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Welcome, welcome to Wharton Money Ball. Welcome to a full hour of sports analytics
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here on the Wharton Podcast Network. This is Cade Massey hosting this week's show
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along with two of my longtime collaborators, colleagues and good friends, Adi Weiner and Shane Jensen.
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Adi showing up from overseas and very late in the day as he's been doing the
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last couple of weeks and will for a couple more. Shane from Philadelphia and Eric is having some
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technical challenges and he may slide in here. He may not slide in here. We're gonna run the show the way we
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have been running it more weeks than not lately and that is have a guest here for the first half and then we'll do
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open lines in the second half. It's a terrifically interesting time of year.
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This is like championship time of year. There's no more championship rich moment in the
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year than probably this week. So we'll dive into that in the second half of the show.
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In the first half of the show, we have a guest, Greg Bond is here. Greg is the Chief Investment Officer at Mangroup.
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Mangroup is one of the world's largest alternative asset managers and Greg's been there for some
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time. I met him first years ago when he was Head of R&D for one of their subsidiaries, ManNumeric, I think, but he is
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their CIO now. He is also Head of Americas for Mangroup and he's the Portfolio Manager for their flagship
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multi-strategy fund. He's typically based in Boston. He is based in Boston, typically in Boston,
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but he's in London joining us from London. Greg, welcome to the show. It's great to see you guys.
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I really enjoy this. This is always find a lot of sort of analogies between sports and investing and sometimes
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it's a lot more fun to do the sports part, to be honest, so it's all good.
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Well, we're impressed that you do anything in addition to the job that you have with
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Mangroup, but we're glad you do. It should be said, and we should find out a little bit about this.
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My impression is that you've done a little bit with Major League Baseball. At least a number of years ago, you
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got involved. I'm not even sure if I'm allowed to say what team, but you were a little
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involved with one of the teams. Yeah, with Red Sox, I reached out. I was working at Numeric at the time,
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Mangroup, and reached out to Tom Tippett, who was in the alumni database, wondering if I
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could do some kind of sabbatical from my day job. And he said, sure, come on over.
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And I was an intern, punching my little card every day, working on their draft strategy.
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So they had a bunch of these draft reports from the scouts and could we turn them into something quantitative.
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And then I didn't quite realize until the very end at the last day, I showed
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up to present to everybody and they brought in Bill James. Apparently they gave him the same data, but
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unfortunately he hadn't done the work yet. So we couldn't go head to head, but
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then he wrote me a memo later telling me how wrong I was on a bunch of stuff, which is great.
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We had a really good time, but it was good to just all of a sudden be in that room and kind of have
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a little bit of a head-to-head going on. It was good. That's a ball. Was Theo Epstein running the team in 2011?
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So 2011 was the famous fried chicken in the dugout stuff, where when I joined, they
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had a 99% chance of making the playoffs. And when I left, they hadn't made the
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playoffs. So I don't know if there's causal inference or correlation or whatever you guys want to
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call it. But that's when Theo then ended up going to the Cubs and there was kind of
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a reshuffling over there. Okay. But I was just a lowly intern. It was fine. You were the only- Laying the seeds
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for the World Series two years later. Yes, I take full credit for that. There you go.
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Now we've found the proper causality. We're having, Greg's not a typical guest for
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us. He's not working for a sports team even though he has that experience. But he is writing interesting papers.
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So he shared with us a few months ago, some work that he's done on trying to identify skill versus chance in sports and
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across sports. And he explicitly builds on our friend, Michael Mobison's work.
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Michael wrote the book, The Success Equation some time ago now, 25 years ago, 20 years
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ago. And Greg's going to build on that. Michael's been on the show. He's been a while, but Michael's also in
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the financial services space. He's also kind of moonlighting as a sports analyst.
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Here's Greg writing academic quality papers, multiple. We thought he was going to send us
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one paper, sends us two papers, while holding down this ridiculous hedge fund manager job.
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I don't know how you do it. No, it's great. And Michael Mobison's work there, I'm trying to
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emulate that. I think, again, going back to tying in financial services and invest in sports concepts and
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going back and reading that book, that was sort of, everybody had to read that book
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that was in investing because it's such a core. And you talk about it here on the
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floor. Even people that are much younger than myself will have heard and read about the book.
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So really influential. Part of it was just going through that book and looking at it and then getting
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re-excited about the space. There was a paper that came out by Gerdie and Newman at the end of 2024.
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And they had done some Bayesian stuff around the Bradley Terry model. Paper was kind of cool because it had
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sports, the depth of competition in sports on one axis and then like animals and like
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dogs and sort of, there's major depth in the natural markets, if you will, but then
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sports was always very constrained. So that got me excited. And then the nice thing is they had
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put a nice Python package together. So I was able to get that going and I needed our CPUs here at man
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group to run some of those Bayesian. I think I oversampled. I'm sure if we talk about some of
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the details, but I cranked up 40,000, four samples at 10,000 each. So I had kind of these four climbers
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going. So that was part of it. And then the other thing that allowed me to do it over COVID, as my COVID
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break, I just decided to collect as many game-by-game results for as many sports
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as possible, going back as far as I could. And part of it is just my philosophy.
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I thought there hadn't been a lot of work about cross-sports analysis, I guess.
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People had gone very deep into baseball or NBA. And I thought there might be something interesting
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if I just took some basic techniques like Bill James' Bad Green Theorem, whatever it might
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be, and just apply it across sports, and maybe I would learn something that way.
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So that started that. So the minute I said I had that data, then I read the paper, the Gertie
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Newman paper, I had the Python package. I had to get a little help from our tech folks to get it all installed.
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And I just cranked it over Christmas, over the Christmas holidays, holidays. As one does, as one does.
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No one was on the server, so I felt like it wasn't too bad. It wasn't crowding out real work.
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And then essentially with the development of some of the large language models and Claude, they
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hadn't contemplated some Davidson model and sort of the ties and the home field advantage and
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stuff in their basic model. And since I was doing things like English Premier League, and I knew the home field
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advantage could be pretty profound in effect, maybe your estimates of competitive depth, sort of making
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mistakes, that I thought if I could just use Claude to say, hey, add the Davidson
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model, add these other things. And it actually built it pretty quickly for me. And then I kind of cranked that out.
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So then I had a full working paper just from the estimation. So looking at competitive depth in markets, in
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this case, sports. And then the final point, because I built a season simulator for every league, using like,
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let's say the 2024 schedule, I was able to have Claude come in and help me optimize that and make that a little quicker.
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So I could take depth of competition, sort of model the league, and then simulate the
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league over the actual season. So I could translate from kind of single game action into what it meant for end
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of season, sort of relative rankings. And so the way I was gonna originally set this up for our investor base is,
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hey, a lot of our job is to find portfolio managers with skill, right? And one of the ways you do that
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is, yeah, you wanna look at their track record, but are there other things that you
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can look at, entry exit skill, other things that aren't just the pure sort of realized
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track, in this case, realized wins. And so just thinking about that as a framework, and then people get the sports and
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the other nice thing in our space, there's probably, in our global reach at the company,
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of the 16 sports I looked at, there's probably at least one fan in the audience
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for each one of those, right? So it's sort of as a no brainer. You can't just do one league, right?
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Because people get very frustrated. So you have to all, the breadth also helped from a marketing perspective.
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You got pretty broad, like super rugby or something down in the South Pacific. I was like, that's getting out there.
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But you looked, I mean, you went, you know, not only all the North American professional,
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but also down into collegiate basketball, men's and women's. You got 14 European soccer leagues.
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So you did a nice canvassing. Let us start at a high level and check my understanding.
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My impression is, again, so kind of riffing off Mobison, I'm jumping over, I'm sure I'm
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jumping over lots of academic references since Mobison, but let's just start with Mobison.
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The way you write it, I believe, is Mobison fundamentally wanted to say which sports are
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more skill-based versus chance-based as a single characteristic. And you come in and say, well, technically
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there's two main things going on here. One is the fundamental chance nature of the
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sport, the competition, but also how compressed are the teams and their abilities.
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And the more compressed, then it's gonna look more like chance, even if it may be
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a very skill-based exercise. And then the more spread out they are, the more depth, the less depth there is,
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the more diagnostic these games will be. And so you're saying we really ought to
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technically separate these things. But then you find that they basically don't
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separate. This is the frustration, I suppose. So you do some real nice modeling, but
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then in the end you're like, well, you can't actually separate the chance nature of the
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sport from the heterogeneity of the quality of the team. So there's a metric that he uses called
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contribution of luck, which is sort of the variance of a coin flip. If your league was completely random, what would
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you expect to see in the cross-sectional variance of team winning percentages? And so on that metric, NBA looks really,
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really good. There's not a lot of luck involved and then you kind of go to the other
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extreme. And if you actually run that, and I did this looking for that cause I had
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this long history of data, it's actually time varying. He talks a lot about it being sport
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specific, but if you go back to the equivalent of the English Premier League in the
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early 1900s, it's like a random number generator back then. You think baseball's bad, English Premier League first
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division pre-World War I is really random. So some of it felt not so intrinsic to the sport, but maybe about the competitive
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environment. And I thought that was a bit missing. And if you actually do some work, and
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there's a couple of papers that came after the Mobison book, that you're not quite completely
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controlling for sample size and the dispersion effect. Yeah, okay. And so the reason NBA looks really good
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in that particular model and what ultimately you see is that it's got good depth, kind
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of in the middle of the pack depth, but they also play 82 games, right? And NFL's very wide, but they don't play
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a lot of games and Major League Baseball, it's very narrow, but they do play a
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lot of games. So you can- Real quickly, Greg, you said middle depth, wide depth, narrow depth.
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Say, elaborate what you mean by that. Cause you just said- So just a measure of how dispersed the talent is in
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a given market, right? Or a given league. So you can, some of the basic models assume a normal distribution of talent, let's say,
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and then what's the standard deviation of that talent would be one way to think about
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that. And just to re-summarize what you said, you said baseball's, on that dimension, baseball's kind
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of, no, no, you said basketball's kind of in the middle, football's, professional football has greater
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spread, and college even greater again. College is greater. And then baseball is the most compressed, just
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in terms of distribution of talent within the - Correct, and over time, it's gotten narrower
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and narrower. Okay, so we have the basics on the table. Okay. Let me get just one more basic on
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the table, cause Greg goes through this exercise to say, basically a win in baseball reveals
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the percentage signal in a single win in Major League Baseball is like 1.7%. It's
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98.3% noise and 1.7% signal. And then he does the same thing for all the other sports, but then you stack
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it up by, baseball plays 162 games. So by the time you get that sample size, you hit more signal, but you can
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do things like, okay, at what point in the season do the team's records have more
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signal than noise? Very helpful way to compare these different leagues. And you do that for all four major
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North American sports. And I got that idea from Tom Tango, actually, in some of his original way back.
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And that was, and I think Michael used a little bit of that as well when building his metrics.
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So yes, it was nice to have that come out to be consistent. Well, let's just put that observation on the
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table and then we'll open up to questions. Because I want our listeners to understand that
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that's in some ways, the most practical implication of all of this is, okay, how do
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the leagues compare on this very important thing? And one thing you need to do when
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you do that is to consider how long the seasons are. So you reach this 50% point at
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different places in the different sports, but then you have to translate that down into percentage
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of the season. So MLB, NHL, NBA, and NFL, you need 69 games in baseball to get to the
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50% signal. So we're saying the record in baseball, major league baseball, after 69 games is half signal,
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half noise. And that's 43% of their regular season schedule. NHL, remarkably to me, this is remarkable, 36
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games on NHL, which is also 44%. So at the same point in the calendar, the NHL, their diagnostics of a game and
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their sample side, their schedule match up with baseball in that way. But contrast that with the two others.
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So for NBA, 14 games into the season, 14 games into the season, you've got as
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much signal as noise in the record. That's only 17% of the season. So this is everyone's sense that basketball season's
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too long. And then for the NFL, it's 12 games, which isn't that much, but it's a big
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fraction of their season. 71% of the major American sports is the deepest into the season that the record
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actually captures 50% of the signal. Okay, with those basic facts, Greg, do I have that roughly?
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Yeah, there's a basic test theory about that and how you take a correlation, sort of
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the correlation between outcomes and talent and then what that would look like over the course
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of a season, Spearman-Brown correlation. There's some math behind it, but yeah, basically
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that's the idea that you've got this, ultimately you care about the final season correlation between
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the talent of your team and what actually is the record. And that can be manipulated either by really
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high accurate sort of game outcomes, or you play with a lot of noise in the games, but you play a lot of games
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basically. And then the other factor is how many teams you have. So you find that the chance of the
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best perform, the best actual underlying team reaching the number one spot is lowest in collegiate
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basketball because there's like 230 teams or whatever. So all these things can combine to, you
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could, if you wanted to, construct like optimal tournament, speaking more broadly about regular season playoffs
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or whatever, if your goal was in fact to crown the best team. Anyway, okay, we've got some questions from Shane
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and I think Shane was first, Shane. Well, I guess just on a methodological issue,
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you have this depth of competition parameter that's pretty important obviously to this entire modeling enterprise.
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But with all these kind of models, as you sort of said, you're assuming kind of
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symmetric talent distributions throughout. And I guess my question is, if you thought about it, if you instead actually modeled
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like non-symmetric talent distributions, and I think we probably all think talent is probably not
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necessarily symmetric. Would you, would that kind of, is that depth of competition parameter kind of capturing the
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extent to which these talent distributions are skewed and there's only like a certain percentage of
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top teams that have all the talents? Like, you know, I kind of feel like you're maybe getting that depth of competition parameter
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is essentially capturing what is actually inherent skewness that's not being captured by your model in
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the underlying talent distributions. So I got concerned about that. There's a couple of other papers where you
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can assume equal distribution of talent and you can kind of derive a lot of interesting
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closed form stuff. But rather than kind of go down that route, I went, I gathered additional data, which
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is not in the paper, which I think I'm eventually put in, where I went to almost all of the leagues and I got
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money line odds from the betting markets to use kind of a model free, sort of what is the, how does the market price
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dispersion? And from that, you can sort of do it in log odds space and they can talk about all the kind of technologies around
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it, but essentially changing, taking money line into a probability that team A beats team B.
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And then you can look at the dispersion of that. And I calculated that and it lines up
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almost perfectly with the bottom-up analysis that I did through the assumption of the, kind
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of the Bradley Terry assumption of normal distribution. So I would say it's not terrible, it's
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not perfect, but I think for the data that I had, none of the conclusions change.
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And in baseball in particular, I think a given game you'd expect it to be very
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tight, depends on who's pitching, those kinds of things. But that data set was a really interesting
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data set that took a while to download, but like, and I can only do 2021 to 2025, but every single game across all
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those leagues and probably monitoring 40 to 50 bookmakers, you can kind of aggregate it up.
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So it's pretty powerful, cool data. There's a company that does this and I
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was able to download all that. Greg, we skipped over the fact that you said bottom-up, model-based evaluation, because you're,
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in the course of this analysis, estimating strength parameters for every team in your data set
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across all of these leagues, a single one for each season, but that's what you're talking
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about. And now you're saying, I can do it that way, or I can go out to the market and get information.
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I can infer those underlying strength parameters from market information top-down.
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And you're saying they coincided very well. They're pretty close. Well, I guess I have too many, so
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I'm gonna have to start with, I guess I'll start at the bottom. I mean, I actually wrote a paper on
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measuring luck and skill sort of in general. And the key, basically two components has to
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do, essentially, how much learning does it take to go from the bottom to the top?
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And you essentially divide up that path in levels. So from the bottom to the top, there
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are levels. And a level is where the team, at one level higher than the one below it,
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consistently beats them. And so that's a very loosey-goosey idea because it's very hard to make perfect sense
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of that stuff. So that leads to the question, and this is how I always like to think about
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skill versus our compression of talent is really what we're talking about here, because it's all
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skill-based. There's very, very little actual dice-oriented luck in this kind of sports.
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Although there is, how we describe that luck is complicated, right? I mean, if you think about it, what
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is luck, right? We just saw a penalty shootout that determined the Champions League.
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Is that luck? Is that skill? I don't wanna get into that. That's sort of a sideshow.
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But the questions that I always ask about sports is, which seems to me to summarize
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it, is if you look at the best team versus the worst team and they played each other, what's the probability of the best
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team winning? And then look at the best team versus, say, the average. Let's say, look at the best team versus
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the first quartile. And sort of look at the dispersion and the probabilities. And we all know that for baseball, for
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example, that's much tighter. You don't see even the very best team playing the very worst team still at most
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might have an 80% chance of probably even less of victory. But we know in basketball, best against worst,
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it's probably nearly 99% chance of victory. And then in football, it's more compressed slightly.
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And that's kind of a way to understand how much skill and luck there is in the game because of that.
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And that directly translates out into questions like what's the probability that the best team wins
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the title at the end? But that, of course, then sneaks back to Shane's question, which is normality is not gonna
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really work. If you have an outlier, they're gonna run up the score. And then I guess those are all statements.
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I'll just sort of finish with a question, which is at any point in your analysis,
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do you look at the actual outcome of the game other than who won and who lost? And doesn't that reveal information?
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So if you, for example, if I destroy my opponent, doesn't that give me inside information?
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Oh, absolutely. I did not do it. This was just the pure outcome. Clearly, you are throwing out tremendous amount of
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information. It's funny, in our high school sports leagues in Massachusetts, they don't even look at wins
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and losses to calculate playoff ranking. It's all about goal differential or point differential
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and strength of schedule, right? So clearly in those very unbalanced situations, that's
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a much more accurate predictor of who's gonna do well in the playoffs. So this was clearly, and you could definitely
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augment the data. My simple augmentation was just really the home away thing. That was not in there.
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That was a meaningful variable that was missing. But yes, you could probably move things into
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goal differential or point differential, which would be much more powerful. Your scaling question's very interesting.
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There is embedded in the paper, borrowing from the Judy Newman paper about what the variance
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is of two teams playing each other. It's about one. You just assume it's one.
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So that coefficient of depth has a scale assumption into it, which then allows you to
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kind of think about, so it's sort of relative ranking, but not in some sort of
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weird absolute sense. Though it does seem that teams typically or so a lot of leagues have a competition
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level of about one as it just turns out. But I think that was more of an outcome that was a bit random.
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But there's a scaling assumption for sure. And again, I will show you the normality
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assumption I think is not terrible. I mean, if you look at it, and there are certain situations and the Dodgers, et
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cetera, maybe in baseball, but it's a good place to start. I don't think it would materially change the
00:22:05
outcome if you did some things in the SKU, but that's definitely something you could test.
00:22:09
You could definitely assume, instead of assuming a prior of normal distribution and talent, you could
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assume SKU normal, whatever kind of distribution you wanna look at. Guys, let's talk a little bit more about
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one higher level methodological question, which is this inability to distinguish the chance nature of the
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sport from the depth of the competitors. This is vexing in a way. You'd like to be able to do that.
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And I came away wondering if you needed a little bit more of an economist approach
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to can you take advantage of some exogenous variation in the world to get some insight
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into that. So for example, you've already mentioned the fact that Premier League changed over the last 100
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years. And you have this fascinating graph in there where Major League Baseball and Premier League looked
00:23:00
about the same in the 30s or 40s, I forget exactly when. And then they depart radically with the Premier,
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not the, I'm saying Premier League. It was first division up until 1992. People get, particularly here in London want me
00:23:12
to say that. Particularly, yeah, exactly. So that was actually a little bump. When they went to Premier League, it accelerates.
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This shoots up where you're seeing more diagnosticity in the outcome. And it seems that pretty obvious it has
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to be that that's because the team quality is more differentiated than it used to be
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because the sport isn't changing as fast. This can't be as changing as fast, it
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seems to me. So can you use that in some way? Similarly, and I think less problematically, you have
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four or five European soccer leagues and they show up similarly in your data, but not
00:23:48
identically in your data. And they are playing the same sport. There's this, you can't believe that there's differences
00:23:53
in the chance nature of the Bundesliga versus La Liga. I mean, maybe there is, but it seems
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like most of that variation that exists has to be coming from differences in heterogeneity of
00:24:05
the quality of the teams. Correct, and that was the basis why I wanted to do the top five European football
00:24:11
leagues. And it was also the rationale for also going back in time. So looking back into the contribution of luck
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metric, if you show that it's time varying for a given sport, very time varying, that's
00:24:22
one control, right? Given that the rules haven't changed that much. And then if you take completely contemporaneous, essentially
00:24:29
the same rule set with fluidity across the leagues, that's another natural experiment.
00:24:34
And I saw contribution to luck was varying there as well. And so that, and if you unpack it
00:24:39
and run the analysis, you start to see these differences, even though they're almost identical structurally,
00:24:44
there are these differences in the depth. And part of that is the spending, the various caps.
00:24:49
There's a lot of league dynamics that I don't understand completely, but there's a lot of,
00:24:54
and we're talking about salary caps in Major League Baseball. I think in Premier League, they have caps
00:24:59
on the amount of losses you're allowed to have. Because people don't make money in Premier League
00:25:03
on a given year. It's all made up in the capital value of the team, right, ultimately.
00:25:08
And so a lot of interesting league dynamics come through there. And you can kind of map back to
00:25:12
that. And there's also one of the leagues plays slightly fewer number of games as well.
00:25:16
There's a little bit of that noise in the background. But the nice thing is there's a really
00:25:21
nice data set. The University of Texas professor catalogs all of these games, all the way down to multiple
00:25:26
divisions, going all the way back through time. And so it was really easy for me
00:25:29
to download and run the analysis to see that when that kind of perfectly controlled.
00:25:34
I thought it was a good control experiment. For sure, for sure. Well, do you come away with a better
00:25:38
sense of the chance contribution in the sport by that? Because throughout the paper, you're basically saying, we're
00:25:47
not able to say, we're not able to say. Well, so the one thing I, so what I want to, there's a little bit of
00:25:51
a distinction between, and I think that, again, going back to the original inspiration, the Judy
00:25:55
Newman paper, how do they define luck, right? So people use it kind of generically.
00:26:01
Is it sample size? Is it this concept of competitive disburden? So all the teams are the same.
00:26:06
So it is a coin flip, but they actually introduced another parameter in the model, which
00:26:11
is this concept of how they define luck, which is essentially the probability of an upset
00:26:16
win. So it's like, what's the probability that an infinitely good team loses or an infinitely bad
00:26:21
team wins, right? And so essentially what they're doing in the model is they shrink the Bradley Terry model,
00:26:27
which will give you your probability that the better team wins back to 50-50 probability,
00:26:32
right? And so in the model, there's another parameter they call alpha, you know, with big letters.
00:26:37
So you have depth of competition beta and this alpha, which is the shrinkage parameter back
00:26:41
to pure coin flip. So if a league was purely a coin flip, then this alpha number, and it would
00:26:47
be a number between zero and one, right? You're going to wait between the Bradley Terry
00:26:50
outcome and the pure coin flip. So econometrically, I thought it was a pretty cool way to diagnose it.
00:26:55
Now, the problem when you go through all of this, and that was the point of their paper too, when you're getting these very
00:27:01
low competitive dispersion leagues, right, there's a lot of collinearity. And so I did a couple of charts
00:27:08
where you don't really improve the model fit by adding this pure luck parameter.
00:27:14
And so that's why, so I think why I'm leaning back to this competitive dispersion explains
00:27:18
a heck of a lot in these sports, and it's a little bit less around what actually goes on in the sports.
00:27:23
Now, I could kind of go a little bit on this concept of sports information ratio
00:27:28
that I stole from the investment literature, which does tie in a little bit and is
00:27:32
a bit more consistent with what was his point. But if you talk about the Bill James
00:27:37
Pythagorean theorem, that parameter, it's sort of- So hold on, Greg. Yeah, I mean, I'm going all over the
00:27:42
place, but we can- No, that's fine. Let's do that because this is- You're
00:27:46
actually getting somewhere with that point because one of the problems that, when you've mentioned it
00:27:52
several times, and I'm gonna make it really specific, if every team is exactly the same
00:27:55
quality, then whatever determines the outcome of the game, it isn't skill, it's something.
00:28:00
You can call it chance at that point, even if it's not an actual dice, we call that chance.
00:28:04
And then as the skill gets a little bit larger, and that chance component abruptly has
00:28:09
to stay the same, then that's the only way you can sort of measure that chance
00:28:13
component. But now the question you might ask is, and this is where Bill James comes into,
00:28:17
well, what is it, what is it the thing that's causing it, right? So the sport that I know best is
00:28:21
baseball, and I can probably tell you where that is, it's when you hit the ball
00:28:25
at a certain angle and a velocity, and sometimes it's a home run, sometimes it's not,
00:28:30
right? And you don't know why, and it could be because it's- Or you throw the
00:28:34
ball and sometimes it curves and sometimes it doesn't. Yeah, little things like that.
00:28:38
I mean, it's not chance as in a dice roll, but it's something completely out of
00:28:42
your control. And that's the thing that we can measure. And what Pythagoras is really not measuring that,
00:28:50
it's measuring the dispersion of runs by game. And if you spread them out, you know,
00:28:54
you don't wanna win by a lot, you only wanna win by a little. That's measuring something different, but almost every sport
00:28:59
now has something called X something, XG, X Woba in baseball. I don't know if basketball yet has an
00:29:05
X, which stands for expected. And the difference between those two is in some level what luck is trying to capture.
00:29:14
And maybe we can actually measure that luck by looking at the difference between X, whatever
00:29:19
X number and goals, X Woba and Woba. And I'll just talk about this because like
00:29:25
Ben Rice right now in baseball, if the Yankees, I'm gonna have to bring up a
00:29:29
Yankee, it's my job, has hit a cover off the ball. And for the last two seasons, he's had
00:29:34
the biggest differential between his actual Woba and his X Woba. And everybody was screaming, there's no way this
00:29:40
guy is, you know, top quartile. This is a superstar. And we're just waiting for him to just
00:29:47
regress back down to what we would call ordinary luck, as opposed to bad luck. And that's kind of a way we can
00:29:54
measure it. Yeah, and it goes all the way back to FIPS, fielding independent, you know, ERAs and
00:30:00
those types of, which is a good easy example with clients. I could just, you know, it's really about
00:30:03
strikeouts and walks and home runs. Once the ball's put into play, it's a
00:30:07
bit of a random outcome, right? And what do people control? So completely by, I think, yeah, yeah, go
00:30:12
ahead. I was like, the one thing, just because you brought basketball, I think the reason we
00:30:16
don't have an X or expected thing in basketball is because the actual outcome happens often
00:30:21
enough that you can just look at, you know, like X is important in soccer, X is important, expected goals is important in soccer
00:30:28
and hockey, where, you know, you've got these underlying, you know, kind of like peripherals that
00:30:33
are predictive of it, but the actual outcome doesn't realize as often as you would like
00:30:37
to. And so you want a more kind of smooth measure of things going on in the game, basically.
00:30:43
Whereas basketball, you don't need that as much. It's more valuable, but people still use it
00:30:46
in basketball. So, you know, whether or not, in fact, who was the Cavs coach against the Knicks
00:30:52
after they were down, I think they were down, they'd lost three games, so they were
00:30:55
close to eliminated. The Cavs coach is like, we're actually winning by expected metrics.
00:31:01
And he means like, you know, three-point shooting. Some studies have said, you know, conditional on
00:31:07
the shot being taken and the degree of coverage is like, it's chance what happens once
00:31:10
it leaves a guy's hand. And so they do use expected some in that way. Greg had this observation, it may not be
00:31:17
in the paper, but he's mentioned it to us on the side, that I think you guys will find interesting.
00:31:20
I'd love to hear him say a little bit more about it. And it's how James's Pythagoras formula, parameter shows
00:31:28
up across sports. So before we do that, Greg, can you just remind our listeners what Bill James's Pythagorean
00:31:35
theorem does? And so we can talk about this. It's a way to translate runs scored and
00:31:40
runs allowed or goals scored, goals allowed for a team over the course of the season
00:31:44
and how it explains their ultimate winning percentage. And theoretically, if there's a big deviation, maybe
00:31:50
that's due to random noise or luck, right? Either direction. Maybe the team got lucky in one inning
00:31:55
games or didn't get lucky in one inning games, but it's sort of back to Adi's
00:31:58
point about, it's more of the expectation over the course of the season run differential matters.
00:32:03
And that's again, back to my Massachusetts high school point, goal differential is how you predict
00:32:07
future performance. And so there's a way, basically the Pythagorean theorem translates scales that runs scored, runs allowed
00:32:13
into a number that maps to the winning percentage of a particular league. And so it does vary by league.
00:32:18
In short, expected number of wins, given the number of runs you scored and gave up
00:32:23
or given the number of goals you scored and gave up. It's expected based on that.
00:32:27
And there's a parameter in there that's vital. It calls it the Pythagoreas because it's the
00:32:32
square root of the sum of the squares of those things, right? But for baseball, it happens to come out
00:32:35
as a two. So that's why, yeah. Okay. So the exponent in that little formula, so
00:32:40
this is classic Bill James. This may be the quintessential Bill James, which is like not sophisticated math, but it's deeply
00:32:47
insightful and parsimonious. And now others have used that and people talk about it.
00:32:52
And so what do you, so the exponent in his formula for basketball happens to be two.
00:32:57
And then you go out and find what trick. Yeah, so baseball two, basketball it's 14 and
00:33:02
whatever the number, you can do this a bunch of different ways, but what I was feeling in this, and it kind of goes
00:33:06
back to maybe our sample size argument about basketball, right? Or maybe baseball.
00:33:11
Imagine baseball, they played every game was a thousand innings, right? So there's a little bit of the game
00:33:16
unit itself has embedded, I think, different observations where the NBA has got a hundred possessions.
00:33:22
Yeah. You're only getting four at bats per, so there's a little bit of noise that comes
00:33:27
across in the baseball. And so what basically, there's a lot of fundamental reasons why this parameter, and you can
00:33:33
make assumptions about is it a Weibull distribution or whatever, the run scoring mechanism.
00:33:38
At the end of the day, think of this Pythagorean coefficient as a scaling number that
00:33:43
translates run scored versus runs allowed to winning, right? And ultimately, if you plot it, and if
00:33:49
you do it for all the 14 sports, you almost get a perfect way. First observed, this is completely empirical.
00:33:53
Then I was able to show it sort of mathematically. It's perfectly linear in what I call the
00:33:59
sports information ratio, which is the mean of goals per game divided by the standard deviation
00:34:04
of goals per game over the course of the season. Okay, and so I call it the high,
00:34:10
so basketball, I'm gonna quote this off the top of my head, basketball is like an
00:34:13
eight, and major league baseball, English Premier League, is like a one or a little over
00:34:18
one. And this relates to the information about a team's quality that you infer from their-
00:34:23
It's basically that game has a lot more, theoretically, that game in the NBA has a
00:34:26
lot more information. So they play 82 games, and each game has a lot of information.
00:34:30
So it kind of builds on that. But what I haven't done yet is build a unifying theory that if you do like
00:34:36
Bradley Terry, I'm close, but this is just, again, me being an amateur, of how the
00:34:41
competitive depth and the sort of sample size of the game or information ratio of the
00:34:46
game come together to describe things. But anyway, if you do the math, you can kind of, with making some very basic
00:34:53
assumptions, there'll be some scaling coefficient, but it should be linear in this information ratio concept.
00:34:57
Are you talking about the sharp, almost like for a sport? Well, so I stole this.
00:35:01
We spend a lot of time in sharp ratio. Philosophically, I know I'm gonna be doing a
00:35:07
little bit more rambling. I always felt like sports, they never spent, it's always about point estimates.
00:35:12
There's very little, usually around uncertainty, around parameters. And I live and die by the mean
00:35:16
and the standard deviation. So a few years ago, at one of our conferences, I came up with a thought
00:35:22
experiment. Which team would win or lose more games? A team that gives up exactly two runs
00:35:28
every game or a team that gives up zero 50% of the time or four 50% of the time.
00:35:34
Which of those two teams would be better? And then you can do the same thing on the offense.
00:35:39
You know, he scores exactly two. And it was this whole Jensen's inequality, blah,
00:35:42
blah, blah, convex, concave. And basically you want a very unstable defense and a very consistent offense was sort of
00:35:48
the method. But it only matters in low sharp sports or low information ratio sports like soccer and
00:35:54
baseball. You get the NBA, it doesn't matter at all. So that was, I added another variable to
00:35:59
the Bill James equation and it loaded, but only in ways that would be consistent with
00:36:04
the IR. And that made a nice story for my investors that you wanna buy high sharp ratio
00:36:07
strategies. Risk management is much more important than low sharps. So anyway, that was the, that's exactly the
00:36:12
point. And back to the paper, you're observing that this, the exponent in James's Pythagorean is linear
00:36:21
in the sports information quotient. There's a scalar to it, but yeah, exactly.
00:36:26
It aligns in the data. It's kind of a cool little paper. That's neat. And that's only because you're able to look
00:36:32
at however many different sports you looked at. That's a, that's a- I just throw
00:36:36
out something that our listeners should sort of know. If you just look at the point differential
00:36:43
as a, for every team, that almost is almost perfectly linear in their winning percentage.
00:36:49
Not over the history of the game, because if you go to the periods of time where no one was scoring and you look
00:36:54
at the massive, you need, then you need the curvature. But if you just look within a season,
00:36:59
for example, at the, at the winning, the delta, your point differential for every sport, goal
00:37:04
differential. Actually I use points for soccer. It's linear in your winning percentage.
00:37:10
It's very, very linear. I mean, and you can see where this comes from. You're looking at this ratio to a power
00:37:16
is the first Taylor series. So first term is linear. And then- Linear, exactly. And then what I could find, that beta,
00:37:22
the slope coefficient that takes that goal differential to map it to winning percentage.
00:37:26
That's the number I'm talking about. We'll scale with this information ratio.
00:37:30
Completely agree with that. Okay, team. We should let Greg go. He's coming to us many hours ahead of
00:37:35
us and we've talked with him longer than we thought we're going to talk with him.
00:37:38
But Greg, thank you for sending us- Please add anything. Yeah, of course. Thanks.
00:37:42
Where, if our listeners are interested in seeing your paper, where could they find it?
00:37:46
So we've got, on this particular paper, it's not quite out yet, but the linear information
00:37:50
ratio is out on our, if you just Google it, Pythagorean theorem man group. There's also a Python package behind it.
00:37:57
If you download it, you can run it. Advertises a bit on our Arctic DB, which is one of our database tools for Python.
00:38:03
Well, you know, speaking of that, I noticed in your footnotes that you grabbed your data.
00:38:08
A big chunk of your data came from Neil Payne, friend of the show, Neil Payne, and sort of a co-founder of FiveThirtyEight.
00:38:16
And it goes all the way, the fact that that's there, goes back to FiveThirtyEight's founding
00:38:20
philosophy. They decided right up front, we're going to use Elo for all of our stuff because
00:38:25
it generalizes across sports. We know it's not perfect, but it generalizes across sports.
00:38:29
But because they had that philosophy and Neil's maintained it, you were able to grab data
00:38:32
and run this. If you are a friend of Neil, have him update for 2025. I had to do that myself.
00:38:38
Well, so he comes to the summer to campus to talk to my students every summer. He said that one of the tricks he
00:38:46
talks to the students about is he's very good about keeping an ongoing log of every
00:38:51
game that happens. So that for all the sports that he follows and he writes about, he's always updating
00:38:57
his database just on a daily basis. So then when he's ready to run something,
00:39:01
he just has it there. He doesn't have to do this mad dash search. If you guys want either the GM data
00:39:06
which I sent over and that other thing, and also any of this game level data going back to the 1870s, I can cost
00:39:12
those 16 leagues. I'm happy to share it if you need it. We would love it. And I'm going to probably have one of
00:39:18
my staff get in touch with you to get that. Greg, we have lots of- Because it would be a fantastic tool.
00:39:22
Yeah, we have students doing projects all the time and they would find that useful, I'm
00:39:27
sure. And perhaps in some world, they might be useful to you. So Greg Bond, thank you for being with
00:39:32
us. Greg, again, Chief Investment Officer at Mangroup. Mangroup is a very large alternative asset management
00:39:38
group. He is based out of Boston. He runs Americas for Mangroup. He's also the PM, the Portfolio Manager for
00:39:45
their flagship fund. Greg, great talking to you and good to see you. Good to meet you all.
00:39:50
Thanks. Interesting time of year, guys. Championship time of year. I think this is as many championships as
00:39:55
we're ever going to see in the calendar. We are starting the NHL Standing Cup Finals
00:40:01
tonight. We've got the Carolina Hurricanes playing the Vegas Golden Knights.
00:40:07
I'm seeing, what, 58, 60% chance on the Hurricanes. Shane, you've got something up there about their
00:40:13
- Yeah, I think my- Yeah, I mean, the Hurricanes definitely are pretty heavily favored
00:40:19
in this, but the Avs are really heavily favored against the Knights, too, so what does
00:40:23
that mean? You know, they've got that- I haven't watched- For some reason, I was all
00:40:28
about the Eastern Conference this year. I haven't seen as much from the West.
00:40:31
When I saw the West, they- Who said this is- I'm going to show all my ignorance here, Shane.
00:40:35
The captain of the Knights was injured for the first couple rounds of the playoffs.
00:40:39
He came back mid-playoffs, and he's this big block of a guy. He does not look like a typical hockey
00:40:46
player. He reminds me of, and this is another name I'm not going to have, Kentucky, years
00:40:52
ago, had this Fred Flintstone Square-looking quarterback, playing quarterback. He was so much fun to watch because
00:41:00
he was so differently shaped than the typical quarterback. Yeah, I think you're talking- I mean,
00:41:03
the first one you're talking about is Mark Stone, right? Oh, Mark Stone, exactly.
00:41:07
I think you have to like- Pretty block of stone. He's like, you know, I feel like he's
00:41:11
like a rock out there. You, I think he's fun to watch and fun to pull for, just because he's so
00:41:17
unusual out there. So, and who's their little guy that's so good? Oh my God, the former Leaf, who just
00:41:24
looks- Oh, Mitch Marner. Mitch Marner. Yeah, Marner, just different than everybody else.
00:41:28
So there are some fun things about the Knights and the way they took care of the abs.
00:41:31
It's not obvious to me, but the Hurricanes did get that 10-day rest. They smoked the freaking Canadiens far worse than
00:41:37
the outcomes looked. Anyway, that's our- No, I mean, Hurricanes have been kind of, you know, they're I
00:41:42
think 12-1 now in the playoffs so far. So they're kind of, you know, I mean,
00:41:47
if they were to, I mean, deal with the Knights in a similar fashion, this would be kind of a relatively historic run.
00:41:55
I think the modern kind of, I put it like the Oilers in like the late 80s had one where they went 60-2,
00:42:02
I think, in the playoffs. So, you know, if you're harping back to kind of Gretzky era Oilers playoff dominance, you're
00:42:09
doing pretty well. Right. And they, so they could drop one and tie or they can sweep and get the
00:42:14
modern record. This is pretty impressive. What about the NBA? The Spurs took down the Thunder in seven.
00:42:21
That was a great series. Back and forth, back and forth. Beginning of the season, we wouldn't have expected
00:42:28
the Spurs and now the Spurs are pretty big favorites against the Knicks. Knicks have looked great, but they haven't played
00:42:33
anybody. You know, these two teams, this was something like only the eighth or ninth team time
00:42:39
that the two best regular season teams played a game seven in the NBA, something like
00:42:45
that. So that was, these are head and shoulders the best records coming into the season.
00:42:50
It'll be interesting to see whether the Knicks. It reminds me a little bit about the,
00:42:54
you know, back in the day when like the Lakers and Kings were battling back and forth and the rest and the West and
00:43:00
like, you know, whoever was in the, sitting there in the East was a little bit of an afterthought because did it, you know,
00:43:06
did it really matter? I mean, I'm not. Am I right? The Knicks have a much worse winning record
00:43:14
during the regular season. Regular season, yes. But they've had a very impressive playoffs.
00:43:18
Yeah, extraordinary playoff. Yes. The question's whether that's just a weak field
00:43:22
or they've actually caught some fire. And of course the big question's what are
00:43:25
they gonna do with Wendy? Yeah. Which would be. Yeah, yeah, I mean, to a certain extent,
00:43:29
them getting stomped by the Spurs wouldn't necessarily be evidence that they're not actually a very
00:43:32
strong team. It seems the Spurs seem. That's right. Relatively palpable right now.
00:43:36
Well, I mean, people, I don't know whether there's any value to this, but the Knicks
00:43:40
did beat the Spurs two out of three, I believe, during the regular season. Does that mean anything?
00:43:44
I don't know. Do we have a regular season for any purpose? Right, it's so sketchy.
00:43:49
It's really, I wanna go back and look at the minutes that Wendy played, that kind
00:43:53
of thing. The Spurs are bigger favorites in that championship series than the Hurricanes are.
00:43:59
I think the Spurs are like 64%. Yeah. Fun fact. The Odds are about 200. They're about minus 200.
00:44:07
So, about that. Guys, one of our listeners sometimes sends information about the ticket prices, and apparently you can
00:44:15
get this off at ESPN. I haven't looked. I think this is like your typical get
00:44:17
-in-the-door ticket prices. So, San Antonio is like the 19th or 20th biggest market, and the NBA, New York's
00:44:23
obviously the biggest market. What do you think the difference is in the price? I don't know whether it's average or marginal,
00:44:30
but ESPN will give you a ticket price to get in the door. What do you think the difference is?
00:44:35
Like, what do you think the ratio is? But for the next Spurs game, New York versus, net games, New York versus San Antonio,
00:44:42
ratio. Are we all supposed to decide now and then hold our numbers so we don't cheat?
00:44:47
I'm sorry, I'm gonna just up and say five. Five to one. I'm gonna just say what I think the
00:44:54
numbers are. I would guess you can get into the, in San Antonio for around $400, and I
00:45:01
don't think you can get into the Garden for under $2,000. So, that's five.
00:45:06
You're a little low on both. Your ratios are very close. The last numbers I saw were about $4
00:45:11
,000 for the Garden, and like $900, $890, $900 for San Antonio. So, not quite five, but in that direction.
00:45:19
So, y'all did good. Odd, your numbers were a little low, but y'all did good.
00:45:23
Okay, that kicks off. Yeah, I'm surprised you can't, I mean, is that, the game hasn't started yet, so there
00:45:29
might be an aftermarket reduction. This is one observation from a couple days ago, but there's gonna, you know, it's the
00:45:36
same, this is the thing, it's the same matchup, just very different cost of taking it
00:45:41
in in person. All right, I wanna do a little bit of college, because this is an extraordinary week
00:45:48
in college sports with a handful of things going on. So, let me give you some rundowns.
00:45:52
I'll try to find something interesting to say in a couple of these. I've got one especially interesting thing for you
00:45:56
guys, you baseball people. But I wanna start with the men's golf tournament, because I talk about this tournament every
00:46:02
year, because it's got such a cool format. They take 30 teams, they play regional qualifiers,
00:46:06
I think five of them, they send six teams out of each to the nationals. That means 30 teams and some individual medalists
00:46:12
arrive. They play four rounds of stroke play to cut it down to 18 teams, eight teams,
00:46:20
eight teams, and then they go to match. Of course, match is where the drama is, match is where the fun is.
00:46:24
So, it's like Ryder Cup style team match play, five guys a side, and that's how
00:46:30
they're gonna decide it in the end, and that's good drama. But to make sure they get the best
00:46:33
teams possible there, they play four freaking rounds of stroke to get there. And so, they ended that yesterday, they started
00:46:41
match play today. How did they do to get the top eight? One of the fun wrinkles here, guys, is
00:46:47
Mark Brody, who we had on like two weeks ago, three weeks ago, Brody has been involved with the ranking system.
00:46:52
So, you can look at what the seeds are of these teams, but you can also look at what their rankings are.
00:46:57
That rankings, according to Brody's of the final eight, one, two, three, five.
00:47:03
So, four of the top five, and then 10, 11, 13, and 30. UCLA, the supposed 30th best, I have a
00:47:10
lot of confidence in Mark's ratings, 30th best team in the country comes in, they land
00:47:15
a spot in the top eight. So, here's what they do. Today, they play the quarters and the semis.
00:47:21
So, four match play team matches this morning, and then two more matches this afternoon to
00:47:28
decide the semifinalists, and then the finalists go in and play tomorrow. So, my Longhorns, number two team in the
00:47:36
country, get a good draw. They got the 30th team, they got UCLA. They should whip those boys, and they got
00:47:43
smoked. They got taken out, first team out this morning. Auburn's number one player, this Jason Coyven, Coyven?
00:47:51
I'm not sure I say his name right. He's like, number one amateur, has turned down
00:47:56
going pro. He lost today. They're playing the semis this afternoon. It's just good fun.
00:48:01
Match play, team match play is great fun. But when I was poking around on this, I found some interesting records.
00:48:05
Let me give you a couple of trivia questions. Who do you think has the most men's
00:48:11
golf titles in NCAA history? They started playing them in 1897, apparently. Can you give us, is it an older,
00:48:25
or are we looking at the first half of golf's history, or the second half? No, no hints, no hints.
00:48:31
You have to guess it out yourself. All right. Adi, predict the home team. Yale dominated for like the first couple of
00:48:41
decades. Even Harvard was smoked by Yale for a long time. Yale is, if you go back to the
00:48:45
18 whatevers, you're gonna hear a lot of Yale. Yeah, a lot of football, a lot of
00:48:51
baseball even. You know, the Yankees used to play in the Yale baseball team. They haven't won a golf title since like
00:48:59
1943, but they still have more than anybody else. Princeton is like tied for third, I think.
00:49:06
But here's a couple of runs that were just amazing I came across. Houston, University of Houston, won 16 in a
00:49:13
30 year stretch. They had this coach, must've been a legendary coach, like from the 50s into the 80s,
00:49:18
16 out of 30 national titles in golf, and they were runner up four other times. So 20 out of 30, they were one
00:49:25
of the top two. That's the- That's amazing, because it's like - It's ridiculous.
00:49:29
You wouldn't necessarily think again about a coach having, I mean- Recruiting maybe?
00:49:37
Must've been a built-in recruit? Right, I mean- You know, that's a great question.
00:49:42
A pipeline with that kind of success over three decades is pretty, given all- Unbelievable,
00:49:48
unbelievable. I wanna raise this point here, because we're talking about a long time ago.
00:49:54
And you immediately went to the pipeline recruiting. I would guess that a sport at that
00:50:01
period in its history probably is, a coach is really gonna take a mediocre player and
00:50:08
can potentially make them quite good. Golf? Yeah, I mean, if you think about it,
00:50:14
I mean, how much, I mean, if you think about it, the game could not have been as advanced in the 50s, 40s, 50s,
00:50:23
as it is even in the 90s. No, that's true, but we are talking 50 through 80s.
00:50:27
50s through 80s. Yeah, 50s through 80s is- By the time you get to the 80s. Peak Palmer Nicholas.
00:50:33
So it's not an advance. But I lived in Texas through some of that era, and I knew a couple of
00:50:38
U of H golfers, but I had no idea they had that kind of record. Anyway, the other one that jumps out, Oklahoma
00:50:43
State, get this. Oklahoma State is, I think they're right up with Princeton.
00:50:49
They're like 12 or so champions. They have won a men's title in golf every decade for the last seven decades.
00:50:58
That's a different kind of streak, right? So now we've made three very different patterns,
00:51:02
but they have been good and competitive and at the top of the game for a long time, but kind of regularly.
00:51:09
And they're in the final four. They won their matchup this morning. I believe they won their matchup this, no,
00:51:14
maybe they were going into, maybe they had them late. They are- They are winning a Superbowl
00:51:21
in every decade or whatever, you know, what they've been. Who is that? Are you gonna tell me they're doing one
00:51:26
game of Superbowl in almost every, like 80s, 90s, 2000s, 2010s? I'm saying, I missed who you're saying.
00:51:34
Who's, I should know who that is, who you're saying. The New York Giants.
00:51:37
The Giants? Oh God, I wouldn't have come up with that. Oh yeah. But then they're like, they're terrible.
00:51:42
The other nine years- Right, yeah, no, again, who knows what the, I don't know
00:51:46
what Oklahoma's doing necessarily in between their like championships. They could be tanking and then coming back.
00:51:51
I don't even know. That's, you know, over like one per decade is kind of, or at least one per
00:51:56
decade. Well, they had a couple of twos. They won three. I think in the 80s, they won three
00:51:59
national titles. Okay, let me give you two other sports. So baseball, y'all's love.
00:52:04
They just finished regionals and the Supers are seeded. The Supers are sweet 16.
00:52:09
Eight teams are hosting super regionals. The cool thing about the baseball, both softball
00:52:15
and baseball play the same structure. 64 team tournament with 16 four team regionals,
00:52:22
but they go through this weird cadence. I've told y'all this before. They do double elimination and then they go
00:52:28
to, at the Supers, they play two out of three. And then in the College World Series, they
00:52:33
first play double elimination and then they play two out of three championships.
00:52:37
So it's this weird iteration between double elimination, two, three, double elimination, two out of three.
00:52:42
And when they do the two out of three, like I was watching the, you know, the college softball or whatever yesterday, and they
00:52:48
do the latter two of the three as a double header. Is that correct? So is that- No, those were elimination
00:52:55
games. So we'll talk about that momentarily, but when they get to the final, that was just
00:53:00
to make the final. So that was the last of the double elimination semis. And then the finals they'll play, I think
00:53:06
for the last 20 years, they've been playing two out of three. Men's baseball, number one and number two got
00:53:11
knocked out in regionals. So UCLA, Georgia Tech got knocked out in regionals. Okay, here's what I want to get to,
00:53:16
softball. So not because Texas is in the final, but we have been paying more attention because
00:53:22
Texas won last year, they beat Texas Tech. They're again playing Texas Tech.
00:53:26
And so we've been watching this thing since regionals and it's just amazing to see a
00:53:29
tournament get whittled down and whittled down. And you'd never think your team's gonna end
00:53:32
up in the finals when there's that many teams, good teams. And the horns are back.
00:53:36
They came out of the loser's bracket, won two yesterday against Tennessee to get into the
00:53:42
finals. And then Texas Tech did the same thing, came out of the loser's bracket, won two
00:53:45
out of three against Alabama. Alabama, number one seed, SEC regular season champ.
00:53:50
Tech is like sitting there at number 11 seed. They went two in a row. Here's the thing though, guys, this is gonna
00:53:57
get a lot of attention. So Tech could easily beat Texas. And I don't know what the odds are,
00:54:01
probably close to even. But they've got, remember Najeri Kennedy, this pitcher
00:54:06
that came from Stanford, Tech got her, they paid a million dollars. She was this first big transfer in softball.
00:54:12
This was two years ago. They got her and they, as the first season she plays for Tech, they go to
00:54:18
the World Series for the first time ever in Tech's history. And they go to the title game first
00:54:23
time. And to remind you what happens in softball, a good pitcher can pitch every game.
00:54:29
Like pitchers sometimes pitch every inning of the post-season. It's not completely unheard of that that to
00:54:34
happen. And pitchers can be dominant in softball. And so it's a huge advantage.
00:54:39
Okay, but here's the thing that's going on right now. This is completely crazy.
00:54:42
And it's gonna be a thing. It's gonna be really interesting. There are rules.
00:54:46
Have you watched softball before and noticed that they can pull a pitcher and they put
00:54:50
the pitcher back in? This is, sometimes it's this jarring thing that happens.
00:54:53
A fielder was injured for like an inning and then they came back in the game. So there's this designated player rule where you
00:55:02
can have a 10th person on the roster that's gonna bat for somebody in the field.
00:55:07
And it just gives them a little latitude in the way they move people around. And then around pitchers, they can pull them
00:55:12
and then put them back in. And so it's not crazy. It's not unheard of that a pitcher might
00:55:16
be pulled and then come back in later in a tight situation. They might start their best pitcher.
00:55:21
They wanna give her a break or whatever. They might bring her back in if the other person gets in trouble.
00:55:25
But it's not super common. It happens, but it's not super common. Okay, the Tex coach, Glasgow, the Tex coach.
00:55:31
So last year he had Kennedy, best pitcher in the game. They got a ton of NIL people and
00:55:37
transfer people and like eight of the nine starters are transfers in a Tech. And they're the best offensive team in like
00:55:43
years. And they've got Kennedy and they've got another great pitcher. Caitlin Terry, I think is her name.
00:55:50
So, and Kennedy's lefty, Terry's righty. Terry's also a good fielder.
00:55:56
She can play left field. Kennedy can play first base or they can sit him on the bench as a special
00:56:00
designated player. And so what Glasgow started doing and he talked about it with the players all season
00:56:07
long and didn't do it. And then in the regionals, no, the super regionals, Tech is 11 seed, which means they're
00:56:14
traveling for the super regional. They go to Florida, they go to game three and all of a sudden Glasgow busts
00:56:19
out this strategy of, I'm gonna start moving, I'm gonna start shifting. I'm gonna platoon my pitchers against your lineup.
00:56:24
Or if a person gets in a little bit of a trouble, I'll pull her and put the other one in, let her regroup
00:56:29
and then switch them back. And so he starts switching multiple times within a game.
00:56:33
No one's ever seen it before. And he's got these two all American pitchers,
00:56:37
one lefty, one righty. And he wins that third game against Florida. They get to the World Series.
00:56:43
The other night against UCLA in an elimination game, he had something like four pitching changes
00:56:50
and three innings or three, five and four, five and three. It's like he was back and forth.
00:56:57
He was literally- But it's not like he was going batter to batter and every time there was a lefty, he'd bring in
00:57:01
like- Not quite that much, but almost like that. Like if the lineup had a bunch of
00:57:07
lefties at the end, he's gonna make sure his righty's in there against them.
00:57:10
He even had one that I was watching where there was the big hitter for UCLA was gonna come up like second in the
00:57:17
order of that inning. And she had already homered off a Kennedy twice. And he's like, by God, I'm not gonna
00:57:21
pitch Kennedy against her now. Let's get Terry in. He pitched Terry against them.
00:57:25
She made it through a few of them. And then he put Kennedy back in. And it's just, I have to admire the
00:57:31
innovation. And I mean, it's not quite rules manipulation, but it's completely- Do we know how
00:57:38
big the handedness advantage is in softball? I knew for a fact that when I came in here and told you all these
00:57:44
stories, you had asked me for some empirical observations that I don't have.
00:57:47
And I do not have that one. It is the relevant question. It is the relevant question.
00:57:51
And I actually asked one about the kind of the pitchers as well, because I was watching it too.
00:57:55
Like they kept talking about like a rising versus sinking. It actually is rising in this case, right?
00:58:01
It's not like, you know, rising fastball in baseball is a misnomer. A rising fastball just is sinking less quickly.
00:58:07
But in softball it actually, the distances and angles are such that it actually is rising,
00:58:12
correct? I don't know for a fact that's true. I don't know for the, people think it
00:58:17
is. It looked like it. It looked like it on TV, but so does a rising fastball in baseball.
00:58:21
Yeah, right. I'm not gonna say for sure. I know for rising fastballs don't rise.
00:58:26
I mean, I played enough softball. I mean, they can release it at a very low point in their body, right?
00:58:31
And it can end up higher, but I guess it can be arcing to get there. Yeah, I'm just saying based on the distance
00:58:36
and speed, like, is it like an upward trajectory when it hits the plate or not? It looks like it, certainly.
00:58:42
But of course in baseball, we all know that that actually isn't the case, you know.
00:58:46
And softballs like baseball in that they want the release to be the same no matter
00:58:50
the pitch. And so they're not gonna deliver a rise ball at least they're gonna try not to
00:58:54
deliver it differently than the others. But I thought y'all were gonna kick out this pitcher strategy.
00:59:00
I totally admire the innovation, even though it might be the downfall of Texas in the
00:59:03
finals for all I know. It may not be. I mean, if it makes it like, in terms of pace of play, it makes me
00:59:07
glad baseball does not do that to a certain extent. Oh my God. Like, I mean, it's crazy enough in baseball
00:59:14
with the more restrictive kind of substitution rules. Right. It's annoying as hell the way it is
00:59:20
right now. I can't imagine it getting worse. Well, I have been saying this for the
00:59:25
last couple of years, but I really enjoy, and I'm sure I'm biased because Texas has
00:59:28
had a good run in softball, but softball's fun. It's faster paced. Anybody, you know, somebody, some, you know, they
00:59:36
have some big sluggers, but then anybody can hit a dinger like this little second baseman
00:59:41
hit a game winning walk off the other day. It's just, that doesn't happen a lot, but
00:59:45
the risk of it is there at a steady pace. Well, I mean, here's a question for Alan
00:59:49
Nathan, our physicist expert. In softball, it seems, and I've watched some of the home runs, they often just stick
00:59:57
their bat out and it goes over the fence in complete swings. Oh yeah. I mean, and listen, they're using an aluminum
01:00:06
bat, which is with incredible springiness and their pitchers are pitching quite fast and the ball
01:00:12
is, I mean, I think it's an interesting question. I mean, how much, let me make it
01:00:17
fundamental. How much of the power in softball is generated from the bat, the batter, or the
01:00:23
pitcher's speed? I'd love to hear that sort of broken down. I can tell you that in baseball-
01:00:27
I can tell you the speeds. The speeds, I don't know the batter part of that, but they report exit velocities during
01:00:34
the games and they're in the high 70s for these home runs. And the pitchers are pitching, their change-ups
01:00:40
might be in the 50s and their top pitches are 60s and the top pitches are typically in the 70s.
01:00:46
I think the Tennessee pitcher famously touched high 70s last year. But so it's 70 in, 70 out, you
01:00:52
know, roughly. 73 in, 73 out. At 40 feet, oh my God. 60. Is it, it's 60 feet, is it not?
01:00:59
No, that's baseball. No, no, it's 42, I think. And with their stride and at that distance,
01:01:07
it is a softball, so it slows down probably substantially from hand to plate because it's
01:01:13
such a big area, surface area. These are lots of questions, but I can tell you that in baseball, the velocity does
01:01:19
matter, but it's about one-tenth relative, matters about one-tenth as much as the velocity
01:01:24
of the bat. Yeah, right, right, right, right. These are aluminum bats, and so therefore a
01:01:29
lot. That's right, that's right. Much less. I mean, if you watch college, you know,
01:01:33
college, they use a different ball in college baseball than they do in major leagues.
01:01:37
It has less bounciness. Oh, exactly. Well, that's because the aluminum bats, I mean,
01:01:42
they come off the bat pretty damn fast. It kills some people. Okay, why don't we leave it there?
01:01:50
Appreciate y'all being up for a little college sports, minor college sports at that, but
01:01:54
it's a fun time of year, and they have some creative tournament designs, which I think
01:01:58
adds a lot of flavor. Why don't we leave it there? We missed Eric Bradlow this week.
01:02:02
Enjoyed the conversation with Greg Bond, Shane Jensen, Adi Wanner. On behalf of the whole team here, many
01:02:06
thanks for Deep Patel and Jacob Grodnick doing things behind the scenes and Marissa Reyna making
01:02:12
the bookings happen. Thanks to you guys for listening. Come back next week. Come back and join us next time.
01:02:16
Between now and then, enjoy your sports.