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Speaker 1: Hey, guys, ready or not, twenty twenty four is here,

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Speaker 1: and we here at breaking points, are already thinking of

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Speaker 1: ways we can up our game for this critical election.

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Speaker 2: We rely on our premium subs to expand coverage, upgrade

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Speaker 2: the studio ad staff, give you, guys, the best independent.

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Speaker 3: Coverage that is possible.

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Speaker 2: If you like what we're all about, it just means

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Speaker 2: the absolute world to have your support. But enough with that,

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Speaker 2: let's get to the show.

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Speaker 1: All right, Let's go and move on to this Epstein story,

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Speaker 1: of which there has been notably no commentary in the

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Speaker 1: Republican media, which is previously very interested in any Epstein

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Speaker 1: type story. So Michael Wolfe will call him controversial.

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Speaker 3: I think the journalist part of the reason why is

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Speaker 3: because it's Michael.

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Speaker 1: No, it's because it's about Trump and not about a Democrat.

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Speaker 1: But anyway, Michael wolf who wrote Fire and Fury, released

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Speaker 1: a bunch of audio recordings that he had with the

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Speaker 1: late Jeffrey Epstein, specifically talking about his relationship with Donald Trump. Notably,

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Speaker 1: in one he talks about how close friends he was

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Speaker 1: with Donald Trump four ten years, and he also seems

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Speaker 1: to have some inside information about the Trump White House.

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Speaker 1: After Trump wins in twenty sixteen, which would also indicate,

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Speaker 1: you know, he had not been excised from Trump's circle

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Speaker 1: to the extent that Trump had previously claimed. Let's take

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Speaker 1: a listen to that.

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Speaker 4: So how do you notice that's closes right?

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Speaker 5: I think we have a snippet from one of the

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Speaker 5: conversations that I recorded with Epstein, and I think this

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Speaker 5: was in a restaurant in twenty seventeen.

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Speaker 4: His people fight each other, right, and then outside is

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Speaker 4: he sort of poisons the well? Outside he will tell

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Speaker 4: ten people bands of scumbag and priest is not doing

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Speaker 4: a good job. And Kelly has a big mouth. What

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Speaker 4: do you think? Jamie diamond says a year of proud

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Speaker 4: and I shouldn't keep you, and I spoke to Paul

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Speaker 4: and call things. I need a new spokespression. So Kelly,

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Speaker 4: even though I hired Kelly as Kelly and is just

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Speaker 4: too much about wild worker and that he tells bat

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Speaker 4: and here, I really want to keep you, but Kelly

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Speaker 4: and hates you.

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Speaker 5: I have more than dozens. I probably have one hundred

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Speaker 5: hours of Epstein talking about the inner workings of the

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Speaker 5: Trump White House and about his long standing, deep relationship

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Speaker 5: with Donald Trump.

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Speaker 1: We can put the tear sheet up on the screen here.

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Speaker 1: This is from the Daily Beast that recorded reported on

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Speaker 1: the contents of this audio. They say they did an

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Speaker 1: audio analysis and matched Epstein's voice to you know, previous

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Speaker 1: depositions that he had given. In addition to the comments

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Speaker 1: we played, he also claimed lots of intimate knowledge of

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Speaker 1: Trump's sexual preferences, including liking to cuck other married men,

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Speaker 1: claimed that he had sex with Millennia for the first

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Speaker 1: time on the Lolita Express.

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Speaker 6: Make of it whatever you will.

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Speaker 1: I'll just say that, you know, if this was about

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Speaker 1: any Democrat, if it was about Bill Clinton, joh Biden, whoever,

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Speaker 1: you would endlessly hear about it from Republicans. And you know,

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Speaker 1: Jeffrey Epstein, the king of like global elite pedophilia, having

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Speaker 1: a close relationship with Donald Trump, which again some of

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Speaker 1: this is already in the public, and they just completely

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Speaker 1: ignore it.

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Speaker 6: You know.

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Speaker 1: It says a lot about their consistency and their principle

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Speaker 1: when it comes to this and literally anything else.

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Speaker 3: I get.

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Speaker 2: Yeah, I mean, yeah, obviously, I don't know. It's just

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Speaker 2: one of those like, oh, you're telling me that people

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Speaker 2: who are obsessed with pedophilia on the internet or weird

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Speaker 2: partisan and don't have consistent standards.

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Speaker 3: Yeah, I mean, I don't know what else to say.

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Speaker 2: Definitely true in terms of the Fire and Fury author

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Speaker 2: Michael wall The weird thing about Michael.

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Speaker 3: Wolfe is it had these tapes for like years.

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Speaker 2: Yeah, because I remembered I think I did a whole

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Speaker 2: mologue about it back in twenty twenty. If I recall

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Speaker 2: also Wolf, I mean, if he didn't have the audio, I.

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Speaker 3: Straight up just wouldn't believe it.

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Speaker 2: Basically, if it's not on tape, I don't believe it

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Speaker 2: because wolf made up so much stuff, particularly in his

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Speaker 2: second book, the one that came out after Fire and Fury,

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Speaker 2: which he was paid like millions of dollars for, So

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Speaker 2: you know, it's a certain extent unless you literally have

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Speaker 2: it basically on tape, I don't believe it. In terms

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Speaker 2: of whether Epstein's telling the truth, I mean, you know,

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Speaker 2: in terms of like he lied a lot about a

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Speaker 2: lot of different people.

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Speaker 3: This is running for cover for Trump. This is basically

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Speaker 3: for all.

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Speaker 2: The people who are around him, and most of the

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Speaker 2: people who had connections with him are definitely sketchy. In

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Speaker 2: terms of Trump's own comments, the one you can nail

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Speaker 2: him dead to rights?

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Speaker 3: Was that comedy made in like nineteen ninety two where he's.

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Speaker 2: Like Jeffrey, he likes him young, where you're like, oh,

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Speaker 2: it's like, well, what knowledge exactly.

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Speaker 3: Did we have here in terms of the other.

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Speaker 6: Man again, and we know he was on the plane

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Speaker 6: a bunch of times.

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Speaker 2: Yeah, he was on the plane. I mean, you know,

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Speaker 2: I'm not running cover Donald Trump or whatever. If you

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Speaker 2: want to call out the pedophilia people online, sure, but

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Speaker 2: you know they have a whole world view well in

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Speaker 2: terms of the pedophilia.

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Speaker 6: This has been a big.

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Speaker 1: Narrative on the right sure for years, and it's just

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Speaker 1: very convenient that when you have Jeffrey Epstein on tape

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Speaker 1: being like, yeah, he was my bestie, and you know

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Speaker 1: plenty of documentary evidence that suggest that that was in

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Speaker 1: fact the case. Nothing total signs Well.

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Speaker 2: They can quan on themselves into I mean, I remember

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Speaker 2: during Q times there was a manage to throwback. But

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Speaker 2: there's this whole theory about how he was actually in

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Speaker 2: with him so he could get inside knowledge to imprison

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Speaker 2: them all. So you know, Christ, you just haven't been

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Speaker 2: exposed enough to the conspiratorial mind. I guess not how

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Speaker 2: they can justify.

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Speaker 1: It's also funny too, whenever Trump gets asked about Epstein

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Speaker 1: or Gallaine Maxwell or whatever gets very case. I remember

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Speaker 1: when he got asked about Gallaine Maxwell and he was.

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Speaker 3: Like, I wish it best.

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Speaker 6: Well, yeah, he got asked again recently.

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Speaker 1: I think during this campaign, will you release you know,

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Speaker 1: the JFK files and the UFO files and the Epstein

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Speaker 1: files and basically the JFK and the UFO Once he

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Speaker 1: was like, yeah, no problem.

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Speaker 3: He was like.

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Speaker 1: Epstein, I don't know, I don't want you know, personal

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Speaker 1: people and their reputations.

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Speaker 6: Like he was very.

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Speaker 1: Hedging around the release of the Epstein files, which again

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Speaker 1: would be you know, used as an indictment if it

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Speaker 1: was against any Democrat that's out there. But you know,

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Speaker 1: for Republicans who are just interested in the story if

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Speaker 1: it happens to go against their own political adversaries, not

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Speaker 1: a word. They're busy running with the like you know,

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Speaker 1: the Diddy List now with regards to Democrats, so you

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Speaker 1: know they've shifted their attention also ignoring you know, plenty

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Speaker 1: of photos out there too.

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Speaker 6: Domald Trump picture.

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Speaker 2: With one of my great life regrets is I sat

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Speaker 2: next to Alex Acosta on a plane and I did

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Speaker 2: not accost him about the Epstein stuff.

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Speaker 3: It was late. It was we were coming from Miami.

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Speaker 3: It was like a midnight flight.

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Speaker 6: But remind people who he was the.

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Speaker 2: Secretary of Labor under Donald Trump. He was the prosecutor

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Speaker 2: who gave Epstein's sweetheart deal. They cut him loose in

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Speaker 2: Florida and allowed him to go to the Palm Beach

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Speaker 2: County jail and continue to victimize people. But I still

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Speaker 2: am angry at myself for not having the courage to

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Speaker 2: cause a scene on the plane and to stick a camera

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Speaker 2: in his face and be like, why did you do it,

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Speaker 2: mister Acosta.

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Speaker 3: But you and I was the only person who obviously

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Speaker 3: even knew who he was. The funny thing. But there

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Speaker 3: you go.

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Speaker 2: Our identity politics over. It's a big question being asked

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Speaker 2: by the New York Times. Thought we would talk about it.

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Speaker 2: I don't know something our audience cares a lot about.

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Speaker 2: Let's put it up there on the screen. From Jeremy

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Speaker 2: Peters in a shift from twenty twenty quote, identity politics

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Speaker 2: loses its grip on the country. He says, the last

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Speaker 2: time Kamala Harris ran for president, people were losing jobs

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Speaker 2: or friends because of something they said or posted online

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Speaker 2: came off as insensitive.

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Speaker 3: Not sure much has changed.

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Speaker 2: There, he goes, but a new, unfamiliar, new language around

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Speaker 2: an identity was catching on. Terms like LATINX and bipock.

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Speaker 2: The homeless were now unhoused. There were pregnant people not women.

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Speaker 2: Back then, the progressive movement tried to establish itself as

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Speaker 2: a bulwark to the Trump White House on considerations of race, gender,

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Speaker 2: and sexual orientation. But now, what he notes is that

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Speaker 2: there has been a reversion to the mean amongst the

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Speaker 2: Harris campaign, amongst a lot of people like Reuben Diego,

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Speaker 2: who has mounted like his own personal crusade against LATINX,

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Speaker 2: which is funny because the data backs him up hundred

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Speaker 2: percent on why there's been such a big shift with that.

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Speaker 2: And then in general what was clearly an electoral backlash

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Speaker 2: in twenty twenty against BLM and anti racism style rhetoric. Now,

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Speaker 2: I guess the big question inside of this thing is

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Speaker 2: we were just talking before. It's kind of confused in

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Speaker 2: the way that it tries to shoehorn its conclusions, But

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Speaker 2: in general, if you put the stuff he's putting.

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Speaker 3: Out of Hollywood and all that shit Aside is not wrong

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Speaker 3: about a basic fact.

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Speaker 2: Racial depolarization has been the story of the twenty twenties,

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Speaker 2: as in, people are not voting more along with their

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Speaker 2: other racial groups as they had before. They are much

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Speaker 2: more likely to vote along educational lines, which break down

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Speaker 2: amongst culture. Now, that's not always a case specifically in

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Speaker 2: the black community. There's still very lot of you know,

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Speaker 2: black people who still vote for the Democratic Party, but it's

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Speaker 2: not less than ever before. Latinos big splits that have

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Speaker 2: happened there, But the big splits I think, you know,

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Speaker 2: if it's twenty thirty and someone will be like, what

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Speaker 2: are the biggest changes that happen, I'd be like, education

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Speaker 2: and gender gap. Those are like the two things which

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Speaker 2: have nothing to do with race. And I mean I

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Speaker 2: think that's a you know, a nice story.

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Speaker 3: Uh.

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Speaker 2: It's a little confused though, because to say identity politic

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Speaker 2: is over, it's like there's actually a lot of gender politics,

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Speaker 2: which is a problem. So anyways, it was an interesting

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Speaker 2: theory I thought we would discuss.

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Speaker 1: Yeah, there's something there, but also I mean, okay, a

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Speaker 1: few things to say about it. First of all, Matt

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Speaker 1: carp tweeted this morning that the New York Times put

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Speaker 1: the oh bit for progressive identity politics on a one

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Speaker 1: while literally the article next door he describes as committing

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Speaker 1: top tier racecraft, which says the headline for that one

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Speaker 1: is white women ask their own.

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Speaker 6: To back hair. That is true, identity politics is not

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Speaker 6: totally dead.

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Speaker 2: Identity politics for whites has actually come back on the

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Speaker 2: left side. I think I credit the white dudes for

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Speaker 2: white straight white dude.

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Speaker 3: Look at this entire Twitter feed. The dude is like

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Speaker 3: straight white guys for Harris. I'm like, okay, I mean,

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Speaker 3: you know, I don't love this language, but okay.

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Speaker 1: One of the things that I always get irritated with

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Speaker 1: in this these types of analyzes is I think it

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Speaker 1: misplaces where the identity politics moment, Like I'm talking about

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Speaker 1: the like very hollow representation only identity politics came from.

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Speaker 1: And it really did come from the Hillary Clinton campaign.

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Speaker 1: Back in twenty sixteen. You had Bernie Sanders running this

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Speaker 1: very class first type of campaign that was about universal values,

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Speaker 1: universal programs, and Hillary Clinton came out and framed him

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Speaker 1: as a racist because he wasn't using this type of

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Speaker 1: language and catering specifically to slicing and dicing every different

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Speaker 1: demographic group in twenty twenty, he actually embraced some of

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Speaker 1: that terminology to try to robut this allegation from Hillary

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Speaker 1: Clinton and her acolytes and the you know, mainstream of

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Speaker 1: the Democratic Party that he was racist and sexist because

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Speaker 1: he didn't talk about LATINX or you know, use bipoc

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Speaker 1: or use whatever you know, stupid academic language is completely

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Speaker 1: off putting to literally every normal voter out there that

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Speaker 1: he talked in this class first, universalist type of language. So,

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Speaker 1: first of all, I just want to point out that

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Speaker 1: that's where this moment specifically came from. And second of all,

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Speaker 1: I guess I would say that I do think there's

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Speaker 1: something to the fact that, you know, the sort of

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Speaker 1: like peak of the cancel culture, PC word policing, all

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Speaker 1: that bullshit. I think the peak of that has subsided,

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Speaker 1: but it has also in some ways just been relocated.

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Speaker 1: Like now there's an attempt on the right and among

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Speaker 1: you know, Zionist Democrats to do just as extensive, if

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Speaker 1: not more aggressive cancelation, shaming, word policing around anyone who

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Speaker 1: would oppose, you know, Israel's genocidal assault on Palestine. So

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Speaker 1: it's sort of just shifted this location in terms of

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Speaker 1: where the cancelation, where all of the like word policing

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Speaker 1: is located. And there's been a huge uptick in terms

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Speaker 1: of policing around ZI in particular.

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Speaker 3: Yeah, I mean that's Look, they like trotting out.

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Speaker 1: College students to talk about their microaggressions and how they

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Speaker 1: feel unsafe.

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Speaker 3: Blah blah blah blaha.

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Speaker 2: It is very interesting to track the rise of people

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Speaker 2: who rose up literally preaching against identity politics the way

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Speaker 2: that they use them, and there definitely is a Zionist

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Speaker 2: exception I think to all of that. I mean, look,

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Speaker 2: it tracks air interesting theory and actually white women for

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Speaker 2: Harris is the apotheosis of this. There's a whole theory

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Speaker 2: called the Great Awokening, which is that there was a

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Speaker 2: divergent shift on racial attitude starting in twenty fourteen. We

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Speaker 2: had a nice little debate about it about Tanahasi Coats,

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Speaker 2: who I still blame for his essay is igniting a

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Speaker 2: lot of that that was igniting right around the same

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Speaker 2: time as Michael Brown. Trayvon Martin actually was the original

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Speaker 2: that was like the first time that this became like

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Speaker 2: a big thing. Ferguson poured gasoline on the fire white

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Speaker 2: women in particular, shifted attitudes on race dramatically divergent from

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Speaker 2: the left, are diverging on the left, even more so

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Speaker 2: away from black voters. And that is exactly the time

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Speaker 2: of the twenty fourteen I think twenty fourteen, late twenty

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Speaker 2: fourteen is when Hillary announces, and that's where it crests

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Speaker 2: in twenty sixteen for her campaign. Then Trump wins the

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Speaker 2: presidency and it goes wild. This is where you get

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Speaker 2: the ACLU abandoning all their foreign principles. They raise a

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Speaker 2: billion dollars. Then you've you know, everything comes to head

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Speaker 2: at BLM. It gets tested, you know, electorally, some of

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Speaker 2: it has died down, the whole Dei machine and all

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Speaker 2: that has definitely been dealt significant number of blows, and

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Speaker 2: a lot of Democrats have moved past it. But institutionally,

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Speaker 2: I feel think it is there and the whole whit

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Speaker 2: white women for Harris or even this is whole like

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Speaker 2: who gets most offended over what which. Jeremy Peters tries

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Speaker 2: to tries to get at it in his article.

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Speaker 3: I wouldn't say we're.

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Speaker 2: Anywhere like near away from it yet, and so and

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Speaker 2: the fights have also changed, you know now it's a

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Speaker 2: lost about race. Gender is the big one, like I said,

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Speaker 2: right now, and it really got.

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Speaker 6: A lot of bro identity politics.

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Speaker 3: Sure, you know, why not?

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Speaker 6: Actually why shouldn't we To be honest with me, I

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Speaker 6: actually think the.

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Speaker 1: Trump campaign has been more identity politics than Kamala Harris

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Speaker 1: has because she is very sensitive. First of all, Democrats

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Speaker 1: learned that like just talking in language about specific racial

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Speaker 1: grievances will not to diminish those you know, those racial

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Speaker 1: issues like that was not the key to unlocking massive

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Speaker 1: performance among black voters or massive performance among brown voters.

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Speaker 1: So just from like a cynical political calculation, they realized, like, Okay,

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Speaker 1: this really isn't it. And number two, I think, as

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Speaker 1: she's very sensitive to the failures of the Hillary Clinton

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Speaker 1: campaign and the fact that she leaned so much into

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Speaker 1: her personal trailblazing identity, that Kamala has really tried to

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Speaker 1: distance herself from that approach and has also really tried to,

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Speaker 1: you know, consistently put out there at the message, sometimes

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Speaker 1: undercut by some of her most prominent surrogates, that she

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Speaker 1: is not taking any voter for granted, regardless of their demographics.

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Speaker 1: So I actually think, like I said that that Trump

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Speaker 1: has leaned more into the identity politics. We were joking

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Speaker 1: about how some staffer is going through like the list

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Speaker 1: of every every like different racial and ethnic group in

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Speaker 1: America and putting specific message to them, having the you know,

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Speaker 1: the Muslim American endorsers on stage with them and they're

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Speaker 1: like religious guards.

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Speaker 2: He put out a statement for Indians. He was like,

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Speaker 2: he's like Indian American Hindus, we will stop the violence

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Speaker 2: against Hindus in Bangladesh. I was like, wow, man, this

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Speaker 2: guy's going deep in terms of what the Hindu community,

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Speaker 2: not even the Hindu community I'm talking about, like Hindus

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Speaker 2: in India, what they're all jazzed.

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Speaker 1: But yeah, the vibe to me of that is like

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Speaker 1: what Democrats were doing in twenty sixty.

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Speaker 4: Yeah.

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Speaker 3: Yeah, I mean maybe it'll work, right.

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Speaker 2: So I look, it's one of those where I don't

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Speaker 2: like this type of stuff. I don't even think it

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Speaker 2: matters that much, to be honest with you, I think

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Speaker 2: almost all of it is just comes down to education.

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Speaker 2: Education specifically, attitudes around race, gender, and sex are all

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Speaker 2: baked in as to whether you have a four year

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Speaker 2: college degree or not. I'm talking about on average, that

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Speaker 2: means and manifests very differently in terms of racial politics.

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Speaker 2: For because the vast majority right of working class photos,

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Speaker 2: you're gonna have a lot more people of color quote

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Speaker 2: unquote you know in that and are going to have

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Speaker 2: very different attitudes on this. And there's women in different ecosism, stuff,

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Speaker 2: their media, their jokes, they like, where they go to

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Speaker 2: eat everything, where go on vacation. Your whole life is

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Speaker 2: totally different, how much money you make, and so that

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Speaker 2: determines almost all of how it plays out for the election.

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Speaker 2: But yeah, if anything, I think that there has been

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Speaker 2: a new identity politics.

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Speaker 3: You're not wrong about Zionism. I think the gender politics of.

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Speaker 2: It of all, especially if the podcast thing plays out,

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Speaker 2: or even if it doesn't. Let's say that the Iowa

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Speaker 2: Seltzer poll is correct and you have this crazy split

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Speaker 2: of women and men. It's like, okay, we're not talking

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Speaker 2: to each other right, Like there's been a full on

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Speaker 2: different ecosystem about how we all understand what's really going

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Speaker 2: on here.

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Speaker 3: And I would love to see poem. But who do

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Speaker 3: you listen to?

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Speaker 1: You?

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Speaker 3: What do you what are you listening to all day?

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Speaker 6: Like?

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Speaker 3: What's your media diet?

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Speaker 2: What are you you know what type of news are

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Speaker 2: you reading, Like what exactly has caused this master divergence

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Speaker 2: from you and your husband?

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Speaker 3: Like how's that working out?

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Speaker 4: You know?

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Speaker 3: In terms of also like what's he doing?

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Speaker 2: You know, how is he not picking up on this

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Speaker 2: for what's happening inside of his house or you know,

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Speaker 2: it's like what is going on for what that split

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Speaker 2: would look like? And then same with the podcast bro stuff.

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Speaker 2: I mean a lot of that. I've talked about this

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Speaker 2: in terms of the whole like forgotten mail theory. A

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Speaker 2: lot of this also stems from the twenty fourteen Great

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Speaker 2: Awakening and the whole like gender pay gap freak out,

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Speaker 2: which is totally reversed by the way since twenty fourteen,

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Speaker 2: and that has also left a lot of men very

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Speaker 2: angry online, and that itself would also play out for

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Speaker 2: identity politics if they do come through for Trump and

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Speaker 2: vote for him in a big way, which I hope

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Speaker 2: would cause actually media to say same thing of like

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Speaker 2: oh my god, like what's going on here?

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Speaker 3: Are all our prior is wrong?

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Speaker 2: Regardless of what happens that that is going to have

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Speaker 2: to cost some reassessment.

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Speaker 1: Yeah, all right, Well, with all of that being said Sager, Okay,

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Speaker 1: let's see your map.

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Speaker 6: What do you got? My friends?

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Speaker 2: All right, everybody, it is my time for the prediction

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Speaker 2: of the electoral map. I want to start very at

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Speaker 2: the top and just say this, I have much less

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Speaker 2: confidence in this one that I did the last one.

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Speaker 2: The last one where I correctly called every state in

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Speaker 2: the electoral college. So with the caveats out there, let's

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Speaker 2: put it up there on the screen. So this is

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Speaker 2: going to be a little bit of a contrarian map,

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Speaker 2: and I'm going to walk you through all of my thinking.

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Speaker 2: So in terms of the swing states, and let's keep

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Speaker 2: it up there so I can just read from it.

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Speaker 2: I've got at the top line, Donald Trump winning the

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Speaker 2: election with two hundred and seventy one electoral votes, literally

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Speaker 2: just one over what he needs to win two hundred

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Speaker 2: and sixty seven there for Kamala Harris. In terms of

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Speaker 2: the swings, I've got Wisconsin and Michigan going blue for Kamala.

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Speaker 2: I've got Pennsylvania going red. I've got North Carolina glowing blue,

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Speaker 2: and then I've got Georgia, Arizona, and Nevada all going red,

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Speaker 2: which gives him exactly two hundred and seventy one electoral

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Speaker 2: votes So let's get to some of my thinking and why.

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Speaker 3: First.

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Speaker 2: The Iowa Seltzer poll had a big impact on my thinking,

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Speaker 2: but not necessarily in a Kamala landslide way. It made

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Speaker 2: me check my assumptions in terms of the blue wall.

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Speaker 2: Can we put the next tear sheet up please on

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Speaker 2: the screen? Here's a key line from Nate Silver and

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Speaker 2: how you should think about the Iowa poll for the

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Speaker 2: rest of the states who Iowa is far better than

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Speaker 2: the other bluewell states in our forty thousand simulations, in

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Speaker 2: cases where Harris won Iowa, her average margin of victory

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Speaker 2: or defeat in Wisconsin was six point seven, in Michigan was

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Speaker 2: five point nine, and in Pennsylvania was three point eight.

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Speaker 2: If the Seltzer pole is right, she'd be heavily favored

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Speaker 2: in all of them. In other words, but Pennsylvania is

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Speaker 2: the least safe of the trio, in part because about

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Speaker 2: two thirds of its population is what we consider the Northeast,

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Speaker 2: not the Midwest, like Iowa. So the reason why I

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Speaker 2: used the Seltzer pole to push those two states of

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Speaker 2: Michigan in Wisconsin blue was because the Seltzer pole showed

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Speaker 2: what I think will show somewhat movement towards Kamala Harris

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Speaker 2: amongst that critical senior women demographic and amongst women who

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Speaker 2: are worried about abortion. But because Pennsylvania is so distinct

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Speaker 2: in its population, it's got more people of color and

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Speaker 2: other places in the northeastern population that have different concerns

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Speaker 2: and are culturally pretty distinct from those other two states.

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Speaker 2: I thought that was enough considering where things lie right now.

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Speaker 2: Let's go to the next element, police, and this will

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Speaker 2: explain a little bit more. The other thing is that

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Speaker 2: in Nate Cone in the New York Times Siena poll analysis,

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Speaker 2: they talked specifically about non response polling bias, as in

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Speaker 2: Democratic response rates ticked up in their survey, White Democratic

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Speaker 2: responses outpaced Republicans by some fifteen percent. That was more

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Speaker 2: than twenty twenty. So what that means is that the

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Speaker 2: response rate amongst Democrats, and this was a whole theory

443
00:20:22,760 --> 00:20:25,240
Speaker 2: in twenty twenty as to why the polls were wrong

444
00:20:25,560 --> 00:20:28,520
Speaker 2: for the Democrats in their direction was because of polling

445
00:20:28,600 --> 00:20:31,959
Speaker 2: response biased and there was overperformance amongst Ems. So if

446
00:20:32,000 --> 00:20:34,920
Speaker 2: Nate Cone is flagging that in a poll of Pennsylvania

447
00:20:34,960 --> 00:20:38,600
Speaker 2: which they have tied again, tiede that they have a

448
00:20:38,640 --> 00:20:41,960
Speaker 2: non response bias there for Democrats. I think that underestimates

449
00:20:42,000 --> 00:20:44,800
Speaker 2: Donald Trump's strength and that means he could win that state.

450
00:20:44,960 --> 00:20:45,960
Speaker 3: Now we'll continue in the map.

451
00:20:46,000 --> 00:20:47,960
Speaker 2: Let's put the next one, please up on the screen

452
00:20:48,640 --> 00:20:50,919
Speaker 2: that shows you exactly what I was talking about, where

453
00:20:51,000 --> 00:20:53,639
Speaker 2: they have. If you factor in a non response bias,

454
00:20:53,880 --> 00:20:58,120
Speaker 2: you have basically Harris and Trump tied in Pennsylvania and Michigan.

455
00:20:58,240 --> 00:21:01,800
Speaker 2: I'm giving Harris Michigan just be because of that Iowa poll.

456
00:21:01,920 --> 00:21:04,480
Speaker 2: But in Pennsylvania, I think with a distinct enough population,

457
00:21:04,680 --> 00:21:07,040
Speaker 2: Trump can pull it out, either thin or frankly, it

458
00:21:07,040 --> 00:21:09,320
Speaker 2: could even win a little bit bigger than the rest.

459
00:21:09,440 --> 00:21:11,840
Speaker 2: And if we continue down, let's go to the next element, please,

460
00:21:12,080 --> 00:21:15,920
Speaker 2: what we'll find is Arizona has had a pretty significant

461
00:21:16,160 --> 00:21:20,480
Speaker 2: response in terms of early ballot returns for Republicans, significantly

462
00:21:20,520 --> 00:21:23,159
Speaker 2: more than they had last time around. From everything I

463
00:21:23,160 --> 00:21:25,080
Speaker 2: have seen, both in terms of the polling and in

464
00:21:25,160 --> 00:21:27,680
Speaker 2: terms of the early vote, there has been enough of

465
00:21:27,720 --> 00:21:31,000
Speaker 2: a Republican surge that I feel pretty confident calling Arizona

466
00:21:31,160 --> 00:21:33,919
Speaker 2: for Donald Trump. Another reason is that there was recently

467
00:21:33,960 --> 00:21:36,720
Speaker 2: a migration analysis that showed that a lot of the

468
00:21:36,760 --> 00:21:40,760
Speaker 2: inflow that's coming into Arizona are Republican Californians who are

469
00:21:40,760 --> 00:21:43,080
Speaker 2: fed up with governance from there, and the state has

470
00:21:43,119 --> 00:21:45,720
Speaker 2: become a little bit more red from last time around.

471
00:21:45,800 --> 00:21:48,320
Speaker 2: If I'm wrong here, the main reason will be because

472
00:21:48,359 --> 00:21:51,480
Speaker 2: of the Arizona abortion ballot referendum, and that will be

473
00:21:51,560 --> 00:21:53,520
Speaker 2: one that was just simply not caught at all.

474
00:21:53,560 --> 00:21:55,800
Speaker 3: So let me get that caveat for right there. Let's

475
00:21:55,800 --> 00:21:56,800
Speaker 3: move to the next one. Please.

476
00:21:57,040 --> 00:21:59,800
Speaker 2: This also shows another some of my calls is that

477
00:22:00,440 --> 00:22:03,400
Speaker 2: is racking with his rally is going every single day

478
00:22:03,560 --> 00:22:06,680
Speaker 2: in North Carolina. Now, the reason why that I'm paying

479
00:22:06,680 --> 00:22:09,080
Speaker 2: attention to this and where the candidates are spending their time,

480
00:22:09,720 --> 00:22:11,720
Speaker 2: is that spending all this time in North Carolina, a

481
00:22:11,760 --> 00:22:14,199
Speaker 2: state that you won twice, is not good and it

482
00:22:14,320 --> 00:22:16,920
Speaker 2: tells us that that's time that you should be spending.

483
00:22:17,040 --> 00:22:20,040
Speaker 2: Theoretically in the blue wall that they are seeing internally

484
00:22:20,240 --> 00:22:22,320
Speaker 2: that they have a problem. I think that problem is

485
00:22:22,359 --> 00:22:25,040
Speaker 2: called Mark Robinson, and he is a major reason why

486
00:22:25,080 --> 00:22:27,040
Speaker 2: I think Kamala is going to win. Let's go to

487
00:22:27,080 --> 00:22:31,320
Speaker 2: the next one. Next slide, please for the real clear politics.

488
00:22:31,400 --> 00:22:33,440
Speaker 2: If you take a look at this, even New York

489
00:22:33,440 --> 00:22:36,479
Speaker 2: Times Sienna. All the quote unquote garbage Republican polls. Every

490
00:22:36,520 --> 00:22:38,320
Speaker 2: single poll out of the state of North Carolina and

491
00:22:38,359 --> 00:22:42,240
Speaker 2: their gubernatorial race has Mark Robinson down by a minimum

492
00:22:42,320 --> 00:22:46,680
Speaker 2: of nine The average the spread is Mark Robinson down

493
00:22:46,720 --> 00:22:49,800
Speaker 2: by fourteen point three points. Some of them have Mark

494
00:22:49,840 --> 00:22:52,800
Speaker 2: Robinson losing by twenty points. So the reason that I

495
00:22:52,840 --> 00:22:55,119
Speaker 2: think Kamala's going to win in North Carolina is I

496
00:22:55,200 --> 00:22:57,919
Speaker 2: just simply don't believe in an electorate that has some

497
00:22:58,320 --> 00:23:01,719
Speaker 2: between nineteen to ten points split tickets swing that is

498
00:23:01,760 --> 00:23:05,200
Speaker 2: going to vote for the gubernatorial candidate as a Democrat

499
00:23:05,359 --> 00:23:07,520
Speaker 2: and then split their ticket for Donald Trump. I think

500
00:23:07,560 --> 00:23:09,639
Speaker 2: Trump had a good chance of winning North Carolina, and

501
00:23:09,680 --> 00:23:12,000
Speaker 2: I think Mark Robinson absolutely sunk it for him the

502
00:23:12,040 --> 00:23:13,680
Speaker 2: amount of time that he's spending there in the state.

503
00:23:13,840 --> 00:23:15,280
Speaker 2: I think that's what it is. Let's go to the

504
00:23:15,280 --> 00:23:18,400
Speaker 2: next part and I can continue here. This is from

505
00:23:18,480 --> 00:23:20,159
Speaker 2: John Rawlston from his early voting blog.

506
00:23:20,320 --> 00:23:22,200
Speaker 3: This one. I'm a little bit less confident about.

507
00:23:22,240 --> 00:23:25,040
Speaker 2: I'll be honest, but there has been significant overperformance for

508
00:23:25,080 --> 00:23:29,680
Speaker 2: Republicans in Clark County, sorry in Nevada. Unless there's some

509
00:23:29,840 --> 00:23:33,439
Speaker 2: major movement in Clark County in Nevada, which does not

510
00:23:33,560 --> 00:23:38,600
Speaker 2: see huge Democratic performance on election day tomorrow. Specifically, John

511
00:23:38,640 --> 00:23:41,520
Speaker 2: Rawlston and other watchers of Nevada are pretty confident that

512
00:23:41,560 --> 00:23:45,320
Speaker 2: there's a major GOP upset happening there. The only way

513
00:23:45,359 --> 00:23:48,240
Speaker 2: that I'm wrong here is again because of abortion, and

514
00:23:48,359 --> 00:23:52,240
Speaker 2: also because of the independent vote specifically, which is categorized

515
00:23:52,280 --> 00:23:55,720
Speaker 2: as other disportionately skews younger. And if that all goes

516
00:23:55,760 --> 00:23:58,640
Speaker 2: disportionately Democrat, then the Republicans are going to have a problem.

517
00:23:58,800 --> 00:24:00,399
Speaker 2: But right now they're up by I think it's like

518
00:24:00,440 --> 00:24:02,480
Speaker 2: forty thousand votes or so in terms of the last

519
00:24:02,680 --> 00:24:04,760
Speaker 2: time that I looked at it, and people in Nevada,

520
00:24:04,760 --> 00:24:07,280
Speaker 2: Republicans and others pretty confident that they're going to win

521
00:24:07,480 --> 00:24:09,560
Speaker 2: that state. It would make sense to in terms of

522
00:24:09,600 --> 00:24:14,359
Speaker 2: the Latino realignment and others. So that's Do I have

523
00:24:14,359 --> 00:24:17,360
Speaker 2: any other other elements? Let's see here one more. Let's

524
00:24:17,400 --> 00:24:20,160
Speaker 2: put that last one up there on freestyling. This is George,

525
00:24:20,200 --> 00:24:23,400
Speaker 2: That's right, I forgot. So this is from my friend Edisante.

526
00:24:23,480 --> 00:24:25,240
Speaker 2: He says, quote, I honestly don't see how you can

527
00:24:25,280 --> 00:24:27,080
Speaker 2: get to Harris plus two in Georgia based on the

528
00:24:27,119 --> 00:24:30,600
Speaker 2: early vote. It feels mathematically impossible currently at fifty five

529
00:24:30,600 --> 00:24:33,520
Speaker 2: percent turnout of registered voters. It was sixty six percent

530
00:24:33,520 --> 00:24:36,040
Speaker 2: turnout in twenty twenty, and it was sixty percent turnout

531
00:24:36,119 --> 00:24:37,720
Speaker 2: in two thousand and sixteen.

532
00:24:37,840 --> 00:24:38,680
Speaker 3: So because you.

533
00:24:38,720 --> 00:24:42,399
Speaker 2: Have lower turnout and you've seen blowout turnout for the

534
00:24:42,440 --> 00:24:45,600
Speaker 2: rural counties for Donald Trump, you have Republicans there that

535
00:24:45,640 --> 00:24:48,000
Speaker 2: are pretty confident. Biden only won it by some ten

536
00:24:48,000 --> 00:24:50,280
Speaker 2: thousand votes or so last time around, which was a

537
00:24:50,320 --> 00:24:53,280
Speaker 2: major upset. Even with the demographic turn you see major

538
00:24:53,320 --> 00:24:56,159
Speaker 2: Republican enthusiasm. You don't have the same beef that they

539
00:24:56,160 --> 00:24:58,359
Speaker 2: had last time with Brian Kemp. Kemp is out there

540
00:24:58,400 --> 00:25:01,800
Speaker 2: stumping for Donald Trump. They seem relatively confident of victory,

541
00:25:01,800 --> 00:25:04,040
Speaker 2: and the math looks like it could be correct. The

542
00:25:04,080 --> 00:25:06,399
Speaker 2: only way again I'm wrong here is if you see

543
00:25:06,640 --> 00:25:09,960
Speaker 2: major white female overperformance, just like you said in Iowa

544
00:25:10,080 --> 00:25:13,280
Speaker 2: abortion style upset, and if you see black turnout that

545
00:25:13,440 --> 00:25:16,840
Speaker 2: skyrockets on election day. So I have lower confidence in

546
00:25:16,880 --> 00:25:19,520
Speaker 2: this map, But in the scenario Donald Trump wins, it'll

547
00:25:19,520 --> 00:25:21,960
Speaker 2: be narrow two seventy one to sixty seven.

548
00:25:22,080 --> 00:25:23,520
Speaker 3: But that's what I've got, crystal.

549
00:25:23,280 --> 00:25:25,080
Speaker 1: Very interesting, I mean, and if you want to hear

550
00:25:25,320 --> 00:25:28,879
Speaker 1: my reaction to Sager's monologue become a premium subscriber today

551
00:25:28,920 --> 00:25:33,000
Speaker 1: at breakingpoints dot Com.

552
00:25:33,160 --> 00:25:35,160
Speaker 6: Well, that's probably a good Bristol. What do you got

553
00:25:35,160 --> 00:25:36,159
Speaker 6: good place to queue up mine?

554
00:25:36,200 --> 00:25:38,119
Speaker 1: Let's go and put my map up on the screen.

555
00:25:38,240 --> 00:25:40,920
Speaker 1: Minus a little simpler to explain, because I do have

556
00:25:41,119 --> 00:25:45,800
Speaker 1: the Kamala landslide scenario, and by landslide, I mean that

557
00:25:45,840 --> 00:25:48,879
Speaker 1: she wins some level of victory in all seven of

558
00:25:49,000 --> 00:25:52,960
Speaker 1: the swing states. I'm not giving her Iowa, no, Ann

559
00:25:53,000 --> 00:25:55,680
Speaker 1: Selzer says, but potentially that is in play because I'm

560
00:25:55,720 --> 00:25:59,359
Speaker 1: just not that bold. But my meta narrative you know,

561
00:26:00,280 --> 00:26:02,560
Speaker 1: of the world that would lead to this map is

562
00:26:02,560 --> 00:26:06,680
Speaker 1: effectively that the combination of January sixth, and in particular,

563
00:26:06,800 --> 00:26:10,440
Speaker 1: the overturning of Roe versus Wade, has represented a political

564
00:26:10,520 --> 00:26:15,040
Speaker 1: earthquake in American politics that pollsters have consistently failed to

565
00:26:15,200 --> 00:26:18,760
Speaker 1: capture this newly shifted electorate and have openly rigged their results.

566
00:26:18,760 --> 00:26:21,480
Speaker 1: I mean, they admit to this to effectively closely match

567
00:26:21,600 --> 00:26:24,560
Speaker 1: the outcome of twenty twenty, just out of terror that

568
00:26:24,600 --> 00:26:28,520
Speaker 1: they will once again underestimate Donald Trump, and in doing

569
00:26:28,560 --> 00:26:33,760
Speaker 1: so they have fundamentally missed a significant shift among women.

570
00:26:34,320 --> 00:26:37,320
Speaker 1: On top of that, Kamala Harris is a fairly ideal

571
00:26:37,480 --> 00:26:41,440
Speaker 1: candidate to champion this new pro choice, anti chaos majority.

572
00:26:41,760 --> 00:26:44,800
Speaker 1: She has built her campaign around appealing to this heavily

573
00:26:44,840 --> 00:26:47,880
Speaker 1: female coalition. And Donald Trump, on the other side, has

574
00:26:47,960 --> 00:26:50,440
Speaker 1: leaned into some of his most toxic traits here down

575
00:26:50,480 --> 00:26:52,760
Speaker 1: the home stretch and is in some ways an ideal

576
00:26:52,840 --> 00:26:56,040
Speaker 1: foil to motivate this coalition, especially since he is the

577
00:26:56,119 --> 00:26:59,639
Speaker 1: individual who is responsible for putting the justices on the

578
00:26:59,640 --> 00:27:03,600
Speaker 1: court to overturn Roe versus wad Okay. So now let

579
00:27:03,600 --> 00:27:07,040
Speaker 1: me give you the data that backs up this theory

580
00:27:07,119 --> 00:27:09,760
Speaker 1: of the world that would result in this map where

581
00:27:09,760 --> 00:27:14,000
Speaker 1: every battleground state ultimately goes to Kamala Harris could put

582
00:27:14,000 --> 00:27:16,160
Speaker 1: this first up on the screen. So this is all

583
00:27:16,240 --> 00:27:21,320
Speaker 1: under the category of pollsters are missing the post Row electorate.

584
00:27:21,960 --> 00:27:27,879
Speaker 1: So even before the Sealser poll, you had when Biden

585
00:27:28,040 --> 00:27:31,240
Speaker 1: was still the top of the ticket, Democrats in almost

586
00:27:31,280 --> 00:27:37,359
Speaker 1: every instance outperforming their polls during special elections. So not

587
00:27:37,400 --> 00:27:39,560
Speaker 1: only that, you also had, if we could put the

588
00:27:39,560 --> 00:27:44,359
Speaker 1: next one up on the screen, polls consistently overestimating Trump.

589
00:27:44,440 --> 00:27:48,879
Speaker 1: Actually after Roe versus Wade and after January sixth, so

590
00:27:48,920 --> 00:27:53,199
Speaker 1: when he was running in the Republican primary, surveys overestimated

591
00:27:53,440 --> 00:27:57,040
Speaker 1: Trump's actual vote share in eight of the ten states

592
00:27:57,280 --> 00:27:59,680
Speaker 1: where there was enough polling for five point thirty eight

593
00:27:59,680 --> 00:28:02,600
Speaker 1: two produce in average. What's more, if we put the

594
00:28:02,640 --> 00:28:06,560
Speaker 1: next one up on the screen, Nicki Haley his most

595
00:28:06,600 --> 00:28:10,879
Speaker 1: significant adversary. Even after she dropped out of the race,

596
00:28:11,840 --> 00:28:16,360
Speaker 1: she continued to rack up significant vote totals, and in

597
00:28:16,400 --> 00:28:20,280
Speaker 1: particular in some of those key suburban counties. So this

598
00:28:20,320 --> 00:28:23,920
Speaker 1: is from a Politico article they write in late April,

599
00:28:23,960 --> 00:28:26,760
Speaker 1: staffers at Joe Biden's headquarters fixated on votes for Nikki

600
00:28:26,840 --> 00:28:30,080
Speaker 1: Haley rolling in during the Pennsylvania primary, as she pulled

601
00:28:30,119 --> 00:28:33,560
Speaker 1: twenty to twenty five percent support in the largely upscale,

602
00:28:33,640 --> 00:28:39,000
Speaker 1: suburban Coller counties around Philadelphia. Most remarkable, Haley had dropped

603
00:28:39,040 --> 00:28:42,480
Speaker 1: out more than six weeks earlier, and this was not

604
00:28:42,560 --> 00:28:46,480
Speaker 1: the only time when this occurred. She continued to perform

605
00:28:46,600 --> 00:28:49,400
Speaker 1: and rack up significant vote totals in states like Florida.

606
00:28:49,400 --> 00:28:51,200
Speaker 1: I think she got seventeen percent of the vote even

607
00:28:51,240 --> 00:28:53,520
Speaker 1: though she'd been out of the race for some significant

608
00:28:53,560 --> 00:28:57,000
Speaker 1: amount of time. And again those votes came largely from

609
00:28:57,200 --> 00:29:02,680
Speaker 1: women in suburban counties. In addition, Trump has done basically

610
00:29:02,720 --> 00:29:05,120
Speaker 1: nothing to reach out to those voters who are at

611
00:29:05,200 --> 00:29:08,240
Speaker 1: least disaffected with him enough to give their vote to

612
00:29:08,320 --> 00:29:11,920
Speaker 1: his primary opponent, even after she had exited the race. Now,

613
00:29:11,960 --> 00:29:15,240
Speaker 1: Nikki Hilly herself has endorsed Trump, but he hasn't used

614
00:29:15,240 --> 00:29:18,040
Speaker 1: her on the campaign trail. He has done very little

615
00:29:18,160 --> 00:29:21,920
Speaker 1: to attempt to reach out to disaffected suburban women who

616
00:29:21,920 --> 00:29:23,960
Speaker 1: may have been part of his coalition and may have

617
00:29:24,040 --> 00:29:27,400
Speaker 1: voted for him in the past, leaving them open to

618
00:29:27,600 --> 00:29:29,760
Speaker 1: what has been, you know, an overwhelming effort. This has

619
00:29:29,800 --> 00:29:32,320
Speaker 1: been a key focus from the Kamala Harris campaign to

620
00:29:32,400 --> 00:29:35,280
Speaker 1: try to persuade some percentage of those voters and try

621
00:29:35,320 --> 00:29:38,840
Speaker 1: to pull them into the Kamala Harris coalition. The next

622
00:29:38,840 --> 00:29:42,400
Speaker 1: piece of evidence here that the pollsters are missing a

623
00:29:42,680 --> 00:29:47,000
Speaker 1: new post Row electorate is the Anne Selzer poll. I mean,

624
00:29:47,040 --> 00:29:50,080
Speaker 1: this has to be the most key piece of data.

625
00:29:50,480 --> 00:29:55,320
Speaker 1: She finds in a shocking upset Kamala Harris beating Trump

626
00:29:55,800 --> 00:30:00,720
Speaker 1: among likely Iowa voters forty seven to forty four. If

627
00:30:00,760 --> 00:30:03,040
Speaker 1: you dig into these numbers, put the next one up

628
00:30:03,040 --> 00:30:07,640
Speaker 1: on the screen. She finds independent women going to Kamala

629
00:30:07,720 --> 00:30:12,320
Speaker 1: Harris by twenty eight points. She finds senior women going

630
00:30:12,360 --> 00:30:16,080
Speaker 1: to Kamala Harris by thirty five points. Now, on the

631
00:30:16,120 --> 00:30:19,600
Speaker 1: one hand, you can say Selzer may be missing a

632
00:30:19,600 --> 00:30:20,400
Speaker 1: lot of what's going on.

633
00:30:20,600 --> 00:30:21,880
Speaker 6: Clearly, this is a.

634
00:30:21,840 --> 00:30:25,840
Speaker 1: Very different portrait than you're seeing from effectively any other polster.

635
00:30:25,880 --> 00:30:28,040
Speaker 1: Of all, tell you some other ones that somewhat line

636
00:30:28,080 --> 00:30:31,600
Speaker 1: up with her in the same region. Unlike other posters,

637
00:30:31,640 --> 00:30:34,200
Speaker 1: she does a lot less waiting, meaning she does a

638
00:30:34,240 --> 00:30:36,600
Speaker 1: lot less going in and applying her own judgment about

639
00:30:36,640 --> 00:30:38,440
Speaker 1: I think the electorate's going to be this, I think

640
00:30:38,440 --> 00:30:41,240
Speaker 1: the electorate is going to be that. Instead, she calls

641
00:30:41,320 --> 00:30:44,240
Speaker 1: up registered voters and talks to them and asks them

642
00:30:44,280 --> 00:30:46,600
Speaker 1: how likely they are to vote, and of course who

643
00:30:46,600 --> 00:30:47,440
Speaker 1: they're going to vote for.

644
00:30:47,760 --> 00:30:49,800
Speaker 6: And so there's a lot less of her.

645
00:30:49,640 --> 00:30:54,440
Speaker 1: Own judgment and her own terror overestimating or underestimating this

646
00:30:54,560 --> 00:30:55,760
Speaker 1: candidate or that candidate.

647
00:30:56,080 --> 00:30:58,360
Speaker 6: It's more directly what the voters say.

648
00:30:58,480 --> 00:31:00,720
Speaker 1: Now that comes with huge risks because in the past,

649
00:31:00,720 --> 00:31:02,720
Speaker 1: of course, there has been a non response bias in

650
00:31:02,800 --> 00:31:06,040
Speaker 1: terms of Trump's supporters, and that could be playing into

651
00:31:06,080 --> 00:31:08,520
Speaker 1: the results that she is seeing here if they end

652
00:31:08,600 --> 00:31:12,600
Speaker 1: up being wildly off. But my bet, based on the

653
00:31:12,600 --> 00:31:14,720
Speaker 1: fact that an Selzer has a better track record that

654
00:31:14,840 --> 00:31:19,400
Speaker 1: basically literally any other Polster in the state of Iore

655
00:31:19,480 --> 00:31:22,840
Speaker 1: or anywhere else, my bet is that she is directionally correct,

656
00:31:23,720 --> 00:31:26,840
Speaker 1: that she is picking up on this post Row shift

657
00:31:27,200 --> 00:31:30,480
Speaker 1: that other Polsters, out of terror and out of the

658
00:31:30,520 --> 00:31:33,560
Speaker 1: fact that they are basically pegging their results to match

659
00:31:33,560 --> 00:31:35,480
Speaker 1: the twenty twenty election because they don't want to be

660
00:31:35,520 --> 00:31:37,080
Speaker 1: on over their skis and they don't want to get

661
00:31:37,080 --> 00:31:40,720
Speaker 1: it too wrong, have completely missed. I mean, for me, logically,

662
00:31:40,760 --> 00:31:43,240
Speaker 1: it makes sense that if you are just trying to

663
00:31:43,240 --> 00:31:45,280
Speaker 1: peg the results to twenty twenty, you are going to

664
00:31:45,320 --> 00:31:48,640
Speaker 1: be missing something because the world is so different from

665
00:31:48,640 --> 00:31:50,640
Speaker 1: how it was in twenty twenty. So I mentioned before,

666
00:31:50,680 --> 00:31:53,560
Speaker 1: we had January sixth, we're out of COVID, we had

667
00:31:53,640 --> 00:31:56,760
Speaker 1: Row being overturned, we had some of the population significant

668
00:31:56,760 --> 00:32:00,640
Speaker 1: population shifts that Sagre talked about across the country. And

669
00:32:00,680 --> 00:32:03,040
Speaker 1: so if you are just trying to clude your result

670
00:32:03,120 --> 00:32:05,600
Speaker 1: to match the way things were back in twenty twenty,

671
00:32:05,920 --> 00:32:09,280
Speaker 1: it is undeniable to me that you're going to be

672
00:32:09,280 --> 00:32:12,800
Speaker 1: missing some significant shifts. And to me, Anne Sellzer gives

673
00:32:12,840 --> 00:32:16,760
Speaker 1: a window into what some of those shifts may have been.

674
00:32:17,720 --> 00:32:19,680
Speaker 1: This is backed up by some other data. We could

675
00:32:19,720 --> 00:32:21,800
Speaker 1: put the next piece up on the screen. Gear from Politico.

676
00:32:22,240 --> 00:32:26,640
Speaker 1: You know, the issue she finds with seniors. Sorry, we

677
00:32:26,640 --> 00:32:29,360
Speaker 1: should have a Politico terachute, but issues she finds with

678
00:32:29,400 --> 00:32:33,040
Speaker 1: seniors also appears to be showing up in other early

679
00:32:33,120 --> 00:32:36,000
Speaker 1: voting data where Republicans are really worried that seniors are

680
00:32:36,040 --> 00:32:36,640
Speaker 1: not turning.

681
00:32:36,480 --> 00:32:36,920
Speaker 6: Up for them.

682
00:32:37,040 --> 00:32:38,640
Speaker 1: And if we leave this one up on the screen,

683
00:32:38,920 --> 00:32:42,680
Speaker 1: we also find that there are some other polls that

684
00:32:42,720 --> 00:32:45,600
Speaker 1: look a little bit like what the Ann Sellzer poll

685
00:32:45,920 --> 00:32:49,120
Speaker 1: looks like. So a Kansas poster that has a good

686
00:32:49,160 --> 00:32:52,800
Speaker 1: reputation just last week found Trump up only by five

687
00:32:53,240 --> 00:32:56,120
Speaker 1: in the state of Kansas. Now that would be an earthquake,

688
00:32:56,360 --> 00:32:59,840
Speaker 1: only five in the state of Kansas. We've seen multiple

689
00:33:00,120 --> 00:33:03,840
Speaker 1: polls of that Nebraska second congressional district, which is a

690
00:33:03,880 --> 00:33:06,840
Speaker 1: swing district meaning it should be close, that has found

691
00:33:06,960 --> 00:33:08,120
Speaker 1: Kamin Harris.

692
00:33:07,880 --> 00:33:10,120
Speaker 6: With a double digit lead.

693
00:33:10,560 --> 00:33:14,600
Speaker 1: That would represent a significant improvement over Biden's performance there.

694
00:33:14,640 --> 00:33:17,160
Speaker 1: I believe Biden won that congressional district by somewhere around

695
00:33:17,160 --> 00:33:20,840
Speaker 1: five points. We also saw a poll of Ohio that

696
00:33:20,960 --> 00:33:25,440
Speaker 1: had Trump up by only three points. Again, that would

697
00:33:25,440 --> 00:33:30,400
Speaker 1: be a significant shift away from Trump visa VI twenty twenty.

698
00:33:30,840 --> 00:33:32,920
Speaker 1: So this person opines, and I think this is true.

699
00:33:32,920 --> 00:33:36,760
Speaker 1: Taken together, Iowa looks like less of an outlier and

700
00:33:36,840 --> 00:33:40,800
Speaker 1: more like something real is happening here. So when you

701
00:33:40,840 --> 00:33:44,960
Speaker 1: piece it together with all these poles and the saleser pole,

702
00:33:45,000 --> 00:33:47,680
Speaker 1: which has so much credibility in such a track record

703
00:33:47,720 --> 00:33:50,240
Speaker 1: behind it, I have to feel like the polsters are

704
00:33:50,320 --> 00:33:54,800
Speaker 1: missing something that is truly happening with women here. In addition,

705
00:33:55,200 --> 00:33:57,720
Speaker 1: we've seen a number of poles. I'll just highlight the

706
00:33:57,760 --> 00:33:59,560
Speaker 1: New York Times one here, Guys, put this up on

707
00:33:59,600 --> 00:34:03,800
Speaker 1: the screen. So the New York Times Sienna Pole, for example,

708
00:34:04,040 --> 00:34:07,960
Speaker 1: found that Kamala Harris was winning these late breaking voters

709
00:34:07,960 --> 00:34:09,920
Speaker 1: by margin of fifty eight to forty two. That's not

710
00:34:09,960 --> 00:34:12,440
Speaker 1: the only poll that has found similar results where those

711
00:34:12,440 --> 00:34:14,840
Speaker 1: who were deciding late in the game are going for

712
00:34:14,920 --> 00:34:17,000
Speaker 1: Kamala Harris's kind of the reverse of what we found

713
00:34:17,000 --> 00:34:20,480
Speaker 1: in twenty sixteen, when after Comy and all of that happened,

714
00:34:20,480 --> 00:34:23,920
Speaker 1: the late breakers significantly broke for Donald Trump and end

715
00:34:24,040 --> 00:34:28,120
Speaker 1: up handing him victory. We also found that early voters

716
00:34:28,560 --> 00:34:31,960
Speaker 1: have been going for Kamala and this is noteworthy as

717
00:34:32,000 --> 00:34:33,480
Speaker 1: well as go and put this up on the screen

718
00:34:33,520 --> 00:34:38,120
Speaker 1: in terms of the numbers with early voters. So recent

719
00:34:38,200 --> 00:34:40,520
Speaker 1: national ABC News IPSOS, New York Times, Santa College and

720
00:34:40,560 --> 00:34:43,520
Speaker 1: CNN polls show Harris with an advantage of nineteen to

721
00:34:43,600 --> 00:34:47,320
Speaker 1: twenty nine points among voters who say they've already cast ballots.

722
00:34:47,600 --> 00:34:50,560
Speaker 1: Those margins range from a fifty nine to forty edge

723
00:34:50,560 --> 00:34:52,400
Speaker 1: in the Times Ciena pulled to a sixty two to

724
00:34:52,440 --> 00:34:56,919
Speaker 1: thirty three edge in the ABC IPSOS one. This cuts

725
00:34:56,960 --> 00:34:59,440
Speaker 1: against the grain of some of the early vote analysis

726
00:34:59,440 --> 00:35:01,759
Speaker 1: that you've seen on the Republican side, where they look

727
00:35:01,760 --> 00:35:03,960
Speaker 1: at the partisan breakdown and they say, okay, well, Republicans

728
00:35:03,960 --> 00:35:06,440
Speaker 1: are holding up pretty well in the early vote, and

729
00:35:06,440 --> 00:35:09,240
Speaker 1: in some places even have a lead in the early vote.

730
00:35:09,520 --> 00:35:11,600
Speaker 6: What this could reflect is the fact that.

731
00:35:12,280 --> 00:35:15,160
Speaker 1: The Harris campaign may be eating into a few percentage

732
00:35:15,160 --> 00:35:17,520
Speaker 1: points of the Republican electorate, where some of those Republicans

733
00:35:17,560 --> 00:35:19,680
Speaker 1: who are turning up are actually voting for Kamala Harris,

734
00:35:19,840 --> 00:35:23,560
Speaker 1: but more significantly, that they are winning a significant number

735
00:35:23,600 --> 00:35:26,879
Speaker 1: of the independent votes who are not registered with any

736
00:35:26,920 --> 00:35:30,960
Speaker 1: particular political party. This in particular is important in the

737
00:35:30,960 --> 00:35:35,080
Speaker 1: state of Nevada, where Republicans have felt the best about

738
00:35:35,120 --> 00:35:38,160
Speaker 1: their early vote. And understandably so you've got John Rawlston

739
00:35:38,160 --> 00:35:40,200
Speaker 1: out they're crunching the numbers, saying, hey, Republicans are.

740
00:35:40,120 --> 00:35:41,479
Speaker 6: Doing something really different here.

741
00:35:41,960 --> 00:35:44,439
Speaker 1: But if you look at this time Siena polling, they

742
00:35:44,480 --> 00:35:47,360
Speaker 1: find okay, yeah, among those people in Nevada who have

743
00:35:47,400 --> 00:35:51,120
Speaker 1: already voted, yes, the Republicans have a two point edge

744
00:35:51,120 --> 00:35:56,640
Speaker 1: by party registration. However, they still found even among that

745
00:35:56,960 --> 00:36:02,920
Speaker 1: two points edge Republican party registration electorate, they still found

746
00:36:02,960 --> 00:36:06,799
Speaker 1: Kamala Harris up by five points because she has a

747
00:36:06,840 --> 00:36:12,239
Speaker 1: wide lead among unaffiliated voters who cast early ballots. So

748
00:36:12,400 --> 00:36:15,440
Speaker 1: the New York Times Siena backing up here some of

749
00:36:15,440 --> 00:36:18,480
Speaker 1: the analysis of what could be going on underneath the

750
00:36:18,520 --> 00:36:21,600
Speaker 1: surface of even in that Nevada early vote, which, as

751
00:36:21,640 --> 00:36:25,319
Speaker 1: I said before, is the area where Republicans have had

752
00:36:25,560 --> 00:36:28,200
Speaker 1: the best numbers and have felt the best about their

753
00:36:28,239 --> 00:36:31,680
Speaker 1: early vote game. Finally, and this may end up being

754
00:36:32,080 --> 00:36:33,920
Speaker 1: one of the key things that we look back on

755
00:36:33,960 --> 00:36:38,200
Speaker 1: as we know, if my map comes to fruition, this

756
00:36:38,280 --> 00:36:40,239
Speaker 1: may be one of the key early indicators that we

757
00:36:40,320 --> 00:36:42,040
Speaker 1: look at in addition to the sales or poll.

758
00:36:42,320 --> 00:36:43,320
Speaker 6: Put this up on the screen.

759
00:36:43,320 --> 00:36:46,840
Speaker 1: There's a massive gender gap in terms of the early vote.

760
00:36:47,000 --> 00:36:50,319
Speaker 1: You've got seventeen point two million people who've voted early

761
00:36:50,360 --> 00:36:53,000
Speaker 1: in the seven swing states. You have one point six

762
00:36:53,120 --> 00:36:56,040
Speaker 1: million more men who have voted women who have voted

763
00:36:56,080 --> 00:37:00,560
Speaker 1: than men. The gender turnout gap is plus thirteen for

764
00:37:00,760 --> 00:37:03,920
Speaker 1: women in Pennsylvania, plus twelve in Georgia, plus eleven in

765
00:37:03,920 --> 00:37:06,719
Speaker 1: North Carolina, plus ten in Michigan, plus eight in Wisconsin,

766
00:37:06,719 --> 00:37:09,600
Speaker 1: plus five in Arizona. And the best result for Republicans

767
00:37:09,640 --> 00:37:13,040
Speaker 1: is in Nevada, where it is only plus one. The

768
00:37:13,080 --> 00:37:16,280
Speaker 1: whole Republican theory of the case is that the bros

769
00:37:16,320 --> 00:37:18,560
Speaker 1: Are going to turn out. They have really bet the

770
00:37:18,600 --> 00:37:21,120
Speaker 1: farm on the group of voters that, frankly, are the

771
00:37:21,200 --> 00:37:24,120
Speaker 1: least likely to turn out, which is young men. And

772
00:37:24,640 --> 00:37:28,240
Speaker 1: these early vote numbers with this large of a gender gap,

773
00:37:28,719 --> 00:37:31,400
Speaker 1: indicate that it is not the men that are turning

774
00:37:31,400 --> 00:37:33,319
Speaker 1: out for this election, that are excited to vote in

775
00:37:33,320 --> 00:37:36,120
Speaker 1: this election, It is women. And you know, the one

776
00:37:36,120 --> 00:37:38,960
Speaker 1: thing that has really been consistent throughout this election, no

777
00:37:39,000 --> 00:37:41,879
Speaker 1: matter what you look at, is the massive gender gap

778
00:37:41,920 --> 00:37:46,440
Speaker 1: with women disproportionately going to Kamala Harris and men disproportionally

779
00:37:46,600 --> 00:37:49,560
Speaker 1: going to Donald Trump. So when you look at which

780
00:37:49,600 --> 00:37:52,200
Speaker 1: gender is showing up at this point, I think that

781
00:37:52,360 --> 00:37:57,120
Speaker 1: has to be a positive indicator for Kamala Harris. And so,

782
00:37:57,400 --> 00:38:00,200
Speaker 1: you know, obviously, if I'm right, Saga very clear what

783
00:38:00,280 --> 00:38:03,160
Speaker 1: happened to her polsters missed the post role electorate. If

784
00:38:03,200 --> 00:38:05,960
Speaker 1: I'm wrong, I also think the reason is pretty simple,

785
00:38:06,000 --> 00:38:08,280
Speaker 1: which is just Biden's really unpopular.

786
00:38:09,040 --> 00:38:11,959
Speaker 6: The economy is, people aren't happy with the economy. Right track,

787
00:38:11,960 --> 00:38:13,360
Speaker 6: wrong track is a disaster.

788
00:38:13,840 --> 00:38:16,919
Speaker 1: Kamala Harris, you know, really obsessed over winning these Nikki

789
00:38:16,960 --> 00:38:20,000
Speaker 1: Hayley voters who potentially were you know, a fantasy and

790
00:38:20,280 --> 00:38:23,640
Speaker 1: you know, didn't show up, didn't end up moving towards

791
00:38:23,680 --> 00:38:25,960
Speaker 1: her whatsoever. They were locked in for Donald Trump. They're

792
00:38:26,000 --> 00:38:27,640
Speaker 1: just partisans at this point. And that's that.

793
00:38:28,200 --> 00:38:30,520
Speaker 6: And you know, I think that is also a very

794
00:38:30,520 --> 00:38:31,280
Speaker 6: possible scenario.

795
00:38:32,000 --> 00:38:34,800
Speaker 2: And if you want to hear my reaction to Crystal's monologue,

796
00:38:34,800 --> 00:38:37,880
Speaker 2: become a premium subscriber today at Breakingpoints dot com.

797
00:38:37,960 --> 00:38:40,560
Speaker 3: All right, so tomorrow we're going to have a morning show.

798
00:38:40,600 --> 00:38:42,279
Speaker 2: Guys, We're gonna be morning show is going to drop

799
00:38:42,320 --> 00:38:45,000
Speaker 2: for everybody in the morning, and then we will also

800
00:38:45,040 --> 00:38:49,200
Speaker 2: be live six thirty pm Eastern time here on the channel.

801
00:38:49,400 --> 00:38:50,919
Speaker 3: All four of us will be here at the desk.

802
00:38:51,040 --> 00:38:53,520
Speaker 2: We've got logan, we've got the software, and we will

803
00:38:53,520 --> 00:38:56,040
Speaker 2: start making calls. We'll have live calls and all that.

804
00:38:56,080 --> 00:38:58,839
Speaker 2: We'll have maps, graphics, et cetera. Everything is totally ready

805
00:38:58,880 --> 00:39:00,879
Speaker 2: to go and we will be with you all week

806
00:39:01,160 --> 00:39:03,040
Speaker 2: as long as we need to until this damn thing

807
00:39:03,120 --> 00:39:03,399
Speaker 2: is over.

808
00:39:03,880 --> 00:39:04,759
Speaker 3: So there you go.

809
00:39:04,840 --> 00:39:05,440
Speaker 6: Buggle up.

810
00:39:05,600 --> 00:39:08,359
Speaker 1: It's gonna be into one way or another. Whatever happens

811
00:39:08,400 --> 00:39:08,960
Speaker 1: gonna be vary.

812
00:39:08,960 --> 00:39:11,120
Speaker 3: It can't wait, can't wait to cover it. I love it.

813
00:39:11,360 --> 00:39:13,320
Speaker 3: I love learning little things. There's always surprises.

814
00:39:13,520 --> 00:39:15,520
Speaker 2: The Latino surprise is one of my favorite moments from

815
00:39:15,560 --> 00:39:18,319
Speaker 2: twenty twenty, just because I love the fact that you

816
00:39:18,360 --> 00:39:21,280
Speaker 2: cannot take anything for granted in this country. Iowa blue state,

817
00:39:21,400 --> 00:39:24,640
Speaker 2: red state now possibly up for grabs. Florida in my

818
00:39:24,680 --> 00:39:27,319
Speaker 2: lifetime went from swing state to red state.

819
00:39:27,360 --> 00:39:28,799
Speaker 3: Ohio went from blue to red.

820
00:39:29,360 --> 00:39:31,560
Speaker 2: I mean, there's been so many of these where people

821
00:39:31,600 --> 00:39:33,640
Speaker 2: are you know, you can't take people for granted. They

822
00:39:33,640 --> 00:39:36,439
Speaker 2: shift all the time, that people move different state. North

823
00:39:36,480 --> 00:39:38,319
Speaker 2: Carolina's up for grabs today. You told me that ten

824
00:39:38,360 --> 00:39:40,400
Speaker 2: years agog. What the hell are you talking about. You know,

825
00:39:40,440 --> 00:39:42,799
Speaker 2: it's like in this country people change their mind all

826
00:39:42,800 --> 00:39:44,560
Speaker 2: the time, and I think that's a great, great thing.

827
00:39:44,600 --> 00:39:47,480
Speaker 2: So we can all witness that in real time together,

828
00:39:47,560 --> 00:39:50,120
Speaker 2: and we'll see you all tomorrow.