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Speaker 1: Pushkin. Welcome back to Risky Business, our show about making

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Speaker 1: better decisions. I'm Maria Kanakova and.

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Speaker 2: I'm Nate Silver. Did you say our show or a show?

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Speaker 1: I said our show, Nate, because it is our show.

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Speaker 1: Sometimes decision say A you know, I like to play

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Speaker 1: around with it the show.

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Speaker 3: How about the show? So I'll tell you how to

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Speaker 3: make better decisions?

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Speaker 1: This is the show about how to make better decisions.

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Speaker 1: What are we going to be talking about today.

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Speaker 3: Night March madness, Maria, March madness. Indeed, it's New York,

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Speaker 3: it's springtime. The weather is improving from mediocre to slightly

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Speaker 3: go to the mediocre. And we're here to watch some

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Speaker 3: basketball and talk about some basketball.

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Speaker 2: Yep.

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Speaker 1: And we're going to start off by talking about some

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Speaker 1: of Nate's models, and then we each made brackets. And

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Speaker 1: I'll also introduce a secret weapon that I used in

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Speaker 1: making my bracket, a certain someone named Monty McNair.

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Speaker 2: Oh well, so you cheated.

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Speaker 1: Oh no, I want to not cheat.

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Speaker 2: I do want to McNair.

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Speaker 1: Nate, I did not cheat. When we don't know how

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Speaker 1: to make a good decision, what do we do? We

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Speaker 1: consult someone with the expertise and the knowledge to help.

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Speaker 1: So I consulted with Monty McNair, who's the GM of

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Speaker 1: the Sacramento Kings and something of a guru when it

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Speaker 1: comes to making March Madness brackets. That full interview is

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Speaker 1: actually going to be on push Can Plus highly recommend

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Speaker 1: that you all listen to it.

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Speaker 3: I feel like the politics that's a bit complicated where

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Speaker 3: the woman has to ask it certified basketball expert. Well,

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Speaker 3: I just kind of go the man just you know,

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Speaker 3: uses a regression based model.

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Speaker 1: So that's that's a great segue night, Let's talk about

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Speaker 1: the regression based ELO model. So you released your Marsh

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Speaker 1: Madness models. How did you you know, how did you

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Speaker 1: make them? Kind of what's the thought process behind them?

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Speaker 1: What goes into them?

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Speaker 3: So some of this like actually literally dates back to

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Speaker 3: two thousand and two or two thousand and three, right

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Speaker 3: where I'm like a junior associate at KPMG. This is

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Speaker 3: an accounting firm. I always was sensitive. I'm you know,

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Speaker 3: it was a consultant at accounting firm, Maria, not an accountant.

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Speaker 1: Just so you know, I always forget that you worked

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Speaker 1: at an accounting firm. Every sing such a year, Oh yeah, right.

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Speaker 3: Such a weird part of the multiverse where I don't

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Speaker 3: know how the fuck that happened. I don't know, but

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Speaker 3: like it's because I parted too much ching college and

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Speaker 3: then had to didn't really make plans anyway. So a

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Speaker 3: friend who had an office pool had these complicated scoring

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Speaker 3: rules where you get points for upsets, and so I

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Speaker 3: took some other rating system I found and modeled that

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Speaker 3: and did a little simulation. Actually it's not a simulations,

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Speaker 3: it's not deterministic exactly, but like, there are only sixty

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Speaker 3: seven games, so you can kind of explicitly do all

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Speaker 3: the conditional probabilities instead of having to rely on a simulation.

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Speaker 2: Right, just a.

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Speaker 3: Technical detail, but it's evolved from there. I think when

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Speaker 3: I was at the New York Times in twenty twelve,

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Speaker 3: I did a big evaluation looking at all these different

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Speaker 3: computer models, all these other different ways to predict the tournament,

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Speaker 3: and found kind of very empirically which did best in

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Speaker 3: predicting tournament games. So a couple of things that if

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Speaker 3: you if you've ever made a model, might sound familiar, right.

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Speaker 3: You know, first, almost always taking a consensus of indicators

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Speaker 3: is better than any one indicator, right, So we wound

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Speaker 3: up with a blend of like five different rating systems,

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Speaker 3: all which do a little differently. Accounting for injuries is

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Speaker 3: very important. You know, you don't want to ignore the

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Speaker 3: fact that Cooper Flag, maybe the best player in the NCAAA,

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Speaker 3: at least the best freshman the nca IS year, got

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Speaker 3: hurt during a tournament. He seems to be helping now,

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Speaker 3: but if he were to be heard, that would gravely

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Speaker 3: impact Duke's prospects.

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Speaker 2: So we account for that.

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Speaker 3: And then also we found that, like there's reversion to

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Speaker 3: the mean, meaning that you play roughly thirty three games

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Speaker 3: or something in a college basketball regular season, not all

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Speaker 3: that biggest samples some of us are against crappy competition,

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Speaker 3: you know from you know, Southwestern State, University prep or whatever. Right,

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Speaker 3: So we looked at pre season rankings as well as

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Speaker 3: part of what we factored into the formula. Sometimes seems

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Speaker 3: that underachieve have actually gotten a little bit unlucky, so

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Speaker 3: believe it or not, giving human ratings some component actually

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Speaker 3: helps a little bit, and this proved to be a

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Speaker 3: popular feature. But like more recently, I began making my

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Speaker 3: own rating system that kind of incorporates some of these

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Speaker 3: different ideas. Right, So are you familiar with ELO ratings.

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Speaker 3: I know some of our readers listeners might be.

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Speaker 1: I should say, well, let's for people who are not familiar.

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Speaker 1: Of course, I personally am very you're familiar with the writings.

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Speaker 1: But for those of us who might not be, why

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Speaker 1: don't we explain.

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Speaker 2: It a little bit?

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Speaker 3: So ELO was designed by let me look at the guys,

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Speaker 3: it's like arpanned Elo, or let me look, it's Hungarian.

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Speaker 3: Probably the Hungarians are quite promiscuous within in kind of

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Speaker 3: this era of intellectual history, are bad Elo a Hungarian

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Speaker 3: American physics professor. But it's a formula where you are

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Speaker 3: adjusting your rating constantly based on whether you beat your opponent,

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Speaker 3: of course, and how good your opponent is. Right, So

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Speaker 3: if I have an ELI rating of a nineteen hundred,

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Speaker 3: and I don't know if that's good in chess, it's

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Speaker 3: probably pretty good. Right, And you're a fourteen hundred, Maria, right,

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Speaker 3: fifteen hundreds, average of four hundreds whatever, mediocre, I barely

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Speaker 3: get any credit because you kind of already forecast to

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Speaker 3: the win.

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Speaker 2: Right.

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Speaker 3: If I'm a nineteen hundred and I beat Magnus Carlson,

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Speaker 3: who's a twenty one, I'm just guessing you might know.

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Speaker 3: Then I get a lot of credit because I've beaten

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Speaker 3: a stronger opponent.

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Speaker 1: So twenty eight thirty three night as Magnus's.

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Speaker 3: Twenty eighth ro' oh my, that's embarrassing. See in basketball,

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Speaker 3: they only get to like twenty two hundred or something, right,

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Speaker 3: And so I'm like, is Magnus Carlson better at chess.

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Speaker 1: Tha twenty eight thirty three? His peak rating was twenty

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Speaker 1: eight eighty two.

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Speaker 3: Okay, that's pretty that's pretty good. I think I'm like

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Speaker 3: a nine hundred or something. But so anyway, we adapted

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Speaker 3: the ELO ratings. Actually other people have done this, but

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Speaker 3: we adapted it first the NFL and then to college basketball.

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Speaker 3: But there are a few twists in ELO. Originally just

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Speaker 3: about the winner loss, right, we also count for the

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Speaker 3: margin of victory. In basketball, we account for home court

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Speaker 3: advantage in kind of an increasingly complicated way. We account

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Speaker 3: for travel distance, so if you fly across the country,

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Speaker 3: your jet lag and so forth. This actually shows up

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Speaker 3: in in performance by the way different teams have different

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Speaker 3: home court advantages. It turns out actually that having like

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Speaker 3: a larger crowd actually helps.

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Speaker 2: I was trying to model this the other day.

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Speaker 3: You know you for every ten thousand fans show up,

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Speaker 3: teams score roughly in additional point, that's right.

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Speaker 1: That's absolutely fascinating, saying, yeah.

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Speaker 3: So you're that's for I do that for women for men,

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Speaker 3: if it's different women's because they have some teams are

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Speaker 3: more popular than men's teams, and some like literally draw

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Speaker 3: you know, twelve people, right, But yeah, the teams that

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Speaker 3: actually have people watching do a little better in their

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Speaker 3: home games controlling for other factors.

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Speaker 1: So I love that you're able to account for these

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Speaker 1: things that might seem kind of more ineffable, right, like

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Speaker 1: like home court advantages, more psychological and more physical like

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Speaker 1: injury risks. How much is like how much of model distinctions?

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Speaker 1: And like your model versus other models is kind of

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Speaker 1: a little bit more subjective, like as you said, the

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Speaker 1: human touch versus kind of the big data crunching. I'm

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Speaker 1: just curious, from like a from a psychologist's standpoint, what

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Speaker 1: the balance is there?

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Speaker 3: So are you saying when I make a bracket or like, when.

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Speaker 1: I'm billion, you're actually building the model.

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Speaker 3: Look, inherently models, you have a lot of different choices

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Speaker 3: you can make. But like, because you have like i'd say,

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Speaker 3: this is like less subjective than some of the other

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Speaker 3: models I've built because you have a lot of data

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Speaker 3: and elo is relatively simple. Right, there are only so

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Speaker 3: many knobs you might have to press, versus more open

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Speaker 3: ended problems.

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Speaker 2: Right.

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Speaker 3: But yeah, in general, like with politics election models that

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Speaker 3: you know, those get to be a little bit more

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Speaker 3: art than science. Not in the sense because people mistake this.

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Speaker 3: Not in the sense that I am like subjectively changing

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Speaker 3: out once the model is designed, but like, but the

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Speaker 3: design questions are harder. And also, you know, so one

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Speaker 3: thing you look at in basketball too, is like how

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Speaker 3: do the parameters of the game change over time?

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Speaker 2: So one thing we do is like, in.

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Speaker 3: Between seasons, how you did last season? Provide some information obviously, right,

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Speaker 3: you know Connecticut was a national champion last year, had

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Speaker 3: a very good year. Right, you wouldn't just toss them

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Speaker 3: in a hat versus you know, writer or Canisius or

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Speaker 3: one of these small schools.

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Speaker 2: I now have beamed him and I had, you.

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Speaker 3: Know, Youngstown State, Texas, Rio Grand Valley. You know, you

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Speaker 3: you you have some information from the prior season. But

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Speaker 3: because there's so much more player turnover now the best

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Speaker 3: players go pro after one year, it's less continuity than

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Speaker 3: there used to be, right, and so and so you

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Speaker 3: should always be asking questions, right, and like you're billion

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Speaker 3: a model, part of what you're doing is saying, Okay,

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Speaker 3: here's this data. It's been well calibrated on literally what's

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Speaker 3: you know, seventy five years of college basketball history, right,

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Speaker 3: but let's make sure that it's performing as advertised on

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Speaker 3: the past couple of years, right, because after all, if

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Speaker 3: you're then adapting this to make predictions, and that's kind

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Speaker 3: of what people are going to care about, whether you

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Speaker 3: Rectro actually got some game right in nineteen seventy two

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Speaker 3: or whatever. And what we found is that the model

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Speaker 3: had I know, I'm saying we because it's just me

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Speaker 3: at this point. What I found is that the model

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Speaker 3: had become overconfident in the in recent years, right, because

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Speaker 3: it was it was overconfident early in the season before

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Speaker 3: catching up. And that was because it you know, overweighted

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Speaker 3: the impact of last season when if you have a

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Speaker 3: good player, it's going to the NBA anyway, and so

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Speaker 3: and so it doesn't help quite as much and so like, yeah,

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Speaker 3: so this is where like domain knowledge helps, right, because

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Speaker 3: you could just say, well, it's a fairly large sample,

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Speaker 3: but you know, it's been a weird couple of years

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Speaker 3: because of COVID and new rules and stuff like that. Right,

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Speaker 3: But it's like, Okay, we have a good hypothesis for why,

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Speaker 3: for why there's more mean reversion now. But by the way,

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Speaker 3: this has actionable implications.

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Speaker 2: Where Okay, so.

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Speaker 3: For our tournament model, which is related to this ELO model,

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Speaker 3: but we combined with other systems. I found myself I

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Speaker 3: do literally make bets based on these, right, And I

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Speaker 3: found myself betting a lot of underdogs to cover the

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Speaker 3: spread in the men's game, but not in the women's

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Speaker 3: game interestingly, right, because women you can't join the WNBA

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Speaker 3: until you're age twenty two, I think, which seems fucking

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Speaker 3: insane to me, Like, how was that legal? By the way,

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Speaker 3: that is very strange.

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Speaker 1: I don't understand.

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Speaker 3: Yeah, the politics THEWWA, you know, it's a whole other segment.

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Speaker 3: But yeah, the teams are still as strong as ever,

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Speaker 3: and so you have less reversion from year to year.

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Speaker 3: So basically for lots of reasons, and the fact that

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Speaker 3: like you know, for a long time in the women's game,

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Speaker 3: they were like, you know, somewhere between a half dozen

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Speaker 3: and two dozen schools. It took women's basketball really seriously,

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Speaker 3: and then some where it just like you go out

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Speaker 3: with a clipboard and say, hey, who wants to play? Right,

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Speaker 3: And so it's almost like professional versus amateur. Now it's

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Speaker 3: become much more popular. There's more money in the sport,

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Speaker 3: more young women are playing the sport, right, But still

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Speaker 3: it's more it's much more lopsided, right, And so you know,

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Speaker 3: so I found and now I'm giving away and replaces

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Speaker 3: my bet. So too late, right, But yeah, so I've

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Speaker 3: ad also fitting a lot of favorites in women's basketball

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Speaker 3: and a lot of underdogs in men's basketball, because there's

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Speaker 3: been more mean reversion in men's basketball.

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Speaker 2: And I don't think that.

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Speaker 3: The models are that the odds are accounting for this enough,

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Speaker 3: although you know, we'll see I'm putting my money where

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Speaker 3: my mouth is at least.

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Speaker 2: Yeah, they're just settle things too.

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Speaker 3: Like you know, big teams that draw huge audiences and

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Speaker 3: have very enthusiastic fans and good in game presentation and

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Speaker 3: nice locker rooms, right, they have a bigger home court

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Speaker 3: advantage which makes their rating look better. But you're playing

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Speaker 3: the NCAA tournament on neutral sites instead, so there have

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Speaker 3: actually been quite a few upsets in the tournament in

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Speaker 3: recent years. So yeah, all this is kind of you know,

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Speaker 3: so that's why it's like, this is what I hate

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Speaker 3: when like academics are like, well you a sports model,

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Speaker 3: election Wedell, this isn't not real science, Like it's actually

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Speaker 3: better science. Sure, because you're like, now I sound like

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Speaker 3: a fucking asshole, but no, but you're like you it's

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Speaker 3: it's still like hypothesis driven, right, you're looking at the

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Speaker 3: real world.

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Speaker 2: You're just actually willing.

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Speaker 3: To test these ideas in the form of of making predictions,

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Speaker 3: in some cases even betting on these predictions.

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Speaker 1: No, that that makes perfect sense. And do I understand

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Speaker 1: correctly that you actually have two versions of your model,

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Speaker 1: one that's elo based and one that's a Bayesian elo version.

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Speaker 3: Is that right? Abazing just because I'm a Doric, right,

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Speaker 3: But so in the Baysing version, like I said before,

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Speaker 3: you uh, the new season begins in the team is

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Speaker 3: kind of the same and kind of a different team.

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Speaker 2: Right.

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Speaker 3: In the original version, it reverts to the mean based

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Speaker 3: on the other teams in your conference, right, because there

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Speaker 3: are clearly differences between like you know, whatever, the ACC

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Speaker 3: and the IVY in the Basing version, then we give

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Speaker 3: weight to the human poles. Instead, we look at the

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Speaker 3: preseason top twenty five. We assume that these people are

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Speaker 3: evaluating recruitment and player aging and Injuriason all this stuff, right,

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Speaker 3: and so we revert based on that instead. It turns

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Speaker 3: out actually that like you do best with a blend

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Speaker 3: of you know, because the humans can add or subtract,

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Speaker 3: they can introduce biases that aren't helpful, right, So it's

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Speaker 3: really it's really kind of a what we call base

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Speaker 3: is really like a blend of the original version and

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Speaker 3: kind of a pure Bayse version. So that accounts for

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Speaker 3: like preseason makings in the human poles, and that has

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Speaker 3: interesting implications.

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Speaker 1: Do they have the same results this year or not?

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Speaker 1: Do your two models differ?

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Speaker 3: Yeah, let me show you it's actually not? Or is

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Speaker 3: it pretty similar that different?

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Speaker 2: I mean, for.

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Speaker 3: So the Basing version has Duke as the best team

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Speaker 3: and the pure version has Floored as the best team.

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Speaker 3: Now these are all within like a few points. It's

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Speaker 3: a very minor difference, right, But like you're counting for

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Speaker 3: like that you're basic kind of counting for like the

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Speaker 3: historical reputation of Duke that gets boosted in these human polls.

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Speaker 2: Got it.

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Speaker 1: Well, that's all super interesting, Nate. So let's see what

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Speaker 1: the results show when it comes to actually choosing brackets.

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Speaker 1: So you got to use all of your models. I

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Speaker 1: did not, but I got to use a Monte McNair.

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Speaker 1: So I got to use a human and after the breaks,

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Speaker 1: let's see how it all turns out. Nay, I have

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Speaker 1: a confession to make. I have never filled out an

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Speaker 1: NC double a bracket until now.

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Speaker 3: Ria.

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Speaker 1: This is my first ever bracket and.

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Speaker 2: We're still allowing on a show.

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Speaker 1: Come on, So this is my first ever bracket.

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Speaker 2: But how can you never filled out a bracket?

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Speaker 1: I've never filled out a bracket, Nate, I confess, I confess.

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Speaker 3: You know I've never done yoga.

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Speaker 1: Oh there you go, Nate. How could you have never

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Speaker 1: done yoga?

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Speaker 3: It feels never the gender stereotype being here. I don't know,

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Speaker 3: but like it's like something you think you would have

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Speaker 3: done once.

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Speaker 1: Right, Okay, Nate, I will I will ever do yoga

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Speaker 1: with you. And now that I filled.

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Speaker 3: Out our bracket, okay, yeah.

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Speaker 1: So so I actually you have your bracket right, and

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Speaker 1: it's probably a normal bracket. I have two brackets, so

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Speaker 1: Here's here's what I did. So first, I know nothing

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Speaker 1: you know about NCAA basketball. I never filled out a

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Speaker 1: bracket before. So I did a bracket based on vibes.

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Speaker 1: So I didn't even look at the at how teams

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Speaker 1: were ranked, didn't look at their record, didn't look at

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Speaker 1: any data whatsoever, and just like went on a totally

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Speaker 1: vibe based like who do I like? What sounds better?

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Speaker 1: What state do I like better? Where's the weather better?

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Speaker 1: Just just random things like that, my ViBe's bracket night.

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Speaker 1: Do you want to guess who won at the end?

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Speaker 1: Who I have winning the whole thing?

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Speaker 2: H Yale?

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Speaker 1: No, I'm a Harvard grad.

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Speaker 2: Okay, it doesn't make sense. I don't.

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Speaker 3: Uh, who would This is a weird what's it like? Laden?

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Speaker 2: Like? What would Maria think? Is exactly?

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Speaker 1: This is a perfect psychology experiment.

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Speaker 2: Uh, give me give me a hint.

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Speaker 1: You weren't You weren't that far off when you said

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Speaker 1: Yale in terms of the types of things I was

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Speaker 1: thinking about. But it was a place. It's so it

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Speaker 1: was an emotional based decision, all right.

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Speaker 3: Okay, So one thing is, are you enough of a

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Speaker 3: college basketball fan to know that you're supposed to hate Duke.

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Speaker 2: Yes, okay, okay, because.

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Speaker 3: That's you know, that's like the point he headad like

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Speaker 3: by the way I was out, Uh, I don't know.

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Speaker 3: I was out with my partner last Saturday, and we'll

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Speaker 3: at least is maybe really too much? Like why is

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Speaker 3: everyone in this bar so so white? Because it's New

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Speaker 3: York and it's a big bar and usually and it's

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Speaker 3: because the New Game was fun?

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Speaker 1: That's funny. But okay, so, so do you give up?

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Speaker 1: Should I just tell you? Let me give give me

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Speaker 1: one more, one more guess?

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Speaker 3: Okay, Saint John's no is there a New York guess?

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Speaker 3: Or not? Maybe? I think, Yeah, I guess you're not

356
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Speaker 3: really like it. I mean, you were live in New York.

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Speaker 3: You don't like it New York born and bread.

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Speaker 2: Okay, I give up?

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Speaker 1: All right? So I had you're gonna laugh. I'm curious

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Speaker 1: to hear your reaction. I had U c l A

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Speaker 1: winning because I felt bad for LA and the wildfires,

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Speaker 1: and I felt like they needed something good to happen

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Speaker 1: to them.

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Speaker 3: Okay, you know, I'm generally pro you. So I like

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Speaker 3: that color mix.

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Speaker 1: Yeah, there was just UCLA had good vibes and I figured,

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Speaker 1: you know, what let's have them win. So I had

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Speaker 1: UCLA beating Duke in the final four.

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Speaker 3: What seedd UCLA even this year there are seven seed probabilistically,

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Speaker 3: let me let me look up the old silver bulletin

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Speaker 3: forecast and see what it says. So you have an

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Speaker 3: zero point three percent chance of being right? All right, yes,

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Speaker 3: but getting it it's like getting aces right.

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Speaker 1: Yeah, absolutely so then okay, So I showed Manty this

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Speaker 1: vibees based bracket and I was like, will you help

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Speaker 1: me correct it? And he was like, dude, we need

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Speaker 1: to start from scratch. So, for listeners who don't know,

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Speaker 1: Monty McNair is the GM of the Sacramento Kings and

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Speaker 1: he went to Princeton and he did his thesis on

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Speaker 1: March Madness and on building brackets. So this is actually

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Speaker 1: what he studied academically. And about eight or nine years

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Speaker 1: ago he came in second in the Cagle bracket contest.

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Speaker 1: So this is someone who is really really good at

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Speaker 1: making brackets. And I asked Monty kind of how he

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Speaker 1: would go about doing this. And Monty has his own

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Speaker 1: models as well, by the way, So let's actually listen

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Speaker 1: to a brief clip of that conversation. And then we'll

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Speaker 1: talk about what the resulting bracket looks like, and we'll

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Speaker 1: talk about your bracket night.

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Speaker 4: I would start with a clean slate, no offense to

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Speaker 4: your ECLA Champion bracket, and I think first we need

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Speaker 4: to set our.

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Speaker 3: Goal, because that's going to dictate. You know, I'll try it.

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Speaker 4: I'll try not too many tortured poker analogies, but I.

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Speaker 1: No, let's do tortured poker analogies. We live for tortured

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Speaker 1: poker analogies on the show.

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Speaker 4: Yes, so head to head cash game versus a World

398
00:20:59,404 --> 00:21:02,004
Speaker 4: Series of Poker tournament, you're going to do different things.

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Speaker 4: I think in your case, really you want to be

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Speaker 4: one person.

401
00:21:05,164 --> 00:21:08,084
Speaker 1: Yes, I'm heads up against Nate Silver, and I want

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Speaker 1: to kick his ass. Take that, Nate. There you go,

403
00:21:13,404 --> 00:21:20,204
Speaker 1: Nate Gottle's been thrown. So so that's I mean, that's

404
00:21:20,284 --> 00:21:23,764
Speaker 1: kind of where Monty said that, you know, the three

405
00:21:23,804 --> 00:21:25,644
Speaker 1: things we need to keep in mind are what's our

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Speaker 1: scoring system? Is there going to be anything different for upsets?

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00:21:28,804 --> 00:21:30,604
Speaker 1: I was like, no, you know, we're just doing something

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Speaker 1: very straightforward. And then he said, you know how big

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00:21:33,084 --> 00:21:34,844
Speaker 1: is your pool? I was like, it's me and Nate

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00:21:35,244 --> 00:21:38,084
Speaker 1: that's it. I just want my bracket to do better.

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Speaker 1: And by the way, we will link to my brackets

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Speaker 1: in the show notes, both the official bracket, which is

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Speaker 1: the Manty bracket as I call it, and then my

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Speaker 1: Vibes bracket, so that you guys can just laugh at

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Speaker 1: how funny my Vib's picks are, because, like I said,

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Speaker 1: I looked at absolutely zero data, ignored the rankings, ignored everything,

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Speaker 1: and just went with my gut. So so you'll be

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00:22:00,844 --> 00:22:04,404
Speaker 1: able to see both of those. One we hope will win,

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Speaker 1: the other we hope we'll give you a good laugh.

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Speaker 1: And yeah, Monty's are our mutual bracket. I did get

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Speaker 1: to make a few decisions, by the way, there were

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Speaker 1: a few close ones where he said, I got to pick,

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Speaker 1: so I picked sometimes things that he would not have picked,

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Speaker 1: But he said that he stood behind the choices. And

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Speaker 1: we do have Duke winning overall. Nate, how about you?

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Speaker 1: Do you have Duke winning or do you have someone

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00:22:26,724 --> 00:22:27,244
Speaker 1: else winning?

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Speaker 3: Well, I don't. Let's going to make things a little

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Speaker 3: easier for people. Let's give our champion, our final four

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Speaker 3: and then our sweet sixteen. So let's say that we're

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Speaker 3: going to get one point for every Sweet sixteen. Pick

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Speaker 3: that's correct, three points for every final four team that

433
00:22:47,084 --> 00:22:49,324
Speaker 3: is correct, and then five points were picking the right champion.

434
00:22:49,564 --> 00:22:51,364
Speaker 2: Okay, okay, okay.

435
00:22:51,484 --> 00:22:54,564
Speaker 3: So you want to start in the South region?

436
00:22:55,124 --> 00:22:55,884
Speaker 1: Uh?

437
00:22:55,924 --> 00:22:58,844
Speaker 3: Sure, Okay, we should take turns going first because it

438
00:22:58,884 --> 00:23:00,964
Speaker 3: might influence. So who are your final four in the South?

439
00:23:02,084 --> 00:23:07,244
Speaker 1: My final four? Let's see Auburn, Texas A and m

440
00:23:07,764 --> 00:23:09,204
Speaker 1: Iowa State and Michigan State.

441
00:23:09,644 --> 00:23:13,404
Speaker 3: Okay, so you have the favorites according to the Silver

442
00:23:13,444 --> 00:23:18,084
Speaker 3: Bulletin model there, I'm gonna I'm gonna just to represent

443
00:23:18,164 --> 00:23:23,804
Speaker 3: my home state. I'm gonna go Auburn Michigan. I mean,

444
00:23:23,964 --> 00:23:25,684
Speaker 3: here's here's the thing that bugs me though, Like our

445
00:23:25,724 --> 00:23:28,124
Speaker 3: model things, Iowa State is underrated but nevertheless the most

446
00:23:28,244 --> 00:23:31,564
Speaker 3: likely team. But anyway, that's fine. Iowa State and Michigan State.

447
00:23:31,644 --> 00:23:33,684
Speaker 3: So a lot of Midwest, a lot of mid West there.

448
00:23:35,044 --> 00:23:38,444
Speaker 3: And then of those four, who do you have emerging

449
00:23:38,444 --> 00:23:39,564
Speaker 3: into the final four.

450
00:23:40,924 --> 00:23:42,124
Speaker 1: Auburn and Iowa State?

451
00:23:44,404 --> 00:23:47,164
Speaker 3: Okays the eight? And then who do you have prevailing?

452
00:23:47,204 --> 00:23:49,284
Speaker 3: We didn't, we were trying to simplify it. So and

453
00:23:49,324 --> 00:23:51,604
Speaker 3: then who do you have prevailing between Auburn and Iowa State?

454
00:23:51,764 --> 00:23:55,724
Speaker 3: Auburn Okay, I also have auburn thing is I have.

455
00:23:55,844 --> 00:23:57,364
Speaker 3: I have this whole model I design. I'm trying to

456
00:23:57,364 --> 00:23:58,764
Speaker 3: sell the people, right, I'm not gonna be like, oh,

457
00:23:59,124 --> 00:24:02,364
Speaker 3: ignore the model. Go to silver Bulletin dot com and

458
00:24:02,364 --> 00:24:03,764
Speaker 3: get temperature. There's no discounty.

459
00:24:03,764 --> 00:24:05,444
Speaker 1: So do you have the same picks as I do

460
00:24:05,564 --> 00:24:06,444
Speaker 1: other than Michigan.

461
00:24:06,684 --> 00:24:06,884
Speaker 2: Yeah.

462
00:24:06,924 --> 00:24:09,804
Speaker 3: In fact, I just had to cheat to over ride

463
00:24:09,884 --> 00:24:12,484
Speaker 3: my model, which actually at Texas A and M and

464
00:24:12,524 --> 00:24:16,404
Speaker 3: the same picks you did, just because like, uh, I

465
00:24:16,444 --> 00:24:19,524
Speaker 3: want some drama for this program. Yea, my model would

466
00:24:19,524 --> 00:24:21,724
Speaker 3: have made those exact same picks. Okay, let's go to

467
00:24:21,844 --> 00:24:22,684
Speaker 3: the And.

468
00:24:22,644 --> 00:24:28,524
Speaker 1: By the way, Michigan versus versus Texas and M was

469
00:24:29,164 --> 00:24:32,564
Speaker 1: one of those where Monty said that I could actually

470
00:24:32,604 --> 00:24:34,484
Speaker 1: pick and I did Texas A and M because I

471
00:24:34,524 --> 00:24:37,244
Speaker 1: thought you would pick Michigan. So I wanted to differentiate

472
00:24:37,284 --> 00:24:38,604
Speaker 1: myself and I was correct.

473
00:24:39,524 --> 00:24:42,644
Speaker 3: Now let's go out west where the regional fib we

474
00:24:42,644 --> 00:24:45,324
Speaker 3: played at the Chase Center in San Francisco, California.

475
00:24:46,724 --> 00:24:47,724
Speaker 2: I'll go FIRSTUS time.

476
00:24:47,844 --> 00:24:51,924
Speaker 3: Okay, and here my model is emphatic about these picks.

477
00:24:51,964 --> 00:24:53,204
Speaker 2: I have no choices to make.

478
00:24:53,284 --> 00:24:57,404
Speaker 3: I have in the final four, Florida Maryland, Texas Tech

479
00:24:57,964 --> 00:25:01,964
Speaker 3: and Saint John's. Go even have a choice to make,

480
00:25:03,204 --> 00:25:06,404
Speaker 3: I mean not really, you know, and then lead eight

481
00:25:06,444 --> 00:25:08,964
Speaker 3: Florida versus Saint John's. I mean Saint John's versus Kansas.

482
00:25:08,964 --> 00:25:11,044
Speaker 3: Excuse me, Sint Johns' sex seconds close, but whatever. I

483
00:25:11,044 --> 00:25:13,564
Speaker 3: live in New York, now, you know. There were some

484
00:25:13,564 --> 00:25:16,484
Speaker 3: some nice Saint John's kids also at the bar the

485
00:25:16,524 --> 00:25:23,444
Speaker 3: other day, nice but loud. So I have Florida advancing

486
00:25:23,484 --> 00:25:24,484
Speaker 3: from the west.

487
00:25:24,364 --> 00:25:29,284
Speaker 2: Over over over Saint John's. Okay, okay, do you who

488
00:25:29,284 --> 00:25:29,604
Speaker 2: do you have?

489
00:25:30,084 --> 00:25:31,764
Speaker 1: I think I have the same thing. I have Florida

490
00:25:31,764 --> 00:25:35,004
Speaker 1: and Maryland, Texas Tech, Saint John's, then Florida Saint John's,

491
00:25:35,004 --> 00:25:35,844
Speaker 1: and then Florida.

492
00:25:35,924 --> 00:25:37,724
Speaker 3: Okay, so so far all we have is this one

493
00:25:37,764 --> 00:25:39,644
Speaker 3: game that may or I'm not even happens.

494
00:25:39,724 --> 00:25:42,244
Speaker 1: So it's really it's interesting though, because you said you

495
00:25:42,284 --> 00:25:49,964
Speaker 1: had no choices, and the one thing that Monty said

496
00:25:50,084 --> 00:25:52,524
Speaker 1: was that the first well, I guess we're not doing

497
00:25:52,524 --> 00:25:55,924
Speaker 1: the We're not doing the Sweet sixteen. So I have

498
00:25:56,004 --> 00:25:59,524
Speaker 1: Colorado State beating Memphis because he said that the five

499
00:25:59,564 --> 00:26:02,324
Speaker 1: and twelve five twelve is one of the most common upsets,

500
00:26:02,404 --> 00:26:03,484
Speaker 1: and that would be fun.

501
00:26:03,484 --> 00:26:05,564
Speaker 2: Okay, you have Coloro State beating Memphis.

502
00:26:05,644 --> 00:26:11,284
Speaker 3: Yeah, so it's a fucking silver bulletin model, Maria, really, yes, yeah, Monty's.

503
00:26:10,884 --> 00:26:12,004
Speaker 2: Just are you sure he was?

504
00:26:12,044 --> 00:26:13,404
Speaker 1: Well, he didn't look. He didn't look.

505
00:26:13,644 --> 00:26:14,204
Speaker 3: Oh my god.

506
00:26:14,284 --> 00:26:16,524
Speaker 1: So, which which bracket are we going to do next?

507
00:26:17,164 --> 00:26:19,004
Speaker 2: Midwest? And now it's your turn to go first?

508
00:26:19,044 --> 00:26:25,524
Speaker 1: Okay, So I have Houston, Purdue, Illinois, and Tennessee. Then

509
00:26:25,564 --> 00:26:30,924
Speaker 1: I have Houston and Tennessee, and then I have Tennessee.

510
00:26:31,804 --> 00:26:36,684
Speaker 3: Okay, so here's where your little counterintuitive friend. Okay, I've

511
00:26:36,684 --> 00:26:39,164
Speaker 3: got Now I don't have to cheat to like to

512
00:26:39,244 --> 00:26:46,684
Speaker 3: have different picks. So Houston Clemson, which you Clemson are Purdue?

513
00:26:46,924 --> 00:26:47,684
Speaker 1: I said, Purdue?

514
00:26:48,044 --> 00:26:48,924
Speaker 3: Houston Clemson.

515
00:26:48,964 --> 00:26:50,404
Speaker 2: Who who's a five seed?

516
00:26:52,364 --> 00:26:54,884
Speaker 3: We think produce a team that you know, gets a

517
00:26:54,924 --> 00:26:57,724
Speaker 3: lot of benefit out of their home court advantage, but

518
00:26:57,804 --> 00:27:01,364
Speaker 3: you're not playing at home in the tournament. Houston Purdue

519
00:27:03,204 --> 00:27:04,884
Speaker 3: A close choice, But I'm going with a model here.

520
00:27:05,004 --> 00:27:07,004
Speaker 3: Kentucky and Tennessee.

521
00:27:07,324 --> 00:27:09,484
Speaker 1: Well, Kentucky was one of the other, so that that

522
00:27:09,524 --> 00:27:11,564
Speaker 1: was one of the choices that he also had me made.

523
00:27:11,604 --> 00:27:14,844
Speaker 1: Illinois versus Kentucky said it was incredibly close, but that

524
00:27:14,924 --> 00:27:16,844
Speaker 1: I was allowed to choose an upset, and I said

525
00:27:16,844 --> 00:27:19,724
Speaker 1: I wanted to since my husband is from Chicago.

526
00:27:23,324 --> 00:27:27,604
Speaker 3: So then I have Houston emerging Tennessee Kentucky. It's a

527
00:27:27,644 --> 00:27:29,884
Speaker 3: classic rivalry those.

528
00:27:29,684 --> 00:27:31,444
Speaker 2: Two long states.

529
00:27:31,804 --> 00:27:34,564
Speaker 3: So I have Houston playing Tennessee in the regional final

530
00:27:34,604 --> 00:27:38,004
Speaker 3: and Houston not Tennessee winning. Huh. Now I feel comfortable.

531
00:27:38,004 --> 00:27:40,284
Speaker 3: I feel like I have an edge here.

532
00:27:40,684 --> 00:27:41,124
Speaker 1: All right?

533
00:27:41,564 --> 00:27:44,604
Speaker 3: Okay, finally whit bracket. Have we not done yet the East?

534
00:27:44,764 --> 00:27:49,524
Speaker 3: So I'm not trying to go first, right yep, Duke Arizona,

535
00:27:50,884 --> 00:27:53,644
Speaker 3: I'm just picking the chalk here because I think I'm

536
00:27:53,644 --> 00:27:57,644
Speaker 3: winning already. Duke Arizona, Wisconsin, Alabama. Do I have any

537
00:27:57,684 --> 00:28:01,084
Speaker 3: choices to make here? I do not Duke beating Alabama

538
00:28:02,044 --> 00:28:04,724
Speaker 3: in the Elite eight to advance to the Final four? Uh?

539
00:28:05,044 --> 00:28:09,884
Speaker 1: Yeah, I have Duke Arizona, Wisconsin, Alabama as well, and

540
00:28:09,924 --> 00:28:12,764
Speaker 1: then Alabama, Duke and Duke.

541
00:28:13,364 --> 00:28:16,804
Speaker 3: Okay, so remind me so I have One of the

542
00:28:16,844 --> 00:28:20,564
Speaker 3: good things about doing a podcast is that it may

543
00:28:20,684 --> 00:28:22,724
Speaker 3: not have Dawn and you. I just have the four

544
00:28:22,804 --> 00:28:26,324
Speaker 3: number one seeds reaching the final four, even though it's

545
00:28:26,324 --> 00:28:29,364
Speaker 3: incredibly unlikely that they all will right collectively, each have

546
00:28:29,444 --> 00:28:31,404
Speaker 3: like a one in two chance roughly or less than

547
00:28:31,404 --> 00:28:32,804
Speaker 3: that actually, so it's like only like a one in

548
00:28:33,844 --> 00:28:37,444
Speaker 3: twenty chance that I'm quote unquote right. But this is

549
00:28:37,444 --> 00:28:39,124
Speaker 3: why you need a funky scoring system. Should have we

550
00:28:39,124 --> 00:28:41,724
Speaker 3: should have? Yeah, Monty, I don't know. Now, we just

551
00:28:41,764 --> 00:28:44,964
Speaker 3: are are very chalky chalk if you don't know the term,

552
00:28:44,964 --> 00:28:46,844
Speaker 3: and you probably wouldn't unless you're a sport's bidding your

553
00:28:46,964 --> 00:28:49,444
Speaker 3: but like chalk just means picking the favorites. So in

554
00:28:49,484 --> 00:28:52,804
Speaker 3: our final four, I need you to pick a national champion,

555
00:28:52,924 --> 00:28:55,164
Speaker 3: Maria or you already revealed your national yep.

556
00:28:55,004 --> 00:28:57,244
Speaker 1: My national champion is Duke. How about you.

557
00:28:57,924 --> 00:29:01,204
Speaker 3: Let me think here? Okay, So this is interesting because

558
00:29:01,244 --> 00:29:03,764
Speaker 3: like the biggest differentially have right now is Houston making

559
00:29:03,844 --> 00:29:09,164
Speaker 3: the final four and not Tennessee. Yep, right, So so

560
00:29:09,244 --> 00:29:14,484
Speaker 3: I don't okay, So if I get if I'm right

561
00:29:14,524 --> 00:29:16,644
Speaker 3: on Houston, but you're right in Okay, I'm gonna do

562
00:29:16,684 --> 00:29:17,444
Speaker 3: something weird here.

563
00:29:19,084 --> 00:29:22,404
Speaker 2: You ready, Yeah, I'm gonna go Florida.

564
00:29:22,564 --> 00:29:25,844
Speaker 1: So you're gonna pick Florida beating Auburn and then this

565
00:29:26,044 --> 00:29:27,244
Speaker 1: is lower.

566
00:29:26,924 --> 00:29:31,524
Speaker 3: Expected value relative to entire field. But I'm trying to

567
00:29:31,524 --> 00:29:33,164
Speaker 3: straut it because if I'm right on Houston making the

568
00:29:33,204 --> 00:29:35,964
Speaker 3: final four, then I have an edge, so I don't

569
00:29:35,964 --> 00:29:38,004
Speaker 3: want to pick Houston, right, and then it's like redundant.

570
00:29:38,044 --> 00:29:39,724
Speaker 3: It's like another way for me to win, right.

571
00:29:41,164 --> 00:29:41,564
Speaker 2: I don't know.

572
00:29:41,644 --> 00:29:44,004
Speaker 3: I don't really I don't really know. I don't even

573
00:29:44,164 --> 00:29:46,524
Speaker 3: you know, I don't know. I don't really like I'm

574
00:29:46,604 --> 00:29:49,644
Speaker 3: really enamored of any of these, you know, Yeah, I.

575
00:29:49,724 --> 00:29:52,564
Speaker 1: Love I love how you're gaming out the game theory

576
00:29:53,284 --> 00:29:55,284
Speaker 1: payoffs in real time on the pod.

577
00:29:55,364 --> 00:29:55,924
Speaker 2: This is great.

578
00:29:56,524 --> 00:29:58,284
Speaker 3: Yeah, because Houston, then you're putting too many eggs in

579
00:29:58,324 --> 00:30:02,204
Speaker 3: the Houston basket, right, But I think I'm gonna get

580
00:30:02,204 --> 00:30:04,884
Speaker 3: that Houston. I mean, you know, yeah, I got Florida. Okay,

581
00:30:04,924 --> 00:30:07,844
Speaker 3: let's let's go through. You have your whole bracket.

582
00:30:07,884 --> 00:30:10,564
Speaker 2: I do I need you to give me three.

583
00:30:10,484 --> 00:30:17,484
Speaker 3: Opening round games where the inferior seed wins and that

584
00:30:17,524 --> 00:30:19,924
Speaker 3: they are at least an eleven seed and they win.

585
00:30:21,684 --> 00:30:24,044
Speaker 1: Well, the only one that I have, well, I have

586
00:30:24,124 --> 00:30:24,924
Speaker 1: Colorado State.

587
00:30:25,284 --> 00:30:27,404
Speaker 2: Okay, that counts.

588
00:30:28,124 --> 00:30:30,084
Speaker 1: Let's see. I think that might be the only one

589
00:30:30,084 --> 00:30:32,124
Speaker 1: that I have that qualifies a no.

590
00:30:32,124 --> 00:30:33,604
Speaker 3: No, somebody else can have to think on the fly,

591
00:30:35,884 --> 00:30:36,844
Speaker 3: you get a freebie.

592
00:30:37,044 --> 00:30:38,164
Speaker 1: This is not fair.

593
00:30:38,284 --> 00:30:45,724
Speaker 5: I don't know any well, I can go back to

594
00:30:45,764 --> 00:30:49,884
Speaker 5: my original vibes bracket and uh that'll that'll I have

595
00:30:50,044 --> 00:30:51,884
Speaker 5: Yale beating Texas A and O.

596
00:30:53,204 --> 00:30:56,644
Speaker 2: All right, so Yale, you've got uh Colorado State.

597
00:30:57,724 --> 00:31:00,364
Speaker 3: And let's see what other Yeah, I go vibes here?

598
00:31:01,164 --> 00:31:06,444
Speaker 1: What other vibe vibe upsides, vibe upsets I had? I

599
00:31:06,524 --> 00:31:13,164
Speaker 1: had Wofford? Is that right word? Beating Tennessee?

600
00:31:13,284 --> 00:31:15,804
Speaker 3: Okay, I think you're weighing the vibes a little too much.

601
00:31:15,844 --> 00:31:17,084
Speaker 3: That's the fifteen verses too.

602
00:31:17,204 --> 00:31:17,644
Speaker 2: I'm gonna.

603
00:31:17,724 --> 00:31:19,524
Speaker 3: I'm gonna so what I'll do as a favor, I

604
00:31:19,564 --> 00:31:21,884
Speaker 3: will not pick any of the ones you pick, not

605
00:31:22,044 --> 00:31:24,324
Speaker 3: that they are exactly at the top of my list,

606
00:31:24,364 --> 00:31:27,044
Speaker 3: apart from Colorado State. I will not let myself choose

607
00:31:27,044 --> 00:31:31,484
Speaker 3: Colorado State due to uh, due to you having picked it. Right,

608
00:31:31,484 --> 00:31:33,964
Speaker 3: that's my little bonus point to you. So let's see,

609
00:31:34,004 --> 00:31:36,004
Speaker 3: let's see where can I where can I get creative here?

610
00:31:36,444 --> 00:31:44,004
Speaker 2: All right? So Drake beating Missouri it's an eleven verses

611
00:31:44,404 --> 00:31:51,164
Speaker 2: six or Model loves Drake VCU beating BYU. It's about

612
00:31:51,164 --> 00:31:53,284
Speaker 2: a forty percent chance according to the model. And now

613
00:31:53,324 --> 00:31:56,564
Speaker 2: I have to do something a little funkier, I think,

614
00:32:00,324 --> 00:32:02,284
Speaker 2: and we'll take High Point.

615
00:32:03,484 --> 00:32:06,804
Speaker 3: Beating Purdue. Perdue tends to choke in the tournament. So

616
00:32:06,964 --> 00:32:10,484
Speaker 3: if the tidebreaker, then will go to the three upset picks, right, Okay,

617
00:32:10,724 --> 00:32:13,884
Speaker 3: if that's tie, they will have a name the location

618
00:32:13,964 --> 00:32:18,164
Speaker 3: of the college contest that probably all win because but yeah,

619
00:32:19,604 --> 00:32:22,484
Speaker 3: hand enter the location of every latitude and lunched it

620
00:32:22,484 --> 00:32:23,964
Speaker 3: at one point before you had to look things up

621
00:32:24,004 --> 00:32:27,804
Speaker 3: and be an efficient programmer. Okay, so we have our picks.

622
00:32:28,964 --> 00:32:30,884
Speaker 3: They are pretty chalky. Do you have women's picks?

623
00:32:31,244 --> 00:32:31,924
Speaker 1: I don't know.

624
00:32:32,684 --> 00:32:33,044
Speaker 3: Okay.

625
00:32:33,404 --> 00:32:38,204
Speaker 1: Monty had very limited time, but I also promised him

626
00:32:38,244 --> 00:32:40,324
Speaker 1: that I would not put that I would not actually

627
00:32:40,324 --> 00:32:42,884
Speaker 1: bet this for any real money. So that was one

628
00:32:42,884 --> 00:32:44,284
Speaker 1: of the conditions of helping me out.

629
00:32:44,684 --> 00:32:47,644
Speaker 3: Okay, Yeah, which I think we came up because they

630
00:32:48,044 --> 00:32:52,124
Speaker 3: barely let me. You know again, They're like, immediately the

631
00:32:52,164 --> 00:32:55,564
Speaker 3: Caesars puts up its lines on women's games, right, and

632
00:32:55,684 --> 00:32:57,964
Speaker 3: like it's after dinner with my partner and I'm like,

633
00:32:58,244 --> 00:33:00,124
Speaker 3: it's like, what are you doing. I'm like, do these

634
00:33:00,204 --> 00:33:02,204
Speaker 3: lunches came out? Man? I have to have to bet

635
00:33:02,204 --> 00:33:04,404
Speaker 3: and help before they change. He's like, are you ignoring

636
00:33:04,444 --> 00:33:06,164
Speaker 3: the conversation. I'm like, this is where you make your money,

637
00:33:06,164 --> 00:33:07,604
Speaker 3: and but it's this the one thing I kind of

638
00:33:07,604 --> 00:33:09,564
Speaker 3: bit serious and they still can't get real money downe.

639
00:33:09,884 --> 00:33:13,524
Speaker 1: Yeah, yeah, no that we've talked about this before. But

640
00:33:13,604 --> 00:33:16,684
Speaker 1: that is obviously a very real problem when you have

641
00:33:16,724 --> 00:33:18,044
Speaker 1: an edge in anything.

642
00:33:18,284 --> 00:33:20,044
Speaker 3: But you know, I want to be the guy who's like,

643
00:33:20,884 --> 00:33:22,924
Speaker 3: for gender equity reasons, I should be allowed to bit

644
00:33:22,924 --> 00:33:26,684
Speaker 3: more on the best. You should, You absolutely should, But no,

645
00:33:26,764 --> 00:33:30,444
Speaker 3: they really limit because they're afraid that like nobody knows anything,

646
00:33:30,444 --> 00:33:33,084
Speaker 3: and so they you know, they're they're very defensive, even

647
00:33:33,084 --> 00:33:35,604
Speaker 3: though women's faketiballl actually the women's tournament final got higher

648
00:33:35,604 --> 00:33:38,844
Speaker 3: TV ratings and the men's last year. But you know,

649
00:33:38,884 --> 00:33:44,044
Speaker 3: whenever a sports book is limiting you to a few

650
00:33:44,084 --> 00:33:46,924
Speaker 3: hundred bucks, right even if they don't let meet you overall,

651
00:33:47,044 --> 00:33:49,164
Speaker 3: whereas a men's I mean I get somewhat limited, right,

652
00:33:49,164 --> 00:33:54,124
Speaker 3: But like that's that's saying they the statistical discourse about

653
00:33:54,124 --> 00:33:56,324
Speaker 3: the game is not very advanced.

654
00:33:57,204 --> 00:34:02,364
Speaker 1: Yeah, that makes sense, Nate. I wonder if our listeners

655
00:34:02,844 --> 00:34:05,724
Speaker 1: want to want to chime in and see who whose

656
00:34:05,764 --> 00:34:07,604
Speaker 1: bracket they think is going to win. Is it going

657
00:34:07,684 --> 00:34:10,604
Speaker 1: to be dug Er Florida. How are we gonna how

658
00:34:10,644 --> 00:34:12,204
Speaker 1: are we going to do it? I think that would be.

659
00:34:12,484 --> 00:34:14,484
Speaker 3: I actually, I just think I think I have more

660
00:34:14,524 --> 00:34:18,484
Speaker 3: ways to win the contest if like, yeah, you probably look,

661
00:34:19,244 --> 00:34:23,684
Speaker 3: I think I don't want Houston. We we think we've

662
00:34:23,724 --> 00:34:26,804
Speaker 3: locked in. If we set Duke, then I'll lose anyway, right,

663
00:34:26,884 --> 00:34:28,564
Speaker 3: I mean, I think that was actually really stupid.

664
00:34:29,404 --> 00:34:30,964
Speaker 2: I don't know, I think it was stupid.

665
00:34:30,644 --> 00:34:33,764
Speaker 3: Probably, But anyway, more drama though, yes, more drama wants

666
00:34:33,764 --> 00:34:36,684
Speaker 3: to drama is always good.

667
00:34:37,324 --> 00:34:39,284
Speaker 1: Now, I don't particularly want to see them win. That's

668
00:34:39,284 --> 00:34:45,604
Speaker 1: why I had U s l A winning my vibes Brocken. Okay, cool,

669
00:34:45,644 --> 00:34:48,484
Speaker 1: well it was it was actually it was fun and

670
00:34:48,604 --> 00:34:51,124
Speaker 1: I I actually think that I'm going to probably enjoy,

671
00:34:52,084 --> 00:34:54,764
Speaker 1: actually enjoy watching some of the games now that I

672
00:34:54,804 --> 00:34:57,404
Speaker 1: actually have a bracket and I know that I'm rooting

673
00:34:57,444 --> 00:35:00,444
Speaker 1: for someone. Rooting is always something that's a little bit

674
00:35:00,484 --> 00:35:05,084
Speaker 1: more fun. Let's uh, yeah, let's let's see who wins.

675
00:35:05,084 --> 00:35:06,764
Speaker 1: I was about to say, may the best man win.

676
00:35:07,564 --> 00:35:09,764
Speaker 1: Let's just say that may the best man win, and

677
00:35:09,844 --> 00:35:13,804
Speaker 1: I hope that that's me slash Meat plus Monty. But

678
00:35:13,804 --> 00:35:17,004
Speaker 1: but we'll see and I'm looking forward to the fight.

679
00:35:21,924 --> 00:35:23,804
Speaker 1: After a quick break, we're going to be back.

680
00:35:32,004 --> 00:35:36,164
Speaker 3: Okay, Maria, now that you're a degenerate gambler, are you

681
00:35:36,204 --> 00:35:37,764
Speaker 3: gonna Are you gonna? By the way, what are we

682
00:35:37,804 --> 00:35:39,764
Speaker 3: betting on this? Should we do like dinner or something?

683
00:35:40,044 --> 00:35:42,644
Speaker 1: Yeah, well we can't. I can't actually bet any money,

684
00:35:42,724 --> 00:35:45,284
Speaker 1: per per Monty, so sure dinner dinner it is?

685
00:35:46,204 --> 00:35:49,724
Speaker 2: Okay, so who pays for dinner next time we see

686
00:35:49,724 --> 00:35:49,924
Speaker 2: each other?

687
00:35:50,044 --> 00:35:52,484
Speaker 3: Okay, are you gonna enjoy watching the games now that

688
00:35:52,524 --> 00:35:54,884
Speaker 3: you have some skin in the game and it could

689
00:35:54,884 --> 00:35:56,044
Speaker 3: be pretty nenice dinner?

690
00:35:56,364 --> 00:36:00,924
Speaker 1: Yeah? Absolutely, I'm uh, I'm excited because this is the

691
00:36:00,964 --> 00:36:03,604
Speaker 1: first time I actually even know what the teams are

692
00:36:03,764 --> 00:36:06,324
Speaker 1: who are playing in the n C Double A. Usually

693
00:36:06,364 --> 00:36:09,284
Speaker 1: I don't tune in until kind of near the end,

694
00:36:09,564 --> 00:36:13,604
Speaker 1: right where I'm like, oh, okay, you know this is important,

695
00:36:13,604 --> 00:36:15,924
Speaker 1: but like there are some of these schools that I'd

696
00:36:15,964 --> 00:36:17,764
Speaker 1: never heard of before. I was like, what in the

697
00:36:17,804 --> 00:36:21,084
Speaker 1: world is this and where is it? So yes, now

698
00:36:21,164 --> 00:36:23,484
Speaker 1: I think I'm actually going to have more skin in

699
00:36:23,524 --> 00:36:26,244
Speaker 1: the game and that should be a lot more fun.

700
00:36:26,404 --> 00:36:29,044
Speaker 1: By the way, our dinner should be something like basketball related,

701
00:36:29,084 --> 00:36:31,404
Speaker 1: like we should get really good burgers or something like that.

702
00:36:31,884 --> 00:36:32,884
Speaker 2: Okay, perfect.

703
00:36:32,964 --> 00:36:35,884
Speaker 1: Do you have any sort of kind of watching rituals

704
00:36:35,964 --> 00:36:38,764
Speaker 1: or anything like that or is it just like whatever goes?

705
00:36:38,804 --> 00:36:41,444
Speaker 1: Do you prefer watching in bars around other people? Do

706
00:36:41,484 --> 00:36:43,844
Speaker 1: you like watching at home? I'm actually just curious. How

707
00:36:44,044 --> 00:36:45,804
Speaker 1: have someone like you who's been doing this a long

708
00:36:45,844 --> 00:36:46,364
Speaker 1: time does it?

709
00:36:47,484 --> 00:36:49,724
Speaker 3: For the tournament in the early rounds, it's fun to

710
00:36:49,764 --> 00:36:51,564
Speaker 3: go to a bar because they can have like four

711
00:36:51,564 --> 00:36:54,004
Speaker 3: different games on it once. I do not have like

712
00:36:54,044 --> 00:36:57,244
Speaker 3: one of those fancy TV setups where multiple monitors kind

713
00:36:57,284 --> 00:36:59,044
Speaker 3: of thing, right, So that's the reason why. Yeah, No,

714
00:36:59,164 --> 00:37:02,844
Speaker 3: the tournament is like fun to like, Yeah, I mean,

715
00:37:02,884 --> 00:37:06,084
Speaker 3: for some basketball and football are kind of the big Yeah,

716
00:37:06,124 --> 00:37:07,564
Speaker 3: it's fun to watch sports at bars.

717
00:37:07,564 --> 00:37:08,044
Speaker 2: What am I say?

718
00:37:08,044 --> 00:37:10,004
Speaker 3: I'm not trying to qualify that, ye too much?

719
00:37:10,124 --> 00:37:13,684
Speaker 1: Yeah, yeah, I actually I totally agree with that. You know,

720
00:37:13,724 --> 00:37:18,284
Speaker 1: I'm not a football fan either, but found myself last year,

721
00:37:18,324 --> 00:37:21,804
Speaker 1: I think or the year before a barbecue place during

722
00:37:21,844 --> 00:37:23,844
Speaker 1: the Super Bowl when it was just starting, and it

723
00:37:23,924 --> 00:37:26,604
Speaker 1: ended up staying there and watching the entire game because

724
00:37:26,844 --> 00:37:29,564
Speaker 1: the energy was just so great and people were you know,

725
00:37:29,844 --> 00:37:31,524
Speaker 1: people were so into it and it was a lot

726
00:37:31,564 --> 00:37:35,524
Speaker 1: of fun. And I actually, like for once enjoyed watching

727
00:37:35,684 --> 00:37:36,524
Speaker 1: a football game.

728
00:37:36,644 --> 00:37:39,124
Speaker 3: And by the way, even if you never bet, a

729
00:37:39,204 --> 00:37:41,884
Speaker 3: sports book is a fun place to watch sports too,

730
00:37:41,884 --> 00:37:46,444
Speaker 3: because people are really into it. Yeah, and they're rooting

731
00:37:46,484 --> 00:37:46,844
Speaker 3: in yeah.

732
00:37:47,204 --> 00:37:47,284
Speaker 5: No.

733
00:37:47,404 --> 00:37:49,244
Speaker 3: But what's weird is like I have so for the

734
00:37:49,284 --> 00:37:52,604
Speaker 3: bets I have, like you know, you sually it's born

735
00:37:52,604 --> 00:37:56,044
Speaker 3: to watch, like the one versus sixteen games, very lopsided ones,

736
00:37:56,044 --> 00:38:00,004
Speaker 3: but like I have like quite a few bets on

737
00:38:00,084 --> 00:38:02,164
Speaker 3: like point spreads in those games. Like I said, in

738
00:38:02,164 --> 00:38:04,324
Speaker 3: the women's often the favorites I expect one, but even

739
00:38:04,324 --> 00:38:07,964
Speaker 3: more and the guys often underdogs, right, But like whether

740
00:38:08,164 --> 00:38:10,724
Speaker 3: you know a team gets to thirty four points or

741
00:38:10,804 --> 00:38:13,924
Speaker 3: thirty six points is like material to me.

742
00:38:14,164 --> 00:38:16,324
Speaker 2: So you maybe I'm probably not a good advertisement.

743
00:38:16,364 --> 00:38:16,844
Speaker 3: Are you watch it?

744
00:38:16,844 --> 00:38:18,604
Speaker 2: I'm like, don't turn away from that blowout? You know.

745
00:38:18,684 --> 00:38:25,044
Speaker 1: Let's well, I'm definitely I'm definitely excited about it. What's

746
00:38:25,084 --> 00:38:28,764
Speaker 1: your take? I'm wondering if you're rational or irrational when

747
00:38:28,804 --> 00:38:32,044
Speaker 1: it comes to this on you know, superstitions. You know,

748
00:38:32,084 --> 00:38:35,364
Speaker 1: there are some people who will like wear the team

749
00:38:35,524 --> 00:38:37,644
Speaker 1: jersey when they watch, and they think that if they

750
00:38:37,644 --> 00:38:40,044
Speaker 1: don't do X, their team's going to lose. How do

751
00:38:40,124 --> 00:38:41,004
Speaker 1: you feel about all of that?

752
00:38:42,964 --> 00:38:44,604
Speaker 3: I mean, I don't you know, you got mad at

753
00:38:44,604 --> 00:38:46,324
Speaker 3: me once, like lost my lucky hat and you're like

754
00:38:46,324 --> 00:38:51,484
Speaker 3: you don't have a lucky hat. Yeah, I'm not too superstitious.

755
00:38:51,604 --> 00:38:53,324
Speaker 1: Really, do you really have a lucky hat?

756
00:38:53,444 --> 00:38:53,564
Speaker 5: Night?

757
00:38:54,444 --> 00:38:56,644
Speaker 3: I thought I did, but then you know, I used

758
00:38:56,644 --> 00:38:59,324
Speaker 3: to have one. I used to have one superstition that

759
00:38:59,444 --> 00:39:04,284
Speaker 3: like I have this one black one chip from from

760
00:39:04,364 --> 00:39:07,804
Speaker 3: the ARIA, right, and like I forgot to cash it in.

761
00:39:08,324 --> 00:39:09,804
Speaker 3: You know, sometimes you have like an X chip in

762
00:39:09,844 --> 00:39:12,164
Speaker 3: your bag or something, right you're leaving for the trip

763
00:39:13,924 --> 00:39:17,164
Speaker 3: and then the next trip, I also forgot to like

764
00:39:17,604 --> 00:39:19,204
Speaker 3: cash and I don't think I was sting at the area.

765
00:39:19,284 --> 00:39:22,124
Speaker 3: So I'm like, this is my tradition now, right, but

766
00:39:22,244 --> 00:39:23,964
Speaker 3: this is good luck. But I haven't happened to the

767
00:39:23,964 --> 00:39:27,044
Speaker 3: good luck in poker, especially that at fucking tournaments.

768
00:39:26,604 --> 00:39:27,284
Speaker 2: You know, or the ARIA.

769
00:39:27,364 --> 00:39:29,364
Speaker 3: So like I'm like fuck this, right, It's like, you know,

770
00:39:34,204 --> 00:39:35,844
Speaker 3: I owed so many money from the sports, so I'm

771
00:39:35,844 --> 00:39:38,364
Speaker 3: like I'm cashing these jumbnuts trying this fucking chip around.

772
00:39:38,404 --> 00:39:41,604
Speaker 3: You have to give white, you know, so so yeah,

773
00:39:41,764 --> 00:39:45,004
Speaker 3: so maybe they'll turn my luck around all right long,

774
00:39:45,044 --> 00:39:45,804
Speaker 3: around my possession.

775
00:39:46,324 --> 00:39:50,124
Speaker 1: Let's see what happens. I love it, love it. Sometimes

776
00:39:50,164 --> 00:39:52,524
Speaker 1: even the most rational people, you know, do you have

777
00:39:52,564 --> 00:39:54,404
Speaker 1: a lucky chip or a lucky hat?

778
00:39:54,844 --> 00:39:57,244
Speaker 3: I do think that, like there's something rational there about

779
00:39:57,244 --> 00:40:00,244
Speaker 3: like not making like if I have like a a

780
00:40:00,284 --> 00:40:03,044
Speaker 3: busy day, especially in loving poker netness. You know, if

781
00:40:03,044 --> 00:40:04,604
Speaker 3: I make day three of something, I'm like, okay, I'm

782
00:40:04,684 --> 00:40:08,044
Speaker 3: going to get up at seven point thirty and like

783
00:40:08,164 --> 00:40:10,644
Speaker 3: and worked out and then eat this place, even have

784
00:40:10,724 --> 00:40:12,884
Speaker 3: to make a reservation, like so I'll try to control

785
00:40:13,004 --> 00:40:14,244
Speaker 3: some factors like that. Right.

786
00:40:14,844 --> 00:40:16,844
Speaker 1: Well, on that note, Nate, good luck to both of us.

787
00:40:17,004 --> 00:40:19,284
Speaker 1: I'm excited. I think this will be a fun competition

788
00:40:19,364 --> 00:40:21,604
Speaker 1: win or lose, although obviously I hope that I win.

789
00:40:22,684 --> 00:40:25,764
Speaker 3: I hope so Tim, you hope I win.

790
00:40:25,884 --> 00:40:27,724
Speaker 1: Yay, that's awesome, thank you.

791
00:40:27,724 --> 00:40:29,484
Speaker 3: You know, it's like when you say it's like when

792
00:40:29,524 --> 00:40:32,884
Speaker 3: someone is like, you know, someone's like, how a goo day,

793
00:40:32,884 --> 00:40:34,924
Speaker 3: and you're like, love you, and it's like you're saying

794
00:40:34,964 --> 00:40:37,444
Speaker 3: that you're a partner or something right, like your boss

795
00:40:37,564 --> 00:40:37,924
Speaker 3: or something.

796
00:40:37,924 --> 00:40:39,364
Speaker 2: It's like, I don't know, do you know, Maury work

797
00:40:39,364 --> 00:40:39,604
Speaker 2: toward you?

798
00:40:39,604 --> 00:40:43,244
Speaker 3: Another podcast I've been up a long hours trying to

799
00:40:43,244 --> 00:40:45,444
Speaker 3: get those brackets and ship up all these models up.

800
00:40:46,804 --> 00:40:48,244
Speaker 2: So I hope I win.

801
00:40:49,004 --> 00:40:49,764
Speaker 5: No, no, you.

802
00:40:49,724 --> 00:40:56,084
Speaker 1: Can't take it back, all right? And on that note, listeners,

803
00:40:56,684 --> 00:40:59,764
Speaker 1: let us know who you think will win. And if

804
00:40:59,804 --> 00:41:02,324
Speaker 1: you tune in to Pushkin Plus today, we're going to

805
00:41:02,404 --> 00:41:05,084
Speaker 1: have the full interview with Monty McNair, which is a

806
00:41:05,084 --> 00:41:08,124
Speaker 1: lot of fun. I highly recommend you listen to it.

807
00:41:15,484 --> 00:41:17,604
Speaker 1: Let us know what you think of the show. Reach

808
00:41:17,684 --> 00:41:21,044
Speaker 1: out to us at Risky Business at pushkin dot fm.

809
00:41:21,364 --> 00:41:23,844
Speaker 1: And by the way, if you're a Pushkin Plus subscriber,

810
00:41:24,004 --> 00:41:26,764
Speaker 1: we have some bonus content for you that's coming up

811
00:41:26,844 --> 00:41:27,964
Speaker 1: right after the credits.

812
00:41:28,124 --> 00:41:31,404
Speaker 3: And if you're not subscribing yet, consider signing up for

813
00:41:31,564 --> 00:41:33,724
Speaker 3: just six ninety nine a month. What a nice price

814
00:41:34,084 --> 00:41:36,964
Speaker 3: you get access to all that premium content and for

815
00:41:37,084 --> 00:41:39,684
Speaker 3: listening across Pushkin's entire network of shows.

816
00:41:39,924 --> 00:41:43,444
Speaker 1: Risky Business is hosted by me Maria Kanikova.

817
00:41:42,764 --> 00:41:44,084
Speaker 2: And by me Nate Silver.

818
00:41:44,484 --> 00:41:47,564
Speaker 3: The show is a co production of Pushkin Industries and iHeartMedia.

819
00:41:48,164 --> 00:41:51,564
Speaker 3: This episode was produced by Isabelle Carter. Our associate producer

820
00:41:51,604 --> 00:41:55,244
Speaker 3: is Gabriel Hunter Chang. Sally Helm is our editor and

821
00:41:55,284 --> 00:41:58,764
Speaker 3: our executive producer is Jacob Goldstein. Mixing by Sarah Bruger.

822
00:41:59,444 --> 00:42:01,844
Speaker 1: If you like this show, please rate and review us

823
00:42:01,844 --> 00:42:04,204
Speaker 1: so other people can find us too, Thanks so much

824
00:42:04,244 --> 00:42:04,804
Speaker 1: for tuning in.