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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 2: coverage that is possible. If you like what we're all about,

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Speaker 2: it just means the absolute world to have your support.

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Speaker 3: But enough with that, let's get to the show. Good morning, everybody,

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Speaker 3: Happy Monday. Have an amazing show for everybody today. What

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Speaker 3: do we have, Crystal.

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Speaker 1: Here we go one day until election day, so we

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Speaker 1: got a big electorally focused show for you. Logan's going

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Speaker 1: to be here talking about whether the state of Iowa. Yes,

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Speaker 1: that's right, Iowa is in play. Ann Selzer shocked the

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Speaker 1: world with her pole drop this weekend, so we'll get

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Speaker 1: into that. Campaigns are also out with their closing ads,

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Speaker 1: so we'll take a look at how they are making

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Speaker 1: their final case to voters. Also, Little Epstein, Little Epstein

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Speaker 1: info some audio tapes released where he talks about how

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Speaker 1: he was Donald Trump's best friend from tenure for ten years,

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Speaker 1: so we'll dig into the relevance of that.

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Speaker 4: Also asking the question if Trump October.

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Speaker 1: Surprised himself with that Medicine Square Garden rally. New York

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Speaker 1: Times is declaring the end of identity politics, but they

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Speaker 1: write about that, and of course Sager and I with

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Speaker 1: the big reveal of our maps making the case for

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Speaker 1: why we think the election is going the way that

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Speaker 1: we think it's going to go. So break down the

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Speaker 1: data reveal both of our maps. We have them going

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Speaker 1: in two different directions, so that should be interesting.

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Speaker 3: Yeah, it'll be fun.

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Speaker 2: We've got our whole thought process laid out, you know,

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Speaker 2: not to brag or anything, just called every state in

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Speaker 2: the electoral college last.

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Speaker 4: Yeah, you nailed the lot I got.

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Speaker 2: Look extremely lucky. I have much lower confidence in this

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Speaker 2: map this time around. But that's why it's fun, right,

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Speaker 2: That's why it's fun to do. As a reminder, thank

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Speaker 2: you to all of our premium subscribers, and if you're not,

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Speaker 2: you're going to want to sign up. At Breakingpoints dot com,

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Speaker 2: we've got exclusive major coverage that's going to be going

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Speaker 2: on from basically now until whenever the election is called.

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Speaker 2: So for context, we've got the show today that's going

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Speaker 2: to include our election previews, like our predictions tomorrow morning.

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Speaker 2: We'll have an update for everybody that will drop sometime. Yeah,

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Speaker 2: we'll we're recorded in the morning, they'll drop beforehand.

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Speaker 3: Yeah.

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Speaker 4: Then Ryan and Emily are going to join us today.

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Speaker 3: That's right.

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Speaker 2: Ryan and Emily will be there, and then all four

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Speaker 2: of us will be here at the desk, including with

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Speaker 2: logan and with Decision Desk HQ software. We're really excited

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Speaker 2: about that because it means we're gonna have full on,

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Speaker 2: you know, basically network style coverage. We have live returns,

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Speaker 2: we can throw to the maps. They're all to be dynamic.

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Speaker 2: We can make calls here at breaking points for when

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Speaker 2: specific races will happen. We'll have literally up to the

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Speaker 2: minute coverage, just like any of the other major networks,

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Speaker 2: so there's no need to go watch anything else. Can

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Speaker 2: we put that beautiful graphic please up on the screen.

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Speaker 2: That's right, Look at six thirty pm Eastern Standard time.

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Speaker 2: It's great to be back on standard time. I just

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Speaker 2: have to put that out there, six thirty pm Eastern time.

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Speaker 2: You can go ahead, you can set your clocks, you

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Speaker 2: can watch it on TV.

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Speaker 3: It'll be on live on YouTube.

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Speaker 2: We'll also be taking questions intermittently throughout the night from

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Speaker 2: our local stream, which again is for our premium subscriber,

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Speaker 2: So if you want to participate in that at Breakingpoints

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Speaker 2: dot Com, you can go ahead and sign up. But

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Speaker 2: all of this is just a second to potheosis of

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Speaker 2: our premium subscribers. Our remember the very first time that

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Speaker 2: we ever did any live election coverage on Rising this.

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Speaker 2: I guess we must have been twenty nineteen for some

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Speaker 2: of those coverage, and people put it on in bars,

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Speaker 2: people in their living room. The number one complaint was always, Hey,

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Speaker 2: we don't have any live like coverage. You don't have

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Speaker 2: a Kernaky style figure, you don't have data and all that,

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Speaker 2: because we had to use others. And so we've worked

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Speaker 2: very very hard to assemble like as big coverage as

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Speaker 2: possibly can. So we've got everything that we need over here.

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Speaker 2: You don't need to go anywhere else except right here for.

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Speaker 3: I have election coverage. Yeah, it's gonna be it's gonna

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Speaker 3: be fun. Yeah, fun. You can't be excited.

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Speaker 1: It's gotta know how election very great flues, right, although

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Speaker 1: even I have to say that, I mean, it's not

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Speaker 1: necessarily that we're really going to know how the story

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Speaker 1: concludes tomorrow.

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Speaker 2: Yeah, I mean, it could go on, right, and let's

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Speaker 2: the other thing. Let's not sell it like you know,

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Speaker 2: tomorrow night we may not know, we're not. We're going

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Speaker 2: to go late, you know, if we need to as

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Speaker 2: late as possible, and then the next morning and we'll

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Speaker 2: wake up and we'll do it all over again. So

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Speaker 2: it is election week, and every single day that this continues,

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Speaker 2: we will have chair on breaking points. Don't worry about

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Speaker 2: it until this thing is called. We're excited for it.

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Speaker 2: The whole team is ready. We're you know, we rested

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Speaker 2: over the weekend. I bought an entire eighteen pack of Celsius,

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Speaker 2: So I'm ready to roll.

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Speaker 3: You're ready, I'm ready to go.

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Speaker 4: Good deal. All right, let's go ahead and get to Logan.

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Speaker 2: Very excited to be joined now by Race to the

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Speaker 2: White House, Logan Phillips, our exclusive election Partner's great to

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Speaker 2: see you.

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Speaker 3: My friend.

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Speaker 5: Hey, great to see you as aways.

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Speaker 3: All right, so let's get into it. Yeah, sure, all

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Speaker 3: of us.

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Speaker 2: At exactly seven oh one pm on Saturday, for election

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Speaker 2: nerds out there, it was a big super Bowl. The

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Speaker 2: Iowa Seltzer poll dropped absolutely shocked the entire political world

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Speaker 2: and really made this thing much more to toss up

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Speaker 2: than I think others were prepared for us. Let's gohea

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Speaker 2: and put this up there on the screen and take

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Speaker 2: a look. So what they have inside of the Des

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Speaker 2: Moines Register, the famed you know, Iowa and Seltzer poll,

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Speaker 2: is that they have Kamala Harris up by three points

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Speaker 2: on Donald Trump in the state logan. Now, the reason

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Speaker 2: why this is such a shock is that this poll,

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Speaker 2: arguably previously was one of the main reasons why Joe

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Speaker 2: Biden dropped out of the race, had him down by

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Speaker 2: eighteen points against Donald Trump, which people were transposing those

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Speaker 2: results onto neighboring Michigan and Wisconsin and saying, if that's

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Speaker 2: the truth, then there's absolutely no way that Joe Biden

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Speaker 2: has a chance at winning any of these any blue

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Speaker 2: wall states. Now, Iowa, I mean what a democrat is

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Speaker 2: not one? I Once is twenty twelve, So the first

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Speaker 2: time in twelve years of the poll has a Democrat

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Speaker 2: leading there. Donald Trump went it by somewhat ten points

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Speaker 2: or so last time around. So give us your analysis,

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Speaker 2: not only in terms of you know, we can give

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Speaker 2: all the caveats if it's true, if it's an outlier,

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Speaker 2: and all that, but let's let's start with and let's

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Speaker 2: suppose it is true.

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Speaker 3: What does that tell us about what's gonna happen tomorrow?

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Speaker 5: Well, you know, I had just finished work for the

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Speaker 5: day on Saturday, and my intern called me and told

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Speaker 5: me the result, and I like had him check five times.

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Speaker 3: Yeah, yeah, I had. I was multiple times. I was like,

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Speaker 3: there's no way. Yeah, we checked like.

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Speaker 1: Eight different Twitter accounts before I believed it, because it

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Speaker 1: was like they must have at least flipped the results

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Speaker 1: because even if it was Trump only up by three,

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Speaker 1: just that ye'd be terrible cataclysmic.

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Speaker 5: For Yeah, because in the post blue check era, you

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Speaker 5: never know if like count as someone that is like

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Speaker 5: Dave washer Men's or something, it's just like slightly off.

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Speaker 5: But no, the poll was legit, and so if it

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Speaker 5: were anyone else, I'd be like, Okay, this had a

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Speaker 5: weird day. Good on them releasing it. But Selzer called

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Speaker 5: prison Obama winning in a landslide in the two thousand

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Speaker 5: and eight Iowa caucus when no one else saw it right.

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Speaker 5: Rick Santorum having a shocking win over Mitt Romney in

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Speaker 5: the coming like from two percent of five weeks ago.

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Speaker 5: In twenty twelve called Donald Trump's surprisingly strong win both

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Speaker 5: cycles in Iowa. So I don't think we can dismiss

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Speaker 5: it given that almost every time she breaks with convention,

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Speaker 5: she's right, this one is breaking so hard more than

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Speaker 5: any of the others with what we expect that I

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Speaker 5: don't think she's right that he's gonna win it. But

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Speaker 5: before I viewed it as a safe race, Mount model

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Speaker 5: had it a two percent chance for them. Yeah, hey,

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Speaker 5: maybe it was. Doesn't reflect well. I mean I should

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Speaker 5: get a little more room for incertainty there. Now I

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Speaker 5: have it up to around twenty or you know, twenty five.

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Speaker 5: Sure which.

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Speaker 4: Chance I want to when.

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Speaker 1: That's I mean to be clear, no one was looking

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Speaker 1: at Iowa as a battle guard, including the campaigns yea,

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Speaker 1: Like they're not campaigning there, they're not running ads there,

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Speaker 1: they're not doing any of that. And so what she

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Speaker 1: finds we can put this up on the screen is

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Speaker 1: she finds effectively huge movement among women. Kamala is wunning

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Speaker 1: independent women in this poll in Iowa by twenty eight points.

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Speaker 1: She's up by a two to one margin among senior women.

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Speaker 1: And so that's basically the story that she's telling is

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Speaker 1: that Roe being overturned was a political earthquake. It has

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Speaker 1: dramatically shifted the electorate, and other pollsters are not capturing

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Speaker 1: the movement among women, in particular towards Kamala Harris and

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Speaker 1: towards Democrats. How much credence do you give to that story?

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Speaker 1: Are there any other data points to support this, because

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Speaker 1: as we know, as we've been covering on the show,

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Speaker 1: almost every other poll is like tie, tie, tie, plus

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Speaker 1: one in this way, plus one that way, maybe plus

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Speaker 1: two if we're getting crazy. But it has been so

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Speaker 1: old stable and such a coin flip this entire time.

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Speaker 5: So I would say the odds are that it is

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Speaker 5: just going to be wrong by quite a bit. There's

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Speaker 5: a case though, that she's right, and if there's a

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Speaker 5: huge pulling miss against Harrs. These are some of the seeds.

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Speaker 5: There's another seed we saw last week. We were talking

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Speaker 5: about hurting last week. Yeah, it's silver. This really good

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Speaker 5: analysis over the weekend, at least I read it over

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Speaker 5: the weekend. So there's like less than a one in

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Speaker 5: a trillion chance that the posters aren't hurting.

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Speaker 2: I think it was nine point five trillion, ye five trillion,

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Speaker 2: chan wow, So I don't even know what the decimal

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Speaker 2: point on that would look.

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Speaker 5: Like, Yeah, my brain isn't big enough to be able

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Speaker 5: to calculate mentally, the difference.

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Speaker 3: Between getting struck by lightning like eight times is more likely. Yeah.

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Speaker 5: So the reason they're doing this is because no one

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Speaker 5: wants to be Quinnipiac being name checked by Donald Trump

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Speaker 5: after a bad cycle, blaming, you know, blaming them, and

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Speaker 5: that hurts their ability to get revenue. Hey, ABC, Washington

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Speaker 5: Post is pole partner. They've been with them for a

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Speaker 5: long time. Theyan one of the best. They had a

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Speaker 5: horrible cycle. They're not with them anymore, so it's easier

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Speaker 5: to stay in the middle. I'm not saying all posters

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Speaker 5: are doing this, but clearly a lot of them are

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Speaker 5: trying to play it safe. The second thought of this

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Speaker 5: is maybe they're not just staying.

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Speaker 6: In the middle.

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Speaker 5: Maybe they're in particular avoiding having out liars the fever Harris,

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Speaker 5: because that's a lot more dangerous. She's not gonna name

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Speaker 5: check pollsters in her speech accepting defeat right.

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Speaker 2: Well, Also, I think it's it wouldn't even be the

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Speaker 2: name checking, it's just that it's a pattern. It would

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Speaker 2: be three cycles in a row rxact and that would

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Speaker 2: mean that you have a systemic problem. And let's just

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Speaker 2: hammer home for people what the past results. Can we

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Speaker 2: put a four please up on the screen. So here

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Speaker 2: you have the final Seltzer fold result poll findings and

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Speaker 2: the results. On twenty twenty two centate she had R

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Speaker 2: plus twelve. The result was R plus twelve. Twenty twenty

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Speaker 2: President she had R plus seven. It was R plus eight.

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Speaker 2: Twenty twenty Senate she had R plus four. It was

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Speaker 2: R plus seven. Twenty eighteen gubernatorial she had this is

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Speaker 2: an interesting she had D plus two and it was

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Speaker 2: R plus three, So that was one of the bigger misses.

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Speaker 2: Twenty sixteen presidents she had R plus seven. It was

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Speaker 2: R plus nine. Twenty fourteen Senate she had R plus seven.

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Speaker 2: It was R plus eight. Twenty twelve President she had

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Speaker 2: D plus five. It was D plus six. So, as

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Speaker 2: he says, about as good as any polster as gets.

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Speaker 2: If we look back in the last twelve years, if

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Speaker 2: your biggest miss is a five point swing towards Republicans,

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Speaker 2: that's roughly two points outside the margin of error. So

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Speaker 2: if we were even think that, you know here three

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Speaker 2: points margin of error, so that would mean it's actually tied.

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Speaker 2: Donald Trump is up by two, Trump up only a

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Speaker 2: two in Iowa, a state that he won by what

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Speaker 2: eight points last time around. That's kind of a disaster, right,

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Speaker 2: So if that's if we take it seriously. The other

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Speaker 2: case is just a massive outlier or you have polling

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Speaker 2: response bias or any of that.

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Speaker 3: So can you run through.

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Speaker 5: Yeah, that's that's still by far the most likely when

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Speaker 5: something wildly goes against your assumptions, even it's a great Polster,

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Speaker 5: that's the probability. That being said, I literally have a

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Speaker 5: Salzer specific contingent in my pulling up a John for

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Speaker 5: anyone else because I found every single cycle and I

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Speaker 5: tested it, the more way that gave to Selzer's pull,

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Speaker 5: the better it was. And so eventually I kind of

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Speaker 5: had to bite the bullet and just do that.

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Speaker 2: So I think Silvera has the same thing. He says

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Speaker 2: higher rates Polster in his entire average.

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Speaker 5: So there you go. Yeah, yeah, and you know what,

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Speaker 5: we have seen similar things in Nebraska. Second, I mean

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Speaker 5: that wass expected Harris to be leaving. She's even like

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Speaker 5: ten points.

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Speaker 1: Yeah, I think what in Biden went in by a

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Speaker 1: last it's like college vote, right, including I believe the

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Speaker 1: New York Times poll of that district had her up

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Speaker 1: by twelve.

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Speaker 4: Yeah, so quite significant.

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Speaker 1: Okay, So let's say that even if Kamwa Hurst isn't

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Speaker 1: winning Iowa, but this is sort of directionally correct, right,

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Speaker 1: She's picking up movement that other pollsters are suppressing out

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Speaker 1: of whatever reason. How does that translate to the other

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Speaker 1: states in the region. Could it be because we actually

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Speaker 1: have some information here? I'm not sure what element it is,

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Speaker 1: but anyway, Iowa has a six week abortion pan that

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Speaker 1: was just instituted and it has been a central issue

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Speaker 1: in terms of the political conversation in the state. So

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Speaker 1: is it possible she is picking up some accurate movement

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Speaker 1: in the state of Iowa, but that is very specific

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Speaker 1: to that state and does not really translate to Michigan, Wisconsin, Pennsylvania.

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Speaker 4: Et cetera.

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Speaker 5: Yeah, I mean those states have had some ability to

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Speaker 5: moderate on abortion in the twenty twenty two midterms, set

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Speaker 5: good point. Iowa didn't have, and you know, I would

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Speaker 5: swung a little bit back. In twenty eighteen, Democrats won

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Speaker 5: three out of four House seats. They might have won

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Speaker 5: four if Steve King, the guy who called himself a

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Speaker 5: white nationalist, to lose his primary. Wow, and then it

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Speaker 5: lurns hard right in twenty and twenty twenty two. Right,

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Speaker 5: there's a possibility like an object in motion in politics,

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Speaker 5: isn't away state in motions of course?

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Speaker 2: Versus that's such an important thing for people to understand.

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Speaker 2: You know, it wasn't that people today, probably if you're young,

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Speaker 2: you're like, oh, I was a red state, Like, well,

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Speaker 2: wasn't that long ago?

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Speaker 3: You know, Obama won it.

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Speaker 2: You want to buy a decent amount the last two

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Speaker 2: times around, Let's go ahead and move on. I think

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Speaker 2: to Emerson, this was this is the counter right a five?

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Speaker 2: Can we put that please on the screen. So the

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Speaker 2: very same day that Seltzer comes out, Emerson also comes

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Speaker 2: out with its own pole. It's got Trump up by

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Speaker 2: ten here in Iowa. Now, the criticism I've seen is

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Speaker 2: that Emerson is more one of the hurting polsters, you

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Speaker 2: know that is out there. They may be using the

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Speaker 2: traditional like recall to vote. I think Chrystal's point is

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Speaker 2: really key. You know, one of the reasons why nobody's

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Speaker 2: been paying attention is that people thought it was read state.

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Speaker 3: Neither the campaign's been paying attention.

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Speaker 2: But you know, locally they did literally just have a

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Speaker 2: six week abortion man that went into effect, and of

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Speaker 2: course that's going to be felt deeply by the people there.

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Speaker 2: And actually in the congressional races, they have all been

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Speaker 2: running crazy ads on abortion in either direction for this

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Speaker 2: entire time, which could be itself obviously contribute to the

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Speaker 2: overall presidential result. But that's a confounding variable, I mean,

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Speaker 2: really is interesting because one of those where it's like, look,

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Speaker 2: somebody's gonna be right here, you know, and she's got

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Speaker 2: a famous reputation and all that, but if it turns

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Speaker 2: out to be catastrophically wrong, it will certainly it'll i

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Speaker 2: mean be her biggest miss ever.

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Speaker 5: I think, yeah, it will be, and I'll give her

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Speaker 5: respect for doing it. She's fearless.

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Speaker 3: Yeah, yeah, yes, she's got what it can.

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Speaker 2: You explain the methodology too, so as I understand, it's

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Speaker 2: not weighted per se. It's more like random digit dialing that.

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Speaker 5: Yeah, she's calling voter rolls, not just random people, so

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Speaker 5: you can't go into that. Yeah, she's trying to get

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Speaker 5: a sense of which different groups are going to vote

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Speaker 5: by just talking to everyone and then getting represented sample

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Speaker 5: the elector, which has worked for her. It's a little

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Speaker 5: old school, but you know, the reality is you don't

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Speaker 5: know which groups are gonna vote more. You use last

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Speaker 5: cycles as a basic principle, but it's part of the

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Speaker 5: reason why you're going to underrate people, Right.

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Speaker 1: So let's do the opposite, so you know, if she's wrong, obviously,

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Speaker 1: this is devastating for her reputation. Everybody else is proved right,

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Speaker 1: including the posters who were just like, let's kind of

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Speaker 1: cluge it and make sure it's not.

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Speaker 4: Too far off of the twenty twenty yearsult.

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Speaker 1: What if she's right, then what does that do to

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Speaker 1: the entire rest of the polling industry If she's correct

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Speaker 1: and we're in the type of landslide territory where Kamala

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Speaker 1: Harris is winning the state of Iowa or even competing

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Speaker 1: contesting the state of Ioway.

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Speaker 5: Yeah, it would definitely free upholsters from hurting next time, I.

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Speaker 1: Said a lot of posters a lot of time in general, Yeah, I.

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Speaker 5: Think everyone's ratings, including myself, for polsters they are forecasting,

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Speaker 5: would emphasize to a greater degree those that don't hurt

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Speaker 5: that would get more positive coverage.

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Speaker 2: Yeah.

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Speaker 5: Now it's kind of laughing at the ones that do that. Right,

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Speaker 5: start to switch ever so slightly now, So I think

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Speaker 5: it would change industry in that regard. Would it decrease

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Speaker 5: trust to a degree? Yeah? I mean we're also you know,

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Speaker 5: we're in a cynical era. I don't if it's a social

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Speaker 5: media thing or what goes viral. But when you mess

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Speaker 5: up everyone sitting more than you get it right.

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Speaker 3: Very true, It's absolutely true.

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Speaker 5: Yeah, that's not fun to talk about which polster was

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Speaker 5: the best.

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Speaker 2: Correct and also the people who are the best in

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Speaker 2: twenty twenty were wrong in twenty sixteen. So it's one

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Speaker 2: of those where it's you can't really use just past

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Speaker 2: results as a perfect predictor.

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Speaker 1: It will be like literally the only polster that anyone

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Speaker 1: listens to.

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Speaker 7: Yeah, if she ends up being correct here, Yeah, that's

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Speaker 7: a good point.

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Speaker 2: Let's go and put New York Times then up on

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Speaker 2: the screen because this is a little bit of a

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Speaker 2: different story and actually a very confounding and interesting one.

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Speaker 2: So the New York Times CNA, this is a poll which,

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Speaker 2: let's give them credit, they haven't heard it in the past, right,

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Speaker 2: and they don't use the weighted sampling, but they've got

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Speaker 2: very very different results here. So in Nevada they have

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Speaker 2: Harris up by two points, North Carolina they have Harris

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Speaker 2: up by two, sorry three points, can't do perfect math here.

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Speaker 2: Wisconsin they have her up by two, Georgia they have

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Speaker 2: her up by one, but in Pennsylvania they have it tied.

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Speaker 2: In Michigan they have it tied as well. And then

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Speaker 2: in Arizona they have Trump up by four. So they

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Speaker 2: actually have Harris in Nevada, in North Carolina and was

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Speaker 2: constant in Georgia and but then tied in Pennsylvania and Michigan.

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Speaker 2: So a tightening there in the blue Wall states, whereas

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Speaker 2: a sunbelt kind of all over the map with an

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Speaker 2: Arizona leading for Trump, but Nevada they're leading for Harris.

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Speaker 2: What can you What do you make of these results

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Speaker 2: and some of the cross tabs as to their explanation

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Speaker 2: as to how they got there.

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Speaker 5: Yeah, I sell of them was straightforward path for Harris

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Speaker 5: is from Pennsylvania. But North Carolina's probably the best back

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Speaker 5: in my buddy.

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Speaker 3: Yeah, it's a number two tipping state I think in

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Speaker 3: the silver analysis.

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Speaker 5: Yeah, and I think for me it's two or three too.

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Speaker 5: So I think that we you know, I've said this before,

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Speaker 5: hay to be broken record. We get too locked into

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Speaker 5: like what the cleanest path is there's so many variations

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Speaker 5: I could easily absolutely yeah, you're right, which I didn't

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Speaker 5: know this before today, but somehow I just found you

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Speaker 5: got every single.

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Speaker 3: Right, totally totally lucky.

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Speaker 5: Like I think you and Larious about are the only

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Speaker 5: two people I've heard of that got both Florida and

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Speaker 5: Georgia right. Oh really, And I think it's a good

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Speaker 5: example for the rest of US mortals that states consume

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Speaker 5: in the other direction we expect, right, because you don't

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Speaker 5: expect Georgia to be to write a Florida going into

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Speaker 5: that cycle. So Carolina could easily end up there. I mean,

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Speaker 5: there's a lot of low kind of like you're talking

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Speaker 5: about a Viowa. North Carolina is being governed right now

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Speaker 5: well by the super majority in the state house. Is

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Speaker 5: if they're a very very deep red state, when it's

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Speaker 5: a swing state, that can easily turn off voters, especially

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Speaker 5: in abortion and yeah.

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Speaker 2: In migration, very liberal. That's a difference with Arizona. I'll

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Speaker 2: talk about this in my prediction, but that's a key

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Speaker 2: part of my prediction.

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Speaker 1: Let's put a seven up on the screen here. This

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Speaker 1: is the some of the abortion stuff that I referred

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Speaker 1: to before So you've got a number of states that

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Speaker 1: have abortion related ballot initiatives that will be you know,

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Speaker 1: on the ballot tomorrow. Nevada and Arizona are two of them,

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Speaker 1: Florida another one.

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Speaker 4: How much if we look.

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Speaker 1: At twenty twenty two, because that's the only post row

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Speaker 1: cycle that we really have to look at, how much

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Speaker 1: did it matter the state specific abortion politics? So you know,

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Speaker 1: do you think, for example, that in Nevada, in Arizona,

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Speaker 1: because they have abortion ballot initiatives that voters are also

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Speaker 1: going to be voting on, that that could shift vote

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Speaker 1: be impactful in terms of the ultimate.

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Speaker 5: Outcome if it we're twenty twenty six, Oh my gosh, yes,

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Speaker 5: because it's a presidential your interest is already higher. It's

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Speaker 5: certainly help but for Democrats. But it's not nearly as

436
00:18:11,480 --> 00:18:12,919
Speaker 5: big of a help as it would be because this

437
00:18:12,960 --> 00:18:15,760
Speaker 5: is the biggest game in town. It already gets people engaged, Right.

438
00:18:15,880 --> 00:18:16,480
Speaker 3: That makes sense.

439
00:18:16,640 --> 00:18:18,879
Speaker 2: Can we put Iowa early vote up on the screen.

440
00:18:19,000 --> 00:18:22,000
Speaker 2: This was a confounder to some of the Steltzer results.

441
00:18:22,160 --> 00:18:23,840
Speaker 2: Uist tic A look, this tell me what you think.

442
00:18:23,880 --> 00:18:26,600
Speaker 2: So in terms of the Iowa early vote, what they

443
00:18:26,640 --> 00:18:30,040
Speaker 2: see is, you know, pretty decent Republican share of early

444
00:18:30,119 --> 00:18:32,600
Speaker 2: vote there highest that it's been, you know, in terms

445
00:18:32,640 --> 00:18:34,520
Speaker 2: of where it is now. Obviously that no party vote

446
00:18:34,560 --> 00:18:37,520
Speaker 2: is still some twenty one percent. Obviously, if Seltzer is correct,

447
00:18:37,560 --> 00:18:39,280
Speaker 2: then that would mean that almost all of that is

448
00:18:39,320 --> 00:18:43,280
Speaker 2: swinging towards the Democrats, which it actually would back up some.

449
00:18:43,240 --> 00:18:43,920
Speaker 3: Of her results.

450
00:18:43,920 --> 00:18:45,840
Speaker 2: But taking a look at the early vote, this is

451
00:18:45,840 --> 00:18:47,920
Speaker 2: what I've saw a lot of Republicans and their criticisms say,

452
00:18:48,000 --> 00:18:49,480
Speaker 2: is like, look, we just don't see any of this

453
00:18:49,560 --> 00:18:52,520
Speaker 2: right now. If anything, we're seeing like Republican enthusiasm coming

454
00:18:52,560 --> 00:18:55,520
Speaker 2: out in terms of their share of the early vote electorate.

455
00:18:55,680 --> 00:18:56,840
Speaker 3: I guess part of that could.

456
00:18:56,720 --> 00:18:58,960
Speaker 2: Be is they're just more use to voting early now

457
00:19:00,080 --> 00:19:01,760
Speaker 2: time around, so you shouldn't read too much. And then,

458
00:19:01,760 --> 00:19:04,320
Speaker 2: of course the Democratic response is yes, but take a

459
00:19:04,320 --> 00:19:06,160
Speaker 2: look at that indie share, and the indie share could

460
00:19:06,160 --> 00:19:07,679
Speaker 2: be overwhelmingly Democratic.

461
00:19:07,760 --> 00:19:09,439
Speaker 5: Yeah, And throwing to the fact that I always been

462
00:19:09,520 --> 00:19:12,439
Speaker 5: zipping right at a fast pace, it was twelve points

463
00:19:12,440 --> 00:19:14,600
Speaker 5: to the right of the popular vote, I believe last cycle,

464
00:19:14,600 --> 00:19:16,680
Speaker 5: if voted dark plus eight, I believe Maybia was a

465
00:19:16,720 --> 00:19:19,280
Speaker 5: little more in that. So for to then all of

466
00:19:19,280 --> 00:19:20,840
Speaker 5: a sudden move. I mean some of the polling and

467
00:19:20,880 --> 00:19:23,160
Speaker 5: the popular vote is showing it tied plus one plus two.

468
00:19:23,160 --> 00:19:26,560
Speaker 5: Hats so that would mean it zipped thirteen the other way.

469
00:19:26,760 --> 00:19:29,840
Speaker 5: Yeah right, unless well, if she's winning Iowa buy three,

470
00:19:29,960 --> 00:19:32,000
Speaker 5: the popular vote at that point is highly likely to

471
00:19:32,119 --> 00:19:32,960
Speaker 5: be like t plus two.

472
00:19:33,160 --> 00:19:34,560
Speaker 3: Yeah, correct, good point.

473
00:19:34,640 --> 00:19:36,639
Speaker 1: Yeah boy, it would be a wild night if that

474
00:19:36,760 --> 00:19:39,480
Speaker 1: ends up being even remotely accurate. It's going to be

475
00:19:39,840 --> 00:19:41,960
Speaker 1: very surprising, I think for all of us if we

476
00:19:41,960 --> 00:19:44,520
Speaker 1: were to see that happen. But you know, take it

477
00:19:44,560 --> 00:19:47,399
Speaker 1: with all the other polling results, and I don't know,

478
00:19:47,720 --> 00:19:51,879
Speaker 1: make of it what you will. Let's go ahead and

479
00:19:51,880 --> 00:19:55,480
Speaker 1: take a look at the Senate and how we're looking there.

480
00:19:55,480 --> 00:19:57,840
Speaker 1: We can put your projection up on the screen. Guys,

481
00:19:57,840 --> 00:19:59,960
Speaker 1: this is a ten. So just take us through the

482
00:20:00,080 --> 00:20:04,320
Speaker 1: US a little bit, how these various races are breaking,

483
00:20:04,800 --> 00:20:08,360
Speaker 1: and what the most likely outcome is Wednesday morning when

484
00:20:08,440 --> 00:20:09,360
Speaker 1: things start to shake down.

485
00:20:09,960 --> 00:20:13,440
Speaker 5: Yeah. So this is where I have the polling average, right,

486
00:20:13,520 --> 00:20:16,919
Speaker 5: And so I think that the pathway for Dams is

487
00:20:16,960 --> 00:20:19,600
Speaker 5: just for one thing to go wrong for Republicans and

488
00:20:19,640 --> 00:20:21,200
Speaker 5: for them to hold on to Ohio where they have

489
00:20:21,240 --> 00:20:23,600
Speaker 5: a small lead. They got four states where that could

490
00:20:23,600 --> 00:20:28,320
Speaker 5: be an option to Braska, Texas, Florida, in Montana. All

491
00:20:28,359 --> 00:20:31,080
Speaker 5: of those except Montana have been moving a little more

492
00:20:31,080 --> 00:20:33,040
Speaker 5: to the right lay in the close, which is kind

493
00:20:33,040 --> 00:20:35,359
Speaker 5: of what we expected. There's still a pathway for it

494
00:20:35,400 --> 00:20:38,120
Speaker 5: to happen, but you know, they're underdogs. Two and three

495
00:20:38,119 --> 00:20:40,400
Speaker 5: shots you hold onto the Senate maybe a touch more.

496
00:20:40,520 --> 00:20:41,640
Speaker 4: Yeah, and I think we have that.

497
00:20:41,680 --> 00:20:44,320
Speaker 1: We can show the projection the next element at age

498
00:20:44,760 --> 00:20:48,440
Speaker 1: eleven that shows the chances of winning a Senate majority.

499
00:20:48,480 --> 00:20:51,879
Speaker 1: So you've got Democrats at roughly a third of you know,

500
00:20:51,960 --> 00:20:55,480
Speaker 1: thirty three percent and Republicans roughly sixty six percent. So

501
00:20:55,560 --> 00:20:57,840
Speaker 1: Democrats was like a one in three shot at keeping

502
00:20:57,840 --> 00:21:00,199
Speaker 1: the Senate majority. So is it your assessment at this

503
00:21:00,240 --> 00:21:02,400
Speaker 1: point their best shot is Montana.

504
00:21:04,080 --> 00:21:07,000
Speaker 5: Yeah, I still think it is. I mean, testers has

505
00:21:07,040 --> 00:21:10,920
Speaker 5: a lot of fundamental strengths. Last selection needed a great job.

506
00:21:10,960 --> 00:21:13,480
Speaker 5: He has a huge fundraising advantage, which is signing candidate

507
00:21:13,520 --> 00:21:16,719
Speaker 5: qualities running against the first time candidate. She has a strength,

508
00:21:17,000 --> 00:21:18,560
Speaker 5: has some weaknesses with the whole gun.

509
00:21:18,760 --> 00:21:19,639
Speaker 3: Yeah, we'll talk about that.

510
00:21:19,840 --> 00:21:22,840
Speaker 2: Yeah, before we get to that, I'm just I'm looking.

511
00:21:23,040 --> 00:21:24,880
Speaker 3: I'm looking at the analysis.

512
00:21:24,880 --> 00:21:29,040
Speaker 2: So for the Democrats to so like in this, you know, overperform,

513
00:21:29,920 --> 00:21:32,480
Speaker 2: Like where would the pickups cause it would be Montana

514
00:21:32,600 --> 00:21:34,200
Speaker 2: and then what else for them.

515
00:21:34,080 --> 00:21:34,960
Speaker 3: To keep a majority?

516
00:21:35,000 --> 00:21:37,120
Speaker 5: Oh they just need Montana, they just Needo wow.

517
00:21:37,200 --> 00:21:38,400
Speaker 3: Oh oh if they hold.

518
00:21:38,160 --> 00:21:40,199
Speaker 5: On to them, which they probably Well when I said probably,

519
00:21:40,240 --> 00:21:41,920
Speaker 5: like I think it might be the closest Senate race,

520
00:21:42,000 --> 00:21:43,720
Speaker 5: right yeah, and by the time they're picking up another

521
00:21:43,720 --> 00:21:46,440
Speaker 5: that they probably have Ohio huh. And he's led most

522
00:21:46,480 --> 00:21:47,000
Speaker 5: of the polling.

523
00:21:47,119 --> 00:21:47,800
Speaker 3: Yeah, he's led.

524
00:21:47,840 --> 00:21:50,840
Speaker 2: You know in most ways. They don't seem all that confident.

525
00:21:50,920 --> 00:21:52,800
Speaker 2: The Marina I mean, even the internal ones that they've

526
00:21:52,800 --> 00:21:55,200
Speaker 2: released show him running like well below Trump. I mean,

527
00:21:55,320 --> 00:21:59,159
Speaker 2: Sherey Brown's a strong candidate. He's an actual credibility on

528
00:21:59,359 --> 00:22:01,760
Speaker 2: union It's deal issues. He's been there for what like

529
00:22:01,800 --> 00:22:04,080
Speaker 2: twelve to two cycles now for We're literally rid a

530
00:22:04,080 --> 00:22:06,480
Speaker 2: book he supported a lot of He's running ads me

531
00:22:06,560 --> 00:22:08,160
Speaker 2: like I support President Trump's tariffs.

532
00:22:08,119 --> 00:22:09,919
Speaker 3: You know, it's like everything you bossibly.

533
00:22:09,600 --> 00:22:12,800
Speaker 5: Don Yeah, exactly, That's what they used to do. That's

534
00:22:12,800 --> 00:22:13,520
Speaker 5: what ends used to do.

535
00:22:13,600 --> 00:22:15,679
Speaker 2: That's my question then about Osborne, So how does he

536
00:22:15,800 --> 00:22:18,200
Speaker 2: factor into all this? How's the polling been looking at him?

537
00:22:18,200 --> 00:22:19,240
Speaker 2: Because we've been keeping our eye on.

538
00:22:19,480 --> 00:22:22,720
Speaker 5: Yeah, it's gotten a little more friendly to Fisher, which

539
00:22:22,840 --> 00:22:25,480
Speaker 5: makes some sense, but it's still Razor tiet. Yeah, he's

540
00:22:25,880 --> 00:22:28,240
Speaker 5: out running Fisher to the right and his ad. It's

541
00:22:28,320 --> 00:22:30,399
Speaker 5: kind of interesting to see saying I stand with Trump

542
00:22:30,400 --> 00:22:32,520
Speaker 5: and all these things, and Fisher's been bought out by

543
00:22:32,840 --> 00:22:36,520
Speaker 5: big donors, right, and you know it. I think it's

544
00:22:36,520 --> 00:22:38,960
Speaker 5: working even if he doesn't win, because it's incredible how

545
00:22:38,960 --> 00:22:39,520
Speaker 5: close it is.

546
00:22:39,760 --> 00:22:42,159
Speaker 1: Yeah, truly, truly, it definitely could be a model for

547
00:22:42,240 --> 00:22:43,040
Speaker 1: future cycles.

548
00:22:43,119 --> 00:22:44,840
Speaker 5: Oh, I think it will be as was Evan McMullen

549
00:22:44,880 --> 00:22:46,760
Speaker 5: for him, I mean, he's running a more monerorate campaign.

550
00:22:46,800 --> 00:22:48,160
Speaker 5: That was the guy who ran in Utah. I only

551
00:22:48,160 --> 00:22:51,879
Speaker 5: lost to Lee by ten points plus thirty our state. Yeah.

552
00:22:51,920 --> 00:22:53,720
Speaker 5: So so one other thing on Ohio. I think part

553
00:22:53,720 --> 00:22:56,520
Speaker 5: of the problem for them is, you know, people don't

554
00:22:56,560 --> 00:22:59,680
Speaker 5: trust politicians just about the only career profession that people

555
00:22:59,680 --> 00:23:02,600
Speaker 5: trust least as he used car salesman and so, you know,

556
00:23:02,640 --> 00:23:05,879
Speaker 5: forget regardless of marinos individual characteristics like that, that's a

557
00:23:05,880 --> 00:23:06,960
Speaker 5: hard thing to get over from me.

558
00:23:07,400 --> 00:23:07,880
Speaker 3: That's his.

559
00:23:10,440 --> 00:23:13,360
Speaker 2: That's absolutely true. They literally have they have a national reputation.

560
00:23:13,400 --> 00:23:16,480
Speaker 2: I think for a reason. Well, you teased up Montana,

561
00:23:16,600 --> 00:23:18,639
Speaker 2: so I guess we have to get to it. There's

562
00:23:18,720 --> 00:23:24,719
Speaker 2: this shocking clip of Megan Kelly interviewing Tim she about

563
00:23:24,760 --> 00:23:28,040
Speaker 2: an alleged gunshot wound incident where he claims what did

564
00:23:28,040 --> 00:23:29,200
Speaker 2: he say the gate?

565
00:23:29,280 --> 00:23:32,879
Speaker 1: Okay, so here's what he claims. He claims he was

566
00:23:32,880 --> 00:23:37,760
Speaker 1: wounded during his military service. And now he's saying that

567
00:23:37,800 --> 00:23:40,800
Speaker 1: when he went to a national park, he fell while

568
00:23:40,840 --> 00:23:43,000
Speaker 1: he was hiking and the bullet fell.

569
00:23:42,840 --> 00:23:43,520
Speaker 4: Out of his arm.

570
00:23:43,920 --> 00:23:46,680
Speaker 1: But then he lied to the park rangers and said

571
00:23:46,680 --> 00:23:49,560
Speaker 1: that he accidentally shot himself while he was at the park,

572
00:23:50,000 --> 00:23:53,399
Speaker 1: and of course the story has shifted, and basically it

573
00:23:53,400 --> 00:23:56,080
Speaker 1: looks like he lied about being wounded during his service

574
00:23:56,080 --> 00:23:58,480
Speaker 1: and how he actually was wounded by was by shooting

575
00:23:58,600 --> 00:24:02,120
Speaker 1: himself international park, which also, by the way, that would

576
00:24:02,119 --> 00:24:04,040
Speaker 1: be a leader. You're not allowed to discharge firearm in

577
00:24:04,280 --> 00:24:07,680
Speaker 1: national park, so there's that as well. So anyway, Megan Kelly,

578
00:24:07,680 --> 00:24:09,920
Speaker 1: who's obviously on the right Trump supporter once Republic gets

579
00:24:09,920 --> 00:24:11,639
Speaker 1: to win, et cetera, was trying to get to the

580
00:24:11,680 --> 00:24:14,399
Speaker 1: bottom of what the hell his story even actually was,

581
00:24:14,560 --> 00:24:17,359
Speaker 1: and even now he still can't really explain it. In

582
00:24:17,400 --> 00:24:19,320
Speaker 1: a way that makes any kind of sense. Let's take

583
00:24:19,320 --> 00:24:20,120
Speaker 1: a listen to how that went.

584
00:24:20,320 --> 00:24:23,000
Speaker 7: They're saying that you were in a park, Glacier Park,

585
00:24:23,400 --> 00:24:26,199
Speaker 7: that you dropped your weapon, that it went off inadvertently

586
00:24:26,240 --> 00:24:28,040
Speaker 7: and it shot you in the arm, and that there's

587
00:24:28,080 --> 00:24:30,320
Speaker 7: a park ranger saying she spoke to you about that.

588
00:24:30,359 --> 00:24:32,239
Speaker 7: It looks like you spoke to the Washington Post and

589
00:24:32,280 --> 00:24:35,240
Speaker 7: you said that you lied when you told the park

590
00:24:35,320 --> 00:24:37,600
Speaker 7: ranger about this.

591
00:24:37,880 --> 00:24:40,199
Speaker 3: So which is it like?

592
00:24:41,080 --> 00:24:44,160
Speaker 7: Did you shoot yourself in the arm inadvertently in Glacier Park.

593
00:24:46,160 --> 00:24:49,400
Speaker 8: No, We've discussed this at length, repeatedly with every media

594
00:24:49,440 --> 00:24:51,160
Speaker 8: outlet for the last year.

595
00:24:51,640 --> 00:24:52,640
Speaker 5: It's been beat to death.

596
00:24:53,000 --> 00:24:57,280
Speaker 8: The point was, at the time I was injured and

597
00:24:57,440 --> 00:24:59,720
Speaker 8: went to the hospital, they required a police report because

598
00:24:59,720 --> 00:25:03,399
Speaker 8: any unshot room requires a police support of any kind. Uh,

599
00:25:03,440 --> 00:25:04,679
Speaker 8: And they said, we have to file this, we have

600
00:25:04,720 --> 00:25:06,800
Speaker 8: to report this law enforcement, but it wouldn't work.

601
00:25:08,160 --> 00:25:08,639
Speaker 5: It happened.

602
00:25:09,160 --> 00:25:11,480
Speaker 3: Did you have a wound in the park.

603
00:25:12,480 --> 00:25:14,399
Speaker 8: Yes, I fell and injured my arm when when we

604
00:25:14,400 --> 00:25:16,919
Speaker 8: were hiking. So that's why I went because you know,

605
00:25:16,920 --> 00:25:18,520
Speaker 8: I could feel the bullet at dis large when I

606
00:25:18,520 --> 00:25:20,119
Speaker 8: when I when I fell and fell on the arm,

607
00:25:20,119 --> 00:25:22,639
Speaker 8: you could feel the bulletet dislodged, and then went to

608
00:25:22,680 --> 00:25:25,520
Speaker 8: the er to say, hey, you know, look, you know

609
00:25:25,600 --> 00:25:27,680
Speaker 8: I've got internal blane going on here, I've injured my arm.

610
00:25:27,680 --> 00:25:28,880
Speaker 8: Can you take a look at this, make sure there's

611
00:25:28,880 --> 00:25:31,680
Speaker 8: nothing serious going on here? And then medical.

612
00:25:31,400 --> 00:25:33,840
Speaker 7: Records for the where the er can say we did

613
00:25:33,880 --> 00:25:37,480
Speaker 7: not treat a gunshot wound, Well.

614
00:25:37,520 --> 00:25:37,880
Speaker 5: There isn't.

615
00:25:37,920 --> 00:25:39,360
Speaker 8: I mean, that's the point. You go in, you check

616
00:25:39,359 --> 00:25:41,480
Speaker 8: on it, and you leave. There's not an extensive medical

617
00:25:41,520 --> 00:25:42,560
Speaker 8: record for any of this stuff.

618
00:25:42,680 --> 00:25:45,240
Speaker 3: So uh yeah, I don't really know anything about what.

619
00:25:45,200 --> 00:25:48,240
Speaker 5: He just said right there, figuring out.

620
00:25:49,359 --> 00:25:51,240
Speaker 3: It fell out of his arm that didn't happen.

621
00:25:51,280 --> 00:25:53,359
Speaker 2: What do you say previously is like navy steal dudes,

622
00:25:53,359 --> 00:25:55,920
Speaker 2: he was involved and whenever, why would you lie.

623
00:25:55,800 --> 00:25:59,280
Speaker 1: To the claim that you shot yourself when actually your

624
00:25:59,320 --> 00:26:01,760
Speaker 1: bullet just out of your arm while you're hiking. Oh

625
00:26:01,840 --> 00:26:03,560
Speaker 1: and conveniently there's no medical records.

626
00:26:03,600 --> 00:26:05,560
Speaker 3: It's a magic bully. That's why I was okay.

627
00:26:05,880 --> 00:26:07,680
Speaker 2: It was like literally like the magic bullet that fell

628
00:26:07,720 --> 00:26:11,000
Speaker 2: out of Governor Connolly with the Kennedy assassin. Anyways, the

629
00:26:11,040 --> 00:26:13,000
Speaker 2: point is that's getting a lot of attention in Montana.

630
00:26:13,040 --> 00:26:13,680
Speaker 3: A lot of gun.

631
00:26:13,520 --> 00:26:16,480
Speaker 2: Owners who are there, and I think he's been hit

632
00:26:16,720 --> 00:26:18,639
Speaker 2: with that. So you think that there's a possibility of

633
00:26:18,640 --> 00:26:19,880
Speaker 2: an upset, there's.

634
00:26:19,680 --> 00:26:22,119
Speaker 5: A possibility for sure that Tester ones he's done it before.

635
00:26:22,119 --> 00:26:26,400
Speaker 5: He's out performing poles before. Yeah, I mean, Tester is

636
00:26:26,760 --> 00:26:29,840
Speaker 5: he crafts quite a persona you know, has three fingers

637
00:26:29,880 --> 00:26:32,160
Speaker 5: lost too on the farm. He comes back literally every

638
00:26:32,200 --> 00:26:36,520
Speaker 5: weekend from the Senate to farm. You know, he's he's

639
00:26:36,720 --> 00:26:39,199
Speaker 5: as committed to his ranch as any Montana rancher.

640
00:26:39,440 --> 00:26:39,680
Speaker 4: Yeah.

641
00:26:39,720 --> 00:26:43,040
Speaker 5: He's also a state that used to elect fifty percent

642
00:26:43,160 --> 00:26:46,000
Speaker 5: of its statewide officials were Democrats for like the first

643
00:26:46,040 --> 00:26:49,000
Speaker 5: twelve or fourteen years of the center of this mollennium,

644
00:26:50,080 --> 00:26:52,600
Speaker 5: and that solely ended one by one, even as it

645
00:26:52,640 --> 00:26:54,240
Speaker 5: was voting for Republicans for a while. But that's the

646
00:26:54,320 --> 00:26:56,720
Speaker 5: ticket voting is reducing, so it's getting tough for every year.

647
00:26:56,800 --> 00:26:58,080
Speaker 3: Yeah, I mean that's I mean, I guess.

648
00:26:58,400 --> 00:27:00,199
Speaker 2: Actually my final thing here on the Senate part is,

649
00:27:00,200 --> 00:27:01,960
Speaker 2: can you give you people a sense of how crazy

650
00:27:02,000 --> 00:27:04,200
Speaker 2: Senate races can be off? Like, what was it main

651
00:27:04,320 --> 00:27:07,200
Speaker 2: last times they had her down by I think twelve

652
00:27:07,480 --> 00:27:10,040
Speaker 2: I actually want to say twelve points for Susan Collins,

653
00:27:10,080 --> 00:27:10,719
Speaker 2: but it might have been eight.

654
00:27:10,760 --> 00:27:11,359
Speaker 3: It was something like that.

655
00:27:11,480 --> 00:27:13,479
Speaker 5: She won by eight and she was down by two

656
00:27:13,600 --> 00:27:14,879
Speaker 5: or three, so the margin might have been.

657
00:27:14,880 --> 00:27:17,920
Speaker 2: The margin's crazy, right, But then they had what Jamie Harrison,

658
00:27:18,000 --> 00:27:19,960
Speaker 2: they had it tied with Lindsay Graham.

659
00:27:19,960 --> 00:27:21,920
Speaker 3: He won by like seventeen or something.

660
00:27:22,040 --> 00:27:24,000
Speaker 5: That's that's the height of bad pulling for the Senate

661
00:27:24,040 --> 00:27:26,840
Speaker 5: was twenty. It was even worse than the presidential pulling.

662
00:27:26,840 --> 00:27:29,880
Speaker 5: You didn't get as much attention. So it's normally as

663
00:27:29,920 --> 00:27:31,479
Speaker 5: bad is that, but it can be. Yeah, I mean,

664
00:27:31,520 --> 00:27:34,680
Speaker 5: Senate poll misses are bigger than presidential on average.

665
00:27:34,240 --> 00:27:35,400
Speaker 3: And people don't spend a lot of money.

666
00:27:35,480 --> 00:27:38,040
Speaker 2: National media outlets are not spending money pulling Montana all

667
00:27:38,080 --> 00:27:39,639
Speaker 2: the time. They're not spending a lot of money pulling

668
00:27:39,840 --> 00:27:42,480
Speaker 2: Ohio as well. So I guess the point of preparing

669
00:27:42,480 --> 00:27:44,920
Speaker 2: people is upsetts can happen in those places in particular,

670
00:27:45,200 --> 00:27:47,040
Speaker 2: Like we just showed everyone with Iowa, no one's poll

671
00:27:47,040 --> 00:27:48,320
Speaker 2: in Iowa except pants Oltzer.

672
00:27:48,640 --> 00:27:50,200
Speaker 3: So you know, we don't have a lot of data.

673
00:27:50,240 --> 00:27:52,679
Speaker 2: It could be, you know, certainly possible, but it just

674
00:27:52,760 --> 00:27:56,040
Speaker 2: shows that big surprises like that can come from places

675
00:27:56,200 --> 00:27:57,960
Speaker 2: that people are not paying all that much attention to

676
00:27:58,000 --> 00:28:01,240
Speaker 2: I remember Texas last time, that big Latino surge. Nobody

677
00:28:01,280 --> 00:28:03,560
Speaker 2: picked it up period in any of the national polling

678
00:28:03,560 --> 00:28:05,760
Speaker 2: because there had no reason to be poll in South Texas.

679
00:28:05,840 --> 00:28:07,080
Speaker 2: And then the polls come in and it was the

680
00:28:07,119 --> 00:28:09,040
Speaker 2: PoTA County or whatever goes in for Trump. You're like,

681
00:28:09,080 --> 00:28:11,159
Speaker 2: what the hell is going on down here? So it

682
00:28:11,200 --> 00:28:13,359
Speaker 2: can be fun for tomorrow night, is what I'm preparing for.

683
00:28:14,000 --> 00:28:16,960
Speaker 1: Last question for you, you know, what is the extent the

684
00:28:17,440 --> 00:28:20,960
Speaker 1: likely extent of ticket splitting that we could see, you know,

685
00:28:21,400 --> 00:28:23,360
Speaker 1: and this is relevant not just in the Senate race.

686
00:28:23,359 --> 00:28:26,000
Speaker 1: I'm thinking of North Carolina in particular. Yeah, this dude

687
00:28:26,240 --> 00:28:28,800
Speaker 1: running for governor, Mark Robinson on the Republican side, is

688
00:28:28,800 --> 00:28:32,200
Speaker 1: probably gonna lose, but definitely double digits, maybe fifteen.

689
00:28:31,920 --> 00:28:32,680
Speaker 4: Maybe twenty.

690
00:28:33,040 --> 00:28:35,080
Speaker 1: And yet it's a state that Trump really kind of

691
00:28:35,119 --> 00:28:37,919
Speaker 1: needs to win. So do you think it's possible to

692
00:28:38,000 --> 00:28:40,320
Speaker 1: have that level of ticket splitting?

693
00:28:40,640 --> 00:28:40,880
Speaker 4: Yes?

694
00:28:41,320 --> 00:28:44,000
Speaker 1: And what about in the Senate races. I've seen some

695
00:28:44,040 --> 00:28:47,600
Speaker 1: indications that, you know, there was more separation between the

696
00:28:47,640 --> 00:28:50,400
Speaker 1: presidential race and the Senate candidates. On the Democratic side

697
00:28:50,520 --> 00:28:52,640
Speaker 1: in particular, it seems like in the blue Wall states.

698
00:28:52,920 --> 00:28:54,920
Speaker 1: That seems to be tightening, and there seems to be

699
00:28:55,000 --> 00:28:57,560
Speaker 1: sort of more direct correlation between the presidential race and

700
00:28:57,560 --> 00:29:00,120
Speaker 1: the Senate races. Are you seeing that in other places?

701
00:29:00,120 --> 00:29:03,040
Speaker 5: Well, yeah, there's some correlation, for sure. They're getting closer.

702
00:29:03,080 --> 00:29:05,200
Speaker 5: But I mean, look, Pennsylvania is like a fifty to

703
00:29:05,240 --> 00:29:07,840
Speaker 5: fifty state for the presidentially my model, it's eighty percent

704
00:29:07,880 --> 00:29:10,360
Speaker 5: for the Senate. That's upset that happened. Yeah, it acquire

705
00:29:10,440 --> 00:29:12,800
Speaker 5: twenty twenty level polling miss for McCormick to win. That's

706
00:29:12,840 --> 00:29:14,760
Speaker 5: happened before, but that's one the biggest polling misses we've

707
00:29:14,760 --> 00:29:18,160
Speaker 5: ever seen. So he's an underdog. He's done a good

708
00:29:18,200 --> 00:29:21,760
Speaker 5: job against Casey, who overperformed Hillary Clinton by like fifteen

709
00:29:21,720 --> 00:29:24,360
Speaker 5: points some red rural districts. Wow, he goes everywhere.

710
00:29:24,440 --> 00:29:27,360
Speaker 2: Yeah, you know, and he's from Western pet Pa. He's

711
00:29:27,360 --> 00:29:30,120
Speaker 2: got the credibility, he's got the family name. He's like,

712
00:29:30,160 --> 00:29:31,040
Speaker 2: it's as good as it gets.

713
00:29:31,080 --> 00:29:33,360
Speaker 5: I think for Pencil, I went to undergrad in Gettysburg

714
00:29:33,560 --> 00:29:35,920
Speaker 5: and he was shown up in Gettysburg more than one really,

715
00:29:36,120 --> 00:29:38,880
Speaker 5: and that's that's deep red area.

716
00:29:39,640 --> 00:29:40,320
Speaker 4: Very interesting.

717
00:29:40,840 --> 00:29:43,240
Speaker 1: All right, Well, we are going to see you tomorrow night.

718
00:29:43,960 --> 00:29:47,240
Speaker 1: Here for the big show. So yeah, thank you. I

719
00:29:47,320 --> 00:29:48,680
Speaker 1: was always great to have your insights.

720
00:29:48,720 --> 00:29:50,320
Speaker 3: Good to see me on guys. All right, let's get

721
00:29:50,360 --> 00:29:50,800
Speaker 3: to it.

722
00:29:53,240 --> 00:29:54,800
Speaker 1: All right, guys, So we are going to spend some

723
00:29:54,840 --> 00:29:58,560
Speaker 1: time taking a look at the closing pitch from both campaigns.

724
00:29:58,680 --> 00:30:01,440
Speaker 1: Up first, this is the fun closing ad from the

725
00:30:01,440 --> 00:30:02,640
Speaker 1: Trump campaignless segalism.

726
00:30:03,720 --> 00:30:06,640
Speaker 6: Four years ago, we took a wrong turn and lost

727
00:30:06,680 --> 00:30:10,560
Speaker 6: our purpose. We lost the strength that makes Americans who

728
00:30:10,560 --> 00:30:14,360
Speaker 6: we are. If we dared to speak the truth, it

729
00:30:14,400 --> 00:30:17,840
Speaker 6: was called hate speech and our values were labeled shameful.

730
00:30:19,200 --> 00:30:23,240
Speaker 6: That's what everything we care about fell apart. We surrendered

731
00:30:23,240 --> 00:30:29,440
Speaker 6: our borders, our paychecks, and our courage. Our patriotism was

732
00:30:29,480 --> 00:30:33,280
Speaker 6: called toxic. Men could beat up women and win medals,

733
00:30:34,240 --> 00:30:36,040
Speaker 6: but there was no prize for the guy who got

734
00:30:36,120 --> 00:30:39,560
Speaker 6: up every day to do his job. Now we're being

735
00:30:39,600 --> 00:30:43,040
Speaker 6: asked to settle for the way things are, and we're

736
00:30:43,080 --> 00:30:48,640
Speaker 6: wondering if America can make a comeback. We can because

737
00:30:48,680 --> 00:30:51,560
Speaker 6: we've done it before. When we get knocked down, we

738
00:30:51,640 --> 00:31:00,000
Speaker 6: don't stay down. We get up again. We fight. We fight.

739
00:31:00,520 --> 00:31:02,920
Speaker 5: I'm Donald J. Trump and I approved this message.

740
00:31:03,080 --> 00:31:06,640
Speaker 1: So closing there with the Trump assassination attempt famous fight

741
00:31:06,680 --> 00:31:08,560
Speaker 1: Fight Fight moment. You know one thing I just noticed

742
00:31:08,560 --> 00:31:12,080
Speaker 1: watching that song, almost one hundred percent men in that ad.

743
00:31:12,200 --> 00:31:17,440
Speaker 2: Yeah, it's well, there was one lady there of I.

744
00:31:17,360 --> 00:31:18,680
Speaker 3: Mean, come on, how cannot watch that?

745
00:31:18,760 --> 00:31:23,280
Speaker 2: It's obviously the most masculine return ad that it is.

746
00:31:23,360 --> 00:31:25,880
Speaker 2: I mean, well, that's the strategy, that's what they need,

747
00:31:25,920 --> 00:31:28,240
Speaker 2: that's how they should come out to win. If you

748
00:31:28,280 --> 00:31:31,320
Speaker 2: think about it, that's a very American carnage message from

749
00:31:31,360 --> 00:31:34,680
Speaker 2: twenty sixteen. It's exactly one in the White House in

750
00:31:35,080 --> 00:31:38,080
Speaker 2: the first time around, and it's one with the imagery.

751
00:31:38,240 --> 00:31:39,720
Speaker 2: The only thing I think you could really change is

752
00:31:39,800 --> 00:31:41,360
Speaker 2: now you have a bunch of it's a very diverse ad,

753
00:31:41,400 --> 00:31:43,800
Speaker 2: I'll put it that way. So it's diverse men as

754
00:31:43,840 --> 00:31:46,240
Speaker 2: opposed to I think it was almost exclusively white in

755
00:31:46,280 --> 00:31:48,400
Speaker 2: twenty sixteen. They're trying to go for the pan racial

756
00:31:48,760 --> 00:31:51,760
Speaker 2: kind of male demographic, and if you look at the

757
00:31:51,800 --> 00:31:54,680
Speaker 2: GOP commentariat, one of the things that they're driving home

758
00:31:54,760 --> 00:31:56,200
Speaker 2: right now is we've got to get men that have

759
00:31:56,520 --> 00:31:58,880
Speaker 2: got to come out and vote because of the historic

760
00:31:58,960 --> 00:32:01,080
Speaker 2: gender gap, and that's a big bet that they're trying

761
00:32:01,080 --> 00:32:04,120
Speaker 2: to make here. Traditionally, women do out vote man, although

762
00:32:04,160 --> 00:32:06,800
Speaker 2: you know, turnout is down relative to twenty twenty, so

763
00:32:06,880 --> 00:32:10,000
Speaker 2: they could still have some interesting surprises. But I thought

764
00:32:10,000 --> 00:32:13,760
Speaker 2: it was vintage Trump. That's very much like a is

765
00:32:13,760 --> 00:32:16,239
Speaker 2: the spectrum of we got to go back. I mean,

766
00:32:16,240 --> 00:32:18,760
Speaker 2: if you think about it too, if you think about

767
00:32:18,840 --> 00:32:21,280
Speaker 2: the affection that people still have for Donald Trump. One

768
00:32:21,320 --> 00:32:23,600
Speaker 2: of the reasons why it's favorables are so much higher

769
00:32:23,680 --> 00:32:25,640
Speaker 2: this time than last time. We see it was literally president,

770
00:32:25,720 --> 00:32:28,480
Speaker 2: and it was president in the before times, mostly before COVID,

771
00:32:28,520 --> 00:32:32,640
Speaker 2: and people remember fondly prices, a feeling of order. This

772
00:32:32,720 --> 00:32:34,560
Speaker 2: is pre a lot of the crazy stuff that happened

773
00:32:34,560 --> 00:32:37,080
Speaker 2: in COVID in twenty twenty one. They blame Biden for that.

774
00:32:37,080 --> 00:32:39,280
Speaker 2: That's one of the reasons Biden so longer in the race.

775
00:32:39,320 --> 00:32:41,360
Speaker 2: And you want to hit all of those notes of

776
00:32:41,400 --> 00:32:44,880
Speaker 2: we got to take our country back from this chaotic period.

777
00:32:44,920 --> 00:32:46,440
Speaker 2: So I think it was a smart ad. It's very

778
00:32:46,480 --> 00:32:49,880
Speaker 2: American carnage. It's the opposite of like a quote unquote

779
00:32:49,920 --> 00:32:50,840
Speaker 2: hopeful message.

780
00:32:50,880 --> 00:32:52,640
Speaker 3: And if he does win, I think that will be

781
00:32:52,680 --> 00:32:53,440
Speaker 3: a smart strategy.

782
00:32:53,520 --> 00:32:53,760
Speaker 4: Yeah.

783
00:32:53,760 --> 00:32:56,080
Speaker 1: And so the other thing I noticed about it is

784
00:32:56,240 --> 00:32:59,360
Speaker 1: outside of the final images of Trump up on stage

785
00:33:00,160 --> 00:33:02,680
Speaker 1: after the assassination attempt, it actually doesn't really have.

786
00:33:02,640 --> 00:33:05,360
Speaker 3: Trump at all, which is smart actually, which.

787
00:33:05,120 --> 00:33:07,640
Speaker 1: Is interesting too, you know, making a referendum on the

788
00:33:07,640 --> 00:33:10,000
Speaker 1: Biden years. And one thing I'll say is, I don't

789
00:33:10,000 --> 00:33:12,200
Speaker 1: know that the campaign has done as an effective job

790
00:33:12,280 --> 00:33:15,680
Speaker 1: as they could trying to tie Kamala directly into the

791
00:33:16,040 --> 00:33:18,040
Speaker 1: Biden record, and that was, you know, one of their

792
00:33:18,080 --> 00:33:20,240
Speaker 1: main goals and jobs from the beginning, from when she

793
00:33:20,360 --> 00:33:22,960
Speaker 1: was swapped in as candidate. But Trump, because he has

794
00:33:23,080 --> 00:33:26,400
Speaker 1: zero message discipline and never has and never will, has

795
00:33:26,440 --> 00:33:30,360
Speaker 1: not really like consistently prosecuted that case. So part of

796
00:33:30,520 --> 00:33:34,520
Speaker 1: the effectiveness of that ad really hinges on how much

797
00:33:34,600 --> 00:33:37,320
Speaker 1: were they able to tie her to a Biden record,

798
00:33:37,360 --> 00:33:40,800
Speaker 1: which is undoubtedly unpopular. On the flip side, we've got

799
00:33:40,840 --> 00:33:43,320
Speaker 1: the Kamala Harris closing ad. Let's take a listen to

800
00:33:43,520 --> 00:33:44,640
Speaker 1: her final pitch to voters.

801
00:33:45,160 --> 00:33:47,120
Speaker 4: Hannah, I'm here, Hanna and shakes my hand.

802
00:33:47,440 --> 00:33:50,400
Speaker 9: How you Dylan gay and bring everybody back to And

803
00:33:50,440 --> 00:33:51,520
Speaker 9: that's exactly how I feel.

804
00:33:51,600 --> 00:33:52,160
Speaker 5: That's why I'm doing.

805
00:33:52,400 --> 00:33:54,080
Speaker 9: Okay, you have to stay in touch with me, Okay,

806
00:33:54,320 --> 00:33:55,520
Speaker 9: I'm very serious about that.

807
00:33:55,880 --> 00:33:56,320
Speaker 3: Okay.

808
00:33:57,080 --> 00:34:00,760
Speaker 9: Throughout this campaign, I've seen the best of America, and

809
00:34:01,040 --> 00:34:03,760
Speaker 9: I've seen what is holding you back and weighing you down.

810
00:34:04,400 --> 00:34:08,520
Speaker 9: High costs, fundamental rights taken away, and politics that have

811
00:34:08,640 --> 00:34:13,560
Speaker 9: driven fear and division. You deserve better. As president, I'll

812
00:34:13,560 --> 00:34:16,720
Speaker 9: bring a new generation of leadership. I'll take on price

813
00:34:16,760 --> 00:34:19,800
Speaker 9: gouging and bring down the cost of groceries and housing

814
00:34:19,840 --> 00:34:20,760
Speaker 9: and prescriptions.

815
00:34:21,600 --> 00:34:22,279
Speaker 3: I'll fight for.

816
00:34:22,280 --> 00:34:25,200
Speaker 9: Your freedom to make your own choices, and i will

817
00:34:25,239 --> 00:34:29,240
Speaker 9: protect your healthcare and your benefits, not take them away.

818
00:34:30,840 --> 00:34:33,640
Speaker 9: The vast majority of people in our country have so

819
00:34:33,760 --> 00:34:37,800
Speaker 9: much more in common than what separates them. Good people,

820
00:34:38,480 --> 00:34:44,400
Speaker 9: hard working people. We see in our fellow Americans, neighbors,

821
00:34:44,960 --> 00:34:50,879
Speaker 9: not enemies. We believe in each other, we believe in

822
00:34:50,920 --> 00:34:51,719
Speaker 9: our country.

823
00:34:52,719 --> 00:34:55,160
Speaker 3: We're not falling for these folks who are trying to

824
00:34:55,239 --> 00:34:57,440
Speaker 3: divide us together.

825
00:34:58,440 --> 00:35:02,080
Speaker 9: We'll build a bright in future for our nation where

826
00:35:02,080 --> 00:35:06,520
Speaker 9: we stand for freedom, we stand for justice, we stand

827
00:35:06,600 --> 00:35:10,560
Speaker 9: for the dignity of work. We haven't yet quite reached

828
00:35:10,719 --> 00:35:14,640
Speaker 9: all of those ideals, but we will die trying because we.

829
00:35:14,640 --> 00:35:15,719
Speaker 3: Love our country.

830
00:35:16,560 --> 00:35:22,319
Speaker 9: Now the baton is in our hands. I pledge to

831
00:35:22,480 --> 00:35:27,799
Speaker 9: seek common sense solutions to make your life better, and

832
00:35:27,920 --> 00:35:33,960
Speaker 9: I pledge to be a president for all Americans. Now

833
00:35:34,080 --> 00:35:37,520
Speaker 9: I'm asking for your vote because as president, I will

834
00:35:37,520 --> 00:35:40,280
Speaker 9: get up every day and fight for the American people.

835
00:35:41,719 --> 00:35:46,080
Speaker 1: I'm Kamala Harris and I approved this message, so pretty significant.

836
00:35:46,120 --> 00:35:47,600
Speaker 4: Contrast in the approach.

837
00:35:47,280 --> 00:35:50,040
Speaker 3: The area literally died nine day right.

838
00:35:50,120 --> 00:35:53,399
Speaker 1: This optimism, even the music is diametrically opposed. She's got

839
00:35:53,400 --> 00:35:57,719
Speaker 1: this like aspirational optimistic score. It's direct to camera from her,

840
00:35:57,920 --> 00:36:00,200
Speaker 1: she's speaking, I mean part of that and putting her

841
00:36:00,200 --> 00:36:03,040
Speaker 1: behind the podium is again trying to help people to

842
00:36:03,120 --> 00:36:06,200
Speaker 1: imagine her in that role as commander in chief. You

843
00:36:06,239 --> 00:36:09,480
Speaker 1: have men and women represented there in that ad, which

844
00:36:09,520 --> 00:36:11,480
Speaker 1: is you know, one thing that I've noted before soccer

845
00:36:11,600 --> 00:36:13,480
Speaker 1: is Democrats have recognized.

846
00:36:13,040 --> 00:36:15,000
Speaker 4: They've got an issue with men.

847
00:36:15,160 --> 00:36:15,319
Speaker 5: You know.

848
00:36:15,360 --> 00:36:17,080
Speaker 1: I don't know that they've done everything they need to

849
00:36:17,080 --> 00:36:19,640
Speaker 1: in terms of outreach, but they certainly have made some efforts,

850
00:36:19,800 --> 00:36:22,280
Speaker 1: whereas the Trump campaign has really just decided like, Nope,

851
00:36:22,560 --> 00:36:24,520
Speaker 1: we can't win over like the women who are concerned

852
00:36:24,560 --> 00:36:26,759
Speaker 1: about abortion, they're gone. We can't win them over. We

853
00:36:26,800 --> 00:36:30,400
Speaker 1: need to just really super serve this male base that

854
00:36:30,440 --> 00:36:32,319
Speaker 1: we're hoping are going to ultimately turn out.

855
00:36:32,440 --> 00:36:33,560
Speaker 3: I honestly agree with that.

856
00:36:33,719 --> 00:36:35,279
Speaker 2: I don't think there as much you can do on

857
00:36:35,360 --> 00:36:38,600
Speaker 2: the abortion issue, Like it is basically as binary as

858
00:36:38,600 --> 00:36:41,399
Speaker 2: it gets. You're either supported or you don't. In terms

859
00:36:41,400 --> 00:36:43,600
Speaker 2: of trying to win people over. If you look at

860
00:36:44,200 --> 00:36:47,359
Speaker 2: opinion polling, you know, it is genuinely and always has

861
00:36:47,360 --> 00:36:49,879
Speaker 2: the number one issue for a lot of these female voters,

862
00:36:49,920 --> 00:36:51,560
Speaker 2: especially these newer female voters.

863
00:36:51,600 --> 00:36:52,960
Speaker 3: But men are very different. They think about a lot

864
00:36:52,960 --> 00:36:53,520
Speaker 3: of different stuff.

865
00:36:53,520 --> 00:36:56,759
Speaker 2: That's another reason why that economy that ad for trum

866
00:36:56,800 --> 00:36:59,359
Speaker 2: Trump was all about the economy, Kamma, it's a little

867
00:36:59,360 --> 00:37:01,400
Speaker 2: bit more of you know, you get a message for everybody,

868
00:37:01,440 --> 00:37:03,560
Speaker 2: you know, for the men who are with you kind

869
00:37:03,600 --> 00:37:04,960
Speaker 2: of on abortion, but you got to give them some

870
00:37:05,160 --> 00:37:07,640
Speaker 2: economy stuff, but you've got a heavy emphasis like I'll

871
00:37:07,640 --> 00:37:10,480
Speaker 2: protect your freedoms. You know, they use that language a lot.

872
00:37:10,520 --> 00:37:14,120
Speaker 2: It's interesting too, if you looked at that big January sixth,

873
00:37:14,760 --> 00:37:18,120
Speaker 2: you know, retrospective rally that she had about democracy, the

874
00:37:18,160 --> 00:37:20,680
Speaker 2: word freedom was everywhere in the comma L added and

875
00:37:20,680 --> 00:37:21,400
Speaker 2: I took notice of that.

876
00:37:21,440 --> 00:37:23,000
Speaker 3: I'm like, you know, this is actually kind of interesting.

877
00:37:23,120 --> 00:37:25,000
Speaker 2: Yeah, in terms of the messaging, I've talked about the

878
00:37:25,040 --> 00:37:28,400
Speaker 2: social libertarianism, how much more popular that is from democrats.

879
00:37:28,440 --> 00:37:31,680
Speaker 2: It's supposed to like ninism, and the comfort stems from

880
00:37:31,680 --> 00:37:34,240
Speaker 2: more of like a woke ideology. So it is interesting

881
00:37:34,239 --> 00:37:37,239
Speaker 2: to see what is obviously popular, what is pulling on.

882
00:37:37,320 --> 00:37:39,560
Speaker 2: I also note there's not a single message of I'm

883
00:37:39,560 --> 00:37:40,920
Speaker 2: a woman and you should elect me.

884
00:37:41,080 --> 00:37:44,239
Speaker 1: There was a little nod to it because she had that,

885
00:37:44,320 --> 00:37:46,399
Speaker 1: you know, she said something like we haven't always lived

886
00:37:46,440 --> 00:37:48,239
Speaker 1: up to rospiration. They have the picture of like the

887
00:37:48,320 --> 00:37:50,759
Speaker 1: little black girl marching and the civil rights but that

888
00:37:50,840 --> 00:37:53,120
Speaker 1: was it. Yeah, you know, it's funny that you read

889
00:37:53,200 --> 00:37:55,400
Speaker 1: that your reaction to the Trump ad was that it

890
00:37:55,400 --> 00:37:57,759
Speaker 1: was mostly about economics, because my reaction to it was

891
00:37:57,800 --> 00:37:58,520
Speaker 1: mostly cultural.

892
00:37:58,680 --> 00:38:00,600
Speaker 4: Well, because you've got.

893
00:38:00,440 --> 00:38:03,680
Speaker 1: You know, they got to throw in the Algerian boxer

894
00:38:04,239 --> 00:38:07,600
Speaker 1: is in there. They talk about, you know, your patriotism

895
00:38:07,719 --> 00:38:09,920
Speaker 1: is labeled toxic. They sort of give this illusion to

896
00:38:09,960 --> 00:38:12,920
Speaker 1: cancel culture and then you know the sense of like chaos.

897
00:38:13,200 --> 00:38:17,960
Speaker 1: So to me it was heavier emphasis on cultural. Kama's

898
00:38:18,000 --> 00:38:20,960
Speaker 1: ad was similar to what she tried to achieve in

899
00:38:21,000 --> 00:38:23,879
Speaker 1: that speech at the Ellipse, which was yes, to make

900
00:38:23,880 --> 00:38:26,239
Speaker 1: the case about like we got to come together, we

901
00:38:26,280 --> 00:38:29,120
Speaker 1: got to put this you know this ugliness in our past.

902
00:38:29,400 --> 00:38:31,359
Speaker 1: But also she got in there like you know, I'm

903
00:38:31,360 --> 00:38:33,040
Speaker 1: going to go after the price gougers. I'm going to

904
00:38:33,080 --> 00:38:35,839
Speaker 1: seek common sense economic solutions, blah blah blah. So trying

905
00:38:35,840 --> 00:38:38,920
Speaker 1: to marry sort of that like democracy argument with the

906
00:38:38,960 --> 00:38:42,319
Speaker 1: more populist parts of her economic pitch. So you know

907
00:38:42,560 --> 00:38:44,520
Speaker 1: which is going to land. We're going to find out

908
00:38:44,640 --> 00:38:48,320
Speaker 1: soon enough. And even if also these are the messages

909
00:38:48,360 --> 00:38:50,239
Speaker 1: that these campaigns want to put out. That's the other

910
00:38:50,239 --> 00:38:53,640
Speaker 1: big question is whether the paid communications really matter that

911
00:38:53,840 --> 00:38:57,320
Speaker 1: much or whether it's more about the meta media narratives,

912
00:38:57,440 --> 00:38:59,000
Speaker 1: which you know about that.

913
00:38:59,120 --> 00:39:01,600
Speaker 3: Yeah, I hold ex ploma about Madison Square Guards exactly,

914
00:39:01,680 --> 00:39:02,880
Speaker 3: turning point right.

915
00:39:03,160 --> 00:39:05,279
Speaker 2: We do have some other info we can share. So

916
00:39:05,360 --> 00:39:07,600
Speaker 2: this literally just came out. I'm gonna mention it. Maybe

917
00:39:07,640 --> 00:39:09,839
Speaker 2: we can edit it in in post production. But the

918
00:39:09,880 --> 00:39:12,640
Speaker 2: final day schedules from the candidates are out. The final

919
00:39:12,719 --> 00:39:15,320
Speaker 2: day schedule from Donald Trump will be Raleigh, North Carolina,

920
00:39:15,640 --> 00:39:20,400
Speaker 2: Reading Pennsylvania, Pittsburgh, Pennsylvania, Grand Rapids, Michigan. Grand Rapids is

921
00:39:20,440 --> 00:39:22,799
Speaker 2: the traditional last time site for Trump. It was his

922
00:39:22,880 --> 00:39:25,560
Speaker 2: last rally in twenty sixteen, last and twenty twenty. Apparently

923
00:39:25,560 --> 00:39:27,560
Speaker 2: he's superstitious because he thinks he won in twenty twenty,

924
00:39:27,560 --> 00:39:28,040
Speaker 2: so he wants to.

925
00:39:28,040 --> 00:39:29,960
Speaker 3: Do it the final day schedule.

926
00:39:30,000 --> 00:39:34,640
Speaker 2: This tells you a lot from Harris Scranton, Allentown, Pittsburgh, Philadelphia,

927
00:39:34,719 --> 00:39:38,200
Speaker 2: so last one BA, so all Pa, whereas Trump's got

928
00:39:38,200 --> 00:39:41,200
Speaker 2: to shore things up in North Carolina. Two stops in PA,

929
00:39:41,600 --> 00:39:45,200
Speaker 2: take notice, Pittsburgh, you know, reading and with the location

930
00:39:45,239 --> 00:39:47,920
Speaker 2: of those, and then finally Grand Rapids. Last time Michigan

931
00:39:47,960 --> 00:39:50,040
Speaker 2: really was such a huge win for him in twenty sixteen.

932
00:39:50,080 --> 00:39:51,200
Speaker 2: That was one that they bet.

933
00:39:50,960 --> 00:39:51,440
Speaker 3: The farm on.

934
00:39:51,719 --> 00:39:53,480
Speaker 2: I remember, you know, so many arguments I had with

935
00:39:53,560 --> 00:39:55,719
Speaker 2: people were like, there's no way Trump is going to

936
00:39:55,760 --> 00:39:58,120
Speaker 2: win Michigan. I remember thinking that and it was like boom.

937
00:39:58,239 --> 00:40:01,160
Speaker 2: In terms of what the true shock of the night was.

938
00:40:01,239 --> 00:40:03,440
Speaker 2: We also should we remind people about Biden about his

939
00:40:03,480 --> 00:40:04,560
Speaker 2: closing message.

940
00:40:04,680 --> 00:40:06,719
Speaker 3: You guys see it highly affected. Do you guys want

941
00:40:06,719 --> 00:40:08,399
Speaker 3: to see his closing message. Let's take a listen.

942
00:40:10,000 --> 00:40:13,680
Speaker 8: This is the kind of guy you're like a smacking ash.

943
00:40:13,760 --> 00:40:16,600
Speaker 4: So even the delivery of that is very like.

944
00:40:17,280 --> 00:40:18,040
Speaker 3: He's going on there.

945
00:40:18,120 --> 00:40:20,480
Speaker 2: You know, he's got the dimension stoneface, like every time

946
00:40:20,480 --> 00:40:23,160
Speaker 2: he speaks, it's like his entire the facial, the mouth

947
00:40:23,239 --> 00:40:24,920
Speaker 2: is moving, nothing else is going on up here.

948
00:40:24,960 --> 00:40:26,320
Speaker 3: They're just blank behind the eyes.

949
00:40:26,360 --> 00:40:29,480
Speaker 1: It's also I mean, he wanted to campaign for Kamala

950
00:40:29,520 --> 00:40:32,359
Speaker 1: and kept reaching out and they just basically were like good, no,

951
00:40:32,880 --> 00:40:36,080
Speaker 1: and he did that those comments.

952
00:40:36,080 --> 00:40:37,160
Speaker 4: What was it that he said that?

953
00:40:37,320 --> 00:40:40,840
Speaker 1: Uh oh, the garbage comment that caused such a problem

954
00:40:41,160 --> 00:40:43,080
Speaker 1: he was supposed to do. I guess what they had

955
00:40:43,120 --> 00:40:45,319
Speaker 1: relegated him to was like, Okay, you can do these

956
00:40:45,400 --> 00:40:47,879
Speaker 1: zoom calls with some of the starget groups. We don't

957
00:40:47,880 --> 00:40:49,920
Speaker 1: think that you can fuck that up too badly. But

958
00:40:50,080 --> 00:40:52,400
Speaker 1: after the garbage comments, all of those.

959
00:40:52,640 --> 00:40:53,600
Speaker 3: They cancel everything.

960
00:40:53,640 --> 00:40:54,560
Speaker 4: They were all pulled.

961
00:40:54,880 --> 00:40:59,080
Speaker 1: So even this very limited role that they felt safe

962
00:40:59,200 --> 00:41:01,360
Speaker 1: having Joe Biden and do, at the end of the

963
00:41:01,440 --> 00:41:03,600
Speaker 1: day they were like, no, sorry, we're even pulling the

964
00:41:03,600 --> 00:41:06,520
Speaker 1: plug on that, which just I mean, it's just it's

965
00:41:06,560 --> 00:41:08,520
Speaker 1: astonishing that this man is still the president.

966
00:41:08,800 --> 00:41:09,000
Speaker 3: Right.

967
00:41:09,040 --> 00:41:12,640
Speaker 1: He can't be trusted to do a zoom surrogate call

968
00:41:12,719 --> 00:41:16,040
Speaker 1: with people who already support the ticket, even that is

969
00:41:16,080 --> 00:41:18,920
Speaker 1: to weight your responsibility for him, And yet he has

970
00:41:18,920 --> 00:41:22,640
Speaker 1: the nuclear codes. There you go, case number example number three,

971
00:41:22,719 --> 00:41:25,440
Speaker 1: thy five hundred and seventy two. Why the Kamlayers campaign

972
00:41:25,520 --> 00:41:28,480
Speaker 1: is like, we're probably not going to be campaigning with

973
00:41:28,520 --> 00:41:29,680
Speaker 1: you because this is just not help.

974
00:41:29,760 --> 00:41:31,200
Speaker 2: Not only are we not campaigning with you, it's like

975
00:41:31,239 --> 00:41:32,920
Speaker 2: we don't even want to mention you. We don't want

976
00:41:32,960 --> 00:41:35,719
Speaker 2: to do with the Biden what administration, who you know

977
00:41:35,840 --> 00:41:39,360
Speaker 2: in terms of totally ticket there, totally yeah. So anyway,

978
00:41:39,440 --> 00:41:41,319
Speaker 2: let's put the next one up there. This is this

979
00:41:41,360 --> 00:41:43,719
Speaker 2: is going to be very interesting. So I mentioned the

980
00:41:43,760 --> 00:41:47,279
Speaker 2: Final Day analysis. Trump is rallying every single day until

981
00:41:47,280 --> 00:41:49,799
Speaker 2: the election in North Carolina they say a swing state

982
00:41:49,840 --> 00:41:52,200
Speaker 2: that he won twice, but that really indicates so I mean,

983
00:41:52,239 --> 00:41:53,839
Speaker 2: both of these schedules we can read a lot into

984
00:41:53,880 --> 00:41:56,000
Speaker 2: this Trump campaign. They're worried about North Carolina. They got

985
00:41:56,040 --> 00:41:58,560
Speaker 2: him there every day all over the weekend. Final Day,

986
00:41:58,560 --> 00:42:01,239
Speaker 2: They've got a state stop there in North Carolina. And

987
00:42:01,280 --> 00:42:04,440
Speaker 2: the Harris campaign they're worried about PA. Four stops all

988
00:42:04,480 --> 00:42:08,879
Speaker 2: in PA. Final stop in Philadelphia. They had a big

989
00:42:08,920 --> 00:42:11,720
Speaker 2: ad that's been running all across the Philly area about

990
00:42:11,719 --> 00:42:15,440
Speaker 2: how Philly like stands up for freedom and about democratic overperformance.

991
00:42:15,480 --> 00:42:17,720
Speaker 2: They're betting the house on the mainline and on urban

992
00:42:18,239 --> 00:42:20,239
Speaker 2: turnout there and they're hoping that that's going to be

993
00:42:20,360 --> 00:42:22,760
Speaker 2: enough to stop what they see is the rural turnout

994
00:42:22,920 --> 00:42:25,400
Speaker 2: in Western PA. That's exactly why, by the way, you

995
00:42:25,480 --> 00:42:27,880
Speaker 2: have Trump there in Pittsburgh for his final stop to

996
00:42:27,880 --> 00:42:30,759
Speaker 2: see where the two you know, demographics specifically of that

997
00:42:31,440 --> 00:42:35,040
Speaker 2: big state, very very different populations even just across the state,

998
00:42:35,200 --> 00:42:37,239
Speaker 2: and who they're trying to turn out for what is

999
00:42:37,360 --> 00:42:39,080
Speaker 2: likely to be the tipping point in the election.

1000
00:42:39,280 --> 00:42:41,960
Speaker 1: Yeah, and both of those states are similar in terms

1001
00:42:41,960 --> 00:42:47,000
Speaker 1: of Electoral College votes. So Pennsylvania has nineteen, North Carolina

1002
00:42:47,040 --> 00:42:51,080
Speaker 1: has sixteen. So both of them, you know, significant prizes electorally,

1003
00:42:51,560 --> 00:42:54,440
Speaker 1: and you know, for either candidate, you know, if Kama

1004
00:42:54,520 --> 00:42:57,520
Speaker 1: can't win Pennsylvania, the map becomes pretty tough for her.

1005
00:42:57,600 --> 00:43:00,839
Speaker 1: North Carolina might be a decent substitute, and same for Trump,

1006
00:43:00,880 --> 00:43:03,640
Speaker 1: if he can't pull off North Carolina, state that you

1007
00:43:03,640 --> 00:43:07,319
Speaker 1: know he won twice, becomes a lot more challenging for

1008
00:43:07,400 --> 00:43:10,000
Speaker 1: him to be able to put together to seventy. Last

1009
00:43:10,000 --> 00:43:13,240
Speaker 1: element we have here is the closing schedule for Kamala Harris,

1010
00:43:13,239 --> 00:43:15,279
Speaker 1: going back a few days to show you know, the

1011
00:43:15,320 --> 00:43:18,920
Speaker 1: focus over really the past week. So Thursday, she was

1012
00:43:18,960 --> 00:43:22,840
Speaker 1: out in those Sun Belt states in Arizona, in Reno

1013
00:43:22,960 --> 00:43:26,239
Speaker 1: and Las Vegas, Nevada. Friday, woke up in Vegas, then

1014
00:43:26,320 --> 00:43:29,640
Speaker 1: headed to Milwaukee. On Saturday, she was in Georgia and

1015
00:43:29,640 --> 00:43:32,279
Speaker 1: North Carolina, and Sunday she was in Michigan and then,

1016
00:43:32,360 --> 00:43:35,160
Speaker 1: as Soccer was saying before, closing out the day with

1017
00:43:35,360 --> 00:43:39,839
Speaker 1: a series of rallies in that critical swing state of Pennsylvania.

1018
00:43:40,160 --> 00:43:41,239
Speaker 4: So that's what it looks like.

1019
00:43:41,360 --> 00:43:43,120
Speaker 3: That's it, folks. It's the closing day.

1020
00:43:43,840 --> 00:43:46,799
Speaker 2: You know, will give you all an update tomorrow, but

1021
00:43:47,080 --> 00:43:48,759
Speaker 2: a lot of the stuff tomorrow is just like b

1022
00:43:48,920 --> 00:43:49,960
Speaker 2: roll of people in line.

1023
00:43:50,239 --> 00:43:52,560
Speaker 3: You know, you'll have some speeches and they're.

1024
00:43:52,320 --> 00:43:53,759
Speaker 4: Trying to read the deal all the lines.

1025
00:43:53,800 --> 00:43:57,120
Speaker 1: Yeah, everything long and wherever, and that means such and

1026
00:43:57,160 --> 00:43:58,240
Speaker 1: such for so and so.

1027
00:43:58,160 --> 00:43:59,960
Speaker 3: And maybe the weather is bad. That's not bad.

1028
00:44:00,080 --> 00:44:03,040
Speaker 2: Weather actually has you know, that has statistically impacted turnout

1029
00:44:03,040 --> 00:44:05,040
Speaker 2: in the past, like especially that's part of the reason

1030
00:44:05,080 --> 00:44:06,000
Speaker 2: people want early vote.

1031
00:44:06,640 --> 00:44:08,640
Speaker 1: So much of the vote is cast early now that

1032
00:44:08,719 --> 00:44:11,240
Speaker 1: those like you know, election day like where the line

1033
00:44:11,280 --> 00:44:13,440
Speaker 1: blong kind of vibes are hard.

1034
00:44:13,200 --> 00:44:15,439
Speaker 2: To I think sixty million people have voted already, maybe

1035
00:44:15,440 --> 00:44:17,759
Speaker 2: even more actually, which is crazy. And we both voted

1036
00:44:17,760 --> 00:44:22,719
Speaker 2: early a lot of people have all my friends. Yeah, fun,

1037
00:44:22,760 --> 00:44:24,440
Speaker 2: should I wear my voting sticker? I saved it?

1038
00:44:24,480 --> 00:44:24,920
Speaker 3: Oh did you?

1039
00:44:25,120 --> 00:44:27,120
Speaker 4: Yeah? No, I didn't get on all right, it was

1040
00:44:27,160 --> 00:44:27,560
Speaker 4: long gone.

1041
00:44:27,600 --> 00:44:29,800
Speaker 1: But you're welcome to if you would like to promote

1042
00:44:29,800 --> 00:44:32,280
Speaker 1: our you know, doing your civic duty and casting your ballot,

1043
00:44:32,320 --> 00:44:34,960
Speaker 1: having your voice heard in whatever way that you choose.

1044
00:44:37,680 --> 00:44:41,680
Speaker 1: Let's get to this question over whether Donald Trump sort

1045
00:44:41,680 --> 00:44:45,840
Speaker 1: of October surprised himself with that people say, controversial rally

1046
00:44:45,880 --> 00:44:47,240
Speaker 1: at Madison Square Garden.

1047
00:44:47,760 --> 00:44:49,600
Speaker 4: Put this up on the screen. So this is this.

1048
00:44:49,719 --> 00:44:52,279
Speaker 1: Data scientist who I'm just gonna put my cards on

1049
00:44:52,320 --> 00:44:56,040
Speaker 1: the table. I don't credit his philosophy here very much

1050
00:44:56,120 --> 00:45:00,840
Speaker 1: because he relies on the like betting ons, he uses

1051
00:45:00,880 --> 00:45:03,640
Speaker 1: the betting odds and then has his own proprietary model

1052
00:45:03,960 --> 00:45:06,600
Speaker 1: to project what he thinks is going to happen in

1053
00:45:06,680 --> 00:45:09,200
Speaker 1: terms of the electoral college. But he has had some

1054
00:45:09,239 --> 00:45:10,880
Speaker 1: success in the past. So let's take a look at

1055
00:45:10,880 --> 00:45:12,200
Speaker 1: what this individual has to say.

1056
00:45:12,400 --> 00:45:13,960
Speaker 4: The headline here from Fortune.

1057
00:45:13,680 --> 00:45:17,160
Speaker 1: Which wrote up these data results are October surprise, Trump

1058
00:45:17,200 --> 00:45:19,399
Speaker 1: just blows huge lead and the Madison Square Garden Raley

1059
00:45:19,400 --> 00:45:22,959
Speaker 1: started the drop, says a top data scientist. They say,

1060
00:45:23,160 --> 00:45:26,040
Speaker 1: Donald Trump is suffering a historic descent of the campaign's

1061
00:45:26,040 --> 00:45:29,000
Speaker 1: final days, an ongoing free fall that is turning what

1062
00:45:29,080 --> 00:45:31,160
Speaker 1: looked like a walkway for the former president into what's

1063
00:45:31,200 --> 00:45:34,239
Speaker 1: most likely a Kamala Harris victory. That is the view

1064
00:45:34,239 --> 00:45:37,799
Speaker 1: from Thomas Miller, data scientist at Northwestern University, who's proprietary

1065
00:45:37,840 --> 00:45:42,000
Speaker 1: models proven right on in past elections. And so basically

1066
00:45:42,400 --> 00:45:46,560
Speaker 1: what Miller takes a look at is not just his

1067
00:45:47,680 --> 00:45:50,040
Speaker 1: not just the betting markets, but then he's got his own,

1068
00:45:50,200 --> 00:45:52,920
Speaker 1: i don't know, special whatever that he applies to it.

1069
00:45:53,480 --> 00:45:58,120
Speaker 1: And he found that prior to Madison Square Garden, at

1070
00:45:58,160 --> 00:46:00,680
Speaker 1: the time when everybody was feeling like all the polls

1071
00:46:00,680 --> 00:46:03,640
Speaker 1: are tightening, and you know, polymarket had Trump is like

1072
00:46:03,640 --> 00:46:05,400
Speaker 1: a seventy percent favorite to win.

1073
00:46:05,320 --> 00:46:06,640
Speaker 4: Et cetera, et cetera.

1074
00:46:06,719 --> 00:46:09,040
Speaker 1: He found it, according to his model, that Trump really

1075
00:46:09,120 --> 00:46:12,319
Speaker 1: was on track for a significant electoral college victory. And

1076
00:46:12,360 --> 00:46:16,520
Speaker 1: then that really falls off a cliff with Madison Square Garden.

1077
00:46:16,680 --> 00:46:17,879
Speaker 4: Tony Hinchcliff the.

1078
00:46:17,840 --> 00:46:20,440
Speaker 1: Other you know comments that were made at that the

1079
00:46:20,600 --> 00:46:23,560
Speaker 1: overall coverage of and vibe of that rally, and that

1080
00:46:23,640 --> 00:46:27,239
Speaker 1: Trump's odds really fell off a cliff after that, the

1081
00:46:27,400 --> 00:46:30,240
Speaker 1: quote here is Miller's numbers show a draw dropping swing

1082
00:46:30,320 --> 00:46:33,200
Speaker 1: to Harris that would have seemed unimaginable two weeks ago.

1083
00:46:33,360 --> 00:46:35,880
Speaker 1: And so, as I said Zager, like, I don't necessarily

1084
00:46:35,880 --> 00:46:38,239
Speaker 1: credit this model that much, because I don't know that

1085
00:46:38,320 --> 00:46:41,759
Speaker 1: I even buy that it's possible to have that dramatic

1086
00:46:41,840 --> 00:46:43,960
Speaker 1: of a swing at this point in the country's history,

1087
00:46:43,960 --> 00:46:46,880
Speaker 1: when voters are by and large pretty locked in about

1088
00:46:46,880 --> 00:46:49,480
Speaker 1: how they think about Roe versus Wade, how they think

1089
00:46:49,480 --> 00:46:52,040
Speaker 1: about Donald Trump, how they think about January sixth, et cetera.

1090
00:46:52,560 --> 00:46:53,600
Speaker 4: But I do think we have some.

1091
00:46:53,600 --> 00:46:58,400
Speaker 1: Additional data that pretty consistently shows that the bulk of

1092
00:46:58,440 --> 00:47:01,440
Speaker 1: the late deciders are to to break for Kamala Harris.

1093
00:47:01,800 --> 00:47:04,359
Speaker 1: And there's been a significant number of focus groups too,

1094
00:47:04,680 --> 00:47:08,279
Speaker 1: where undecided voters to describe the Madison Square Garden and

1095
00:47:08,320 --> 00:47:10,640
Speaker 1: the rhetoric there as kind of like the final straw

1096
00:47:10,880 --> 00:47:12,759
Speaker 1: that reminded them of all the things they didn't like

1097
00:47:12,800 --> 00:47:14,880
Speaker 1: about Donald Trump, and they just felt like they couldn't,

1098
00:47:15,000 --> 00:47:16,759
Speaker 1: you know, suck it up and vote for them, or

1099
00:47:16,800 --> 00:47:21,080
Speaker 1: you know, deal with that level of ugliness, chaos, et cetera,

1100
00:47:21,520 --> 00:47:24,000
Speaker 1: And so reminding of them of that in those final

1101
00:47:24,120 --> 00:47:27,680
Speaker 1: days really did kind of eat into his margins and

1102
00:47:27,960 --> 00:47:31,879
Speaker 1: cause some shift among those few late deciding voters towards coming.

1103
00:47:32,000 --> 00:47:32,160
Speaker 3: Yeah.

1104
00:47:32,160 --> 00:47:33,440
Speaker 2: I mean that's why I want to do this segment

1105
00:47:33,520 --> 00:47:35,839
Speaker 2: is Yeah, you know, it genuinely is a Ruschak test.

1106
00:47:35,960 --> 00:47:39,479
Speaker 2: Like for me, it seems psychotic that it's like, oh yeah,

1107
00:47:39,520 --> 00:47:43,200
Speaker 2: it's the msg Tony Hinchcliff joke. That's what put you

1108
00:47:43,239 --> 00:47:45,440
Speaker 2: over the edge, not all the other stuff that Trump

1109
00:47:45,440 --> 00:47:49,000
Speaker 2: has said. He's been in national life for almost what

1110
00:47:49,120 --> 00:47:51,080
Speaker 2: twelve years? Yeah, I mean if you go back, what

1111
00:47:51,160 --> 00:47:54,160
Speaker 2: birthurism started in twenty eleven? Is that correct? So that's

1112
00:47:54,239 --> 00:47:56,799
Speaker 2: thirteen years ago. So that's what put you over the edge,

1113
00:47:56,840 --> 00:47:58,839
Speaker 2: not even something in Trump's said. It was at is rally,

1114
00:47:58,880 --> 00:48:00,920
Speaker 2: and it was what other people were saying is rally. Okay,

1115
00:48:01,000 --> 00:48:02,920
Speaker 2: you know, frankly, you should have your head examine. But

1116
00:48:03,320 --> 00:48:07,080
Speaker 2: that is the big theory that the Harris campaign media

1117
00:48:07,160 --> 00:48:09,759
Speaker 2: wants us to believe it to and that's why I

1118
00:48:09,760 --> 00:48:10,120
Speaker 2: want to do it.

1119
00:48:10,200 --> 00:48:11,759
Speaker 3: I'm like, I don't buy this stuff at all.

1120
00:48:11,840 --> 00:48:14,440
Speaker 2: I just do not see how these quote unquote late

1121
00:48:14,520 --> 00:48:18,080
Speaker 2: deciders who are making up their mind are doing so

1122
00:48:18,440 --> 00:48:21,640
Speaker 2: based upon something then again, is not even Trump related

1123
00:48:21,719 --> 00:48:24,640
Speaker 2: per se. I think the only case that you could

1124
00:48:24,640 --> 00:48:28,240
Speaker 2: make that it is hurt Trump is the media coverage

1125
00:48:28,280 --> 00:48:31,880
Speaker 2: of it, if anything, and the ongoing discussion relative to

1126
00:48:31,920 --> 00:48:34,680
Speaker 2: any positive message that he's been able to put out there,

1127
00:48:34,680 --> 00:48:36,799
Speaker 2: which I think that's actually a fair one, right, which

1128
00:48:36,840 --> 00:48:39,600
Speaker 2: is that it could have people reminded of like, oh,

1129
00:48:39,719 --> 00:48:41,480
Speaker 2: is this what the Trump presidency is going to be

1130
00:48:41,800 --> 00:48:44,400
Speaker 2: like again? Or we just constantly going to have Fox

1131
00:48:44,400 --> 00:48:46,759
Speaker 2: and Friends segments about whether he what did he say

1132
00:48:46,760 --> 00:48:49,319
Speaker 2: about Mika Brazinski said she had like a shitty face left?

1133
00:48:49,520 --> 00:48:49,719
Speaker 3: Yes?

1134
00:48:50,480 --> 00:48:51,960
Speaker 2: Right, So it's like, is that what the presidency he's

1135
00:48:51,960 --> 00:48:53,279
Speaker 2: going to book like? Or is it going to be

1136
00:48:53,320 --> 00:48:55,840
Speaker 2: something that actually has to do with my own life?

1137
00:48:55,960 --> 00:48:58,719
Speaker 2: The data scientist stuff I thought was interesting because the

1138
00:48:58,760 --> 00:49:00,839
Speaker 2: thing is that this has been a big media talking point.

1139
00:49:00,840 --> 00:49:03,000
Speaker 2: Can we put C four please up on the screen?

1140
00:49:03,120 --> 00:49:05,719
Speaker 2: Is the Harris campaign is running with this wild They

1141
00:49:05,719 --> 00:49:10,560
Speaker 2: are claiming that absolutely MSG is what really pushed these

1142
00:49:10,600 --> 00:49:14,120
Speaker 2: late deciding voters to come home to the Democratic Party.

1143
00:49:14,360 --> 00:49:17,319
Speaker 2: They have put out say, say a quote significant advantage

1144
00:49:17,760 --> 00:49:20,479
Speaker 2: in these late deciding voters and that they have seen

1145
00:49:20,640 --> 00:49:22,799
Speaker 2: significant movement as like what is a two to one

1146
00:49:23,160 --> 00:49:27,080
Speaker 2: margin over the garbage thing. I saw what some quote

1147
00:49:27,280 --> 00:49:29,840
Speaker 2: that they had where somebody was even reacting to Trump

1148
00:49:29,840 --> 00:49:31,960
Speaker 2: in the garbage truck, and they're like, well, you know

1149
00:49:32,000 --> 00:49:34,920
Speaker 2: that only reminded me of the whole Puerto Rico situation.

1150
00:49:35,160 --> 00:49:37,840
Speaker 1: Well, they didn't even know about the Biden comments, so

1151
00:49:37,880 --> 00:49:39,680
Speaker 1: when they saw him in the garbage truck, they thought

1152
00:49:39,680 --> 00:49:40,839
Speaker 1: he was just like doubling down.

1153
00:49:40,880 --> 00:49:42,879
Speaker 3: Which, by the way, garbage island.

1154
00:49:42,760 --> 00:49:46,359
Speaker 2: Reminder two of what people in the median voter. They're

1155
00:49:46,400 --> 00:49:50,480
Speaker 2: not paying attention, you know, respected to people who's let's say,

1156
00:49:50,800 --> 00:49:53,040
Speaker 2: watch this show. But again, for me to believe it

1157
00:49:53,040 --> 00:49:55,080
Speaker 2: would be that there are all these Latinos out there

1158
00:49:55,120 --> 00:49:56,560
Speaker 2: who was like, I'm about to vote for Trump, but

1159
00:49:56,600 --> 00:49:59,759
Speaker 2: because of what Tony Hinchcliff said about Puerto Rico, that's

1160
00:49:59,800 --> 00:50:00,799
Speaker 2: an for me to come home.

1161
00:50:01,280 --> 00:50:02,759
Speaker 3: Kamala Harris, Sorry, I just.

1162
00:50:02,680 --> 00:50:03,200
Speaker 5: Don't buy it.

1163
00:50:03,480 --> 00:50:06,480
Speaker 2: I absolutely don't buy it at all. Even if you

1164
00:50:06,520 --> 00:50:08,680
Speaker 2: are straight up where to reaken, that's really enough for

1165
00:50:08,719 --> 00:50:11,319
Speaker 2: you to switch your vote seems nuts, especially if you're

1166
00:50:11,360 --> 00:50:12,920
Speaker 2: the type of person who's going to vote for Trump

1167
00:50:13,520 --> 00:50:17,200
Speaker 2: the first time. Anyway, It's just again ridiculous. But the

1168
00:50:17,320 --> 00:50:20,120
Speaker 2: data that they have, that's what they're claiming. The other reason,

1169
00:50:20,239 --> 00:50:22,160
Speaker 2: that's another reason that I suspect of it is it

1170
00:50:22,200 --> 00:50:24,600
Speaker 2: just seems all too cute, all too perfect for a

1171
00:50:24,640 --> 00:50:26,719
Speaker 2: media narrative they already hate anyway, and that we know

1172
00:50:26,800 --> 00:50:30,440
Speaker 2: love racism, conversation whatever for them to have this, And

1173
00:50:30,480 --> 00:50:32,719
Speaker 2: that's why the Harris campaign is leaking it to them.

1174
00:50:32,760 --> 00:50:32,840
Speaker 10: Now.

1175
00:50:32,880 --> 00:50:35,200
Speaker 2: Of course, you know, should we really take what the

1176
00:50:35,200 --> 00:50:36,960
Speaker 2: Harris campaign itself seriously?

1177
00:50:37,320 --> 00:50:38,320
Speaker 3: I take it just seriously.

1178
00:50:38,600 --> 00:50:41,240
Speaker 2: When the Trump people are like, we're seeing absolutely blowout

1179
00:50:41,239 --> 00:50:43,279
Speaker 2: turnout in New Hampshire, I'm like, no, you're not.

1180
00:50:43,320 --> 00:50:44,280
Speaker 3: You're not gonna win New Hampster.

1181
00:50:44,360 --> 00:50:46,840
Speaker 1: But what I'm looking at is actually the polling data

1182
00:50:46,840 --> 00:50:48,640
Speaker 1: that suggests like the New York Times Siana pole that

1183
00:50:48,640 --> 00:50:51,840
Speaker 1: we talked about earlier showed late breaking voters going, you know,

1184
00:50:52,040 --> 00:50:55,520
Speaker 1: lopsidedly towards Kamala Harris. There was a Univision you Go

1185
00:50:55,800 --> 00:50:59,680
Speaker 1: poll of Pennsylvania Latino voters. They did not like the

1186
00:50:59,680 --> 00:51:04,160
Speaker 1: common and they also had a huge divide in favor

1187
00:51:04,280 --> 00:51:07,040
Speaker 1: of Kamala Harris. She led Trump sixty four to thirty

1188
00:51:07,360 --> 00:51:09,400
Speaker 1: among Pennsylvania Latinos.

1189
00:51:09,440 --> 00:51:12,480
Speaker 4: So here's why I don't want to overstate it, right.

1190
00:51:12,960 --> 00:51:16,680
Speaker 1: But when Trump was at his best during his campaign,

1191
00:51:17,120 --> 00:51:19,759
Speaker 1: was and his poll numbers were the highest and he

1192
00:51:19,800 --> 00:51:21,840
Speaker 1: looked like he had the highest probability to win. Is

1193
00:51:21,840 --> 00:51:25,960
Speaker 1: when he was being relatively quiet, and it allowed his

1194
00:51:26,000 --> 00:51:30,160
Speaker 1: approval rating to improve. You know, where people have short

1195
00:51:30,280 --> 00:51:32,640
Speaker 1: term memories, and they kind of forgot, like some of

1196
00:51:32,640 --> 00:51:35,080
Speaker 1: the things about him that they really hated last time around.

1197
00:51:35,080 --> 00:51:37,120
Speaker 1: They kind of forgot, you know, some of the ugliness

1198
00:51:37,160 --> 00:51:39,680
Speaker 1: and the divisiveness and all of that stuff. And so

1199
00:51:40,000 --> 00:51:43,080
Speaker 1: you pair the fact that you got the Kamala Harris

1200
00:51:43,160 --> 00:51:46,120
Speaker 1: campaign prosecuting a case not just about like, oh, Trump's

1201
00:51:46,120 --> 00:51:48,799
Speaker 1: a fascist, but this guy's a divider, right, Like we're

1202
00:51:48,800 --> 00:51:50,239
Speaker 1: the ones that are going to put the country back

1203
00:51:50,280 --> 00:51:52,960
Speaker 1: together and we need to leave this ugliness in the past.

1204
00:51:53,440 --> 00:51:55,720
Speaker 1: And at the very time they're trying to prosecute that case,

1205
00:51:56,080 --> 00:51:58,120
Speaker 1: Trump and his campaign are out there on Madison Square

1206
00:51:58,120 --> 00:52:00,759
Speaker 1: Garden and with other comments, by the way, proving the

1207
00:52:00,840 --> 00:52:03,359
Speaker 1: point of all of this stuff you don't like about

1208
00:52:03,360 --> 00:52:06,640
Speaker 1: this guy, like it's still there here, it is on display,

1209
00:52:06,800 --> 00:52:09,400
Speaker 1: et cetera. So, yeah, one of the reasons Trump has

1210
00:52:09,440 --> 00:52:11,720
Speaker 1: really stayed in the game and had such a strong

1211
00:52:11,719 --> 00:52:14,000
Speaker 1: shot this time around is that his approval rating is

1212
00:52:14,040 --> 00:52:17,640
Speaker 1: significantly better than it was in the past. So you know,

1213
00:52:17,680 --> 00:52:20,560
Speaker 1: I think there's a reasonable case to be made that

1214
00:52:21,280 --> 00:52:24,480
Speaker 1: reminding people of their least favorite parts of his character

1215
00:52:24,520 --> 00:52:27,040
Speaker 1: traits here at the end has not served the campaign.

1216
00:52:27,400 --> 00:52:30,880
Speaker 1: And again, when you look at the actual pulling of

1217
00:52:31,040 --> 00:52:34,400
Speaker 1: late breaking voters, it does seem to pretty consistently show

1218
00:52:34,560 --> 00:52:36,960
Speaker 1: that they are moving towards Kamala Harris. So you know,

1219
00:52:37,000 --> 00:52:38,960
Speaker 1: I wouldn't say it's like, oh, just because of this

1220
00:52:39,000 --> 00:52:43,200
Speaker 1: one joke. It's that the whole tenor of that rally

1221
00:52:43,760 --> 00:52:48,080
Speaker 1: fit into a Kamala Harris campaign narrative and was for

1222
00:52:48,160 --> 00:52:50,920
Speaker 1: some voters kind of like a final reminder or a

1223
00:52:50,960 --> 00:52:54,239
Speaker 1: final straw that really pushed them into the Harris camp.

1224
00:52:54,520 --> 00:52:56,680
Speaker 1: So in that way, you know, we're talking about small

1225
00:52:56,719 --> 00:52:59,280
Speaker 1: margins here, et cetera. I do think that it probably

1226
00:52:59,400 --> 00:53:03,000
Speaker 1: that it may have been if if Kamala Harris wins

1227
00:53:03,000 --> 00:53:04,319
Speaker 1: at the end of the day, we may look back

1228
00:53:04,320 --> 00:53:06,120
Speaker 1: at that rally and say that it did end up

1229
00:53:06,120 --> 00:53:08,480
Speaker 1: being consequential. It also came at a time when they

1230
00:53:08,520 --> 00:53:11,759
Speaker 1: were feeling really hyper, super confident and felt like they

1231
00:53:11,800 --> 00:53:13,919
Speaker 1: could kind of just like let it all hang out.

1232
00:53:14,400 --> 00:53:17,040
Speaker 1: And I'm not sure the voting public were excited about

1233
00:53:17,040 --> 00:53:17,680
Speaker 1: what that had to see.

1234
00:53:17,680 --> 00:53:19,680
Speaker 2: I think that's a convenient narrative for people who always

1235
00:53:19,680 --> 00:53:20,960
Speaker 2: want to get jazz uff about these things.

1236
00:53:21,000 --> 00:53:21,680
Speaker 3: I just don't believe it.

1237
00:53:21,719 --> 00:53:23,719
Speaker 2: I think if Kamala lose it, or so if Kamala wins,

1238
00:53:23,719 --> 00:53:25,680
Speaker 2: it can be all a portion all day. I really do.

1239
00:53:26,800 --> 00:53:30,440
Speaker 1: On the polling that shows late breaking voters going for her, like,

1240
00:53:30,480 --> 00:53:31,719
Speaker 1: do you do you buy that pulling out?

1241
00:53:32,239 --> 00:53:35,280
Speaker 2: Don't, because first of all, it's such a small sample. Second,

1242
00:53:35,440 --> 00:53:37,640
Speaker 2: like in the New York Times, Tanna, we're going crosstab

1243
00:53:37,680 --> 00:53:40,400
Speaker 2: diving on what like eight hundred to two thousand people.

1244
00:53:41,160 --> 00:53:43,359
Speaker 2: Even within that it's like, you know, the sample gets

1245
00:53:43,400 --> 00:53:45,839
Speaker 2: down to like a couple a dozen. Then we're talking

1246
00:53:45,840 --> 00:53:48,880
Speaker 2: about late breaking univision. I mean again, for Univision to

1247
00:53:48,880 --> 00:53:51,719
Speaker 2: be true, then the entire Latino realignment is fake. If

1248
00:53:51,760 --> 00:53:54,440
Speaker 2: sixty to thirty break is basically a twenty sixteen margin

1249
00:53:54,600 --> 00:53:56,400
Speaker 2: for a latinos So that you have to ask me,

1250
00:53:56,440 --> 00:53:59,279
Speaker 2: after eight years of watching this movement happen, do I

1251
00:53:59,320 --> 00:53:59,719
Speaker 2: believe that?

1252
00:53:59,840 --> 00:54:00,040
Speaker 3: No?

1253
00:54:00,239 --> 00:54:04,040
Speaker 2: Absolutely not, especially whenever we've seen previous Well, why aren't

1254
00:54:04,040 --> 00:54:07,400
Speaker 2: you telling me? In Philadelphia that the biggest movement was

1255
00:54:07,400 --> 00:54:11,759
Speaker 2: the Puerto Rican neighborhoods towards Trump. Okay, okay again, Like,

1256
00:54:11,880 --> 00:54:13,759
Speaker 2: if I want to put myself in the headspace Puerto

1257
00:54:13,880 --> 00:54:16,879
Speaker 2: Rican guy lives in Philly who supports Trump is the

1258
00:54:16,920 --> 00:54:19,480
Speaker 2: Tony Hinchcliff joke. That's really gonna be like, you know what,

1259
00:54:19,680 --> 00:54:22,319
Speaker 2: I'm supporting Kamala Harris, I just don't buy it. Like

1260
00:54:22,320 --> 00:54:24,279
Speaker 2: these are the type of people who hate political correctness.

1261
00:54:24,320 --> 00:54:26,680
Speaker 2: They're exactly the type people are on YouTube watching you know,

1262
00:54:26,719 --> 00:54:29,840
Speaker 2: Tony Hinchcliff comedy and think it's hilarious. Like that's the

1263
00:54:29,880 --> 00:54:33,279
Speaker 2: cultural milieu that if you're a Latino supporting Trump, that's

1264
00:54:33,360 --> 00:54:35,239
Speaker 2: kind of what you swim in is saying. You know,

1265
00:54:35,320 --> 00:54:38,000
Speaker 2: if we've seen the data, the vast majority of the

1266
00:54:38,120 --> 00:54:42,360
Speaker 2: Latino movement to the Republican Party started with Latin X

1267
00:54:42,640 --> 00:54:45,840
Speaker 2: that terminology of some four or five years ago and

1268
00:54:45,960 --> 00:54:50,480
Speaker 2: increasingly is moving along blue collar cultural lines away from

1269
00:54:50,719 --> 00:54:53,000
Speaker 2: political correctness. Was the exact type of people who get

1270
00:54:53,040 --> 00:54:55,960
Speaker 2: all spun up about Madison Square gardens. So you really

1271
00:54:56,000 --> 00:54:59,080
Speaker 2: would have to believe that the entire Latino shift and

1272
00:54:59,120 --> 00:55:01,560
Speaker 2: all that is fake. Now, look, it actually could be

1273
00:55:01,640 --> 00:55:05,960
Speaker 2: possible that it's all totally overstated. If there's a Harris landslide,

1274
00:55:06,040 --> 00:55:07,560
Speaker 2: it will be fake, and I you know, I'll eat

1275
00:55:07,560 --> 00:55:08,080
Speaker 2: it on the sock.

1276
00:55:09,239 --> 00:55:10,319
Speaker 3: We could do it again, just the.

1277
00:55:10,360 --> 00:55:11,160
Speaker 4: Lay other us of these times.

1278
00:55:11,239 --> 00:55:14,239
Speaker 1: So, they asked Pennsylvania Latino voters, generally speaking, do you

1279
00:55:14,280 --> 00:55:18,960
Speaker 1: feel Trump is disrespectful or respectful towards Latino community? Sixty

1280
00:55:19,000 --> 00:55:22,239
Speaker 1: four percent said disrespectful, twenty eight percent said respectful.

1281
00:55:23,000 --> 00:55:24,799
Speaker 4: They asked what is closest to.

1282
00:55:24,760 --> 00:55:26,960
Speaker 1: Your view about the remarks at Madison Square Garden by

1283
00:55:27,000 --> 00:55:30,120
Speaker 1: the Trump campaign. Sixty nine percent said they were more

1284
00:55:30,200 --> 00:55:33,800
Speaker 1: racist than humorous, seventeen percent said they were more humorous

1285
00:55:33,920 --> 00:55:37,200
Speaker 1: than racist. Another only fourteen percent actually so they hadn't

1286
00:55:37,280 --> 00:55:39,359
Speaker 1: heard enough about the remarks, which might be the most

1287
00:55:39,680 --> 00:55:43,359
Speaker 1: interesting number there. But you know, I find it to

1288
00:55:43,400 --> 00:55:45,839
Speaker 1: me it's not so hard to imagine that it's not

1289
00:55:45,920 --> 00:55:47,759
Speaker 1: just this vote. It's like that brings up, oh, I

1290
00:55:47,800 --> 00:55:53,320
Speaker 1: remember how he treated, you know, Puerto Ricans after Hurricane Maria.

1291
00:55:54,360 --> 00:55:57,799
Speaker 1: And there's a corollary to like the sense that some

1292
00:55:57,880 --> 00:56:00,400
Speaker 1: men have gotten from the Democratic side of I just

1293
00:56:00,440 --> 00:56:03,080
Speaker 1: feel like they don't want me, even want me in

1294
00:56:03,080 --> 00:56:05,400
Speaker 1: their coalition, Like I feel a sense of contempt, and

1295
00:56:05,520 --> 00:56:07,680
Speaker 1: course voters are going to pick up on that sense

1296
00:56:07,680 --> 00:56:09,920
Speaker 1: of like, oh this is this is not a club

1297
00:56:10,080 --> 00:56:13,880
Speaker 1: that I'm really ever going to be part of. So again,

1298
00:56:14,040 --> 00:56:16,080
Speaker 1: I don't want to overstate it, but I do think

1299
00:56:16,120 --> 00:56:18,239
Speaker 1: that there's something to the fact that, certainly, if you

1300
00:56:18,280 --> 00:56:20,319
Speaker 1: wanted to choose how you were going to close out

1301
00:56:20,360 --> 00:56:24,240
Speaker 1: the campaign, Madison Square Garden and some of the comments

1302
00:56:24,280 --> 00:56:28,839
Speaker 1: that Trump has made since then are not all that helpful. Yeah,

1303
00:56:28,840 --> 00:56:31,160
Speaker 1: so we have C two.

1304
00:56:31,480 --> 00:56:32,600
Speaker 3: Oh sorry, that's on Puerto Rico.

1305
00:56:32,760 --> 00:56:32,960
Speaker 4: Yeah.

1306
00:56:33,000 --> 00:56:36,239
Speaker 1: C two, where Trump was, you know, asked about the

1307
00:56:36,440 --> 00:56:39,120
Speaker 1: you know, the the Puerto Rico joke. Now, his campaign

1308
00:56:39,160 --> 00:56:40,719
Speaker 1: thought this was a problem right away. They put on

1309
00:56:40,840 --> 00:56:43,879
Speaker 1: a statement trying to distance themselves from Tony Hinchcliffe. Trump,

1310
00:56:43,960 --> 00:56:46,680
Speaker 1: of course, though, he's you know, again totally incapable of

1311
00:56:46,680 --> 00:56:49,560
Speaker 1: any sort of message discipline. So he says, I didn't

1312
00:56:49,560 --> 00:56:51,040
Speaker 1: see any problem with it. Let's take listen to what

1313
00:56:51,080 --> 00:56:51,600
Speaker 1: he had to say.

1314
00:56:51,760 --> 00:56:54,319
Speaker 11: Well, I guess somebody put it on a comedian, any joke,

1315
00:56:54,440 --> 00:56:58,120
Speaker 11: here's a comedian a joke. You mentioned Puerto Rico. All

1316
00:56:58,160 --> 00:57:01,040
Speaker 11: of a sudden, the Democrat and they are good at

1317
00:57:01,040 --> 00:57:02,680
Speaker 11: this stuff. By the way, that's the only thing they're

1318
00:57:02,719 --> 00:57:05,040
Speaker 11: good at. They're no good at policy, they're no good

1319
00:57:05,040 --> 00:57:08,000
Speaker 11: at government. They're good at other things. They're very good

1320
00:57:08,040 --> 00:57:10,400
Speaker 11: at cheating. But other than that, they're not good at anything.

1321
00:57:10,760 --> 00:57:13,480
Speaker 11: So let me just so. They come up and a

1322
00:57:13,520 --> 00:57:17,080
Speaker 11: comedian put it early in the show as a filler.

1323
00:57:17,120 --> 00:57:19,480
Speaker 11: In all fairness, I guess he said some joke. I

1324
00:57:19,520 --> 00:57:21,600
Speaker 11: haven't heard the joke, but he said some joke and

1325
00:57:21,680 --> 00:57:24,400
Speaker 11: he mentioned Puerto Rico. All of a sudden, they come

1326
00:57:24,440 --> 00:57:27,720
Speaker 11: out with something about Puerto Rico. Nobody's been better to

1327
00:57:27,760 --> 00:57:30,560
Speaker 11: Puerto Rico than me. I saved Puerto Rico when they

1328
00:57:30,560 --> 00:57:34,080
Speaker 11: had they had some of the worst hurricanes. Really, bet

1329
00:57:34,240 --> 00:57:37,360
Speaker 11: I brought in the hospital ship, the mercy I bought

1330
00:57:37,400 --> 00:57:40,800
Speaker 11: in this massive hospital ship I brought is nobody in

1331
00:57:40,880 --> 00:57:43,800
Speaker 11: Puerto Rican's will tell you that nobody's done more for

1332
00:57:43,880 --> 00:57:44,880
Speaker 11: Puerto Rico than me.

1333
00:57:45,080 --> 00:57:47,400
Speaker 1: Classic Trump. Nobody's done more for Puerto Rico than me.

1334
00:57:47,920 --> 00:57:51,560
Speaker 1: He also made some comments at recent rally. I'll just

1335
00:57:51,680 --> 00:57:54,560
Speaker 1: playing for you. Part of it is talking about how,

1336
00:57:55,600 --> 00:57:57,760
Speaker 1: you know, he wouldn't mind so much if someone had

1337
00:57:57,800 --> 00:57:59,280
Speaker 1: to shoot through the fake news, and then the other

1338
00:57:59,360 --> 00:58:01,520
Speaker 1: part is him talking about how maybe he should never

1339
00:58:01,520 --> 00:58:03,880
Speaker 1: have left the White House even after he lost. Of course,

1340
00:58:03,880 --> 00:58:05,640
Speaker 1: he doesn't acknowledge that he lost the stick a lesson

1341
00:58:05,640 --> 00:58:05,840
Speaker 1: of that.

1342
00:58:06,240 --> 00:58:10,320
Speaker 10: I have a piece of glass over here, and I

1343
00:58:10,360 --> 00:58:16,800
Speaker 10: don't have a piece of last there, and I have

1344
00:58:16,960 --> 00:58:21,520
Speaker 10: this piece of glass here. But all we have really

1345
00:58:21,560 --> 00:58:30,640
Speaker 10: over here is the fake news, right, and to get

1346
00:58:30,800 --> 00:58:35,160
Speaker 10: me somebody would have to shoot through the fake news.

1347
00:58:37,480 --> 00:58:38,960
Speaker 10: And I don't mind that so much.

1348
00:58:41,040 --> 00:58:43,800
Speaker 5: I don't mind. I don't mind tho.

1349
00:58:45,200 --> 00:58:45,760
Speaker 3: Our country.

1350
00:58:45,800 --> 00:58:48,680
Speaker 5: The day that I left, I shouldn't have left.

1351
00:58:48,720 --> 00:58:52,200
Speaker 10: I mean, honestly, because we did so, we did so well,

1352
00:58:52,240 --> 00:58:56,320
Speaker 10: we had such a great So now, I mean, every

1353
00:58:56,360 --> 00:58:57,560
Speaker 10: polling booth has.

1354
00:58:58,040 --> 00:58:59,760
Speaker 11: Hundreds of lawyers standing there.

1355
00:59:00,000 --> 00:59:03,360
Speaker 10: It's all about the lawyers. Everybody's standing at lawyers. Nobody

1356
00:59:03,440 --> 00:59:04,040
Speaker 10: should have that.

1357
00:59:04,440 --> 00:59:07,480
Speaker 1: So in the closing week here much more of the

1358
00:59:07,520 --> 00:59:11,200
Speaker 1: focus has been on Madison Square Garden Trump's own comments,

1359
00:59:11,280 --> 00:59:14,280
Speaker 1: et cetera, highlighting some of his most off putting and

1360
00:59:14,360 --> 00:59:17,560
Speaker 1: most toxic traits. And so, you know, I don't think

1361
00:59:17,600 --> 00:59:20,200
Speaker 1: that's probably how the campaign when it's closed to I mean.

1362
00:59:20,120 --> 00:59:22,160
Speaker 2: Look, well, first of all, it's Trump's campaign, so he

1363
00:59:22,200 --> 00:59:24,640
Speaker 2: can close it how he wants. Now, the only problematic

1364
00:59:24,680 --> 00:59:26,640
Speaker 2: one electorally, I actually think was the last one about

1365
00:59:26,640 --> 00:59:27,600
Speaker 2: when not wanting to leave.

1366
00:59:27,840 --> 00:59:28,320
Speaker 3: That was the one.

1367
00:59:28,320 --> 00:59:29,600
Speaker 2: I mean, no one's going to cry for a joke

1368
00:59:29,600 --> 00:59:32,560
Speaker 2: about shooting through journalists like no one's going to be again.

1369
00:59:32,680 --> 00:59:36,840
Speaker 2: I don't believe all this Latino brujaja around Puerto Rico

1370
00:59:36,880 --> 00:59:39,120
Speaker 2: could be wrong, you know, totally willing to eat it

1371
00:59:39,320 --> 00:59:42,120
Speaker 2: if I am, we'll find out tomorrow. But the stop

1372
00:59:42,200 --> 00:59:44,640
Speaker 2: the steal was a big problem in Pennsylvania, and that

1373
00:59:44,760 --> 00:59:47,480
Speaker 2: actually is the one where you can see in terms

1374
00:59:47,520 --> 00:59:50,360
Speaker 2: of his message discipline, that's the one which always has

1375
00:59:50,440 --> 00:59:53,480
Speaker 2: hurt him the most. Doug Mastriano lost by such massive

1376
00:59:53,520 --> 00:59:56,560
Speaker 2: margin last time around. That that is the one actually

1377
00:59:56,560 --> 00:59:58,640
Speaker 2: where if you were an advisor or something, you would

1378
00:59:58,640 --> 01:00:01,479
Speaker 2: be cringing about not wanting to leave the White House

1379
01:00:01,480 --> 01:00:04,600
Speaker 2: because it was specifically that itself that turned a lot

1380
01:00:04,640 --> 01:00:07,440
Speaker 2: of people off in PA. People were already Republicans, and

1381
01:00:07,480 --> 01:00:10,360
Speaker 2: that also cost them to doctor oz His. I don't

1382
01:00:10,400 --> 01:00:12,440
Speaker 2: even remember a mess of an answer he had on

1383
01:00:12,440 --> 01:00:14,920
Speaker 2: stop it was bad even I remember that in the

1384
01:00:15,040 --> 01:00:18,080
Speaker 2: disastrous Spetterman debate, even that one stood out of like

1385
01:00:18,600 --> 01:00:22,800
Speaker 2: that was rough in terms of that iant I mean

1386
01:00:22,800 --> 01:00:25,680
Speaker 2: it probably was that up there with abortion. So you know,

1387
01:00:25,760 --> 01:00:28,280
Speaker 2: in terms of the two things that turned voters off,

1388
01:00:28,400 --> 01:00:31,000
Speaker 2: I think that that last clip was the most consequential

1389
01:00:31,040 --> 01:00:33,919
Speaker 2: because apparently this to say something he said a long time.

1390
01:00:33,960 --> 01:00:36,120
Speaker 3: I mean, look, it's just a bunch of bullshit talk.

1391
01:00:36,160 --> 01:00:37,520
Speaker 3: They did leave at the end of the day.

1392
01:00:37,960 --> 01:00:40,400
Speaker 2: But it's like, if you want to remind people of, like,

1393
01:00:41,000 --> 01:00:43,440
Speaker 2: genuinely one of the things that has turned the most

1394
01:00:43,480 --> 01:00:46,400
Speaker 2: voters off about you, that is the one which you

1395
01:00:46,440 --> 01:00:47,320
Speaker 2: would walk away from.

1396
01:00:47,320 --> 01:00:48,840
Speaker 3: But look, we'll see again.

1397
01:00:48,920 --> 01:00:50,760
Speaker 2: My theory on Trump is all the shit is just

1398
01:00:50,800 --> 01:00:53,320
Speaker 2: baked in at this point, especially for a lot of

1399
01:00:53,320 --> 01:00:54,760
Speaker 2: men who are out there, they laugh, you know, if

1400
01:00:54,760 --> 01:00:57,120
Speaker 2: the criticism, they're like, it's funny. They you know, the

1401
01:00:57,160 --> 01:01:00,000
Speaker 2: media bed wedding, they love it. There's nothing they love more.

1402
01:00:59,760 --> 01:01:02,640
Speaker 2: They want the jokes, play it, you know, let's spread

1403
01:01:02,640 --> 01:01:04,960
Speaker 2: them around. They think it's hilarious. Now, big question is

1404
01:01:04,960 --> 01:01:06,920
Speaker 2: whether they're going to vote. And I do think it's

1405
01:01:06,920 --> 01:01:09,560
Speaker 2: a very open question. If there's a Harris landslide and

1406
01:01:09,600 --> 01:01:11,520
Speaker 2: it's because of the what is it, the hidden women

1407
01:01:11,840 --> 01:01:12,800
Speaker 2: effect or something like.

1408
01:01:12,720 --> 01:01:14,760
Speaker 3: That, there should be a male reassessment.

1409
01:01:15,040 --> 01:01:18,120
Speaker 2: There should be some there should be some therapizing that

1410
01:01:18,200 --> 01:01:20,280
Speaker 2: is happening in the male community. I mean, I've been

1411
01:01:20,280 --> 01:01:22,520
Speaker 2: trying to lead the charge on this by just saying,

1412
01:01:22,560 --> 01:01:24,800
Speaker 2: if your wife is afraid to tell you who they

1413
01:01:24,800 --> 01:01:26,920
Speaker 2: are voting for, you are a loser.

1414
01:01:26,960 --> 01:01:29,480
Speaker 3: And it means that you have let me tell you

1415
01:01:29,480 --> 01:01:29,520
Speaker 3: that