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Welcome, welcome to Wharton Moneyball here on the Wharton podcast network. This is Eric Bradlo, professor of
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marketing, statistics, and data science here at the Wharton School. I'm here
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today with my 11 and a half year collaborators on Wharton Moneyball, but of course a lot longer than that as
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friends and colleagues. I'm here today with Professor Shane Jensen, Professor
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Audi Winer, both professors of statistics and data science. Some combination of the three of us and Cade
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Massie are here every week on Wharton Moneyball. And of course, this is the last show of 2025 as we're sitting here
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on December the 30th recording the show. It's been a great year of interesting
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things in statistics and data science, especially applied to sports. Even more so happening on telecast now, even
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broadcasters using them more and more, including more in writing. Uh something I talked with Aaron Shatz about last
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week on the show, we're starting to see award winners even more reflective of
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advanced statistics. So it's been a and you know we now have thanks to Audi and
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Wasabi we have a Wharton sports business journal here at the Wharton School which
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has supplemented the already great publications that are happening at the application of statistics data science
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and sports of which I think it's fair to say the two of you I wouldn't say the
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pioneers but the two of you were in the early boat legitimizing statist the application and methodological
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development of statistics and data science and sports and so uh it's great to be on the show with you guys for the
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last show of uh 2025. >> Yeah, I I'll take the acknowledgement. Shane and I we did remember that years
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back when Shane had first started as a professor and we got money from ESPN um to do research into baseball statistics.
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That's the paper that we wrote I think is still maybe my top favorite paper of
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all time. The only reason the only reason I didn't call you guys the pioneers is of course
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you know uh Shane remembers I mean we were both graduate students at Harvard stat uh Fred Meller had written many
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many papers of course the original Efron and Morris shrinkage estimation paper uses baseball data um you know our
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former colleague and you know friend who passed away recently Dave Schmidline wrote a number of papers including you
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know can a hot goalie take you win you the Stanley Cup. So the only reason I was saying is I was pulling you guys in
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the first wave or in that >> we weren't the first to do sports at >> No, no, no, no. And of course a mutual
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>> your son's very old as well. So that can't can't be >> and also we all have a mutual connection
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to this person. Of course, one of my adviserss, one of Shane's adviserss, Hal
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Stern, obviously did a lot of work in there. Obviously, his PhD adviser was Tom Cover, someone that Audi knew
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extremely well. Tom Cover is is uh is possibly one of the most influential statisticians in my career. Not only did
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I uh go to Stanford because he was a professor there, but I did research information theory because that's what
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he did. And he wrote one of the classics and I today we kind of we don't we discount it, but he wrote one of the
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most original and beautiful papers in baseball analytics um called the offensive erra. It's a classic paper
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from 1975. Hm. I I it's not a paper I've seen, but I will look. The stuff of Tom's that I
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was familiar with is because I, you know, I was dabbling in lottery stuff. He had done a lot of work there. Uh
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specifically what h how do you infer sell entries of a contingency table? You only observe the margins, which is the
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classic thing, you know, for the lottery. They tell you how much how frequently each number is used in the
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lottery, but they don't tell you the probability of the n tupils. Like they will not give how many times, but how do
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you infer something from that? So I became familiar with a lot of his work uh but not the offensive erra one. I I I
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paper it's it's cover and king. We could devote a um we could we can devote a
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session to talking about it some future time. We could also talk about how how the oldfashioned method that they used
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wouldn't would be um just too simplistic for today's analyses. But uh listen, he
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also, you know, Kofer was right there with Shannon and Thorp inventing gambling um techniques to be used in the
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casinos. And I remember going to his house as a grad student. He showed me the old roulette um device that they had
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made made to predict what fraction of the wheel they expected the ball to land in with excess probability. And they
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would let they would use that to uh place bets on a roulette wheel. Incredible. and they just f felt it was
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too risky. I mean, because of course it was monumentally illegal to use that kind of device, [laughter]
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but but they they they were doing it. And of course, Thorp um who was at MIT wrote the first book called Beat the
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Dealer on on how to beat blackjack. Impenetrable book. Today, you can you can ask Chat TBT to make you an expert
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in a few hours. Um but back then, he had that book and it was impossible to decipher. And I remember spending quite
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a bit of time learning how to be a a uh a a a competent card counter from that book. Um it kept the the pool of people
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able to do it low because the barrier to entry was so high. Probably a good thing for all. Well
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guys, we always um today we don't have a guest on the show. Uh it is December the
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30th or we could have gotten a guest. I figured why not just you know you guys are the guests. So, I figured I'd take
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this opportunity to, you know, what caught your eye in sports. And of course, we always do it from a
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statistics angle because we're a sports and statistics show here. So, Shane, I
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thought why not? Let's start with you and we'll just alternate between you and
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Audi and I might chirp in every now and then with some stuff, but Shane, let's
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just start with you. >> Well, I mean, obviously, I guess what's caught my eye. I'm absolutely over the
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moon with Drake May and the Patriots this season. And I mean, the my expectations were low at the start of
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the season. I think most people's word, but to have somebody who is in the MVP
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conversation and to be a team in the conversation for the number one seed in the conference, I just I I mean, it's
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one of the many I think very unexpected things uh to happen this season. Not not
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the least of which is kind of some of the sort of top contenders not being anything uh this year. So, yeah. So, I
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guess that's what's kind of caught my eye. I continue to be amazed by the run
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they're on. And maybe you get you can kind of temper my excitement a little bit or something. Tell me something that
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you know makes me not absolutely gaga over this Drake May situation for potentially the next 10 years.
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>> I would think something you might be equally happy about and I'd love your
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thoughts on this. Maybe just from a statistical perspective or even if it's I I don't know that I'm looking for a
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precise answer, but how you think about it, I think you have to be equally happy
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that Mike Frabel's the coach >> because you know, you put in an average
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coach there and you know, it's hard to know what the counterfactual is, but it's not obvious that there's a 13 and3
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team sitting there. It's not obvious. You know, Mike Frabel's relatively young
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for a coach and he's got a long way to go. He could be there for all of Drake.
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Matter of fact, if he's rational, he should be there for all of Drake May's
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career. How do you think about the role? >> No. No. I mean, I I think you it speaks
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to I think the difficult like, you know, kind of the good fortune I guess the Patriots perhaps have had here or just
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the difficulty of turning around a franchise that had like a top three pick in the last like three or four drafts is
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that you need to kind of, you know, teams that are that bad usually need both the coach and the quarterback kind
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of like refreshed. And getting both those kind of an elite level version of both those at the same time is not an
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easy feat. And of course they correlate with you know kind of I I I think they feed back on each other once you uh have
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them together. But again, the I guess good luck basically that the the Patriots have had that I think that's
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really facilitated their quick turnaround. Whereas I think the Jets might have had I think the Jets had a
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good coach and uh you know Salah a couple years ago but not the good quarterback and they you know it's hard
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lining those things up. >> So let me ask you a question now. Um how much do you basianly update? In other
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words, I think we all know last year I think they were three and 14 or whatever they were last year. They weren't a good
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team last year. >> Yeah. Um, let's suppose this year your prior might have been, well, Drake May
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is going to improve. You know, you got Mike Brael, maybe there would be a 500 team. Well, let's put this way. I think
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a reasonable confidence band might have been between six to 10 wins somewhere in
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that band. Um, they've obviously exceeded that, but now that they're in the playoffs,
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possibly at least the two seed, maybe the one seed. I think they've locked in
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at least the two. >> Yeah. >> Yeah. Well, well, I mean, I Yes. I think
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Jacksonville can maybe catch them, but I mean >> Oh, you're right. Maybe. So, okay. But
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they're one of the top. >> I mean, they're hosting they're certainly hosting a home their first
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they're hosting game in the divisional round at the minimum. >> Um and and possibly uh skipping uh the
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uh wild card round two. They still could >> right how much how much do you believe they have a
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legitimate chance to win the Super Bowl? >> I mean, it seems very wide open. I I
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mean I I can talk myself into it. I I mean and Audi probably wants to jump in. I I they haven't I would love to see
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them beat more good teams. You know, they haven't did they did not play the toughest schedule.
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>> Just so you know, Aaron Shats just on before just to interrupt just for one
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second statistic. I think I tweeted out or exed out on W Moneyball last week. Aaron Shatz had an interesting stat
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which was this Patriot team up till last week. I assume the same might be true has the third easiest schedule since
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1978. Like it's a fact. Like that's >> Yeah. I mean it's certainly not going to
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look harder after last week. >> Yeah. Right. So now maybe it's the but
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I'm Yeah. Go ahead, Audi. >> I mean so this is what this is what confuses I mean, all season long people
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thinking that that we're saying that the Patriots are the best the I mean, the
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the the most overrated team g I mean, given their opponents >> and they just, you know, and there's
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been and this is a short season. I mean, no matter how you slice it, we've got 16
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games, not a lot, right? And the standard deviation in a football game is about 14 points. So, what how truly good
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are they? And and and that would be the my question. >> I think that is the primary question. I
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I would just say that that all all the AFC teams I think it's just particularly
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wide open the year that all the AFC teams kind of have also questions. I mean, Denver has had a tougher schedule,
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but has been barely ekking out their wins and, you know, I mean, if you had to talk I mean, they would probably be
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my pick to go to the Super Bowl, but like but you know, I could certainly argue that the Patriots could beat them
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in a game, you know, and and and similarly, you know, the I mean, the the Jaguars, I mean, think about all the
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top, you know, the teams that are going to be playoff teams. I think you could argue certainly um the pay I bet you the
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Bills will be something like the sixth or seventh seed and will probably be favored in like every
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>> Let me just let me just bring let me just bring that up by the way. It's
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actually something very interesting. So um this is something that's fascinating.
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I did I did just use chat GPT for this but it's not that anybody could do this.
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It's not that impressive a thing. So right now the betting favorite for the Super Bowl, the betting favorite
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to win the Super Bowl are the Rams plus 475. Now they lost yesterday to the Falcons.
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>> They're going to be like the five or six seed, right? >> They're likely the six. They could be
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the five. So I just want to say this again to everybody and I want to add a couple stats from Aaron Shats and then
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Audi get your perspective on this. Right now, they're the betting favorite as potentially the six seed facing the
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world champion Philadelphia Eagles on the road. Okay. According to Aaron, according to his
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DVOA metric, which he invented, you know, it's something that pretty well known in the analytics literature in
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football, the Seattle Seahawks and the um Rams, according to sorry, the Seahawks and the Rams, according to him,
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by the way, are two of the strongest teams like in the top 10 ever by his metric. So do you find it
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odd and I also this is something I did do by the way I did ask chat GPT took it only about 15 seconds to do I told it to
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uh provide me a plot on the x-axis of the betting line implied probability so on the x-axis is you take the betting
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line you get an implied probability of winning the super bowl on the y ais is a strengthbased probability of winning the
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super bowl now we can define what that is it ended up using some ver some amalgam of ELO and other based ratings
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from profocus NFL.com etc. I asked to do a bariat scatter plot for me and I also
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asked it maybe to Shane's point tell me the and so then asked to put a 45 degree
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line on the plot and then tell me which team is farthest from the 45 degree line. It turns out, by the way, it's got
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the Bills being the farthest, meaning it has the Bills to Shane's point stronger than the implied betting odds
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by the largest gap of any team. So, Aie, any >> hold on just just sure I'm correct. So,
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I understand very clearly what the implied odds of winning the Super Bowl are. That's easy. Um you're using now a
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probability of winning the Super Bowl using using essentially a statistical model. Is that
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>> so what it did is I'll tell you what I asked it >> and then I'll aren't the probabilities
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pretty small. >> They are small. I'll tell you what it did. >> So I just this is literally I always
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think it's important when you tell someone you used a large language model. What exactly was your prompt?
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>> Here's exactly the only thing I asked it. I'll say verbatim to our listeners
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here on Morton Moneyball. I mean, tomorrow will be a different answer anyway, even if it is.
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>> Yeah, of course. But here's what I said. I said, provide me a plot of betting
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line implied probability on the x-axis versus strength probability on the y ais for each team to win the Super Bowl. And
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so, and that's what it did. And I know what it did because it now that chat GPT
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5.2 into thinking mode exists. It's telling me what it's doing at each step.
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So, I'm watching it. I'm not just getting the output of this. I'm watching
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it. It's creating a simulation. It simulated 10,000 playoff scenarios going forward and simulated those out based on
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strength parameters from an amalgam of NFL.com, Profoot Focus, and ESPN. That's
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how it got the strengthbased probabilities. And the other ones it got from I think it was BM MGM and some
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other and some other betting line. That's that's what it did. >> No. So did you adjust for the vig
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directly or did you not bother with that? >> Good question. It automatically now
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adjusts for the vig when it it I didn't tell it. I just told all our listeners
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exactly what I typed in. It in its thinking adjusts for the vig when computing those Super Bowl-based
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probabilities. In other words, it know it it adds up to more than one because of the vig. It renormalizes it because
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of the vig. It does that automatically now when you ask it to give you an implied probability.
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>> I assume the point is that the bills are are are the bills the one that are the
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most diff. >> Yeah, they're most likely to win. They're the biggest they're much more
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likely to win according to the statistical model than the implied. >> That is correct.
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They're the most under underbet team if you want to call it that. They should be
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they should have a better betting odds than given the strength model uh predicts. Uh they have the largest
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deviation from the 45 degree line. Now now the betting odds have to do with um how much action there is. So on some
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level you can say it's because they're being underb, right? Not enough people
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are are taking them. Um or it could be that the statistical models have see things that the public just doesn't
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agree with. >> I'm kind of fascinated on the other side here that the Rams
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um are the you said where the betting market comes. >> They are there. This is from from the
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from ESPN and FanDuel. The Rams are the number they're both at they're plus call
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it 475. >> Because you know the number uh the two other teams in their division are
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playing off for the number one seed. Well, that's that's why I brought up
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this upcoming weekend. So, I think that's why I'm speaking to you guys. >> I I mean, I I I don't know if again,
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this is maybe uh >> that is interesting. I don't know if there's some kind of built-in
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>> um you know, Stafford effect or something like that, though. He certainly looked uh less than impressive
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uh last night. >> Well, let me ask you guys a question. How much value do you guys place?
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Suppose I say the following. Um, I'm going to keep the teams the same because
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obviously if you take Stafford off the team and you put some average quarterback on it, obviously the Rams
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are very different. How much value do you put on, you know, let's look at it.
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Rams, Shawn McVey has won the Super Bowl. Denver Broncos, Shawn Payton has won the Super Bowl. Mike Frael, I know,
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hasn't won the Super Bowl as a coach. He's won it many times as a player.
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>> Tommy Mc has also lost the Super Bowl. >> He's also lost the Super Bowl. Yeah, but
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he's been he's been to the Super Bowl and he certainly has his teams have
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performed well in the Super Bowl. Do you guys put any value on the coach, let's
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call it let's even winning the Super Bowl cuz maybe that's a that's too
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specific a metric. How about playoff success? Like suppose we did the followup the po I'll give you another
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coach that's won the Super Bowl. How much would you change the Rams odds or the Broncos odds if I told you Mike
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Tomlin was the coach of their team? Well, I I mean, >> he's won the Super Bowl. I
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>> I mean, obviously, I think playoff success does move the needle a bit, especially coaches that have
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demonstrated playoff success. At the same time, you know, I think the McVey, it's based much more on what we know
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McVeyy's body of work in general than his particular playoff success. For example, Nick Serriani, I bet you has a
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better playoff record than Shawn McVey or than probably anybody we're talking
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about right now. consider him based on that like probably the best playoff record of any playoff coach out there
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right now. Would you consider him the best head coach kind of among the playoff teams? Obviously not or at least
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I wouldn't either. So >> no I would not. >> So I I kind of think it's you know
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there's a lot of small sample randomness to kind of play off record. Shawn McVey
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has been around for long enough where I think yeah I think his body of work both
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regular season and playoffs clearly suggest he's elite coach. Let me just stick with the Patriots for a second.
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Then Audi, I want to get to you about what caught your eye in sports. There's
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an going to be an interesting debate about the MVP of the league this year. I think it's down to one of two players. I
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think everyone agrees it's either going to be Drake May or Matthew Stafford. I
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think that's likely to be true. Okay. What's interesting, back to the analytics versus not
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passing yards, Matthew Stafford. Touchdown, Matthew Stafford. touchdown interception ratio Matthew Stafford. But
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if you look at any let's call it more advanced metric >> any rate stat
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>> Drake may >> so I think it's going to be fascinating and by the way not that there's let's
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call let's we all look at there's a recency bias I don't think particularly
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Matthew Stafford paid particularly well in the game last night against Atlanta and I would say I don't know um I'll
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just put what Shane put in the rundown May was the first player in NFL history I understand it's you know they p cherry
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picked these He threw for five plus touchdowns, 250 plus yards, and completed 90% of his passes in the game.
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Not bad. >> Yeah, he was out of the game by the middle of the third quarter.
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>> Right. So, I all I'm saying is I think this will be another piece of evidence
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because based if if I just took the names off and I just provide you the advanced statistics, I think Drake May
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would win the MVP this year. I do. I personally, it's not about deserve it. I
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actually do not think he will win because I think Stafford leads in the traditional metrics, especially
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touchdowns by so many. I think it'll just overwhelm it. But that's just my
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opinion. >> Yeah, you're probably right. Um I I I think it's kind of similar to, you know,
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kind maybe like the Mike Trout uh um Miguel Cabrera kind of, you know, MVP race from a few years ago where Miguel
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Cabera, you know, Mike Trout kind of had the sort of rate stat like was was was dominant but Miguel Cabrera was, you
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know, had had these kind of gotty kind of totals. >> Absolutely. >> So Audi, what caught your eye? Why don't
00:21:02
we move on to you? I mean, not that by the way, this is the nice thing. We've
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talked about we talked about the Patriots in some sense, but we got to talk a little bit about coaching. We got
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to talk about, I'll call it advanced stats for the MVP versus kind of more traditional stats. We got to talk about
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the Rams and maybe they're the um you know, they're the third, you just pointed out the third best team in their
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division and they're the favorite to win the Super Bowl according to the betting
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odds. They're the third. Forget again, you might say, well, strength is they're
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the third best in their division. They play other teams in their division the same as Seattle and the 49ers do.
00:21:38
They're in the same division. >> All right. So, a lot of things have caught my eye. I I I'll start at the
00:21:44
bottom. Um the Jets, that's my my defunct my def de facto favorite team or historically the favorite team. Um how
00:21:54
awful they've been and is just sort of disappointing and also considering I I
00:21:59
been spending time thinking about Sam Darnold. If you're thinking if Aaron Shat says the Seahawks are this, you
00:22:04
know, one of a great team, how could how does Sam Darnold go being so bad with the Jets to being I mean, I don't I
00:22:12
don't think he's particularly extraordinary with the with with Seattle. I mean, he's had a very good
00:22:17
season, but and so it makes me wonder like how how good are we really are at assessing the quality of these players?
00:22:24
And I think that reflects back on our discussion about Drake May. I mean, what do we really know? It does. It this
00:22:30
season does seem to be like ex with the exception of some very awful teams including the Jets, the Giants, the
00:22:36
Raiders. Um, and if you look at it, there's there's at least three or four
00:22:41
horrible teams. There's a a whole bunch of teams top 10 that are not necessarily
00:22:47
that distinguishable from each other. Um, and then I mean I watched the the Eagles game um on uh Sunday and they're
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not a good team. I mean they're they're not a good team. Um >> they have a defense. They have a good
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defense. Can >> they have a good defense and by and defense just just to talk about as
00:23:04
general defense is the least predictable aspect of a football team. And I mean that not only from next season
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>> to next season that doesn't predict well. But even next game it doesn't
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predict that well. >> Um so what do we really know? And so I I'll turn it to you. Um uh is the Jets
00:23:25
are the Jets problems so managerial coachwise ownership? What do you see the problem there? I mean I mentioned them
00:23:33
but Jets pay people just I mean they put their hand on their back and they go h it's really a weird feeling. I mean what
00:23:40
is it? >> I so let me first say it's it's I don't I don't buy particularly any of that
00:23:47
franchise stuff like franchises matter, ownership matters. um them having first class facilities matter, but obviously
00:23:54
the Jets have a great market, infinite wealth, um all of that. I think you have to I go back to what Shane said earlier.
00:24:02
The Jets sooner than later have to decide whether Aaron Glenn is the coach that can build and turn around that
00:24:11
team. Because if he's not, what's going to happen is you're going to draft
00:24:17
probably, you know, uh, another quarterback, right? >> Yep. Yep. You are. And now the question
00:24:24
is, if he can't develop that quarterback, maybe it's the quarterback's no good or maybe Aaron
00:24:30
Glenn's no good as a coach or who has offensive coordinator is no good. Shane,
00:24:34
I know you wanted to jump in, please. Yeah, I mean I would just sort of I think what the Jets have exhibited over
00:24:40
the last like like part of their continual mediocrity and I think it's kind of partly the age we live in I
00:24:47
suppose is there there's no patience, you know, like like Aaron Glenn's got
00:24:51
like a year or two before, you know, he's going to get canned and he doesn't
00:24:56
look I mean I mean, you know, I'm not really I don't think he necessarily is a
00:25:00
keeper, but you know, part of the reason, you know, the Darnolds, you know, and and the Gino Smiths and
00:25:05
everything of the world is that you've got like a year or two to really hit big
00:25:09
or you've got essentially a year to hit big in New York in the New York market
00:25:12
or or you know all of a sudden the pressure is on you starting to be considered a bus. I kind of like look at
00:25:17
some of some of the like like Pton Manning's first year. Look at look at like or or Rogers sitting for a few
00:25:23
years. Like there's like a lot of I I I think we >> Tom Brady's first year
00:25:28
>> that's not I I I mean sure if you want that for the standard, but like I I
00:25:33
think people like need to be more patient with quarterbacks and recognize that they'll often struggle because
00:25:39
they're usually on a bad team um coming out of the draft and and you kind of
00:25:44
have to build a whole organization around them. And I think, you know, the Jets keep kind of hoping for a quick fix
00:25:51
or like, you know, that a number one will come in without support and like, you know, just be awesome out of the
00:25:55
gate. And sometimes that does happen. You know, there there have been, you know, teams that have kind of hit very
00:26:01
quickly like that. But I I think >> I think there doesn't seem to be a lot
00:26:05
of long-term vision there. >> Well, I'm going to say I'm gonna let me
00:26:08
say one thing about Sam Darnold, then I want to ask a question. So, let me start
00:26:11
with Sam Darnold. So, I just saw something a stat the other day. There are only six quarterbacks in the history
00:26:17
of the NFL to win 13 games two seasons in a row. Okay, Jim Darnold is now one of them.
00:26:26
Now, great. Maybe it's teams around him. Maybe they're be he's being coached
00:26:32
well. Now, of course, Brady's on that list five times, but let's I'm just I'm
00:26:36
counting Brady just once. I'm not counting Brady five times. But Sam Darnold
00:26:42
is one of six quarterbacks on two different teams now. By the way, he may be the only to do it on two different
00:26:50
teams to win 13 games in a row twice. I mean, it's incredible. I mean, at some point,
00:26:56
you have to give something to Sam Darnold. Great. Maybe he's a system quarterback. Great. He's a system
00:27:02
quarterback that maybe win 14 games and be the number one seed in the NFC. Just make sure you didn't say he's
00:27:07
hasn't won 13 in a row two seasons in a row twice. >> He's won 13 in a row.
00:27:13
>> But I'm saying we're two different teams. Yeah, he's done it twice last
00:27:16
year and this year he's still got a game to go. But I'm saying he was the Vikings
00:27:21
quarterback last year and now he's >> Yeah. >> And that was my point. I mean, he's not
00:27:25
just good. He's very good yet we all thought of him as terrible with the Jets.
00:27:29
>> That's my point. Really? >> Maybe I'll give you another name you
00:27:32
speak to. Tell us again the whiner rating system of Trevor Lawrence. Maybe it takes a little bit of time and how's
00:27:41
Trevor Lawrence playing? How would you feel? By the way, if you're the Jaguars
00:27:45
right now, I know there's there's probably half a dozen teams there if I
00:27:49
know the Patriots would be one of them. If you're the Jaguars, you wouldn't
00:27:53
trade Trevor Lawrence for anybody right now. >> No, like not remotely. But my question
00:27:57
is this. Let's maybe But there's a handful of teams that Yeah. No, no, no. I'm saying there's a
00:28:05
handful of teams that would make that same statement. The Chargers might make that statement. The Patriots would
00:28:10
definitely make that statement, but the Jaguars are now one of those teams that would make that
00:28:15
>> like top 10 quarterback. Finally. >> Oh, yeah. Let me let me ask you guys a
00:28:19
statistical question. Yeah. >> Know, one of the things that we brought often in baseball, we have these ways of
00:28:24
measuring the quality of a player outside of their quality of opponent and quality of the team around them. for
00:28:30
baseball, it's easy because the their their players around them don't matter
00:28:33
that much. Opponents matter um and we and we and we can adjust for that and we can generally measure the quality of a
00:28:39
player in the abs in the in the outside of their context. >> Have we gotten any qu ability to do that
00:28:46
for quarterbacks? Is there any way to really question measures? >> Well, the problem with EPA, and I'm I'll
00:28:54
bring this up. problem with EPA is you're if you do you really care about EPA or do you really care about
00:29:02
>> I mean are you ask yes I mean as as a peripheral as a ratebased peripheral it's kind of like
00:29:10
do I really as a pitcher do I really care about strikeout rate I mean yeah >> but that is kind of like pretty what I'm
00:29:18
trying to do >> let me try to answer your question because I looked at this so if you also
00:29:23
look in the rundown down. Although I had a mistake. I I I don't know. I was dreaming the Bucks were 6-1 at one
00:29:28
point. They were 6-2. Whatever this Let me type in again what I asked chat GPT. How has Baker Mayfield's quality of play
00:29:38
declined from when the Buccaneers were I should have said 6-2. 6-2 until now? Please be specific on what metrics you
00:29:46
are scoring him on. Is there any way to know whether it is in quotes his fault or whether it is due to poor coaching or
00:29:54
a poor offensive line? So, I only copied the first half of what it showed, but Audi, it gave me a bunch of metrics
00:30:03
that it thinks are predictive of quality. His yards per attempt has gone down. His passer rating's gone down. His
00:30:09
completion percentage has gone down. His interception rate has quadrupled. His touchdown rate has been flat. his sack
00:30:16
rate has gone up. And so it also had a bunch of other more advanced stats that I I probably should have put there. But
00:30:25
it >> it probably doesn't have it probably doesn't even have access to PFF, but his
00:30:29
PFF kind of grades per game have been going down. >> It had it had PFF grades, but it did. By
00:30:34
the way, this is beauty. By the way, look, just because I'm Morton's vice
00:30:38
dean of AI doesn't mean I'm all in. But let me just say I'm sort of all in.
00:30:40
>> No. Yeah. Yeah. You understand? For those of you that have access to it,
00:30:45
>> use chat GPT 5.2 thinking mode. And and let me just say why. It's I'm not saying
00:30:51
its answers are perfect, but I'm saying you get to watch in real time its thought process. It's literally telling
00:30:59
you step by step. So even said Shane, it it's almost said in like you and I were
00:31:03
speaking. It said, "I'd love to have PFF data, but that's behind a firewall."
00:31:10
>> It said that. Okay. Yeah. So I I did some play. Let's get back to the So the
00:31:16
metrics. The reason I brought it up is those are metrics. >> Yeah. No, the thing is so but let's go
00:31:20
back to my question. Those metrics would say that Sam Darnold was bad when he was
00:31:25
with the Jets and now he's good with the two teams that he's been very good with
00:31:29
the Vikings and Seattle. What I'm asking for is it because Sam Darnold is now
00:31:34
better or is it because we just weren't evaluating him properly? Well, I mean,
00:31:39
clearly for now, >> I think you can conclusively say and and he also was not good with Carolina, if I
00:31:46
remember correctly. I mean, he's been around. >> He was not >> um
00:31:49
>> the Jets did not put him in his in the best position to win. That that's a
00:31:54
pretty safe statement. I mean, the ceiling on Sam Darnold has been demonstrated to be much higher than the
00:31:59
Jets could even, you know, muster. So, that I I mean, there's obviously an organizational effect here, I think.
00:32:06
>> Well, let me ask you a question. We've seen it happen not just to Sam Darnold,
00:32:09
Gino Smith, you know, I mean, >> let me ask you guys both a question. When evaluating Sam Darnold or any such
00:32:17
quarterback, will you allow me to compute a set of metrics, but I'm in a condition on various things? For
00:32:26
example, I might condition on a bin of win loss records, or I might condition on the team making the playoffs. And so
00:32:35
how does his metric how do his metrics look compared to other quarterbacks that have played on similarly successful
00:32:45
teams? And I won't even use postseason I won't use postseason outcomes. I'll use
00:32:50
let's take all teams that have won 10 plus games. Look at the metrics of their
00:32:54
quarterback. look at in a multivariate way where Sam Darnold lies on those multivariat distributions and try to say
00:33:03
is Sam Darnold good compared to quarterbacks that have been on successful regular season teams. Is that
00:33:10
a fair to condition on that and do it that way? >> I mean yes and one one piece of
00:33:13
information we that have already conditioned is you know that previous MVP discussion we had even though he is
00:33:19
on a 13- win team did Sam Darnold come up at all? So we kind of implicitly know his
00:33:27
metrics are perhaps, you know, not what we at least see as driving the success of his team.
00:33:36
>> You have more do you have another way of so I mean my my here's what I would love
00:33:39
if I if I had if I were the, you know, the the uh the oracle or the you know the deciser who can decide what I can
00:33:47
observe. I would love to observe to know how much pressure there is on a quarterback
00:33:52
>> on every play and and I want it. Well, can you um >> You can you can get it publicly on on
00:33:57
every play. How much? >> Yeah, you can get some good proxy pocket last. I Yeah, I should have
00:34:04
printed this out when I asked the question about Baker Mayfield. The advanced stat it had was pocket pressure
00:34:11
rate, how he did against pocket pressure. Those were stats that it was found. No. So those are aggregate
00:34:17
information. What I would like to do is on every play, every drop back, every I want to know how much pressure and I
00:34:22
want to know how open the receivers were and and and so what I'm trying to do is
00:34:26
get something that's like like a what we would call in baseball peripheral. Like
00:34:30
how good at you are doing your your basic job, which is open. Exactly. PF, right?
00:34:37
>> No, PFFs does that. Yes, they do that with with ratings by by raiders. They
00:34:42
like look at it and they Yeah, they they do that. You can get a B you can't the
00:34:46
peripheral you can get is a B kind of a a binary count variable but playbyplay did they do a good job or not as graded.
00:34:54
Yeah. >> But you would agree that we're not far away from a video enabled large language model
00:35:03
trained by humans to be able to ingest film data. Determine some definition of pocket pressure. determine some
00:35:13
definition of how open the receivers are. Determine some definition of expected yards gained given this
00:35:19
situation. I mean that data will become like even if it's not PFF doing it. Um
00:35:26
somebody could that AI that is what AI is extraordinary at. It's trained to do
00:35:33
that some blackbox version of that soon. >> Yeah. Doing that's what they're doing in
00:35:38
soccer. are doing it in and continuous. >> Nobody will be telling it to anybody.
00:35:45
>> Yep. >> Well, guys, >> all right. But before I let you go, guy,
00:35:49
before you go on to your your I just want to say just >> I know this is football football, but we
00:35:54
are at the baseball solstice today at this very moment. We're exactly halfway
00:35:59
between the last pitch of the World Series and the first pitch of spring training. It just needed to be noted.
00:36:04
>> Like it's been a particularly cold stove. No, >> it has been the news.
00:36:10
>> Well, guys, since we are I just one other quick thing. This is not this is
00:36:14
not genius math. I just wanted to see if it could do what we teach in our basic stat classes. We obviously have might as
00:36:20
well we should spend a few minutes on it. We have four NCA games coming up. Uh NCA football games coming up. Um Oregon
00:36:28
a 2 and a half point favorite over Texas Tech even though Texas Tech was the four
00:36:32
seed. That's fine. Indiana by seven over Alabama. We could question, you know, if
00:36:37
we look historically, there's no reason. I mean, this is another thing like how
00:36:41
much weight do you put on Alabama's historical success? Should they be less than a seven-point underdog? We have
00:36:48
Georgia 6 and a half point favorite on Miss. We have Ohio State 9 and a half point favorite on Miami. I just asked
00:36:54
Chad GPT a very simple question, which is what's the probability that at least
00:36:59
one of the underdogs advances? And of course it can do it can do what I call you know high school level math stat.
00:37:07
The probability of least at least one is one minus the probability of none. So I
00:37:12
I put this in the rundown. It actually computed that. It assumed independence. It took the betting odds. It implied it
00:37:18
implied win probability. It can I mean it's not shocking that it can do that
00:37:23
calculation. But I'm just saying you know it's not like I gave it any real I
00:37:29
didn't tell it where to get the data from. I didn't tell it to do, you know,
00:37:32
it could have done it in a more brute force way. All I'm commenting on is that
00:37:37
apparently there's an 81% chance, which again, if you asked most pe This is why
00:37:42
we need a show like ours in my view. If you asked most people on the street, what's the probability that
00:37:49
Oregon, Indiana, Georgia, and OSU are all going to win? They're going to say a
00:37:56
lot more than 19%. A lot more a lot more than 90. >> You think so? I mean, aren't aren't
00:38:04
don't most people think of the games is pretty tight? >> No. >> No.
00:38:08
>> See, they're one tight game, which is Oregon, Texas, Tech, and and maybe, by
00:38:13
the way, a lot of people have Oregon as their prediction to win the whole thing.
00:38:17
Um, and then I, you know, the betting odds don't have Indiana, Georgia, or Ohio State as particularly close games.
00:38:24
I don't think anybody would be surprised. As a matter of fact, if you remove the Oregon game out of there and
00:38:29
I said, "What's the probability one of Alabama, Old Miss, or Miami advances or
00:38:34
at least one?" I think that probability would probably be no more than one minus.8, so I don't know, uh, maybe
00:38:44
10%. Yeah, I just felt like most people thought there of there not being an overwhelmingly dominant team in uh,
00:38:53
college football this year. I think I think I remember Kate saying like OSU and Indiana are significantly above
00:38:59
everybody else this year. We could debate whether Georgia, you know, Georgia will be there or not at the end,
00:39:05
but either way, I just thought it was interesting. I I just thought that it's
00:39:08
not that the calculation was very sophisticated or getting the data was that hard or sophisticated. I just
00:39:13
thought it surprising that there's an 81% probability assuming independence,
00:39:17
which by the way, I'm not an independence guy. I look there's no reason the game should be dependent on
00:39:23
each other. No reason whatsoever except >> there's none. Except I can tell a story
00:39:31
where if the first game happens and let's say you know well it only takes one of these events to happen for my
00:39:39
event to happen that I asked for. But let's say Miami plays a really close game against OSU. Does that give any
00:39:47
increased belief for whatever that's worth to any of the other underdog teams? Any rational
00:39:53
>> answer that can I go first? Yeah, >> I would say yes. It means to suggest you
00:39:57
have model miscalibration. Um, and remember the same model is applied to always. Will it
00:40:03
>> exogenous latent variable like it's you know what >> crazy monsoon rains throughout of the
00:40:10
United States that don't that basically you know knowing one game 63 tells you a
00:40:15
lot about the Nets game being 6-3 or something like that there I mean that that's an example of something that
00:40:20
could cause >> No but I think more dependent model if Miami I mean >> Ohio's pretty heavily st favored over
00:40:27
Miami. No, that's that's the first game by the way. That's actually there's
00:40:31
>> if Miami wins. >> Yeah. >> Um there there can be one simple
00:40:34
explanation for that is they just they just you know underdog wins, right? Big deal. And that's probably the the more
00:40:40
likely explanation. But this an alternative explanation is your model was miscalibrated to start with and my
00:40:45
posteriors on on each of those possibilities will in will change after Miami victory. And that so if Miami
00:40:52
wins, I would suggest that there's probably a a greater probability that I've done something wrong and the the
00:40:59
model is wrong across all of them. And so I would therefore imagine that the numbers are that it's more likely.
00:41:06
>> I hadn't thought about this connection, but let me tell you about a paper I'm
00:41:09
literally just about to submit with a doctoral student maybe today, tomorrow, as soon as we finish up a couple things.
00:41:15
So, I'm sure you guys know this in the this a good statistics point for our listeners out here on Morton Moneyball.
00:41:22
And again, this is Eric Brado. I'm here with Shane Jensen and Audi Winer, some
00:41:25
combination of the three of us and Kate Massie here every week on the Wharton podcast network. So, you guys know,
00:41:31
let's assume you're being basian for a second and you assume that your data
00:41:35
generative process is Gaussian. So, a normally distributed, right? And let's
00:41:39
say you put a prior distribution on the mean of that distribution which is also Gaussian. So you have what's called the
00:41:45
standard normal normal model. The nice thing is they're what's called conjugate
00:41:49
which all our listeners probably know what I mean by that. The posterior is normal. But one thing for sure is true
00:41:54
is that the posterior variance has to be lower than the prior variance. That's
00:41:58
just a mathematical result of the normal normal model. Now Audi so it turns out that the minute you move away from these
00:42:06
families that of course isn't true. Matter of fact, if you just make a simple assumption of a mixture
00:42:12
distribution as your prior, then that phenomenon goes away. The reason I bring this up is I'll use your words. I'll
00:42:18
just repeat back to our listeners what you just said. Let's suppose Miami beats
00:42:23
Ohio State. So, one possibility is now I'm less certain about my model. I have
00:42:30
to raise the posterior variance compared to the prior variance. Now, I've got an
00:42:34
observation that suggests my model might need a wider variance. Now that means even if I don't change the mean
00:42:40
strengths of the teams of the other six teams playing the probability of the lower team winning would go up just by
00:42:46
the widening of the distribution. And no, no, I know I'm saying of course because you're a statistician, but I'm
00:42:52
just pointing out to people that >> wait and I'm not even sure that your
00:42:56
first assertion is true that the posterior variance has to be less than the >> It is 100% true.
00:43:01
>> Even if even if there's a mean bias because there's that, you know, the
00:43:04
discrepancy part of it where there's there's the extra variance blow up if
00:43:07
the prior mean and the data mean are super misaligned. >> So there's two ways by the way in a
00:43:12
mixture you can actually get increased variance. One is if you get a larger separation of the means which we call a
00:43:20
change in polarity and the second way you can do it is of course if the uh within group variances change but under
00:43:28
the standard normal normal model if you look at the equation for the posformation information adds so you can
00:43:35
say 1 / sigma^ 2 + 1 / to^ 2 inverse that's the posterior variance it always
00:43:43
has to be smaller than 1 sigma squar So the normal normal model always implies always implies a lowering of variance no
00:43:52
matter what information signals received and I'm pointing out that that doesn't
00:43:57
recognize the fact that there could be a model misspecification or other things.
00:44:03
So I'll be happy to send you the paper Shane. It's also we're going to post it
00:44:06
on SSRN but I find it and the example we use is I don't know Shane what's your
00:44:11
you live down in center city. You live in Philadelphia. What's What's your
00:44:14
favorite restaurant in Philadelphia? >> I like Barcelona down in East Pass.
00:44:19
>> Barcelona. Great. And so you have a strong belief that it's a good restaurant with a pretty narrow
00:44:24
posterior. Okay. Because you've been there a bunch probably and you like it a
00:44:27
lot. And now all of a sudden imagine you go to Barcelona and you have a bad experience. You could easily imagine
00:44:33
your posterior variance being a little bit wider than it was before. But the normal normal model will never allow for
00:44:39
that. it will only allow for a smaller posterior variance no matter what that information signal is. And that's just
00:44:46
an it's a mathematical restriction of the normal normal model. And the reason
00:44:50
I know this, by the way, I found this out. I I'll use Audi as an example. Audi's a brilliant man. I give him an
00:44:56
I'll use the words of educational testing because I ETSs for a number of years. I'm giving Audi an SAT. Audi's
00:45:03
getting questions right right. I think Audi's really smart with a low posterior variance. I give him an
00:45:09
easy question. He gets it wrong. Well, a normal normal model will lower the variance of Audi's ability, but of
00:45:16
course any rational person would probably raise the variance of now because now he's gotten now I'm less
00:45:22
sure that he's as smart as I thought he was. And so I published a paper of this.
00:45:27
This is my first ever academic publication. I literally in homage to Fiser and it is related to that. I
00:45:33
called it negative information that you can actually receive signals that raise posterior variance but not under a
00:45:39
simple normal normal model. >> That would also but you would also think I'm stupider too.
00:45:44
>> Yeah, it would take the mean. >> Yes. Yes. Yeah. I'm talking purely about
00:45:49
variance stories. Of course I can compute the posterior mean. I mean of course that's going to go downward. That
00:45:54
wouldn't surprise anybody. But the and the normal normal model will allow for
00:45:58
that. It just won't allow for the variance to go up. It will not allow for the variance to go up. Either way,
00:46:03
that's a total aside. It was just based on your point about if Miami were to
00:46:08
somehow beat OSU, we have to put more variance now in our model beliefs, which would by de facto raise the
00:46:15
probabilities of the underdog teams. >> You got it. That's a good that's a
00:46:19
technical explanation for what I was predicting. Um, >> I want to say what caught my eye, and
00:46:24
I'd love to get Shane's thought on this. So, now this is the third week since I
00:46:28
pointed this out. that the Colorado Avalanche still only have two regulation losses. They've played 38
00:46:39
games. They've won 29, lost two, and lost seven in overtime. Now, by the way, you might say it's an
00:46:46
equally statistical anomaly that they've lost seven in overtime given how good
00:46:50
they are. They're on pace for 140 points, which would break the record of 135. But, you know, I say this every
00:46:56
year about halfway and the teams peter out and they might end up in the high 120s. I mean, there's no way you would
00:47:01
predict that they're going to win 140 points. Shane, just give I mean to me,
00:47:08
they've played almost half the season and they've only lost two regular season
00:47:14
games. Isn't that nuts? >> Yeah. I mean, it's it's it's it's it's a
00:47:20
good record. Like you said, I I I feel like we've done this to ourselves several seasons now. um in the last like
00:47:25
decade or so where uh I mean first of all even if they kind of maintain that pace you know you know like they may not
00:47:32
even want to be a historical pace going into the postseason because the last couple teams that were on that
00:47:38
historical pace flamed out in the first round of the postseason >> like the Lightning I remember at least
00:47:42
>> Lightning and the Bruins the Bruins who have the third point record that team
00:47:47
that year they flamed out quick >> yeah they won they lost in the first round same as the and they have the
00:47:52
record at 135 points Yes. Yeah. Yeah. I mean, again, these are all sort of the 82 game kind of
00:47:59
records or whatever. You know, this is uh you know, the Montreal Canadians had like uh I think won like
00:48:06
>> 131 or 132 or something for years for like 40 years theirs was considered the
00:48:10
real record. But uh but yeah, I mean I I do think they are on a historical pace.
00:48:15
The specifically the two losses does yeah stand out. I mean, if they had if they finished the season with like, you
00:48:22
know, say less than, you know, eight losses or something like that, that'll probably be like the loss record
00:48:28
certainly. Um, and I know, I mean, they they've just been absolutely excellent
00:48:32
uh um looking unstoppable. It just it does seem I I think it is a curiosity that the last few years we've seen these
00:48:39
kind of seemingly unstoppable teams roll through the regular season and then get
00:48:44
immediately stopped. I don't think it >> is there any reason you see that um any
00:48:48
reason I maybe we've talked about this I just don't remember why you know I think
00:48:53
it was the the Canadians record was something like 1977 I may have or 81 or somewhere whatever '7s
00:49:00
>> in the 70s okay and then that record held for 40some years and then all of a
00:49:06
sudden now the Bruins broke it and now we're seeing pot and the lightning had a
00:49:09
great season and then the Bru any explanation for that I mean just there are a bunch of really crappy teams
00:49:14
teams. We're seeing the variation in team strengths go. Is that what's happening?
00:49:18
>> Yeah, it does seem like that. But I don't actually know. I mean, we should
00:49:22
need we we would need to get a real hockey expert on that kind of is looking at that distribution, I guess, season to
00:49:28
season a little bit more. It could just be kind of these sort of one, you know, I mean, c certainly comparatively
00:49:37
to like the when the Canadians were doing, I feel like the lead is less dynastic. I mean the Canadians back I
00:49:44
mean this is pre free agency and all this type of stuff. I I feel like you you'd kind of think that most of the
00:49:49
sort of forces that are around now influencing hockey would go towards more parody not less. Uh but again the tail
00:49:58
behavior of that we can we've sort of seen in baseball that like you know there's been obviously a lot of parody
00:50:04
kind of in general I think over the last couple decades in baseball in terms of the teams that contend versus not and
00:50:09
some of the forces in play but you know there still is like occasionally you'll
00:50:14
have a season where there's like three 100 win teams and there'll be like none
00:50:18
the next season. I don't I don't know the kind of tail behavior even if you
00:50:21
don't necessarily have a even if you have a relatively stationary system. I I
00:50:25
I don't know what I don't have a good intuition about that. >> Yeah. I think as I remember maybe just
00:50:30
go back to baseball for a second. Am I I'm right. Right. There was no 100 win
00:50:32
team in baseball last year. Right. >> There was none. >> And we were shocked. Shocking. Right.
00:50:38
>> Dodgers started off with like 13 wins in a row or something and they were their
00:50:41
preseason prediction was nearly 100 to start with and after winning so many we would have thought for sure they would
00:50:46
have won. We we thought it we made those. >> We've also had a couple teams that have
00:50:50
kind of hit historical lows over the last couple years in baseball. Is that the new rule or is that just kind of a a
00:50:55
two-off kind of scenario or something? >> Yeah, I lost that bet. Remember this
00:50:59
year I I predicted I forget who it was. Was it the >> Colorado? You thought they would be
00:51:03
>> Yeah, I thought and I don't know how many did they end up at 50. >> They they did not set any records. No,
00:51:08
they they >> was the record, but I I know that I had predicted I think I predicted like 35
00:51:12
and you guys said they're going to start winning. >> Yeah, I just followed Gton. You know, I
00:51:16
regression to the mean in the end of the day. Hard to top. Well, guys, in the last few minutes, let me throw out some
00:51:21
topics just to get your reaction to. I put this in the rundown before last night's game where he got injured and
00:51:27
now he's out for four weeks. I'm talking about Nicole Joic, uh, the three-time
00:51:32
MVP of the NBA. Let me just tell you guys his stats. 29.9 points a game, 11.1 assists, and
00:51:43
12.4 rebounds. He leads the league in assists and rebounds and he's averaging
00:51:50
30 points a game and he's a center. So at some point we're going to have to start Am I wrong
00:51:58
that we're going to have to start listing this man? I if he hadn't injured
00:52:01
his knee he's literally injured it last or yeah injured his knee. He's out for
00:52:05
four weeks now. He might have won the MVP again which puts him at four MVPs but he's got three. Not bad.
00:52:12
You're an NBA guy. Is this one of the greatest players in NBA history? >> I mean, starting to look that way. I I I
00:52:19
think it's kind of um you know, I mean, I I think with these er era kind of comparisons, I actually
00:52:26
am not the student of his that NBA kind of history. Um so I I I guess you know, if we can kind of try and talk at least
00:52:35
anecdotally about the kind of dominance he's displaying, what the what what kind
00:52:38
of a historical analog would be. Would it be kind of Shaquille O'Neal? Is that
00:52:42
kind of, you know, I'm trying to think of sort of center points and reboundated
00:52:48
in in this gamechanging way. I know Shaquille probably doesn't have the point totals, but would that be kind of
00:52:54
the closest analog that we've been looking at? >> I think so, except he's a much better
00:52:58
passer. I'm pretty sure I don't know that this is true. I'm my I'm going to
00:53:02
say a statement and our our listeners at W Moneyball can say Eric doesn't remember when this happened. I'm pretty
00:53:08
sure Wilt Chamberlain was the last center to lead the league in assists. >> Wow. Yeah. So, that's pretty amazing.
00:53:17
>> I mean, to me, the 11.1 assists are more impressive for a center than the 30
00:53:22
points and 12 rebounds. I mean, that's impressive, too. But you I'm sure you
00:53:27
could look it up while I'm speaking here. I'm I'm I know I know for a fact
00:53:30
Chamber led the league in assists one year, but it might be the last time a center led the league in assists,
00:53:36
>> right? And and would you sort of say like you know physically like is he even
00:53:42
the kind of most unique sort of center in the league right now because you've
00:53:45
got somebody like Wemmen Yama. So like you know >> oh Joe gets can't jump. He can't run
00:53:52
fast. He he's got no lift. Um he's not particularly muscled. Um might have you
00:53:58
know no I know. >> Is he he is he the most uninterestingly dominant player ever? [laughter]
00:54:04
>> Yes. Yeah. I would say that's I I'd say that's I would say that's fair to say.
00:54:09
You know, it's also, you know, since I'm a big Larry Bird fan, I was always a fan
00:54:13
of his that and partially because of that they always said, you know, Larry Bird couldn't jump, he couldn't run
00:54:18
fast, you know, um, etc. But his knowledge of the game, you know, and his ability to lead teams, I think when I
00:54:26
see Jokic play, I'm just, you know, guy almost never makes the wrong play. Either way, I just think we have to
00:54:31
start talking to him about the best ever. Um, I you mentioned golf. We have to, you know, in homage to him today.
00:54:38
Um, today's Tiger Wood's 50th birthday. >> Wow. Congrats. >> Young man. Young man. You know, you have
00:54:45
to remember for me, Tiger Woods, we were both at Stanford at the same time. Um, so
00:54:51
>> his undergraduate and your graduate school. >> Yeah. I I was finishing my graduate
00:54:55
degree. But here's my question. >> Yeah. something magically happens in the
00:55:00
world of golf which could help Tiger Woods when you turn 50 is there's this thing called the senior tour
00:55:06
big advantage you get to use a cart and we know part of the challenge is he says
00:55:10
I can swing a club I just can't walk well you can use a cart third they only
00:55:14
second they only play three rounds instead of four so this is one of those interesting
00:55:22
questions like I mean he can still play the Masters by the way and he can still play all the majors I think I'm going to
00:55:29
make a forecast here. I think he moves to the senior tour just because his body he just I mean he has played one
00:55:34
tournament in two and a half years. He could play as many senior tournaments as he wants and then plays the four majors
00:55:40
like if he comes back why Shane any thoughts? >> Yeah. No, I mean the only thing I would
00:55:45
sort of say is you know we don't know kind of like motivation for actually coming back you know I mean he he could
00:55:51
just ride off into retirement. He certainly got the accolades and everything like that. I think it will be
00:55:56
kind of whether his accumulated injury history if he can kind of have that more great you can kind of put it together
00:56:02
physically for the for the senior tour. I mean it would be wide open. I mean he'd instantly be favor, right? I think
00:56:11
coming into that uh situation. So um so yeah. No, I mean I would I kind of I would love to see it. I just you know um
00:56:19
don't know necessarily it'll be kind of I think a motivation factor. So, I have one more topic I want to ask
00:56:26
you guys about. So, I I wasted time on I guess it was Sunday morning and I watched this awful made for TV tennis
00:56:33
event where Nick Curios do you know who Nick Curios is, guys? >> Yes, he's a pretty bad tennis player.
00:56:41
>> Well, he's he used to be okay. He was a top 10 player at one point. He made it
00:56:44
to the Wimbleton final. He lost in the Wimbleton final. He's currently number
00:56:49
672 in the world >> because he hasn't played much. Um, he played the number one woman in the
00:56:54
world, Arena Sabalanka, in a best of three match. Okay. And but the reason it was awful was because you only allowed
00:57:05
one serve, both players. >> His court was actually wider than her court. So literally, Shane, they made a
00:57:12
court that looked like this on one side of the net and then like this on the other side of the net.
00:57:17
>> For our listeners who weren't seeing, it was substantially bigger. his his he was
00:57:22
he was very limited. >> He was Yeah, it was limited. He won 6363. >> And by the way, he was also toying.
00:57:30
>> He was No. No. So, look. So, I asked Here's what I asked GPT. Can you rate the strength of men's and
00:57:42
women's tennis players in one ranking? Now, here's what it did. It did it. But
00:57:51
I'll give it credit. It says tennis abstracts ELO is calculated separately within each tour's match pool because
00:57:58
there's no cross tour match data. The absolute ENO numbers aren't calibrated
00:58:03
for men's versus women comparison. So this combined list is best read as top
00:58:08
by ELO within their respective tours. So I give it credit for recognizing that. Interestingly,
00:58:14
Center and Alcarz are at the >> I'm glad TPT is trying now pushing back
00:58:18
on some of your more uh more elaborative plans here. >> I agree with that. Um it has S and
00:58:23
Alcarez on top and then it has the next four players are all women. Sabalanka third, Switite fourth, Rabbakana fifth
00:58:32
and Koko Goff sixth. So my only point is is that um it is true it was this funny
00:58:40
match but look even in their primes remember this isn't Billy Jean King against a 55year-old Bobby Riggs by the
00:58:47
way you know which was the original Battle of the Sex's back in 73 I mean Curios is a 30-year-old former top 10
00:58:55
player and they're playing best of three um you know even in her prime Naverova
00:59:01
they always wanted her to play the and her comment was she couldn't even beat
00:59:05
the top hundth man in the world and said it wouldn't even be close. She her comment was she would have lost six love
00:59:10
six love to number 100 man in the world. All I'm saying is this to me was not an
00:59:16
event to show the relative. That's what we I mean that's kind of what we're
00:59:20
getting at is if it if it was really a fair sort of you know fair playing surface literally um
00:59:28
>> yeah play the match >> the usual what we call a tennis match but ranking with the top women like what
00:59:35
would be the kind of fair ranking of like say number one in the uh women's side versus like the men's side and you
00:59:42
know Naverova's estimate is like for herself when it was herself is like at the 100 level. No, no. She said she
00:59:48
would have gotten beaten easily by theund. She might put herself at 500. >> So either way, it was just I I just
00:59:57
found it interesting. I I thought put this way. Um it was an interesting match to watch,
01:00:04
but it didn't tell me anything about like I do want I wouldn't mind answering
01:00:08
the question. I mean, look, >> why don't every tournament we just kind
01:00:11
of have like a round like some play like like you know the people that like lose
01:00:15
out in the early rounds? Couldn't we do a little bit of extra like kind of like
01:00:18
games to like like across the across the g uh genders to sort of uh try and sus this out slowly?
01:00:24
>> I love even today I love women's tennis. I might love women's tennis more than I
01:00:29
love men's tennis. I love women's I love I have so much uh respect for the
01:00:34
women's game. I love everything about the women's game. But you know just as
01:00:38
an academic I must admit you know I I have to admit I'm thinking to myself is
01:00:43
there some way because look we did this at ETS2 like if there's no overlap between two like can I like let's take
01:00:52
an a simp that's not a simple example but it's a simple enough example suppose
01:00:56
I want to know who's smarter someone that gets a 780 on the physics AP exam and someone that gets a 780 on the
01:01:06
French seven AP exam. And of course, if there's overlap, then you could potentially try to statistically answer
01:01:13
that question. But I'm trying to the the bigger picture I'm talking about isn't
01:01:16
men's versus women's tennis, cuz I I love them both. Um what do you do when
01:01:21
you don't have overlap between distributions? Do you guys remember the bridging eras in sports paper? That's
01:01:28
the way they did it is that you overlap and you know Mickey Man Ruth played with
01:01:32
Dagi or Garrick played with Dagio and Deagio played with Mantle and then Mantle played with this so you have
01:01:38
overlapping designs but here we essentially have a zero overlap design a can be done well and some overlap you
01:01:46
got the mixed doubles right >> you know that's right >> that's not I mean a is there anything
01:01:52
that can be done >> no in fairness I mean until you have unless you have un there is some
01:01:59
overlap. It's just not at the professional level. You have to go down to the amateur level and from there you
01:02:04
can make some you can do something. But listen, it's it's it's monumental. It's
01:02:08
just absolutely I mean it was not only Martino I think it was also did wasn't
01:02:12
it um Serena Williams didn't she say she'd be beaten six love six love against
01:02:18
>> Serena Williams said the same thing >> and uh and and basically she says it's a
01:02:22
different game. I mean they they they're much faster. They hit much harder. Um,
01:02:25
and they're not they're not I mean the it's just to speak back of that about
01:02:30
that overlapping errors paper. You can't even do that without without modeling.
01:02:34
You still need >> I mean you the way you can achieve overlap is through model assumptions
01:02:39
through model assumptions. >> I mean it's not like timing. I mean if with swimming and track it's just a time
01:02:46
you can you can compare that. Um that's about the only sport that has an absolute metric. And even then people
01:02:51
talk about Jesse Owens he didn't have he didn't have the track. He didn't have
01:02:55
the shoes. He didn't have, you know, and then you you might want to wonder what
01:02:58
would how fast would Jesse Owens have been if he had been trained and and geared up in today's world. And people
01:03:05
try to make forecasts of that. >> I think what we can all agree on here is, you know, since time happens,
01:03:12
weather happens, different equipment happens, there's no really such thing as an exact applesto
01:03:20
apples replication. Like you you you always need to like there's like and we
01:03:24
can decide those variables are >> under the assumption that time is a straight arrow and not a flat circle.
01:03:29
>> Yeah. >> If time is a flat circle we will come back to a replicatable. I mean we we do
01:03:34
like I mean a great question for us as statisticians to ask is across all sports sporting events but baseball
01:03:39
football not only team but also individual which what is the oldest what is the performance of yester year that
01:03:47
would still be considered dominant by today's standards considering there's so much improvement
01:03:54
in athleticism gear etc and what obviously comes to my mind immediately is secretariat who still has all the
01:04:01
records and that's about 50 years ago know um >> 52 years still holds the record all
01:04:06
three tracks >> and [clears throat] it's not even close I don't think um and so beyond that I
01:04:10
mean you can't you can you can go back to you know Hack Wilson 180 plus RBI's
01:04:15
190 that's a different time you wouldn't even think about it Chad Williams
01:04:19
hitting 406 again it's a different time you can't it's hard to compare them no
01:04:24
tennis player um no none of the athletes from the basketball or baseball or football era would you imagine um If you
01:04:33
transform them to today, unless you this sport has changed too fundamentally in each of those.
01:04:39
>> Maybe the the one that came to my mind I I like I like Hack Wilson. I like all of
01:04:44
those things >> to me. I I forget if he won 10 11 Michael Phelps at the Olympics
01:04:51
that won 11. >> Yeah, but but listen, Michael Phelps records are all gone. His last record
01:04:55
>> I just No, no. I'm not saying his records are still there. I'm just saying
01:04:59
the his dominance dominance >> every event he competed in >> eight gold medals in the Olympics. Yeah.
01:05:06
Yeah. >> Oh no. I mean that even the fact that he beat out that Spitz guy who was dominant
01:05:11
and like the fact that there seems to be like once a generation somebody who comes and completely sweeps across
01:05:19
>> it. It says to me that maybe this maybe it's less impre I mean Phelps was the
01:05:23
one that did it. So I'm not taking anything away from him. But right it's a
01:05:26
once in a generation >> sport where you can sweep across 12 medals because they're all basic.
01:05:32
>> I mean what I mean here question be what would Chamberlain have looked like if he
01:05:35
had grown up in today? >> He probably would have been I mean he wouldn't have been trained the way he
01:05:40
was back then >> or Bill had a completely different competition, a different style. would
01:05:45
have been I mean you so you do have to recognize that that people wouldn't would would go into the you know one of
01:05:51
the things I remember my favorite was when I when when my kids were on college tours I'm not not sure you did this Eric
01:05:56
you probably you're all your boys went are went to Penn but my kids were looking at different schools and I
01:06:02
remember once um I was at at Yale for a reunion and and I had a senior or a junior in high school and I went to the
01:06:09
admissions committee and they said none of you would have gotten in today based on the standards and most of us are
01:06:14
looking each other is and we wouldn't have looked on paper the way we looked
01:06:19
back then today because we would have been in the same environment as the kids today we would have done all the things
01:06:23
that they would have done to make themselves look differently and similarly with athletes I mean if you
01:06:28
put Will Chamberlain in today's world he would look look like he looked back then
01:06:31
if you put Babe Ruth in the world today he wouldn't be eating hot dogs like that
01:06:35
it wouldn't happen right that world is gone he would have been forced to be as
01:06:39
as as uh as athletically and nutritionally minded as any athlete is >> why we like the secretariat example
01:06:45
because we're not Oh, Secretariat would would have slacked off or something like
01:06:49
that. [laughter] >> Oh, Secretariat had heart though, right? >> Take that out of the equation.
01:06:54
>> Well, Secretary had the biggest heart. We have to remember >> biggest heart we've always heard. Well,
01:06:59
guys, >> if we keep talking, we're going to be talking right into the new year.
01:07:03
>> All right. Thank you. This has been a great edition here of Wharton Moneyball
01:07:06
and the Wharton podcast network. Uh on behalf of all of us, myself, Audi Winer, Shane Jensen, uh we'd like to thank you
01:07:12
for joining us today. uh and abstensia Cade Massie, we like to thank you for being a big part of our show for all of
01:07:19
2025. And um we always like to think I hope all of my co-hosts agree with me, the best is yet to come, 11 and a half
01:07:27
years, but uh we've got a lot more in us. And so please join us for our next
01:07:32
show. Thanks to our producers, uh Marissa Ren and De Patel. Thanks to our associate producer and sound engineer,
01:07:37
Dion Simpkins between now and next week. There's a lot of it. Enjoy your sports.
01:07:42
Enjoy your statistics. and we'll see you next week here on the Wharton Podcast
01:07:45
Network.