March 19, 2025 • 42 mins
This week, Nate and Maria talk college basketball. Nate explains how he made his recently-released March Madness model. Then, they compare their tournament brackets. Maria gets some help from Sacramento Kings GM Monte McNair, but also makes one bracket entirely based on vibes. Nate mostly sticks to his model, but makes a few surprising picks.
Check out Maria’s brackets here.
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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 Kanakova and.
Speaker 2 (00:31):
I'm Nate Silver. Did you say our show or a show?
Speaker 1 (00:34):
I said our show, Nate, because it is our show.
Sometimes decision say A you know, I like to play
around with it the show.
Speaker 3 (00:42):
How about the show? So I'll tell you how to
make better decisions?
Speaker 1 (00:47):
This is the show about how to make better decisions.
What are we going to be talking about today.
Speaker 3 (00:55):
Night March madness, Maria, March madness. Indeed, it's New York,
it's springtime. The weather is improving from mediocre to slightly
go to the mediocre. And we're here to watch some
basketball and talk about some basketball.
Speaker 2 (01:08):
Yep.
Speaker 1 (01:08):
And we're going to start off by talking about some
of Nate's models, and then we each made brackets. And
I'll also introduce a secret weapon that I used in
making my bracket, a certain someone named Monty McNair.
Speaker 2 (01:23):
Oh well, so you cheated.
Speaker 1 (01:25):
Oh no, I want to not cheat.
Speaker 2 (01:26):
I do want to McNair.
Speaker 1 (01:30):
Nate, I did not cheat. When we don't know how
to make a good decision, what do we do? We
consult someone with the expertise and the knowledge to help.
So I consulted with Monty McNair, who's the GM of
the Sacramento Kings and something of a guru when it
comes to making March Madness brackets. That full interview is
(01:51):
actually going to be on push Can Plus highly recommend
that you all listen to it.
Speaker 3 (01:58):
I feel like the politics that's a bit complicated where
the woman has to ask it certified basketball expert. Well,
I just kind of go the man just you know,
uses a regression based model.
Speaker 1 (02:11):
So that's that's a great segue night, Let's talk about
the regression based ELO model. So you released your Marsh
Madness models. How did you you know, how did you
make them? Kind of what's the thought process behind them?
What goes into them?
Speaker 3 (02:27):
So some of this like actually literally dates back to
two thousand and two or two thousand and three, right
where I'm like a junior associate at KPMG. This is
an accounting firm. I always was sensitive. I'm you know,
it was a consultant at accounting firm, Maria, not an accountant.
Speaker 1 (02:44):
Just so you know, I always forget that you worked
at an accounting firm. Every sing such a year, Oh yeah, right.
Speaker 3 (02:50):
Such a weird part of the multiverse where I don't
know how the fuck that happened. I don't know, but
like it's because I parted too much ching college and
then had to didn't really make plans anyway. So a
friend who had an office pool had these complicated scoring
rules where you get points for upsets, and so I
took some other rating system I found and modeled that
(03:12):
and did a little simulation. Actually it's not a simulations,
it's not deterministic exactly, but like, there are only sixty
seven games, so you can kind of explicitly do all
the conditional probabilities instead of having to rely on a simulation.
Speaker 2 (03:25):
Right, just a.
Speaker 3 (03:25):
Technical detail, but it's evolved from there. I think when
I was at the New York Times in twenty twelve,
I did a big evaluation looking at all these different
computer models, all these other different ways to predict the tournament,
and found kind of very empirically which did best in
predicting tournament games. So a couple of things that if
you if you've ever made a model, might sound familiar, right.
(03:48):
You know, first, almost always taking a consensus of indicators
is better than any one indicator, right, So we wound
up with a blend of like five different rating systems,
all which do a little differently. Accounting for injuries is
very important. You know, you don't want to ignore the
fact that Cooper Flag, maybe the best player in the NCAAA,
at least the best freshman the nca IS year, got
(04:08):
hurt during a tournament. He seems to be helping now,
but if he were to be heard, that would gravely
impact Duke's prospects.
Speaker 2 (04:15):
So we account for that.
Speaker 3 (04:17):
And then also we found that, like there's reversion to
the mean, meaning that you play roughly thirty three games
or something in a college basketball regular season, not all
that biggest samples some of us are against crappy competition,
you know from you know, Southwestern State, University prep or whatever. Right,
So we looked at pre season rankings as well as
(04:38):
part of what we factored into the formula. Sometimes seems
that underachieve have actually gotten a little bit unlucky, so
believe it or not, giving human ratings some component actually
helps a little bit, and this proved to be a
popular feature. But like more recently, I began making my
own rating system that kind of incorporates some of these
different ideas. Right, So are you familiar with ELO ratings.
(05:01):
I know some of our readers listeners might be.
Speaker 1 (05:03):
I should say, well, let's for people who are not familiar.
Of course, I personally am very you're familiar with the writings.
But for those of us who might not be, why
don't we explain.
Speaker 2 (05:16):
It a little bit?
Speaker 3 (05:17):
So ELO was designed by let me look at the guys,
it's like arpanned Elo, or let me look, it's Hungarian.
Probably the Hungarians are quite promiscuous within in kind of
this era of intellectual history, are bad Elo a Hungarian
American physics professor. But it's a formula where you are
adjusting your rating constantly based on whether you beat your opponent,
(05:42):
of course, and how good your opponent is. Right, So
if I have an ELI rating of a nineteen hundred,
and I don't know if that's good in chess, it's
probably pretty good. Right, And you're a fourteen hundred, Maria, right,
fifteen hundreds, average of four hundreds whatever, mediocre, I barely
get any credit because you kind of already forecast to
the win.
Speaker 2 (05:59):
Right.
Speaker 3 (06:00):
If I'm a nineteen hundred and I beat Magnus Carlson,
who's a twenty one, I'm just guessing you might know.
Then I get a lot of credit because I've beaten
a stronger opponent.
Speaker 1 (06:09):
So twenty eight thirty three night as Magnus's.
Speaker 3 (06:12):
Twenty eighth ro' oh my, that's embarrassing. See in basketball,
they only get to like twenty two hundred or something, right,
And so I'm like, is Magnus Carlson better at chess.
Speaker 1 (06:21):
Tha twenty eight thirty three? His peak rating was twenty
eight eighty two.
Speaker 3 (06:26):
Okay, that's pretty that's pretty good. I think I'm like
a nine hundred or something. But so anyway, we adapted
the ELO ratings. Actually other people have done this, but
we adapted it first the NFL and then to college basketball.
But there are a few twists in ELO. Originally just
about the winner loss, right, we also count for the
margin of victory. In basketball, we account for home court
(06:50):
advantage in kind of an increasingly complicated way. We account
for travel distance, so if you fly across the country,
your jet lag and so forth. This actually shows up
in in performance by the way different teams have different
home court advantages. It turns out actually that having like
a larger crowd actually helps.
Speaker 2 (07:07):
I was trying to model this the other day.
Speaker 3 (07:09):
You know you for every ten thousand fans show up,
teams score roughly in additional point, that's right.
Speaker 1 (07:16):
That's absolutely fascinating, saying, yeah.
Speaker 3 (07:18):
So you're that's for I do that for women for men,
if it's different women's because they have some teams are
more popular than men's teams, and some like literally draw
you know, twelve people, right, But yeah, the teams that
actually have people watching do a little better in their
home games controlling for other factors.
Speaker 1 (07:34):
So I love that you're able to account for these
things that might seem kind of more ineffable, right, like
like home court advantages, more psychological and more physical like
injury risks. How much is like how much of model distinctions?
And like your model versus other models is kind of
a little bit more subjective, like as you said, the
(07:56):
human touch versus kind of the big data crunching. I'm
just curious, from like a from a psychologist's standpoint, what
the balance is there?
Speaker 3 (08:05):
So are you saying when I make a bracket or like, when.
Speaker 1 (08:08):
I'm billion, you're actually building the model.
Speaker 3 (08:12):
Look, inherently models, you have a lot of different choices
you can make. But like, because you have like i'd say,
this is like less subjective than some of the other
models I've built because you have a lot of data
and elo is relatively simple. Right, there are only so
many knobs you might have to press, versus more open
(08:35):
ended problems.
Speaker 2 (08:36):
Right.
Speaker 3 (08:36):
But yeah, in general, like with politics election models that
you know, those get to be a little bit more
art than science. Not in the sense because people mistake this.
Not in the sense that I am like subjectively changing
out once the model is designed, but like, but the
design questions are harder. And also, you know, so one
thing you look at in basketball too, is like how
(08:58):
do the parameters of the game change over time?
Speaker 2 (09:01):
So one thing we do is like, in.
Speaker 3 (09:05):
Between seasons, how you did last season? Provide some information obviously, right,
you know Connecticut was a national champion last year, had
a very good year. Right, you wouldn't just toss them
in a hat versus you know, writer or Canisius or
one of these small schools.
Speaker 2 (09:22):
I now have beamed him and I had, you.
Speaker 3 (09:24):
Know, Youngstown State, Texas, Rio Grand Valley. You know, you
you you have some information from the prior season. But
because there's so much more player turnover now the best
players go pro after one year, it's less continuity than
there used to be, right, and so and so you
should always be asking questions, right, and like you're billion
a model, part of what you're doing is saying, Okay,
(09:46):
here's this data. It's been well calibrated on literally what's
you know, seventy five years of college basketball history, right,
but let's make sure that it's performing as advertised on
the past couple of years, right, because after all, if
you're then adapting this to make predictions, and that's kind
of what people are going to care about, whether you
Rectro actually got some game right in nineteen seventy two
(10:08):
or whatever. And what we found is that the model
had I know, I'm saying we because it's just me
at this point. What I found is that the model
had become overconfident in the in recent years, right, because
it was it was overconfident early in the season before
catching up. And that was because it you know, overweighted
(10:31):
the impact of last season when if you have a
good player, it's going to the NBA anyway, and so
and so it doesn't help quite as much and so like, yeah,
so this is where like domain knowledge helps, right, because
you could just say, well, it's a fairly large sample,
but you know, it's been a weird couple of years
because of COVID and new rules and stuff like that. Right,
But it's like, Okay, we have a good hypothesis for why,
for why there's more mean reversion now. But by the way,
(10:55):
this has actionable implications.
Speaker 2 (10:57):
Where Okay, so.
Speaker 3 (11:02):
For our tournament model, which is related to this ELO model,
but we combined with other systems. I found myself I
do literally make bets based on these, right, And I
found myself betting a lot of underdogs to cover the
spread in the men's game, but not in the women's
(11:24):
game interestingly, right, because women you can't join the WNBA
until you're age twenty two, I think, which seems fucking
insane to me, Like, how was that legal? By the way,
that is very strange.
Speaker 1 (11:36):
I don't understand.
Speaker 3 (11:38):
Yeah, the politics THEWWA, you know, it's a whole other segment.
But yeah, the teams are still as strong as ever,
and so you have less reversion from year to year.
So basically for lots of reasons, and the fact that
like you know, for a long time in the women's game,
they were like, you know, somewhere between a half dozen
(11:58):
and two dozen schools. It took women's basketball really seriously,
and then some where it just like you go out
with a clipboard and say, hey, who wants to play? Right,
And so it's almost like professional versus amateur. Now it's
become much more popular. There's more money in the sport,
more young women are playing the sport, right, But still
it's more it's much more lopsided, right, And so you know,
(12:20):
so I found and now I'm giving away and replaces
my bet. So too late, right, But yeah, so I've
ad also fitting a lot of favorites in women's basketball
and a lot of underdogs in men's basketball, because there's
been more mean reversion in men's basketball.
Speaker 2 (12:36):
And I don't think that.
Speaker 3 (12:38):
The models are that the odds are accounting for this enough,
although you know, we'll see I'm putting my money where
my mouth is at least.
Speaker 2 (12:45):
Yeah, they're just settle things too.
Speaker 3 (12:47):
Like you know, big teams that draw huge audiences and
have very enthusiastic fans and good in game presentation and
nice locker rooms, right, they have a bigger home court
advantage which makes their rating look better. But you're playing
the NCAA tournament on neutral sites instead, so there have
actually been quite a few upsets in the tournament in
(13:08):
recent years. So yeah, all this is kind of you know,
so that's why it's like, this is what I hate
when like academics are like, well you a sports model,
election Wedell, this isn't not real science, Like it's actually
better science. Sure, because you're like, now I sound like
a fucking asshole, but no, but you're like you it's
it's still like hypothesis driven, right, you're looking at the
(13:30):
real world.
Speaker 2 (13:30):
You're just actually willing.
Speaker 3 (13:31):
To test these ideas in the form of of making predictions,
in some cases even betting on these predictions.
Speaker 1 (13:36):
No, that that makes perfect sense. And do I understand
correctly that you actually have two versions of your model,
one that's elo based and one that's a Bayesian elo version.
Speaker 3 (13:45):
Is that right? Abazing just because I'm a Doric, right,
But so in the Baysing version, like I said before,
you uh, the new season begins in the team is
kind of the same and kind of a different team.
Speaker 2 (13:57):
Right.
Speaker 3 (13:58):
In the original version, it reverts to the mean based
on the other teams in your conference, right, because there
are clearly differences between like you know, whatever, the ACC
and the IVY in the Basing version, then we give
weight to the human poles. Instead, we look at the
preseason top twenty five. We assume that these people are
evaluating recruitment and player aging and Injuriason all this stuff, right,
(14:20):
and so we revert based on that instead. It turns
out actually that like you do best with a blend
of you know, because the humans can add or subtract,
they can introduce biases that aren't helpful, right, So it's
really it's really kind of a what we call base
is really like a blend of the original version and
kind of a pure Bayse version. So that accounts for
(14:45):
like preseason makings in the human poles, and that has
interesting implications.
Speaker 1 (14:50):
Do they have the same results this year or not?
Do your two models differ?
Speaker 3 (14:56):
Yeah, let me show you it's actually not? Or is
it pretty similar that different?
Speaker 2 (15:01):
I mean, for.
Speaker 3 (15:03):
So the Basing version has Duke as the best team
and the pure version has Floored as the best team.
Now these are all within like a few points. It's
a very minor difference, right, But like you're counting for
like that you're basic kind of counting for like the
historical reputation of Duke that gets boosted in these human polls.
Speaker 2 (15:24):
Got it.
Speaker 1 (15:24):
Well, that's all super interesting, Nate. So let's see what
the results show when it comes to actually choosing brackets.
So you got to use all of your models. I
did not, but I got to use a Monte McNair.
So I got to use a human and after the breaks,
let's see how it all turns out. Nay, I have
(15:58):
a confession to make. I have never filled out an
NC double a bracket until now.
Speaker 3 (16:05):
Ria.
Speaker 1 (16:06):
This is my first ever bracket and.
Speaker 2 (16:08):
We're still allowing on a show.
Speaker 1 (16:10):
Come on, So this is my first ever bracket.
Speaker 2 (16:14):
But how can you never filled out a bracket?
Speaker 1 (16:16):
I've never filled out a bracket, Nate, I confess, I confess.
Speaker 3 (16:22):
You know I've never done yoga.
Speaker 1 (16:24):
Oh there you go, Nate. How could you have never
done yoga?
Speaker 3 (16:28):
It feels never the gender stereotype being here. I don't know,
but like it's like something you think you would have
done once.
Speaker 1 (16:34):
Right, Okay, Nate, I will I will ever do yoga
with you. And now that I filled.
Speaker 3 (16:40):
Out our bracket, okay, yeah.
Speaker 1 (16:42):
So so I actually you have your bracket right, and
it's probably a normal bracket. I have two brackets, so
Here's here's what I did. So first, I know nothing
you know about NCAA basketball. I never filled out a
bracket before. So I did a bracket based on vibes.
So I didn't even look at the at how teams
(17:03):
were ranked, didn't look at their record, didn't look at
any data whatsoever, and just like went on a totally
vibe based like who do I like? What sounds better?
What state do I like better? Where's the weather better?
Just just random things like that, my ViBe's bracket night.
Do you want to guess who won at the end?
Who I have winning the whole thing?
Speaker 2 (17:25):
H Yale?
Speaker 1 (17:27):
No, I'm a Harvard grad.
Speaker 2 (17:29):
Okay, it doesn't make sense. I don't.
Speaker 3 (17:31):
Uh, who would This is a weird what's it like? Laden?
Speaker 2 (17:38):
Like? What would Maria think? Is exactly?
Speaker 1 (17:40):
This is a perfect psychology experiment.
Speaker 2 (17:44):
Uh, give me give me a hint.
Speaker 1 (17:46):
You weren't You weren't that far off when you said
Yale in terms of the types of things I was
thinking about. But it was a place. It's so it
was an emotional based decision, all right.
Speaker 3 (17:57):
Okay, So one thing is, are you enough of a
college basketball fan to know that you're supposed to hate Duke.
Speaker 2 (18:03):
Yes, okay, okay, because.
Speaker 3 (18:06):
That's you know, that's like the point he headad like
by the way I was out, Uh, I don't know.
I was out with my partner last Saturday, and we'll
at least is maybe really too much? Like why is
everyone in this bar so so white? Because it's New
York and it's a big bar and usually and it's
(18:28):
because the New Game was fun?
Speaker 1 (18:32):
That's funny. But okay, so, so do you give up?
Should I just tell you? Let me give give me
one more, one more guess?
Speaker 3 (18:39):
Okay, Saint John's no is there a New York guess?
Or not? Maybe? I think, Yeah, I guess you're not
really like it. I mean, you were live in New York.
You don't like it New York born and bread.
Speaker 2 (18:50):
Okay, I give up?
Speaker 1 (18:51):
All right? So I had you're gonna laugh. I'm curious
to hear your reaction. I had U c l A
winning because I felt bad for LA and the wildfires,
and I felt like they needed something good to happen
to them.
Speaker 3 (19:03):
Okay, you know, I'm generally pro you. So I like
that color mix.
Speaker 1 (19:09):
Yeah, there was just UCLA had good vibes and I figured,
you know, what let's have them win. So I had
UCLA beating Duke in the final four.
Speaker 3 (19:19):
What seedd UCLA even this year there are seven seed probabilistically,
let me let me look up the old silver bulletin
forecast and see what it says. So you have an
zero point three percent chance of being right? All right, yes,
but getting it it's like getting aces right.
Speaker 1 (19:39):
Yeah, absolutely so then okay, So I showed Manty this
vibees based bracket and I was like, will you help
me correct it? And he was like, dude, we need
to start from scratch. So, for listeners who don't know,
Monty McNair is the GM of the Sacramento Kings and
he went to Princeton and he did his thesis on
(19:59):
March Madness and on building brackets. So this is actually
what he studied academically. And about eight or nine years
ago he came in second in the Cagle bracket contest.
So this is someone who is really really good at
making brackets. And I asked Monty kind of how he
(20:21):
would go about doing this. And Monty has his own
models as well, by the way, So let's actually listen
to a brief clip of that conversation. And then we'll
talk about what the resulting bracket looks like, and we'll
talk about your bracket night.
Speaker 4 (20:35):
I would start with a clean slate, no offense to
your ECLA Champion bracket, and I think first we need
to set our.
Speaker 3 (20:42):
Goal, because that's going to dictate. You know, I'll try it.
Speaker 4 (20:46):
I'll try not too many tortured poker analogies, but I.
Speaker 1 (20:49):
No, let's do tortured poker analogies. We live for tortured
poker analogies on the show.
Speaker 4 (20:54):
Yes, so head to head cash game versus a World
Series of Poker tournament, you're going to do different things.
I think in your case, really you want to be
one person.
Speaker 1 (21:05):
Yes, I'm heads up against Nate Silver, and I want
to kick his ass. Take that, Nate. There you go,
Nate Gottle's been thrown. So so that's I mean, that's
kind of where Monty said that, you know, the three
things we need to keep in mind are what's our
(21:25):
scoring system? Is there going to be anything different for upsets?
I was like, no, you know, we're just doing something
very straightforward. And then he said, you know how big
is your pool? I was like, it's me and Nate
that's it. I just want my bracket to do better.
And by the way, we will link to my brackets
in the show notes, both the official bracket, which is
the Manty bracket as I call it, and then my
(21:46):
Vibes bracket, so that you guys can just laugh at
how funny my Vib's picks are, because, like I said,
I looked at absolutely zero data, ignored the rankings, ignored everything,
and just went with my gut. So so you'll be
able to see both of those. One we hope will win,
the other we hope we'll give you a good laugh.
(22:07):
And yeah, Monty's are our mutual bracket. I did get
to make a few decisions, by the way, there were
a few close ones where he said, I got to pick,
so I picked sometimes things that he would not have picked,
But he said that he stood behind the choices. And
we do have Duke winning overall. Nate, how about you?
Do you have Duke winning or do you have someone
else winning?
Speaker 3 (22:27):
Well, I don't. Let's going to make things a little
easier for people. Let's give our champion, our final four
and then our sweet sixteen. So let's say that we're
going to get one point for every Sweet sixteen. Pick
that's correct, three points for every final four team that
is correct, and then five points were picking the right champion.
Speaker 2 (22:49):
Okay, okay, okay.
Speaker 3 (22:51):
So you want to start in the South region?
Speaker 1 (22:55):
Uh?
Speaker 3 (22:55):
Sure, Okay, we should take turns going first because it
might influence. So who are your final four in the South?
Speaker 1 (23:02):
My final four? Let's see Auburn, Texas A and m
Iowa State and Michigan State.
Speaker 3 (23:09):
Okay, so you have the favorites according to the Silver
Bulletin model there, I'm gonna I'm gonna just to represent
my home state. I'm gonna go Auburn Michigan. I mean,
here's here's the thing that bugs me though, Like our
model things, Iowa State is underrated but nevertheless the most
likely team. But anyway, that's fine. Iowa State and Michigan State.
(23:31):
So a lot of Midwest, a lot of mid West there.
And then of those four, who do you have emerging
into the final four.
Speaker 1 (23:40):
Auburn and Iowa State?
Speaker 3 (23:44):
Okays the eight? And then who do you have prevailing?
We didn't, we were trying to simplify it. So and
then who do you have prevailing between Auburn and Iowa State?
Auburn Okay, I also have auburn thing is I have.
I have this whole model I design. I'm trying to
sell the people, right, I'm not gonna be like, oh,
ignore the model. Go to silver Bulletin dot com and
get temperature. There's no discounty.
Speaker 1 (24:03):
So do you have the same picks as I do
other than Michigan.
Speaker 2 (24:06):
Yeah.
Speaker 3 (24:06):
In fact, I just had to cheat to over ride
my model, which actually at Texas A and M and
the same picks you did, just because like, uh, I
want some drama for this program. Yea, my model would
have made those exact same picks. Okay, let's go to
the And.
Speaker 1 (24:22):
By the way, Michigan versus versus Texas and M was
one of those where Monty said that I could actually
pick and I did Texas A and M because I
thought you would pick Michigan. So I wanted to differentiate
myself and I was correct.
Speaker 3 (24:39):
Now let's go out west where the regional fib we
played at the Chase Center in San Francisco, California.
Speaker 2 (24:46):
I'll go FIRSTUS time.
Speaker 3 (24:47):
Okay, and here my model is emphatic about these picks.
Speaker 2 (24:51):
I have no choices to make.
Speaker 3 (24:53):
I have in the final four, Florida Maryland, Texas Tech
and Saint John's. Go even have a choice to make,
I mean not really, you know, and then lead eight
Florida versus Saint John's. I mean Saint John's versus Kansas.
Excuse me, Sint Johns' sex seconds close, but whatever. I
live in New York, now, you know. There were some
(25:13):
some nice Saint John's kids also at the bar the
other day, nice but loud. So I have Florida advancing
from the west.
Speaker 2 (25:24):
Over over over Saint John's. Okay, okay, do you who
do you have?
Speaker 1 (25:30):
I think I have the same thing. I have Florida
and Maryland, Texas Tech, Saint John's, then Florida Saint John's,
and then Florida.
Speaker 3 (25:35):
Okay, so so far all we have is this one
game that may or I'm not even happens.
Speaker 1 (25:39):
So it's really it's interesting though, because you said you
had no choices, and the one thing that Monty said
was that the first well, I guess we're not doing
the We're not doing the Sweet sixteen. So I have
Colorado State beating Memphis because he said that the five
and twelve five twelve is one of the most common upsets,
(26:02):
and that would be fun.
Speaker 2 (26:03):
Okay, you have Coloro State beating Memphis.
Speaker 3 (26:05):
Yeah, so it's a fucking silver bulletin model, Maria, really, yes, yeah, Monty's.
Speaker 2 (26:10):
Just are you sure he was?
Speaker 1 (26:12):
Well, he didn't look. He didn't look.
Speaker 3 (26:13):
Oh my god.
Speaker 1 (26:14):
So, which which bracket are we going to do next?
Speaker 2 (26:17):
Midwest? And now it's your turn to go first?
Speaker 1 (26:19):
Okay, So I have Houston, Purdue, Illinois, and Tennessee. Then
I have Houston and Tennessee, and then I have Tennessee.
Speaker 3 (26:31):
Okay, so here's where your little counterintuitive friend. Okay, I've
got Now I don't have to cheat to like to
have different picks. So Houston Clemson, which you Clemson are Purdue?
Speaker 1 (26:46):
I said, Purdue?
Speaker 3 (26:48):
Houston Clemson.
Speaker 2 (26:48):
Who who's a five seed?
Speaker 3 (26:52):
We think produce a team that you know, gets a
lot of benefit out of their home court advantage, but
you're not playing at home in the tournament. Houston Purdue
A close choice, But I'm going with a model here.
Kentucky and Tennessee.
Speaker 1 (27:07):
Well, Kentucky was one of the other, so that that
was one of the choices that he also had me made.
Illinois versus Kentucky said it was incredibly close, but that
I was allowed to choose an upset, and I said
I wanted to since my husband is from Chicago.
Speaker 3 (27:23):
So then I have Houston emerging Tennessee Kentucky. It's a
classic rivalry those.
Speaker 2 (27:29):
Two long states.
Speaker 3 (27:31):
So I have Houston playing Tennessee in the regional final
and Houston not Tennessee winning. Huh. Now I feel comfortable.
I feel like I have an edge here.
Speaker 1 (27:40):
All right?
Speaker 3 (27:41):
Okay, finally whit bracket. Have we not done yet the East?
So I'm not trying to go first, right yep, Duke Arizona,
I'm just picking the chalk here because I think I'm
winning already. Duke Arizona, Wisconsin, Alabama. Do I have any
choices to make here? I do not Duke beating Alabama
(28:02):
in the Elite eight to advance to the Final four? Uh?
Speaker 1 (28:05):
Yeah, I have Duke Arizona, Wisconsin, Alabama as well, and
then Alabama, Duke and Duke.
Speaker 3 (28:13):
Okay, so remind me so I have One of the
good things about doing a podcast is that it may
not have Dawn and you. I just have the four
number one seeds reaching the final four, even though it's
incredibly unlikely that they all will right collectively, each have
like a one in two chance roughly or less than
that actually, so it's like only like a one in
(28:33):
twenty chance that I'm quote unquote right. But this is
why you need a funky scoring system. Should have we
should have? Yeah, Monty, I don't know. Now, we just
are are very chalky chalk if you don't know the term,
and you probably wouldn't unless you're a sport's bidding your
but like chalk just means picking the favorites. So in
our final four, I need you to pick a national champion,
Maria or you already revealed your national yep.
Speaker 1 (28:55):
My national champion is Duke. How about you.
Speaker 3 (28:57):
Let me think here? Okay, So this is interesting because
like the biggest differentially have right now is Houston making
the final four and not Tennessee. Yep, right, So so
I don't okay, So if I get if I'm right
on Houston, but you're right in Okay, I'm gonna do
something weird here.
Speaker 2 (29:19):
You ready, Yeah, I'm gonna go Florida.
Speaker 1 (29:22):
So you're gonna pick Florida beating Auburn and then this
is lower.
Speaker 3 (29:26):
Expected value relative to entire field. But I'm trying to
straut it because if I'm right on Houston making the
final four, then I have an edge, so I don't
want to pick Houston, right, and then it's like redundant.
It's like another way for me to win, right.
Speaker 2 (29:41):
I don't know.
Speaker 3 (29:41):
I don't really I don't really know. I don't even
you know, I don't know. I don't really like I'm
really enamored of any of these, you know, Yeah, I.
Speaker 1 (29:49):
Love I love how you're gaming out the game theory
payoffs in real time on the pod.
Speaker 2 (29:55):
This is great.
Speaker 3 (29:56):
Yeah, because Houston, then you're putting too many eggs in
the Houston basket, right, But I think I'm gonna get
that Houston. I mean, you know, yeah, I got Florida. Okay,
let's let's go through. You have your whole bracket.
Speaker 2 (30:07):
I do I need you to give me three.
Speaker 3 (30:10):
Opening round games where the inferior seed wins and that
they are at least an eleven seed and they win.
Speaker 1 (30:21):
Well, the only one that I have, well, I have
Colorado State.
Speaker 2 (30:25):
Okay, that counts.
Speaker 1 (30:28):
Let's see. I think that might be the only one
that I have that qualifies a no.
Speaker 3 (30:32):
No, somebody else can have to think on the fly,
you get a freebie.
Speaker 1 (30:37):
This is not fair.
Speaker 5 (30:38):
I don't know any well, I can go back to
my original vibes bracket and uh that'll that'll I have
Yale beating Texas A and O.
Speaker 2 (30:53):
All right, so Yale, you've got uh Colorado State.
Speaker 3 (30:57):
And let's see what other Yeah, I go vibes here?
Speaker 1 (31:01):
What other vibe vibe upsides, vibe upsets I had? I
had Wofford? Is that right word? Beating Tennessee?
Speaker 3 (31:13):
Okay, I think you're weighing the vibes a little too much.
That's the fifteen verses too.
Speaker 2 (31:17):
I'm gonna.
Speaker 3 (31:17):
I'm gonna so what I'll do as a favor, I
will not pick any of the ones you pick, not
that they are exactly at the top of my list,
apart from Colorado State. I will not let myself choose
Colorado State due to uh, due to you having picked it. Right,
that's my little bonus point to you. So let's see,
let's see where can I where can I get creative here?
Speaker 2 (31:36):
All right? So Drake beating Missouri it's an eleven verses
six or Model loves Drake VCU beating BYU. It's about
a forty percent chance according to the model. And now
I have to do something a little funkier, I think,
(32:00):
and we'll take High Point.
Speaker 3 (32:03):
Beating Purdue. Perdue tends to choke in the tournament. So
if the tidebreaker, then will go to the three upset picks, right, Okay,
if that's tie, they will have a name the location
of the college contest that probably all win because but yeah,
hand enter the location of every latitude and lunched it
at one point before you had to look things up
(32:24):
and be an efficient programmer. Okay, so we have our picks.
They are pretty chalky. Do you have women's picks?
Speaker 1 (32:31):
I don't know.
Speaker 3 (32:32):
Okay.
Speaker 1 (32:33):
Monty had very limited time, but I also promised him
that I would not put that I would not actually
bet this for any real money. So that was one
of the conditions of helping me out.
Speaker 3 (32:44):
Okay, Yeah, which I think we came up because they
barely let me. You know again, They're like, immediately the
Caesars puts up its lines on women's games, right, and
like it's after dinner with my partner and I'm like,
it's like, what are you doing. I'm like, do these
lunches came out? Man? I have to have to bet
and help before they change. He's like, are you ignoring
the conversation. I'm like, this is where you make your money,
(33:06):
and but it's this the one thing I kind of
bit serious and they still can't get real money downe.
Speaker 1 (33:09):
Yeah, yeah, no that we've talked about this before. But
that is obviously a very real problem when you have
an edge in anything.
Speaker 3 (33:18):
But you know, I want to be the guy who's like,
for gender equity reasons, I should be allowed to bit
more on the best. You should, You absolutely should, But no,
they really limit because they're afraid that like nobody knows anything,
and so they you know, they're they're very defensive, even
though women's faketiballl actually the women's tournament final got higher
TV ratings and the men's last year. But you know,
(33:38):
whenever a sports book is limiting you to a few
hundred bucks, right even if they don't let meet you overall,
whereas a men's I mean I get somewhat limited, right,
But like that's that's saying they the statistical discourse about
the game is not very advanced.
Speaker 1 (33:57):
Yeah, that makes sense, Nate. I wonder if our listeners
want to want to chime in and see who whose
bracket they think is going to win. Is it going
to be dug Er Florida. How are we gonna how
are we going to do it? I think that would be.
Speaker 3 (34:12):
I actually, I just think I think I have more
ways to win the contest if like, yeah, you probably look,
I think I don't want Houston. We we think we've
locked in. If we set Duke, then I'll lose anyway, right,
I mean, I think that was actually really stupid.
Speaker 2 (34:29):
I don't know, I think it was stupid.
Speaker 3 (34:30):
Probably, But anyway, more drama though, yes, more drama wants
to drama is always good.
Speaker 1 (34:37):
Now, I don't particularly want to see them win. That's
why I had U s l A winning my vibes Brocken. Okay, cool,
well it was it was actually it was fun and
I I actually think that I'm going to probably enjoy,
actually enjoy watching some of the games now that I
actually have a bracket and I know that I'm rooting
(34:57):
for someone. Rooting is always something that's a little bit
more fun. Let's uh, yeah, let's let's see who wins.
I was about to say, may the best man win.
Let's just say that may the best man win, and
I hope that that's me slash Meat plus Monty. But
but we'll see and I'm looking forward to the fight.
(35:21):
After a quick break, we're going to be back.
Speaker 3 (35:32):
Okay, Maria, now that you're a degenerate gambler, are you
gonna Are you gonna? By the way, what are we
betting on this? Should we do like dinner or something?
Speaker 1 (35:40):
Yeah, well we can't. I can't actually bet any money,
per per Monty, so sure dinner dinner it is?
Speaker 2 (35:46):
Okay, so who pays for dinner next time we see
each other?
Speaker 3 (35:50):
Okay, are you gonna enjoy watching the games now that
you have some skin in the game and it could
be pretty nenice dinner?
Speaker 1 (35:56):
Yeah? Absolutely, I'm uh, I'm excited because this is the
first time I actually even know what the teams are
who are playing in the n C Double A. Usually
I don't tune in until kind of near the end,
right where I'm like, oh, okay, you know this is important,
but like there are some of these schools that I'd
never heard of before. I was like, what in the
(36:17):
world is this and where is it? So yes, now
I think I'm actually going to have more skin in
the game and that should be a lot more fun.
By the way, our dinner should be something like basketball related,
like we should get really good burgers or something like that.
Speaker 2 (36:31):
Okay, perfect.
Speaker 1 (36:32):
Do you have any sort of kind of watching rituals
or anything like that or is it just like whatever goes?
Do you prefer watching in bars around other people? Do
you like watching at home? I'm actually just curious. How
have someone like you who's been doing this a long
time does it?
Speaker 3 (36:47):
For the tournament in the early rounds, it's fun to
go to a bar because they can have like four
different games on it once. I do not have like
one of those fancy TV setups where multiple monitors kind
of thing, right, So that's the reason why. Yeah, No,
the tournament is like fun to like, Yeah, I mean,
for some basketball and football are kind of the big Yeah,
it's fun to watch sports at bars.
Speaker 2 (37:07):
What am I say?
Speaker 3 (37:08):
I'm not trying to qualify that, ye too much?
Speaker 1 (37:10):
Yeah, yeah, I actually I totally agree with that. You know,
I'm not a football fan either, but found myself last year,
I think or the year before a barbecue place during
the Super Bowl when it was just starting, and it
ended up staying there and watching the entire game because
the energy was just so great and people were you know,
people were so into it and it was a lot
(37:31):
of fun. And I actually, like for once enjoyed watching
a football game.
Speaker 3 (37:36):
And by the way, even if you never bet, a
sports book is a fun place to watch sports too,
because people are really into it. Yeah, and they're rooting
in yeah.
Speaker 5 (37:47):
No.
Speaker 3 (37:47):
But what's weird is like I have so for the
bets I have, like you know, you sually it's born
to watch, like the one versus sixteen games, very lopsided ones,
but like I have like quite a few bets on
like point spreads in those games. Like I said, in
the women's often the favorites I expect one, but even
more and the guys often underdogs, right, But like whether
(38:08):
you know a team gets to thirty four points or
thirty six points is like material to me.
Speaker 2 (38:14):
So you maybe I'm probably not a good advertisement.
Speaker 3 (38:16):
Are you watch it?
Speaker 2 (38:16):
I'm like, don't turn away from that blowout? You know.
Speaker 1 (38:18):
Let's well, I'm definitely I'm definitely excited about it. What's
your take? I'm wondering if you're rational or irrational when
it comes to this on you know, superstitions. You know,
there are some people who will like wear the team
jersey when they watch, and they think that if they
don't do X, their team's going to lose. How do
(38:40):
you feel about all of that?
Speaker 3 (38:42):
I mean, I don't you know, you got mad at
me once, like lost my lucky hat and you're like
you don't have a lucky hat. Yeah, I'm not too superstitious.
Speaker 1 (38:51):
Really, do you really have a lucky hat?
Speaker 5 (38:53):
Night?
Speaker 3 (38:54):
I thought I did, but then you know, I used
to have one. I used to have one superstition that
like I have this one black one chip from from
the ARIA, right, and like I forgot to cash it in.
You know, sometimes you have like an X chip in
your bag or something, right you're leaving for the trip
and then the next trip, I also forgot to like
(39:17):
cash and I don't think I was sting at the area.
So I'm like, this is my tradition now, right, but
this is good luck. But I haven't happened to the
good luck in poker, especially that at fucking tournaments.
Speaker 2 (39:26):
You know, or the ARIA.
Speaker 3 (39:27):
So like I'm like fuck this, right, It's like, you know,
I owed so many money from the sports, so I'm
like I'm cashing these jumbnuts trying this fucking chip around.
You have to give white, you know, so so yeah,
so maybe they'll turn my luck around all right long,
around my possession.
Speaker 1 (39:46):
Let's see what happens. I love it, love it. Sometimes
even the most rational people, you know, do you have
a lucky chip or a lucky hat?
Speaker 3 (39:54):
I do think that, like there's something rational there about
like not making like if I have like a a
busy day, especially in loving poker netness. You know, if
I make day three of something, I'm like, okay, I'm
going to get up at seven point thirty and like
and worked out and then eat this place, even have
to make a reservation, like so I'll try to control
some factors like that. Right.
Speaker 1 (40:14):
Well, on that note, Nate, good luck to both of us.
I'm excited. I think this will be a fun competition
win or lose, although obviously I hope that I win.
Speaker 3 (40:22):
I hope so Tim, you hope I win.
Speaker 1 (40:25):
Yay, that's awesome, thank you.
Speaker 3 (40:27):
You know, it's like when you say it's like when
someone is like, you know, someone's like, how a goo day,
and you're like, love you, and it's like you're saying
that you're a partner or something right, like your boss
or something.
Speaker 2 (40:37):
It's like, I don't know, do you know, Maury work
toward you?
Speaker 3 (40:39):
Another podcast I've been up a long hours trying to
get those brackets and ship up all these models up.
Speaker 2 (40:46):
So I hope I win.
Speaker 5 (40:49):
No, no, you.
Speaker 1 (40:49):
Can't take it back, all right? And on that note, listeners,
let us know who you think will win. And if
you tune in to Pushkin Plus today, we're going to
have the full interview with Monty McNair, which is a
lot of fun. I highly recommend you listen to it.
(41:15):
Let us know what you think of the show. Reach
out to us at Risky Business at pushkin dot fm.
And by the way, if you're a Pushkin Plus subscriber,
we have some bonus content for you that's coming up
right after the credits.
Speaker 3 (41:28):
And if you're not subscribing yet, consider signing up for
just six ninety nine a month. What a nice price
you get access to all that premium content and for
listening across Pushkin's entire network of shows.
Speaker 1 (41:39):
Risky Business is hosted by me Maria Kanikova.
Speaker 2 (41:42):
And by me Nate Silver.
Speaker 3 (41:44):
The show is a co production of Pushkin Industries and iHeartMedia.
This episode was produced by Isabelle Carter. Our associate producer
is Gabriel Hunter Chang. Sally Helm is our editor and
our executive producer is Jacob Goldstein. Mixing by Sarah Bruger.
Speaker 1 (41:59):
If you like this show, please rate and review us
so other people can find us too, Thanks so much
for tuning in.
Pushkin. Welcome back to Risky Business, our show about making
better decisions. I'm Maria Kanakova and.
Speaker 2 (00:31):
I'm Nate Silver. Did you say our show or a show?
Speaker 1 (00:34):
I said our show, Nate, because it is our show.
Sometimes decision say A you know, I like to play
around with it the show.
Speaker 3 (00:42):
How about the show? So I'll tell you how to
make better decisions?
Speaker 1 (00:47):
This is the show about how to make better decisions.
What are we going to be talking about today.
Speaker 3 (00:55):
Night March madness, Maria, March madness. Indeed, it's New York,
it's springtime. The weather is improving from mediocre to slightly
go to the mediocre. And we're here to watch some
basketball and talk about some basketball.
Speaker 2 (01:08):
Yep.
Speaker 1 (01:08):
And we're going to start off by talking about some
of Nate's models, and then we each made brackets. And
I'll also introduce a secret weapon that I used in
making my bracket, a certain someone named Monty McNair.
Speaker 2 (01:23):
Oh well, so you cheated.
Speaker 1 (01:25):
Oh no, I want to not cheat.
Speaker 2 (01:26):
I do want to McNair.
Speaker 1 (01:30):
Nate, I did not cheat. When we don't know how
to make a good decision, what do we do? We
consult someone with the expertise and the knowledge to help.
So I consulted with Monty McNair, who's the GM of
the Sacramento Kings and something of a guru when it
comes to making March Madness brackets. That full interview is
(01:51):
actually going to be on push Can Plus highly recommend
that you all listen to it.
Speaker 3 (01:58):
I feel like the politics that's a bit complicated where
the woman has to ask it certified basketball expert. Well,
I just kind of go the man just you know,
uses a regression based model.
Speaker 1 (02:11):
So that's that's a great segue night, Let's talk about
the regression based ELO model. So you released your Marsh
Madness models. How did you you know, how did you
make them? Kind of what's the thought process behind them?
What goes into them?
Speaker 3 (02:27):
So some of this like actually literally dates back to
two thousand and two or two thousand and three, right
where I'm like a junior associate at KPMG. This is
an accounting firm. I always was sensitive. I'm you know,
it was a consultant at accounting firm, Maria, not an accountant.
Speaker 1 (02:44):
Just so you know, I always forget that you worked
at an accounting firm. Every sing such a year, Oh yeah, right.
Speaker 3 (02:50):
Such a weird part of the multiverse where I don't
know how the fuck that happened. I don't know, but
like it's because I parted too much ching college and
then had to didn't really make plans anyway. So a
friend who had an office pool had these complicated scoring
rules where you get points for upsets, and so I
took some other rating system I found and modeled that
(03:12):
and did a little simulation. Actually it's not a simulations,
it's not deterministic exactly, but like, there are only sixty
seven games, so you can kind of explicitly do all
the conditional probabilities instead of having to rely on a simulation.
Speaker 2 (03:25):
Right, just a.
Speaker 3 (03:25):
Technical detail, but it's evolved from there. I think when
I was at the New York Times in twenty twelve,
I did a big evaluation looking at all these different
computer models, all these other different ways to predict the tournament,
and found kind of very empirically which did best in
predicting tournament games. So a couple of things that if
you if you've ever made a model, might sound familiar, right.
(03:48):
You know, first, almost always taking a consensus of indicators
is better than any one indicator, right, So we wound
up with a blend of like five different rating systems,
all which do a little differently. Accounting for injuries is
very important. You know, you don't want to ignore the
fact that Cooper Flag, maybe the best player in the NCAAA,
at least the best freshman the nca IS year, got
(04:08):
hurt during a tournament. He seems to be helping now,
but if he were to be heard, that would gravely
impact Duke's prospects.
Speaker 2 (04:15):
So we account for that.
Speaker 3 (04:17):
And then also we found that, like there's reversion to
the mean, meaning that you play roughly thirty three games
or something in a college basketball regular season, not all
that biggest samples some of us are against crappy competition,
you know from you know, Southwestern State, University prep or whatever. Right,
So we looked at pre season rankings as well as
(04:38):
part of what we factored into the formula. Sometimes seems
that underachieve have actually gotten a little bit unlucky, so
believe it or not, giving human ratings some component actually
helps a little bit, and this proved to be a
popular feature. But like more recently, I began making my
own rating system that kind of incorporates some of these
different ideas. Right, So are you familiar with ELO ratings.
(05:01):
I know some of our readers listeners might be.
Speaker 1 (05:03):
I should say, well, let's for people who are not familiar.
Of course, I personally am very you're familiar with the writings.
But for those of us who might not be, why
don't we explain.
Speaker 2 (05:16):
It a little bit?
Speaker 3 (05:17):
So ELO was designed by let me look at the guys,
it's like arpanned Elo, or let me look, it's Hungarian.
Probably the Hungarians are quite promiscuous within in kind of
this era of intellectual history, are bad Elo a Hungarian
American physics professor. But it's a formula where you are
adjusting your rating constantly based on whether you beat your opponent,
(05:42):
of course, and how good your opponent is. Right, So
if I have an ELI rating of a nineteen hundred,
and I don't know if that's good in chess, it's
probably pretty good. Right, And you're a fourteen hundred, Maria, right,
fifteen hundreds, average of four hundreds whatever, mediocre, I barely
get any credit because you kind of already forecast to
the win.
Speaker 2 (05:59):
Right.
Speaker 3 (06:00):
If I'm a nineteen hundred and I beat Magnus Carlson,
who's a twenty one, I'm just guessing you might know.
Then I get a lot of credit because I've beaten
a stronger opponent.
Speaker 1 (06:09):
So twenty eight thirty three night as Magnus's.
Speaker 3 (06:12):
Twenty eighth ro' oh my, that's embarrassing. See in basketball,
they only get to like twenty two hundred or something, right,
And so I'm like, is Magnus Carlson better at chess.
Speaker 1 (06:21):
Tha twenty eight thirty three? His peak rating was twenty
eight eighty two.
Speaker 3 (06:26):
Okay, that's pretty that's pretty good. I think I'm like
a nine hundred or something. But so anyway, we adapted
the ELO ratings. Actually other people have done this, but
we adapted it first the NFL and then to college basketball.
But there are a few twists in ELO. Originally just
about the winner loss, right, we also count for the
margin of victory. In basketball, we account for home court
(06:50):
advantage in kind of an increasingly complicated way. We account
for travel distance, so if you fly across the country,
your jet lag and so forth. This actually shows up
in in performance by the way different teams have different
home court advantages. It turns out actually that having like
a larger crowd actually helps.
Speaker 2 (07:07):
I was trying to model this the other day.
Speaker 3 (07:09):
You know you for every ten thousand fans show up,
teams score roughly in additional point, that's right.
Speaker 1 (07:16):
That's absolutely fascinating, saying, yeah.
Speaker 3 (07:18):
So you're that's for I do that for women for men,
if it's different women's because they have some teams are
more popular than men's teams, and some like literally draw
you know, twelve people, right, But yeah, the teams that
actually have people watching do a little better in their
home games controlling for other factors.
Speaker 1 (07:34):
So I love that you're able to account for these
things that might seem kind of more ineffable, right, like
like home court advantages, more psychological and more physical like
injury risks. How much is like how much of model distinctions?
And like your model versus other models is kind of
a little bit more subjective, like as you said, the
(07:56):
human touch versus kind of the big data crunching. I'm
just curious, from like a from a psychologist's standpoint, what
the balance is there?
Speaker 3 (08:05):
So are you saying when I make a bracket or like, when.
Speaker 1 (08:08):
I'm billion, you're actually building the model.
Speaker 3 (08:12):
Look, inherently models, you have a lot of different choices
you can make. But like, because you have like i'd say,
this is like less subjective than some of the other
models I've built because you have a lot of data
and elo is relatively simple. Right, there are only so
many knobs you might have to press, versus more open
(08:35):
ended problems.
Speaker 2 (08:36):
Right.
Speaker 3 (08:36):
But yeah, in general, like with politics election models that
you know, those get to be a little bit more
art than science. Not in the sense because people mistake this.
Not in the sense that I am like subjectively changing
out once the model is designed, but like, but the
design questions are harder. And also, you know, so one
thing you look at in basketball too, is like how
(08:58):
do the parameters of the game change over time?
Speaker 2 (09:01):
So one thing we do is like, in.
Speaker 3 (09:05):
Between seasons, how you did last season? Provide some information obviously, right,
you know Connecticut was a national champion last year, had
a very good year. Right, you wouldn't just toss them
in a hat versus you know, writer or Canisius or
one of these small schools.
Speaker 2 (09:22):
I now have beamed him and I had, you.
Speaker 3 (09:24):
Know, Youngstown State, Texas, Rio Grand Valley. You know, you
you you have some information from the prior season. But
because there's so much more player turnover now the best
players go pro after one year, it's less continuity than
there used to be, right, and so and so you
should always be asking questions, right, and like you're billion
a model, part of what you're doing is saying, Okay,
(09:46):
here's this data. It's been well calibrated on literally what's
you know, seventy five years of college basketball history, right,
but let's make sure that it's performing as advertised on
the past couple of years, right, because after all, if
you're then adapting this to make predictions, and that's kind
of what people are going to care about, whether you
Rectro actually got some game right in nineteen seventy two
(10:08):
or whatever. And what we found is that the model
had I know, I'm saying we because it's just me
at this point. What I found is that the model
had become overconfident in the in recent years, right, because
it was it was overconfident early in the season before
catching up. And that was because it you know, overweighted
(10:31):
the impact of last season when if you have a
good player, it's going to the NBA anyway, and so
and so it doesn't help quite as much and so like, yeah,
so this is where like domain knowledge helps, right, because
you could just say, well, it's a fairly large sample,
but you know, it's been a weird couple of years
because of COVID and new rules and stuff like that. Right,
But it's like, Okay, we have a good hypothesis for why,
for why there's more mean reversion now. But by the way,
(10:55):
this has actionable implications.
Speaker 2 (10:57):
Where Okay, so.
Speaker 3 (11:02):
For our tournament model, which is related to this ELO model,
but we combined with other systems. I found myself I
do literally make bets based on these, right, And I
found myself betting a lot of underdogs to cover the
spread in the men's game, but not in the women's
(11:24):
game interestingly, right, because women you can't join the WNBA
until you're age twenty two, I think, which seems fucking
insane to me, Like, how was that legal? By the way,
that is very strange.
Speaker 1 (11:36):
I don't understand.
Speaker 3 (11:38):
Yeah, the politics THEWWA, you know, it's a whole other segment.
But yeah, the teams are still as strong as ever,
and so you have less reversion from year to year.
So basically for lots of reasons, and the fact that
like you know, for a long time in the women's game,
they were like, you know, somewhere between a half dozen
(11:58):
and two dozen schools. It took women's basketball really seriously,
and then some where it just like you go out
with a clipboard and say, hey, who wants to play? Right,
And so it's almost like professional versus amateur. Now it's
become much more popular. There's more money in the sport,
more young women are playing the sport, right, But still
it's more it's much more lopsided, right, And so you know,
(12:20):
so I found and now I'm giving away and replaces
my bet. So too late, right, But yeah, so I've
ad also fitting a lot of favorites in women's basketball
and a lot of underdogs in men's basketball, because there's
been more mean reversion in men's basketball.
Speaker 2 (12:36):
And I don't think that.
Speaker 3 (12:38):
The models are that the odds are accounting for this enough,
although you know, we'll see I'm putting my money where
my mouth is at least.
Speaker 2 (12:45):
Yeah, they're just settle things too.
Speaker 3 (12:47):
Like you know, big teams that draw huge audiences and
have very enthusiastic fans and good in game presentation and
nice locker rooms, right, they have a bigger home court
advantage which makes their rating look better. But you're playing
the NCAA tournament on neutral sites instead, so there have
actually been quite a few upsets in the tournament in
(13:08):
recent years. So yeah, all this is kind of you know,
so that's why it's like, this is what I hate
when like academics are like, well you a sports model,
election Wedell, this isn't not real science, Like it's actually
better science. Sure, because you're like, now I sound like
a fucking asshole, but no, but you're like you it's
it's still like hypothesis driven, right, you're looking at the
(13:30):
real world.
Speaker 2 (13:30):
You're just actually willing.
Speaker 3 (13:31):
To test these ideas in the form of of making predictions,
in some cases even betting on these predictions.
Speaker 1 (13:36):
No, that that makes perfect sense. And do I understand
correctly that you actually have two versions of your model,
one that's elo based and one that's a Bayesian elo version.
Speaker 3 (13:45):
Is that right? Abazing just because I'm a Doric, right,
But so in the Baysing version, like I said before,
you uh, the new season begins in the team is
kind of the same and kind of a different team.
Speaker 2 (13:57):
Right.
Speaker 3 (13:58):
In the original version, it reverts to the mean based
on the other teams in your conference, right, because there
are clearly differences between like you know, whatever, the ACC
and the IVY in the Basing version, then we give
weight to the human poles. Instead, we look at the
preseason top twenty five. We assume that these people are
evaluating recruitment and player aging and Injuriason all this stuff, right,
(14:20):
and so we revert based on that instead. It turns
out actually that like you do best with a blend
of you know, because the humans can add or subtract,
they can introduce biases that aren't helpful, right, So it's
really it's really kind of a what we call base
is really like a blend of the original version and
kind of a pure Bayse version. So that accounts for
(14:45):
like preseason makings in the human poles, and that has
interesting implications.
Speaker 1 (14:50):
Do they have the same results this year or not?
Do your two models differ?
Speaker 3 (14:56):
Yeah, let me show you it's actually not? Or is
it pretty similar that different?
Speaker 2 (15:01):
I mean, for.
Speaker 3 (15:03):
So the Basing version has Duke as the best team
and the pure version has Floored as the best team.
Now these are all within like a few points. It's
a very minor difference, right, But like you're counting for
like that you're basic kind of counting for like the
historical reputation of Duke that gets boosted in these human polls.
Speaker 2 (15:24):
Got it.
Speaker 1 (15:24):
Well, that's all super interesting, Nate. So let's see what
the results show when it comes to actually choosing brackets.
So you got to use all of your models. I
did not, but I got to use a Monte McNair.
So I got to use a human and after the breaks,
let's see how it all turns out. Nay, I have
(15:58):
a confession to make. I have never filled out an
NC double a bracket until now.
Speaker 3 (16:05):
Ria.
Speaker 1 (16:06):
This is my first ever bracket and.
Speaker 2 (16:08):
We're still allowing on a show.
Speaker 1 (16:10):
Come on, So this is my first ever bracket.
Speaker 2 (16:14):
But how can you never filled out a bracket?
Speaker 1 (16:16):
I've never filled out a bracket, Nate, I confess, I confess.
Speaker 3 (16:22):
You know I've never done yoga.
Speaker 1 (16:24):
Oh there you go, Nate. How could you have never
done yoga?
Speaker 3 (16:28):
It feels never the gender stereotype being here. I don't know,
but like it's like something you think you would have
done once.
Speaker 1 (16:34):
Right, Okay, Nate, I will I will ever do yoga
with you. And now that I filled.
Speaker 3 (16:40):
Out our bracket, okay, yeah.
Speaker 1 (16:42):
So so I actually you have your bracket right, and
it's probably a normal bracket. I have two brackets, so
Here's here's what I did. So first, I know nothing
you know about NCAA basketball. I never filled out a
bracket before. So I did a bracket based on vibes.
So I didn't even look at the at how teams
(17:03):
were ranked, didn't look at their record, didn't look at
any data whatsoever, and just like went on a totally
vibe based like who do I like? What sounds better?
What state do I like better? Where's the weather better?
Just just random things like that, my ViBe's bracket night.
Do you want to guess who won at the end?
Who I have winning the whole thing?
Speaker 2 (17:25):
H Yale?
Speaker 1 (17:27):
No, I'm a Harvard grad.
Speaker 2 (17:29):
Okay, it doesn't make sense. I don't.
Speaker 3 (17:31):
Uh, who would This is a weird what's it like? Laden?
Speaker 2 (17:38):
Like? What would Maria think? Is exactly?
Speaker 1 (17:40):
This is a perfect psychology experiment.
Speaker 2 (17:44):
Uh, give me give me a hint.
Speaker 1 (17:46):
You weren't You weren't that far off when you said
Yale in terms of the types of things I was
thinking about. But it was a place. It's so it
was an emotional based decision, all right.
Speaker 3 (17:57):
Okay, So one thing is, are you enough of a
college basketball fan to know that you're supposed to hate Duke.
Speaker 2 (18:03):
Yes, okay, okay, because.
Speaker 3 (18:06):
That's you know, that's like the point he headad like
by the way I was out, Uh, I don't know.
I was out with my partner last Saturday, and we'll
at least is maybe really too much? Like why is
everyone in this bar so so white? Because it's New
York and it's a big bar and usually and it's
(18:28):
because the New Game was fun?
Speaker 1 (18:32):
That's funny. But okay, so, so do you give up?
Should I just tell you? Let me give give me
one more, one more guess?
Speaker 3 (18:39):
Okay, Saint John's no is there a New York guess?
Or not? Maybe? I think, Yeah, I guess you're not
really like it. I mean, you were live in New York.
You don't like it New York born and bread.
Speaker 2 (18:50):
Okay, I give up?
Speaker 1 (18:51):
All right? So I had you're gonna laugh. I'm curious
to hear your reaction. I had U c l A
winning because I felt bad for LA and the wildfires,
and I felt like they needed something good to happen
to them.
Speaker 3 (19:03):
Okay, you know, I'm generally pro you. So I like
that color mix.
Speaker 1 (19:09):
Yeah, there was just UCLA had good vibes and I figured,
you know, what let's have them win. So I had
UCLA beating Duke in the final four.
Speaker 3 (19:19):
What seedd UCLA even this year there are seven seed probabilistically,
let me let me look up the old silver bulletin
forecast and see what it says. So you have an
zero point three percent chance of being right? All right, yes,
but getting it it's like getting aces right.
Speaker 1 (19:39):
Yeah, absolutely so then okay, So I showed Manty this
vibees based bracket and I was like, will you help
me correct it? And he was like, dude, we need
to start from scratch. So, for listeners who don't know,
Monty McNair is the GM of the Sacramento Kings and
he went to Princeton and he did his thesis on
(19:59):
March Madness and on building brackets. So this is actually
what he studied academically. And about eight or nine years
ago he came in second in the Cagle bracket contest.
So this is someone who is really really good at
making brackets. And I asked Monty kind of how he
(20:21):
would go about doing this. And Monty has his own
models as well, by the way, So let's actually listen
to a brief clip of that conversation. And then we'll
talk about what the resulting bracket looks like, and we'll
talk about your bracket night.
Speaker 4 (20:35):
I would start with a clean slate, no offense to
your ECLA Champion bracket, and I think first we need
to set our.
Speaker 3 (20:42):
Goal, because that's going to dictate. You know, I'll try it.
Speaker 4 (20:46):
I'll try not too many tortured poker analogies, but I.
Speaker 1 (20:49):
No, let's do tortured poker analogies. We live for tortured
poker analogies on the show.
Speaker 4 (20:54):
Yes, so head to head cash game versus a World
Series of Poker tournament, you're going to do different things.
I think in your case, really you want to be
one person.
Speaker 1 (21:05):
Yes, I'm heads up against Nate Silver, and I want
to kick his ass. Take that, Nate. There you go,
Nate Gottle's been thrown. So so that's I mean, that's
kind of where Monty said that, you know, the three
things we need to keep in mind are what's our
(21:25):
scoring system? Is there going to be anything different for upsets?
I was like, no, you know, we're just doing something
very straightforward. And then he said, you know how big
is your pool? I was like, it's me and Nate
that's it. I just want my bracket to do better.
And by the way, we will link to my brackets
in the show notes, both the official bracket, which is
the Manty bracket as I call it, and then my
(21:46):
Vibes bracket, so that you guys can just laugh at
how funny my Vib's picks are, because, like I said,
I looked at absolutely zero data, ignored the rankings, ignored everything,
and just went with my gut. So so you'll be
able to see both of those. One we hope will win,
the other we hope we'll give you a good laugh.
(22:07):
And yeah, Monty's are our mutual bracket. I did get
to make a few decisions, by the way, there were
a few close ones where he said, I got to pick,
so I picked sometimes things that he would not have picked,
But he said that he stood behind the choices. And
we do have Duke winning overall. Nate, how about you?
Do you have Duke winning or do you have someone
else winning?
Speaker 3 (22:27):
Well, I don't. Let's going to make things a little
easier for people. Let's give our champion, our final four
and then our sweet sixteen. So let's say that we're
going to get one point for every Sweet sixteen. Pick
that's correct, three points for every final four team that
is correct, and then five points were picking the right champion.
Speaker 2 (22:49):
Okay, okay, okay.
Speaker 3 (22:51):
So you want to start in the South region?
Speaker 1 (22:55):
Uh?
Speaker 3 (22:55):
Sure, Okay, we should take turns going first because it
might influence. So who are your final four in the South?
Speaker 1 (23:02):
My final four? Let's see Auburn, Texas A and m
Iowa State and Michigan State.
Speaker 3 (23:09):
Okay, so you have the favorites according to the Silver
Bulletin model there, I'm gonna I'm gonna just to represent
my home state. I'm gonna go Auburn Michigan. I mean,
here's here's the thing that bugs me though, Like our
model things, Iowa State is underrated but nevertheless the most
likely team. But anyway, that's fine. Iowa State and Michigan State.
(23:31):
So a lot of Midwest, a lot of mid West there.
And then of those four, who do you have emerging
into the final four.
Speaker 1 (23:40):
Auburn and Iowa State?
Speaker 3 (23:44):
Okays the eight? And then who do you have prevailing?
We didn't, we were trying to simplify it. So and
then who do you have prevailing between Auburn and Iowa State?
Auburn Okay, I also have auburn thing is I have.
I have this whole model I design. I'm trying to
sell the people, right, I'm not gonna be like, oh,
ignore the model. Go to silver Bulletin dot com and
get temperature. There's no discounty.
Speaker 1 (24:03):
So do you have the same picks as I do
other than Michigan.
Speaker 2 (24:06):
Yeah.
Speaker 3 (24:06):
In fact, I just had to cheat to over ride
my model, which actually at Texas A and M and
the same picks you did, just because like, uh, I
want some drama for this program. Yea, my model would
have made those exact same picks. Okay, let's go to
the And.
Speaker 1 (24:22):
By the way, Michigan versus versus Texas and M was
one of those where Monty said that I could actually
pick and I did Texas A and M because I
thought you would pick Michigan. So I wanted to differentiate
myself and I was correct.
Speaker 3 (24:39):
Now let's go out west where the regional fib we
played at the Chase Center in San Francisco, California.
Speaker 2 (24:46):
I'll go FIRSTUS time.
Speaker 3 (24:47):
Okay, and here my model is emphatic about these picks.
Speaker 2 (24:51):
I have no choices to make.
Speaker 3 (24:53):
I have in the final four, Florida Maryland, Texas Tech
and Saint John's. Go even have a choice to make,
I mean not really, you know, and then lead eight
Florida versus Saint John's. I mean Saint John's versus Kansas.
Excuse me, Sint Johns' sex seconds close, but whatever. I
live in New York, now, you know. There were some
(25:13):
some nice Saint John's kids also at the bar the
other day, nice but loud. So I have Florida advancing
from the west.
Speaker 2 (25:24):
Over over over Saint John's. Okay, okay, do you who
do you have?
Speaker 1 (25:30):
I think I have the same thing. I have Florida
and Maryland, Texas Tech, Saint John's, then Florida Saint John's,
and then Florida.
Speaker 3 (25:35):
Okay, so so far all we have is this one
game that may or I'm not even happens.
Speaker 1 (25:39):
So it's really it's interesting though, because you said you
had no choices, and the one thing that Monty said
was that the first well, I guess we're not doing
the We're not doing the Sweet sixteen. So I have
Colorado State beating Memphis because he said that the five
and twelve five twelve is one of the most common upsets,
(26:02):
and that would be fun.
Speaker 2 (26:03):
Okay, you have Coloro State beating Memphis.
Speaker 3 (26:05):
Yeah, so it's a fucking silver bulletin model, Maria, really, yes, yeah, Monty's.
Speaker 2 (26:10):
Just are you sure he was?
Speaker 1 (26:12):
Well, he didn't look. He didn't look.
Speaker 3 (26:13):
Oh my god.
Speaker 1 (26:14):
So, which which bracket are we going to do next?
Speaker 2 (26:17):
Midwest? And now it's your turn to go first?
Speaker 1 (26:19):
Okay, So I have Houston, Purdue, Illinois, and Tennessee. Then
I have Houston and Tennessee, and then I have Tennessee.
Speaker 3 (26:31):
Okay, so here's where your little counterintuitive friend. Okay, I've
got Now I don't have to cheat to like to
have different picks. So Houston Clemson, which you Clemson are Purdue?
Speaker 1 (26:46):
I said, Purdue?
Speaker 3 (26:48):
Houston Clemson.
Speaker 2 (26:48):
Who who's a five seed?
Speaker 3 (26:52):
We think produce a team that you know, gets a
lot of benefit out of their home court advantage, but
you're not playing at home in the tournament. Houston Purdue
A close choice, But I'm going with a model here.
Kentucky and Tennessee.
Speaker 1 (27:07):
Well, Kentucky was one of the other, so that that
was one of the choices that he also had me made.
Illinois versus Kentucky said it was incredibly close, but that
I was allowed to choose an upset, and I said
I wanted to since my husband is from Chicago.
Speaker 3 (27:23):
So then I have Houston emerging Tennessee Kentucky. It's a
classic rivalry those.
Speaker 2 (27:29):
Two long states.
Speaker 3 (27:31):
So I have Houston playing Tennessee in the regional final
and Houston not Tennessee winning. Huh. Now I feel comfortable.
I feel like I have an edge here.
Speaker 1 (27:40):
All right?
Speaker 3 (27:41):
Okay, finally whit bracket. Have we not done yet the East?
So I'm not trying to go first, right yep, Duke Arizona,
I'm just picking the chalk here because I think I'm
winning already. Duke Arizona, Wisconsin, Alabama. Do I have any
choices to make here? I do not Duke beating Alabama
(28:02):
in the Elite eight to advance to the Final four? Uh?
Speaker 1 (28:05):
Yeah, I have Duke Arizona, Wisconsin, Alabama as well, and
then Alabama, Duke and Duke.
Speaker 3 (28:13):
Okay, so remind me so I have One of the
good things about doing a podcast is that it may
not have Dawn and you. I just have the four
number one seeds reaching the final four, even though it's
incredibly unlikely that they all will right collectively, each have
like a one in two chance roughly or less than
that actually, so it's like only like a one in
(28:33):
twenty chance that I'm quote unquote right. But this is
why you need a funky scoring system. Should have we
should have? Yeah, Monty, I don't know. Now, we just
are are very chalky chalk if you don't know the term,
and you probably wouldn't unless you're a sport's bidding your
but like chalk just means picking the favorites. So in
our final four, I need you to pick a national champion,
Maria or you already revealed your national yep.
Speaker 1 (28:55):
My national champion is Duke. How about you.
Speaker 3 (28:57):
Let me think here? Okay, So this is interesting because
like the biggest differentially have right now is Houston making
the final four and not Tennessee. Yep, right, So so
I don't okay, So if I get if I'm right
on Houston, but you're right in Okay, I'm gonna do
something weird here.
Speaker 2 (29:19):
You ready, Yeah, I'm gonna go Florida.
Speaker 1 (29:22):
So you're gonna pick Florida beating Auburn and then this
is lower.
Speaker 3 (29:26):
Expected value relative to entire field. But I'm trying to
straut it because if I'm right on Houston making the
final four, then I have an edge, so I don't
want to pick Houston, right, and then it's like redundant.
It's like another way for me to win, right.
Speaker 2 (29:41):
I don't know.
Speaker 3 (29:41):
I don't really I don't really know. I don't even
you know, I don't know. I don't really like I'm
really enamored of any of these, you know, Yeah, I.
Speaker 1 (29:49):
Love I love how you're gaming out the game theory
payoffs in real time on the pod.
Speaker 2 (29:55):
This is great.
Speaker 3 (29:56):
Yeah, because Houston, then you're putting too many eggs in
the Houston basket, right, But I think I'm gonna get
that Houston. I mean, you know, yeah, I got Florida. Okay,
let's let's go through. You have your whole bracket.
Speaker 2 (30:07):
I do I need you to give me three.
Speaker 3 (30:10):
Opening round games where the inferior seed wins and that
they are at least an eleven seed and they win.
Speaker 1 (30:21):
Well, the only one that I have, well, I have
Colorado State.
Speaker 2 (30:25):
Okay, that counts.
Speaker 1 (30:28):
Let's see. I think that might be the only one
that I have that qualifies a no.
Speaker 3 (30:32):
No, somebody else can have to think on the fly,
you get a freebie.
Speaker 1 (30:37):
This is not fair.
Speaker 5 (30:38):
I don't know any well, I can go back to
my original vibes bracket and uh that'll that'll I have
Yale beating Texas A and O.
Speaker 2 (30:53):
All right, so Yale, you've got uh Colorado State.
Speaker 3 (30:57):
And let's see what other Yeah, I go vibes here?
Speaker 1 (31:01):
What other vibe vibe upsides, vibe upsets I had? I
had Wofford? Is that right word? Beating Tennessee?
Speaker 3 (31:13):
Okay, I think you're weighing the vibes a little too much.
That's the fifteen verses too.
Speaker 2 (31:17):
I'm gonna.
Speaker 3 (31:17):
I'm gonna so what I'll do as a favor, I
will not pick any of the ones you pick, not
that they are exactly at the top of my list,
apart from Colorado State. I will not let myself choose
Colorado State due to uh, due to you having picked it. Right,
that's my little bonus point to you. So let's see,
let's see where can I where can I get creative here?
Speaker 2 (31:36):
All right? So Drake beating Missouri it's an eleven verses
six or Model loves Drake VCU beating BYU. It's about
a forty percent chance according to the model. And now
I have to do something a little funkier, I think,
(32:00):
and we'll take High Point.
Speaker 3 (32:03):
Beating Purdue. Perdue tends to choke in the tournament. So
if the tidebreaker, then will go to the three upset picks, right, Okay,
if that's tie, they will have a name the location
of the college contest that probably all win because but yeah,
hand enter the location of every latitude and lunched it
at one point before you had to look things up
(32:24):
and be an efficient programmer. Okay, so we have our picks.
They are pretty chalky. Do you have women's picks?
Speaker 1 (32:31):
I don't know.
Speaker 3 (32:32):
Okay.
Speaker 1 (32:33):
Monty had very limited time, but I also promised him
that I would not put that I would not actually
bet this for any real money. So that was one
of the conditions of helping me out.
Speaker 3 (32:44):
Okay, Yeah, which I think we came up because they
barely let me. You know again, They're like, immediately the
Caesars puts up its lines on women's games, right, and
like it's after dinner with my partner and I'm like,
it's like, what are you doing. I'm like, do these
lunches came out? Man? I have to have to bet
and help before they change. He's like, are you ignoring
the conversation. I'm like, this is where you make your money,
(33:06):
and but it's this the one thing I kind of
bit serious and they still can't get real money downe.
Speaker 1 (33:09):
Yeah, yeah, no that we've talked about this before. But
that is obviously a very real problem when you have
an edge in anything.
Speaker 3 (33:18):
But you know, I want to be the guy who's like,
for gender equity reasons, I should be allowed to bit
more on the best. You should, You absolutely should, But no,
they really limit because they're afraid that like nobody knows anything,
and so they you know, they're they're very defensive, even
though women's faketiballl actually the women's tournament final got higher
TV ratings and the men's last year. But you know,
(33:38):
whenever a sports book is limiting you to a few
hundred bucks, right even if they don't let meet you overall,
whereas a men's I mean I get somewhat limited, right,
But like that's that's saying they the statistical discourse about
the game is not very advanced.
Speaker 1 (33:57):
Yeah, that makes sense, Nate. I wonder if our listeners
want to want to chime in and see who whose
bracket they think is going to win. Is it going
to be dug Er Florida. How are we gonna how
are we going to do it? I think that would be.
Speaker 3 (34:12):
I actually, I just think I think I have more
ways to win the contest if like, yeah, you probably look,
I think I don't want Houston. We we think we've
locked in. If we set Duke, then I'll lose anyway, right,
I mean, I think that was actually really stupid.
Speaker 2 (34:29):
I don't know, I think it was stupid.
Speaker 3 (34:30):
Probably, But anyway, more drama though, yes, more drama wants
to drama is always good.
Speaker 1 (34:37):
Now, I don't particularly want to see them win. That's
why I had U s l A winning my vibes Brocken. Okay, cool,
well it was it was actually it was fun and
I I actually think that I'm going to probably enjoy,
actually enjoy watching some of the games now that I
actually have a bracket and I know that I'm rooting
(34:57):
for someone. Rooting is always something that's a little bit
more fun. Let's uh, yeah, let's let's see who wins.
I was about to say, may the best man win.
Let's just say that may the best man win, and
I hope that that's me slash Meat plus Monty. But
but we'll see and I'm looking forward to the fight.
(35:21):
After a quick break, we're going to be back.
Speaker 3 (35:32):
Okay, Maria, now that you're a degenerate gambler, are you
gonna Are you gonna? By the way, what are we
betting on this? Should we do like dinner or something?
Speaker 1 (35:40):
Yeah, well we can't. I can't actually bet any money,
per per Monty, so sure dinner dinner it is?
Speaker 2 (35:46):
Okay, so who pays for dinner next time we see
each other?
Speaker 3 (35:50):
Okay, are you gonna enjoy watching the games now that
you have some skin in the game and it could
be pretty nenice dinner?
Speaker 1 (35:56):
Yeah? Absolutely, I'm uh, I'm excited because this is the
first time I actually even know what the teams are
who are playing in the n C Double A. Usually
I don't tune in until kind of near the end,
right where I'm like, oh, okay, you know this is important,
but like there are some of these schools that I'd
never heard of before. I was like, what in the
(36:17):
world is this and where is it? So yes, now
I think I'm actually going to have more skin in
the game and that should be a lot more fun.
By the way, our dinner should be something like basketball related,
like we should get really good burgers or something like that.
Speaker 2 (36:31):
Okay, perfect.
Speaker 1 (36:32):
Do you have any sort of kind of watching rituals
or anything like that or is it just like whatever goes?
Do you prefer watching in bars around other people? Do
you like watching at home? I'm actually just curious. How
have someone like you who's been doing this a long
time does it?
Speaker 3 (36:47):
For the tournament in the early rounds, it's fun to
go to a bar because they can have like four
different games on it once. I do not have like
one of those fancy TV setups where multiple monitors kind
of thing, right, So that's the reason why. Yeah, No,
the tournament is like fun to like, Yeah, I mean,
for some basketball and football are kind of the big Yeah,
it's fun to watch sports at bars.
Speaker 2 (37:07):
What am I say?
Speaker 3 (37:08):
I'm not trying to qualify that, ye too much?
Speaker 1 (37:10):
Yeah, yeah, I actually I totally agree with that. You know,
I'm not a football fan either, but found myself last year,
I think or the year before a barbecue place during
the Super Bowl when it was just starting, and it
ended up staying there and watching the entire game because
the energy was just so great and people were you know,
people were so into it and it was a lot
(37:31):
of fun. And I actually, like for once enjoyed watching
a football game.
Speaker 3 (37:36):
And by the way, even if you never bet, a
sports book is a fun place to watch sports too,
because people are really into it. Yeah, and they're rooting
in yeah.
Speaker 5 (37:47):
No.
Speaker 3 (37:47):
But what's weird is like I have so for the
bets I have, like you know, you sually it's born
to watch, like the one versus sixteen games, very lopsided ones,
but like I have like quite a few bets on
like point spreads in those games. Like I said, in
the women's often the favorites I expect one, but even
more and the guys often underdogs, right, But like whether
(38:08):
you know a team gets to thirty four points or
thirty six points is like material to me.
Speaker 2 (38:14):
So you maybe I'm probably not a good advertisement.
Speaker 3 (38:16):
Are you watch it?
Speaker 2 (38:16):
I'm like, don't turn away from that blowout? You know.
Speaker 1 (38:18):
Let's well, I'm definitely I'm definitely excited about it. What's
your take? I'm wondering if you're rational or irrational when
it comes to this on you know, superstitions. You know,
there are some people who will like wear the team
jersey when they watch, and they think that if they
don't do X, their team's going to lose. How do
(38:40):
you feel about all of that?
Speaker 3 (38:42):
I mean, I don't you know, you got mad at
me once, like lost my lucky hat and you're like
you don't have a lucky hat. Yeah, I'm not too superstitious.
Speaker 1 (38:51):
Really, do you really have a lucky hat?
Speaker 5 (38:53):
Night?
Speaker 3 (38:54):
I thought I did, but then you know, I used
to have one. I used to have one superstition that
like I have this one black one chip from from
the ARIA, right, and like I forgot to cash it in.
You know, sometimes you have like an X chip in
your bag or something, right you're leaving for the trip
and then the next trip, I also forgot to like
(39:17):
cash and I don't think I was sting at the area.
So I'm like, this is my tradition now, right, but
this is good luck. But I haven't happened to the
good luck in poker, especially that at fucking tournaments.
Speaker 2 (39:26):
You know, or the ARIA.
Speaker 3 (39:27):
So like I'm like fuck this, right, It's like, you know,
I owed so many money from the sports, so I'm
like I'm cashing these jumbnuts trying this fucking chip around.
You have to give white, you know, so so yeah,
so maybe they'll turn my luck around all right long,
around my possession.
Speaker 1 (39:46):
Let's see what happens. I love it, love it. Sometimes
even the most rational people, you know, do you have
a lucky chip or a lucky hat?
Speaker 3 (39:54):
I do think that, like there's something rational there about
like not making like if I have like a a
busy day, especially in loving poker netness. You know, if
I make day three of something, I'm like, okay, I'm
going to get up at seven point thirty and like
and worked out and then eat this place, even have
to make a reservation, like so I'll try to control
some factors like that. Right.
Speaker 1 (40:14):
Well, on that note, Nate, good luck to both of us.
I'm excited. I think this will be a fun competition
win or lose, although obviously I hope that I win.
Speaker 3 (40:22):
I hope so Tim, you hope I win.
Speaker 1 (40:25):
Yay, that's awesome, thank you.
Speaker 3 (40:27):
You know, it's like when you say it's like when
someone is like, you know, someone's like, how a goo day,
and you're like, love you, and it's like you're saying
that you're a partner or something right, like your boss
or something.
Speaker 2 (40:37):
It's like, I don't know, do you know, Maury work
toward you?
Speaker 3 (40:39):
Another podcast I've been up a long hours trying to
get those brackets and ship up all these models up.
Speaker 2 (40:46):
So I hope I win.
Speaker 5 (40:49):
No, no, you.
Speaker 1 (40:49):
Can't take it back, all right? And on that note, listeners,
let us know who you think will win. And if
you tune in to Pushkin Plus today, we're going to
have the full interview with Monty McNair, which is a
lot of fun. I highly recommend you listen to it.
(41:15):
Let us know what you think of the show. Reach
out to us at Risky Business at pushkin dot fm.
And by the way, if you're a Pushkin Plus subscriber,
we have some bonus content for you that's coming up
right after the credits.
Speaker 3 (41:28):
And if you're not subscribing yet, consider signing up for
just six ninety nine a month. What a nice price
you get access to all that premium content and for
listening across Pushkin's entire network of shows.
Speaker 1 (41:39):
Risky Business is hosted by me Maria Kanikova.
Speaker 2 (41:42):
And by me Nate Silver.
Speaker 3 (41:44):
The show is a co production of Pushkin Industries and iHeartMedia.
This episode was produced by Isabelle Carter. Our associate producer
is Gabriel Hunter Chang. Sally Helm is our editor and
our executive producer is Jacob Goldstein. Mixing by Sarah Bruger.
Speaker 1 (41:59):
If you like this show, please rate and review us
so other people can find us too, Thanks so much
for tuning in.
Hosts And Creators
Maria Konnikova
Nate Silver
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