September 12, 2024 • 38 mins
Nate and Maria discuss the Harris-Trump debate. Then, Nate gives an update on his election model, and Maria shares the outcome of her irrational move at a poker tournament in Barcelona.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:15):
Pushkin. Welcome back to Risky Business, our show about making
better decisions. I'm Maria Kanikova.
Speaker 2 (00:31):
And I'm Nate Silver.
Speaker 1 (00:32):
So today, Yeah, Nate, I.
Speaker 2 (00:35):
Think I think so much better at the intros, in
the positions, and now we're reverting me. It's been a
couple of weeks since we've had like a normal episode.
Speaker 1 (00:42):
That's true, it's true. It's good to see you. You
are in Jackson Hole.
Speaker 2 (00:47):
Yeah, Jackson Hole, Wyoming, a very you know, God's country
as they might call it, and that is very pretty.
Speaker 1 (00:54):
Yes, and so it's actually quite appropriate. I'm back in
New York City, So we're in two different parts of
the country to discuss something that hopefully will help unite
those different parts of the country, the presidential debate from
last night. So we are recording this the morning of Wednesday,
September eleventh. So last night you and I both were
(01:17):
listening to the debate from our respective different parts of
the country, and Kamala Harris tried to make her appeal
to both of us, right to all Americans all over
the country. So what'd you think, Nate.
Speaker 2 (01:29):
You know, I'm here in Teaton County. Well, actually it's
the one blue County in all of Wyoming.
Speaker 1 (01:35):
Yeah, I love your voices. I love your voice.
Speaker 2 (01:38):
I thought Kamala Harris had an excellent debate, or maybe
maybe I think subtly more that former President Trump had
a really bad debate, playing into basically every critique that
you might have of Donald Trump, the fact that repeatedly
she was able to tease them about his rally crowd
(01:59):
sizes and things like that, and how foreign leaders don't
really respect him, and every single time he took the
bait and got distracted and had these long two minute
segments with no mics off, no interruption, and would spend
the first minute complaining about whatever Kamala said, and then
the next thirty seconds with a coherent point, and then
veering off in some other random direction for another thirty seconds.
(02:21):
A lot of talk about things like a reference to
if you're not really into politics, this will sound bizarre
because it is, but Haitian immigrants, oh.
Speaker 1 (02:31):
My god, yes, in.
Speaker 2 (02:32):
Ohio killing dogs and cats, you know, talking about Sean
Hanny and Laura Ingram and like just kind of not
really able to escape the fact that he's trying to
persuade over this tiny fraction five percent of the country
that is still undecided.
Speaker 1 (02:48):
Yeah, can we just pause for a second. Not just
killing dogs and cats, but eating them, eating them. And
this is a conspiracy theory that came from like the
deep bowels of conspiracy theoryedom and here we have the
former president just like losing it and completely, you know,
just going off on this. He was actually fact chucked
(03:09):
on this by the moderators during the debate, which they
did a few times, which was new, right, they haven't
done that before, where they said, you know, actually there's
zero evidence of this. But he just didn't know where
to go. I think, you know, I was worried, you know,
going into this debate. I thought it could go a
lot of different ways, and I was just hoping that
(03:30):
she wouldn't let him get to her right because as
a female also, the risk is so much bigger because
if she starts feeling defensive or indignant or anything, you know,
because she's female, it's going to come off a lot
worse and it's going to just completely blow up. She
did not, and it was actually the other way around
where she was able to kind of just needle him,
and he every single time squandered most of his airtime
(03:54):
in responding to the needles as opposed to saying anything substantive,
which I think was the entire point.
Speaker 2 (04:00):
Yeah, she is very different about gender issues tactically than
Hillary Clinton was, and to my male eye, more effect
I think in part by like sometimes jabbing. In poker terms,
we might call it. This is probably too esoteric. A
donk bet donk bet is when you unexpectedly lead with
a bet into the person that you're expecting to bet right, donk,
(04:23):
di O, and k. A donkey in poker is a
name for a bad player. A fish donk is a
shortening of donkey. So this was thought to be a
play that a bad player makes it doesn't know to
check to the person who made the last bet. It
turns out that and the fancy computer simulations you're supposd
to this sometimes but can also be a way to
exploit somebody, to get under their skin. And it can
(04:45):
I'm not gonna talk about the game theory here, but
it can throw them off rhythm. Sometimes it is correct
actually under game theory, but it's a way to take
charge of the hand. And she did that a couple
of times, like kind of premptively saying, Hey, Trump's going
to lie a lot, you know, even on issues that
would seem like relative weaknesses for her, like, for example,
the unpopularity of President Biden in the fact that she's
been the vice president for three and a half years.
(05:07):
She would say, by the way, I'm not Joe Biden,
and like actually lean into that. And I think she
was well prepared for this event. And I don't know
to what extent that credit goes to her versus her campaign.
The Democrats to have a lot of data. I mean,
Donald Trump has had one, two, three, four or five.
This is his seventh general election debate, and he doesn't
(05:28):
really change form that much, except in the Biden debate,
where Biden was so bad that some instinctual part of
Trump knew that you have to maybe not go for
the jugular that much. Although Trump was actually kind of
bad in that debate too, it's just that when Biden.
Speaker 1 (05:41):
In comparison, he was he was fine.
Speaker 2 (05:44):
Yeah, but you know, Biden couldn't like nail him on
the things he were saying that were very very false
and or crazy. So I mean, first of all, if
nothing else, if Harris loses and we'll talk about this
in a minute, probably still in the vicinity of fifty
to fifty. If nothing else, you have this a b
test where replacing Joe Biden with Kamal Harris was a
really good idea.
Speaker 1 (06:05):
Yeah, and I actually think your dunk bet analogy is
really really a good one. First of all, just talking
from personal experience. The first, you know, I've actually let
people get under my skin doing that, you know, because
it's so it's unexpected, and the attitude is, you know,
you check to the pre flop aggressor, right, so the
person who has them, has the aggressive lead, is supposed
(06:27):
to be allowed to continue that aggression. And there have
been a few times I even remember when I've made
big mistakes and hands because someone has donked it to me,
and I'm like, no, you fucker, you don't get to
do that, and I raise, which is exactly what I
shouldn't be doing in some specific scenarios where they actually,
like clearly are donk betting because they have something. And
I remember those hands vividly because it's very unlike me
(06:51):
to to start tilting in that particular way. I've gotten
over it now, but it's actually, you know, it can
be a very effective strategy. And she did that. There
was actually one time where at first I was like, wait,
what the hell did she just do? And then I
realized what she was doing right when she was answering
a question and all of a sudden she says, by
the way, you know, I invite you to go to
(07:11):
Donald Trump's rallies. And I'm like, wait, you know what
a non sequort or what did she Why is she
doing that? And then it became very clear was a
very good strategic move because she was kind of deflecting
away from her record on something that she didn't really
want to talk about, and instead then Trump kept, you know,
for the next two minutes talking about how he has
the biggest rallies in history.
Speaker 2 (07:30):
Yeah, and she is taking some I mean, she is
veering off topic, and she often did it on questions
like immigration that are not great topics for her. Yeah,
And if Trump had responded coherently and not taken the bait,
then you'd have his media buzz about how she went
really off topic and was evasive, right, And instead he's
more often absolutely absolutely.
Speaker 1 (07:50):
But yes, I actually thought, you know, it was it
was quite funny because I was like, oh, you know,
she's learned to play the game of not answering the question,
because Donald Trump never answers the question. And as the
debate went on, she was doing the exact same thing,
and I'm like, huh, okay, well you know what that's
I guess how these debates go. But she she definitely
learned to play that game, and I actually thought she
(08:11):
became much stronger as a night war on. I was
very nervous when she started giving her first response, because
you could tell she was nervous, you know, as someone
who has done public speaking, who's kind of watched a
lot of debaters, you know, you and I were both
debaters all through school. You could tell, like the first
few minutes, you know, she was nervous and she was
(08:32):
a little stiff. But then she kind of got over it,
and I think she kind of became much more fluid
and really kind of picked up in style. And I
was very happy to see that. And he had kind
of some of the opposite trajectory where kind of he
started off thinking that, you know, I'm gonna just completely
demolish her, and then he started getting very defensive.
Speaker 2 (08:52):
Yeah, public speaking for me, at least, I don't know
if you're different, Maria, but like it's not It isn't
actually like just having a conversation with a friend at
a bar that your body recognizes that this is a
high stakes momentolutely and you're operating under a elevated person
but also a stress response. Now, in my experience, what
(09:14):
you have to do is just kind of be a
little bit self conscious about it. Right, You're like, I
am on terrain that I either rehearsed or that I've
spoken about many times, and I'm just gonna say words
and lo and behold, they come across coherent most of
the time. If you're tired or fatigued, or or you're
in a room where the setting is weird, the lighting is,
you can be thrown off and that can be hard
(09:35):
to recover from. But you have to like you can't
overthink it too much, and you have to trust your
experience base that like, yeah, this is actually gonna go fine,
and some people are even better in that setting than
they would be a meeting or something.
Speaker 1 (09:49):
Yeah, I think that's absolutely true. And I actually have
a stress response every single time I give a talk
right before I go on stage every single time, and
I give lots of talks. So this is something that
that definitely happens. But then you know, you could you
could tell that she does have the experience, she was prepared,
and she was able to kind of establish the momentum.
The other thing that I thought was very interesting was,
(10:11):
you know, Trump won the coin toss and decided that
he was going to give the closing, you know, the
final closing, and I actually think that that worked against him,
because he spent most of his closing remarks yelling at
what she said, as opposed to kind of giving any
summary of anything that he had said. So so I think
that she came across much better at the end of
the debate. And the way that you know, impressions and
(10:31):
memory formation works, I think that that really works in
her favor.
Speaker 2 (10:36):
And the beginning too. I mean, in the beginning, I
agreed at the very first question on the economy is
strong territory for Trump. But then you have extended remarks
on abortion for a moment, I thought that might be
the story of the night. And then you have which is,
by the way, if you're not a very good issue
for Democrats.
Speaker 1 (10:52):
Since she did very well, she did very well Onlke Biden,
who kind of you know, pivoted to immigration instead of
talking about abortion. She really nailed the abortion.
Speaker 2 (11:02):
And then you have the bait taking phase. I mean, look,
we may look back if Harris wins and say that
the Trump can made a series of really predictable mistakes.
The lack of preparation tonight were even in the closing statement.
He can't really stay on focus and talk about Harris's record,
for example, I mean other things too. I mean, naming
Jadie Vance is one. Not preparing for the transition from
(11:25):
Biden to Harris when they had been telegraphed at least
for a few weeks as a possibility, a strong possibility.
His convention speech where he rambled off topic after a
more subdued thirty minutes on the assassination attempt, you know,
frankly accepting that first debate with Biden. I mean, I
guess you may not have played out all the tactics,
but if he hadn't done that, then Biden's on stage
(11:48):
tonight having that debate from June tonight, and Democrats are
appropriately panicking because they're probably totally fucked at that point.
So thanks to the White House for moving that forward.
If you're a Democrat, I guess yeah, for sure. By
the way, did you notice that he did not call
her by her name a single time during the night.
He kept saying she and pointing at her, and he
(12:10):
never said vice President Harris. He never said Kamala Harris.
He used her name exactly zero times. I've actually haven't
seen anyone mention that, But that's something that really struck
me and kind of shows how, you know, it shows
you so much about his attitude about kind of his
conception of women, his conception of her. You know, she's
not even worthy of calling by the name. Because I
(12:31):
was I was actually paying attention to it because I
was wondering if he was going to mispronounce her name
on purpose the way he's always done, and what her
reaction was going to be.
Speaker 1 (12:39):
But he didn't. He didn't even say her name. So
I thought that that was something that was an interesting point,
and I thought that she tried to do a really
she tried to do a nice job since she was
in Pennsylvania of talking about the fact that she wasn't
going to stop fracking and that she, you know, was
upping well production in all of these things. And it's
a very fine line for her to walk right to
(13:01):
try to say, you know, I want a green future.
I'm worried about climate change, but believe me, I'm not
going to hurt your jobs. So I think that she
tried her best to appeal to Pennsylvania, which is obviously crucial, and.
Speaker 2 (13:15):
She is flip flopping on a lot of this stuff.
So there's a lot of fertile ground that Trump could
what's the metaphere they could have trod or I'm not
sure what their metaphor is exactly. Yep. And again the
lack of discipline from Trump, you know, just to say
she's a radical left winger, as she showed in San
Francisco and in twenty nineteen when she wanted to do
(13:36):
X and Y and Z, and now she's flip flopping
because she's desperate to, you know, make up for Joe
Biden's mistakes. You can do a fair amount of that.
I do think that's an issue for Harris with some voters.
But look, they got another data point. So the convention,
we can talk about the trajectory of the horse race.
Maybe in a moment. I thought she gave a very
good convention speech. Also that was also kind of agro
(13:59):
and aimed at male voters. And she 's some of
these same themes tonight, talking about competitiveness with China, talking
about how aggressive and lethal our military is. For instance,
sometimes when you hear something for the second time, it
closes a deal, right, you know, So obviously there's some
chance that there's a bounce in the polls Harris, by
(14:20):
the way, so we're not just totally giving you our
subjective opinion. In the CNN post debate poll, she won
sixty three thirty seven or something, and Market.
Speaker 1 (14:28):
As well also had her her odds going up, So
I think that that is also I think by three points, right,
she moved up by thy point.
Speaker 2 (14:36):
Before the debate, it had been like Trump fifty four,
Harris forty six. These are not vote shares, these are
win probabilities, and after it's fifty to fifty technically forty
nine forty nine, they price in some chance that I
don't know, somebody has to drop out due to a
health issue, I guess, But basically drew the race back
to even. In the Silver Bulletin forecast, she had been
(15:00):
at thirty eight thirty nine percent heading in. If she
were to get that polymarket bounce, then she'd be in
the you know, forty five percent range. So fairly close
to fifty to fifty. The way the model works, the
model was very disappointed. Shall we say, not that it
has emotions. It's not some large language model that the
(15:24):
model is disappointed with Harris's post convention polling, where typically
after a convention is one of the best periods for
a candidate throughout the life of the election cycle. And
Harris was not only not getting a sustained bounce, she
may have gotten one for a couple of days and
then RFK drops out the new cycle changes, but actually
(15:46):
been declining. She had lost a point or two in
polls of Michigan, Wisconsin, you know, Pennsylvania, most of those
rust belt blue wall swing states. So it's like this
should be a really good period and it's a really
bad period. Therefore, it had gotten really grumpy about her polling,
and therefore a lot of Democrats, some of whom are
completely insane and should go, I don't know, moved to
(16:11):
Fiji or some nice country where they don't have to
blow up my Twitter mentions. We're upset with me as
a result, But we have to talk about that.
Speaker 1 (16:18):
Yes we do, Nate, Yes we do. Because you had
such a great takedown all right, I'm going to read
a tweet from yesterday night. Hey bet, understand you may
still be frustrated. Silence of the Lambs came out the
same year as for the Boys, so you were pretty
much always going to lose best Actress to Jodie Foster
in poker. We'd call that a bad beat. But that's
no reason to tweet out conspiracy theories.
Speaker 2 (16:39):
Please discuss no, so Bette Midler among other Democrats, you know,
Stuart Stevens, this guy at the Lincoln Project. Yeah, you
have certain people who are convinced that.
Speaker 1 (16:51):
You've been bought.
Speaker 2 (16:54):
If I've been I must have been bought. This is
like literally what Stuart Stevens and Bette Midler are implying.
And sometimes it comes up in the context of so
I consult. I'm an advisor to Pollymarket, and Polly Market
has been invested in by Founder's Fund, which is you know,
one of the principles is Peter Teal at Founder's Fund,
So it therefore gets twisted into Peter Teal is secretly
(17:16):
funding Nate, which is bizarre. I mean, Founder's Fund has
invested in in Lift and Airbnb and Facebook and and
you know all these all these startups eventually get invested
in by most of the big VC firms at some
stage of fundraising. I mean, if you work for open Ai.
Elon Musk co founded open Ai, are you on Elon
Musk's payroll? You know substack Mark Andreeson and recent Horror,
(17:41):
which is a big investor in substack. I think he
isn't as triggering for people as Elon or Peter Teal,
but like, but yeah, you know, you have to understand
how business works and how vet your capital works. But
people people cannot fathom because their brains are so poisoned
by politicscept Like, you know, it's not like I want Trump.
I mean, you know, I'm disclosed here. I'm trying to
(18:02):
be nonpartisan to our audience. You know, I'm gonna vote
for Harris if if I'm being super duper honest, I mean,
most of the audience for kind of high brow news
content are Democratic leaning voters. The audience at the newsletter
is very is very colle educated. You know, when when
(18:24):
Harris is doing well in the polls, then we probably
convert a little better from we get traffic either way,
we convert a little better to paid subscriptions when there's
good news for Harris.
Speaker 1 (18:34):
Of course, of course, I mean confirmation bias, right, and
people people really when they when you're saying things that
they agree with and they like you, and then otherwise
you're you know, you're a paid shill, and I can't
I can't believe that I'm associated.
Speaker 2 (18:48):
With you, but like people, I think people are. I
call it the uh. It's called the called the Bonyar
threshold after a particularly hackish online Democrat named Tom Bonyer,
who just cannot seem to understand that anybody could ever
try to want to be true seeking about anything. But
once you pass the Bonyar threshold, you're no longer capable
(19:11):
of assuming anyone else acts in good faith. This is
a mistake poker players make. Actually they assume that everybody
else plays the same way that they do, right that
if they'd be bluffing in a particular spot, and maybe
they have the wrong bluffing frequency, we call it that.
Therefore the other player must be also and they're mapping
themselves to the rest of the world.
Speaker 3 (19:28):
So yeah, we'll be back in a minute with yet
more on the election, the debate, the polls, and the models.
Speaker 2 (19:53):
Here's the guide to how to interpret the polls and
the models in the coming week or two. And this
is going to preview a post. It's actually the post
and silver builting will probably be up by the time
you listen to this, so go check that post out
too if you want, like a more precise version. The
first thing that will come out are poles that are
kind of crappy, the reason being that like poles that
(20:17):
you conduct in just today, are gonna have a harder
time capturing a representative sample electorate. And also the news
story itself. Part of the benefit of the debate is
people watching debate night, but also then these clips circulate
of Trump talking about immigrants eating dogs and cats, and
the abortion stuff circulates, and you get buzz and favorable
(20:40):
headlines for Harris. By the way, Taylor Swift, yeah, I.
Speaker 1 (20:44):
Was gonna I was gonna bring that up for sure,
because that's also a little bounce. While we can talk
about whether it's a bounce or not.
Speaker 2 (20:50):
It's interesting that she did that, whether they chose to.
I'm sure I'm sure Tat is coordinating this with her
friends and the Biden excuse me, not by her, the
Harris Walls campaign. So they really wanted to drive like
a big boom news cycle instead of like if it
were me, I would not have done that tonight. I
have said I think my debate was strong enough. We'll
(21:11):
tell Taylor, Hey, Taylor, just hold off. Let's save us
for a couple of weeks from now, when if there
is a debate bounce that it might fade in the
new cycle changes. But anyway, so first you get these
kind of either online polls that have these large panels,
that have very large samples, which can be fine poles,
but tend not to show a lot of movement for
reasons that aren't getting worth into or really crappy one
day IVR automated polls, So I wouldn't pay too mu
(21:35):
attention to anything until maybe this weekend. This weekend, the
first traditional polls that survey people Wednesday, Thursday, Wednesday, Thursday,
Friday will come out probably Saturday or Sunday. Those might
show more movement. If there is movement, those are a
bigger deal. And then in the following week you'll start
to see probably more state polling data. So by the
(21:57):
end of next week we'll have a pretty good idea
of where the race stands. Now, if I had to
guess you look at that number in the CNN poll
typically that does actually correlate with movement in the head
to have polls. You know, one of the biggest numbers
ever because gaps ever in the c and M pole
was in the first debate in twenty twelve when ntt Romney,
(22:19):
you know, kind of clean Barack Obama's clock and Rodney
gained about temporarily it faded, but gained about three points
in polls as a result. After the first debate with
Trump and and Biden, Trump only gained three points or so.
Right as disastrous that the bit was for Biden. Now,
maybe that Trump is like near his theoretical ceiling anyway, Right,
(22:42):
it's hard for Trump to get much more than fifty
percent of the electorate. He's a pretty unpopular guy. But
you know, so Beasonville, the guest might be that Harris
gets half that a point or a point and a half.
The question is kind of what the baseline is exactly.
If you look at the Silver bulletin pulling average, she
had been ahead by two point two points, I think
(23:02):
or something. So that gets her to three and a
half or so. On top of that, our model assumes
that we're no longer in this convention period, so it's
no longer penalizing Harris's polling. If she were to get
up to three and a half point lead, depending on
what happens in the swing stage, she would emerge out
of that as frankly a slight favorite in the model. Now.
(23:25):
I don't want to guarantee that, though, because there's a
little bit of ambiguity about where the race stood beforehand.
In some of the very recent national polling, The New
York Times has this very good poll to do with
Santa College, very large sample size, very rigorous methodology. Trump
had been ahead by a point in the popular vote,
and Trump's a head by a point nationally. He wins
electoral college like for sure. So there have been some
(23:46):
bad data for Harris recently. If you can get back
though to the three and a half point range three
four that you know, that would count as a as
a big tournament events.
Speaker 1 (23:55):
Yeah, but it's still it's still incredibly close, right and
we're still how many weeks out from the election, right?
Six weeks? We still have we still have quite some
time to go where things can change, which is why
I actually I totally agree with you. I thought it
was so strange that the Taylor Swift endorsement came right now,
because it kind of got buried right because it was
(24:17):
late at night, and obviously her her followers are gonna
are going to be looking at it. But I think
that you do need to have some of these things
that you can sprinkle in along the way because it's
a long road ahead, and as we've talked about before,
like for Harris, any little slip up is going to
really really matter. And she doesn't even have to mess up,
she just has to, you know, do something that's perceived
(24:40):
the wrong way. She's the one who has to prove,
I think, and to be kind of on she has
to be perfect, right, she has to be one hundred percent.
Speaker 2 (24:50):
Although although I think that, you know, let's do another
poker term, I think they've been a little bit nitty,
meaning risk averse and neurotic about, for example, not doing
more media at sure.
Speaker 1 (25:01):
Yeah, yeah that that actually I really do not understand.
Speaker 2 (25:05):
She right now is at forty nine percent of the
vote in polls. To win, she has to get to
fifty one percent fifty one because she has a disadvantage
and all likely hid in the electoral college, so she
needs to find two percent of the electorate somewhere, which
seems easy, But there's only about six percent that's undecided
or voting third party at the moment. Probably one percent
(25:26):
of that will stay with Jill Stein or whatever else. Right,
but like, you know, maybe you have five percent of
the actual electorate in play or four percent or something
like that, right, you need to get probably half that.
It's non trivial problem, actually, And like so really reaching
into like the marginal constituencies, maybe not doing the mainstream
media hits, but doing some of the weird podcasts and find,
(25:47):
you know, profiling who are the undecided voters. Maybe it's
maybe it's men who just are a little uncomfortable with
voting for a woman. Don't like Trump, but maybe a
little uncomfortable voting for a woman or things like that.
I don't know, you know, I think they tend to
over index on like the Nikki Hayley, Liz Cheney voters.
(26:08):
They do fine with those. But maybe the more marginalized
it might be all you know, it might be younger
black voters and Hispanic voters who you know, do not
grow up with memories of the Civil rights era and
require a little bit more persuasion.
Speaker 1 (26:20):
Well, we can invite her to come talk on our podcast,
right U, Kamala Harris, come on on and help convince
Donald Trump.
Speaker 2 (26:29):
Donald Trump, yept, what if we moderate a debate? That
would be funny, that would be amazing.
Speaker 1 (26:34):
Okay, we will, we will host a debate on risky business.
Let's let's go, and.
Speaker 2 (26:39):
Actually we were you know, some of us is even
like the area I describe as the river in the book,
it's like some of the Silicon Valley stuff. And she's
tried to moderate. I mean she's like, you know, reduced,
I have not paid all that much touch in this
policy stuff. She like has a less aggressive plan for
taxing capital gains then Biden has. You know, Silicon Valley
seems a little bit less opposed to Harris than they
(27:02):
were to Biden. I mean, it's a tiny constituency, but
like the crypto people are a group that, like Trump,
has gone out after bitcoin declined after debate last night
because they saw Trump as losing. For instance, areas like that,
perhaps she.
Speaker 1 (27:18):
Does have some low hanging fruit where if she actually
comes out, you know, and says something that shows that
she's going to be supportive of crypto, which right now,
her actions are not showing right, given given who she
showsen for her administration, if she does a few key
things like that, I think she might be able to
pick up like one percent, right which, as we know,
like one percent matters, every single percent matters.
Speaker 2 (27:39):
Or point or point one percent, which also matters. Kamala coin, Yes,
Kamala coin, Yep.
Speaker 1 (27:47):
Absolutely, she can do a cat she can do a
cat coin for cat ladies, right. I think that that
would work. I mean, Taylor Swift's endorsement had a cat
in the picture. So lean into that, you know, show
that you understand the culture. Nate, I do have a question,
and you don't have to answer if you don't want to.
But I'm curious, how strongly do you believe kind of
(28:08):
in the current output of your model, Like, do you
do you think that Harris right now is kind of
poised to lose?
Speaker 2 (28:16):
Not right now because now you have an absence of
post debate data that could move the numbers quite a bit,
but you know, as of like as of uh, Tuesday
morning or Tuesday night before the debate, when she's at
thirty eight percent, thirty eight and a half percent, yeah,
more or less. I mean, look, I'm aware that there
are other models out there, some of which are good,
(28:38):
some of which you are less good. I'm aware that
prediction markets are generally pretty smart, and those also Harris,
you know, Polly market her whatever, forty five is, forty
four is percent range instead of thirty eight. That's like
not a huge gap. But look, one reason to believe
the model is that like is that, you know, if
(28:59):
I were doing this subjectively, then I'd be rooting for Harris,
probably right. I am not a Trump fan, not a
huge fan of Harris's politics, frankly either, but I consider
after January sixth, there are lots of reasons why I
would never vote for Trump, and she's the alternative, you know,
for various reasons. I think. Probably also, if you were
(29:20):
somebody who was really agitating, as I was, for somebody,
not necessarily Harris, frankly, but somebody to replace Biden, then
you're gonna look really smart if you're one of the
people who said you gotta get rid of Biden, and
then that person wins and Harris is probably good for
site traffic and newsletter subscriptions. Frankly. So the fact that
I have all these incentives to root for Harris is
(29:43):
the reason to trust the process I set up ahead
of time, before I knew how this would affect anything,
and the same process yielded reliable model results since two
thousand and eight over many elections, And so I'm not
involving my emotions. So yeah, if I were, you know, look,
as a Bayesian, you'd kind of combine your forecast with
(30:04):
other information, other data. So probably realisticly would be like
forty two percent, taking the mid point of like polymarkets
forty six and our models thirty eight, and you get to,
like Harris forty two or forty three is probably my
subjective belief that seems not to go out on too
far of a limb.
Speaker 1 (30:21):
That makes sense.
Speaker 2 (30:22):
It would now be higher now she's gained, you know,
but now I think, you know, if I did predict
what our model will say in two weeks, it might
be fifty percent Harris fifty three or something like that.
Speaker 1 (30:30):
Well, here's hoping after the break travel with us to Barcelona,
where I was just playing in EPT Barcelona, the European
Poker Tour, and we'll have a little poker for you.
Speaker 2 (31:00):
So Maria, well, I've been bouncing back and forth between
the West coast and the East Coast and the UK
for a couple of days. You have been in fabulously
beautiful Barcelona, Spain playing poker. I see from the hand
and mom database that you cashed three times, including one
rather large cash. Do you want to tell us about that? Yeah?
Speaker 1 (31:21):
Absolutely, I had a nice trip. This was the Poker
Star's twentieth anniversary of the European Poker Tour, so it
was a huge event with some record setting fields and
there was one event. So there's a new format that
was introduced a few years ago in poker. That's the
mystery bounty, which appeals to djens such as Nate. Because
(31:43):
I'm not a djen. Nate as a jen, right, I
mean it does.
Speaker 2 (31:47):
I don't like mystery bounties that much. I mean, I
don't know KOs are more from. These are different, We're
not maybe a little above people's heads. But yeah, so
Wester go ahead. So the mystery bounty is that if
you knock out a player, usually after some threshold, if
you get to day two of a tournament, then you
knock out a player, you get like a little card
that says you get to draw from this like big
(32:10):
vat literally of envelopes, and some of the envelopes contain
very large amounts of money, such as what was top
prize here, like something one hundred thousand, one hundred thousand
euro Yes, one hundred thousand euros. It's one hundred and
ten whatever thousand American dollars. So yeah, it's a way
for a cheap entry to have. Like usually you can
(32:32):
only make these six figure sums if you like win
the tournament or finish in the top like two or three.
This is a way to have another opportunity without having
to win, without having to beat everybody, just knocking one
player out and you win one hundred thousand euros or
a million dollars and some of the worldsairs of poker ones.
So did you win a big bounty then.
Speaker 1 (32:49):
Maria, Yeah, so well, first of all, you know, so
in this mystery bounty tournament, it was a three three
thousand euro buy in. I ended up coming in fifth,
which is huge. I beat out over eleven hundred players
in order to make the final table, so really nice,
deep run. I was very happy with the result. And
(33:10):
so I had ten bounties to pull, which is a lot, right,
ten bounties is a good amount and I have never
yet wow, and I have never pulled anything other than
a min bounty. So a min bounty is a minimum bounty.
In this particular tournament, that was one thousand euros, and
(33:31):
this was by far the most common bounty. So we
are going to now pivot to a very non risky
business way of thinking. So here I am with ten
bounties to draw. I draw the first one. It's a men.
I wait a while to draw another one. It's another men.
So at some point I've drawn five men bounties and
I have five left. Now the one hundred k's are gone.
(33:54):
And there is a player who everyone in the poker
world knows and anyone who was following the main event
this year knows as well. Nicholas Sausted, otherwise known as
Lena by his online screen name. He had a very
deep run in the main event, and a lot of
poker players, including me, were rooting for him to win
because he's a phenomenal player and a really really nice guy.
(34:17):
Lena is also someone who's quite lucky and runs incredibly well.
He plays well. But see this is why I said,
We're about to pivot into a non risky business way
of thinking. By this point, he had been knocked out
of the bounty event, and he had drawn two bounties.
He had drawn a twenty five K and a ten K.
So what I decided to do was send a text
(34:39):
message to Nicholas and ask him if he would draw
my remaining bounties. This is an incredibly non scientific, irrational
way of thinking. I was like, I am going to
get this very lucky player who's very good at drawing bounties.
By the way, we know, right, you cannot be good
or bad at drawing bounties. This is random. But I'm
(35:00):
going to get him to draw my remaining bounties. So
he said, sure, he would do it.
Speaker 2 (35:04):
He charge a fee.
Speaker 1 (35:05):
Now he did not charge a fee because he's a
nice guy. So here I am playing, did not even know,
you know, if he was actually going to show up.
So we're down to two tables. You know, Nicholas is
still not there. You know, I'm getting worried that I'm
gonna have to draw my other bounties. Tap on my shoulder,
there he is. He said, are you ready to draw?
He said, oh my god, yes, So I give him
the five bounties. We go up and he says what
(35:27):
are the top bounties left? And I said, well, there's
a twenty five K left and a ten K left.
And he said, okay, we got this. So he draws
and he draws, and you know, he draws my five
bounties and he said, do you want to open them?
Do I open them? I said, let's take turns. So
I opened the first one. It's a twenty five K.
He opens the second one it's a ten K, and
he's like, okay, I did it for you. He's like,
(35:49):
the only other ones left are the one case right,
And I was like, yep, so then the other three
were one case.
Speaker 2 (35:54):
So my little bounties were there remaining at this point
about forty so there are two good ones out of forty.
Speaker 1 (36:02):
Yep in the restaurant and he picks those ten picked five,
He picked five.
Speaker 2 (36:06):
He picked five, yep, so five out of forty and
they have to I mean, do some combinatorics readers. I'm
sure we have readers who can do the math here,
but like, are you sure that? Because there are these
envelope politic conspiracies if you're an NBA fan. Supposedly, the
NBA lottery was rigged for New York Knicks to get
in Patrick Ewing out of Georgetown, for example, And like
(36:28):
one of the envelopes was like the others had like
a bet edge.
Speaker 1 (36:33):
There were any no marked envelopes, and I saw him drawing.
He just he has the magic touch, which is the single.
Speaker 2 (36:40):
Special contacts or glasses.
Speaker 1 (36:42):
Maybe this is the single most irrational thing I will
ever say on this podcast. Thank you so much, Nicholas
for adding thirty five thousand euros to my life.
Speaker 2 (36:53):
Let's be here. What if he had said I wanted
two percent feet? Uh huh? Would you have accepted that feet?
He gets two percent of whatever bounty you in above
the minimum.
Speaker 1 (37:05):
I don't know.
Speaker 2 (37:06):
We didn't.
Speaker 1 (37:07):
We didn't have to go there. Okay, I did not
think he would ask. I think I would pay him
one percent too, but as a basi, and at the
end I said that I would buy him a nice
dinner wherever he wanted to go. But he did not
take it up on me. Take me up on it,
(37:28):
and I will reiterate the invitation, Nicholas, if you're listening
any stop anywhere, it's on me. Thank you so much.
And if we're ever playing a mystery bounty together, I
hope that you will draw my envelopes for me again,
because I'm a very rational person who does not aspire
to be superstitious ever.
Speaker 2 (37:58):
Risky Business is hosted by me Nate Silver and me
Maria Kanakova. The show was a co production of Pushkin
Industries and iHeartMedia. This episode was produced by Isabel Carter.
Our associate produce is Gabriel Hunter Chang. Our engineer is
Sarah Bruger. Our executive producer is Jacob Goldstein.
Speaker 1 (38:16):
If you want to listen to an ad free version,
sign up for Pushkin Plus for six ninety nine a month.
If you get access to ad free listening, Thanks for
tuning in.
Pushkin. Welcome back to Risky Business, our show about making
better decisions. I'm Maria Kanikova.
Speaker 2 (00:31):
And I'm Nate Silver.
Speaker 1 (00:32):
So today, Yeah, Nate, I.
Speaker 2 (00:35):
Think I think so much better at the intros, in
the positions, and now we're reverting me. It's been a
couple of weeks since we've had like a normal episode.
Speaker 1 (00:42):
That's true, it's true. It's good to see you. You
are in Jackson Hole.
Speaker 2 (00:47):
Yeah, Jackson Hole, Wyoming, a very you know, God's country
as they might call it, and that is very pretty.
Speaker 1 (00:54):
Yes, and so it's actually quite appropriate. I'm back in
New York City, So we're in two different parts of
the country to discuss something that hopefully will help unite
those different parts of the country, the presidential debate from
last night. So we are recording this the morning of Wednesday,
September eleventh. So last night you and I both were
(01:17):
listening to the debate from our respective different parts of
the country, and Kamala Harris tried to make her appeal
to both of us, right to all Americans all over
the country. So what'd you think, Nate.
Speaker 2 (01:29):
You know, I'm here in Teaton County. Well, actually it's
the one blue County in all of Wyoming.
Speaker 1 (01:35):
Yeah, I love your voices. I love your voice.
Speaker 2 (01:38):
I thought Kamala Harris had an excellent debate, or maybe
maybe I think subtly more that former President Trump had
a really bad debate, playing into basically every critique that
you might have of Donald Trump, the fact that repeatedly
she was able to tease them about his rally crowd
(01:59):
sizes and things like that, and how foreign leaders don't
really respect him, and every single time he took the
bait and got distracted and had these long two minute
segments with no mics off, no interruption, and would spend
the first minute complaining about whatever Kamala said, and then
the next thirty seconds with a coherent point, and then
veering off in some other random direction for another thirty seconds.
(02:21):
A lot of talk about things like a reference to
if you're not really into politics, this will sound bizarre
because it is, but Haitian immigrants, oh.
Speaker 1 (02:31):
My god, yes, in.
Speaker 2 (02:32):
Ohio killing dogs and cats, you know, talking about Sean
Hanny and Laura Ingram and like just kind of not
really able to escape the fact that he's trying to
persuade over this tiny fraction five percent of the country
that is still undecided.
Speaker 1 (02:48):
Yeah, can we just pause for a second. Not just
killing dogs and cats, but eating them, eating them. And
this is a conspiracy theory that came from like the
deep bowels of conspiracy theoryedom and here we have the
former president just like losing it and completely, you know,
just going off on this. He was actually fact chucked
(03:09):
on this by the moderators during the debate, which they
did a few times, which was new, right, they haven't
done that before, where they said, you know, actually there's
zero evidence of this. But he just didn't know where
to go. I think, you know, I was worried, you know,
going into this debate. I thought it could go a
lot of different ways, and I was just hoping that
(03:30):
she wouldn't let him get to her right because as
a female also, the risk is so much bigger because
if she starts feeling defensive or indignant or anything, you know,
because she's female, it's going to come off a lot
worse and it's going to just completely blow up. She
did not, and it was actually the other way around
where she was able to kind of just needle him,
and he every single time squandered most of his airtime
(03:54):
in responding to the needles as opposed to saying anything substantive,
which I think was the entire point.
Speaker 2 (04:00):
Yeah, she is very different about gender issues tactically than
Hillary Clinton was, and to my male eye, more effect
I think in part by like sometimes jabbing. In poker terms,
we might call it. This is probably too esoteric. A
donk bet donk bet is when you unexpectedly lead with
a bet into the person that you're expecting to bet right, donk,
(04:23):
di O, and k. A donkey in poker is a
name for a bad player. A fish donk is a
shortening of donkey. So this was thought to be a
play that a bad player makes it doesn't know to
check to the person who made the last bet. It
turns out that and the fancy computer simulations you're supposd
to this sometimes but can also be a way to
exploit somebody, to get under their skin. And it can
(04:45):
I'm not gonna talk about the game theory here, but
it can throw them off rhythm. Sometimes it is correct
actually under game theory, but it's a way to take
charge of the hand. And she did that a couple
of times, like kind of premptively saying, Hey, Trump's going
to lie a lot, you know, even on issues that
would seem like relative weaknesses for her, like, for example,
the unpopularity of President Biden in the fact that she's
been the vice president for three and a half years.
(05:07):
She would say, by the way, I'm not Joe Biden,
and like actually lean into that. And I think she
was well prepared for this event. And I don't know
to what extent that credit goes to her versus her campaign.
The Democrats to have a lot of data. I mean,
Donald Trump has had one, two, three, four or five.
This is his seventh general election debate, and he doesn't
(05:28):
really change form that much, except in the Biden debate,
where Biden was so bad that some instinctual part of
Trump knew that you have to maybe not go for
the jugular that much. Although Trump was actually kind of
bad in that debate too, it's just that when Biden.
Speaker 1 (05:41):
In comparison, he was he was fine.
Speaker 2 (05:44):
Yeah, but you know, Biden couldn't like nail him on
the things he were saying that were very very false
and or crazy. So I mean, first of all, if
nothing else, if Harris loses and we'll talk about this
in a minute, probably still in the vicinity of fifty
to fifty. If nothing else, you have this a b
test where replacing Joe Biden with Kamal Harris was a
really good idea.
Speaker 1 (06:05):
Yeah, and I actually think your dunk bet analogy is
really really a good one. First of all, just talking
from personal experience. The first, you know, I've actually let
people get under my skin doing that, you know, because
it's so it's unexpected, and the attitude is, you know,
you check to the pre flop aggressor, right, so the
person who has them, has the aggressive lead, is supposed
(06:27):
to be allowed to continue that aggression. And there have
been a few times I even remember when I've made
big mistakes and hands because someone has donked it to me,
and I'm like, no, you fucker, you don't get to
do that, and I raise, which is exactly what I
shouldn't be doing in some specific scenarios where they actually,
like clearly are donk betting because they have something. And
I remember those hands vividly because it's very unlike me
(06:51):
to to start tilting in that particular way. I've gotten
over it now, but it's actually, you know, it can
be a very effective strategy. And she did that. There
was actually one time where at first I was like, wait,
what the hell did she just do? And then I
realized what she was doing right when she was answering
a question and all of a sudden she says, by
the way, you know, I invite you to go to
(07:11):
Donald Trump's rallies. And I'm like, wait, you know what
a non sequort or what did she Why is she
doing that? And then it became very clear was a
very good strategic move because she was kind of deflecting
away from her record on something that she didn't really
want to talk about, and instead then Trump kept, you know,
for the next two minutes talking about how he has
the biggest rallies in history.
Speaker 2 (07:30):
Yeah, and she is taking some I mean, she is
veering off topic, and she often did it on questions
like immigration that are not great topics for her. Yeah,
And if Trump had responded coherently and not taken the bait,
then you'd have his media buzz about how she went
really off topic and was evasive, right, And instead he's
more often absolutely absolutely.
Speaker 1 (07:50):
But yes, I actually thought, you know, it was it
was quite funny because I was like, oh, you know,
she's learned to play the game of not answering the question,
because Donald Trump never answers the question. And as the
debate went on, she was doing the exact same thing,
and I'm like, huh, okay, well you know what that's
I guess how these debates go. But she she definitely
learned to play that game, and I actually thought she
(08:11):
became much stronger as a night war on. I was
very nervous when she started giving her first response, because
you could tell she was nervous, you know, as someone
who has done public speaking, who's kind of watched a
lot of debaters, you know, you and I were both
debaters all through school. You could tell, like the first
few minutes, you know, she was nervous and she was
(08:32):
a little stiff. But then she kind of got over it,
and I think she kind of became much more fluid
and really kind of picked up in style. And I
was very happy to see that. And he had kind
of some of the opposite trajectory where kind of he
started off thinking that, you know, I'm gonna just completely
demolish her, and then he started getting very defensive.
Speaker 2 (08:52):
Yeah, public speaking for me, at least, I don't know
if you're different, Maria, but like it's not It isn't
actually like just having a conversation with a friend at
a bar that your body recognizes that this is a
high stakes momentolutely and you're operating under a elevated person
but also a stress response. Now, in my experience, what
(09:14):
you have to do is just kind of be a
little bit self conscious about it. Right, You're like, I
am on terrain that I either rehearsed or that I've
spoken about many times, and I'm just gonna say words
and lo and behold, they come across coherent most of
the time. If you're tired or fatigued, or or you're
in a room where the setting is weird, the lighting is,
you can be thrown off and that can be hard
(09:35):
to recover from. But you have to like you can't
overthink it too much, and you have to trust your
experience base that like, yeah, this is actually gonna go fine,
and some people are even better in that setting than
they would be a meeting or something.
Speaker 1 (09:49):
Yeah, I think that's absolutely true. And I actually have
a stress response every single time I give a talk
right before I go on stage every single time, and
I give lots of talks. So this is something that
that definitely happens. But then you know, you could you
could tell that she does have the experience, she was prepared,
and she was able to kind of establish the momentum.
The other thing that I thought was very interesting was,
(10:11):
you know, Trump won the coin toss and decided that
he was going to give the closing, you know, the
final closing, and I actually think that that worked against him,
because he spent most of his closing remarks yelling at
what she said, as opposed to kind of giving any
summary of anything that he had said. So so I think
that she came across much better at the end of
the debate. And the way that you know, impressions and
(10:31):
memory formation works, I think that that really works in
her favor.
Speaker 2 (10:36):
And the beginning too. I mean, in the beginning, I
agreed at the very first question on the economy is
strong territory for Trump. But then you have extended remarks
on abortion for a moment, I thought that might be
the story of the night. And then you have which is,
by the way, if you're not a very good issue
for Democrats.
Speaker 1 (10:52):
Since she did very well, she did very well Onlke Biden,
who kind of you know, pivoted to immigration instead of
talking about abortion. She really nailed the abortion.
Speaker 2 (11:02):
And then you have the bait taking phase. I mean, look,
we may look back if Harris wins and say that
the Trump can made a series of really predictable mistakes.
The lack of preparation tonight were even in the closing statement.
He can't really stay on focus and talk about Harris's record,
for example, I mean other things too. I mean, naming
Jadie Vance is one. Not preparing for the transition from
(11:25):
Biden to Harris when they had been telegraphed at least
for a few weeks as a possibility, a strong possibility.
His convention speech where he rambled off topic after a
more subdued thirty minutes on the assassination attempt, you know,
frankly accepting that first debate with Biden. I mean, I
guess you may not have played out all the tactics,
but if he hadn't done that, then Biden's on stage
(11:48):
tonight having that debate from June tonight, and Democrats are
appropriately panicking because they're probably totally fucked at that point.
So thanks to the White House for moving that forward.
If you're a Democrat, I guess yeah, for sure. By
the way, did you notice that he did not call
her by her name a single time during the night.
He kept saying she and pointing at her, and he
(12:10):
never said vice President Harris. He never said Kamala Harris.
He used her name exactly zero times. I've actually haven't
seen anyone mention that, But that's something that really struck
me and kind of shows how, you know, it shows
you so much about his attitude about kind of his
conception of women, his conception of her. You know, she's
not even worthy of calling by the name. Because I
(12:31):
was I was actually paying attention to it because I
was wondering if he was going to mispronounce her name
on purpose the way he's always done, and what her
reaction was going to be.
Speaker 1 (12:39):
But he didn't. He didn't even say her name. So
I thought that that was something that was an interesting point,
and I thought that she tried to do a really
she tried to do a nice job since she was
in Pennsylvania of talking about the fact that she wasn't
going to stop fracking and that she, you know, was
upping well production in all of these things. And it's
a very fine line for her to walk right to
(13:01):
try to say, you know, I want a green future.
I'm worried about climate change, but believe me, I'm not
going to hurt your jobs. So I think that she
tried her best to appeal to Pennsylvania, which is obviously crucial, and.
Speaker 2 (13:15):
She is flip flopping on a lot of this stuff.
So there's a lot of fertile ground that Trump could
what's the metaphere they could have trod or I'm not
sure what their metaphor is exactly. Yep. And again the
lack of discipline from Trump, you know, just to say
she's a radical left winger, as she showed in San
Francisco and in twenty nineteen when she wanted to do
(13:36):
X and Y and Z, and now she's flip flopping
because she's desperate to, you know, make up for Joe
Biden's mistakes. You can do a fair amount of that.
I do think that's an issue for Harris with some voters.
But look, they got another data point. So the convention,
we can talk about the trajectory of the horse race.
Maybe in a moment. I thought she gave a very
good convention speech. Also that was also kind of agro
(13:59):
and aimed at male voters. And she 's some of
these same themes tonight, talking about competitiveness with China, talking
about how aggressive and lethal our military is. For instance,
sometimes when you hear something for the second time, it
closes a deal, right, you know, So obviously there's some
chance that there's a bounce in the polls Harris, by
(14:20):
the way, so we're not just totally giving you our
subjective opinion. In the CNN post debate poll, she won
sixty three thirty seven or something, and Market.
Speaker 1 (14:28):
As well also had her her odds going up, So
I think that that is also I think by three points, right,
she moved up by thy point.
Speaker 2 (14:36):
Before the debate, it had been like Trump fifty four,
Harris forty six. These are not vote shares, these are
win probabilities, and after it's fifty to fifty technically forty
nine forty nine, they price in some chance that I
don't know, somebody has to drop out due to a
health issue, I guess, But basically drew the race back
to even. In the Silver Bulletin forecast, she had been
(15:00):
at thirty eight thirty nine percent heading in. If she
were to get that polymarket bounce, then she'd be in
the you know, forty five percent range. So fairly close
to fifty to fifty. The way the model works, the
model was very disappointed. Shall we say, not that it
has emotions. It's not some large language model that the
(15:24):
model is disappointed with Harris's post convention polling, where typically
after a convention is one of the best periods for
a candidate throughout the life of the election cycle. And
Harris was not only not getting a sustained bounce, she
may have gotten one for a couple of days and
then RFK drops out the new cycle changes, but actually
(15:46):
been declining. She had lost a point or two in
polls of Michigan, Wisconsin, you know, Pennsylvania, most of those
rust belt blue wall swing states. So it's like this
should be a really good period and it's a really
bad period. Therefore, it had gotten really grumpy about her polling,
and therefore a lot of Democrats, some of whom are
completely insane and should go, I don't know, moved to
(16:11):
Fiji or some nice country where they don't have to
blow up my Twitter mentions. We're upset with me as
a result, But we have to talk about that.
Speaker 1 (16:18):
Yes we do, Nate, Yes we do. Because you had
such a great takedown all right, I'm going to read
a tweet from yesterday night. Hey bet, understand you may
still be frustrated. Silence of the Lambs came out the
same year as for the Boys, so you were pretty
much always going to lose best Actress to Jodie Foster
in poker. We'd call that a bad beat. But that's
no reason to tweet out conspiracy theories.
Speaker 2 (16:39):
Please discuss no, so Bette Midler among other Democrats, you know,
Stuart Stevens, this guy at the Lincoln Project. Yeah, you
have certain people who are convinced that.
Speaker 1 (16:51):
You've been bought.
Speaker 2 (16:54):
If I've been I must have been bought. This is
like literally what Stuart Stevens and Bette Midler are implying.
And sometimes it comes up in the context of so
I consult. I'm an advisor to Pollymarket, and Polly Market
has been invested in by Founder's Fund, which is you know,
one of the principles is Peter Teal at Founder's Fund,
So it therefore gets twisted into Peter Teal is secretly
(17:16):
funding Nate, which is bizarre. I mean, Founder's Fund has
invested in in Lift and Airbnb and Facebook and and
you know all these all these startups eventually get invested
in by most of the big VC firms at some
stage of fundraising. I mean, if you work for open Ai.
Elon Musk co founded open Ai, are you on Elon
Musk's payroll? You know substack Mark Andreeson and recent Horror,
(17:41):
which is a big investor in substack. I think he
isn't as triggering for people as Elon or Peter Teal,
but like, but yeah, you know, you have to understand
how business works and how vet your capital works. But
people people cannot fathom because their brains are so poisoned
by politicscept Like, you know, it's not like I want Trump.
I mean, you know, I'm disclosed here. I'm trying to
(18:02):
be nonpartisan to our audience. You know, I'm gonna vote
for Harris if if I'm being super duper honest, I mean,
most of the audience for kind of high brow news
content are Democratic leaning voters. The audience at the newsletter
is very is very colle educated. You know, when when
(18:24):
Harris is doing well in the polls, then we probably
convert a little better from we get traffic either way,
we convert a little better to paid subscriptions when there's
good news for Harris.
Speaker 1 (18:34):
Of course, of course, I mean confirmation bias, right, and
people people really when they when you're saying things that
they agree with and they like you, and then otherwise
you're you know, you're a paid shill, and I can't
I can't believe that I'm associated.
Speaker 2 (18:48):
With you, but like people, I think people are. I
call it the uh. It's called the called the Bonyar
threshold after a particularly hackish online Democrat named Tom Bonyer,
who just cannot seem to understand that anybody could ever
try to want to be true seeking about anything. But
once you pass the Bonyar threshold, you're no longer capable
(19:11):
of assuming anyone else acts in good faith. This is
a mistake poker players make. Actually they assume that everybody
else plays the same way that they do, right that
if they'd be bluffing in a particular spot, and maybe
they have the wrong bluffing frequency, we call it that.
Therefore the other player must be also and they're mapping
themselves to the rest of the world.
Speaker 3 (19:28):
So yeah, we'll be back in a minute with yet
more on the election, the debate, the polls, and the models.
Speaker 2 (19:53):
Here's the guide to how to interpret the polls and
the models in the coming week or two. And this
is going to preview a post. It's actually the post
and silver builting will probably be up by the time
you listen to this, so go check that post out
too if you want, like a more precise version. The
first thing that will come out are poles that are
kind of crappy, the reason being that like poles that
(20:17):
you conduct in just today, are gonna have a harder
time capturing a representative sample electorate. And also the news
story itself. Part of the benefit of the debate is
people watching debate night, but also then these clips circulate
of Trump talking about immigrants eating dogs and cats, and
the abortion stuff circulates, and you get buzz and favorable
(20:40):
headlines for Harris. By the way, Taylor Swift, yeah, I.
Speaker 1 (20:44):
Was gonna I was gonna bring that up for sure,
because that's also a little bounce. While we can talk
about whether it's a bounce or not.
Speaker 2 (20:50):
It's interesting that she did that, whether they chose to.
I'm sure I'm sure Tat is coordinating this with her
friends and the Biden excuse me, not by her, the
Harris Walls campaign. So they really wanted to drive like
a big boom news cycle instead of like if it
were me, I would not have done that tonight. I
have said I think my debate was strong enough. We'll
(21:11):
tell Taylor, Hey, Taylor, just hold off. Let's save us
for a couple of weeks from now, when if there
is a debate bounce that it might fade in the
new cycle changes. But anyway, so first you get these
kind of either online polls that have these large panels,
that have very large samples, which can be fine poles,
but tend not to show a lot of movement for
reasons that aren't getting worth into or really crappy one
day IVR automated polls, So I wouldn't pay too mu
(21:35):
attention to anything until maybe this weekend. This weekend, the
first traditional polls that survey people Wednesday, Thursday, Wednesday, Thursday,
Friday will come out probably Saturday or Sunday. Those might
show more movement. If there is movement, those are a
bigger deal. And then in the following week you'll start
to see probably more state polling data. So by the
(21:57):
end of next week we'll have a pretty good idea
of where the race stands. Now, if I had to
guess you look at that number in the CNN poll
typically that does actually correlate with movement in the head
to have polls. You know, one of the biggest numbers
ever because gaps ever in the c and M pole
was in the first debate in twenty twelve when ntt Romney,
(22:19):
you know, kind of clean Barack Obama's clock and Rodney
gained about temporarily it faded, but gained about three points
in polls as a result. After the first debate with
Trump and and Biden, Trump only gained three points or so.
Right as disastrous that the bit was for Biden. Now,
maybe that Trump is like near his theoretical ceiling anyway, Right,
(22:42):
it's hard for Trump to get much more than fifty
percent of the electorate. He's a pretty unpopular guy. But
you know, so Beasonville, the guest might be that Harris
gets half that a point or a point and a half.
The question is kind of what the baseline is exactly.
If you look at the Silver bulletin pulling average, she
had been ahead by two point two points, I think
(23:02):
or something. So that gets her to three and a
half or so. On top of that, our model assumes
that we're no longer in this convention period, so it's
no longer penalizing Harris's polling. If she were to get
up to three and a half point lead, depending on
what happens in the swing stage, she would emerge out
of that as frankly a slight favorite in the model. Now.
(23:25):
I don't want to guarantee that, though, because there's a
little bit of ambiguity about where the race stood beforehand.
In some of the very recent national polling, The New
York Times has this very good poll to do with
Santa College, very large sample size, very rigorous methodology. Trump
had been ahead by a point in the popular vote,
and Trump's a head by a point nationally. He wins
electoral college like for sure. So there have been some
(23:46):
bad data for Harris recently. If you can get back
though to the three and a half point range three
four that you know, that would count as a as
a big tournament events.
Speaker 1 (23:55):
Yeah, but it's still it's still incredibly close, right and
we're still how many weeks out from the election, right?
Six weeks? We still have we still have quite some
time to go where things can change, which is why
I actually I totally agree with you. I thought it
was so strange that the Taylor Swift endorsement came right now,
because it kind of got buried right because it was
(24:17):
late at night, and obviously her her followers are gonna
are going to be looking at it. But I think
that you do need to have some of these things
that you can sprinkle in along the way because it's
a long road ahead, and as we've talked about before,
like for Harris, any little slip up is going to
really really matter. And she doesn't even have to mess up,
she just has to, you know, do something that's perceived
(24:40):
the wrong way. She's the one who has to prove,
I think, and to be kind of on she has
to be perfect, right, she has to be one hundred percent.
Speaker 2 (24:50):
Although although I think that, you know, let's do another
poker term, I think they've been a little bit nitty,
meaning risk averse and neurotic about, for example, not doing
more media at sure.
Speaker 1 (25:01):
Yeah, yeah that that actually I really do not understand.
Speaker 2 (25:05):
She right now is at forty nine percent of the
vote in polls. To win, she has to get to
fifty one percent fifty one because she has a disadvantage
and all likely hid in the electoral college, so she
needs to find two percent of the electorate somewhere, which
seems easy, But there's only about six percent that's undecided
or voting third party at the moment. Probably one percent
(25:26):
of that will stay with Jill Stein or whatever else. Right,
but like, you know, maybe you have five percent of
the actual electorate in play or four percent or something
like that, right, you need to get probably half that.
It's non trivial problem, actually, And like so really reaching
into like the marginal constituencies, maybe not doing the mainstream
media hits, but doing some of the weird podcasts and find,
(25:47):
you know, profiling who are the undecided voters. Maybe it's
maybe it's men who just are a little uncomfortable with
voting for a woman. Don't like Trump, but maybe a
little uncomfortable voting for a woman or things like that.
I don't know, you know, I think they tend to
over index on like the Nikki Hayley, Liz Cheney voters.
(26:08):
They do fine with those. But maybe the more marginalized
it might be all you know, it might be younger
black voters and Hispanic voters who you know, do not
grow up with memories of the Civil rights era and
require a little bit more persuasion.
Speaker 1 (26:20):
Well, we can invite her to come talk on our podcast,
right U, Kamala Harris, come on on and help convince
Donald Trump.
Speaker 2 (26:29):
Donald Trump, yept, what if we moderate a debate? That
would be funny, that would be amazing.
Speaker 1 (26:34):
Okay, we will, we will host a debate on risky business.
Let's let's go, and.
Speaker 2 (26:39):
Actually we were you know, some of us is even
like the area I describe as the river in the book,
it's like some of the Silicon Valley stuff. And she's
tried to moderate. I mean she's like, you know, reduced,
I have not paid all that much touch in this
policy stuff. She like has a less aggressive plan for
taxing capital gains then Biden has. You know, Silicon Valley
seems a little bit less opposed to Harris than they
(27:02):
were to Biden. I mean, it's a tiny constituency, but
like the crypto people are a group that, like Trump,
has gone out after bitcoin declined after debate last night
because they saw Trump as losing. For instance, areas like that,
perhaps she.
Speaker 1 (27:18):
Does have some low hanging fruit where if she actually
comes out, you know, and says something that shows that
she's going to be supportive of crypto, which right now,
her actions are not showing right, given given who she
showsen for her administration, if she does a few key
things like that, I think she might be able to
pick up like one percent, right which, as we know,
like one percent matters, every single percent matters.
Speaker 2 (27:39):
Or point or point one percent, which also matters. Kamala coin, Yes,
Kamala coin, Yep.
Speaker 1 (27:47):
Absolutely, she can do a cat she can do a
cat coin for cat ladies, right. I think that that
would work. I mean, Taylor Swift's endorsement had a cat
in the picture. So lean into that, you know, show
that you understand the culture. Nate, I do have a question,
and you don't have to answer if you don't want to.
But I'm curious, how strongly do you believe kind of
(28:08):
in the current output of your model, Like, do you
do you think that Harris right now is kind of
poised to lose?
Speaker 2 (28:16):
Not right now because now you have an absence of
post debate data that could move the numbers quite a bit,
but you know, as of like as of uh, Tuesday
morning or Tuesday night before the debate, when she's at
thirty eight percent, thirty eight and a half percent, yeah,
more or less. I mean, look, I'm aware that there
are other models out there, some of which are good,
(28:38):
some of which you are less good. I'm aware that
prediction markets are generally pretty smart, and those also Harris,
you know, Polly market her whatever, forty five is, forty
four is percent range instead of thirty eight. That's like
not a huge gap. But look, one reason to believe
the model is that like is that, you know, if
(28:59):
I were doing this subjectively, then I'd be rooting for Harris,
probably right. I am not a Trump fan, not a
huge fan of Harris's politics, frankly either, but I consider
after January sixth, there are lots of reasons why I
would never vote for Trump, and she's the alternative, you know,
for various reasons. I think. Probably also, if you were
(29:20):
somebody who was really agitating, as I was, for somebody,
not necessarily Harris, frankly, but somebody to replace Biden, then
you're gonna look really smart if you're one of the
people who said you gotta get rid of Biden, and
then that person wins and Harris is probably good for
site traffic and newsletter subscriptions. Frankly. So the fact that
I have all these incentives to root for Harris is
(29:43):
the reason to trust the process I set up ahead
of time, before I knew how this would affect anything,
and the same process yielded reliable model results since two
thousand and eight over many elections, And so I'm not
involving my emotions. So yeah, if I were, you know, look,
as a Bayesian, you'd kind of combine your forecast with
(30:04):
other information, other data. So probably realisticly would be like
forty two percent, taking the mid point of like polymarkets
forty six and our models thirty eight, and you get to,
like Harris forty two or forty three is probably my
subjective belief that seems not to go out on too
far of a limb.
Speaker 1 (30:21):
That makes sense.
Speaker 2 (30:22):
It would now be higher now she's gained, you know,
but now I think, you know, if I did predict
what our model will say in two weeks, it might
be fifty percent Harris fifty three or something like that.
Speaker 1 (30:30):
Well, here's hoping after the break travel with us to Barcelona,
where I was just playing in EPT Barcelona, the European
Poker Tour, and we'll have a little poker for you.
Speaker 2 (31:00):
So Maria, well, I've been bouncing back and forth between
the West coast and the East Coast and the UK
for a couple of days. You have been in fabulously
beautiful Barcelona, Spain playing poker. I see from the hand
and mom database that you cashed three times, including one
rather large cash. Do you want to tell us about that? Yeah?
Speaker 1 (31:21):
Absolutely, I had a nice trip. This was the Poker
Star's twentieth anniversary of the European Poker Tour, so it
was a huge event with some record setting fields and
there was one event. So there's a new format that
was introduced a few years ago in poker. That's the
mystery bounty, which appeals to djens such as Nate. Because
(31:43):
I'm not a djen. Nate as a jen, right, I
mean it does.
Speaker 2 (31:47):
I don't like mystery bounties that much. I mean, I
don't know KOs are more from. These are different, We're
not maybe a little above people's heads. But yeah, so
Wester go ahead. So the mystery bounty is that if
you knock out a player, usually after some threshold, if
you get to day two of a tournament, then you
knock out a player, you get like a little card
that says you get to draw from this like big
(32:10):
vat literally of envelopes, and some of the envelopes contain
very large amounts of money, such as what was top
prize here, like something one hundred thousand, one hundred thousand
euro Yes, one hundred thousand euros. It's one hundred and
ten whatever thousand American dollars. So yeah, it's a way
for a cheap entry to have. Like usually you can
(32:32):
only make these six figure sums if you like win
the tournament or finish in the top like two or three.
This is a way to have another opportunity without having
to win, without having to beat everybody, just knocking one
player out and you win one hundred thousand euros or
a million dollars and some of the worldsairs of poker ones.
So did you win a big bounty then.
Speaker 1 (32:49):
Maria, Yeah, so well, first of all, you know, so
in this mystery bounty tournament, it was a three three
thousand euro buy in. I ended up coming in fifth,
which is huge. I beat out over eleven hundred players
in order to make the final table, so really nice,
deep run. I was very happy with the result. And
(33:10):
so I had ten bounties to pull, which is a lot, right,
ten bounties is a good amount and I have never
yet wow, and I have never pulled anything other than
a min bounty. So a min bounty is a minimum bounty.
In this particular tournament, that was one thousand euros, and
(33:31):
this was by far the most common bounty. So we
are going to now pivot to a very non risky
business way of thinking. So here I am with ten
bounties to draw. I draw the first one. It's a men.
I wait a while to draw another one. It's another men.
So at some point I've drawn five men bounties and
I have five left. Now the one hundred k's are gone.
(33:54):
And there is a player who everyone in the poker
world knows and anyone who was following the main event
this year knows as well. Nicholas Sausted, otherwise known as
Lena by his online screen name. He had a very
deep run in the main event, and a lot of
poker players, including me, were rooting for him to win
because he's a phenomenal player and a really really nice guy.
(34:17):
Lena is also someone who's quite lucky and runs incredibly well.
He plays well. But see this is why I said,
We're about to pivot into a non risky business way
of thinking. By this point, he had been knocked out
of the bounty event, and he had drawn two bounties.
He had drawn a twenty five K and a ten K.
So what I decided to do was send a text
(34:39):
message to Nicholas and ask him if he would draw
my remaining bounties. This is an incredibly non scientific, irrational
way of thinking. I was like, I am going to
get this very lucky player who's very good at drawing bounties.
By the way, we know, right, you cannot be good
or bad at drawing bounties. This is random. But I'm
(35:00):
going to get him to draw my remaining bounties. So
he said, sure, he would do it.
Speaker 2 (35:04):
He charge a fee.
Speaker 1 (35:05):
Now he did not charge a fee because he's a
nice guy. So here I am playing, did not even know,
you know, if he was actually going to show up.
So we're down to two tables. You know, Nicholas is
still not there. You know, I'm getting worried that I'm
gonna have to draw my other bounties. Tap on my shoulder,
there he is. He said, are you ready to draw?
He said, oh my god, yes, So I give him
the five bounties. We go up and he says what
(35:27):
are the top bounties left? And I said, well, there's
a twenty five K left and a ten K left.
And he said, okay, we got this. So he draws
and he draws, and you know, he draws my five
bounties and he said, do you want to open them?
Do I open them? I said, let's take turns. So
I opened the first one. It's a twenty five K.
He opens the second one it's a ten K, and
he's like, okay, I did it for you. He's like,
(35:49):
the only other ones left are the one case right,
And I was like, yep, so then the other three
were one case.
Speaker 2 (35:54):
So my little bounties were there remaining at this point
about forty so there are two good ones out of forty.
Speaker 1 (36:02):
Yep in the restaurant and he picks those ten picked five,
He picked five.
Speaker 2 (36:06):
He picked five, yep, so five out of forty and
they have to I mean, do some combinatorics readers. I'm
sure we have readers who can do the math here,
but like, are you sure that? Because there are these
envelope politic conspiracies if you're an NBA fan. Supposedly, the
NBA lottery was rigged for New York Knicks to get
in Patrick Ewing out of Georgetown, for example, And like
(36:28):
one of the envelopes was like the others had like
a bet edge.
Speaker 1 (36:33):
There were any no marked envelopes, and I saw him drawing.
He just he has the magic touch, which is the single.
Speaker 2 (36:40):
Special contacts or glasses.
Speaker 1 (36:42):
Maybe this is the single most irrational thing I will
ever say on this podcast. Thank you so much, Nicholas
for adding thirty five thousand euros to my life.
Speaker 2 (36:53):
Let's be here. What if he had said I wanted
two percent feet? Uh huh? Would you have accepted that feet?
He gets two percent of whatever bounty you in above
the minimum.
Speaker 1 (37:05):
I don't know.
Speaker 2 (37:06):
We didn't.
Speaker 1 (37:07):
We didn't have to go there. Okay, I did not
think he would ask. I think I would pay him
one percent too, but as a basi, and at the
end I said that I would buy him a nice
dinner wherever he wanted to go. But he did not
take it up on me. Take me up on it,
(37:28):
and I will reiterate the invitation, Nicholas, if you're listening
any stop anywhere, it's on me. Thank you so much.
And if we're ever playing a mystery bounty together, I
hope that you will draw my envelopes for me again,
because I'm a very rational person who does not aspire
to be superstitious ever.
Speaker 2 (37:58):
Risky Business is hosted by me Nate Silver and me
Maria Kanakova. The show was a co production of Pushkin
Industries and iHeartMedia. This episode was produced by Isabel Carter.
Our associate produce is Gabriel Hunter Chang. Our engineer is
Sarah Bruger. Our executive producer is Jacob Goldstein.
Speaker 1 (38:16):
If you want to listen to an ad free version,
sign up for Pushkin Plus for six ninety nine a month.
If you get access to ad free listening, Thanks for
tuning in.
Hosts And Creators
Maria Konnikova
Nate Silver
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