March Madness Bracket Extravaganza

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Welcome back to Risky Business, our show about making better decisions.

I'm Maria Konakova.

And I'm Nate Silver.

Did you say our show or a show?

I said our show, Nate, because it is our show.

Sometimes I say our show.

Sometimes I say a.

You know, I like to play around with it.

The show.

How about the show?

I don't know if the show will tell you how to make better decisions.

This is the show about how to make better decisions.

What are we going to be talking about today, Nate?

March Madness, Maria.

March Madness, indeed.

It's New York.

It's springtime.

The weather is improving from mediocre to slightly better than mediocre.

And we're here to watch some basketball and talk about some basketball.

Yep.

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.

Oh, so you cheated?

Oh, no, I did not cheat.

I did not.

You, Monte McNair.

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 actually going to be on Pushkin Plus.

Highly recommend that you all listen to it.

I feel like the politics of this are a bit complicated where the woman has to ask a certified basketball expert.

Well, I just kind of go, the man just, you know, uses a regression-based ELO model.

So, so that's, that's a great segue, Nate.

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?

So, some of this, like, actually literally dates back to 2002 or 2003, right?

Where I'm like

a junior associate at KPMG.

This is an accounting firm.

I always was sensitive.

I'm, you know, I was a consultant at the accounting firm, Maria, not an accountant, just so you know.

I always forget that you worked at an accounting firm every time you choose it.

I'm like, oh, yeah, right.

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 partied too much in 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 and did a little simulation.

Actually, it's not a simulation.

It's not deterministic exactly, but like there are only 67 games.

So you can kind of explicitly do all the conditional probabilities instead of having to rely on a simulation, right?

Just a technical detail.

But it's evolved from there.

I think when I was at the New York Times in 2012, 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've ever made a model might sound familiar, right?

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 of 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 NCAA, at least the best freshman in the NCAA this year,

got hurt during the ACC tournament.

He seems to be helping now.

But if he were to be hurt, that would gravely impact Duke's prospects.

So we account for that.

And then also we found that like...

There's reversion to the mean, meaning that you play roughly 33 games or something in a college basketball regular season.

Not all that big a sample.

Some of us are against crappy competition, you know, from

southwestern State University prep or whatever, right?

So we looked at preseason rankings as well as part of what we factored into the formula.

Sometimes teams 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 pretty 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?

I know some of our readers, listeners might be, I should say.

Well, let's, for people who are not familiar, of course, I personally am very familiar with ELO ratings.

But for those of us who might not be,

why don't we explain it a little bit?

So, ELO was designed by, let me look at the guys, it's like Arpand Elo, or let me look, is Hungarian probably.

The Hungarians are quite promiscuous with in kind of this era of intellectual history.

Arpad 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, of course, and how good your opponent is, right?

So if I have an Elo rating of 1900, I don't know if that's good in chess, it's probably pretty good, right?

And you're a 1400 Maria, right?

1400 is 1500 is average, a 1400 is whatever, mediocre.

I barely get any credit because you kind of already forecasted the win, right?

If I'm a 1900 and I beat Magnus Carlson, who's a 21, I'm just guessing,

you might know, then I get a lot of credit because I've beaten a stronger opponent, right?

2833 Nate is Magnus' Elo rating.

2833.

Oh my, that's embarrassing.

See, in basketball, they only get up to like 2,200 or something, right?

And so I'm like, is Magnus Carlson better at chess than?

Yeah, 2833.

His peak rating was 2882.

Okay, that's pretty, that's pretty good.

I think I'm like a 900 or something.

But so anyway, we adapted the ELO ratings.

Actually, other people have done this, but we adapted it first to the NFL and then to college basketball.

But there are a few twists.

In ELO,

originally just about the winner-loss, right?

We also account for the margin of victory.

in basketball.

We account for home court advantage in kind of an increasingly complicated way.

We account for travel distance.

So if you fly across the country, you're jet lagged and so forth.

This actually shows up in performance.

By the way, different teams have different home court advantages.

It turns out actually that having like a larger crowd actually helps.

I was trying to model this the other day.

You know, if you for every 10,000 fans that show up, teams score roughly an additional point.

That's that's absolutely fascinating.

So yeah, so you're that's for I did that for women.

I don't know for men if it's different.

Women's option because they have some teams are more popular than men's teams and some like literally draw, you know, 12 people, right?

But yeah, the teams that actually have people watching do a little better in their home games, controlling for other factors.

So I love that you're able to account for these things that might seem kind of more ineffable, right?

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 human touch versus kind of the big data crunching.

I'm just curious from like a from a

standpoint, what the balance is there.

So are you saying when I make a bracket or like when I'm building a model?

No, when you're actually building the model.

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-ended problems, right?

But yet in general, like with politics,

election models,

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 it 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 do the parameters of the game change over time?

So one

we do is like in between seasons, how you did last season provides 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, Ryder or Canisius or one of these small schools that I now have beamed in my head, you know, Youngstown State,

Texas, Rio Grande Valley.

You know,

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, and you're building a model, part of what you're doing is saying, okay,

here's this data.

It's been well calibrated on literally what's, you know, 75 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, then that's kind of what people are going to care about, whether you retroactively got some game right in 1972 or whatever.

And what we found

is that the model had, I don't know, I'm saying we because it's just me at this point.

What I found is that the model had become

overconfident in recent years.

Okay.

Right.

Because it was, it was overconfident early in the season before catching up.

And that was because it,

you know, overweighted the impact of last last season.

When, if you have a good player, he'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, been a weird couple of years because of COVID and new rules and stuff like that, right?

But it's like, no, okay, we have a good hypothesis for why, for why there's more mean reversion now.

But by the way, this has actionable implications where,

okay, so

for our tournament tournament model, which is related to this ELO model, but we combine it 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 game, interestingly, right?

Because women, you can't join the WNBA until you're age 22, I think, which seems fucking insane to me like how is that legal by the way that is very strange i don't understand yeah um the politics of the wmba i didn't you know it's a whole nother segment but yeah the teams are still as strong as ever and so you you have less reversion from year to year so basically for lots of reasons and the fact that like um you know for a long time in the women's game they were like

you know, somewhere between a half dozen and two dozen schools that took women's basketball really seriously.

And then some were 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, so I found, and now I'm giving away information, but I already placed my bets, so too late, right?

But yeah, so I found myself fitting a lot of favorites in women's basketball and a lot of underdogs in men's basketball because there's been more meme reversion in men's basketball

and I don't think that 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 yeah at least yeah they're just subtle things too like

you know big teams that draw huge audiences and 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 nstate tournament on neutral sites instead so there have actually been quite a few upsets in in the tournament in 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 uh

academics are like only a sportsman election model this is not real science like it's actually better science sure a 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 real world you're just actually willing to test these ideas in the form of of making predictions and in some cases even betting on these predictions no that 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 what's that's a bayesian elo version is that right i call it bayesian just because i'm a dork right but so in the bayesian version like i said before um

you uh

the new season begins and the team is kind of the same and kind of a different team right um

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 League.

In the Bayesian version, then we give weight to the human polls instead.

We look at the preseason top 25.

We assume that these people are evaluating recruitment and player aging and injuries and all this stuff, right?

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 kind of a, what we call Bayes is really like a blend of the original version and kind of a pure

Bayes version.

So that accounts for like preseason rankings in the human polls.

And that has interesting implications.

Do they have the same results this year or not?

Do your two models differ?

Yeah, let me show you.

It's actually not

that different.

I mean, for

so the Bayesian version has Duke as the best team,

and the pure version has Florida 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 the, you're basically kind of counting for like the historical reputation of Duke that gets boosted in these human polls.

Got it.

Well, that's all super interesting, Nate.

So, 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 Monty McNair.

So I got to use a human.

And after the break, let's see how it all turns out.

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Nate, I have a confession to make.

I have never filled out an NCAA bracket until now.

Maria, this is my first ever bracket.

We're still allowing you on the show.

Come on.

So, this is my first ever bracket.

How can you not have filled out a bracket?

I've never filled out a bracket, Nate.

I confess, I confess.

You know, I've never done yoga.

Oh, there you go.

Nate, how could you have never done yoga?

It feels now we're through the gender stereotype in here.

I don't know, but like, it's like something you think you would have done, would have done once, right?

Okay, Nate, I will, I will do yoga with you.

And now that I filled out a bracket.

Okay.

Yeah.

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, nothing, you know, about NCAA basketball.

I've never filled out a bracket before.

So I did a bracket based on vibes.

So I didn't even look at the C at how teams 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 random things like that.

My vibes bracket, Nate, do you want to guess who won at the end?

Who I have winning the whole thing?

Yale?

No.

It doesn't really matter.

I'm a Harvard grad.

Okay, it doesn't make sense.

I don't know.

Who would this is a weird, what's it like Ladden?

Like, what would Maria think is a college graduate?

Exactly.

This is a perfect psychology experiment.

Give me a hint.

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.

so it was an emotional-based decision.

Okay, so one thing is, are you enough of a college basketball fan to know that you're supposed to hate Duke?

Yes.

Okay.

Okay.

Cause that's, you know, that's like the pointy-headed,

you know, like, by the way, I was out, uh, I don't know, I was out with my partner

last Saturday.

Um,

and we're like, this is maybe revealing too much.

We're like, why is everyone in this bar so

white?

Because it's New York and it's a big bar, and usually, and it's because the Duke game was.

That's funny.

But, okay, so do you give up?

Should I just tell you?

Let me give, give me one more.

One more guess.

Okay.

St.

John's?

No.

Because they're in New York.

Maybe.

Yeah, I guess you're not really like it.

I mean, you live in New York.

You're not like a New York born and bred.

Okay, I give up.

All right.

So I had, you're going to laugh.

I'm curious to hear your reaction.

I had UCLA winning because I felt bad for LA and the wildfires, and I felt like they needed something good to happen to them.

Okay.

You know, I'm generally pro

UCLA.

I like that color mix.

Yeah.

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.

What seed is UCLA even this year?

They're a seven seed.

Probabilistically, let me look up the old silver bulletin forecast and see what it says.

So you have an 0.3% chance of being right.

It's like getting aces, right?

Yeah, absolutely.

So then, okay, so I showed Monty this vibes-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 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 Kaggle bracket contest.

So this is someone who is

really, really good at making brackets.

And I asked Monty kind of how he 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, Nate.

I would start with a clean slate.

No offense to your UCLA champion bracket.

And I think first we need to set our goal because that's going to dictate, you know, I'll try.

I'll try not too many tortured poker analogies, but I do know a little bit more.

No, let's do tortured poker analogies.

We live for tortured poker analogies on this show.

Yes.

So a 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 beat one person.

Yes.

I'm heads up against Nate Silver, and I want to kick his ass.

Take that, Nate.

There you go, Nate.

Gauntlet's been thrown.

I love it.

So, so that's, I mean, that's kind of where

Monty said that, you know, the three three things we need to keep in mind are what's our 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 Monty bracket, as I call it, and then my vibes bracket so that you guys can just laugh at how funny my vibes pics are because like I said, I looked at absolutely zero data, ignored the rankings, ignored everything, and just went with my gut.

So you'll be able to see both of those.

One, we hope, will win, the other, we hope will give you a good laugh.

And yeah, Monty's

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 um do you have duke winning or do you have someone else winning well i i don't let's to make things a little easier for people um let's give our champion

our final four and then our sweet 16.

so let's say that we're gonna get um

one point for every sweet 16 pick that's correct uh

Three points for every final four team that is correct, and then five points for picking the right champion.

Okay?

Okay.

Okay, so

you want to start in the the south region?

Sure.

Okay.

We should take turns going first because it might influence.

So who are your final four in the south?

My final four, let's see.

Auburn, Texas AM, Iowa State, and Michigan State.

Okay, so

you have the favorites according to the silver bulletin model there.

I'm going to I'm going to just to represent my home state, I'm going to go Auburn, Michigan.

I mean, mean,

here's the thing that bugs me, though.

Like, our model thinks Iowa State is underrated, but nevertheless, the most likely team.

But anyway, that's fine.

Iowa State and Michigan State.

So a lot of Midwest, a lot of Midwest there.

And then of those four, who do you have emerging into the final four?

Auburn and Iowa State.

Okay, it's the Elite 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.

The thing is, I fucking have this whole model I designed.

I'm trying to sell the people, right?

I'm not going to be like, oh, ignore the model.

Go to silverbulletin.com and get temporary.

Actually, there's no discount.

So do you have the same picks as I do other than Michigan?

Yeah.

In fact, I just had to cheat to override my model, which actually at Texas A ⁇ M, the same picks you did, just because like,

I want some drama for this program.

My model would have made those exact same picks.

Okay, let's go to the.

And by the way, Nate, Michigan

versus Texas A ⁇ M

was one of those where Monty said that I could actually pick.

And I did Texas A ⁇ M because I thought you would pick Michigan.

So I wanted to differentiate myself.

And I was correct.

Now let's go out west

where the regional finally played at the Chase Center in San Francisco, California.

I'll go first this time.

Okay.

And here, my model is emphatic about these picks.

I have no choices to make.

I have in the final four: Florida, Maryland, Texas Tech, and St.

John's.

Don't even have a choice to make.

I mean, not really, you know, and then

lead eight, Florida versus St.

John's.

I mean, St.

John's versus Kansas, or excuse me, St.

John's versus Texas Tech is close, but whatever.

I live in New York now.

You know, there were some

nice St.

John's kids also at the bar the other day.

Nice but loud.

So I have Florida advancing from the West over

St.

John's.

Okay.

Okay.

Who do you have?

I think I have the same thing.

I have Florida, Maryland, Texas, Tech, St.

John's, then Florida, St.

John's, and then Florida.

Okay, so so far all we have is this one game that may or may not even happen.

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 the sweet 16.

So I have Colorado State beating Memphis because he said that the

512 is one of the most common upsets, and that would be fun.

Okay, you have Colorado State beating Memphis.

Yeah.

So is the fucking silver bulletin model, Maria.

Really?

Yes.

Yeah, Monty is just, are you sure he was?

No, he didn't look.

He didn't look.

Oh, my God.

So, which bracket are we going to do next?

Midwest.

And now it's your turn to go first.

Okay.

So I have Houston, Purdue, Illinois, and Tennessee.

Then I have Houston and Tennessee.

And then I have Tennessee.

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,

wait, Duke, Clemson or Purdue?

I said Purdue.

Houston, Clemson, who is a lower, who is a five-seed.

We think Purdue's a team that gets a lot of benefit out of their home court advantage,

but you're not playing at home in the tournament.

Houston-Purdue

is a close choice, but I'm going with a model here: Kentucky and Tennessee.

Well, Kentucky was one of the other.

So that was one of the choices that he also had me made.

Illinois versus Kentucky.

He 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.

So then I have Houston emerging.

Tennessee, Kentucky.

It's a classic rivalry.

Those two long states.

So I have Houston playing Tennessee in the regional final and Houston, not Tennessee, winning.

Now I feel comfortable.

I feel like I have an edge here.

All right.

Okay.

Finally, what bracket are we not done yet?

The East.

So my turn 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 in the Elite Eight to advance to the final four.

Yeah, I have Duke, Arizona, Wisconsin, Alabama

as well.

And then Alabama, Duke, and Duke.

Okay, so remind me.

So I have

one of the good things about doing a podcast is that it may not have dawned on you that 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

20 chance that I'm quote unquote right.

But this is why you need a funky scoring system.

We should have, yeah, Monty, I don't know.

Now we just are very chalky.

Chalk, if you don't know the term, and you probably wouldn't unless you're a sports biddinger, 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 champion.

Yep, my national champion is Duke.

How about you?

Let me think here.

Okay, so this is interesting because the biggest difference I have right now is Houston making the final four and not Tennessee.

Yep.

Right.

So

I don't.

Okay.

So if I get, if I'm right on Houston, but you're right on.

Okay, I'm going to do something weird here.

You ready?

Yeah.

I'm going to go Florida.

So you're going to pick Florida beating Auburn and then this has lower expected value

relative to entire field, but I'm trying to strategy.

Because if I'm right on Houston making the Final Four, then I don't 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?

I don't know.

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 not really enamored of any of these.

You know,

I love how you're gaming out the game theory payoffs in real time on the pod.

This is great.

Yeah, because Houston, then you're putting too many eggs in the Houston basket, right?

But I think I'm going to get that Houston.

I mean, you know, yeah, I got Florida.

Okay, let's go through.

Do you have your whole bracket?

I do.

I need you to give me three opening round games

where

the inferior seed wins.

and that they are at least an 11 seed and they win.

Well, the only one that I have...

well, I have Colorado State.

Okay, that counts.

Let's see.

I think that might be the only one that I have that qualifies.

Yes, and now somebody else is going to have to think of the fly.

You get a freebie.

This is not fair.

I don't know anything.

Well, I can go back to my original vibes bracket, and that'll, that'll, I have Yale beating Texas A ⁇ F.

All right, so you got got Yale, you've got

Colorado State.

And let's see what other.

You got to go vibes here.

What other

vibe upsets I had?

I had Wooford.

Is that right?

Wofford.

Wofford

beating Tennessee.

Okay, I think you're weighing the vibes a little too much.

That's a 15 versus two.

I'm going to...

So what I'll do is a favor, I will not pick any of the ones you pick.

Not that they were exactly at the top of my list, apart from Colorado State, but I will not let myself choose Colorado State

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 get creative here?

All right.

So, Drake

beating Missouri.

It's an 11 versus 6.

Our model loves Drake.

VCU beating BYU.

It's about a 40% chance according to the model.

And now I have to do something a little funkier, I think.

And we'll take high point

beating Purdue.

Purdue tends to choke in the tournament.

So if it's a tiebreaker, then we'll go to the three upset picks, right?

Okay.

If that's tied, then we'll have a name the location of the college contest that probably I'll win.

Because I've studied this shit for

all

hand enter the location of every latitude and longitude at one point before you had to look things things up and be an efficient programmer.

Okay, so we have our picks.

They are pretty chalky.

Do you have women's picks, Maria?

I don't, no.

Okay.

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.

Okay.

Yeah.

Which I think it's a good thing.

It was shit up because they barely let me, you know, again, they were 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, he's like, what are you doing?

I'm like, dude, these lines just came out, man.

I 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.

And, but it's just the one thing I kind of bet seriously, I still can't get real money down.

Yeah.

Yeah.

No, that

we, we've talked about this before, but that is obviously a very real problem when you have an edge in anything.

But, you know, I'm going to be the guy who's like, for gender equity reasons, I should be allowed to bet more on women's basketball.

You should.

You absolutely should.

But no, they really limit because they're afraid that

nobody knows anything.

And so

they're very defensive.

Even though women's basketball, actually, the women's tournament final got higher TV ratings than the men's last year.

But, you know, whenever a sports book is limiting you to

a few hundred bucks, right?

even if they don't limit you overall, whereas a men's, I mean, I get somewhat limited, right?

But like, that's that's saying they, they, the statistical

discourse about the game is is not very advanced.

Yeah, that

that makes sense.

Nate, I wonder if our listeners want to want to chime in and see who's whose bracket they think is going to win.

Is it going to be Duke or Florida?

How are we going to do it?

I think that would be

I think I have more ways to win the contest if like

you probably look.

I don't know.

I think I don't want Houston.

I think that's we blocked blocked in the middle of the house.

I mean, if I hit Duke, then I lose anyway, right?

I mean, I think that was actually really stupid.

I don't know.

I think it was stupid.

But anyway, more drama, though.

Yes, more drama.

Who wants to see Duke win?

Drama is always good.

No, I don't particularly want to see them win.

That's why I had UCLA winning my vibes bracket.

Okay.

Cool.

Well, it was, it was actually, it was fun.

And 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 for someone.

Rooting is always something that's a little bit more fun.

Let's, yeah, 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 me plus Monty,

but but we'll see.

And I'm looking forward to the fight.

After a quick break, we're gonna be back.

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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 yeah well we can't i i can't actually bet any money per per monty so sure dinner dinner it is

okay um

so who pays for dinner next time we see each other okay are you gonna enjoy watching the games now that you have some skin in the game i mean it could be a pretty nice dinner Yeah, absolutely.

I'm excited because this is the first time I actually even know what the teams are who are playing in the NCAA.

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 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.

Okay, perfect.

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 someone like you who's been doing this a long time does it.

For the tournament in the early rounds, it's fun to go to a bar because they can have like four different games on at once.

And 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 I mean, for some, basketball and football are kind of the big,

yeah, it's fun to watch sports at bars.

What am I saying?

I'm not trying to qualify that

too much.

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, in a barbecue place during the Super Bowl when it was just starting.

And I 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 of fun.

and i actually like for once enjoyed watching a football game and by the way even if you never bet a sports book is a fun place to watch sports to because people are really into it yeah um

and they're rooting and yeah no but with weird is like i have so for the bets i have like um

you know usually it's boring to watch like the one versus 16 games the very lopsided ones but like i have like quite a few bets um

on like point spreads in those games like i said in the women's it's often the favorites.

I expect to women buy even more.

In the guys, it's often underdogs, right?

But like, whether a team gets demolished by 34 points or 36 points is like material

to me.

So maybe probably not a good advertisement to you watching.

I'll be like, don't turn away from that blowout.

Well,

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

superstitions.

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 you feel about all of that?

I mean, I don't, you know, you got mad at me once because I like lost my lucky hat, and you're like, you don't have a lucky hat.

Yeah, I'm not too superstitious, really.

Do you really have a lucky hat, Nate?

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 $100 chip from

the ARIA, right?

And, like, I forgot to cash it in.

You know, sometimes you have like an extra chip in your bag or something, right?

You're leaving for the trip.

And then the next trip, I also forgot to like cash in.

I don't think I was staying at the ARIA.

So I'm like, this is my tradition now, right?

But this is good luck.

But I haven't had particularly good luck in poker, especially not at fucking tournaments, you know, or the Aria.

So like, I'm like, fuck this, right?

It's like, you know.

all right.

I owed so many money from a sports bus.

I'm like, I'm cashing these chips.

I'm not carrying this fucking chip around.

I don't have to give white, you know?

So,

so yeah, so maybe I'll turn my luck around.

All right.

Chip is no longer on my possession.

Let's see what happens.

Love it.

Love it.

Sometimes even the most rational people, you know, do have a lucky chip or a lucky hat.

I do think that like.

There's something rational there about like not making, like if I have like a

busy day, especially involving poker, but not if I make day three of something, I'm like, okay, I'm going to get up at 7.30 and like and work out and then eat breakfast at this place, even if I have to make a reservation.

So I'll try to control some factors like that, right?

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.

I hope so too, Maria.

You hope I win?

Yay, that's awesome.

Thank you.

You know, it's like when you say, it's like when someone is like,

you know, when someone's like, have a good good day.

And you're like, love you.

And it's like, you're saying it's your partner or something, right?

And it's like your boss or something.

It's like, I don't know.

You know, Marie, we're toward the end of the podcast.

I've been up a long hours trying to get all this brackets and shit up, all these models up.

So I hope I win.

Thank you.

No, no, you can't take it.

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

let us know what you think of the show reach out to us at riskybusiness at pushkin.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 and if you're not subscribing yet consider signing up for just 6.99 a month, what a nice price.

You get access to all that premium content and ad-free listing across Pushkin's entire network of shows.

Risky Business is hosted by me, Maria Konakova.

And by me, Nate Silver.

The show is a co-production of Pushkin Industries and iHeartMedia.

This episode was produced by Isabel Carter.

Our associate producer is Gabriel Hunter Chang.

Sally Helm is our editor.

And our executive producer is Jacob Goldstein.

Mixing by Sarah Bruguer.

If you like this show, please rate and review us so other people can find us too.

Thanks so much for tuning in.

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