1 00:00:15,604 --> 00:00:28,724 Speaker 1: Pushkin. Welcome back to Risky Business, our show about making 2 00:00:28,724 --> 00:00:31,324 Speaker 1: better decisions. I'm Maria Kanikova. 3 00:00:31,164 --> 00:00:32,204 Speaker 2: And I'm Nate Silver. 4 00:00:32,804 --> 00:00:35,404 Speaker 1: So today, Yeah, Nate, I. 5 00:00:35,324 --> 00:00:37,124 Speaker 2: Think I think so much better at the intros, in 6 00:00:37,164 --> 00:00:39,844 Speaker 2: the positions, and now we're reverting me. It's been a 7 00:00:39,884 --> 00:00:42,204 Speaker 2: couple of weeks since we've had like a normal episode. 8 00:00:42,324 --> 00:00:45,524 Speaker 1: That's true, it's true. It's good to see you. You 9 00:00:45,564 --> 00:00:46,964 Speaker 1: are in Jackson Hole. 10 00:00:47,044 --> 00:00:51,084 Speaker 2: Yeah, Jackson Hole, Wyoming, a very you know, God's country 11 00:00:51,124 --> 00:00:54,364 Speaker 2: as they might call it, and that is very pretty. 12 00:00:54,284 --> 00:00:57,524 Speaker 1: Yes, and so it's actually quite appropriate. I'm back in 13 00:00:57,524 --> 00:01:01,924 Speaker 1: New York City, So we're in two different parts of 14 00:01:01,964 --> 00:01:05,924 Speaker 1: the country to discuss something that hopefully will help unite 15 00:01:05,924 --> 00:01:08,564 Speaker 1: those different parts of the country, the presidential debate from 16 00:01:08,724 --> 00:01:12,244 Speaker 1: last night. So we are recording this the morning of Wednesday, 17 00:01:12,284 --> 00:01:16,444 Speaker 1: September eleventh. So last night you and I both were 18 00:01:17,324 --> 00:01:20,484 Speaker 1: listening to the debate from our respective different parts of 19 00:01:20,524 --> 00:01:23,964 Speaker 1: the country, and Kamala Harris tried to make her appeal 20 00:01:24,004 --> 00:01:26,804 Speaker 1: to both of us, right to all Americans all over 21 00:01:26,844 --> 00:01:28,364 Speaker 1: the country. So what'd you think, Nate. 22 00:01:29,564 --> 00:01:31,764 Speaker 2: You know, I'm here in Teaton County. Well, actually it's 23 00:01:31,764 --> 00:01:33,804 Speaker 2: the one blue County in all of Wyoming. 24 00:01:35,324 --> 00:01:37,924 Speaker 1: Yeah, I love your voices. I love your voice. 25 00:01:38,244 --> 00:01:41,084 Speaker 2: I thought Kamala Harris had an excellent debate, or maybe 26 00:01:41,324 --> 00:01:45,484 Speaker 2: maybe I think subtly more that former President Trump had 27 00:01:45,484 --> 00:01:50,724 Speaker 2: a really bad debate, playing into basically every critique that 28 00:01:50,764 --> 00:01:55,524 Speaker 2: you might have of Donald Trump, the fact that repeatedly 29 00:01:55,604 --> 00:01:59,564 Speaker 2: she was able to tease them about his rally crowd 30 00:01:59,604 --> 00:02:02,284 Speaker 2: sizes and things like that, and how foreign leaders don't 31 00:02:02,284 --> 00:02:05,044 Speaker 2: really respect him, and every single time he took the 32 00:02:05,084 --> 00:02:07,684 Speaker 2: bait and got distracted and had these long two minute 33 00:02:07,684 --> 00:02:11,844 Speaker 2: segments with no mics off, no interruption, and would spend 34 00:02:11,884 --> 00:02:15,964 Speaker 2: the first minute complaining about whatever Kamala said, and then 35 00:02:16,004 --> 00:02:18,004 Speaker 2: the next thirty seconds with a coherent point, and then 36 00:02:18,084 --> 00:02:21,244 Speaker 2: veering off in some other random direction for another thirty seconds. 37 00:02:21,564 --> 00:02:25,924 Speaker 2: A lot of talk about things like a reference to 38 00:02:26,004 --> 00:02:28,284 Speaker 2: if you're not really into politics, this will sound bizarre 39 00:02:28,324 --> 00:02:31,204 Speaker 2: because it is, but Haitian immigrants, oh. 40 00:02:31,044 --> 00:02:32,124 Speaker 1: My god, yes, in. 41 00:02:32,124 --> 00:02:37,724 Speaker 2: Ohio killing dogs and cats, you know, talking about Sean 42 00:02:37,764 --> 00:02:40,604 Speaker 2: Hanny and Laura Ingram and like just kind of not 43 00:02:40,644 --> 00:02:42,884 Speaker 2: really able to escape the fact that he's trying to 44 00:02:42,924 --> 00:02:46,204 Speaker 2: persuade over this tiny fraction five percent of the country 45 00:02:47,204 --> 00:02:48,404 Speaker 2: that is still undecided. 46 00:02:48,724 --> 00:02:51,444 Speaker 1: Yeah, can we just pause for a second. Not just 47 00:02:51,564 --> 00:02:55,764 Speaker 1: killing dogs and cats, but eating them, eating them. And 48 00:02:55,804 --> 00:02:58,604 Speaker 1: this is a conspiracy theory that came from like the 49 00:02:58,684 --> 00:03:03,124 Speaker 1: deep bowels of conspiracy theoryedom and here we have the 50 00:03:03,164 --> 00:03:07,004 Speaker 1: former president just like losing it and completely, you know, 51 00:03:07,124 --> 00:03:09,404 Speaker 1: just going off on this. He was actually fact chucked 52 00:03:09,484 --> 00:03:12,564 Speaker 1: on this by the moderators during the debate, which they 53 00:03:12,564 --> 00:03:15,164 Speaker 1: did a few times, which was new, right, they haven't 54 00:03:15,204 --> 00:03:18,484 Speaker 1: done that before, where they said, you know, actually there's 55 00:03:18,644 --> 00:03:22,244 Speaker 1: zero evidence of this. But he just didn't know where 56 00:03:22,284 --> 00:03:25,364 Speaker 1: to go. I think, you know, I was worried, you know, 57 00:03:25,404 --> 00:03:28,364 Speaker 1: going into this debate. I thought it could go a 58 00:03:28,364 --> 00:03:30,444 Speaker 1: lot of different ways, and I was just hoping that 59 00:03:30,524 --> 00:03:33,764 Speaker 1: she wouldn't let him get to her right because as 60 00:03:33,764 --> 00:03:36,804 Speaker 1: a female also, the risk is so much bigger because 61 00:03:36,804 --> 00:03:40,124 Speaker 1: if she starts feeling defensive or indignant or anything, you know, 62 00:03:40,204 --> 00:03:42,284 Speaker 1: because she's female, it's going to come off a lot 63 00:03:42,324 --> 00:03:44,564 Speaker 1: worse and it's going to just completely blow up. She 64 00:03:44,604 --> 00:03:46,764 Speaker 1: did not, and it was actually the other way around 65 00:03:46,884 --> 00:03:50,444 Speaker 1: where she was able to kind of just needle him, 66 00:03:50,524 --> 00:03:53,844 Speaker 1: and he every single time squandered most of his airtime 67 00:03:54,244 --> 00:03:57,484 Speaker 1: in responding to the needles as opposed to saying anything substantive, 68 00:03:57,564 --> 00:03:59,484 Speaker 1: which I think was the entire point. 69 00:04:00,284 --> 00:04:04,084 Speaker 2: Yeah, she is very different about gender issues tactically than 70 00:04:04,124 --> 00:04:09,324 Speaker 2: Hillary Clinton was, and to my male eye, more effect 71 00:04:09,764 --> 00:04:12,484 Speaker 2: I think in part by like sometimes jabbing. In poker terms, 72 00:04:12,484 --> 00:04:15,364 Speaker 2: we might call it. This is probably too esoteric. A 73 00:04:15,444 --> 00:04:19,884 Speaker 2: donk bet donk bet is when you unexpectedly lead with 74 00:04:19,924 --> 00:04:23,564 Speaker 2: a bet into the person that you're expecting to bet right, donk, 75 00:04:23,644 --> 00:04:27,324 Speaker 2: di O, and k. A donkey in poker is a 76 00:04:27,404 --> 00:04:29,964 Speaker 2: name for a bad player. A fish donk is a 77 00:04:30,004 --> 00:04:33,724 Speaker 2: shortening of donkey. So this was thought to be a 78 00:04:33,724 --> 00:04:35,804 Speaker 2: play that a bad player makes it doesn't know to 79 00:04:35,964 --> 00:04:37,964 Speaker 2: check to the person who made the last bet. It 80 00:04:38,004 --> 00:04:40,724 Speaker 2: turns out that and the fancy computer simulations you're supposd 81 00:04:40,724 --> 00:04:43,044 Speaker 2: to this sometimes but can also be a way to 82 00:04:43,284 --> 00:04:45,604 Speaker 2: exploit somebody, to get under their skin. And it can 83 00:04:45,924 --> 00:04:47,684 Speaker 2: I'm not gonna talk about the game theory here, but 84 00:04:47,724 --> 00:04:50,004 Speaker 2: it can throw them off rhythm. Sometimes it is correct 85 00:04:50,044 --> 00:04:53,604 Speaker 2: actually under game theory, but it's a way to take 86 00:04:53,684 --> 00:04:55,284 Speaker 2: charge of the hand. And she did that a couple 87 00:04:55,324 --> 00:04:57,844 Speaker 2: of times, like kind of premptively saying, Hey, Trump's going 88 00:04:57,884 --> 00:05:00,284 Speaker 2: to lie a lot, you know, even on issues that 89 00:05:00,324 --> 00:05:03,484 Speaker 2: would seem like relative weaknesses for her, like, for example, 90 00:05:03,564 --> 00:05:05,644 Speaker 2: the unpopularity of President Biden in the fact that she's 91 00:05:05,644 --> 00:05:07,164 Speaker 2: been the vice president for three and a half years. 92 00:05:07,404 --> 00:05:09,084 Speaker 2: She would say, by the way, I'm not Joe Biden, 93 00:05:09,124 --> 00:05:12,564 Speaker 2: and like actually lean into that. And I think she 94 00:05:12,724 --> 00:05:16,764 Speaker 2: was well prepared for this event. And I don't know 95 00:05:16,844 --> 00:05:19,244 Speaker 2: to what extent that credit goes to her versus her campaign. 96 00:05:20,124 --> 00:05:21,804 Speaker 2: The Democrats to have a lot of data. I mean, 97 00:05:22,204 --> 00:05:25,084 Speaker 2: Donald Trump has had one, two, three, four or five. 98 00:05:25,204 --> 00:05:28,164 Speaker 2: This is his seventh general election debate, and he doesn't 99 00:05:28,164 --> 00:05:30,604 Speaker 2: really change form that much, except in the Biden debate, 100 00:05:30,604 --> 00:05:33,884 Speaker 2: where Biden was so bad that some instinctual part of 101 00:05:33,884 --> 00:05:36,204 Speaker 2: Trump knew that you have to maybe not go for 102 00:05:36,244 --> 00:05:38,244 Speaker 2: the jugular that much. Although Trump was actually kind of 103 00:05:38,244 --> 00:05:41,484 Speaker 2: bad in that debate too, it's just that when Biden. 104 00:05:41,164 --> 00:05:43,244 Speaker 1: In comparison, he was he was fine. 105 00:05:44,124 --> 00:05:46,964 Speaker 2: Yeah, but you know, Biden couldn't like nail him on 106 00:05:47,044 --> 00:05:49,044 Speaker 2: the things he were saying that were very very false 107 00:05:49,084 --> 00:05:52,644 Speaker 2: and or crazy. So I mean, first of all, if 108 00:05:52,644 --> 00:05:56,124 Speaker 2: nothing else, if Harris loses and we'll talk about this 109 00:05:56,164 --> 00:05:58,364 Speaker 2: in a minute, probably still in the vicinity of fifty 110 00:05:58,364 --> 00:06:00,524 Speaker 2: to fifty. If nothing else, you have this a b 111 00:06:00,724 --> 00:06:03,924 Speaker 2: test where replacing Joe Biden with Kamal Harris was a 112 00:06:03,964 --> 00:06:04,884 Speaker 2: really good idea. 113 00:06:05,004 --> 00:06:07,844 Speaker 1: Yeah, and I actually think your dunk bet analogy is 114 00:06:07,964 --> 00:06:10,884 Speaker 1: really really a good one. First of all, just talking 115 00:06:10,884 --> 00:06:13,964 Speaker 1: from personal experience. The first, you know, I've actually let 116 00:06:14,004 --> 00:06:16,684 Speaker 1: people get under my skin doing that, you know, because 117 00:06:16,724 --> 00:06:20,684 Speaker 1: it's so it's unexpected, and the attitude is, you know, 118 00:06:20,524 --> 00:06:23,604 Speaker 1: you check to the pre flop aggressor, right, so the 119 00:06:23,644 --> 00:06:27,684 Speaker 1: person who has them, has the aggressive lead, is supposed 120 00:06:27,684 --> 00:06:30,964 Speaker 1: to be allowed to continue that aggression. And there have 121 00:06:31,004 --> 00:06:32,924 Speaker 1: been a few times I even remember when I've made 122 00:06:32,964 --> 00:06:35,364 Speaker 1: big mistakes and hands because someone has donked it to me, 123 00:06:35,604 --> 00:06:37,684 Speaker 1: and I'm like, no, you fucker, you don't get to 124 00:06:37,724 --> 00:06:40,204 Speaker 1: do that, and I raise, which is exactly what I 125 00:06:40,204 --> 00:06:43,924 Speaker 1: shouldn't be doing in some specific scenarios where they actually, 126 00:06:43,964 --> 00:06:47,364 Speaker 1: like clearly are donk betting because they have something. And 127 00:06:47,444 --> 00:06:50,884 Speaker 1: I remember those hands vividly because it's very unlike me 128 00:06:51,404 --> 00:06:55,524 Speaker 1: to to start tilting in that particular way. I've gotten 129 00:06:55,524 --> 00:06:58,684 Speaker 1: over it now, but it's actually, you know, it can 130 00:06:58,724 --> 00:07:01,164 Speaker 1: be a very effective strategy. And she did that. There 131 00:07:01,204 --> 00:07:04,004 Speaker 1: was actually one time where at first I was like, wait, 132 00:07:04,044 --> 00:07:05,444 Speaker 1: what the hell did she just do? And then I 133 00:07:05,484 --> 00:07:07,644 Speaker 1: realized what she was doing right when she was answering 134 00:07:07,644 --> 00:07:09,804 Speaker 1: a question and all of a sudden she says, by 135 00:07:09,844 --> 00:07:11,684 Speaker 1: the way, you know, I invite you to go to 136 00:07:11,724 --> 00:07:14,044 Speaker 1: Donald Trump's rallies. And I'm like, wait, you know what 137 00:07:14,124 --> 00:07:15,964 Speaker 1: a non sequort or what did she Why is she 138 00:07:16,164 --> 00:07:18,044 Speaker 1: doing that? And then it became very clear was a 139 00:07:18,124 --> 00:07:20,884 Speaker 1: very good strategic move because she was kind of deflecting 140 00:07:20,924 --> 00:07:23,564 Speaker 1: away from her record on something that she didn't really 141 00:07:23,604 --> 00:07:27,204 Speaker 1: want to talk about, and instead then Trump kept, you know, 142 00:07:27,204 --> 00:07:29,004 Speaker 1: for the next two minutes talking about how he has 143 00:07:29,044 --> 00:07:30,324 Speaker 1: the biggest rallies in history. 144 00:07:30,444 --> 00:07:32,484 Speaker 2: Yeah, and she is taking some I mean, she is 145 00:07:32,564 --> 00:07:34,724 Speaker 2: veering off topic, and she often did it on questions 146 00:07:34,724 --> 00:07:38,324 Speaker 2: like immigration that are not great topics for her. Yeah, 147 00:07:38,364 --> 00:07:41,564 Speaker 2: And if Trump had responded coherently and not taken the bait, 148 00:07:41,764 --> 00:07:44,284 Speaker 2: then you'd have his media buzz about how she went 149 00:07:44,324 --> 00:07:47,204 Speaker 2: really off topic and was evasive, right, And instead he's 150 00:07:47,364 --> 00:07:49,164 Speaker 2: more often absolutely absolutely. 151 00:07:50,044 --> 00:07:52,524 Speaker 1: But yes, I actually thought, you know, it was it 152 00:07:52,524 --> 00:07:55,564 Speaker 1: was quite funny because I was like, oh, you know, 153 00:07:55,644 --> 00:07:58,124 Speaker 1: she's learned to play the game of not answering the question, 154 00:07:58,404 --> 00:08:01,604 Speaker 1: because Donald Trump never answers the question. And as the 155 00:08:01,644 --> 00:08:04,404 Speaker 1: debate went on, she was doing the exact same thing, 156 00:08:04,444 --> 00:08:06,844 Speaker 1: and I'm like, huh, okay, well you know what that's 157 00:08:06,884 --> 00:08:09,804 Speaker 1: I guess how these debates go. But she she definitely 158 00:08:09,844 --> 00:08:11,724 Speaker 1: learned to play that game, and I actually thought she 159 00:08:11,804 --> 00:08:14,484 Speaker 1: became much stronger as a night war on. I was 160 00:08:14,564 --> 00:08:17,844 Speaker 1: very nervous when she started giving her first response, because 161 00:08:17,844 --> 00:08:19,724 Speaker 1: you could tell she was nervous, you know, as someone 162 00:08:19,764 --> 00:08:21,924 Speaker 1: who has done public speaking, who's kind of watched a 163 00:08:21,964 --> 00:08:23,804 Speaker 1: lot of debaters, you know, you and I were both 164 00:08:23,804 --> 00:08:28,244 Speaker 1: debaters all through school. You could tell, like the first 165 00:08:28,284 --> 00:08:32,244 Speaker 1: few minutes, you know, she was nervous and she was 166 00:08:32,284 --> 00:08:34,364 Speaker 1: a little stiff. But then she kind of got over it, 167 00:08:34,404 --> 00:08:36,924 Speaker 1: and I think she kind of became much more fluid 168 00:08:37,084 --> 00:08:39,524 Speaker 1: and really kind of picked up in style. And I 169 00:08:39,564 --> 00:08:41,524 Speaker 1: was very happy to see that. And he had kind 170 00:08:41,564 --> 00:08:43,964 Speaker 1: of some of the opposite trajectory where kind of he 171 00:08:44,044 --> 00:08:48,004 Speaker 1: started off thinking that, you know, I'm gonna just completely 172 00:08:49,004 --> 00:08:51,524 Speaker 1: demolish her, and then he started getting very defensive. 173 00:08:52,244 --> 00:08:55,244 Speaker 2: Yeah, public speaking for me, at least, I don't know 174 00:08:55,284 --> 00:08:57,764 Speaker 2: if you're different, Maria, but like it's not It isn't 175 00:08:57,764 --> 00:09:00,964 Speaker 2: actually like just having a conversation with a friend at 176 00:09:01,004 --> 00:09:04,004 Speaker 2: a bar that your body recognizes that this is a 177 00:09:04,124 --> 00:09:09,404 Speaker 2: high stakes momentolutely and you're operating under a elevated person 178 00:09:09,764 --> 00:09:14,764 Speaker 2: but also a stress response. Now, in my experience, what 179 00:09:14,804 --> 00:09:16,524 Speaker 2: you have to do is just kind of be a 180 00:09:16,564 --> 00:09:19,324 Speaker 2: little bit self conscious about it. Right, You're like, I 181 00:09:19,364 --> 00:09:21,364 Speaker 2: am on terrain that I either rehearsed or that I've 182 00:09:21,364 --> 00:09:25,644 Speaker 2: spoken about many times, and I'm just gonna say words 183 00:09:25,684 --> 00:09:29,084 Speaker 2: and lo and behold, they come across coherent most of 184 00:09:29,124 --> 00:09:32,164 Speaker 2: the time. If you're tired or fatigued, or or you're 185 00:09:32,164 --> 00:09:33,964 Speaker 2: in a room where the setting is weird, the lighting is, 186 00:09:34,164 --> 00:09:35,524 Speaker 2: you can be thrown off and that can be hard 187 00:09:35,524 --> 00:09:37,684 Speaker 2: to recover from. But you have to like you can't 188 00:09:38,004 --> 00:09:42,004 Speaker 2: overthink it too much, and you have to trust your 189 00:09:42,044 --> 00:09:44,564 Speaker 2: experience base that like, yeah, this is actually gonna go fine, 190 00:09:44,844 --> 00:09:47,324 Speaker 2: and some people are even better in that setting than 191 00:09:47,324 --> 00:09:49,004 Speaker 2: they would be a meeting or something. 192 00:09:49,284 --> 00:09:51,364 Speaker 1: Yeah, I think that's absolutely true. And I actually have 193 00:09:51,404 --> 00:09:54,244 Speaker 1: a stress response every single time I give a talk 194 00:09:54,324 --> 00:09:56,684 Speaker 1: right before I go on stage every single time, and 195 00:09:56,724 --> 00:09:59,764 Speaker 1: I give lots of talks. So this is something that 196 00:09:59,764 --> 00:10:02,404 Speaker 1: that definitely happens. But then you know, you could you 197 00:10:02,404 --> 00:10:05,004 Speaker 1: could tell that she does have the experience, she was prepared, 198 00:10:05,684 --> 00:10:08,324 Speaker 1: and she was able to kind of establish the momentum. 199 00:10:08,644 --> 00:10:11,204 Speaker 1: The other thing that I thought was very interesting was, 200 00:10:11,244 --> 00:10:13,164 Speaker 1: you know, Trump won the coin toss and decided that 201 00:10:13,164 --> 00:10:14,804 Speaker 1: he was going to give the closing, you know, the 202 00:10:15,204 --> 00:10:18,284 Speaker 1: final closing, and I actually think that that worked against him, 203 00:10:18,324 --> 00:10:21,364 Speaker 1: because he spent most of his closing remarks yelling at 204 00:10:21,444 --> 00:10:23,884 Speaker 1: what she said, as opposed to kind of giving any 205 00:10:23,924 --> 00:10:27,284 Speaker 1: summary of anything that he had said. So so I think 206 00:10:27,284 --> 00:10:29,244 Speaker 1: that she came across much better at the end of 207 00:10:29,284 --> 00:10:31,844 Speaker 1: the debate. And the way that you know, impressions and 208 00:10:31,924 --> 00:10:34,444 Speaker 1: memory formation works, I think that that really works in 209 00:10:34,484 --> 00:10:35,004 Speaker 1: her favor. 210 00:10:36,484 --> 00:10:38,244 Speaker 2: And the beginning too. I mean, in the beginning, I 211 00:10:38,244 --> 00:10:40,444 Speaker 2: agreed at the very first question on the economy is 212 00:10:40,484 --> 00:10:44,604 Speaker 2: strong territory for Trump. But then you have extended remarks 213 00:10:44,684 --> 00:10:47,724 Speaker 2: on abortion for a moment, I thought that might be 214 00:10:47,724 --> 00:10:50,004 Speaker 2: the story of the night. And then you have which is, 215 00:10:50,124 --> 00:10:52,084 Speaker 2: by the way, if you're not a very good issue 216 00:10:52,084 --> 00:10:52,644 Speaker 2: for Democrats. 217 00:10:52,644 --> 00:10:55,564 Speaker 1: Since she did very well, she did very well Onlke Biden, 218 00:10:55,644 --> 00:10:59,084 Speaker 1: who kind of you know, pivoted to immigration instead of 219 00:10:59,084 --> 00:11:01,964 Speaker 1: talking about abortion. She really nailed the abortion. 220 00:11:02,284 --> 00:11:04,604 Speaker 2: And then you have the bait taking phase. I mean, look, 221 00:11:04,924 --> 00:11:08,804 Speaker 2: we may look back if Harris wins and say that 222 00:11:08,804 --> 00:11:11,524 Speaker 2: the Trump can made a series of really predictable mistakes. 223 00:11:12,204 --> 00:11:14,844 Speaker 2: The lack of preparation tonight were even in the closing statement. 224 00:11:15,124 --> 00:11:18,364 Speaker 2: He can't really stay on focus and talk about Harris's record, 225 00:11:18,404 --> 00:11:21,044 Speaker 2: for example, I mean other things too. I mean, naming 226 00:11:21,124 --> 00:11:25,324 Speaker 2: Jadie Vance is one. Not preparing for the transition from 227 00:11:25,364 --> 00:11:27,924 Speaker 2: Biden to Harris when they had been telegraphed at least 228 00:11:27,924 --> 00:11:30,724 Speaker 2: for a few weeks as a possibility, a strong possibility. 229 00:11:31,764 --> 00:11:35,444 Speaker 2: His convention speech where he rambled off topic after a 230 00:11:35,484 --> 00:11:39,804 Speaker 2: more subdued thirty minutes on the assassination attempt, you know, 231 00:11:39,884 --> 00:11:42,884 Speaker 2: frankly accepting that first debate with Biden. I mean, I 232 00:11:42,884 --> 00:11:45,044 Speaker 2: guess you may not have played out all the tactics, 233 00:11:45,044 --> 00:11:48,284 Speaker 2: but if he hadn't done that, then Biden's on stage 234 00:11:48,284 --> 00:11:51,724 Speaker 2: tonight having that debate from June tonight, and Democrats are 235 00:11:52,324 --> 00:11:55,324 Speaker 2: appropriately panicking because they're probably totally fucked at that point. 236 00:11:56,684 --> 00:11:59,164 Speaker 2: So thanks to the White House for moving that forward. 237 00:11:59,164 --> 00:12:02,644 Speaker 2: If you're a Democrat, I guess yeah, for sure. By 238 00:12:02,684 --> 00:12:04,404 Speaker 2: the way, did you notice that he did not call 239 00:12:04,444 --> 00:12:06,364 Speaker 2: her by her name a single time during the night. 240 00:12:06,604 --> 00:12:10,124 Speaker 2: He kept saying she and pointing at her, and he 241 00:12:10,204 --> 00:12:13,364 Speaker 2: never said vice President Harris. He never said Kamala Harris. 242 00:12:13,484 --> 00:12:16,124 Speaker 2: He used her name exactly zero times. I've actually haven't 243 00:12:16,124 --> 00:12:18,684 Speaker 2: seen anyone mention that, But that's something that really struck 244 00:12:18,764 --> 00:12:22,844 Speaker 2: me and kind of shows how, you know, it shows 245 00:12:22,844 --> 00:12:26,044 Speaker 2: you so much about his attitude about kind of his 246 00:12:26,084 --> 00:12:28,764 Speaker 2: conception of women, his conception of her. You know, she's 247 00:12:28,804 --> 00:12:31,404 Speaker 2: not even worthy of calling by the name. Because I 248 00:12:31,484 --> 00:12:33,564 Speaker 2: was I was actually paying attention to it because I 249 00:12:33,604 --> 00:12:36,324 Speaker 2: was wondering if he was going to mispronounce her name 250 00:12:36,364 --> 00:12:38,644 Speaker 2: on purpose the way he's always done, and what her 251 00:12:38,684 --> 00:12:39,764 Speaker 2: reaction was going to be. 252 00:12:39,964 --> 00:12:42,204 Speaker 1: But he didn't. He didn't even say her name. So 253 00:12:42,244 --> 00:12:45,764 Speaker 1: I thought that that was something that was an interesting point, 254 00:12:46,724 --> 00:12:48,764 Speaker 1: and I thought that she tried to do a really 255 00:12:49,004 --> 00:12:50,684 Speaker 1: she tried to do a nice job since she was 256 00:12:50,684 --> 00:12:54,244 Speaker 1: in Pennsylvania of talking about the fact that she wasn't 257 00:12:54,284 --> 00:12:57,204 Speaker 1: going to stop fracking and that she, you know, was 258 00:12:57,804 --> 00:12:59,844 Speaker 1: upping well production in all of these things. And it's 259 00:12:59,884 --> 00:13:01,804 Speaker 1: a very fine line for her to walk right to 260 00:13:01,884 --> 00:13:04,084 Speaker 1: try to say, you know, I want a green future. 261 00:13:04,164 --> 00:13:07,804 Speaker 1: I'm worried about climate change, but believe me, I'm not 262 00:13:07,804 --> 00:13:10,084 Speaker 1: going to hurt your jobs. So I think that she 263 00:13:10,284 --> 00:13:15,044 Speaker 1: tried her best to appeal to Pennsylvania, which is obviously crucial, and. 264 00:13:15,004 --> 00:13:16,804 Speaker 2: She is flip flopping on a lot of this stuff. 265 00:13:17,884 --> 00:13:20,684 Speaker 2: So there's a lot of fertile ground that Trump could 266 00:13:20,804 --> 00:13:22,444 Speaker 2: what's the metaphere they could have trod or I'm not 267 00:13:22,484 --> 00:13:26,404 Speaker 2: sure what their metaphor is exactly. Yep. And again the 268 00:13:26,524 --> 00:13:30,604 Speaker 2: lack of discipline from Trump, you know, just to say 269 00:13:30,764 --> 00:13:34,044 Speaker 2: she's a radical left winger, as she showed in San 270 00:13:34,044 --> 00:13:36,604 Speaker 2: Francisco and in twenty nineteen when she wanted to do 271 00:13:36,804 --> 00:13:39,244 Speaker 2: X and Y and Z, and now she's flip flopping 272 00:13:39,244 --> 00:13:42,044 Speaker 2: because she's desperate to, you know, make up for Joe 273 00:13:42,084 --> 00:13:45,404 Speaker 2: Biden's mistakes. You can do a fair amount of that. 274 00:13:45,724 --> 00:13:47,764 Speaker 2: I do think that's an issue for Harris with some voters. 275 00:13:48,924 --> 00:13:52,244 Speaker 2: But look, they got another data point. So the convention, 276 00:13:52,284 --> 00:13:53,724 Speaker 2: we can talk about the trajectory of the horse race. 277 00:13:53,724 --> 00:13:55,884 Speaker 2: Maybe in a moment. I thought she gave a very 278 00:13:55,884 --> 00:13:59,564 Speaker 2: good convention speech. Also that was also kind of agro 279 00:13:59,804 --> 00:14:02,604 Speaker 2: and aimed at male voters. And she 's some of 280 00:14:02,604 --> 00:14:05,324 Speaker 2: these same themes tonight, talking about competitiveness with China, talking 281 00:14:05,364 --> 00:14:09,244 Speaker 2: about how aggressive and lethal our military is. For instance, 282 00:14:11,204 --> 00:14:14,004 Speaker 2: sometimes when you hear something for the second time, it 283 00:14:14,124 --> 00:14:17,604 Speaker 2: closes a deal, right, you know, So obviously there's some 284 00:14:17,684 --> 00:14:20,124 Speaker 2: chance that there's a bounce in the polls Harris, by 285 00:14:20,164 --> 00:14:22,684 Speaker 2: the way, so we're not just totally giving you our 286 00:14:22,684 --> 00:14:26,044 Speaker 2: subjective opinion. In the CNN post debate poll, she won 287 00:14:26,204 --> 00:14:29,084 Speaker 2: sixty three thirty seven or something, and Market. 288 00:14:28,884 --> 00:14:31,484 Speaker 1: As well also had her her odds going up, So 289 00:14:31,524 --> 00:14:34,164 Speaker 1: I think that that is also I think by three points, right, 290 00:14:34,884 --> 00:14:35,964 Speaker 1: she moved up by thy point. 291 00:14:36,964 --> 00:14:40,364 Speaker 2: Before the debate, it had been like Trump fifty four, 292 00:14:40,564 --> 00:14:42,844 Speaker 2: Harris forty six. These are not vote shares, these are 293 00:14:42,884 --> 00:14:46,364 Speaker 2: win probabilities, and after it's fifty to fifty technically forty 294 00:14:46,404 --> 00:14:48,924 Speaker 2: nine forty nine, they price in some chance that I 295 00:14:48,964 --> 00:14:51,124 Speaker 2: don't know, somebody has to drop out due to a 296 00:14:51,124 --> 00:14:55,244 Speaker 2: health issue, I guess, But basically drew the race back 297 00:14:55,324 --> 00:15:00,884 Speaker 2: to even. In the Silver Bulletin forecast, she had been 298 00:15:00,924 --> 00:15:06,804 Speaker 2: at thirty eight thirty nine percent heading in. If she 299 00:15:06,924 --> 00:15:10,244 Speaker 2: were to get that polymarket bounce, then she'd be in 300 00:15:10,284 --> 00:15:13,604 Speaker 2: the you know, forty five percent range. So fairly close 301 00:15:13,644 --> 00:15:16,644 Speaker 2: to fifty to fifty. The way the model works, the 302 00:15:16,724 --> 00:15:20,044 Speaker 2: model was very disappointed. Shall we say, not that it 303 00:15:20,084 --> 00:15:24,844 Speaker 2: has emotions. It's not some large language model that the 304 00:15:24,884 --> 00:15:31,284 Speaker 2: model is disappointed with Harris's post convention polling, where typically 305 00:15:31,324 --> 00:15:34,524 Speaker 2: after a convention is one of the best periods for 306 00:15:34,604 --> 00:15:38,084 Speaker 2: a candidate throughout the life of the election cycle. And 307 00:15:38,164 --> 00:15:41,884 Speaker 2: Harris was not only not getting a sustained bounce, she 308 00:15:41,884 --> 00:15:43,364 Speaker 2: may have gotten one for a couple of days and 309 00:15:43,364 --> 00:15:46,564 Speaker 2: then RFK drops out the new cycle changes, but actually 310 00:15:46,564 --> 00:15:50,164 Speaker 2: been declining. She had lost a point or two in 311 00:15:50,284 --> 00:15:55,164 Speaker 2: polls of Michigan, Wisconsin, you know, Pennsylvania, most of those 312 00:15:55,364 --> 00:15:59,604 Speaker 2: rust belt blue wall swing states. So it's like this 313 00:15:59,644 --> 00:16:01,124 Speaker 2: should be a really good period and it's a really 314 00:16:01,124 --> 00:16:04,044 Speaker 2: bad period. Therefore, it had gotten really grumpy about her polling, 315 00:16:04,084 --> 00:16:06,404 Speaker 2: and therefore a lot of Democrats, some of whom are 316 00:16:06,444 --> 00:16:10,484 Speaker 2: completely insane and should go, I don't know, moved to 317 00:16:11,604 --> 00:16:13,604 Speaker 2: Fiji or some nice country where they don't have to 318 00:16:13,844 --> 00:16:17,004 Speaker 2: blow up my Twitter mentions. We're upset with me as 319 00:16:17,004 --> 00:16:18,244 Speaker 2: a result, But we have to talk about that. 320 00:16:18,724 --> 00:16:21,084 Speaker 1: Yes we do, Nate, Yes we do. Because you had 321 00:16:21,204 --> 00:16:23,164 Speaker 1: such a great takedown all right, I'm going to read 322 00:16:23,204 --> 00:16:28,004 Speaker 1: a tweet from yesterday night. Hey bet, understand you may 323 00:16:28,044 --> 00:16:30,564 Speaker 1: still be frustrated. Silence of the Lambs came out the 324 00:16:30,604 --> 00:16:32,564 Speaker 1: same year as for the Boys, so you were pretty 325 00:16:32,604 --> 00:16:35,044 Speaker 1: much always going to lose best Actress to Jodie Foster 326 00:16:35,404 --> 00:16:37,684 Speaker 1: in poker. We'd call that a bad beat. But that's 327 00:16:37,724 --> 00:16:39,804 Speaker 1: no reason to tweet out conspiracy theories. 328 00:16:39,964 --> 00:16:44,804 Speaker 2: Please discuss no, so Bette Midler among other Democrats, you know, 329 00:16:44,884 --> 00:16:48,084 Speaker 2: Stuart Stevens, this guy at the Lincoln Project. Yeah, you 330 00:16:48,164 --> 00:16:51,084 Speaker 2: have certain people who are convinced that. 331 00:16:51,204 --> 00:16:52,964 Speaker 1: You've been bought. 332 00:16:54,004 --> 00:16:55,884 Speaker 2: If I've been I must have been bought. This is 333 00:16:55,924 --> 00:16:58,444 Speaker 2: like literally what Stuart Stevens and Bette Midler are implying. 334 00:16:58,884 --> 00:17:00,604 Speaker 2: And sometimes it comes up in the context of so 335 00:17:00,644 --> 00:17:05,404 Speaker 2: I consult. I'm an advisor to Pollymarket, and Polly Market 336 00:17:05,404 --> 00:17:09,884 Speaker 2: has been invested in by Founder's Fund, which is you know, 337 00:17:09,964 --> 00:17:12,764 Speaker 2: one of the principles is Peter Teal at Founder's Fund, 338 00:17:12,964 --> 00:17:16,004 Speaker 2: So it therefore gets twisted into Peter Teal is secretly 339 00:17:16,604 --> 00:17:19,284 Speaker 2: funding Nate, which is bizarre. I mean, Founder's Fund has 340 00:17:19,324 --> 00:17:23,484 Speaker 2: invested in in Lift and Airbnb and Facebook and and 341 00:17:24,004 --> 00:17:28,484 Speaker 2: you know all these all these startups eventually get invested 342 00:17:28,524 --> 00:17:30,684 Speaker 2: in by most of the big VC firms at some 343 00:17:30,804 --> 00:17:33,444 Speaker 2: stage of fundraising. I mean, if you work for open Ai. 344 00:17:33,644 --> 00:17:36,044 Speaker 2: Elon Musk co founded open Ai, are you on Elon 345 00:17:36,124 --> 00:17:41,844 Speaker 2: Musk's payroll? You know substack Mark Andreeson and recent Horror, 346 00:17:41,844 --> 00:17:44,084 Speaker 2: which is a big investor in substack. I think he 347 00:17:44,244 --> 00:17:46,564 Speaker 2: isn't as triggering for people as Elon or Peter Teal, 348 00:17:46,684 --> 00:17:49,724 Speaker 2: but like, but yeah, you know, you have to understand 349 00:17:49,804 --> 00:17:51,764 Speaker 2: how business works and how vet your capital works. But 350 00:17:51,804 --> 00:17:56,244 Speaker 2: people people cannot fathom because their brains are so poisoned 351 00:17:56,244 --> 00:18:00,884 Speaker 2: by politicscept Like, you know, it's not like I want Trump. 352 00:18:00,924 --> 00:18:02,724 Speaker 2: I mean, you know, I'm disclosed here. I'm trying to 353 00:18:02,724 --> 00:18:04,844 Speaker 2: be nonpartisan to our audience. You know, I'm gonna vote 354 00:18:04,844 --> 00:18:12,044 Speaker 2: for Harris if if I'm being super duper honest, I mean, 355 00:18:12,204 --> 00:18:15,564 Speaker 2: most of the audience for kind of high brow news 356 00:18:15,604 --> 00:18:19,684 Speaker 2: content are Democratic leaning voters. The audience at the newsletter 357 00:18:19,764 --> 00:18:24,924 Speaker 2: is very is very colle educated. You know, when when 358 00:18:24,964 --> 00:18:27,884 Speaker 2: Harris is doing well in the polls, then we probably 359 00:18:27,924 --> 00:18:31,604 Speaker 2: convert a little better from we get traffic either way, 360 00:18:31,644 --> 00:18:33,604 Speaker 2: we convert a little better to paid subscriptions when there's 361 00:18:33,604 --> 00:18:34,524 Speaker 2: good news for Harris. 362 00:18:34,404 --> 00:18:37,764 Speaker 1: Of course, of course, I mean confirmation bias, right, and 363 00:18:37,844 --> 00:18:40,404 Speaker 1: people people really when they when you're saying things that 364 00:18:40,444 --> 00:18:44,124 Speaker 1: they agree with and they like you, and then otherwise 365 00:18:44,164 --> 00:18:46,884 Speaker 1: you're you know, you're a paid shill, and I can't 366 00:18:46,924 --> 00:18:48,884 Speaker 1: I can't believe that I'm associated. 367 00:18:48,364 --> 00:18:51,324 Speaker 2: With you, but like people, I think people are. I 368 00:18:51,364 --> 00:18:55,564 Speaker 2: call it the uh. It's called the called the Bonyar 369 00:18:55,644 --> 00:19:00,884 Speaker 2: threshold after a particularly hackish online Democrat named Tom Bonyer, 370 00:19:00,924 --> 00:19:03,364 Speaker 2: who just cannot seem to understand that anybody could ever 371 00:19:03,724 --> 00:19:07,764 Speaker 2: try to want to be true seeking about anything. But 372 00:19:07,844 --> 00:19:11,444 Speaker 2: once you pass the Bonyar threshold, you're no longer capable 373 00:19:11,484 --> 00:19:14,004 Speaker 2: of assuming anyone else acts in good faith. This is 374 00:19:14,004 --> 00:19:16,604 Speaker 2: a mistake poker players make. Actually they assume that everybody 375 00:19:16,604 --> 00:19:20,204 Speaker 2: else plays the same way that they do, right that 376 00:19:20,284 --> 00:19:22,564 Speaker 2: if they'd be bluffing in a particular spot, and maybe 377 00:19:22,564 --> 00:19:25,484 Speaker 2: they have the wrong bluffing frequency, we call it that. 378 00:19:25,524 --> 00:19:27,444 Speaker 2: Therefore the other player must be also and they're mapping 379 00:19:27,484 --> 00:19:28,564 Speaker 2: themselves to the rest of the world. 380 00:19:28,924 --> 00:19:35,364 Speaker 3: So yeah, we'll be back in a minute with yet 381 00:19:35,404 --> 00:19:41,804 Speaker 3: more on the election, the debate, the polls, and the models. 382 00:19:53,204 --> 00:19:56,484 Speaker 2: Here's the guide to how to interpret the polls and 383 00:19:56,524 --> 00:19:59,844 Speaker 2: the models in the coming week or two. And this 384 00:19:59,924 --> 00:20:02,124 Speaker 2: is going to preview a post. It's actually the post 385 00:20:02,124 --> 00:20:03,724 Speaker 2: and silver builting will probably be up by the time 386 00:20:03,764 --> 00:20:06,044 Speaker 2: you listen to this, so go check that post out 387 00:20:06,044 --> 00:20:10,004 Speaker 2: too if you want, like a more precise version. The 388 00:20:10,004 --> 00:20:12,284 Speaker 2: first thing that will come out are poles that are 389 00:20:13,004 --> 00:20:17,524 Speaker 2: kind of crappy, the reason being that like poles that 390 00:20:17,604 --> 00:20:21,164 Speaker 2: you conduct in just today, are gonna have a harder 391 00:20:21,204 --> 00:20:26,004 Speaker 2: time capturing a representative sample electorate. And also the news 392 00:20:26,004 --> 00:20:28,804 Speaker 2: story itself. Part of the benefit of the debate is 393 00:20:28,804 --> 00:20:32,244 Speaker 2: people watching debate night, but also then these clips circulate 394 00:20:32,444 --> 00:20:36,644 Speaker 2: of Trump talking about immigrants eating dogs and cats, and 395 00:20:36,684 --> 00:20:40,684 Speaker 2: the abortion stuff circulates, and you get buzz and favorable 396 00:20:40,684 --> 00:20:43,884 Speaker 2: headlines for Harris. By the way, Taylor Swift, yeah, I. 397 00:20:44,204 --> 00:20:45,924 Speaker 1: Was gonna I was gonna bring that up for sure, 398 00:20:45,964 --> 00:20:48,324 Speaker 1: because that's also a little bounce. While we can talk 399 00:20:48,364 --> 00:20:49,604 Speaker 1: about whether it's a bounce or not. 400 00:20:50,644 --> 00:20:53,364 Speaker 2: It's interesting that she did that, whether they chose to. 401 00:20:53,844 --> 00:20:57,444 Speaker 2: I'm sure I'm sure Tat is coordinating this with her 402 00:20:57,444 --> 00:20:59,484 Speaker 2: friends and the Biden excuse me, not by her, the 403 00:20:59,484 --> 00:21:03,124 Speaker 2: Harris Walls campaign. So they really wanted to drive like 404 00:21:03,164 --> 00:21:06,284 Speaker 2: a big boom news cycle instead of like if it 405 00:21:06,364 --> 00:21:09,244 Speaker 2: were me, I would not have done that tonight. I 406 00:21:09,604 --> 00:21:11,684 Speaker 2: have said I think my debate was strong enough. We'll 407 00:21:11,684 --> 00:21:15,164 Speaker 2: tell Taylor, Hey, Taylor, just hold off. Let's save us 408 00:21:15,164 --> 00:21:17,444 Speaker 2: for a couple of weeks from now, when if there 409 00:21:17,484 --> 00:21:19,244 Speaker 2: is a debate bounce that it might fade in the 410 00:21:19,244 --> 00:21:21,444 Speaker 2: new cycle changes. But anyway, so first you get these 411 00:21:21,524 --> 00:21:24,404 Speaker 2: kind of either online polls that have these large panels, 412 00:21:24,444 --> 00:21:27,444 Speaker 2: that have very large samples, which can be fine poles, 413 00:21:27,444 --> 00:21:28,724 Speaker 2: but tend not to show a lot of movement for 414 00:21:28,764 --> 00:21:31,524 Speaker 2: reasons that aren't getting worth into or really crappy one 415 00:21:31,564 --> 00:21:35,724 Speaker 2: day IVR automated polls, So I wouldn't pay too mu 416 00:21:35,724 --> 00:21:41,764 Speaker 2: attention to anything until maybe this weekend. This weekend, the 417 00:21:41,844 --> 00:21:47,124 Speaker 2: first traditional polls that survey people Wednesday, Thursday, Wednesday, Thursday, 418 00:21:47,124 --> 00:21:50,204 Speaker 2: Friday will come out probably Saturday or Sunday. Those might 419 00:21:50,244 --> 00:21:52,644 Speaker 2: show more movement. If there is movement, those are a 420 00:21:52,644 --> 00:21:55,044 Speaker 2: bigger deal. And then in the following week you'll start 421 00:21:55,084 --> 00:21:57,724 Speaker 2: to see probably more state polling data. So by the 422 00:21:57,804 --> 00:21:59,684 Speaker 2: end of next week we'll have a pretty good idea 423 00:22:00,044 --> 00:22:02,124 Speaker 2: of where the race stands. Now, if I had to 424 00:22:02,164 --> 00:22:06,364 Speaker 2: guess you look at that number in the CNN poll 425 00:22:06,404 --> 00:22:09,164 Speaker 2: typically that does actually correlate with movement in the head 426 00:22:09,164 --> 00:22:12,484 Speaker 2: to have polls. You know, one of the biggest numbers 427 00:22:12,484 --> 00:22:14,244 Speaker 2: ever because gaps ever in the c and M pole 428 00:22:14,404 --> 00:22:18,284 Speaker 2: was in the first debate in twenty twelve when ntt Romney, 429 00:22:19,444 --> 00:22:23,524 Speaker 2: you know, kind of clean Barack Obama's clock and Rodney 430 00:22:23,564 --> 00:22:26,484 Speaker 2: gained about temporarily it faded, but gained about three points 431 00:22:26,484 --> 00:22:29,724 Speaker 2: in polls as a result. After the first debate with 432 00:22:29,844 --> 00:22:35,284 Speaker 2: Trump and and Biden, Trump only gained three points or so. 433 00:22:35,564 --> 00:22:38,044 Speaker 2: Right as disastrous that the bit was for Biden. Now, 434 00:22:38,044 --> 00:22:41,524 Speaker 2: maybe that Trump is like near his theoretical ceiling anyway, Right, 435 00:22:42,404 --> 00:22:44,244 Speaker 2: it's hard for Trump to get much more than fifty 436 00:22:44,244 --> 00:22:47,004 Speaker 2: percent of the electorate. He's a pretty unpopular guy. But 437 00:22:47,364 --> 00:22:49,364 Speaker 2: you know, so Beasonville, the guest might be that Harris 438 00:22:49,404 --> 00:22:52,684 Speaker 2: gets half that a point or a point and a half. 439 00:22:53,804 --> 00:22:56,404 Speaker 2: The question is kind of what the baseline is exactly. 440 00:22:56,444 --> 00:22:59,044 Speaker 2: If you look at the Silver bulletin pulling average, she 441 00:22:59,044 --> 00:23:02,244 Speaker 2: had been ahead by two point two points, I think 442 00:23:02,564 --> 00:23:05,004 Speaker 2: or something. So that gets her to three and a 443 00:23:05,044 --> 00:23:09,844 Speaker 2: half or so. On top of that, our model assumes 444 00:23:09,844 --> 00:23:11,604 Speaker 2: that we're no longer in this convention period, so it's 445 00:23:11,644 --> 00:23:14,924 Speaker 2: no longer penalizing Harris's polling. If she were to get 446 00:23:14,964 --> 00:23:17,404 Speaker 2: up to three and a half point lead, depending on 447 00:23:17,444 --> 00:23:19,364 Speaker 2: what happens in the swing stage, she would emerge out 448 00:23:19,404 --> 00:23:24,884 Speaker 2: of that as frankly a slight favorite in the model. Now. 449 00:23:25,724 --> 00:23:27,564 Speaker 2: I don't want to guarantee that, though, because there's a 450 00:23:27,564 --> 00:23:30,284 Speaker 2: little bit of ambiguity about where the race stood beforehand. 451 00:23:30,604 --> 00:23:32,844 Speaker 2: In some of the very recent national polling, The New 452 00:23:32,924 --> 00:23:34,524 Speaker 2: York Times has this very good poll to do with 453 00:23:34,564 --> 00:23:38,484 Speaker 2: Santa College, very large sample size, very rigorous methodology. Trump 454 00:23:38,524 --> 00:23:41,204 Speaker 2: had been ahead by a point in the popular vote, 455 00:23:41,284 --> 00:23:43,084 Speaker 2: and Trump's a head by a point nationally. He wins 456 00:23:43,084 --> 00:23:45,844 Speaker 2: electoral college like for sure. So there have been some 457 00:23:46,084 --> 00:23:48,644 Speaker 2: bad data for Harris recently. If you can get back 458 00:23:48,644 --> 00:23:50,164 Speaker 2: though to the three and a half point range three 459 00:23:50,244 --> 00:23:53,724 Speaker 2: four that you know, that would count as a as 460 00:23:53,764 --> 00:23:55,404 Speaker 2: a big tournament events. 461 00:23:55,924 --> 00:23:59,124 Speaker 1: Yeah, but it's still it's still incredibly close, right and 462 00:23:59,164 --> 00:24:01,844 Speaker 1: we're still how many weeks out from the election, right? 463 00:24:01,924 --> 00:24:06,244 Speaker 1: Six weeks? We still have we still have quite some 464 00:24:06,404 --> 00:24:09,124 Speaker 1: time to go where things can change, which is why 465 00:24:09,204 --> 00:24:11,924 Speaker 1: I actually I totally agree with you. I thought it 466 00:24:11,964 --> 00:24:14,964 Speaker 1: was so strange that the Taylor Swift endorsement came right now, 467 00:24:15,004 --> 00:24:17,844 Speaker 1: because it kind of got buried right because it was 468 00:24:17,924 --> 00:24:21,124 Speaker 1: late at night, and obviously her her followers are gonna 469 00:24:21,164 --> 00:24:24,124 Speaker 1: are going to be looking at it. But I think 470 00:24:24,164 --> 00:24:26,284 Speaker 1: that you do need to have some of these things 471 00:24:26,324 --> 00:24:28,484 Speaker 1: that you can sprinkle in along the way because it's 472 00:24:28,484 --> 00:24:31,284 Speaker 1: a long road ahead, and as we've talked about before, 473 00:24:31,484 --> 00:24:33,884 Speaker 1: like for Harris, any little slip up is going to 474 00:24:34,004 --> 00:24:37,124 Speaker 1: really really matter. And she doesn't even have to mess up, 475 00:24:37,484 --> 00:24:40,604 Speaker 1: she just has to, you know, do something that's perceived 476 00:24:40,764 --> 00:24:44,204 Speaker 1: the wrong way. She's the one who has to prove, 477 00:24:44,324 --> 00:24:47,604 Speaker 1: I think, and to be kind of on she has 478 00:24:47,644 --> 00:24:50,204 Speaker 1: to be perfect, right, she has to be one hundred percent. 479 00:24:50,324 --> 00:24:52,884 Speaker 2: Although although I think that, you know, let's do another 480 00:24:52,924 --> 00:24:54,684 Speaker 2: poker term, I think they've been a little bit nitty, 481 00:24:55,164 --> 00:24:59,324 Speaker 2: meaning risk averse and neurotic about, for example, not doing 482 00:24:59,364 --> 00:25:01,124 Speaker 2: more media at sure. 483 00:25:01,284 --> 00:25:04,284 Speaker 1: Yeah, yeah that that actually I really do not understand. 484 00:25:05,484 --> 00:25:07,924 Speaker 2: She right now is at forty nine percent of the 485 00:25:08,004 --> 00:25:10,684 Speaker 2: vote in polls. To win, she has to get to 486 00:25:10,684 --> 00:25:14,284 Speaker 2: fifty one percent fifty one because she has a disadvantage 487 00:25:14,324 --> 00:25:15,964 Speaker 2: and all likely hid in the electoral college, so she 488 00:25:16,004 --> 00:25:19,484 Speaker 2: needs to find two percent of the electorate somewhere, which 489 00:25:19,484 --> 00:25:22,764 Speaker 2: seems easy, But there's only about six percent that's undecided 490 00:25:23,004 --> 00:25:26,004 Speaker 2: or voting third party at the moment. Probably one percent 491 00:25:26,004 --> 00:25:28,884 Speaker 2: of that will stay with Jill Stein or whatever else. Right, 492 00:25:28,884 --> 00:25:31,004 Speaker 2: but like, you know, maybe you have five percent of 493 00:25:31,044 --> 00:25:34,124 Speaker 2: the actual electorate in play or four percent or something 494 00:25:34,164 --> 00:25:36,324 Speaker 2: like that, right, you need to get probably half that. 495 00:25:37,324 --> 00:25:40,364 Speaker 2: It's non trivial problem, actually, And like so really reaching 496 00:25:40,404 --> 00:25:44,004 Speaker 2: into like the marginal constituencies, maybe not doing the mainstream 497 00:25:44,004 --> 00:25:47,324 Speaker 2: media hits, but doing some of the weird podcasts and find, 498 00:25:47,644 --> 00:25:50,404 Speaker 2: you know, profiling who are the undecided voters. Maybe it's 499 00:25:50,844 --> 00:25:56,764 Speaker 2: maybe it's men who just are a little uncomfortable with 500 00:25:58,084 --> 00:25:59,764 Speaker 2: voting for a woman. Don't like Trump, but maybe a 501 00:25:59,764 --> 00:26:02,084 Speaker 2: little uncomfortable voting for a woman or things like that. 502 00:26:02,204 --> 00:26:05,244 Speaker 2: I don't know, you know, I think they tend to 503 00:26:05,244 --> 00:26:08,244 Speaker 2: over index on like the Nikki Hayley, Liz Cheney voters. 504 00:26:08,244 --> 00:26:10,364 Speaker 2: They do fine with those. But maybe the more marginalized 505 00:26:10,364 --> 00:26:12,244 Speaker 2: it might be all you know, it might be younger 506 00:26:12,284 --> 00:26:15,924 Speaker 2: black voters and Hispanic voters who you know, do not 507 00:26:15,924 --> 00:26:17,764 Speaker 2: grow up with memories of the Civil rights era and 508 00:26:17,764 --> 00:26:20,004 Speaker 2: require a little bit more persuasion. 509 00:26:20,404 --> 00:26:23,844 Speaker 1: Well, we can invite her to come talk on our podcast, 510 00:26:23,964 --> 00:26:27,724 Speaker 1: right U, Kamala Harris, come on on and help convince 511 00:26:27,764 --> 00:26:28,364 Speaker 1: Donald Trump. 512 00:26:29,124 --> 00:26:31,924 Speaker 2: Donald Trump, yept, what if we moderate a debate? That 513 00:26:31,964 --> 00:26:33,324 Speaker 2: would be funny, that would be amazing. 514 00:26:34,124 --> 00:26:36,924 Speaker 1: Okay, we will, we will host a debate on risky business. 515 00:26:37,324 --> 00:26:38,804 Speaker 1: Let's let's go, and. 516 00:26:39,484 --> 00:26:41,284 Speaker 2: Actually we were you know, some of us is even 517 00:26:41,404 --> 00:26:44,204 Speaker 2: like the area I describe as the river in the book, 518 00:26:44,204 --> 00:26:47,044 Speaker 2: it's like some of the Silicon Valley stuff. And she's 519 00:26:47,084 --> 00:26:49,644 Speaker 2: tried to moderate. I mean she's like, you know, reduced, 520 00:26:49,644 --> 00:26:50,924 Speaker 2: I have not paid all that much touch in this 521 00:26:50,924 --> 00:26:53,564 Speaker 2: policy stuff. She like has a less aggressive plan for 522 00:26:53,604 --> 00:26:59,084 Speaker 2: taxing capital gains then Biden has. You know, Silicon Valley 523 00:26:59,124 --> 00:27:02,564 Speaker 2: seems a little bit less opposed to Harris than they 524 00:27:02,564 --> 00:27:05,764 Speaker 2: were to Biden. I mean, it's a tiny constituency, but 525 00:27:05,884 --> 00:27:08,924 Speaker 2: like the crypto people are a group that, like Trump, 526 00:27:08,924 --> 00:27:12,124 Speaker 2: has gone out after bitcoin declined after debate last night 527 00:27:12,164 --> 00:27:16,244 Speaker 2: because they saw Trump as losing. For instance, areas like that, 528 00:27:16,524 --> 00:27:18,204 Speaker 2: perhaps she. 529 00:27:18,084 --> 00:27:20,644 Speaker 1: Does have some low hanging fruit where if she actually 530 00:27:20,684 --> 00:27:23,524 Speaker 1: comes out, you know, and says something that shows that 531 00:27:23,564 --> 00:27:25,964 Speaker 1: she's going to be supportive of crypto, which right now, 532 00:27:26,004 --> 00:27:28,924 Speaker 1: her actions are not showing right, given given who she 533 00:27:29,004 --> 00:27:31,524 Speaker 1: showsen for her administration, if she does a few key 534 00:27:31,564 --> 00:27:33,164 Speaker 1: things like that, I think she might be able to 535 00:27:33,204 --> 00:27:36,044 Speaker 1: pick up like one percent, right which, as we know, 536 00:27:36,164 --> 00:27:39,044 Speaker 1: like one percent matters, every single percent matters. 537 00:27:39,084 --> 00:27:44,484 Speaker 2: Or point or point one percent, which also matters. Kamala coin, Yes, 538 00:27:44,884 --> 00:27:46,244 Speaker 2: Kamala coin, Yep. 539 00:27:47,044 --> 00:27:49,404 Speaker 1: Absolutely, she can do a cat she can do a 540 00:27:49,444 --> 00:27:53,924 Speaker 1: cat coin for cat ladies, right. I think that that 541 00:27:54,044 --> 00:27:56,444 Speaker 1: would work. I mean, Taylor Swift's endorsement had a cat 542 00:27:56,484 --> 00:27:59,284 Speaker 1: in the picture. So lean into that, you know, show 543 00:27:59,324 --> 00:28:02,204 Speaker 1: that you understand the culture. Nate, I do have a question, 544 00:28:02,284 --> 00:28:04,764 Speaker 1: and you don't have to answer if you don't want to. 545 00:28:04,804 --> 00:28:08,684 Speaker 1: But I'm curious, how strongly do you believe kind of 546 00:28:08,804 --> 00:28:11,124 Speaker 1: in the current output of your model, Like, do you 547 00:28:11,324 --> 00:28:13,924 Speaker 1: do you think that Harris right now is kind of 548 00:28:14,164 --> 00:28:14,924 Speaker 1: poised to lose? 549 00:28:16,284 --> 00:28:20,484 Speaker 2: Not right now because now you have an absence of 550 00:28:20,524 --> 00:28:22,804 Speaker 2: post debate data that could move the numbers quite a bit, 551 00:28:23,004 --> 00:28:26,244 Speaker 2: but you know, as of like as of uh, Tuesday 552 00:28:26,324 --> 00:28:30,124 Speaker 2: morning or Tuesday night before the debate, when she's at 553 00:28:30,524 --> 00:28:34,324 Speaker 2: thirty eight percent, thirty eight and a half percent, yeah, 554 00:28:34,324 --> 00:28:36,164 Speaker 2: more or less. I mean, look, I'm aware that there 555 00:28:36,244 --> 00:28:38,644 Speaker 2: are other models out there, some of which are good, 556 00:28:38,644 --> 00:28:41,124 Speaker 2: some of which you are less good. I'm aware that 557 00:28:41,164 --> 00:28:45,124 Speaker 2: prediction markets are generally pretty smart, and those also Harris, 558 00:28:45,164 --> 00:28:48,084 Speaker 2: you know, Polly market her whatever, forty five is, forty 559 00:28:48,084 --> 00:28:50,764 Speaker 2: four is percent range instead of thirty eight. That's like 560 00:28:50,844 --> 00:28:55,164 Speaker 2: not a huge gap. But look, one reason to believe 561 00:28:55,204 --> 00:28:59,684 Speaker 2: the model is that like is that, you know, if 562 00:28:59,684 --> 00:29:03,804 Speaker 2: I were doing this subjectively, then I'd be rooting for Harris, 563 00:29:03,804 --> 00:29:06,004 Speaker 2: probably right. I am not a Trump fan, not a 564 00:29:06,124 --> 00:29:10,244 Speaker 2: huge fan of Harris's politics, frankly either, but I consider 565 00:29:10,684 --> 00:29:13,484 Speaker 2: after January sixth, there are lots of reasons why I 566 00:29:13,484 --> 00:29:17,004 Speaker 2: would never vote for Trump, and she's the alternative, you know, 567 00:29:17,324 --> 00:29:20,444 Speaker 2: for various reasons. I think. Probably also, if you were 568 00:29:20,484 --> 00:29:25,084 Speaker 2: somebody who was really agitating, as I was, for somebody, 569 00:29:25,764 --> 00:29:28,844 Speaker 2: not necessarily Harris, frankly, but somebody to replace Biden, then 570 00:29:28,884 --> 00:29:30,364 Speaker 2: you're gonna look really smart if you're one of the 571 00:29:30,364 --> 00:29:31,764 Speaker 2: people who said you gotta get rid of Biden, and 572 00:29:31,804 --> 00:29:34,484 Speaker 2: then that person wins and Harris is probably good for 573 00:29:35,324 --> 00:29:39,644 Speaker 2: site traffic and newsletter subscriptions. Frankly. So the fact that 574 00:29:39,804 --> 00:29:43,484 Speaker 2: I have all these incentives to root for Harris is 575 00:29:43,484 --> 00:29:45,964 Speaker 2: the reason to trust the process I set up ahead 576 00:29:45,964 --> 00:29:49,324 Speaker 2: of time, before I knew how this would affect anything, 577 00:29:49,604 --> 00:29:53,244 Speaker 2: and the same process yielded reliable model results since two 578 00:29:53,324 --> 00:29:56,124 Speaker 2: thousand and eight over many elections, And so I'm not 579 00:29:56,204 --> 00:30:00,364 Speaker 2: involving my emotions. So yeah, if I were, you know, look, 580 00:30:00,404 --> 00:30:03,244 Speaker 2: as a Bayesian, you'd kind of combine your forecast with 581 00:30:04,164 --> 00:30:06,804 Speaker 2: other information, other data. So probably realisticly would be like 582 00:30:07,364 --> 00:30:11,604 Speaker 2: forty two percent, taking the mid point of like polymarkets 583 00:30:11,644 --> 00:30:13,884 Speaker 2: forty six and our models thirty eight, and you get to, 584 00:30:13,964 --> 00:30:16,604 Speaker 2: like Harris forty two or forty three is probably my 585 00:30:16,644 --> 00:30:20,364 Speaker 2: subjective belief that seems not to go out on too 586 00:30:20,364 --> 00:30:21,044 Speaker 2: far of a limb. 587 00:30:21,444 --> 00:30:22,004 Speaker 1: That makes sense. 588 00:30:22,044 --> 00:30:23,924 Speaker 2: It would now be higher now she's gained, you know, 589 00:30:24,204 --> 00:30:25,844 Speaker 2: but now I think, you know, if I did predict 590 00:30:25,844 --> 00:30:28,084 Speaker 2: what our model will say in two weeks, it might 591 00:30:28,124 --> 00:30:30,404 Speaker 2: be fifty percent Harris fifty three or something like that. 592 00:30:30,764 --> 00:30:40,084 Speaker 1: Well, here's hoping after the break travel with us to Barcelona, 593 00:30:40,324 --> 00:30:44,124 Speaker 1: where I was just playing in EPT Barcelona, the European 594 00:30:44,244 --> 00:30:47,004 Speaker 1: Poker Tour, and we'll have a little poker for you. 595 00:31:00,324 --> 00:31:02,884 Speaker 2: So Maria, well, I've been bouncing back and forth between 596 00:31:02,924 --> 00:31:04,884 Speaker 2: the West coast and the East Coast and the UK 597 00:31:05,004 --> 00:31:08,164 Speaker 2: for a couple of days. You have been in fabulously 598 00:31:08,604 --> 00:31:14,044 Speaker 2: beautiful Barcelona, Spain playing poker. I see from the hand 599 00:31:14,084 --> 00:31:17,404 Speaker 2: and mom database that you cashed three times, including one 600 00:31:17,764 --> 00:31:21,044 Speaker 2: rather large cash. Do you want to tell us about that? Yeah? 601 00:31:21,084 --> 00:31:25,204 Speaker 1: Absolutely, I had a nice trip. This was the Poker 602 00:31:25,204 --> 00:31:28,444 Speaker 1: Star's twentieth anniversary of the European Poker Tour, so it 603 00:31:28,524 --> 00:31:31,804 Speaker 1: was a huge event with some record setting fields and 604 00:31:32,244 --> 00:31:35,324 Speaker 1: there was one event. So there's a new format that 605 00:31:35,444 --> 00:31:38,164 Speaker 1: was introduced a few years ago in poker. That's the 606 00:31:38,204 --> 00:31:42,964 Speaker 1: mystery bounty, which appeals to djens such as Nate. Because 607 00:31:43,004 --> 00:31:47,244 Speaker 1: I'm not a djen. Nate as a jen, right, I 608 00:31:47,244 --> 00:31:47,764 Speaker 1: mean it does. 609 00:31:47,964 --> 00:31:51,164 Speaker 2: I don't like mystery bounties that much. I mean, I 610 00:31:51,204 --> 00:31:54,404 Speaker 2: don't know KOs are more from. These are different, We're 611 00:31:54,444 --> 00:31:56,924 Speaker 2: not maybe a little above people's heads. But yeah, so 612 00:31:57,284 --> 00:32:01,284 Speaker 2: Wester go ahead. So the mystery bounty is that if 613 00:32:01,324 --> 00:32:03,484 Speaker 2: you knock out a player, usually after some threshold, if 614 00:32:03,524 --> 00:32:05,324 Speaker 2: you get to day two of a tournament, then you 615 00:32:05,364 --> 00:32:07,804 Speaker 2: knock out a player, you get like a little card 616 00:32:07,884 --> 00:32:10,484 Speaker 2: that says you get to draw from this like big 617 00:32:10,564 --> 00:32:15,764 Speaker 2: vat literally of envelopes, and some of the envelopes contain 618 00:32:16,524 --> 00:32:18,524 Speaker 2: very large amounts of money, such as what was top 619 00:32:18,524 --> 00:32:22,164 Speaker 2: prize here, like something one hundred thousand, one hundred thousand 620 00:32:22,204 --> 00:32:25,044 Speaker 2: euro Yes, one hundred thousand euros. It's one hundred and 621 00:32:25,084 --> 00:32:29,884 Speaker 2: ten whatever thousand American dollars. So yeah, it's a way 622 00:32:29,964 --> 00:32:32,004 Speaker 2: for a cheap entry to have. Like usually you can 623 00:32:32,044 --> 00:32:34,484 Speaker 2: only make these six figure sums if you like win 624 00:32:34,484 --> 00:32:37,364 Speaker 2: the tournament or finish in the top like two or three. 625 00:32:37,764 --> 00:32:40,924 Speaker 2: This is a way to have another opportunity without having 626 00:32:40,924 --> 00:32:43,004 Speaker 2: to win, without having to beat everybody, just knocking one 627 00:32:43,004 --> 00:32:45,284 Speaker 2: player out and you win one hundred thousand euros or 628 00:32:45,324 --> 00:32:47,524 Speaker 2: a million dollars and some of the worldsairs of poker ones. 629 00:32:48,204 --> 00:32:49,444 Speaker 2: So did you win a big bounty then. 630 00:32:49,404 --> 00:32:53,124 Speaker 1: Maria, Yeah, so well, first of all, you know, so 631 00:32:53,204 --> 00:32:55,684 Speaker 1: in this mystery bounty tournament, it was a three three 632 00:32:55,684 --> 00:32:58,964 Speaker 1: thousand euro buy in. I ended up coming in fifth, 633 00:32:59,244 --> 00:33:03,284 Speaker 1: which is huge. I beat out over eleven hundred players 634 00:33:03,804 --> 00:33:06,364 Speaker 1: in order to make the final table, so really nice, 635 00:33:06,364 --> 00:33:09,764 Speaker 1: deep run. I was very happy with the result. And 636 00:33:10,324 --> 00:33:14,124 Speaker 1: so I had ten bounties to pull, which is a lot, right, 637 00:33:15,204 --> 00:33:18,564 Speaker 1: ten bounties is a good amount and I have never 638 00:33:18,804 --> 00:33:24,084 Speaker 1: yet wow, and I have never pulled anything other than 639 00:33:24,164 --> 00:33:27,884 Speaker 1: a min bounty. So a min bounty is a minimum bounty. 640 00:33:28,124 --> 00:33:31,364 Speaker 1: In this particular tournament, that was one thousand euros, and 641 00:33:31,404 --> 00:33:34,364 Speaker 1: this was by far the most common bounty. So we 642 00:33:34,404 --> 00:33:37,684 Speaker 1: are going to now pivot to a very non risky 643 00:33:37,724 --> 00:33:41,164 Speaker 1: business way of thinking. So here I am with ten 644 00:33:41,204 --> 00:33:44,004 Speaker 1: bounties to draw. I draw the first one. It's a men. 645 00:33:44,644 --> 00:33:46,884 Speaker 1: I wait a while to draw another one. It's another men. 646 00:33:47,924 --> 00:33:51,164 Speaker 1: So at some point I've drawn five men bounties and 647 00:33:51,204 --> 00:33:53,764 Speaker 1: I have five left. Now the one hundred k's are gone. 648 00:33:54,884 --> 00:33:58,924 Speaker 1: And there is a player who everyone in the poker 649 00:33:58,964 --> 00:34:02,084 Speaker 1: world knows and anyone who was following the main event 650 00:34:02,124 --> 00:34:05,404 Speaker 1: this year knows as well. Nicholas Sausted, otherwise known as 651 00:34:05,524 --> 00:34:08,324 Speaker 1: Lena by his online screen name. He had a very 652 00:34:08,324 --> 00:34:10,604 Speaker 1: deep run in the main event, and a lot of 653 00:34:10,884 --> 00:34:13,164 Speaker 1: poker players, including me, were rooting for him to win 654 00:34:13,484 --> 00:34:16,244 Speaker 1: because he's a phenomenal player and a really really nice guy. 655 00:34:17,204 --> 00:34:21,644 Speaker 1: Lena is also someone who's quite lucky and runs incredibly well. 656 00:34:23,604 --> 00:34:25,844 Speaker 1: He plays well. But see this is why I said, 657 00:34:26,044 --> 00:34:28,324 Speaker 1: We're about to pivot into a non risky business way 658 00:34:28,364 --> 00:34:31,004 Speaker 1: of thinking. By this point, he had been knocked out 659 00:34:31,004 --> 00:34:33,644 Speaker 1: of the bounty event, and he had drawn two bounties. 660 00:34:33,684 --> 00:34:35,604 Speaker 1: He had drawn a twenty five K and a ten K. 661 00:34:36,124 --> 00:34:39,204 Speaker 1: So what I decided to do was send a text 662 00:34:39,204 --> 00:34:42,284 Speaker 1: message to Nicholas and ask him if he would draw 663 00:34:42,404 --> 00:34:47,804 Speaker 1: my remaining bounties. This is an incredibly non scientific, irrational 664 00:34:47,884 --> 00:34:49,924 Speaker 1: way of thinking. I was like, I am going to 665 00:34:50,004 --> 00:34:53,844 Speaker 1: get this very lucky player who's very good at drawing bounties. 666 00:34:54,004 --> 00:34:56,204 Speaker 1: By the way, we know, right, you cannot be good 667 00:34:56,284 --> 00:35:00,124 Speaker 1: or bad at drawing bounties. This is random. But I'm 668 00:35:00,164 --> 00:35:02,404 Speaker 1: going to get him to draw my remaining bounties. So 669 00:35:02,444 --> 00:35:03,724 Speaker 1: he said, sure, he would do it. 670 00:35:04,164 --> 00:35:05,004 Speaker 2: He charge a fee. 671 00:35:05,164 --> 00:35:06,684 Speaker 1: Now he did not charge a fee because he's a 672 00:35:06,764 --> 00:35:10,364 Speaker 1: nice guy. So here I am playing, did not even know, 673 00:35:10,804 --> 00:35:12,524 Speaker 1: you know, if he was actually going to show up. 674 00:35:12,844 --> 00:35:15,604 Speaker 1: So we're down to two tables. You know, Nicholas is 675 00:35:15,604 --> 00:35:17,844 Speaker 1: still not there. You know, I'm getting worried that I'm 676 00:35:17,844 --> 00:35:21,124 Speaker 1: gonna have to draw my other bounties. Tap on my shoulder, 677 00:35:21,324 --> 00:35:23,004 Speaker 1: there he is. He said, are you ready to draw? 678 00:35:23,164 --> 00:35:24,884 Speaker 1: He said, oh my god, yes, So I give him 679 00:35:24,884 --> 00:35:27,244 Speaker 1: the five bounties. We go up and he says what 680 00:35:27,324 --> 00:35:29,044 Speaker 1: are the top bounties left? And I said, well, there's 681 00:35:29,044 --> 00:35:30,884 Speaker 1: a twenty five K left and a ten K left. 682 00:35:31,004 --> 00:35:33,724 Speaker 1: And he said, okay, we got this. So he draws 683 00:35:33,924 --> 00:35:37,324 Speaker 1: and he draws, and you know, he draws my five 684 00:35:37,364 --> 00:35:38,844 Speaker 1: bounties and he said, do you want to open them? 685 00:35:38,924 --> 00:35:42,044 Speaker 1: Do I open them? I said, let's take turns. So 686 00:35:42,164 --> 00:35:43,884 Speaker 1: I opened the first one. It's a twenty five K. 687 00:35:44,364 --> 00:35:46,644 Speaker 1: He opens the second one it's a ten K, and 688 00:35:47,284 --> 00:35:49,324 Speaker 1: he's like, okay, I did it for you. He's like, 689 00:35:49,364 --> 00:35:51,124 Speaker 1: the only other ones left are the one case right, 690 00:35:51,124 --> 00:35:53,204 Speaker 1: And I was like, yep, so then the other three 691 00:35:53,244 --> 00:35:53,924 Speaker 1: were one case. 692 00:35:54,124 --> 00:35:58,444 Speaker 2: So my little bounties were there remaining at this point 693 00:35:58,644 --> 00:36:02,484 Speaker 2: about forty so there are two good ones out of forty. 694 00:36:02,244 --> 00:36:05,524 Speaker 1: Yep in the restaurant and he picks those ten picked five, 695 00:36:05,684 --> 00:36:06,324 Speaker 1: He picked five. 696 00:36:06,524 --> 00:36:09,004 Speaker 2: He picked five, yep, so five out of forty and 697 00:36:09,044 --> 00:36:12,244 Speaker 2: they have to I mean, do some combinatorics readers. I'm 698 00:36:12,244 --> 00:36:13,884 Speaker 2: sure we have readers who can do the math here, 699 00:36:13,924 --> 00:36:17,724 Speaker 2: but like, are you sure that? Because there are these 700 00:36:17,804 --> 00:36:22,324 Speaker 2: envelope politic conspiracies if you're an NBA fan. Supposedly, the 701 00:36:22,924 --> 00:36:25,244 Speaker 2: NBA lottery was rigged for New York Knicks to get 702 00:36:25,284 --> 00:36:27,764 Speaker 2: in Patrick Ewing out of Georgetown, for example, And like 703 00:36:28,084 --> 00:36:31,284 Speaker 2: one of the envelopes was like the others had like 704 00:36:31,284 --> 00:36:32,084 Speaker 2: a bet edge. 705 00:36:33,444 --> 00:36:36,084 Speaker 1: There were any no marked envelopes, and I saw him drawing. 706 00:36:37,004 --> 00:36:40,084 Speaker 1: He just he has the magic touch, which is the single. 707 00:36:40,844 --> 00:36:42,444 Speaker 2: Special contacts or glasses. 708 00:36:42,484 --> 00:36:45,844 Speaker 1: Maybe this is the single most irrational thing I will 709 00:36:45,884 --> 00:36:49,324 Speaker 1: ever say on this podcast. Thank you so much, Nicholas 710 00:36:49,324 --> 00:36:53,724 Speaker 1: for adding thirty five thousand euros to my life. 711 00:36:53,964 --> 00:36:57,284 Speaker 2: Let's be here. What if he had said I wanted 712 00:36:57,284 --> 00:37:01,484 Speaker 2: two percent feet? Uh huh? Would you have accepted that feet? 713 00:37:01,484 --> 00:37:03,844 Speaker 2: He gets two percent of whatever bounty you in above 714 00:37:03,884 --> 00:37:05,124 Speaker 2: the minimum. 715 00:37:05,684 --> 00:37:06,204 Speaker 1: I don't know. 716 00:37:06,644 --> 00:37:07,324 Speaker 2: We didn't. 717 00:37:07,444 --> 00:37:13,044 Speaker 1: We didn't have to go there. Okay, I did not 718 00:37:13,164 --> 00:37:16,924 Speaker 1: think he would ask. I think I would pay him 719 00:37:16,964 --> 00:37:20,324 Speaker 1: one percent too, but as a basi, and at the 720 00:37:20,404 --> 00:37:22,684 Speaker 1: end I said that I would buy him a nice 721 00:37:22,684 --> 00:37:25,084 Speaker 1: dinner wherever he wanted to go. But he did not 722 00:37:25,164 --> 00:37:27,684 Speaker 1: take it up on me. Take me up on it, 723 00:37:28,004 --> 00:37:32,124 Speaker 1: and I will reiterate the invitation, Nicholas, if you're listening 724 00:37:32,644 --> 00:37:36,524 Speaker 1: any stop anywhere, it's on me. Thank you so much. 725 00:37:36,804 --> 00:37:39,164 Speaker 1: And if we're ever playing a mystery bounty together, I 726 00:37:39,204 --> 00:37:41,764 Speaker 1: hope that you will draw my envelopes for me again, 727 00:37:42,004 --> 00:37:46,204 Speaker 1: because I'm a very rational person who does not aspire 728 00:37:46,564 --> 00:37:47,884 Speaker 1: to be superstitious ever. 729 00:37:58,164 --> 00:38:01,084 Speaker 2: Risky Business is hosted by me Nate Silver and me 730 00:38:01,204 --> 00:38:04,004 Speaker 2: Maria Kanakova. The show was a co production of Pushkin 731 00:38:04,044 --> 00:38:08,084 Speaker 2: Industries and iHeartMedia. This episode was produced by Isabel Carter. 732 00:38:08,564 --> 00:38:12,444 Speaker 2: Our associate produce is Gabriel Hunter Chang. Our engineer is 733 00:38:12,484 --> 00:38:15,644 Speaker 2: Sarah Bruger. Our executive producer is Jacob Goldstein. 734 00:38:16,364 --> 00:38:18,124 Speaker 1: If you want to listen to an ad free version, 735 00:38:18,164 --> 00:38:20,844 Speaker 1: sign up for Pushkin Plus for six ninety nine a month. 736 00:38:20,844 --> 00:38:23,484 Speaker 1: If you get access to ad free listening, Thanks for 737 00:38:23,524 --> 00:38:23,924 Speaker 1: tuning in.