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