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Speaker 1: Welcome to Worst Year Ever, a production of I Heart Radio.

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Speaker 1: Welcome Yet Everything, So don't Oh my gosh, everybody, Welcome

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Speaker 1: back to the Worst Year Ever. This is part two

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Speaker 1: of the first two episodes, Part two of episode one.

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Speaker 1: Oh my gosh, Yeah, you get to you get too

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Speaker 1: on the first day. Is that exciting because we didn't

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Speaker 1: budget our time. Well, but it's a gift for you guys. Yeah,

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Speaker 1: we just have too much to say to each other.

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Speaker 1: I do. And speaking of gifts, oh wait, no, it's

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Speaker 1: not time for products and services. I am just a hack.

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Speaker 1: I'm so sorry. Okay, I accept your apology on behalf

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Speaker 1: of everyone listening. Right. We don't traditionally open with ads.

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Speaker 1: We actually I have an addictive personality and I have

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Speaker 1: gotten addicted to add pivots and it's a real problem.

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Speaker 1: If I don't do it twice a day, I just

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Speaker 1: get the shakes comes. I'll pull my car over in

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Speaker 1: the street and just shout at a passing police officers.

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Speaker 1: It's hims like a diaper for dudes, adult diaper. No,

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Speaker 1: it gets your dick hard. Oh, it's a hard pill.

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Speaker 1: It's a dick pill. It's a dick hard pill. Is

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Speaker 1: it spelled like the word him or the other women,

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Speaker 1: but with an s him No, not like what what

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Speaker 1: religious people do, although selling hymns has become my religion.

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Speaker 1: I mean, I'm sure that gets some people hard. Is

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Speaker 1: this podcast sponsored by HIMS? Maybe one day we can

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Speaker 1: only in horrible at this crossed has crossed everybody? Co

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Speaker 1: Do you want to talk about the Washington Post? Yeah,

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Speaker 1: Penis is crossed and then talking about the New York Times.

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Speaker 1: All right, it's actually gonna talk about the New York

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Speaker 1: Times and Washington Post kind of together because I think

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Speaker 1: they have kind of similar problems and kind of similar positives.

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Speaker 1: They are two of America's papers of record. Phrase it

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Speaker 1: tends to mean that they're, you know, very rigorous. They

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Speaker 1: show attention to detail and have accountability in their editorial process,

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Speaker 1: and they have like a truly like a national view

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Speaker 1: and attempt to be the best possible record of what

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Speaker 1: happened in the nation, not just in a region for

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Speaker 1: the day. And we could debate how much they do

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Speaker 1: that or not um but as as I think we

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Speaker 1: all can guess or remember, the New York Times the

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Speaker 1: Washington Post are both generally pro war. Another theme that

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Speaker 1: I think we all sort of get with a lot

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Speaker 1: of these mainstream news organizations, cody, unless it's war against

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Speaker 1: Nazi Germany in the late nineteen thirties, in which case

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Speaker 1: they really think we should see how this Hitler guy

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Speaker 1: plays out. Let's hear him out, let him, let him

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Speaker 1: do what he's gonna do, and we'll we'll just stay

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Speaker 1: keep to ourselves. Yeah, just it's just another you know,

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Speaker 1: balanced viewpoint. What could America not second? Yeah, exactly, um whatever.

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Speaker 1: Whatever the synonym for not second is to also specifically

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Speaker 1: pro the war in Iraq is interesting. I think that

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Speaker 1: like the war stance that draws claims of bias is

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Speaker 1: the anti war stance. No one really accuses anybody of

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Speaker 1: bias if they're pro war. Interesting thing about our culture.

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Speaker 1: Both of Your Times and Washington Post have also been

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Speaker 1: heavily criticized for the reporting on the Obama administration's drone

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Speaker 1: strike program, their general lack of reporting on civilian casualties

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Speaker 1: for that program. Which civilian casualties are I guess one

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Speaker 1: would describe it as a problem with drone strikes. Yeah,

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Speaker 1: I certainly wouldn't. I think that's fair. And to to

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Speaker 1: quote a study from Jay Bachman from the School of

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Speaker 1: International Service at American University. The willingness of The New

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Speaker 1: York Times and Washington Post to state positively that only

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Speaker 1: legitimate targets were killed in the immediate aftermath of drone

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Speaker 1: strikes and Yemen and Pakistan is especially problematic because exactly

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Speaker 1: who was killed in drone strikes is difficult to immediately confirm. Uh.

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Speaker 1: The study found not always, not always, not always, difficult

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Speaker 1: always Yeah. The other side, the site that I write

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Speaker 1: for Bellancat did a fun investigation last year where they

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Speaker 1: traced back one of the missiles that we sent Saudi

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Speaker 1: Arabia that was used to blow up a school bus

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Speaker 1: with a couple of dozen small children on it. Fun.

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Speaker 1: Legitimate targets seem like a school bus, hospitals, weddings, things

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Speaker 1: of things of that nature. It's a war, you guy, Hey, okay, Um,

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Speaker 1: it's a very old long war. Um. This this. This

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Speaker 1: study found that civilian deaths there were reported in stories

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Speaker 1: nine of the cases where civilian deaths were actually proven

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Speaker 1: to have occurred, so they did not report on them. Um.

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Speaker 1: Neither New York Times nor the Washington Post have corrected

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Speaker 1: their errors and failing to accurately report on the effects

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Speaker 1: of drone strikes. Uh, and the editorial departments of both

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Speaker 1: papers have said that it is either not up to

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Speaker 1: them or not in the papers policy to run corrections

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Speaker 1: on such stories. So Papers of Record, Yeah, I love

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Speaker 1: that response. Um. Another fun fact, the New York Times

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Speaker 1: is nick name is the Gray Lady, because the entire

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Speaker 1: staff are huge fans of the erotic novel Shades of Gray. Yeah. Yeah, yeah,

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Speaker 1: very very cool. I wasn't going to include that because

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Speaker 1: it seemed a little blue, you know, a little little

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Speaker 1: rough around the edges for our very clean podcast. But

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Speaker 1: it sounds pretty basic to me, well, you know, not

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Speaker 1: for not for the Paper of Record. So I actually

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Speaker 1: hate describing things as basic because it just describes something

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Speaker 1: that's popular than that. People. Yeah, we move on from

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Speaker 1: the drone strikes, but I think it is hard to

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Speaker 1: understate the detrimental effect of a misinformed public getting sort

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Speaker 1: of like normalized too civilian deaths from drone strikes. Like

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Speaker 1: we've talked about normalization a lot and normally of violence,

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Speaker 1: but like if you have this president who's not really

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Speaker 1: criticized for a lot, especially stuff like this, and you're

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Speaker 1: not even reporting on the civilian deaths, and you're sort

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Speaker 1: of creating this idea that drone strikes are actually like

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Speaker 1: very safe and effective when they're totally not and they

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Speaker 1: kill people, uh pretty indiscriminately, um, with a lot of

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Speaker 1: collateral damage. It's bad. I would say, yeah, we'll talk

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Speaker 1: about that a bit with me and okay, yeah, my

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Speaker 1: theories that that it's bad. Yeah, I concur my theory

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Speaker 1: is that that pod is going to in fact save America. Well, okay,

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Speaker 1: will you in that? Alright? Well I thought they were,

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Speaker 1: but I guess not. They do good reporting to like

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Speaker 1: the Washington Post has a great like investigative team, um,

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Speaker 1: people like Dave farettole Like they do a lot of Yeah,

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Speaker 1: I tend to like stuff a lot of things that

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Speaker 1: the Washington Post specifically puts out. And that's why I

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Speaker 1: want to make that clear. Like they do good reporting.

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Speaker 1: And I mean the New York Times was the first

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Speaker 1: to publish the Pentagon papers. They got they got sued

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Speaker 1: for it. Like they do good things. That's a long

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Speaker 1: time ago. But actual reporters at the New York Times

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Speaker 1: tend to be very I've worked with the number of them,

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Speaker 1: tend to be very very very very are we good

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Speaker 1: at their jobs? Like the actual investigative journalists and stuff

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Speaker 1: who were doing the work. Like most of it people

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Speaker 1: get piste off at is like the op ed section

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Speaker 1: and stuff like, there's a lot of really good original

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Speaker 1: reporting done by the Times, And I would never want

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Speaker 1: to pretend like there isn't yes exactly, I want to

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Speaker 1: make that clear. Um, and we'll yeah, we're gonna get

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Speaker 1: to the op ed stuff. Yeah. But again like sort

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Speaker 1: of going with the theme, I think, uh that is

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Speaker 1: generally true. Is it's just there they're very pro war

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Speaker 1: uh and uh not questioning of the military things like that. Um.

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Speaker 1: The Washington Post, by their own admission, ran one forty

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Speaker 1: editorials and articles supporting Bush's wars. Um. They at one

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Speaker 1: point apologize for its practice of burying articles. But the

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Speaker 1: problem wasn't that they buried stories. It was just that

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Speaker 1: the anti war stories were vastly outnumbered by the pro

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Speaker 1: war stories, so they did not do the reporting. UM.

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Speaker 1: One study positive that there was just sort of general

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Speaker 1: attitude among the editors that boiled down to the idea that, like,

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Speaker 1: the war was inevitable, so why published these contrary voices?

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Speaker 1: Interesting that you want balance, but not when it comes

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Speaker 1: to maybe saying that like to war, you know, I mean,

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Speaker 1: is it possible that war is a good thing? Though

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Speaker 1: for their paper, I haven't considered it. I guess I

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Speaker 1: should read there are many many articles about how why

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Speaker 1: don't why don't you start with that? Do some research? Okay?

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Speaker 1: I will read their articles? How bushes um? So they

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Speaker 1: have that problem? Um. But like Robert you alluded to,

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Speaker 1: what I think we really should talk about are the

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Speaker 1: Washington Post and New York Times calumness and fact checkers,

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Speaker 1: um on some more news. We actually recently did an

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Speaker 1: episode written by Creature features own Katie Golden about Brett Stephens,

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Speaker 1: specifically he's a New York Times columnist and how he's

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Speaker 1: basically used his columns for his own petty bullshit combined

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Speaker 1: with climate denial. Um. And that's why the New York

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Speaker 1: Times op ed seems to be a place for out

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Speaker 1: of touch elites to sort of air their petty personal grievances,

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Speaker 1: mostly involving how kids on college camp? Is there totalitarian?

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Speaker 1: In one column by Barry Weiss as her only evidence

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Speaker 1: of this totalitarianism, she linked to a fake Twitter account.

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Speaker 1: Recently columns Brett Stevens after being called a bedbug by

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Speaker 1: a Jewish professor in a tweet that got five likes.

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Speaker 1: Brett wrote in an entire column that likened him to

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Speaker 1: a Nazi, and his evidence shared a link to Google

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Speaker 1: books with the search field reading Jews as bedbugs, which

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Speaker 1: led to him finding a quote about bed bugs that

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Speaker 1: completely undermined his point because the quote was about literal bedbugs.

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Speaker 1: So it seems it seems like maybe there's like a

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Speaker 1: complete lack of oversight and editorial rigor um, which a

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Speaker 1: paper records should probably have. It also seems like if

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Speaker 1: they fired Brett Stevens for his salary, they could afford

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Speaker 1: to pay a couple of dozen really talented journalists to

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Speaker 1: investigate like six to ten really complicated stories a year

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Speaker 1: each and provide like more than weekly content that was

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Speaker 1: incredibly useful in groundbreaking. But because they don't, we get

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Speaker 1: to know that Brett Stevens googled Jews as bed bugs

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Speaker 1: and did not read the quote that he found. He

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Speaker 1: didn't even unbelievable, why why would he? He found it

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Speaker 1: in a book? Therefore, what that professor did to him

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Speaker 1: was the same as the Holocaust exactly, que fucking d

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Speaker 1: you just got bloom up, random Jewish professor who I

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Speaker 1: think is a Nazi. Now it's just a it's just

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Speaker 1: real problem. Yeah. And so this actually kind of started

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Speaker 1: due to new leadership from James Bennett. He was put

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Speaker 1: in charge of the editorial page in um interesting year

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Speaker 1: for this to start. I don't know if anyone's familiar

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Speaker 1: with that year and how good it was everybody. Yeah,

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Speaker 1: So James hired a bunch of Brett bugs U two,

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Speaker 1: as he claims, we're looking to challenge our own and

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Speaker 1: our reader's assumptions. Uh, never mind that the paper only

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Speaker 1: seems to be challenging any assumptions from the right, and

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Speaker 1: any of the reader's assumptions about climate change are true.

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Speaker 1: So don't need to be challenged by Brett fucking Stevens.

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Speaker 1: But in general, the spectrum of challenge is pretty narrow.

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Speaker 1: To Glenn Greenwald points out, it spans the small gap

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Speaker 1: from establishment centrist Democrats to establishment centrist Republicans. Again, just

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Speaker 1: sort of all these viewpoints that are approved and none

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Speaker 1: that are, like war bad, maybe more taxes, I don't know,

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Speaker 1: things like that. This all reminds me of the liberal consensus.

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Speaker 1: This is something I think about a lot, and I

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Speaker 1: think it helps to understand some of what we're seeing

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Speaker 1: here and on the internet and culturally in general. In

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Speaker 1: the seventies, there was something developing called the liberal consensus. Uh.

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Speaker 1: One can make an argument that this is because reality

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Speaker 1: sort of leans liberal, but basically there was there are

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Speaker 1: these nonpartisan organizations like the New York Times and the

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Speaker 1: Brookings Institute, and conservatives saw them as incredibly biased. Like

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Speaker 1: the Brookings Institute would do studies, and they would study

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Speaker 1: issues and do research and gather facts and sort of

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Speaker 1: come to conclusions and propose policy. It was a nonpartisan

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Speaker 1: organization meant to do that, and a lot of their

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Speaker 1: policies were liberal leaning, right, but it was the result

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Speaker 1: of a nonpartisan effort to research and understand an issue.

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Speaker 1: So in response to this, people were very mad that

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Speaker 1: Nixon was sort of buying into the liberal consensus. So

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Speaker 1: we started to see things pop up like the American

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Speaker 1: Enterprise Institute and the Heritage Foundation. UH, these sort of

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Speaker 1: conservative think tanks that were responding to what they saw

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Speaker 1: as body So they're saying, you're biased, therefore we're going

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Speaker 1: to be biased, right, So you have like all these

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Speaker 1: institutions were basically admitting their own bias and their goal

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Speaker 1: being to find facts and information that they can use

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Speaker 1: to prove a conservative policy that's worth pursuing. We see

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Speaker 1: this a lot, the shades of it, at least in

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Speaker 1: people like Ben Shapiro. Praguer University is great at this.

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Speaker 1: You'll notice that a lot of their speakers are actually

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Speaker 1: from the American Enterprise Institute. So you start with conclusion,

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Speaker 1: you find facts can support it, as opposed to finding facts,

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Speaker 1: doing research, and then analyzing it to a right a conclusion.

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Speaker 1: So there's always been this sort of push against the

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Speaker 1: liberal consensus and liberal media. In the sixties, the first

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Speaker 1: instance of it was used by George Wallace and it

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Speaker 1: was used to basically discredit the civil rights movement, saying

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Speaker 1: the media only supported the civil rights movement because of

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Speaker 1: their liberal bias. So it's been going on for as

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Speaker 1: been going on for a while, and it's this sort

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Speaker 1: of general idea that has been building and building over

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Speaker 1: the years. Um, we're becoming more and more polarized, right,

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Speaker 1: is this push against the liberal consensus, liberal media and

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Speaker 1: saying that like, well now we're gonna do it even

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Speaker 1: though they weren't doing it right. It's like Fox News

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Speaker 1: they go full far right conspiracy nut job as a

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Speaker 1: response to what they perceive as bias, and then we

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Speaker 1: get to sort of where we are today. I think

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Speaker 1: we're still grappling with it. Um. There's more to say

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Speaker 1: about like the Powell memo that sort of pushed more

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Speaker 1: think tanks, more lobbying in favor of big business, regardless

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Speaker 1: of the facts. So I think there's always this see

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Speaker 1: mean call for balance when balance isn't warranted um, which

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Speaker 1: again is sort of what we've been talking about, where

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Speaker 1: you have fifty articles about climate change and then fifty

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Speaker 1: articles denying climate change. But that's not balance, that's the

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Speaker 1: illusion of balance in the face of what reality actually is, right,

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Speaker 1: because denying climate change is just lying. Well, you're not

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Speaker 1: being balanced at all. You're you're putting something out there

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Speaker 1: that isn't true. Yeah, And so you have these I

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Speaker 1: think a lot of these institutions, like the New York Times,

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Speaker 1: who are being accused of this bias, and they're like, well,

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Speaker 1: you don't have any of these voices and any of

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Speaker 1: these voices, So they're trying to create a balance that

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Speaker 1: doesn't actually exist. They're getting all these calmness to write

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Speaker 1: about how we need more nukes and how climate change

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Speaker 1: isn't really how all the all the college campuses are

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Speaker 1: just a bunch of lips snowflakes. While I'm going to

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Speaker 1: accuse some critic of being hitler and that's why we

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Speaker 1: are sort of here now, it seems more criticizing their

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Speaker 1: articles about war less criticizing liberal Yeah, and like again,

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Speaker 1: like I'm not gonna say there's no liberal bias and

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Speaker 1: things like that, but this is sort of where we're

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Speaker 1: getting at well. And I think also like a big

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Speaker 1: chunk of the liberal bias that like doesn't get written

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Speaker 1: down as bias is actually they're biased towards wanting to

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Speaker 1: be that fucking kind of chronkite voice that like like

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Speaker 1: like that that leads them to giving weight to sides

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Speaker 1: that ought not be given weight just because they want

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Speaker 1: to be seen as being fair, because that's more important

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Speaker 1: to them than accurately reporting on the truth, because getting

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Speaker 1: attacked is like they just can't handle it. And I

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Speaker 1: think part of that to do with a lot of

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Speaker 1: the fact that a lot of our most prominent voices

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Speaker 1: in mainstream journalism are rich kids who got the job

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Speaker 1: because they were able to take an unpaid internship, who

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Speaker 1: went to j school and learned all the things you

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Speaker 1: learn at j school, which often doesn't interact with like

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Speaker 1: what actually makes a good journalist and whose family is

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Speaker 1: tied into like the media industry somehow. Like it's it's

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Speaker 1: a bunch of people who when they get actually like attacked,

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Speaker 1: can't push back because they don't have any kind of

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Speaker 1: like they're they're gormless at a fundamental level, and that

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Speaker 1: is not good. Yeah. Right, as soon as they're accused

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Speaker 1: of that bias, you're like, oh, I guess I got,

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Speaker 1: I got, I guess we are we have to fix that.

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Speaker 1: I got overcorrect. Yeah, it's all. Yeah, it's a lot

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Speaker 1: of overcorrection and in general, just saying saying what your

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Speaker 1: narrative is. I know head in New York Times recently

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Speaker 1: said like, oh, we focus on Russia for a couple

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Speaker 1: of years, and now we're not going to do that

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Speaker 1: because that, you know, that didn't work out. So now

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Speaker 1: we're gonna focus on race. And this idea of like

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Speaker 1: choosing your narrative for like the next year and a half, Well,

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Speaker 1: that's not that's not that's not journalism. You you like,

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Speaker 1: you research, you react, you analyze and stuff. You don't

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Speaker 1: just say like this is this is our narrative now,

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Speaker 1: Um so I don't care for that. But speaking of

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Speaker 1: people airing personal grievances and papers of record and wanting balance,

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Speaker 1: except when it comes to the left. The Washington Post

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Speaker 1: today Thursday, September twelve ran an op ed called I

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Speaker 1: like Elizabeth Warren too bad. She's a hypocrite, and I'm

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Speaker 1: gonna I'm gonna briefly read this thing, and I want

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Speaker 1: you to stop me when you think that I've made

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Speaker 1: my point. Warren attacked former Vice President Joe Biden off

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Speaker 1: to a good start list personally, this is me saying

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Speaker 1: good for holding a kickoff fundraiser in Philadelphia in April,

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Speaker 1: which she criticized as a swanky, private fundraiser for wealthy

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Speaker 1: donors in an email to supporters the next day. Well,

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Speaker 1: I helped organize that affair, And I thought her attack

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Speaker 1: was extremely hypocritical because nearly twenty of us who attended

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Speaker 1: the Biden fundraiser had also given her two thousand or

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Speaker 1: more closed door fundraisers and swanky locations. You didn't stop me,

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Speaker 1: but if you didn't catch that, well, I helped organize

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Speaker 1: that affair. This is a person who just like they

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Speaker 1: organized the thing and they're upset, so they're writing an

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Speaker 1: article about itulous and like we will. I'm sure in

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Speaker 1: other episodes talk about Elizabeth Warren and her donors, but like,

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Speaker 1: come on, Washington Post, Uh, that's crazy, right, Like it's

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Speaker 1: is this person who's totally involved and at least they

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Speaker 1: mentioned it. Um. They don't always do that, but they

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Speaker 1: seemingly have a huge bias against anything on the left

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Speaker 1: of the establishment Democratic Party, especially Bernard Sanders. Sixteen they

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Speaker 1: ran sixteen negative articles about him in sixteen hours. Yeah, recently,

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Speaker 1: but I bet they ran sixteen positive articles about him

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Speaker 1: in sixteen hours. I bet they did. Yeah, I bet

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Speaker 1: that proved their balance. Recently, Glenn Kessler gave Brie Sanders

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Speaker 1: three pinocchio is about a claim that five thousand people

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Speaker 1: go bankrupt every year because they can't pay outrageous medical bills. Um.

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Speaker 1: So he fact checked this gave it three pinocchios. The

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Speaker 1: claim was proven to be true within Glenn's article. His

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Speaker 1: source for the fact check said that it was true.

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Speaker 1: That's so, I mean, but it's kind of like, guys,

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Speaker 1: if you multiply negative one times negative one times negative one,

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Speaker 1: you get positive three. I don't understand math, but I

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Speaker 1: think that means that three pinocchios equals of truth. Well,

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Speaker 1: he he lost a couple of pinocchios just because he

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Speaker 1: don't like it, you know, took something about Bernie, just

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Speaker 1: like knock those pinocchios like, but also it's Bernie, so

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Speaker 1: take two off. Yeah. Maybe that's something to do with

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Speaker 1: the Washington Post being owned by the richest man on

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Speaker 1: the planet, Jeff Bezos, somebody money criticizing all the time.

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Speaker 1: I don't know. Maybe that's like suggesting that I scw

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Speaker 1: my coverage in favor of dick Pills because they support

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Speaker 1: my podcast. What else did they support? Yea, they support

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Speaker 1: of healthy rectile balance, they support blood slow that's unbiased.

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Speaker 1: There claims that Bezos is pretty hands off with the

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Speaker 1: Washington Post. I don't know. Maybe he is. Um that

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Speaker 1: doesn't mean that there is not this sort of push again,

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Speaker 1: we see it. We see the New York Times too,

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Speaker 1: We see the established media in general. One quick note.

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Speaker 1: Also today September twelve, a nineteen, the Washington Post published

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Speaker 1: their last issue of their express publication. Because you know,

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Speaker 1: money digital media sort of, we're all going under the cover,

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Speaker 1: says hope you enjoy your sticking phones, um as if

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Speaker 1: the Washington Post isn't owned by the richest man in

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Speaker 1: the planets, right, But that's the gist. I didn't think

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Speaker 1: we we should be wary of things like that, of

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Speaker 1: specifically their editorial columns. You'll see a lot of disclaimers.

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Speaker 1: One thing I've noticed a lot of There are a

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Speaker 1: lot of articles that are like, oh, the Democrats are

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Speaker 1: going too far left for me to vote for them.

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Speaker 1: But then you're like, oh, it's written by like Dick Cheney,

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Speaker 1: Ari Fleischer or something like that, Like these people were

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Speaker 1: you going to vote for them anyway. It's this sort

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Speaker 1: of like taking a moderate or a centrist or a

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Speaker 1: Republican and saying we're going nuts, which I think is

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Speaker 1: a bit of a dishonest framing. I agree with you completely. Uh,

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Speaker 1: we got to take a quick break for things. Okay,

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Speaker 1: you appear to be done speaking. I thought you said

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Speaker 1: I love crepes, but we're not love crepes. I love

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Speaker 1: crepes too, but that's not I assume that's not what

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Speaker 1: we're selling. I'm selling that right now by crepes together everything,

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Speaker 1: don't don't We have returned from break now? How those

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Speaker 1: are great ads? I loved them. I bought them all again.

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Speaker 1: You're gonna run out of money them, but that money

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Speaker 1: is coming back to you in a way. So all right,

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Speaker 1: we got a lot to get through. I'm gonna be

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Speaker 1: pretty quick on what I'm here to talk about now,

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Speaker 1: which is pontive America. You guys are familiar. That's the

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Speaker 1: goal of this podcast, right we are are we are

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Speaker 1: we trying to save or destroy America? I have I

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Speaker 1: have forgotten. I think destroy America so that we can

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Speaker 1: build it back up again in the way that we

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Speaker 1: prefer if we if like, if we have time, I mean,

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Speaker 1: I guess like, well, well, well we'll pencil that in

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Speaker 1: after destroying it, like if we if we wind up

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Speaker 1: you know, you know, all podcasts are an evolution, so

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Speaker 1: we'll see what happens if we want to rebuild it

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Speaker 1: back up. Yeah, because part of me is thinking that

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Speaker 1: after we destroy America, that's probably the time for us

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Speaker 1: to launch our podcast about Frasier when we break down

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Speaker 1: every episode an exhaustive, forty five minute detail. I love

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Speaker 1: that idea. Yeah, so maybe we'll destroy America. We'll talk

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Speaker 1: about Frasier for three years, and then we'll rebuild it. Okay,

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Speaker 1: that's a good plan. You guys on board. All right,

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Speaker 1: I'm gonna push for saving America. I don't want to.

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Speaker 1: I don't want to rock the boat here, but I

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Speaker 1: think I'm going to convince you both by the end

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Speaker 1: of the year that we should save it. Robert, Okay,

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Speaker 1: Cody's out. All right, let's see we can Can we

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Speaker 1: marketplace ideas? Huh? Can we get the ghost of the

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Speaker 1: dog that played Eddie on Frasier? All right, we'll get

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Speaker 1: to be a perfect fit. Um. All right, we're gonna

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Speaker 1: talk about America specifically. Sophie's right here, Sophie. Oh, Sophie's

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Speaker 1: right here. I was you would hid Anderson. Wait are

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Speaker 1: you saying Sophie is the ghost of the dog from Fraser.

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Speaker 1: I was just a friend that you would hire a

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Speaker 1: dog that isn't my own mine, Sophie, it's the ghost

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Speaker 1: of a dog. I know, but your sibling is in

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Speaker 1: this room and you're not going to hire. And the

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Speaker 1: dog has a lot of star power. But come on,

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Speaker 1: I've never read employment law or talked to an employment lawyer,

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Speaker 1: but I do think we're legally bound to hire ghosts

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Speaker 1: before living dogs. But we'll check into that, and we'll

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Speaker 1: circle back Okay, we won't go stack of of two

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Speaker 1: thousand four. All right, America, Pod save Bros. Because they're bros, right,

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Speaker 1: common phrase, God Save America? Yeah, alright, okay, that's another.

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Speaker 1: In the first episode, we talked about Jake Tapper writing

427
00:23:56,880 --> 00:23:59,919
Speaker 1: himbo and then calling it a day. They were brain's

428
00:24:00,080 --> 00:24:03,480
Speaker 1: arming on names, and somebody said, what about pod Save America?

429
00:24:03,560 --> 00:24:07,280
Speaker 1: And that was the day's work got done? I know. Okay,

430
00:24:07,880 --> 00:24:11,800
Speaker 1: Uh so pods America is comprised of Jon Favreau, not

431
00:24:11,960 --> 00:24:16,520
Speaker 1: that Jon Favreau, John Lovett, not that John Lovett, Tommy Vtur,

432
00:24:16,680 --> 00:24:21,639
Speaker 1: and Dan Phifer during the that but yeah, that did

433
00:24:21,680 --> 00:24:26,080
Speaker 1: you say dog Phifer? Yes, mad dog Phifer, you know,

434
00:24:26,200 --> 00:24:29,480
Speaker 1: as we like to call them. Uh. During the election,

435
00:24:29,520 --> 00:24:33,080
Speaker 1: they hosted Keeping It six also not a great name.

436
00:24:33,680 --> 00:24:37,520
Speaker 1: But then after after Trump won, they rebranded, as you said,

437
00:24:37,880 --> 00:24:41,160
Speaker 1: we guessed it, Pods of America and started their own

438
00:24:41,240 --> 00:24:45,960
Speaker 1: podcast network called Crooked Media. Yes, that is a quippy

439
00:24:46,480 --> 00:24:50,920
Speaker 1: Hillary reference. Very cool. Um. I think it's important to

440
00:24:51,080 --> 00:24:54,760
Speaker 1: distinguish that they do have some interesting shows on that network.

441
00:24:55,160 --> 00:24:57,720
Speaker 1: Pod Save the People is hosted by civil rights activist

442
00:24:57,760 --> 00:25:01,119
Speaker 1: Deray McKesson, and that's very well well done. And doesn't

443
00:25:01,160 --> 00:25:04,240
Speaker 1: have the same uh you know, kind of BROWI perspective

444
00:25:04,560 --> 00:25:08,200
Speaker 1: of the pod save guys, Uh with friends like these

445
00:25:08,280 --> 00:25:10,800
Speaker 1: dives into understanding people on either side of the aisle.

446
00:25:11,000 --> 00:25:12,679
Speaker 1: And I've been known to listen to the all female

447
00:25:12,760 --> 00:25:17,359
Speaker 1: panel show Hysteria, which how Stuff Works host Danish Schwartz

448
00:25:17,520 --> 00:25:19,680
Speaker 1: is a frequent guest on. So they do have some

449
00:25:19,800 --> 00:25:22,560
Speaker 1: good programs to offer. But we're here to talk about

450
00:25:22,560 --> 00:25:26,040
Speaker 1: positive America specifically. There's too many of them to go

451
00:25:26,160 --> 00:25:29,240
Speaker 1: into all of their backgrounds, but suffice to say that

452
00:25:29,280 --> 00:25:32,480
Speaker 1: they all met while working for the Obama administration. Fifer

453
00:25:32,600 --> 00:25:35,880
Speaker 1: was the communications director, Vieter was the assistant Press secretary

454
00:25:35,960 --> 00:25:39,120
Speaker 1: and National security spokesperson. Favreau and love It we're both

455
00:25:39,160 --> 00:25:42,880
Speaker 1: speech writers. Uh. One little thing that I found interesting, uh,

456
00:25:43,359 --> 00:25:45,800
Speaker 1: was I guess at one point Love It secretly officiated

457
00:25:45,880 --> 00:25:50,240
Speaker 1: the first gay wedding at the White House, but because

458
00:25:50,560 --> 00:25:53,240
Speaker 1: it was during a time when Obama's views on same

459
00:25:53,280 --> 00:25:56,280
Speaker 1: sex marriage were still ambiguous, he straight up did not

460
00:25:56,520 --> 00:25:59,600
Speaker 1: have permission, like they told him not to. In several interviews,

461
00:25:59,680 --> 00:26:02,000
Speaker 1: Love It has been quoted as saying, we were very nervous.

462
00:26:02,040 --> 00:26:03,720
Speaker 1: They were nervous because they were getting married, and I

463
00:26:03,800 --> 00:26:05,560
Speaker 1: was nervous because I snuck into my boss's house to

464
00:26:05,600 --> 00:26:08,680
Speaker 1: perform a wedding against his wishes in his backyard. Um,

465
00:26:09,240 --> 00:26:11,400
Speaker 1: that's actually pretty cool. I know, it is pretty cool.

466
00:26:12,600 --> 00:26:14,920
Speaker 1: I actually did the same thing in Jack O'Brien's back

467
00:26:15,520 --> 00:26:17,840
Speaker 1: but the people who got married were unaware of the

468
00:26:17,880 --> 00:26:22,480
Speaker 1: wedding too, so doubling down. So you're really nervous. It

469
00:26:22,600 --> 00:26:24,600
Speaker 1: was the same but different. How are they going to

470
00:26:24,680 --> 00:26:28,000
Speaker 1: react when I tell them that they're married? Now? One

471
00:26:28,320 --> 00:26:31,480
Speaker 1: tidbit that I found less fun slightly is it? Apparently

472
00:26:31,560 --> 00:26:35,240
Speaker 1: at one point, Jon Favreau posted a picture on Facebook

473
00:26:35,520 --> 00:26:38,439
Speaker 1: of him posing with a cardboard cutout of Hillary Clinton

474
00:26:38,520 --> 00:26:42,760
Speaker 1: and grabbing her boob. As scandals go, that's not a

475
00:26:42,840 --> 00:26:46,360
Speaker 1: big one, but it is pretty BROI And again, that's

476
00:26:46,400 --> 00:26:49,919
Speaker 1: the thesis that I keep coming back to about. I mean,

477
00:26:49,960 --> 00:26:52,080
Speaker 1: this is just a general bob. That's my opinion listening

478
00:26:52,119 --> 00:26:54,680
Speaker 1: to the show. It's why I don't listen to it anymore.

479
00:26:55,359 --> 00:26:57,680
Speaker 1: So I don't know how if they've evolved a bit

480
00:26:57,840 --> 00:27:02,680
Speaker 1: since then, But that's kind of a consensus that people have. Um,

481
00:27:02,880 --> 00:27:04,879
Speaker 1: but people love to tune in here their perspective. I mean,

482
00:27:04,920 --> 00:27:07,359
Speaker 1: they've got a really unique one. They they've been inside

483
00:27:07,440 --> 00:27:09,399
Speaker 1: the party for a long time, They've seen what a

484
00:27:09,480 --> 00:27:12,600
Speaker 1: functioning White House looks like. Um. But they get a

485
00:27:12,640 --> 00:27:16,000
Speaker 1: lot of criticism, uh, based on the very valid perception

486
00:27:16,080 --> 00:27:19,080
Speaker 1: that because of this access that they have, you know,

487
00:27:19,280 --> 00:27:21,639
Speaker 1: they have been in the Democratic for a Party for

488
00:27:21,720 --> 00:27:24,480
Speaker 1: a long time, they have access to that they that

489
00:27:24,560 --> 00:27:28,040
Speaker 1: they have some blind spots criticizing the establishment and the

490
00:27:28,119 --> 00:27:31,680
Speaker 1: people in the establishment, or that they don't want to

491
00:27:31,800 --> 00:27:35,040
Speaker 1: lose that access, you know, by pushing too hard, by

492
00:27:35,080 --> 00:27:39,480
Speaker 1: pushing too many buttons. Um. For example, when you listen

493
00:27:39,520 --> 00:27:41,800
Speaker 1: to Pods of America, you're gonna get no real criticisms

494
00:27:41,880 --> 00:27:45,160
Speaker 1: of Obama. They were a part of the Obama administration,

495
00:27:45,680 --> 00:27:50,640
Speaker 1: even during like we're in a period right now where

496
00:27:50,680 --> 00:27:54,320
Speaker 1: we're seeing these masks child detentions and that's the policy

497
00:27:54,400 --> 00:27:57,560
Speaker 1: that did start under the Obama administration. Yes, Trump has

498
00:27:57,600 --> 00:28:00,280
Speaker 1: made it worse and exactly changed it and like actually

499
00:28:00,280 --> 00:28:03,760
Speaker 1: did this whole thing up. But there's a real conversation

500
00:28:03,880 --> 00:28:08,280
Speaker 1: that needs to happen about what are the Democratic Party,

501
00:28:08,320 --> 00:28:11,320
Speaker 1: what Obama's administration has, what role they played in all

502
00:28:11,400 --> 00:28:13,439
Speaker 1: of this. Um. But whenever something like that comes up,

503
00:28:13,480 --> 00:28:16,800
Speaker 1: they dodged the issue. Same thing with talking about drone strikes.

504
00:28:17,920 --> 00:28:21,480
Speaker 1: The drone program that Obama you know, that's started there,

505
00:28:21,760 --> 00:28:23,840
Speaker 1: it continues to this day, and nobody talks about it.

506
00:28:24,520 --> 00:28:29,159
Speaker 1: And it's just and and that's it's very frustrating. And

507
00:28:29,200 --> 00:28:31,400
Speaker 1: that's something that's frustrating to me in general when we're

508
00:28:31,440 --> 00:28:35,160
Speaker 1: talking going through this whole primary process. I think it's

509
00:28:35,200 --> 00:28:41,040
Speaker 1: fair to criticize Obama. It doesn't make him a completely

510
00:28:41,120 --> 00:28:43,520
Speaker 1: bad person. It doesn't mean that everything he did was bad.

511
00:28:43,600 --> 00:28:45,800
Speaker 1: But you have to be able to have some self reflection.

512
00:28:46,040 --> 00:28:47,880
Speaker 1: And that is the main thing for me that's missing

513
00:28:48,240 --> 00:28:52,640
Speaker 1: from Pod Save America. Um, you know, the whole thing.

514
00:28:52,720 --> 00:28:56,080
Speaker 1: Like just this week, Cody shared this with me. Uh.

515
00:28:56,520 --> 00:28:59,400
Speaker 1: But John Favreau tweeted a glowing endorsement of former UN

516
00:28:59,400 --> 00:29:01,600
Speaker 1: Ambassador im At the Power's new book, The Education of

517
00:29:01,640 --> 00:29:04,800
Speaker 1: an Idealist, which is, I guess, a memoir that takes

518
00:29:04,800 --> 00:29:07,480
Speaker 1: a look at US foreign policy from the inside. It

519
00:29:07,600 --> 00:29:10,760
Speaker 1: apparently talks a lot about Bashar al Assad and Russia,

520
00:29:10,840 --> 00:29:15,760
Speaker 1: but fails to acknowledge anything about Obama's drone program or

521
00:29:15,880 --> 00:29:19,600
Speaker 1: how the US armed al Kada in Syria, you know,

522
00:29:20,200 --> 00:29:23,160
Speaker 1: or about Yemen, you know, the stuff that's going on

523
00:29:23,200 --> 00:29:29,560
Speaker 1: in Yemen with America's support. So it's again just whitewashing,

524
00:29:29,720 --> 00:29:32,360
Speaker 1: you know, highlighting what they want and like ignoring the

525
00:29:32,560 --> 00:29:36,160
Speaker 1: actual issues that we have. And someone who helps yea

526
00:29:36,400 --> 00:29:38,840
Speaker 1: a situation like Yemen, like she was involved with that

527
00:29:39,040 --> 00:29:41,520
Speaker 1: and just being like, hey, check out this book. I

528
00:29:41,600 --> 00:29:45,080
Speaker 1: don't know. It's is that it shouldn't be a source

529
00:29:45,320 --> 00:29:47,560
Speaker 1: you go to for news. It's a source where like

530
00:29:47,640 --> 00:29:49,880
Speaker 1: the obviously, as you pointed out, they have some unique

531
00:29:49,920 --> 00:29:53,400
Speaker 1: perspective that's not not valuable, Like there's certainly value when

532
00:29:53,480 --> 00:29:56,320
Speaker 1: hearing from people who've worked inside a functioning White House. Absolutely,

533
00:29:56,440 --> 00:29:59,600
Speaker 1: but they're not journalists. They're a propaganda arm of the

534
00:29:59,640 --> 00:30:04,920
Speaker 1: Obama the administration. Yes, yes, that's why I originally did

535
00:30:05,080 --> 00:30:07,440
Speaker 1: listen to their show and liked it because most of

536
00:30:07,520 --> 00:30:10,320
Speaker 1: what they talked about was like related to like their

537
00:30:10,360 --> 00:30:12,160
Speaker 1: experience in the White House and like how the White

538
00:30:12,200 --> 00:30:14,640
Speaker 1: House functions, and it was interesting to get that perspective.

539
00:30:15,400 --> 00:30:18,680
Speaker 1: But then the election happened and they kept sort of

540
00:30:19,120 --> 00:30:21,680
Speaker 1: being wrong and like doubling down and like not sort

541
00:30:21,720 --> 00:30:24,280
Speaker 1: of reflecting on that, and it became way less well,

542
00:30:24,320 --> 00:30:28,120
Speaker 1: but didn't didn't they keep it sixteen hundred? Oh no,

543
00:30:28,440 --> 00:30:31,680
Speaker 1: oh no, they didn't. They didn't. That might mean the

544
00:30:31,760 --> 00:30:36,720
Speaker 1: pod won't save America. I don't know it won't. Uh yeah.

545
00:30:36,840 --> 00:30:39,720
Speaker 1: The Parts of America team has also supported the United

546
00:30:39,760 --> 00:30:42,080
Speaker 1: States of Care. UM. I'm gonna let Cody talk a

547
00:30:42,080 --> 00:30:43,960
Speaker 1: bit about this because he knows more about it. But basically,

548
00:30:44,000 --> 00:30:46,880
Speaker 1: the United States of Care is an initiative that aims

549
00:30:46,920 --> 00:30:51,360
Speaker 1: to put healthcare over politics and to change the conversation.

550
00:30:51,640 --> 00:30:55,080
Speaker 1: I mean, health care is politics, but okay. Also, the

551
00:30:55,120 --> 00:30:57,400
Speaker 1: council working on this initiative is comprised of a ton

552
00:30:57,440 --> 00:31:00,160
Speaker 1: of healthcare executives, people have spent their lives ripping, pull

553
00:31:00,200 --> 00:31:03,160
Speaker 1: off and change for their lives, and they claim to

554
00:31:03,200 --> 00:31:06,280
Speaker 1: be a safe space to discuss common sense solutions. Uh.

555
00:31:06,640 --> 00:31:09,640
Speaker 1: And their mission statement says, the United States of Care

556
00:31:09,760 --> 00:31:12,480
Speaker 1: is a new movement to ensure that every single American

557
00:31:12,600 --> 00:31:17,080
Speaker 1: has access to quality, affordable healthcare, regardless of health status,

558
00:31:17,320 --> 00:31:21,920
Speaker 1: social need, or income. And that's a pretty conservative talking point.

559
00:31:22,200 --> 00:31:27,000
Speaker 1: Access is very different than having um and there's been

560
00:31:27,000 --> 00:31:29,200
Speaker 1: a lot of pushback online from that, and they've waffled,

561
00:31:29,240 --> 00:31:31,200
Speaker 1: you know, they've come out as saying they're for Medicare

562
00:31:31,280 --> 00:31:34,120
Speaker 1: for all. But then you know, they start, you know,

563
00:31:34,160 --> 00:31:37,440
Speaker 1: support this, or they're tweeting, I'm a Medicare for all advocate.

564
00:31:37,520 --> 00:31:39,080
Speaker 1: This is from John Favrew. By the way, I'm a

565
00:31:39,120 --> 00:31:41,200
Speaker 1: Medicare for All advocate. I also want to make sure

566
00:31:41,200 --> 00:31:43,640
Speaker 1: that there's a path to get there, which involves building

567
00:31:43,640 --> 00:31:46,120
Speaker 1: a movement and persuading people who don't currently agree. So

568
00:31:46,200 --> 00:31:48,920
Speaker 1: it's a different approach. It's more of a centrist uh

569
00:31:49,240 --> 00:31:52,440
Speaker 1: you know, yeah, I mean it's yeah, super centrist. I

570
00:31:53,320 --> 00:31:55,760
Speaker 1: find issue with a lot of this framing a lot

571
00:31:56,120 --> 00:31:58,920
Speaker 1: from like Favro, where he's like, you know, and it

572
00:31:59,000 --> 00:32:02,640
Speaker 1: involves building a movement persuading people. Well, John, I don't

573
00:32:02,640 --> 00:32:06,280
Speaker 1: know if you're familiar, but in sixteen there was a movement.

574
00:32:06,960 --> 00:32:10,520
Speaker 1: You dismissed it, and now that it's a popular idea,

575
00:32:10,680 --> 00:32:12,720
Speaker 1: you're adopting it but still sort of couching it in

576
00:32:12,760 --> 00:32:17,120
Speaker 1: this language of access, Like it's just like movement happens

577
00:32:17,160 --> 00:32:19,480
Speaker 1: and a candidate says for two years healthcare as a

578
00:32:19,560 --> 00:32:21,520
Speaker 1: human right, and he said it so many times and

579
00:32:21,600 --> 00:32:25,640
Speaker 1: people now think that because it's true. Um, and now

580
00:32:25,720 --> 00:32:28,760
Speaker 1: they're sort of like piggybacking on that movement, but also

581
00:32:29,800 --> 00:32:32,520
Speaker 1: like to the detriment of the movement by like allign

582
00:32:32,680 --> 00:32:35,880
Speaker 1: with like what former Senator Bill Frist is involved in

583
00:32:35,920 --> 00:32:38,479
Speaker 1: this organization. A lot of private insurance companies are involved,

584
00:32:38,560 --> 00:32:41,920
Speaker 1: and you still have like love it doing these sort

585
00:32:41,960 --> 00:32:44,200
Speaker 1: of rants about how we got to keep the private

586
00:32:44,320 --> 00:32:46,040
Speaker 1: health insurance if you like it and things like that,

587
00:32:46,320 --> 00:32:51,280
Speaker 1: which stops the actual momentum of the movement and persuading

588
00:32:51,360 --> 00:32:53,520
Speaker 1: people that this is a better pack. If you're saying

589
00:32:53,560 --> 00:32:54,840
Speaker 1: like we gotta do this, we gotta do this in

590
00:32:54,880 --> 00:32:57,080
Speaker 1: this incremental change, and like we gotta protect this, then

591
00:32:57,080 --> 00:32:59,440
Speaker 1: you're not actually persuading people of the thing that you

592
00:32:59,480 --> 00:33:02,920
Speaker 1: actually want, which is Medicare for all. So saying you're

593
00:33:02,920 --> 00:33:04,640
Speaker 1: for Medicare for all but doing all this other stuff

594
00:33:04,960 --> 00:33:07,520
Speaker 1: tells me that you're not really Another thing that people

595
00:33:07,560 --> 00:33:09,880
Speaker 1: find frustrating about Pots of America again, that they have

596
00:33:09,960 --> 00:33:12,040
Speaker 1: access to all these candidates, but they rarely ask them

597
00:33:12,680 --> 00:33:17,720
Speaker 1: hard hitting questions, and when they do, or they just

598
00:33:17,840 --> 00:33:19,680
Speaker 1: let the person, you know, get off the hook with

599
00:33:19,720 --> 00:33:21,840
Speaker 1: a pretty slippery answer. I'm sure that has to do

600
00:33:22,000 --> 00:33:24,320
Speaker 1: again with the access question of not wanting to burn bridges,

601
00:33:24,400 --> 00:33:25,880
Speaker 1: wanting to be a place that people want to go,

602
00:33:26,320 --> 00:33:28,800
Speaker 1: but you're squandering an opportunity and they're not journalists, and

603
00:33:28,800 --> 00:33:33,360
Speaker 1: they're not journalists, Like, um, it takes like speaking as

604
00:33:33,400 --> 00:33:35,960
Speaker 1: someone who's who's done a lot of interviews, like there's

605
00:33:35,960 --> 00:33:38,120
Speaker 1: a thing you have to get over when you're kind

606
00:33:38,160 --> 00:33:40,360
Speaker 1: of new to it, where like we're trying to be

607
00:33:40,440 --> 00:33:44,200
Speaker 1: kind of non confrontational, um, just in civilized society, and

608
00:33:44,320 --> 00:33:46,440
Speaker 1: like you have to be willing to say things to

609
00:33:46,600 --> 00:33:50,280
Speaker 1: someone's face that will make them hate you. And if

610
00:33:50,320 --> 00:33:53,880
Speaker 1: you're not a journalist and not experienced doing that, you're

611
00:33:53,920 --> 00:33:55,760
Speaker 1: just not going to do it in a conversation, Like

612
00:33:55,840 --> 00:33:59,440
Speaker 1: you're just not especially in that community especially I mean

613
00:34:00,400 --> 00:34:03,080
Speaker 1: friends and like having been there, I mean sure in

614
00:34:03,160 --> 00:34:05,200
Speaker 1: the interviews, but they also have big people come on.

615
00:34:05,240 --> 00:34:07,280
Speaker 1: They've had the presidential candidates coming on. Love It or

616
00:34:07,360 --> 00:34:11,440
Speaker 1: Leave It John Lovett's own show and do this like

617
00:34:11,760 --> 00:34:14,000
Speaker 1: Queen for a Day type segment where they'll ask him

618
00:34:14,000 --> 00:34:16,919
Speaker 1: a bunch of really intense questions like as president, would

619
00:34:16,920 --> 00:34:19,120
Speaker 1: you recognize a hot dog as a sandwich? And like

620
00:34:20,000 --> 00:34:23,839
Speaker 1: part of me is interested in seeing breaking groundbreaking, part

621
00:34:23,880 --> 00:34:26,680
Speaker 1: of me is curious about seeing how uh, these very

622
00:34:26,760 --> 00:34:31,000
Speaker 1: serious people are in a less formal situation, but not really.

623
00:34:31,080 --> 00:34:33,279
Speaker 1: What's more important to me is to like take that

624
00:34:33,400 --> 00:34:36,960
Speaker 1: time to ask Amy klobish Er something important right now.

625
00:34:37,160 --> 00:34:40,560
Speaker 1: They're there, you're there, you're there. Why do you Why

626
00:34:40,560 --> 00:34:44,480
Speaker 1: are you wandering this opportunity? UM, it's like having Donald

627
00:34:44,520 --> 00:34:47,239
Speaker 1: Trump on your show and like testing his two pay

628
00:34:47,560 --> 00:34:50,719
Speaker 1: and like trying to humanize him. It is ignoring the

629
00:34:50,800 --> 00:34:53,440
Speaker 1: fact that, like he's calling a huge chunk of the

630
00:34:53,520 --> 00:34:58,359
Speaker 1: country vermin, Jimmy Fallon might as well be a UM.

631
00:34:58,680 --> 00:35:01,160
Speaker 1: And one little thing I want to say this is,

632
00:35:01,200 --> 00:35:05,520
Speaker 1: according to Cody oh Love, it is the one that

633
00:35:05,600 --> 00:35:08,920
Speaker 1: suggested to Elizabeth Warren that she take the DNA test

634
00:35:09,400 --> 00:35:12,680
Speaker 1: that proved that she is one percent that bitch who

635
00:35:12,760 --> 00:35:15,200
Speaker 1: doesn't have very much Native American blood at all. I

636
00:35:15,320 --> 00:35:16,960
Speaker 1: was wanted to do a Lizzo joke. I'm sorry, I

637
00:35:17,000 --> 00:35:19,960
Speaker 1: did hear that. It was a really bad call, and

638
00:35:20,040 --> 00:35:22,160
Speaker 1: it was the kind of really bad call you'd make

639
00:35:22,360 --> 00:35:24,799
Speaker 1: if like. One of the things that makes these guys

640
00:35:24,920 --> 00:35:28,200
Speaker 1: so bad at actually analyzing a real presidential election is

641
00:35:28,280 --> 00:35:31,440
Speaker 1: that their only experience getting a guy elected was quote

642
00:35:31,480 --> 00:35:35,399
Speaker 1: unquote helping to get the most charismatic politician of any

643
00:35:35,440 --> 00:35:37,880
Speaker 1: of our lifetimes elected like Barack Obama didn't need that

644
00:35:38,000 --> 00:35:43,200
Speaker 1: much help, like um So, I don't trust that these

645
00:35:43,280 --> 00:35:45,560
Speaker 1: guys actually have good advice on how to get a

646
00:35:45,640 --> 00:35:49,799
Speaker 1: president elected because their previous experience is the most charismatic

647
00:35:49,920 --> 00:35:54,240
Speaker 1: man alive in the country got elected and they were around. Also,

648
00:35:54,520 --> 00:35:57,080
Speaker 1: love It didn't love It, didn't join the administration until

649
00:35:57,080 --> 00:36:01,239
Speaker 1: after he was Hillary didn't get her to either, Sure

650
00:36:01,320 --> 00:36:03,960
Speaker 1: did not neither time. They all also work for John Gary,

651
00:36:04,280 --> 00:36:06,440
Speaker 1: So maybe not all of them, but most of them did.

652
00:36:06,920 --> 00:36:09,799
Speaker 1: I might say they are the worst people to ask

653
00:36:09,880 --> 00:36:11,680
Speaker 1: about how to get a president elect, and they might

654
00:36:11,719 --> 00:36:17,280
Speaker 1: be the worst people to try and save America. Yeah, yeah, yeah, yeah, Okay,

655
00:36:17,560 --> 00:36:20,200
Speaker 1: we're gonna take a real quick break again, and then

656
00:36:20,239 --> 00:36:22,480
Speaker 1: we're gonna come back and hear what Robert has to

657
00:36:22,520 --> 00:36:27,000
Speaker 1: say about stuff. We're gonna talk about Nate silver. But first,

658
00:36:27,239 --> 00:36:29,560
Speaker 1: enjoy these ads for silver that you put up your

659
00:36:29,600 --> 00:36:32,720
Speaker 1: butt to cure diseases. We're selling butt silver, right, Sophie,

660
00:36:32,840 --> 00:36:35,800
Speaker 1: that's that's one of our sponsors. She's nodding yes, Okay,

661
00:36:36,000 --> 00:36:47,280
Speaker 1: because she's confirmed. She's not just confirmed. It products together everything.

662
00:36:50,120 --> 00:36:55,040
Speaker 1: We're back now, we have to get through this last

663
00:36:55,080 --> 00:36:58,719
Speaker 1: bit quickly, so we're gonna have fewer interjections, which is

664
00:36:58,760 --> 00:37:01,759
Speaker 1: why this is a a good time for me to

665
00:37:02,000 --> 00:37:06,080
Speaker 1: talk about Nate Silver, a man who Cody Johnston personally

666
00:37:06,160 --> 00:37:10,640
Speaker 1: finds deeply engaging and dare I say, powerfully erotic onto

667
00:37:10,680 --> 00:37:15,320
Speaker 1: the story politically engaged person who feared the rise of

668
00:37:15,360 --> 00:37:17,879
Speaker 1: Donald Trump at two thousand sixteen. There is a better

669
00:37:17,960 --> 00:37:20,120
Speaker 1: than even chance you spent a lot of time refreshing

670
00:37:20,160 --> 00:37:22,920
Speaker 1: the website for Nate Silver's pole aggregating thing of the

671
00:37:23,040 --> 00:37:26,040
Speaker 1: jig five thirty eight. Presumably many folks will do the

672
00:37:26,080 --> 00:37:29,279
Speaker 1: same thing throughout, sighing an ease a Trump's chances fall

673
00:37:29,600 --> 00:37:32,960
Speaker 1: and cringing as they rise throughout the campaign. On the surface,

674
00:37:33,640 --> 00:37:36,160
Speaker 1: seems like a totally down to business, numbers focused endeavor

675
00:37:36,239 --> 00:37:38,920
Speaker 1: that can be relied upon for objective analysis of the polls.

676
00:37:39,400 --> 00:37:43,759
Speaker 1: The reality, of course, is murkier. Nathaniel Read Silver was

677
00:37:43,840 --> 00:37:47,319
Speaker 1: born on January nineteen seventy eight. He fell in love

678
00:37:47,360 --> 00:37:49,520
Speaker 1: with baseball from an early age. If you aren't a

679
00:37:49,600 --> 00:37:51,320
Speaker 1: fan of the sport, this may come as a surprise

680
00:37:51,360 --> 00:37:53,880
Speaker 1: to you, but baseball is essentially dungeons and dragons with

681
00:37:53,920 --> 00:37:56,080
Speaker 1: an optional component where human beings, get out in the

682
00:37:56,160 --> 00:37:59,200
Speaker 1: field and do stuff too many many fans, baseball is

683
00:37:59,239 --> 00:38:02,239
Speaker 1: about the number, calculating run averages and turning players in

684
00:38:02,280 --> 00:38:05,520
Speaker 1: two collections of Statistics. In a two thousand six Chicago

685
00:38:05,600 --> 00:38:10,000
Speaker 1: Tribune profile, William Hagman wrote this about Silver quote. Silver

686
00:38:10,080 --> 00:38:11,960
Speaker 1: caught the baseball bug when he was six growing up

687
00:38:12,000 --> 00:38:14,560
Speaker 1: in East Lansing, Michigan. It was nineteen eighty four, the

688
00:38:14,640 --> 00:38:17,120
Speaker 1: year the Detroit Tigers won the World Series. The Tigers

689
00:38:17,200 --> 00:38:19,120
Speaker 1: became his team, and baseball his sport. And if there's

690
00:38:19,120 --> 00:38:21,560
Speaker 1: anything that goes hand in glove with baseball, it's numbers,

691
00:38:21,640 --> 00:38:25,240
Speaker 1: another of Silver's childhood interests. It is always more interesting

692
00:38:25,320 --> 00:38:28,040
Speaker 1: to apply it to batting averages than algebra class. Silver

693
00:38:28,160 --> 00:38:31,319
Speaker 1: said now. As a high school student, Nate was an

694
00:38:31,320 --> 00:38:33,880
Speaker 1: avid debater at a state level champion. He was not

695
00:38:34,000 --> 00:38:36,480
Speaker 1: a big fan of public speaking, but he loved delving

696
00:38:36,520 --> 00:38:39,840
Speaker 1: into research and crafting arguments. His childhood and East Lansing,

697
00:38:39,920 --> 00:38:42,280
Speaker 1: Michigan was pretty normal outside of his love for baseball,

698
00:38:42,320 --> 00:38:44,680
Speaker 1: which is objectively a red flag. He wrote for his

699
00:38:44,760 --> 00:38:47,600
Speaker 1: high school newspaper and showed an interest in both journalism

700
00:38:47,640 --> 00:38:50,680
Speaker 1: and economics. Nate went to the university of Chicago, where

701
00:38:50,680 --> 00:38:53,400
Speaker 1: he thrived and eventually got a degree in economics. He

702
00:38:53,480 --> 00:38:55,760
Speaker 1: spent some time working as a consultant for a gigantic

703
00:38:55,760 --> 00:38:58,480
Speaker 1: accounting firm in the early aughts, but his true love

704
00:38:58,560 --> 00:39:01,440
Speaker 1: remained baseball. He's been most of his free time crafting

705
00:39:01,440 --> 00:39:04,600
Speaker 1: a system for protecting the performance of professional baseball players.

706
00:39:05,000 --> 00:39:09,239
Speaker 1: He named the Player Empirical Comparison and Optimization Test Algorithm,

707
00:39:09,440 --> 00:39:11,960
Speaker 1: or PAKOTA, a reference to the name of a relatively

708
00:39:12,120 --> 00:39:16,200
Speaker 1: obscure infielder named Bill Pacoda. In that Tribune article major

709
00:39:16,280 --> 00:39:20,120
Speaker 1: League Yeah, he's a huge nerd um huge nerds also,

710
00:39:20,400 --> 00:39:22,320
Speaker 1: and he said he was a big fan of public speaking.

711
00:39:22,360 --> 00:39:26,799
Speaker 1: That's why he loves Twitter. In that Tribune article, major

712
00:39:26,840 --> 00:39:30,480
Speaker 1: League Baseball consultant Gary Huckaday described how Pacoda worked. Quote,

713
00:39:30,680 --> 00:39:33,840
Speaker 1: Nate isolated five components how a player provides value. Then

714
00:39:33,880 --> 00:39:36,520
Speaker 1: Pacoda looks through the history to find similar players who

715
00:39:36,600 --> 00:39:39,200
Speaker 1: have had similar performance shapes at the same age. It

716
00:39:39,239 --> 00:39:40,839
Speaker 1: takes a look at all those guys in the past

717
00:39:40,880 --> 00:39:42,480
Speaker 1: to have been similar, and takes a look at what

718
00:39:42,560 --> 00:39:45,560
Speaker 1: their careers did after a certain point. Keep that in

719
00:39:45,719 --> 00:39:48,279
Speaker 1: mind because it will be relevant to Nate's later work

720
00:39:48,320 --> 00:39:52,560
Speaker 1: in politics. Now Pacoda was successful enough that Silver eventually

721
00:39:52,640 --> 00:39:55,359
Speaker 1: sold it to a site called Baseball Prospectus. He got

722
00:39:55,400 --> 00:39:57,279
Speaker 1: a job there as a managing editor and did that

723
00:39:57,360 --> 00:40:00,440
Speaker 1: for a while. As George W. Bush's president, he wound

724
00:40:00,480 --> 00:40:03,400
Speaker 1: down and the two thousand eight election campaign started winding up.

725
00:40:03,520 --> 00:40:06,760
Speaker 1: Nates Silver found himself focusing more and more on national politics.

726
00:40:07,120 --> 00:40:10,520
Speaker 1: He started an anonymous blog under the name Pablano and

727
00:40:10,600 --> 00:40:13,480
Speaker 1: provided his analysis of the polls, which often differed from

728
00:40:13,480 --> 00:40:17,000
Speaker 1: the conventional wisdom of the pundit classmates. Hot takes were

729
00:40:17,040 --> 00:40:19,200
Speaker 1: based in large part on applying the lessons he learned

730
00:40:19,239 --> 00:40:21,920
Speaker 1: analyzing sports to politics. I'm going to quote from a

731
00:40:21,960 --> 00:40:25,640
Speaker 1: New York magazine profile. Now. Sports and politics offers several

732
00:40:25,719 --> 00:40:29,480
Speaker 1: obvious parallels. Both involved a competition, essentially between two teams.

733
00:40:29,719 --> 00:40:32,640
Speaker 1: Both involved dreams of statistical data available for devotees to

734
00:40:32,719 --> 00:40:36,320
Speaker 1: sort through or more commonly, for intermediary experts to sort through, analyze,

735
00:40:36,320 --> 00:40:38,719
Speaker 1: and then interpret for you. In baseball, these stats track

736
00:40:38,800 --> 00:40:41,040
Speaker 1: player performance, how many hits the player gets and win,

737
00:40:41,120 --> 00:40:43,400
Speaker 1: and against what kind of pictures, while in politics, the

738
00:40:43,440 --> 00:40:46,000
Speaker 1: data tracts voter preferences. Who do you like and why?

739
00:40:46,320 --> 00:40:47,719
Speaker 1: What kind of choice are you going to make on

740
00:40:47,840 --> 00:40:50,799
Speaker 1: election day. These stats, on their face seemed pretty straightforward.

741
00:40:50,880 --> 00:40:53,400
Speaker 1: If a hitter hits three hundred, he's valuable. If Obama

742
00:40:53,440 --> 00:40:56,200
Speaker 1: opens up a six point national lead, he's in good shape.

743
00:40:57,040 --> 00:41:00,440
Speaker 1: So in May of two thousand eight, eight had his

744
00:41:00,520 --> 00:41:03,560
Speaker 1: first major win. Poster started predicting Hillary Clinton would win

745
00:41:03,600 --> 00:41:05,600
Speaker 1: big in Indiana and only lose by eight points in

746
00:41:05,680 --> 00:41:08,440
Speaker 1: North Carolina. This was seen as evidence that her campaign

747
00:41:08,520 --> 00:41:11,400
Speaker 1: was finally recovering from the temporary lead Obama had accrued

748
00:41:11,480 --> 00:41:14,359
Speaker 1: over the past few months. Silver blogging as Pablana only

749
00:41:14,400 --> 00:41:16,759
Speaker 1: got around eight hundred daily visitors to his side at

750
00:41:16,800 --> 00:41:19,040
Speaker 1: the time. That changed when he published an in depth

751
00:41:19,080 --> 00:41:21,759
Speaker 1: breakdown of Clinton's polling numbers, which suggested she would lose

752
00:41:21,800 --> 00:41:24,400
Speaker 1: by sixteen points in North Carolina and only win by

753
00:41:24,440 --> 00:41:27,399
Speaker 1: two in Indiana. The final members were a one point

754
00:41:27,440 --> 00:41:30,080
Speaker 1: win for Clinton in Indiana and a fifteen point loss

755
00:41:30,239 --> 00:41:33,279
Speaker 1: in North Carolina. So maybe it had been almost exactly right,

756
00:41:33,520 --> 00:41:36,480
Speaker 1: very close, and every major pundit had been wrong. Nate

757
00:41:36,560 --> 00:41:39,040
Speaker 1: went on to correctly predict forty nine out of fifty

758
00:41:39,080 --> 00:41:41,399
Speaker 1: states in the two thousand eight election, or at least

759
00:41:42,040 --> 00:41:44,799
Speaker 1: that's how things were reported. Time put him on their

760
00:41:44,840 --> 00:41:47,600
Speaker 1: list of a hundred most influential people for doing that.

761
00:41:48,239 --> 00:41:50,800
Speaker 1: Two years later, Nate's new website five thirty eight was

762
00:41:50,800 --> 00:41:53,040
Speaker 1: bought by The New York Times, netting him a pile

763
00:41:53,120 --> 00:41:57,040
Speaker 1: of money. Now, the reality of that award he got

764
00:41:57,120 --> 00:41:59,560
Speaker 1: for predicting the outcomes of Fordnite in the fifty States

765
00:41:59,680 --> 00:42:02,400
Speaker 1: was that eight had not, in fact predicted anything. His

766
00:42:02,520 --> 00:42:04,839
Speaker 1: model had calculated what it saw is the most likely

767
00:42:04,920 --> 00:42:07,719
Speaker 1: outcomes for all fifty states, and in forty nine of

768
00:42:07,800 --> 00:42:10,799
Speaker 1: those cases, the end result had jelled with that calculation.

769
00:42:11,239 --> 00:42:14,120
Speaker 1: There's a difference though Nate had not said Obama is

770
00:42:14,200 --> 00:42:16,799
Speaker 1: going to win all these states and said he'd said,

771
00:42:16,880 --> 00:42:19,400
Speaker 1: my analysis of these polls shows Obama ahead in all

772
00:42:19,440 --> 00:42:22,040
Speaker 1: these states, and the media had sort of translated that

773
00:42:22,160 --> 00:42:26,640
Speaker 1: into Nate Silver correctly predicts the results. Yeah, this is

774
00:42:26,719 --> 00:42:29,839
Speaker 1: not hair splitting. There's a very important difference between the two. Yeah, yeah,

775
00:42:30,160 --> 00:42:33,120
Speaker 1: this actually happens, uh quite a lot with like him

776
00:42:33,520 --> 00:42:37,800
Speaker 1: and just even like on the reverse side, everyone everyone

777
00:42:37,840 --> 00:42:40,719
Speaker 1: often says like everyone got it wrong. Trump won, Everyone

778
00:42:40,760 --> 00:42:42,960
Speaker 1: got it wrong. Not then to get it wrong. If

779
00:42:43,040 --> 00:42:46,960
Speaker 1: you say, uh, someone has chance of winning and they win,

780
00:42:47,800 --> 00:42:53,560
Speaker 1: you're right chance, Like that's how that's how this works. Yeah,

781
00:42:53,719 --> 00:42:58,359
Speaker 1: that's that's how numbers work. Yeah. So Nate builds this blog,

782
00:42:58,800 --> 00:43:00,239
Speaker 1: and as he did it, he was again sort of

783
00:43:00,320 --> 00:43:02,719
Speaker 1: taking the ideas that had first earned him success in

784
00:43:02,760 --> 00:43:06,000
Speaker 1: the realm of baseball and applying them to politics. There

785
00:43:06,120 --> 00:43:08,479
Speaker 1: is a lot of logic to this system. Uh here's

786
00:43:08,520 --> 00:43:12,320
Speaker 1: New York Magazine quote. In concocting eight, Silver decided the

787
00:43:12,360 --> 00:43:13,880
Speaker 1: best way to read the polls was to put them

788
00:43:13,920 --> 00:43:16,200
Speaker 1: all together, with the idea that averaging tin poles would

789
00:43:16,200 --> 00:43:17,759
Speaker 1: give you a better result than trying to pick out

790
00:43:17,800 --> 00:43:20,120
Speaker 1: the best one. Again, he wasn't the first person to

791
00:43:20,200 --> 00:43:22,600
Speaker 1: do this. Other sites like Real Clear Politics and Poster

792
00:43:22,719 --> 00:43:25,760
Speaker 1: offered the same service. But as Silver told me, sometimes

793
00:43:25,800 --> 00:43:27,959
Speaker 1: the answer is in looking at other alternatives that exist

794
00:43:28,040 --> 00:43:29,720
Speaker 1: in the market and saying they have the right idea,

795
00:43:29,800 --> 00:43:32,080
Speaker 1: but they're not doing it the right way. So he

796
00:43:32,160 --> 00:43:34,880
Speaker 1: came up with a system that predicts posters future performance

797
00:43:34,920 --> 00:43:36,719
Speaker 1: based on how good it's been in the past, and

798
00:43:36,800 --> 00:43:39,400
Speaker 1: finding his average Silver ways each pole differently ranking them

799
00:43:39,440 --> 00:43:43,160
Speaker 1: according to his own statistic pie Poster induced error based

800
00:43:43,200 --> 00:43:45,360
Speaker 1: on a number of factors, including its track record and

801
00:43:45,440 --> 00:43:48,000
Speaker 1: its methodology. When advantage of this system is that during

802
00:43:48,040 --> 00:43:50,560
Speaker 1: the primaries, the system actually got smarter because each time

803
00:43:50,600 --> 00:43:53,240
Speaker 1: a pole performed well in the primary, it's ranking improved.

804
00:43:54,160 --> 00:43:57,480
Speaker 1: So that's what Nate does. And uh, you know. For years,

805
00:43:58,000 --> 00:44:00,080
Speaker 1: Nate Silver was considered to be something very close to

806
00:44:00,160 --> 00:44:02,520
Speaker 1: a profit by many in particularly sort of like the

807
00:44:02,600 --> 00:44:05,320
Speaker 1: liberal side of things. Uh. In the two thousand and

808
00:44:05,360 --> 00:44:08,320
Speaker 1: twelve election, five thirty eight was lauded for correctly predicting

809
00:44:08,360 --> 00:44:10,720
Speaker 1: the results of all fifty states. It won a Webby

810
00:44:10,760 --> 00:44:13,560
Speaker 1: Award in two thousand thirteen at one another, and Nate

811
00:44:13,640 --> 00:44:16,080
Speaker 1: sold the site to ESPN and became its editor in

812
00:44:16,160 --> 00:44:18,879
Speaker 1: chief or I guess the New York Times, so yeah.

813
00:44:19,320 --> 00:44:21,759
Speaker 1: Five thirty eight relaunched as something more akin to a

814
00:44:21,880 --> 00:44:24,359
Speaker 1: major news site in two thousand fourteen, covering a wide

815
00:44:24,440 --> 00:44:27,719
Speaker 1: variety of stories with the perspective of data journalism. And

816
00:44:27,880 --> 00:44:31,160
Speaker 1: then came the two thousand sixteen election. It would be

817
00:44:31,239 --> 00:44:33,560
Speaker 1: a stretch to say this election was Nates undoing, but

818
00:44:33,680 --> 00:44:36,319
Speaker 1: it was a blow to his reputation. Like everyone else,

819
00:44:36,400 --> 00:44:39,600
Speaker 1: five thirty eight predicted a Clinton win as most likely. However,

820
00:44:39,920 --> 00:44:42,560
Speaker 1: that bird's eye view alone is not fair. Nates Silver

821
00:44:42,680 --> 00:44:45,080
Speaker 1: actually took a huge amount of flak from the left

822
00:44:45,160 --> 00:44:48,359
Speaker 1: from you know, giving Donald Trump a thirty percent chance

823
00:44:48,440 --> 00:44:50,960
Speaker 1: of victory. On November five, two thousand and sixteen, the

824
00:44:51,040 --> 00:44:54,440
Speaker 1: Huffington Posts Ryan Grimm published a very dumb article titled,

825
00:44:54,680 --> 00:44:58,240
Speaker 1: Nate Silver is unskewing polls, all of them in Trump's direction.

826
00:44:58,920 --> 00:45:01,120
Speaker 1: Now that unskewing line as a reference to a bit

827
00:45:01,160 --> 00:45:03,160
Speaker 1: of political history some of you may remember. Back in

828
00:45:03,200 --> 00:45:05,360
Speaker 1: two thousand twelve, a moron grew convinced that all of

829
00:45:05,400 --> 00:45:08,399
Speaker 1: the polls were biased against Mitt Romney. Using nonsense math,

830
00:45:08,440 --> 00:45:11,840
Speaker 1: he unskewed them to predict a massive victory formt This

831
00:45:11,960 --> 00:45:15,640
Speaker 1: did not happen. So while outlets like Poe gave Trump

832
00:45:15,719 --> 00:45:17,839
Speaker 1: like a one to two percent chance of victory, five

833
00:45:17,920 --> 00:45:20,680
Speaker 1: thirty eight showed his campaign as having a serious fighting chance,

834
00:45:20,920 --> 00:45:23,760
Speaker 1: and Nate Silver, to his credit, repeatedly outlined that Trump

835
00:45:23,800 --> 00:45:26,440
Speaker 1: had a very viable route to an electoral college victory.

836
00:45:26,760 --> 00:45:29,759
Speaker 1: I'm gonna quote from Ryan Grimm's article. Now, the short

837
00:45:29,880 --> 00:45:32,600
Speaker 1: version is that Silver is changing the results of polls

838
00:45:32,680 --> 00:45:35,160
Speaker 1: to fit where he thinks the polls truly are, rather

839
00:45:35,239 --> 00:45:37,279
Speaker 1: than simply entering the poll numbers into his model and

840
00:45:37,360 --> 00:45:41,120
Speaker 1: crunching them. Silver called this unskewing thank you a trend

841
00:45:41,239 --> 00:45:44,319
Speaker 1: line adjustment. He compares it a poll to previous polls

842
00:45:44,320 --> 00:45:46,880
Speaker 1: conducted by the same polling firm, makes a series of assumptions,

843
00:45:46,960 --> 00:45:49,520
Speaker 1: runs a regression analysis, and gets a new poll number.

844
00:45:49,680 --> 00:45:51,480
Speaker 1: That's the number he sticks in his model, not the

845
00:45:51,520 --> 00:45:54,279
Speaker 1: original number. He may end up being right, but he's

846
00:45:54,360 --> 00:45:57,879
Speaker 1: just guessing. So this guy basically said that, like, Trump

847
00:45:57,920 --> 00:46:00,279
Speaker 1: benefited unfairly from the skewing and of course eat his

848
00:46:00,360 --> 00:46:03,600
Speaker 1: words hardcore when Trump won, And like, you know, I

849
00:46:03,640 --> 00:46:06,120
Speaker 1: think it's actually unfair that Silver took some credit, like

850
00:46:06,320 --> 00:46:08,279
Speaker 1: took flak for like not giving Trump more of a

851
00:46:08,400 --> 00:46:11,160
Speaker 1: chance of victory, because he really did repeatedly point out

852
00:46:11,320 --> 00:46:14,640
Speaker 1: he could totally win if the things that happened happened.

853
00:46:14,800 --> 00:46:17,920
Speaker 1: There's a chance he will win. Yeah. Yeah, Silver gave

854
00:46:18,000 --> 00:46:20,080
Speaker 1: him a solid chance of winning, and he won, And

855
00:46:20,160 --> 00:46:21,959
Speaker 1: you got to give the dude credit for that. Also,

856
00:46:22,080 --> 00:46:23,759
Speaker 1: like this, like he may end up right, but he's

857
00:46:23,760 --> 00:46:30,920
Speaker 1: just guessing. Everyone. Yeah, everyone, homeboy, yeah, none of you. Yeah. Now,

858
00:46:31,360 --> 00:46:32,960
Speaker 1: I just gave Nate a lot of credit for his

859
00:46:33,040 --> 00:46:35,600
Speaker 1: performance in the two thousand and sixteen elections, so it's

860
00:46:35,600 --> 00:46:39,120
Speaker 1: also fair that I criticized him for the things that

861
00:46:39,200 --> 00:46:43,320
Speaker 1: he was wrong about in that election. Two thousand fifteen,

862
00:46:44,239 --> 00:46:48,120
Speaker 1: eight published this article under Nate Silver's byline, Donald Trump

863
00:46:48,239 --> 00:46:58,239
Speaker 1: is winning the polls and losing the nomination like that

864
00:46:58,520 --> 00:47:01,920
Speaker 1: is just guessing now. In that article, Nate predicted that

865
00:47:01,960 --> 00:47:04,040
Speaker 1: Trump would lose the nomination, and his evidence as to

866
00:47:04,120 --> 00:47:06,840
Speaker 1: why was largely not based on hard data science but

867
00:47:07,080 --> 00:47:10,399
Speaker 1: on history. Quote twelve years ago in two thousand three,

868
00:47:10,600 --> 00:47:13,200
Speaker 1: Joe Lieberman led in most polls of the Democratic primary.

869
00:47:13,400 --> 00:47:16,360
Speaker 1: Eight years ago, on August two thousand seven, Rudy Giuliani

870
00:47:16,400 --> 00:47:18,920
Speaker 1: maintained a clear lead in polls of Republicans, while Hillary

871
00:47:19,000 --> 00:47:22,359
Speaker 1: Clinton led poles of the Democratic nomination contest four years

872
00:47:22,400 --> 00:47:24,919
Speaker 1: ago in August two thousand eleven, when Mitt Romney began

873
00:47:24,960 --> 00:47:26,640
Speaker 1: with a lead in the polls of Republican voters, but

874
00:47:26,680 --> 00:47:28,080
Speaker 1: he would be surpassed by the end of the month

875
00:47:28,120 --> 00:47:30,600
Speaker 1: by Rick Perry, the first of four Republican rivals who

876
00:47:30,600 --> 00:47:35,720
Speaker 1: would at some point overtake Romney in the national polling averages. Lieberman, Clinton, Giuliani, Perry,

877
00:47:35,800 --> 00:47:37,880
Speaker 1: as you probably gathered, are not the phases of top

878
00:47:37,960 --> 00:47:41,040
Speaker 1: mount Rushmore. Only Clinton came close to winning the nomination.

879
00:47:41,960 --> 00:47:44,840
Speaker 1: Following that, Silver included one of his own Twitter posts

880
00:47:44,880 --> 00:47:47,960
Speaker 1: in the article, which makes him look pretty dumb and retrospect.

881
00:47:48,440 --> 00:47:50,520
Speaker 1: So this is a tweet from August nine, two thousand

882
00:47:50,560 --> 00:47:53,000
Speaker 1: and fifteen, by Nate Silver, and he's like, he's doing

883
00:47:53,080 --> 00:47:56,840
Speaker 1: it as like a little script. Media, Trump's doing great, Nerds, No,

884
00:47:57,080 --> 00:47:59,520
Speaker 1: those polls don't meet what you think. Media. A new

885
00:47:59,600 --> 00:48:04,600
Speaker 1: poll shows Trump doing great. Proved you wrong. Yeah, he's uh, yeah,

886
00:48:05,080 --> 00:48:07,600
Speaker 1: how can you be like that? Not so aware? Like

887
00:48:07,719 --> 00:48:13,360
Speaker 1: that's just parity. Also, it's interesting that he's done this

888
00:48:13,480 --> 00:48:15,160
Speaker 1: sort of like if you look at history, actually left

889
00:48:15,160 --> 00:48:17,279
Speaker 1: poles changed that he doesn't. He doesn't do that for

890
00:48:17,400 --> 00:48:21,480
Speaker 1: Joe Biden. Does he know? He does not? Interesting? Interesting,

891
00:48:21,760 --> 00:48:25,719
Speaker 1: very interest Interesting. It's September and he's has not has

892
00:48:25,800 --> 00:48:30,560
Speaker 1: not made Maybe he's learned his lesson. Yeah. Now, at

893
00:48:30,680 --> 00:48:33,120
Speaker 1: this point, the problem with Nate Silver is pretty obvious.

894
00:48:33,200 --> 00:48:35,440
Speaker 1: He's never gotten out of the habit of treating political

895
00:48:35,520 --> 00:48:39,360
Speaker 1: predictions like sports. In baseball, appealing to history makes sense

896
00:48:39,440 --> 00:48:42,520
Speaker 1: because there are thousands upon thousands of games and thousands

897
00:48:42,600 --> 00:48:44,800
Speaker 1: upon thousands of players, and you have a lot of

898
00:48:44,920 --> 00:48:47,640
Speaker 1: data with which you can use to draw useful conclusions.

899
00:48:48,200 --> 00:48:52,080
Speaker 1: American politics does not work that way, there are comparatively speaking,

900
00:48:52,200 --> 00:48:55,759
Speaker 1: relatively few congress people and elections in recorded history, and

901
00:48:55,880 --> 00:48:59,480
Speaker 1: even fewer presidents. Outliers then matter a lot more, and

902
00:48:59,600 --> 00:49:03,279
Speaker 1: making accurate predictions based on history is much harder. But

903
00:49:03,400 --> 00:49:05,600
Speaker 1: even in that article, Nate fell directly back on his

904
00:49:05,640 --> 00:49:08,160
Speaker 1: baseball history to help justify his opinion that Trump had

905
00:49:08,200 --> 00:49:11,960
Speaker 1: no chance quote, so should you ignore those national polls entirely?

906
00:49:12,120 --> 00:49:14,320
Speaker 1: In a literal sense? They do have some correlation with

907
00:49:14,400 --> 00:49:16,680
Speaker 1: election outcomes, even this far out at candidate near the

908
00:49:16,719 --> 00:49:18,480
Speaker 1: top of the polls is a somewhat better bet to

909
00:49:18,520 --> 00:49:20,839
Speaker 1: win the nomination than one near the bottom. But that's

910
00:49:20,880 --> 00:49:24,160
Speaker 1: like projecting a major league pitchers numbers from high school stats. Sure,

911
00:49:24,200 --> 00:49:26,279
Speaker 1: you'd rather draft a random seventeen year old with a

912
00:49:26,360 --> 00:49:28,120
Speaker 1: two point one for e r A than another one

913
00:49:28,200 --> 00:49:29,759
Speaker 1: with a three point three one e r A if

914
00:49:29,840 --> 00:49:31,759
Speaker 1: that's all the information you have to go by, But

915
00:49:31,880 --> 00:49:34,160
Speaker 1: that data doesn't reveal very much, and it's predictive power

916
00:49:34,160 --> 00:49:38,960
Speaker 1: it tends to be swamped by other indicators, so wild Like, yeah,

917
00:49:39,120 --> 00:49:44,319
Speaker 1: it's really dumb. Like an actual like you can look

918
00:49:44,400 --> 00:49:47,880
Speaker 1: at a players like actual performance over their entire career

919
00:49:48,160 --> 00:49:51,000
Speaker 1: and like judge them based off of that, how do

920
00:49:51,120 --> 00:49:54,520
Speaker 1: you how do you how do you bring that logic

921
00:49:54,640 --> 00:49:58,800
Speaker 1: to politics? I mean it worked for a while. You

922
00:49:58,960 --> 00:50:00,800
Speaker 1: got to give him that for a while. But like,

923
00:50:00,880 --> 00:50:05,080
Speaker 1: even the logic of it isn't right. Yeah, it's not

924
00:50:06,120 --> 00:50:09,279
Speaker 1: um yes, a lot of it is luck. And in

925
00:50:09,360 --> 00:50:12,480
Speaker 1: that article Nate made it it stated that our emphatic

926
00:50:12,560 --> 00:50:15,080
Speaker 1: prediction is simply that Trump will not win the nomination.

927
00:50:15,200 --> 00:50:17,120
Speaker 1: It's not even clear that he's trying to do so.

928
00:50:18,120 --> 00:50:22,080
Speaker 1: Um So I don't know that Trump was trying to

929
00:50:22,120 --> 00:50:25,800
Speaker 1: actually win. That is a fair point. Now, I'm obviously

930
00:50:25,840 --> 00:50:27,920
Speaker 1: far from the first person to criticize Nate for treating

931
00:50:27,920 --> 00:50:31,000
Speaker 1: politics like the baseballs. Among the community of people who

932
00:50:31,080 --> 00:50:33,759
Speaker 1: understand statistics, he has quite a few detractors. One of

933
00:50:33,840 --> 00:50:36,040
Speaker 1: them is not Seemed to Leb not Seem is one

934
00:50:36,080 --> 00:50:38,400
Speaker 1: of a galaxy of thinkers who got famous thanks to

935
00:50:38,520 --> 00:50:41,279
Speaker 1: Malcolm Gladwell. He's an investor with a unique philosophy on

936
00:50:41,400 --> 00:50:43,719
Speaker 1: risk that I don't really understand in a very successful

937
00:50:43,800 --> 00:50:46,600
Speaker 1: history of making money via predicting bad economic ship and

938
00:50:46,640 --> 00:50:49,080
Speaker 1: then capitalizing on it. I understand that seemed to Leb

939
00:50:49,480 --> 00:50:52,279
Speaker 1: does not like Nate. Nate does not like not Seem,

940
00:50:52,440 --> 00:50:54,480
Speaker 1: and starting in two thousand fifteen or so, the two

941
00:50:54,560 --> 00:50:57,880
Speaker 1: started ship fighting on Twitter. Their disagreements are fairly nerdy,

942
00:50:57,920 --> 00:50:59,719
Speaker 1: and I am not competent to get into the weeds

943
00:50:59,760 --> 00:51:02,560
Speaker 1: of their deeper statistical slap flights or to say who's right.

944
00:51:02,880 --> 00:51:04,520
Speaker 1: What's important is that you know that this was not

945
00:51:04,600 --> 00:51:07,040
Speaker 1: a friendly rivalry, and the Seem took to commenting on

946
00:51:07,120 --> 00:51:10,240
Speaker 1: Silver's methods on Twitter. He regularly chimed in on comments

947
00:51:10,280 --> 00:51:13,359
Speaker 1: made by other individuals criticizing five thirty eight, and then

948
00:51:13,680 --> 00:51:17,160
Speaker 1: in April of two thou eighteen, this happened, so guy

949
00:51:17,280 --> 00:51:21,880
Speaker 1: named Joachim Marnet's like that he posts like a criticism

950
00:51:21,960 --> 00:51:25,760
Speaker 1: of five now cast, and it seemed to leab comments

951
00:51:25,800 --> 00:51:28,239
Speaker 1: to that. Yes, the now cast came out later in

952
00:51:28,320 --> 00:51:31,719
Speaker 1: his bs and Nate Silver ways in the Seame, it's

953
00:51:31,719 --> 00:51:33,759
Speaker 1: pathetic that for the last two years you favorite it

954
00:51:33,800 --> 00:51:36,560
Speaker 1: retweeted hundreds of comments from random Twitter nobodies that take

955
00:51:36,600 --> 00:51:38,880
Speaker 1: your side of the argument. Maybe you should save some

956
00:51:38,960 --> 00:51:42,799
Speaker 1: of your dignity and debate me, to which the Seam responds, Silver,

957
00:51:42,960 --> 00:51:45,239
Speaker 1: You're a quack. Do not engage me. Just broadcast to

958
00:51:45,280 --> 00:51:48,000
Speaker 1: your followers three million or three billion doesn't make a difference.

959
00:51:48,120 --> 00:51:51,640
Speaker 1: I write formal papers, to which Nate responds, l O L.

960
00:51:51,680 --> 00:51:53,360
Speaker 1: I can't believe all it took was for someone to

961
00:51:53,440 --> 00:51:55,400
Speaker 1: stand up to your bullying by trolling you back, to

962
00:51:55,480 --> 00:52:01,640
Speaker 1: turn you into such a cuck, calls him a cut

963
00:52:04,600 --> 00:52:11,959
Speaker 1: Nates liver, Yeah, classy. I love it, And ironically calling

964
00:52:12,040 --> 00:52:14,919
Speaker 1: someone a cock like that, and like the scene comes

965
00:52:14,960 --> 00:52:16,400
Speaker 1: out as a bit of a dick in that too,

966
00:52:16,560 --> 00:52:19,040
Speaker 1: Like they both seem like unpleasant people. I wouldn't want

967
00:52:19,040 --> 00:52:21,840
Speaker 1: to have dinner with their having a moment online. I

968
00:52:22,000 --> 00:52:27,960
Speaker 1: highly doubt that Nate actually laughed out loud. I love

969
00:52:28,040 --> 00:52:30,920
Speaker 1: that his response, Like the sim comes out with the

970
00:52:31,000 --> 00:52:34,040
Speaker 1: incredibly arrogant, Like in any other argument, somebody like trying

971
00:52:34,120 --> 00:52:36,480
Speaker 1: to argue that they're right by saying I write formal

972
00:52:36,560 --> 00:52:39,120
Speaker 1: papers would be the person I sympathize with least in

973
00:52:39,200 --> 00:52:42,399
Speaker 1: that argument, Nate follow us up calling him a cock

974
00:52:44,960 --> 00:52:49,320
Speaker 1: and end of argument. Oh it's amazing, he just no

975
00:52:49,360 --> 00:52:53,000
Speaker 1: one looks good here. Yeah, it's incredible. So in December

976
00:52:53,080 --> 00:52:55,920
Speaker 1: of that year, Isaac Farber wrote a really interesting breakdown

977
00:52:55,960 --> 00:52:58,160
Speaker 1: of the spat between the same and Nate on the

978
00:52:58,280 --> 00:53:01,800
Speaker 1: medium blog towards Data Science. It is a very wonky

979
00:53:01,920 --> 00:53:04,320
Speaker 1: dissection of five thirty eight, and I will not pretend

980
00:53:04,360 --> 00:53:06,719
Speaker 1: to understand every piece of the math that this guy

981
00:53:06,800 --> 00:53:09,200
Speaker 1: brings up in it. Favor does not call five thirty

982
00:53:09,239 --> 00:53:12,520
Speaker 1: eight useless and certainly seems to see value innates analysis

983
00:53:12,560 --> 00:53:15,680
Speaker 1: of the polls, but he also criticizes Silver for accepting

984
00:53:15,719 --> 00:53:18,040
Speaker 1: praise for his not a prediction predictions of the two

985
00:53:18,080 --> 00:53:21,440
Speaker 1: thousand eight election results. Quote he should not have accepted

986
00:53:21,480 --> 00:53:23,520
Speaker 1: the honor if he didn't call a winner in any

987
00:53:23,600 --> 00:53:26,040
Speaker 1: of the states, which is a fair point. If he

988
00:53:26,080 --> 00:53:30,759
Speaker 1: didn't predict anything, don't. Favor points out that there are

989
00:53:30,800 --> 00:53:34,360
Speaker 1: two types of uncertainty in any prediction, aliatory and epistemic.

990
00:53:34,600 --> 00:53:37,440
Speaker 1: If aliatory uncertainty is the probability of, say, rolling a

991
00:53:37,520 --> 00:53:40,160
Speaker 1: six on a standard die, epistemic would have more to

992
00:53:40,239 --> 00:53:42,680
Speaker 1: do with the uncertainty involved in getting certain results in

993
00:53:42,760 --> 00:53:45,040
Speaker 1: a specific game, or with specific other types of dice

994
00:53:45,160 --> 00:53:46,600
Speaker 1: or whatever like. It has to do with more with

995
00:53:46,680 --> 00:53:49,360
Speaker 1: the system. I'm going to quote from his right up

996
00:53:49,400 --> 00:53:52,600
Speaker 1: now quote. Bespoke models like five thirty eight's only report

997
00:53:52,640 --> 00:53:56,280
Speaker 1: to the public aliatory uncertainty as it concerns their statistical outputs.

998
00:53:56,600 --> 00:53:59,880
Speaker 1: The trouble is that epistemic uncertainty is very difficult sometimes

999
00:54:00,120 --> 00:54:03,000
Speaker 1: possible to estimate. For example, why didn't five thirty eight

1000
00:54:03,160 --> 00:54:05,919
Speaker 1: model incorporate before it happened a chance that Comey would

1001
00:54:05,920 --> 00:54:09,719
Speaker 1: reopen his investigation into the Clinton emails. Sports, like other

1002
00:54:09,840 --> 00:54:12,680
Speaker 1: games of chance, have very well defined mechanisms which lend

1003
00:54:12,719 --> 00:54:16,759
Speaker 1: themselves to statistical analysis. On the other hand, highly nonlinear

1004
00:54:16,840 --> 00:54:20,400
Speaker 1: events that contested elections may not. With fewer data points,

1005
00:54:20,480 --> 00:54:23,280
Speaker 1: you can see the variation of the Senate predictions is enormous.

1006
00:54:23,600 --> 00:54:26,080
Speaker 1: Gauging the performance of models on these types of events

1007
00:54:26,160 --> 00:54:29,080
Speaker 1: becomes doubly difficult. It isn't clear if a prediction is

1008
00:54:29,160 --> 00:54:31,600
Speaker 1: wrong owing to the quality of the model, epistemic, or

1009
00:54:31,719 --> 00:54:35,719
Speaker 1: just luck aliatory. Because there is so much uncertainty around

1010
00:54:35,760 --> 00:54:39,200
Speaker 1: nonlinear events like an election, it could reasonably be considered

1011
00:54:39,239 --> 00:54:43,000
Speaker 1: frivolous to report early stage forecasts. The only conceivable reason

1012
00:54:43,080 --> 00:54:45,760
Speaker 1: to do so is to capture and monetize the interest

1013
00:54:45,840 --> 00:54:47,680
Speaker 1: of a public which is hungry to know the future.

1014
00:54:47,880 --> 00:54:50,000
Speaker 1: I will not go into the technical arguments. Tell Of

1015
00:54:50,040 --> 00:54:52,000
Speaker 1: has written and published a paper on the key issues.

1016
00:54:52,040 --> 00:54:54,880
Speaker 1: With a solution. Here, we can say with some confidence

1017
00:54:55,000 --> 00:54:58,520
Speaker 1: that five thirty eight predictions are not always reliable probabilities

1018
00:55:00,320 --> 00:55:05,680
Speaker 1: seems fair, solid solid maths lapdown on Nate there. Yeah, Now,

1019
00:55:06,120 --> 00:55:08,160
Speaker 1: if the last several months or anything to judge by

1020
00:55:08,360 --> 00:55:10,680
Speaker 1: Nate Silver still suffers from the problem of weighing his

1021
00:55:10,760 --> 00:55:12,880
Speaker 1: own biases too heavily in his predictions, even when the

1022
00:55:12,960 --> 00:55:15,680
Speaker 1: numbers don't back him up. Back in August two thousand nineteen,

1023
00:55:15,800 --> 00:55:19,000
Speaker 1: Nate tweeted about an analysis of campaign finance data published

1024
00:55:19,040 --> 00:55:21,759
Speaker 1: by the Washington Post. This analysis looked at the people

1025
00:55:21,800 --> 00:55:24,800
Speaker 1: who had donated to Democratic presidential campaigns and concluded that

1026
00:55:24,880 --> 00:55:27,760
Speaker 1: Sanders had the most loyal base of any campaign because

1027
00:55:27,800 --> 00:55:30,279
Speaker 1: more than eighty percent of his donors gave just to him.

1028
00:55:30,760 --> 00:55:34,160
Speaker 1: Here sound Nate interpreted that data on Twitter, so basically,

1029
00:55:34,239 --> 00:55:38,480
Speaker 1: Bernie fans only like Bernie, and only Bernie fans like Bernie. Now,

1030
00:55:38,640 --> 00:55:40,400
Speaker 1: a number of people pointed out that this was not

1031
00:55:40,520 --> 00:55:42,600
Speaker 1: a fair analysis. I found a good website on the

1032
00:55:42,760 --> 00:55:45,520
Speaker 1: very biased Splinter news website that nonetheless hits at some

1033
00:55:45,600 --> 00:55:47,960
Speaker 1: of Nate's core issues. Quote. First of all, it should

1034
00:55:48,000 --> 00:55:51,200
Speaker 1: not be surprising whatsoever that of people who have donated

1035
00:55:51,239 --> 00:55:53,759
Speaker 1: to Sanders anti establishment campaign did not then turn around

1036
00:55:53,800 --> 00:55:56,640
Speaker 1: and give money to former Vice President Joe Biden. Sanders's

1037
00:55:56,680 --> 00:55:58,560
Speaker 1: base is loyal, and there are a few other candidates

1038
00:55:58,560 --> 00:56:00,839
Speaker 1: who truly share his goals like metic care for all. Plus,

1039
00:56:00,920 --> 00:56:02,920
Speaker 1: many of his donors have given in small amounts, so

1040
00:56:03,000 --> 00:56:04,440
Speaker 1: it makes sense to think that they may not have

1041
00:56:04,600 --> 00:56:07,600
Speaker 1: money to give elsewhere. But the chart and the story

1042
00:56:07,680 --> 00:56:10,880
Speaker 1: clearly showed. Despite silver suggestion that roughly twenty of Sanders

1043
00:56:10,880 --> 00:56:14,080
Speaker 1: supporters gave to other campaigns, Sanders and Senator Elizabeth Warren

1044
00:56:14,120 --> 00:56:17,560
Speaker 1: share sixty thousand donors. Sanders supporters also gave to Reptulci

1045
00:56:17,600 --> 00:56:21,000
Speaker 1: Gabbard in significant amounts. Most overlapping donors gave more money

1046
00:56:21,040 --> 00:56:24,040
Speaker 1: to Sanders. Now. A number of people also pointed out

1047
00:56:24,080 --> 00:56:26,600
Speaker 1: that while Sanders donors tended to be loyal, a number

1048
00:56:26,680 --> 00:56:30,120
Speaker 1: of donors to other Democratic candidates also donated to Sanders,

1049
00:56:30,160 --> 00:56:33,200
Speaker 1: which would suggest that supporters of other candidates don't hate Bernie,

1050
00:56:33,239 --> 00:56:35,920
Speaker 1: even if they do prefer Kamala Harris or mayor Pete.

1051
00:56:36,360 --> 00:56:38,560
Speaker 1: More to the point is the fact that since Sanders

1052
00:56:38,600 --> 00:56:40,879
Speaker 1: has so many small dollar donors who only gave to him,

1053
00:56:41,080 --> 00:56:44,040
Speaker 1: this suggests something else very important that Nate missed entirely.

1054
00:56:44,560 --> 00:56:47,080
Speaker 1: Malaka Jabali, who writes for the Guardian the intercept in

1055
00:56:47,120 --> 00:56:49,760
Speaker 1: the route pointed this out in a tweet, quote, today,

1056
00:56:49,800 --> 00:56:51,960
Speaker 1: we learned that Sanders get support from people who may

1057
00:56:52,040 --> 00:56:55,320
Speaker 1: not normally be drawn to Democratic candidates. Something tells me

1058
00:56:55,440 --> 00:56:59,560
Speaker 1: that might come in handy in November. Now, that's all

1059
00:56:59,560 --> 00:57:02,440
Speaker 1: pretty their evidence that Nate, like everyone, lets his personal

1060
00:57:02,480 --> 00:57:04,840
Speaker 1: opinions cloud his analysis, even when he claims to just

1061
00:57:04,920 --> 00:57:07,520
Speaker 1: be looking at the numbers. Data can be interpreted in

1062
00:57:07,640 --> 00:57:10,080
Speaker 1: numerous ways, and it's clear to me that Silver has

1063
00:57:10,120 --> 00:57:13,799
Speaker 1: an anti Bernie bias. His actual articles on eight are

1064
00:57:14,000 --> 00:57:16,880
Speaker 1: fairer about Sanders. In July he published an article about

1065
00:57:16,880 --> 00:57:19,280
Speaker 1: how Bernie was quote ahead of the pack on healthcare,

1066
00:57:19,400 --> 00:57:22,160
Speaker 1: and in February, one of his editors, Claire Malone, wrote

1067
00:57:22,200 --> 00:57:25,520
Speaker 1: an article about how Bernie could win the nomination. But

1068
00:57:25,640 --> 00:57:28,560
Speaker 1: even in his supposedly unbiased coverage, Nate has a tendency

1069
00:57:28,640 --> 00:57:31,240
Speaker 1: to reveal himself, as best embodied by this June six,

1070
00:57:31,440 --> 00:57:35,240
Speaker 1: two thou nineteen headline, Bernie Sanders has the highest floor

1071
00:57:35,520 --> 00:57:40,120
Speaker 1: and it's pretty damn low. Okay, where he's like pointing

1072
00:57:40,160 --> 00:57:42,720
Speaker 1: out that Sanders has like the highest like floor of

1073
00:57:42,800 --> 00:57:45,040
Speaker 1: support of any of the Democratic candidates, but it's low,

1074
00:57:45,160 --> 00:57:48,919
Speaker 1: so it's not a big come on dude, like, yeah, ah,

1075
00:57:49,040 --> 00:57:51,960
Speaker 1: he does that all the time. Yes. Sorry. We were

1076
00:57:52,000 --> 00:57:54,120
Speaker 1: supposed to write brief summaries of these people, and I

1077
00:57:54,160 --> 00:57:56,720
Speaker 1: wrote nine pages, and I apologize, but you got through

1078
00:57:56,760 --> 00:57:59,760
Speaker 1: that so fast it was hard to I was biding

1079
00:57:59,800 --> 00:58:02,919
Speaker 1: my tongue so much because I knew we had limited time.

1080
00:58:03,200 --> 00:58:06,600
Speaker 1: I didn't want any reject It was interesting. Yeah, that

1081
00:58:06,800 --> 00:58:08,360
Speaker 1: caused a lot of a lot of my already held

1082
00:58:08,400 --> 00:58:11,040
Speaker 1: opinions about Nate Solver. I mean, we could go through

1083
00:58:11,080 --> 00:58:12,480
Speaker 1: every one of his tweets and be like, well, here's

1084
00:58:12,480 --> 00:58:15,920
Speaker 1: how you're misrepresenting it. We're not going to I will

1085
00:58:15,960 --> 00:58:18,560
Speaker 1: not be reading all of my responses to Nate Silver's tweets.

1086
00:58:18,960 --> 00:58:21,720
Speaker 1: He's got some singers in there, got some singers, there's

1087
00:58:21,720 --> 00:58:24,800
Speaker 1: some hey folks, there's some good tweets in there. All right, um,

1088
00:58:25,520 --> 00:58:30,280
Speaker 1: but boy, he uh yeah, just just take take take

1089
00:58:30,320 --> 00:58:34,600
Speaker 1: him with a grain of salt. Yeah, yeah, take everybody,

1090
00:58:36,320 --> 00:58:41,160
Speaker 1: everybody with a grain of salt. But again, particularly people

1091
00:58:41,200 --> 00:58:44,400
Speaker 1: who claim to have some sort of like that their

1092
00:58:44,400 --> 00:58:48,240
Speaker 1: analysis is based purely on the numbers, because there's always

1093
00:58:48,320 --> 00:58:52,920
Speaker 1: more to it than that. And and again, predictions like

1094
00:58:53,000 --> 00:58:54,760
Speaker 1: one of the most valuable things I think about that.

1095
00:58:54,920 --> 00:58:59,240
Speaker 1: Um that, Uh. The quote that I read from that

1096
00:58:59,400 --> 00:59:04,160
Speaker 1: like stick histical wank medium blog was the point that, um, you,

1097
00:59:04,640 --> 00:59:08,040
Speaker 1: when you're analyzing sports, there are rules to baseball. You

1098
00:59:08,160 --> 00:59:10,520
Speaker 1: know that a game, if baseball, is not going to

1099
00:59:10,680 --> 00:59:13,520
Speaker 1: be impacted by one of the players pulling out a

1100
00:59:13,720 --> 00:59:16,520
Speaker 1: gun and shooting the ball out of the air, because

1101
00:59:16,560 --> 00:59:18,880
Speaker 1: then that that that guy would be ejected and that

1102
00:59:18,920 --> 00:59:20,600
Speaker 1: would not be part of the game because it's not

1103
00:59:20,680 --> 00:59:22,520
Speaker 1: part of the rules of baseball. You can't shoot a

1104
00:59:22,560 --> 00:59:25,000
Speaker 1: ball out of the air with a gun. You can't

1105
00:59:25,080 --> 00:59:27,800
Speaker 1: do the same thing with politics because all sorts of

1106
00:59:27,840 --> 00:59:30,640
Speaker 1: ship happens, and there really aren't rules, And so at

1107
00:59:30,720 --> 00:59:34,520
Speaker 1: any moment, the FBI director can reopen an investigation into

1108
00:59:34,560 --> 00:59:38,080
Speaker 1: a candidate's emails, or a candidate could pull his dick out,

1109
00:59:38,720 --> 00:59:43,040
Speaker 1: or like like anything, there are variables. You cannot account for.

1110
00:59:43,720 --> 00:59:47,000
Speaker 1: The idea that like you can treat politics the way

1111
00:59:47,040 --> 00:59:53,200
Speaker 1: you treat sports is fundamentally asinine also dangerous, I think dangerous, right,

1112
00:59:53,280 --> 00:59:56,200
Speaker 1: it's sort of like reinforces this sort of like the

1113
00:59:56,600 --> 00:59:59,720
Speaker 1: racehorse sort of narrative like how politics go, but also

1114
00:59:59,800 --> 01:00:04,440
Speaker 1: like I mean baseball, like you can observe somebody's actual

1115
01:00:04,520 --> 01:00:07,640
Speaker 1: skill level and what they accomplish, and like translating that

1116
01:00:07,760 --> 01:00:11,200
Speaker 1: to what people think of a candidate. That's not how

1117
01:00:11,400 --> 01:00:15,120
Speaker 1: that's not how it is. It's completely different. Yeah, And

1118
01:00:15,200 --> 01:00:17,320
Speaker 1: there would be like if we if if Donald Trump

1119
01:00:17,440 --> 01:00:20,120
Speaker 1: was the thousandth president, Nate would have more of a

1120
01:00:20,200 --> 01:00:22,120
Speaker 1: like to stand on because then there would be enough

1121
01:00:22,200 --> 01:00:24,440
Speaker 1: numbers that like it would kind of boil out some

1122
01:00:24,560 --> 01:00:27,120
Speaker 1: of like the weird fringe chances of things happening, just

1123
01:00:27,160 --> 01:00:29,840
Speaker 1: because there would be so much data. But there's not.

1124
01:00:30,520 --> 01:00:33,680
Speaker 1: You haven't even had fifty Yeah, I don't think he

1125
01:00:33,680 --> 01:00:40,320
Speaker 1: would uh that sample size? Yeah, got a ways to

1126
01:00:40,400 --> 01:00:44,280
Speaker 1: go before we had any Before we go, I wanted

1127
01:00:44,320 --> 01:00:47,560
Speaker 1: to give Washington Post its to do. Yeah. In regards

1128
01:00:47,720 --> 01:00:51,200
Speaker 1: to Bernie Sanders coverage, um and share with you this

1129
01:00:51,520 --> 01:00:54,479
Speaker 1: this headline from I want to you both to guess

1130
01:00:54,560 --> 01:00:58,520
Speaker 1: who you think wrote it. People came to see Bernie

1131
01:00:58,560 --> 01:01:00,880
Speaker 1: Sanders in Boston. Why aren't we talking more about it?

1132
01:01:01,800 --> 01:01:07,840
Speaker 1: Fair question? Who wrote it? Oh? God, I don't know, Chris. Yeah,

1133
01:01:08,920 --> 01:01:11,160
Speaker 1: I was wrong. Yeah, I didn't even know he wrote

1134
01:01:11,200 --> 01:01:14,640
Speaker 1: for the Washington Post. I didn't either. Unbelievable, good take Chris,

1135
01:01:14,760 --> 01:01:18,520
Speaker 1: you're one good take. Well this has been great. Yeah,

1136
01:01:18,560 --> 01:01:20,880
Speaker 1: we know what to read and listen to and what

1137
01:01:21,200 --> 01:01:26,560
Speaker 1: you guys still clear on where to get your information? Yes,

1138
01:01:26,960 --> 01:01:30,880
Speaker 1: Breitbart Right dot com. Yeah, yeah, that's where we landed. Good.

1139
01:01:33,960 --> 01:01:42,360
Speaker 1: DA's great. The Daily Stormer some really hot takes. Yeah,

1140
01:01:42,800 --> 01:01:47,520
Speaker 1: Daily Mail, all the Daily The Daily Mail is when

1141
01:01:47,560 --> 01:01:50,480
Speaker 1: The Daily Mail reposted the entirety of a mass shooters

1142
01:01:50,560 --> 01:01:55,400
Speaker 1: manifesto uncritically, just like plastered it on. I didn't know

1143
01:01:55,480 --> 01:01:57,760
Speaker 1: they did that. What the hell? Yeah, they did that

1144
01:01:57,920 --> 01:02:02,080
Speaker 1: because they're responsible journalists. Cody, Oh, that's why. That's what

1145
01:02:02,320 --> 01:02:06,240
Speaker 1: that looks like. That's why. Yes, So stay tuned for

1146
01:02:06,360 --> 01:02:09,520
Speaker 1: the next episode of this podcast, wherein I read Anders

1147
01:02:09,560 --> 01:02:15,880
Speaker 1: Brevick's manifesto with no commentary. Um, you mean you're gonna

1148
01:02:15,880 --> 01:02:21,240
Speaker 1: read twelve Rules for life. God not to say that

1149
01:02:21,320 --> 01:02:24,400
Speaker 1: they're the same, but um, I feel like I feel

1150
01:02:24,440 --> 01:02:27,320
Speaker 1: like maybe Jordan Peterson read a little little Anders and

1151
01:02:27,480 --> 01:02:29,280
Speaker 1: was like, oh man, it's kind of right about a

1152
01:02:29,280 --> 01:02:32,760
Speaker 1: lot of stuff. He had that decision we all have

1153
01:02:32,920 --> 01:02:36,200
Speaker 1: where you have to choose between being a rich political

1154
01:02:36,280 --> 01:02:39,440
Speaker 1: personality who gets paid to lecture at colleges and shooting

1155
01:02:39,520 --> 01:02:46,200
Speaker 1: dozens of children. Uh, and thankfully he chose for him.

1156
01:02:46,440 --> 01:02:48,520
Speaker 1: I feel like the root of the problem is still there.

1157
01:02:48,720 --> 01:02:52,840
Speaker 1: But yeah, how the tree grew um is a better, Yes,

1158
01:02:52,960 --> 01:02:56,440
Speaker 1: absolutely better tree. So stay tuned for more of the

1159
01:02:56,520 --> 01:03:00,280
Speaker 1: Worst Year Ever. Yeah. Also stay tuned for the literal

1160
01:03:00,400 --> 01:03:03,320
Speaker 1: Worst Year Ever. And yeah, the actual year and the

1161
01:03:03,320 --> 01:03:07,400
Speaker 1: show about the year. We'll be ratching this up yeah later. Yeah,

1162
01:03:07,480 --> 01:03:09,320
Speaker 1: not every episode. It's going to be us talking about

1163
01:03:09,600 --> 01:03:12,760
Speaker 1: media sources. It's gonna be talking about a lot of

1164
01:03:12,800 --> 01:03:14,960
Speaker 1: other stuff. But I feel good about this. I think

1165
01:03:15,000 --> 01:03:17,640
Speaker 1: this is a great place for us to start primary

1166
01:03:18,880 --> 01:03:24,560
Speaker 1: trust only us. That's the takeaway that this two parter

1167
01:03:24,920 --> 01:03:29,480
Speaker 1: is to give you only us, and uh you know

1168
01:03:29,800 --> 01:03:33,320
Speaker 1: that that way you'll just kind of uncritically accepted. When

1169
01:03:33,400 --> 01:03:38,320
Speaker 1: we start pushing our secret dark horse candidate for Theodor Kazynski,

1170
01:03:39,920 --> 01:03:41,760
Speaker 1: I thought you were going to kill with Vermin Supreme,

1171
01:03:41,920 --> 01:03:47,360
Speaker 1: but you went with Okay, okay, okay, um. Real quick

1172
01:03:47,360 --> 01:03:49,360
Speaker 1: before you go, make sure to check out our website

1173
01:03:50,360 --> 01:03:56,760
Speaker 1: www dot Worst Year pot dot calm, and on both

1174
01:03:56,800 --> 01:04:02,240
Speaker 1: Instagram and Twitter, we're at Worst Year Pod. Simple ease

1175
01:04:02,360 --> 01:04:04,959
Speaker 1: to remember we all just followed it. I said ease

1176
01:04:05,240 --> 01:04:10,840
Speaker 1: because it was easier than saying easy saying I'm really

1177
01:04:10,960 --> 01:04:14,960
Speaker 1: good at ending podcasts, So like and subscribe, and in

1178
01:04:15,040 --> 01:04:18,840
Speaker 1: the meantime, maybe stockpile some dried food, maybe by a

1179
01:04:18,880 --> 01:04:21,120
Speaker 1: machete and some bolt cutters. Maybe pick up, you know,

1180
01:04:21,200 --> 01:04:24,960
Speaker 1: a couple of months of extra your prescription, whatever that

1181
01:04:25,040 --> 01:04:27,680
Speaker 1: happens to be. Maybe Horde insulin, maybe by m oh

1182
01:04:27,760 --> 01:04:33,760
Speaker 1: whatever sounds good. Order all of that from Amazon dot com.

1183
01:04:35,520 --> 01:04:38,360
Speaker 1: Choose one day delivery to put lots of people's lives

1184
01:04:38,400 --> 01:04:41,800
Speaker 1: and risk. Right, isn't that a thing? And stay tuned

1185
01:04:41,840 --> 01:04:44,200
Speaker 1: for our next episode on why Medicare for all just

1186
01:04:44,480 --> 01:04:50,560
Speaker 1: isn't very practical hard degree, I think, I think good

1187
01:04:50,640 --> 01:04:57,080
Speaker 1: things are impossible. We have a new tagline, good things

1188
01:04:57,120 --> 01:05:03,480
Speaker 1: are impossible. What was the one failing? Just like yeah

1189
01:05:03,680 --> 01:05:07,120
Speaker 1: from the last episode, feeling something don't expectacularly something like that.

1190
01:05:08,680 --> 01:05:14,000
Speaker 1: Guess sure, guys, we did it, don't you know? What?

1191
01:05:14,640 --> 01:05:24,480
Speaker 1: Come by? Everything? Everything? So dump it's got again. I

1192
01:05:24,600 --> 01:05:30,480
Speaker 1: tried Daniel Lovely. Worst Year Ever is a production of

1193
01:05:30,520 --> 01:05:33,280
Speaker 1: I Heart Radio. For more podcasts from my heart Radio,

1194
01:05:33,400 --> 01:05:36,320
Speaker 1: visit the I heart Radio app, Apple Podcasts, or wherever

1195
01:05:36,400 --> 01:05:37,720
Speaker 1: you listen to your favorite shows.