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Welcome, welcome everyone to the Wharton Podcast Network here on Wharton Money Ball. I'm Eric
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Bradlow, professor of marketing statistics and data science here at the Wharton School.
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I'm joined today by my two longtime friends, collaborators, and co-hosts, Adi Weiner and
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Shane Gentian, professors of statistics and data science. Some combination of the three of us,
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and Cade Massey are here every week on Wharton Money Ball. Guys, I've always said from the
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that the best part of our show is we get to interview people that are doing what we do for
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a living, but actually in the real world, if you'd like. I mean, we have day jobs and our next guest,
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Michael McCurdy, longtime recurring guest here on Wharton Money Ball, everybody knows him. He's a
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mathematician. We were just discussing off air what he teaches, but you would expect a mathematician
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at a university to teach. He was educated in Canada, England, and Australia. He returned to
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his home of Canada to raise a family, teach math, and of course the most important part of his life
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to help us better understand hockey. He runs the, and I don't know why he put this word in his own
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description, the moderately popular, we think it's very popular, hockey visualization site,
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HockeyViz. He lives in Halifax, Nova Scotia with his wife and two children. Micah, welcome back to Wharton Money Ball. Thanks for having me.
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Well, it's great to have you back, and you know, this is an exciting time for hockey.
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It really is. It's, you know, it's a big part of the season. Let me just start with the basics.
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Let's start with HockeyViz, which you could imagine, this might stand for visualization,
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and I know a lot of the work you do is visual. Let's, for our listeners that haven't heard you
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on the show before, what made you think about not only doing, let's call it the analytics of hockey,
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but representing it in a multi-dimensional visual way? So the really short origin story is that I was real homesick for Canada doing my PhD in Australia,
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and I missed having hockey around. And I was a pretty casual fan in Canada, but then in Australia,
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I found because I missed it, I reached out to it and got a lot more interested in it,
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and wanted to do some little simulations to figure out how likely the Senators were to take,
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you know, 2-4-6, somewhere in between points from a California road trip. And so I thought,
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well, I'll have to simulate that, and the teams aren't the same strength, and then it kind of all
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got out of hand. That was 15 years ago now, and it was just a hobby for myself, just for friends,
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just a little, you know, a little curiosity. But the visualization part came about not because of
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any real deliberate strategy, except the fact that I, despite stereotypes, am not particularly
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fond of numbers. In particular, I don't understand information by reading numbers.
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And so some of the academic training that I had from some of my previous stuff was all strongly
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emphasizing just graphing data all the time. Just if you think you understand what you're doing,
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just make a graph. And that you can use your visual intuition about two-dimensional space
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together with your symbolic intuition with your other knowledges. And so it was, at first,
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it was just a debugging tool. I just didn't understand anything until I saw it in a picture.
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And then I started sharing it first on Twitter, now on Blue Sky. In fact, first on Facebook forever,
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then on Twitter, and then on Blue Sky. And gradually people started to say, oh, you know,
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can you look into this for me? Can you look into that for me? And so what started as purely
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descriptive statistics so that I could understand my own simulations, you know, just sort of slowly
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snowballed. But it's not sort of deliberate, like I'm going to sit down and make a business.
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That part came afterwards to my great surprise. Although my wife doesn't like it when I say
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that out loud. She says, I always knew it was going to work, and I shouldn't be so self-deprecating.
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So there you go. Let me ask you a question. A lot of our listeners here on Morton Moneyball might say, wait a second,
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I thought you said that Mike is a mathematician. What's he doing all this simulation stuff for?
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So could you tell us like how much of what you do, and look, I'm only looking at Adi,
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I'm looking at for our listeners here, Adi is trained as not only a statistician,
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but a probabilist that spent maybe the first half of his career, I'll call it proving things,
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deriving things, et cetera. And now we live in a world of simulation, which can allow us to
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model richer things, but maybe not in a more as general a way in some ways. How do you think of
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the role of simulation in your work versus mathematics in your work? Well, so I made a serious career change away from science into mathematics,
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into pure mathematics. In fact, my PhD is in extremely pure mathematics. And while I love it
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still, it does not engender itself to study employment, even more so than all the other
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kinds of mathematics. And moreover, I found in addition to that homesickness, I found that I was
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also kind of jonesing for that computational aspect. In some of my high school work for myself,
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and in some university work too, I did some physics papers I wrote with a man named Andrew
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Rutenberg, who's still a mentor to me. We're singing a choir now together. But he's a simulationist.
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And that's his primary work. And he taught me a lot about simulation physics. And so it's funny,
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I left physics behind, so I thought at the age of 22, only to recover it 10, now 20 years later.
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And part of it was the sort of fun of just saying, well, here's a little gadget. What happens when
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it has a kind of a crafty aspect to it? You don't have to convince me about the joy of
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figuring something out from first principles and then convincing somebody else of it,
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just on a sheet of paper or just on a whiteboard, like that kind of thing that's really satisfying.
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But it doesn't have that kind of like, oh, here's a little thing, what does it do?
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And so simulation was part of why I chose that project as like a fun project. And then from
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there, it just gets more and more powerful. Once you have measurements for players that are half
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decent, now all of a sudden your simulations are a lot more interesting than just here's a fun little
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toy. And because it grows naturally like that from a little toy into something serious, which you
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don't get so much with a kind of like, I'm going to sit down and give you a treatment of some
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subject from the bare metal. If you can do that, then so much the better. But you can kind of
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bootstrap your way into something interesting with simulation work. And that's no pun intended
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on bootstrap. This is the right show. That was a good pun. Hold on a second. Wait, wait. I got
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a lead in for you, Adi. You're next, but I got to make a lead in. I'm sitting here knowing Adi for
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25 years. I have no idea whether he's going to ask you about math, simulation, or you're singing
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right now. It could be any of the three of them, given who he is, but Adi Weiner, please.
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Yeah, I'm going to pass on the singing, although I'd love to talk about that offline.
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But I'm actually, you know, I'm preparing for my course next week, which is on sports analytics.
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It's to our NBAs. And so I want to teach them, if they had more math, I would show them how to do
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empirical Bayes, conjugate priors, and forecast, say, next year's or the second half of the
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season's batting averages or whatever it is that you're doing with sports analytics in the classic
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mathematical framework. But I can't do that because I don't have that time and they don't have that
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knowledge. So I've discovered that I could teach this purely with simulation and essentially first
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principles. And it's remarkable. You can use split samples and use, oh, you have to do a little bit
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of math just because you can't get away from it 100%. But it's incredible what you can do. And
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there's a whole school of statistics teaching, which is really apropos to sports, where you
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essentially dispense with the asymptotics and all the glorious work that has made statistics possible
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for so long because we didn't have computers and replace it with things like bootstrap,
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permutation tests, split samples to get prediction intervals and confidence ranges
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and bootstrapping. And it's remarkable. And I wonder whether or not all that theory, which was
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wonderful for us to get us to where we are, will eventually just be just one of the tools in the
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bag and not the main event. So it's funny you should put it that way because I have been trying
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to persuade my university to let me offer a course a little bit like that, targeted not at MBA folks,
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but instead at people who want like a funny quirk on a math degree. Would you like some
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sort of data science-y kind of angle? Would you like some sort of... But I, despite being a
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mathematician, pure by training and applied by work, I'm not a statistician. And I've made a lot
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of friends with a lot of statisticians, but every now and again, when they talk real turkey, I have
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to say, sorry, you have to prove this to me. I don't know what to talk. And so I'm in this sort
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of peculiar situation of knowing a lot of stats, but not actually knowing any stats in a way.
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I'm going to give Shane the next question, but this will be the last generic question. Because
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actually I want to talk to Mike about this year's hockey, but he's so interesting just in the way
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you're thinking about solving stuff. But please, Shane, go ahead. And of course, another pride of
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Canada here. It is another generic observation, Tom, maybe feeding off of what Adi said about
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how we're doing more kind of simulation-based teaching and less of teaching maybe the
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asymptotics. I kind of feel like even when I was teaching in intro courses, the asymptotics,
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I was teaching it kind of via simulation as a thought experiment. I feel like when you think
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about the sampling distribution of the mean or something like that, you're automatically kind
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of doing a thought experiment simulation in your head anyway. I kind of feel like it's maybe like,
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maybe they're kind of more deeply linked even than that. But regardless, that was just kind
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of the observation. And I feel like maybe my background or whatever, I always taught
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asymptotics via simulation anyway. Sports really encourages that. Yep. There's no question about it. So, Michael, here's the first question. It's not on your list,
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but given we're in the home soon to be of champions, the Flyers. So let me ask you to
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think about the Flyers. No, but I have a very specific question. Okay. So you come up with a
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visualization that says right now, maybe the Flyers have, what is it, about 60 something percent
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to beat the Penguins? Something like that. Yeah. Okay. So when you think about that, there are two
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factors I can imagine. One is they're up two to nothing. And so what you do is you compute the
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probability that they would win given they're up to nothing. Okay. Another thing is that you're
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going to do that because they are up to nothing, but have to adjust your assessment of their
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underlying theta because they're up two to nothing. When you think about this,
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how stable do you think about the strengths when you're coming up with these probabilities?
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So I actually take a pretty doctrinaire approach to this kind of thing. And it may not be what
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people expect. In particular, the attitude that I take is that everything that you're measuring
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is either a person or it's geometry. And you can't do physics maybe even more broadly than
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just geometry. And so the people in particular, I understand to change really slowly. And so if you
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have a good bead on the ability of the players, that's a big if, but if you do, then the fact
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that the Flyers have won one game or two games or even 10 games weighs surprisingly little on the
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ability of the players. And so the simulations that I'm doing, I'm starting from, this is how
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good I think the people that the Flyers are going to put on the ice are, as well as this is how good
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I think their coaches, this is how good I think anyone else who makes a relevant decision for on
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ice stuff. And so I try not to include anything about laundry, if you like, anything about the
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teams themselves, like I, as such. So I- Is the whole equal to the sum of its parts? Are there interaction effects like between,
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like how do you, let's imagine I give you all these strengths of the players, the coach, etc.
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Do you a little, is it a summation or is it something more complicated than that?
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Well, so, so in my case, the, the, I have a simulation model at another level where I'm
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simulating the game itself, how, who is going to play with whom, who is going to actually be on the
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ice against whom. And so I'll just run any number of simulations of the game. So, so it's not just
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that I'm simulating the remainder of the series, but, but then of course, and of course the shifts
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are also in a way being simulated once you've, so it's sort of simulation all the way down. And,
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and that, that if you like is just physics training, I suppose. But I find it really
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satisfactory to explain, at least to myself and sometimes to other people too, even though,
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of course it has to be simplified in places, you know, it's not actually the hockey game.
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I saw Shane's hand up. I just want to ask a clarifying question on this simulation,
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just to be clear. So let's imagine I've got, I'm even showing you my lack of knowledge.
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There's a goalie and five, six players on the ice at once. Five as a rule? Five. Yeah. Goalie and five. If I told you the strengths of this five players and the strengths
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of this five players playing against each other and the strengths of the goalie, would your
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simulation then output a probability of scoring? Like those are the primitives and that's when you
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simulate and players are going in and out, but at any point in time, there's my six strengths
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against your six strengths and that's, what's determining what goes on. Yep. In fact, it's, it's sort of two or three models, but then taken together, they do exactly
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that. Ah, great. Sorry, Shane. I saw your hand up. Yeah, no, and obviously you're, you're, you're modeling and simulating this at kind of a,
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at a, you know, shift by shift level, it sounds like, as opposed to kind of like a, you know,
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a series by series level. But I, I wouldn't kind of, when I, I thought about sort of simulating,
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say the hockey playoffs. And I, I specifically have been thinking a bunch about this kind of
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the president's trophy curse or whatever, the fact that, you know, we often have the top team
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in hockey knocked out in the first round, or that seems to happen more probably than it should.
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Um, and it got me kind of thinking about Eric's, uh, frequent, frequent kind of comment on these
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shows, which is momentum versus, you know, kind of badly, you know, a simulation of hockey
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playoffs where the two factors are basically either the quality of the team or the momentum
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of the team. And I do kind of feel like hockey playoffs are uniquely designed to kind of pair
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because, you know, the top teams by Colorado, say, for example, are always kind of going up
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against wildcard teams. And could we see, you know, so basically is there kind of a curse to
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being in the top place in hockey because you get paired up against the team that is most likely
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going to be on like a momentum-based run going into the playoffs just because of that kind of
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one versus eight sort of seeding. It doesn't seem implausible, but, but I have a sort of asceticism
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of sorts where, where if, if there is such a thing, then I, I don't quite consider it legitimate to
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include until I can model it somehow, you know, where, where does it, well, let me just tell you,
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it's real. All right. But sorry. I don't, um, the, yeah, I, I guess the way, the way one,
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if it existed, I agree. If it existed the way I think you would build in the model is you said
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before that your primitives like player ability to player ability essentially are very slowly
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evolving, maybe not actually even evolving once you get to the playoff parameters. And this would
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be kind of like, it'd be kind of like doing, doing some kind of more like, kind of like short-term,
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you know, maybe for a subset team, some of those parameters are, are changing more rapidly.
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Um, that, that would be kind of how I think I would build a concept of momentum or something
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like that. What you could do is we're in, in Mike, maybe Micah does this already, which would be,
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let's imagine we, we take, we're sitting here at game 82, just before the playoff start, right?
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We're going to take a window going backwards. Maybe that's all 82 games. Maybe it's a shorter
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number of games. You could fit the window size that gives you the best predictive ability
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historically over the dataset. So another simple way to do what Shane's talking about would be
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to just take model abilities in some more localized window around the playoffs and call that,
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if you want to call it non-stationarity, I'm fine with that too, or some form of momentum.
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So I actually, I don't do this right now, but I have done it in the past. Um, and, and, uh, in, in fact, I found this anecdotal, of course, but I found that 25 games,
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um, was a useful window to look back. Uh, not, not instead of, but in addition to a longer
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time, um, uh, precisely how those got mixed is a little bit ad hoc. Um, this is part of why I left it behind. Um, but, but, but now I use a sort of more
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instantaneous, like kind of trying to get a beat on player ability. That's a little bit more like,
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like the speedometer in your car, like, um, where it's understood that it's,
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it's a continuous measurement. And, and then I suppose if you were going to layer on more
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characteristics on top of that, you know, the, the question is going to be, well, how, you know,
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and, and so you could, you could just take a window like that, but then I always sort of want to get a little bit deeper into it, you know, into some, maybe it's something
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about coming from science rather than coming from statistics is that, is that if I can't
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get ahold of something where I can say, look, this is, this is what people are like, specifically
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these interesting people, you know, with their extremely fancy jobs and, and, you know, where
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they play hockey for us to, for our amusement, you know, that, that aspect of personality,
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even if I have to get at it very clumsily, I would still rather try and fail than just say,
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well, let's just throw some big number on it. I like it. Yeah. Adi, please. All right. So I've got two questions. Um, I'll start with this one because it's been
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burning on my mind based on a competition that we just did with high school students
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with a simulated hockey season. Shane was, was part of this as well. Um, and it has to do with the goalie. So we built a model where the goalie effect was actually quite
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large. It was simulated hockey, not real hockey. And part of the reason we did that is that if I,
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based on real data, um, if I told you, for example, that the, the two factors about a team,
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one was the goalie's historical G over XG. In other words, how many goals did he give up versus
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how many shots would you expected versus your opponents, you know, the XG differential for
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the two teams. So like the quantity of quality shots they're been making, they both were kind
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of equal in terms of their predictive quality. Um, I thought that the goalie would get,
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get trounced by, you know, the quality and number of shots that each side was taking,
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but it turned out the goalie is really, really important in describing what happens in the past.
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And I'm really not sure, sure whether that really describes what happens so much in the future.
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Um, or is this another goalie performance? We've talked about that on our show is very important
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in figuring out who wins a match, but, and I'm, but I'm not so sure how important it is in
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predicting who will win matches. Um, and so what, what we hear is goalie actually matters a lot
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and goalies can get hot or is that hot because it's non-stationary or is it hot because it's
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just a psychology? It's not really hot. It just looks like good, good, good work. Or is it, um,
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uh, you know, uh, or is it genuine psychological heat of the classic momentum definition?
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So I'd love to hear your opinions. And once you do that, when I have a second question,
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so I want to cue that up. I, so I, I, on the one hand, I strongly suspect that, that there is,
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that there is something just latent, just that we're not catching that I don't have data for,
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or that I'm not perceiving correctly, you know, a really classic hidden variable, kind of, you
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know, it looks blurry to you because you're just not resolving the things that you should be
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resolving. There are features here. You know, how much time does that guy have to take that shot?
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How much time does that goalie have to actually make a read? You know, that, that kind of stuff,
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like how occluded, how good is that screen? You know, all kinds of stuff like that, that we're
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just, you know, that we were getting really, I'm getting really clumsy proxies on if, if anything.
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And, and so, but on the other hand, it's, it seems tempting to me to imagine that you could have
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some kind of, some kind of literally psychological treatment there, where especially, part of why
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it's on my mind actually is because Lena Solmark, the goaltender for the Ottawa Senators, took a
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leave of absence to deal with anxiety problems in the middle of this regular season. The, and,
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and then was extraordinary last night. And, and it's not just, you know, it's not just unusual for,
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for a male athlete to do such a thing in the middle of a pro season, but it's also striking
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because you could see him playing badly before it. And then he took a good-ish chunk of time off.
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I don't know exactly what kind of care he got, but it seems to have been reasonably good. And
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now he came back and he's been much, much stronger. And so that, you know, anecdotal, I suppose,
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but it points to a reasonably plain psychological explanation for on-ice results. The, you know,
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the mechanism of which I think is going to be really tricky to model. Adam, you had a second question?
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Yeah. Here's my second question is, so when, I love to be what I would call smart, stupid
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modelers. So what does that mean? Take a sport that I don't know much about that makes me stupid
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and then model it in what I would call decently smart techniques. I've done this with like
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football, for example, and I've demonstrated pretty convincingly that while I can't beat the
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smart, smart modelers, I can get pretty close in terms of forecasting probabilities of outcomes
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with very, very simple stuff. So in hockey, my question is, if I were to just model teams based
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on score differentials, you know, I mean, a schedule adjusted score differential type thing,
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not, so that's what I mean by smart, stupid. How would I do in terms of predicting outcomes of
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these playoff games, as opposed to someone who did fancy simulations and was smart, smart?
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What fraction of the signal do I get by being smart, stupid? A lot. I don't know. It's sort of not precisely well posed as a question, but something like 80,
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85%. It's funny, the phrase that you use, the smart, stupid, I call physicist thinking,
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just because there's this really kind of uncomfortable habit of physicists to sort
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of wander into any discipline and just say, you know, have you considered approximating this with
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a tangent plane and like, you know, sort of swanning out after doing nothing useful whatsoever.
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But, but, you know, really basic simulation principles and a head on your shoulders can
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take you a very long way. Like I noticed, for instance, one of like I run a prediction contest
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for the regular season every year where I give whoever wins a lot of sour candy. That's for
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historical interest. But one of the things that you can really tell is that some people,
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some people are just winging it. They're just putting in whatever numbers based on, you know,
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whatever they feel like. And other people are actually doing simple things like making sure
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that the number of points adds up to a number of points that is possible for the, for the whole
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season, you know, or possibly going further than that and actually like simulating something,
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who knows. And so, and you see a really, really big difference. And then after that,
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you, so as long as you do something sensible, you can get sort of to the same place that all
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of the other people doing something sensible get to. And then, then the real struggle comes from,
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you know, now what do you do? What are you going to try to actually get some juice out of something?
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And there you find almost immediately, you know, well, let's try this. It doesn't help. Let's try
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that. It doesn't help. You know, you can, like I would say, if you've got, if you went one step
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further than what you said and actually used both goaltenders and finishers, so just measured with
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goals per expected. And then also something about chances, pure chances without finishing
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ability. Like if you, if you separated out those two features into two separate things,
00:24:57
you would already be, you would already have something like 90, 95%. I think the other nice thing about, I want to move on to one other question out here,
00:25:07
but I think the other nice thing about simulation of course, is you can answer a lot of other
00:25:11
questions along the way that aren't necessarily, that's, I mean, that's the thing is that once you
00:25:16
have, as Shane mentioned earlier, the, like the shift level, once you have a full simulation,
00:25:21
all kinds of things you could answer. And in some sense, it's consistent and coherent
00:25:28
because it's coming from the same underlying primitives. In fact, if you like, one of my
00:25:33
favorite examples of this is that during, during one of the worst parts of COVID, when everybody
00:25:37
was stuck in their bedrooms, I dug out the simulator and just loaded up the 1819 Detroit
00:25:45
Red Wings. And I forget precisely which year, the darkest depths of the tank Buffalo Sabres
00:25:51
and the two worst teams I think we've seen in the last two decades and imagined a seven game
00:25:56
series between them. And I just got the simulation to sort of just tell me it's debugging output.
00:26:00
And I just turned it into a story and, and people loved it. It was just, it was just a,
00:26:06
just a hoot. There wasn't any real point to it, but once you have a simulation model,
00:26:09
you can play games with it. You don't even have to do real work with it. So let me ask you a couple of questions from your list in the last few minutes that we have. So
00:26:17
one of the things you wrote down is isolating individual player and referee impacts on
00:26:22
minor penalties drawn and taken. So without knowing what you're going to do, I'm going to
00:26:27
go back to my ETS days and say how I would answer the question. Okay. And then you'll tell me whether
00:26:32
what I'm saying is totally stupid. Let's imagine a table where the rows of the referees and the
00:26:39
columns of the players. Okay. And so we have entries when certain referees are calling penalties on
00:26:47
certain players, right? In the educational testing world, one could be examinees, the other being
00:26:54
test items. And we estimate row and column effects. So some players are more likely to get penalties
00:27:00
than others. Some referees are more likely to give penalties than others. And this, depending
00:27:05
on what model we fit, we can decompose the two of those things. So is what you're doing anything
00:27:12
like that, or am I totally off base what you're thinking about or doing? Surprisingly similar in
00:27:17
the end. It's slightly more sophisticated in terms of structure where I can like nest things inside
00:27:23
one another. But broadly, yeah, that's the approach. You know, does your presence on the ice
00:27:29
cause your team to take penalties or the opposite or neither? And I didn't get as
00:27:34
far as actually putting individual referee names on stuff. I've been yelled at before.
00:27:50
Okay. Okay. So, Michael, let me ask you another question. One of the other topics on here was age curves. And I love the way it's phrased, solving age curves alongside
00:28:15
teammate and competition effects. Like, am I old or the people next to me suck or I just happen
00:28:20
to be playing against really good people? How do we figure all that out? Well, so this is something
00:28:26
that I have gotten some progress on. But so far, until very recently, the aging work and the sort
00:28:35
of specific ability work has been separate. So I have like a model for who's a good finisher,
00:28:42
a model for who's a good shot generator, a model for who's a good shot suppressor, all these other
00:28:47
specific hockey skills. And then I can go and take those measurements afterwards and say,
00:28:52
okay, now let's figure out an aging curve for those things. But of course, it doesn't take
00:28:58
too long of doing that, in my case, a year or two before you realize you really want to do those
00:29:02
things at the same time. Because among other things, you get into the what I call the moving
00:29:08
elevators problem, where if you take an older, strong player who's declining, because they're
00:29:14
and then pair them with a younger player who's improving. Sometimes you do this, sometimes,
00:29:20
you know, you have to worry about bias all the time. Sometimes these things happen accidentally,
00:29:23
you know, or sort of along the way. But other times they happen on purpose. You know, we like
00:29:27
that as a coaching choice. We're going to put this guy, you know, his legs are no good anymore.
00:29:32
So we're going to play him with a young guy whose legs are amazing. Also, this young player who's
00:29:36
with amazing legs is actually a superstar in the making. So we hope and we would like him to
00:29:42
get better. We'd like, you know, the specific example that I had in my mind from last year
00:29:46
was Claude Giroux, Flyers legend, now in his older age for a hockey player in Ottawa, with
00:29:52
being routinely paired with Tim Stutzler. And worrying that I wasn't getting the credit
00:29:59
portion right, because you have two players who are staple to one another. And that's already a
00:30:02
problem, except you know, in sort of generalities that the aging curves are taking them in the
00:30:08
opposite direction. So if you take something naive, where you're not including the aging,
00:30:12
the measurements that you're going to get themselves are going to be slightly wrong
00:30:17
for that reason. And so I think, I think at last, I have figured out a way that I can start to
00:30:23
integrate the aging curve measurements into the model measurements. And so we'll see if that works.
00:30:29
Maybe you can have me back on in a month or two. But I considered this to be a huge pain in the
00:30:33
neck until sort of all of a sudden, I have a model idea for a regression technique that might
00:30:39
be able to solve with the computers I currently have. So maybe just in the last minute we have,
00:30:44
so how do you see this Stanley Cup playoffs playing out? Like, you know, do you have a,
00:30:49
well, let's wait a couple things. One is, according to your sims, there has to be a
00:30:54
predicted winner by the definition of a sim, like it will give you a predicted winner of the playoffs.
00:30:58
Both, who is that? And how certain are we? Like, I'll ask you in two ways. What's the probability
00:31:04
of whatever team that is actually winning? Like how much higher is it than, I've got this right,
00:31:09
there are 16 teams in the playoffs right now. And so how much higher than one over 16 is it?
00:31:14
And then number two, how many teams would I have to take? We always like to ask this question
00:31:20
so that it would be even money. Like I give you three teams and I get 13, does that, is that an even money bet? Or you have five and I have 11. So I'm just wondering,
00:31:28
what's the distribution of winning percentages? So how do you view both? What do you see for both
00:31:34
of those things? So first of all, the short answer is Colorado. They're the best team in the league
00:31:39
this year. Their probability as of right now, and they're up one, nothing in their series.
00:31:43
Their probability is about 37% to win the cup. That is unusually high for a single team.
00:31:53
Most years, so to answer your second question, most years, if you wanted to make an even money
00:31:58
field, it would be five versus 11 would be typical. Sometimes six, sometimes four. This
00:32:05
year it's two. And the second team is Carolina, 20% as of the moment. And so then 20 plus 37,
00:32:14
there's you're already over 50. And they weren't both over 50 or very, very close even before the
00:32:18
playoffs started. Carolina, of course, is up to nothing on all the way to the first round.
00:32:21
But that is unusual. This year specifically, there are very few bad teams and a lot of
00:32:29
middling teams, some of which made the playoffs and only a couple good teams. And so they look
00:32:34
really good by comparison to the very average field. Well, Micah, that's fascinating. Shane,
00:32:42
before we let Micah go, any reaction to 37 and 20? Does that seem like, oh, my God.
00:32:47
It sounds high. But I mean, you know, when you were first starting to talk about Colorado,
00:32:51
I was thinking to myself, oh, well, what about like I was thinking, oh, maybe Carolina should
00:32:56
actually have the higher percentage because it's maybe got the easier path. But the fact that it's
00:33:00
basically the second highest anyway, maybe maybe if you could talk a little just very briefly,
00:33:06
like the 37 percent, like you've got kind of Colorado almost twice as much as the next
00:33:12
high, like Carolina kind of those are two relatively independent trajectories, at least
00:33:18
the two of those that have. Do you like is it kind of in the privatives? Is Colorado really
00:33:24
that much better? Yes, that's and that's part of why I don't I don't feel bad of saying
00:33:30
so, even though, you know, for six, seven, eight years in a row, I would have said, oh,
00:33:34
that's a mistake. Thirty seven percent, you know, that's far too high for a single team.
00:33:37
But this year, I feel a lot happier about it because I've been watching the primitives,
00:33:41
if you like, about, you know, Nathan McKinnon, Cale McCarr, the Wedgwood having a tremendous year,
00:33:48
you know, a lot of just really high quality skaters. Maybe a corollary to Shane's question.
00:33:53
Then we're going to let you go. If Carolina were to play Colorado, I guess that would be in the
00:33:59
Stanley Cup finals, right? Yeah. What would Colorado's win probability? How much of that
00:34:06
3720 is coming from a head to head matchup if they just get there or how much of it is
00:34:11
Shane's intuition that a lot of it could be the path to get there? So the path is easy for
00:34:17
Carolina. That's part of. So it would be even greater. So you're saying then if the path is
00:34:22
easier, then it's got to be even more extreme than 3720 in the finals. It is. Yeah, it is.
00:34:29
It's more like 65, 62, maybe. I looked it up the other day and I don't have it right in front of
00:34:35
me, but it's in that territory. It's hefty. Wow. Wow. Yeah. And Carolina are no slouches. They're
00:34:40
extremely good. Huh? Well, Mike, as always, it's great to have you here on Wharton Moneyball. We've
00:34:46
been joined by Mike McCurdy, as he told me before the show started, 100% time mathematician,
00:34:51
100% time father and husband, 100% of the time the creator of hockey biz. Micah, thank you again for joining us here on Wharton Moneyball. Thank you all very much.
00:35:05
So please stay with us and join us right after the break. Welcome back. Welcome back to Wharton
00:35:11
Moneyball here on the Wharton Podcast Network. Eric Bradlow, professor of marketing statistics
00:35:15
and data science here at the Wharton School. I'm joined by my two longtime friends and
00:35:18
collaborators, Shane Jensen and Adi Weiner, both professors of statistics and data science.
00:35:23
And again, some combination of us and Kate Massey here every week on Wharton Moneyball.
00:35:28
Guys, I just want to say, I'll tell you why I felt so good about that interview we just did
00:35:33
with Micah McCurdy. First, he's great. And I think his insights are tremendous. But secondly,
00:35:38
more so for Adi and myself. I mean, Shane, I expect you to ask intelligent questions about
00:35:42
hockey, but I don't think Adi and my questions were totally moronic. I think we may have actually
00:35:47
learned something over 12 years. What the hell? No, yeah. The only thing the only thing moronic
00:35:52
was that expectation that I would only ask intelligent questions. But yeah, yeah, no, no.
00:35:58
Either way, we're all doing fine. We're doing fine. So, guys, as you know, in the second half
00:36:02
of the show, we always we typically do what caught our eye in sports. Adi, I'll start with you.
00:36:08
There's lots to talk about. Baseball. Yeah, plenty to talk about. Any NFL draft coming up? Whatever
00:36:13
you want to talk about. What caught your eye? There's a lot. I mean, so as you guys all know,
00:36:18
everyone, our listeners know, you guys know, I try to catch most Yankee games. I actually went to a Yankee game. I went to a Phillies game. So two games since we last got
00:36:25
together. It's always nice to go to a baseball game. So I'll just point out one thing general,
00:36:32
one thing specific about baseball. First of all, the American League is getting its ass handed to
00:36:36
them by the National League. Only four teams in the American League have a winning record
00:36:41
as of actually as of before the American League teams played the National League team.
00:36:48
Interleague play, you mean specifically? No, no, no. Just in general, their records.
00:36:52
So it has to I mean, you'd think that there should be some more or less balance if they're
00:36:56
only playing each other, but they're not. They do play a lot of interleague. So there are nine
00:37:01
teams in the National League with winning records and only four in the American League. That was
00:37:05
prior to last night's games. I didn't I didn't update what happened last night.
00:37:08
And I'm thinking I'm because it's like the Yankees have the best record in the AL and
00:37:14
it's crap. I mean, and there's only a couple other teams who are like hanging in there
00:37:19
and and in the National League, like whole divisions are above 500. So just an observation,
00:37:26
it definitely caught my eye. And I'm kind of wondering whether this is just the vagaries
00:37:29
of small samples or is it really. Like last year, I feel like around this time, maybe preseason last year, we went in saying like, oh, well, all the basically all the you know,
00:37:41
the top like there might be one or two AL teams that are in the top eight. Right. Right. Going
00:37:47
in. So I kind of feel like this is maybe I mean, maybe it's not the same teams or whatever.
00:37:52
But no, I kind of feel like this is a lot of the kind of elite player movement, at least that we've seen. It's gone to the NL. Well, the Dodgers have been sucking them up.
00:38:00
And the Mets and the Mets, Shane Mets, you look at just to build on that, if you look at fan graphs,
00:38:06
let's just go through it quickly. Top eight. This is projected wins by the end of the season. Last
00:38:11
time I checked, Dodgers National League, right. Braves National League. Yankees American League.
00:38:16
Last time I checked, Pirates National League. Cubs National League. Tigers American League.
00:38:22
Mariners, I think, are still in the American League. Padres National League. Phillies National
00:38:26
League. Brewers National League. So I've just named the top ten. Did you say Pirates?
00:38:31
At four? Pirates are really good this year. They're shocking. Yeah. The Pirates are predicted
00:38:37
85 total wins. I mean, it's a four-way tie for fourth or whatever. The Pirates are projected
00:38:42
85 total wins right now on fan graphs, which is fourth highest projection right now.
00:38:49
Yeah, I did. I said the Pirates. Interesting. All right. All right. I mean, the Bucs fans in Pittsburgh are quaking. This can't be right. There's got to be something
00:39:01
wrong with the map. Whether you want to take the top ten where it's seven to three or the top five,
00:39:05
it's four to one. The Yankees are the lone team sitting there amongst the Dodgers, Braves, Pirates,
00:39:12
and Cubs. And so we're not even counting some other teams that are off to some really good
00:39:19
start. Well, I mean, that's the thing is, you know, I mean, in part that's fan graphs. Fan
00:39:23
graphs sounds like it's waiting tremendously towards in-season so far versus preseason
00:39:29
expectations. If you had any kind of... No, fan graphs is pretty slow learner. What was the Pirates preseason? Were they in the top ten?
00:39:39
It's just a little over 500 because right now the Pirates are playing roughly 600 ball,
00:39:44
but fan graphs only has them going 520 the rest of the season, like 72 and 68. But you add that
00:39:51
to the 13 wins they have now. To be fair, the Pirates are really building. They have a tremendous
00:39:57
young talent, Paul Skeens at the top, but still only 520. We're not expecting them to be that
00:40:03
good. Let's go back to you. I forget if that was your specific or general... No, so that was my
00:40:07
first observation. The second observation, I'm going to bring this up every week until I get
00:40:10
an answer. We can actually discuss this. We talked about it a little bit last week.
00:40:14
What do you do with Trent Grisham, the Trent Grisham problem? I talk about this constantly.
00:40:19
He did manage to hit a home run the other night. Here's a guy who I looked at his record carefully,
00:40:24
six years of complete lower than mediocrity, barely getting a win over a placement.
00:40:29
Last year, he had a couple. He was pretty good. The Yankees gave him a $20 million contract.
00:40:34
I thought that's just insane. And of course, coming out of the gate this year...
00:40:38
Is the problem that the Yankees actually have mediocre players at some positions?
00:40:42
To me, it's that they would fall for... What's the problem here? To me, serious analytics staff shouldn't fall for recency bias,
00:40:50
the way they appear to me to have fallen pretty hard for recency bias. And your model should take care of that. I haven't done the real gritty work yet.
00:41:01
Maybe they're not looking at just performance data. A lot of things that the actual
00:41:05
teams are looking at is this peripheral information that we don't get. I mean, we could get or we don't generally dig into the hard hit rate, the pull rates,
00:41:14
the expectations of how you'll do in Yankee Stadium compared to... He was playing in San
00:41:17
Diego before. That's a crap park. And he pulls the ball. And they clearly hit more home runs last
00:41:22
year. But maybe they're seeing something that I'm not. But I'm just, as again, let's be what
00:41:28
I called before, smart stupid. So I'm not going to take all the fancy information that the high
00:41:34
tech people will use. That's the stupid part. But I'm going to use the information that you
00:41:38
have smartly. And it just doesn't seem to me that that was a good move. And so that's mine.
00:41:44
I'll just keep my Trent Grisham watch going for the rest of the season. Obviously, as a Yankee
00:41:49
fan, I hope he cleans up and does fantastic. But as a statistician, I'm thinking not a good move.
00:41:56
All right. That's my take. Shane, what caught your eye in sports? Maybe it's around hockey or it could be around any sport.
00:42:01
Yeah, I'll talk baseball as well. But first of all, I just want to say, Audie, I'm glad you've got somebody else to kvetch about. Is that the right way to say it?
00:42:09
Kvetch. You've got to say your units properly. Kvetch. Kvetch about besides Anthony Volpe. It sounds like you found a second target there.
00:42:16
Yeah, definitely. But yeah, I guess I'll do another baseball one because we are kind of, I think, still
00:42:24
in sort of the silly kind of like overly interpreting small sample kind of part of the
00:42:30
season. The one that kind of stood out to me is Mason Miller. The closer for the Padres
00:42:36
has faced 38 batters this season and struck out 27 of them. He's actually slumping, Shane. I looked at his stats like four days ago and he had struck out
00:42:46
23 of 29. He's only struck out four of his last nine. Shane, it's not looking good.
00:42:51
K-rate coming down from like 15 or whatever the heck it is right now. It's just like.
00:42:55
By the way, last time I checked, Shane, I don't know if this is true. When I looked, he had struck out 23 and walked zero.
00:43:03
Yeah, I mean, his whip is like point three or something. No, but I'm saying he had struck out 23 of 29 and walked zero.
00:43:12
So so I mean, obviously, we're all our reactions all say there's no way. I mean,
00:43:18
this is not sustainable. And I guess it kind of leads me like I'd love to hear from you guys.
00:43:23
What do you think the best sustainable relief like what the sustainable version of like this
00:43:31
is like? What like what? You know, if you had to get a percent strikeout rate, that's like a whip, a whip, more like point like is whips going to be double that by the end?
00:43:41
Oh, yeah. Yeah. Oh, yeah. But no, no, very many walks. Shane's bringing up three interesting points. One interesting point with three prongs to it.
00:43:49
One is there's three things that are amazing by what he's doing. His strikeout rate,
00:43:53
his era, which, of course, zero and his whip. Which of these three do we think will be the most
00:44:00
outlying or, you know, in a multidimensional space, which of these will be most outlined by
00:44:05
the end of the season? And is the combination of these three going to be even more outlying?
00:44:10
Like he'll be at the ninety ninth percentile of strikeout rate, maybe era and rip, which in a
00:44:16
three dimensional space will make it even more impressive what he's doing. That's what I'm
00:44:20
talking about. Are you talking Manolobis distance? Yeah, probably. Yeah. Yeah. Collectively. I mean,
00:44:27
listen, there's there's been absurdly good seasons by relievers over historically
00:44:33
era's around or even less than one ridiculous strikeout rates. I think he's in line to be
00:44:39
one of those. Will he compete? Will he continue what he's done so far? No, that's that's that's
00:44:45
immortal. It'll it'll regress down. But is a long way to regress down and still be in the
00:44:50
greatness area? Yeah. And I guess like I'm using him as a way of thinking about like what is kind
00:44:55
of the what is the reasonable like lower bound on era for a full season and whip? And it's like
00:45:03
got to be at least double what he's doing right now on the whip front, at least. So I'm going to
00:45:07
stay with baseball with what my caught my eye. I've been texting you guys about this.
00:45:12
So as if we didn't have to talk about another great thing of Shohei Ohtani. So he actually is currently on a 52 game on base streak. Now, that, of course,
00:45:25
includes walks hit by pitches, other stuff. Now, just to remind everybody, everybody knows Joe
00:45:30
DiMaggio streak is a hit streak. That's 56. The all time on base streak is Ted Williams.
00:45:37
Eighty four. OK, the next highest after that, I think, is like in the low 70s. So Ted Williams
00:45:42
is very outlying. But Shohei's 52 is the 28th longest all time, but it is the longest since
00:45:49
A-Rod in 2004. So and the longest Dodger one ever is 58 games, and I think it was Gil Hodges or
00:45:57
somebody like that. But like it's getting to the point where, you know, he's setting records at
00:46:05
a very fast pace. And the other thing I did, which was fun for Chat GPT, I actually like Chat GPT's answer here. I said, which one's more impressive,
00:46:15
Ted Williams, 84 game streak or DiMaggio's 56 game streak? And it did a bunch of calculations.
00:46:22
First of all, I like that it reflected some uncertainty. It said, well, you'd have to make
00:46:25
lots of assumptions, et cetera, et cetera, et cetera. But it basically said there's no
00:46:29
comparison. It's what Adi said also on the text chain. It said, conservatively, DiMaggio streak
00:46:36
is probably a hundred times as rare and non-conservatively, it might be a thousand times
00:46:42
or maybe the odd one conservative. It's not even close, which it's orders of magnitude more rare,
00:46:50
56 hits than 84 on base, which was a consistent Adi with yours. Like you answered in 10 seconds
00:46:58
or two seconds, not close. Well, I actually do that calculation in Moneyball Academy with my
00:47:03
students in the full Moneyball Academy, the full three-week program. And basically DiMaggio had
00:47:09
three things going for him that made it even remotely possible. Obviously a very high batting
00:47:14
average. So when he did put the ball in play, he did- It's 357 that season. That season. And during the streak, it was around not even that much higher,
00:47:23
closer to 400 during the streak. He also almost never walked, which is generally a bad thing
00:47:29
for baseball players, but a great thing for hitting streaks. You really need that low walk rate
00:47:37
in order to get the ad bats to generate these hit streaks. And frankly, nobody does that. No modern
00:47:43
player today doesn't walk. In fact, DiMaggio was asked in some level, why don't you walk?
00:47:49
And it really reflects the changing understanding of the game. He said, the fans don't come to see
00:47:54
me walk, which is an interesting question. It's almost like softball when you're playing
00:47:58
pickup softball. Nobody wants to walk. You want to get to hit the ball. That's not major league
00:48:03
baseball. You could also make an argument, Ted Williams had the most famous vision of any player.
00:48:07
Ted Williams walked a ton. Yeah. Well, it's funny because he also had the completely opposite
00:48:11
attitude towards the fans. DiMaggio said, they're coming to see me. I need to be as good as I can
00:48:17
possibly be every single time. And it was quite of his part of his personality. Why does he play so
00:48:23
hard? And he would say, because someone might be here to see me for the very first time. And Ted
00:48:29
Williams, his attitude was, I don't give a crap. He was so notoriously anti the fans. And I mean,
00:48:35
so much so if you've read a biography of Williams, it's fascinating. But Ted Williams would say
00:48:40
repeatedly, I'm here to hit and I can't hit a ball that's out of the strike zone and no way I'm
00:48:44
swinging at it. And even more tellingly, I can't hit a pitch that might even be in the strike zone.
00:48:51
It's just in a place I can't do anything with. And that's a testimony to his brilliance as a
00:48:55
hitter, which really was, I think, probably unsurpassed by anyone. I hate to admit this,
00:49:00
but yes. But so DiMaggio had those three things together, the high bat, I didn't say the third
00:49:05
one, the third one, which kind of, which is actually also very important. He almost never
00:49:09
struck out. So he didn't strike out. He didn't walk. And when he did put the ball in play,
00:49:15
he hit it hard. So, so many opportunities to get hits, whether those were picking up infield hits,
00:49:20
because he didn't strike out, whether not by not walking, he had, he would typically have five,
00:49:25
sometimes six at bats in a game. And Williams would have three. It was very common, right?
00:49:31
So you can't have hitting streaks if you're only batting three times a game. And finally,
00:49:35
of course, he hit the ball so damn hard and getting and on the line. So he got a lot of
00:49:38
hits that way. So when you talk about the probability of 56, it's 100 times greater.
00:49:44
And that's for show, hey, you're just not going to see 56. It's never going to happen.
00:49:48
They said it was something of 250 years. It's done. Nobody hits that way anymore.
00:49:56
They said Ted Williams could be broken. You know, maybe it's 25 years, 30 years,
00:50:00
40 years. But I mean, that's a, I know people say, wow, 40 years, 250. Oh, that's a big difference between 40 years and 250 years.
00:50:07
The only one who had a chance to beat DiMaggio was Ichiro. Same, same style. Right. Right. All right. Adi, you're next. Let's go round two. Anything else caught your eye in
00:50:17
sports this week? Anything caught my eye in sports. So I haven't really been paying attention
00:50:23
much to the playoffs. I was looking a little bit at the NBA playoff sort of lineup. And I,
00:50:29
in preparation for the class in previous two seasons, there was an extremeness about the
00:50:36
Celtics two years ago when they won insofar as that they were not only the favorite during the
00:50:41
regular season, they significantly underperformed in terms of wins, their expected wins using a
00:50:47
Pythagorean-type identity. In other words, they did great, but we expect them to do even better.
00:50:53
And they won. Last year, of course, there was just, they were just saying, I guess, who won
00:50:59
last year? Jason Tatum. Jason Tatum went down with an ACL, with Achilles injury.
00:51:04
Right. And it was totally, totally different. But last year, it was almost as expected. It
00:51:08
was tight that there were three teams that all were extraordinary in terms of their differential.
00:51:13
This year, again, all three top super teams were, none of them over- or underperformed during their
00:51:21
season. So I think it's going to be, I think, I mean, it's pretty lopsided. It's surprising.
00:51:27
The real good question is, would you take the field this year versus the other ones?
00:51:32
I mean, in our previous hockey discussion with Micah, we were talking about, oh, how many teams
00:51:37
would you have to take to cover 50% of the chance of winning the Stanley Cup? And it was like,
00:51:42
you know, two in this year, but like, you know, usually three, four, something like that.
00:51:46
It sounds like you, well, the last couple of years in the NBA, if you give me three teams,
00:51:51
I can cover 100% of the probability of winning it all. Getting close to it. So yeah, last year, this year, I think OKC is responsible for over half
00:51:59
easily. It's not half yet. I mean, betting odds, they're plus 150. But, but, and so it would take
00:52:07
the second team, which is the Celtics, by the way, which are like at plus 500 or 550, it would
00:52:12
take the two of them to get over 50%. But, and I think it would be hard for me to argue. No,
00:52:20
what's interesting to me, actually, glad Shane brought up Micah, because I think everybody here would agree, in totality, the stronger teams are in the West.
00:52:30
San Antonio's in the West. They had a winning record against OKC this year. Denver's in the West.
00:52:35
They had a winning record against OKC this year. They're all in the West. But still,
00:52:42
OKC is plus 150 and Boston's 550. It was the same question Shane asked Micah,
00:52:47
how the hell is Colorado, you know, 62, 38 or whatever it is over Carolina, when Carolina,
00:52:56
you know, Colorado in some sense has the harder path. Well, OKC has the harder path,
00:53:01
and they're still a three to one favorite. It's crazy how that distribution is so skewed in the
00:53:08
NBA right now. It seems like, I mean, I know in general that happens in the NBA. It's a very
00:53:14
dynastic. Well, there's so many teams, so many teams competing for the bottom. Yes. It just
00:53:19
takes like a third of them right out. It's nuts. Yep. Yeah, that's right. So, Shane, anything else
00:53:25
caught your eye? Well, I'll just kind of throw one shout out to Jose Ramirez. This is a baseball one
00:53:31
that I noticed Jose Ramirez recently became the Cleveland Guardians all time leader in games
00:53:39
played. So he is the player that has played the most games for the Cleveland Guardians slash
00:53:44
Indians franchise in that franchise's history. Yeah. And actually, the list of kind of, you know,
00:53:51
the list of most kind of like most all time leaders for across the different franchises
00:53:56
are fascinating when it gives me the opportunity to also we can celebrate Garrett Anderson is
00:54:02
actually the Angels all time leader in terms of games played. He just passed away this week,
00:54:08
shockingly. Do you guys know who it is for the Yankees without looking it up? The most games
00:54:13
ever in Yankee history? Yeah, the Yankee that's played the most games for the Yankees.
00:54:18
It's not Derek Jeter. It is Derek Jeter. It is. I guess you do know. There you go. Well,
00:54:25
I'm just saying you think Lou Gehrig, but he was he played every game, but for 16 years, not
00:54:30
basically 20 years. Mickey Mantle played 17 or 18 seasons. Imagine a lot, much less. He only played
00:54:38
13. Yeah. Imagine only played three minutes, like five for the wars. Right. And so those are the
00:54:43
long time. And then there's a bunch of pitchers. But pitchers don't play every day. Yeah. Yeah.
00:54:47
So, I mean, all basically looking at just kind of the top five across base of kind of franchise
00:54:53
leaders in terms of games played, it's it's all position players. I don't think they're.
00:54:58
Yeah. By the way, just in case people are interested, the Red Sox, the Stremski played thirty three hundred and eight games to the Red Sox. That's top all time.
00:55:07
And then Hank Aaron for the Braves is next. Then Stan Musil for the Cardinals. And she's ahead of Aaron. That's that's a surprise. Wow. Well, yes, that's right. Yeah.
00:55:19
All right, guys. So just the last thing in the last minute or two, I can't believe the following.
00:55:25
Well, if you know the answer to this. So, Adi, I'll ask you in basketball. OK,
00:55:31
I don't know the answer then. OK, so let me just say, you obviously know every year they announce
00:55:37
a defensive player of the year, right? I do. I couldn't name one, but no, that's not true.
00:55:42
There's only been one in the history of basketball who's been the unanimous defensive player of the
00:55:48
year has voted on. I'm not surprised, actually. Very surprised. And it was just yesterday.
00:55:55
This year, it's women. I can't believe you're telling me I'll make it up. Michael Jordan,
00:56:01
Will Chamberlain, whoever it is, has never there's never been another unanimous
00:56:07
defensive player of the year. Well, but those players who are primarily known for their offense.
00:56:14
Right. Yeah. But I mean, has there ever been anyone? Well, yeah, I mean, I guess you.
00:56:20
I guess what we're saying is defensive talent somehow doesn't sort of stand out. You'd have
00:56:28
to look at the distribution of these, quote, unquote, we're talking kind of a defensive MVP,
00:56:32
right? So it's what the distribution of that those votes looks like. Yeah, I'm just surprised that there is even if it's just a hurting mentality,
00:56:41
that there hasn't been a herd of votes that has gone towards one player, like, you know,
00:56:46
whether it's I don't know, Dekembe Mutombo, who is like the five time defensive player of the year,
00:56:51
was never the unanimous defensive MVP or I don't know, Scott. Yao Ming was Yao Ming really good at defense? Yeah, yeah. Yao Ming. I don't know if he was.
00:57:00
Yao Ming. Yeah. Dennis Rodman, you know, never defensive, never unanimous. Either way,
00:57:05
that just caught my eye. One last thing I'll end with, guys. So, so Alkaraz, you know, I love
00:57:14
talking tennis, is injured. He has a wrist injury. He had to pull out of two tournaments now. He's
00:57:20
saying he may not play the French. Now, if he doesn't play the French, here's my question to
00:57:27
you. Two part question. How much odds do you give to Joker? Not Joker. Yes, that Joker. But how much
00:57:34
odds do you give to Sinner? Remember, Sinner had like eight match points against Alkaraz last year.
00:57:40
Sinner should have won the French last year. How much probability do you give to Sinner? You have
00:57:45
to take Sinner well over the rest of the field, right? And number two, does it give Djokovic a
00:57:51
chance if he's on the opposite side of the draw? Because then he only has to potentially beat one
00:57:57
of them. Yeah, I guess. Yeah, I mean, I mean, my intuition, can you give me a little bit more
00:58:04
like what before this injury, how would you, what would you have given? What would be the
00:58:10
probability of somebody besides Alkaraz and Sinner winning? Oh, I would have said at most
00:58:19
5%, 10%. Oh, 10%. Yeah. I would have gone with that. Yeah. Yep. Yep. So I think,
00:58:27
and again, it's kind of one of those things where, you know, this is what we call the
00:58:30
violation of the IIA property in marketing, which means I'm not going to redistribute the
00:58:36
probability in proportion to the market shares of the other brands. I'm giving it all to the
00:58:41
other dominant brand. I'm giving it all to Sinner. Yeah, that makes sense. All right, guys. Well,
00:58:48
that's been one hour of Wharton Money Ball. I'd like to obviously thank our guests, Michael
00:58:52
McCurdy, but on behalf of myself, Eric Bradlow, on behalf of my colleagues, Shane Jensen and Adi
00:58:56
Weiner, thanks to our producer, Deep Patel. Thanks to our other producer, Marissa Renna.
00:59:01
Thanks to our associate producer and sound engineer, Dion Simpkins. On behalf of all of us
00:59:06
at Wharton Money Ball, enjoy your statistics, enjoy your sports, and we'll see you next week here
00:59:11
on the Wharton Podcast Network and Wharton Money Ball.