmer as as my producer. Um,

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Speaker 1: you've been listening to Masters in Business on Bloomberg Radio