00:00:00
Welcome everyone to this week's edition of Wharton Moneyball. I'm Eric Bradlow,
00:00:04
professor of marketing statistics and data science here at the Wharton School. Some combination of myself, my two
00:00:09
colleagues are here today, Shane Jensen and Adi Wyner, both professors of statistics and data science, and Cade
00:00:15
Massey are here every week on Wharton Moneyball here on the Wharton Podcast Network.
00:00:21
Well, Adi and Shane, uh nothing changes when I say the best part of our, I guess I'll call it jobs,
00:00:27
non-paying jobs, uh doing what doing Wharton Moneyball is interviewing people who have made seminal contributions to
00:00:34
the applications of statistics and sports. Uh today's guest, Ken Pomeroy, many-time returning guest, is certainly
00:00:42
no exception to that. Um just for our fans that don't know Ken, although I'm
00:00:46
sure most of our fans on Wharton Moneyball do, uh Ken is a college basketball statistician. He's the
00:00:52
founder of the analytics website KenPom. It publishes both tempo-based efficiency
00:00:57
ratings and predictive rankings for every D1 team. So, Ken, on behalf of myself, Adi, and Shane, welcome back to
00:01:04
Wharton Moneyball. Yeah, thanks, Eric. It's uh great to be back on. So, let's just start with the beginning.
00:01:10
I always like to go back in time, and then we'll catch up, if you'd like to,
00:01:14
2026 in this year's NCAA tournament. Um for those people that don't know, when
00:01:20
you first built the KenPom ratings and website, like usually someone does that because they see either a deficiency in
00:01:27
what's being done out there, they see a better way to do something out there, or
00:01:32
they just want to be, you know, build a following and a community. Um why did you build your KenPom system to begin
00:01:39
with? Yeah, it was a a little bit of all of those things. Um when I, you know, when I first built it,
00:01:47
it was the kind of the advent of uh uh you know, kind of random people logging on the internet.
00:01:54
Uh the the was just starting to kind of mature, and uh uh certainly uh the baseball side of
00:01:59
analytics was starting to explode, and there were just, you know, these people online who were getting into advanced
00:02:04
baseball stats, and you know, kind of like outfoxing like traditional media in terms of analysis,
00:02:10
and you know, I like baseball, but I really love basketball, and I really love college basketball, and I I looked and
00:02:15
looked for the the version of that online for for months, and didn't find it. And so, that's kind of
00:02:21
what uh motivated me to to start my site, and I never, you know, imagined it would get to to this point, but I I did
00:02:27
think there would be, you know, a little bit of a following just because there was, you know, you were seeing that
00:02:30
following develop for baseball. Mhm. Um what's What do you think, as you know, it's been uh so, a few seasons
00:02:36
when the internet started to take off, has Kenpom, as you know, a site, as the statistics, has it been around Am I
00:02:43
saying 25 years now? Is that about the right like it? Yeah, you know, when I uh first
00:02:50
uh posted offensive and defensive uh efficiency for all the teams, that was at the end of the
00:02:55
2004 season. Uh Kenpom in some form, like the website existed before that, and I was doing
00:03:00
college basketball ratings, kind of basic ratings, like you'd see uh you know, Jeff Sagarin do. He was He was
00:03:05
really an inspiration for me uh growing up. But uh yeah, the kind of the the modern version started in 2004, and
00:03:11
I've I've backdated, you know, some seasons before that. But yeah, it's, you
00:03:15
know, we're we're definitely over 20 years at this point. So, what would you say is the most,
00:03:20
like, you know, if you had to be for a minute, you tend to be modest, if you were not modest, what would be you would
00:03:26
say would be the biggest impact that your work has had? Like, what's been the
00:03:29
biggest misconception the way that you've modeled things has either corrected, or what do you think you've
00:03:35
added to, you know, as we sit here in 2026, what do you think you've added to
00:03:40
the way in which, let's call it player or teams effectiveness or efficiency, or, you know, what are the
00:03:47
sufficient statistics one should measure when one's thinking about team strength?
00:03:51
How do you think about your contribution? I think in a in a really general sense,
00:03:56
it's just about trying to account for the opportunities that teams or players
00:04:00
have to do things. Um so, you know, the the biggest impact on the team level where, you know, we talk
00:04:08
about possessions per game or pace or tempo or whatever you want to call it. Uh you know, in college basketball,
00:04:13
there's such a wide range of styles in that regard that you can't just Traditionally, you know, when I started
00:04:19
people just looked at good points scored per game and points allowed per game and
00:04:22
attributed that to offense and defensive quality, but you really do have to look
00:04:25
at points per possession and points allowed per possession uh to give you the the true picture of of which teams
00:04:31
are most effective and um you know, people were thinking about that before I started my site, but I
00:04:37
just to have kind of a an easy reference to see how all the teams did in those stats and you know, make it easy to
00:04:42
compare. That's really I think what what my main contribution has been. I would think also and Audie I mean just
00:04:47
Audie wants to jump in in just a second. I would also think that number of possessions, you know, and you know, big
00:04:52
end teams that get lots of offensive rebounds, teams that push the ball more, uh teams that get turnovers. I would
00:04:58
imagine that, you know, it's some, you know, as I always say, you know, I'll
00:05:02
use my language of business here in the marketing department. What the hell, I'm
00:05:04
sitting in my office in the marketing department. What do retailers care about? They care about making money per
00:05:09
unit and they care about inventory turn. So, what do I care about in basketball?
00:05:13
I care about getting a lot of possessions and being efficient on each of those possessions. Is that too simple
00:05:17
a way to think about it? No, not at all. I uh No, that's it gets back to kind of
00:05:22
the opportunity thing, right? Like that you know, that was one of the early discoveries, too, is you'd see these
00:05:26
teams that you know, functionally you'd watch them and they they'd look really ugly
00:05:31
offensively, you know, uh you wouldn't you wouldn't say they had an effective
00:05:35
offense, but uh the trick was, you know, they were getting like a lot of offensive rebounds or maybe they weren't
00:05:40
committing a lot of turnovers, you know, it's very easy to count things that do
00:05:43
happen, but to notice things that don't happen, like not committing turnovers is
00:05:46
something that humans don't necessarily do very well. So, you know, you you'd see like teams
00:05:51
ranked pretty high in my offensive ratings and maybe didn't look pretty, you know, functionally, but it was
00:05:55
because they were you know, just getting a lot more opportunities at shots like you're
00:05:59
talking about. So, you since we're almost talking historically here. So, Dean Oliver's
00:06:04
spoken to uh he's been on the show many times. He's come to speak to our our our
00:06:08
programs here at at Wharton and Penn. And one of the things that he talks about is that when he first started
00:06:13
doing basketball analytics and he's sort of the Bill James of basketball analytics if you will, he said that's
00:06:19
the statistic of possessions didn't even exist. It wasn't even something that was
00:06:23
ever recorded, right? And he almost had to had to take in basketball paper he talks about how he actually had to go
00:06:29
and record and figure out like how you can figure out what the pace of play is cuz it wasn't there. So, uh scroll now
00:06:34
to your work in in college basketball. How did you get to possessions? Is it now a stat or is it did you have to work
00:06:40
that as as hard as he did to create that number? And then of course once you have
00:06:44
possessions you can talk about efficiency. So, how did it come to be for in your in your perspective?
00:06:49
Yeah, well first of all I you know, Dean was a a huge inspiration for for my work and his book, you know,
00:06:54
his original book I think came out in 2002, which so it's not a coincidence that like my site started shortly after
00:07:00
that. Like just the ideas he had really resonated with me. Um yeah, I yeah, so possessions aren't a published stat. Uh
00:07:06
so, you can either count them uh specifically or you can estimate them from box score data and I still estimate
00:07:12
them from box score data for for most things I do. Um that gets you really close enough to the number, but uh
00:07:18
yeah, I mean there's no doubt that uh there were there were some hurdles, you
00:07:22
know, early on to kind of getting things started and that was one of them, but like Dean kind
00:07:27
of blazed the trail on that. So, it actually, you know, made it really easy for me to just, you know, follow follow
00:07:32
his work which he helpfully laid out in his book. Do you do the four factors in college
00:07:37
basketball? Is that part of your So, can you explain to our listeners what the four factors are, please? That'd be
00:07:42
great. Yeah, so the the four factors are, you know, just a basic kind of the basic
00:07:49
building blocks of offense or defense. Uh so, you have shooting, you have turnovers, you have offensive rebounds,
00:07:55
and you have free throws. And uh the way those are measured again is by opportunity. So, you know, field goal
00:08:01
percentage is not straight field goal percentage. It it's called effective field goal percentage. It accounts for
00:08:06
the value of a made three-point shot. So, you know, if I'm shooting >> That's all these favorite math. Three is
00:08:10
50% more than two. Sure [laughter] is. In basketball. Yeah. Yeah. And you find teams that shoot a
00:08:16
lot of three-pointers tend to have lower field goal percentages than teams that don't. But, obviously, there's a
00:08:21
trade-off there. When they make their shots, they're, you know, getting more
00:08:23
points out of it. So, so, there's that. You know, rebounding is offensive rebounding percentage. So, it's
00:08:28
basically the percentage of times that you have a chance to get an offensive rebound, how often you do so. Turnovers
00:08:33
is turnover percent percentage, so the percentage of possessions you get a turnover on. So, yeah, those are the
00:08:38
four factors, and they're they're just great at you know, basic explanation of what a
00:08:43
team does or doesn't do well offensively and defensively. >> [snorts] >> So, one of the things that I've noticed,
00:08:47
just to jump in on the four factors, I actually teach this in my senior capstone.
00:08:52
And one of the things that's remarkable, I'm not a basketball person by nature. I
00:08:56
mean, I've certainly played enough basketball as a kid and in high school. But, I don't really study it. It's one
00:09:02
of my weakest sports, I think, in terms of the majors. But, I so I've I've come to learn about
00:09:08
basketball through statistics, which is kind of odd, right? But, those four factors are oddly
00:09:13
uncorrelated, which is shocking to me. And at least at the professional level. Is that Is that Can you explain that?
00:09:20
And is it true at collegiate level, too? I would imagine the collegiate level's
00:09:22
got to be more correlated, cuz good is good. And when you have tremendous variation, which you do at in the NCAA,
00:09:29
you probably your best teams are probably better in some level. But, I like to >> And just before you answer that, Ken, I
00:09:33
just want to be clear. What Audi may be referring to or not, correct me, correct him.
00:09:39
At the individual player level, this is a classic aggregation issue I would I could imagine.
00:09:44
If I'm a good three-point shooter, I may also be a very good free throw shooter,
00:09:48
but at the aggregate level, at the team level, they may be uncorrelated when I look across teams. So, if you could I'm
00:09:54
just modifying Audi's question. I just want to know if this uncorrelatedness is
00:09:58
at the player level, the team level when we aggregate, or is that not true at all? And then then I'll take Curtis
00:10:03
mean. Uh yeah, that's a lot to ponder. I I'll It's It's more I I would say it's more
00:10:11
uncorrelated at the team level than the player level. Um so, Audi made a good point that, you know, in college, the
00:10:16
distribution of talent is much greater than it is in in the NBA, right? It's
00:10:19
the the difference in the best and worst team is enormous in college. And so, yeah, so you do tend to see that like
00:10:26
teams that are good in one thing are going to be good in other things. But, uh but there's no like there's no reason
00:10:32
for you to be good at shooting and also good at offensive rebounding, right? Like those two things really aren't that
00:10:37
related fundamentally skills. And in fact, you see like almost no teams are great at every single four factor. Like
00:10:44
most teams are great at even the great teams. They might be great at like four of the eight four factors if you
00:10:50
consider both sides of the ball. Um and they might be good at two others, and they might be like below average at two
00:10:57
others. Like nobody's great at everything. So, teams have all sorts of different styles, and they specialize in
00:11:03
different things, and uh and yeah, the fact of the matter is like the way those four factors are defined, they're
00:11:08
basically orthogonal. Like they're not the stats don't bleed into to other four
00:11:11
factors. So, you can be great at one thing and poor at another, and it's not
00:11:15
unusual to see very good teams behave that way. Yeah, well, I guess you're uh
00:11:18
just to kind of follow up, you're talking about kind of specialization at the
00:11:23
team level. I when when kind of I thought about what might drive a lack of correlation, uh it would be more
00:11:29
specialization at the individual level. And I know like cuz I I think about baseball. Obviously in baseball it's
00:11:34
very there's very much of a you know, like fielding even like fielding and hitting are not are often negatively
00:11:39
correlated because there's a real specialization there. Basketball obviously there's not as much
00:11:44
positional specialization, but is is that a large part of what's driving maybe a lack of correlation? Yeah, I
00:11:50
mean there's there is some specialization like often times you if you look at a you know, a team so first
00:11:55
of all like in basketball we really struggle to measure measure individual defense, right? We don't have really
00:11:59
many good stats for that. You know, block shots are good for for tall guys, but for small guys
00:12:04
you know, they're not going to block shots and they still can be effective defenders. Um so often times when you
00:12:08
look at like uh season stats and you see like a player who doesn't contribute
00:12:12
much offensively like it's almost certain that they're a good defender. Like that's why they're on the court. So
00:12:17
you do see that. I think another like another great example of aggregation is that when you compare offensive
00:12:22
rebounding percentage and defensive rebounding percentage for a team and and look at all of division one, there
00:12:26
really is hardly any correlation between those two. So if I tell you that a team
00:12:30
is a great offensive rebounding team, I really haven't told you anything about
00:12:33
whether they're a good defensive rebounding team. And yet on the player level there obviously is a high
00:12:37
correlation there, right? Like a good offensive rebounder individually is probably a good offensive rebound or a
00:12:42
good defensive rebounder as well. So that's to me that's the best example of
00:12:45
that effect you guys are talking about. Let me ask you the following question. Let's imagine
00:12:50
I took just those four factors and I use those as a predictive model to predict the outcome of winning versus
00:12:57
something more sophisticated that I'm sure you done and built over the last 22
00:13:02
23 plus years. How much do we lose? Like are those sufficient statistics? I don't
00:13:08
mean literally, but quasi sufficient for predicting game outcomes? And if the answer's yes,
00:13:14
great. If not, what else is kind of missing? No, there's not there's not much
00:13:19
missing. Like if all you have are are box score stats, those four factors explain like you
00:13:23
know, 99% of offense and defense. The only thing that's missing are like data
00:13:27
errors and uh you know, the possession estimation and things like that. But yeah, I mean for the most part, you
00:13:33
know, yeah, you're going to find you plug those guys into a regression or whatever and you know, obviously like
00:13:38
shooting's going to dominate. It's going to be the most important thing and then
00:13:41
offensive rebounding and turnovers are somewhere like tied for a decent second and then like free throws are like
00:13:46
you know, the weakest thing. But yeah, you you you'd have a really nice model
00:13:50
if you if you use those four things. Can I uh let me just jump in cuz actually I
00:13:53
have this data for the NBA, so not the collegiate. So if you try to predict wins using
00:13:59
point differential, that's about as best as you can get. I mean the game is down
00:14:02
to points, right? So yeah, you can end up have a lot of blowouts and your your point differential might not that be
00:14:08
perfectly accurate with your wins. You can it's it that's you can't expect
00:14:12
perfection. But four factors works almost as good. It's just That's the point in the
00:14:18
>> It's it really is amazing how how I mean there's leftover residual
00:14:22
variance because of how you distribute your points. But you're and I'm talking
00:14:26
about season-wide four factors are and I just use four. I'm but I use the differential. So the way I use I can
00:14:32
create a delta four factor for each of them and instead of having eight, you know, four on each side. So
00:14:38
I'm sure I'd have even better if I did if I went down to that. It's just amazing. I'm very excited
00:14:43
about this and I when I teach this I get excited about my students usually look at me and I'm like, why do you care? But
00:14:48
it's because they don't have experience and they don't know how valuable that
00:14:51
is. >> a dream regression scenario you where you've got like four different factors
00:14:55
all which are important but not particularly highly correlated with each other. Yeah. Well, I was just going to say I
00:15:02
mean that's you know, that's why Dean Oliver chose those. That was really
00:15:04
intentional the way he you know, it's pretty obvious like when you think about it, it's obvious like
00:15:08
those things should be the four factors. But just the way they're they're
00:15:11
measured and the fact that they're you know, again, they're like the measurements are completely orthogonal,
00:15:16
so they're not you know, directly related to each other in any way. So let me ask you a follow-up to that.
00:15:21
So I in the when I introduced you, I I read and predictive rankings for every D1 team.
00:15:27
So, how do you I mean, obviously you have a ranking vector. Obviously we get outcomes at the end of the season,
00:15:33
which, you know, get the NCAA tournament, but how do you score yourself? Like, anybody that does
00:15:40
predictions always has, let's call it an error metric of some sort and says, "How
00:15:44
well did I do?" And then possibly that leads in good models to model modification if you notice some sort of
00:15:51
systematic errors or stuff. So, I So, how do you score how well you've done in
00:15:56
ranking teams? And how does that lead, well, if you notice something to any type of modification of how you do
00:16:02
things? Right. So, the the main way I score myself is by looking at my game predictions.
00:16:09
Um, yeah, so I do look at the overall ranking as well, but primarily I I use that to like judge my I have a
00:16:19
preseason ranking as well, which is completely separate calculation, but they're really like judged on, you know,
00:16:23
compared to the final ranking. But, overall like the algorithm, I yeah, I basically focus on game prediction. So,
00:16:29
both uh, you know, predicted uh, point differential and uh, win probability. And, you know, just a basic How is your
00:16:36
model How is your model We always like to ask this question. Is your model properly calibrated? Which means when
00:16:42
you just I want to make sure our listeners understand, but they do. Um, when the KenPom system says team A has a
00:16:47
70% chance, if you bucket all of those around 70%, is it near 70%? If you bucket the 20% ones, is it around 20%?
00:16:55
So, what do you find there and do you have the classic problem Shane talks about this all the time? Do you have
00:17:00
problems out in the tails, which is typically where we find, you know, models don't do as well out in the
00:17:06
extremes? Yeah, so it is pretty reliable. Um, yeah, I have like I have a page for
00:17:12
those for those stats that updates in in real time basically. And I'm probably
00:17:15
the only probably the only one that looks at it, but you know, I'm always curious. Uh, Uh yeah, it it does really
00:17:21
well. In the extremes, it probably um under predicts the favorite slightly. Like I think this year there were like
00:17:28
260-ish games where I gave the underdog like less than a 2% chance of winning. And they won zero of those games.
00:17:38
And they should have probably won like three or something, you know, based on the probabilities. So, there's a little
00:17:43
bit of uh overconfidence, I guess, with with extreme underdogs. But outside of that, like for the most
00:17:50
part, it does pretty well. Certainly does pretty well in the probabilities. It has you know, it has another issue
00:17:54
with like scoring margin for extreme underdogs as well. It's constantly like
00:17:58
overconfident on these just really super lopsided matchups. Um but Overconfident in in favor of the
00:18:05
underdog. Exactly, yeah. So, like, you know, when the market says a team's favored by 40, like I have them favored
00:18:11
by like 32 or something. And Well, Oddie's all for that. Oddie's all for
00:18:15
regressing back toward some average. Well, I mean, let me let me just point out a couple things. Um first with
00:18:20
response to that, this is something I also do with my class. There's a sort of the big home dog
00:18:25
effect in betting, um which is that when a team is playing at home and they're the big underdog,
00:18:32
they have a tendency historically to cover the spread. And uh fascinating because that goes
00:18:37
back to the '70s. And I I look at this historical data in football in particular where I have that data. And
00:18:43
it just in every 5-year period, it just sticks out, sticks out, sticks out. And it goes all the way through like 2015
00:18:49
and then it stops. It's just sort of gone. As if And that may be because people are starting to bet it more more
00:18:54
um uh globally. Hard to know exactly. But that's that's potentially what you're seeing in
00:18:59
basketball is that the model, because you're not really talking about betting,
00:19:02
the model says 40 points, but we know that home team's going to outplay there
00:19:06
when they're the big dog. Maybe you're seeing that. But my question is, um Um,
00:19:10
way back in the way In fact, you were one of our earliest guests in in in in what in what Moneyball. Um, we were
00:19:16
trying to figure out what some of the secret sauces are in NCAA tournament prediction. And I have to thank you
00:19:21
right away because, you know, I I only play in a small family tournament, but I've won 40 per 40 four out of five of
00:19:27
them. Um, and I just use your your inside information. So, I should send you send you a check, but if only for
00:19:32
you for a couple bucks. Just to get that out there. So, thank you, Ken. But, the
00:19:35
question, um, this is I remember Nate Silver pointing this out. Um, he used to say some of the secret sauce was
00:19:41
shrinkage or regression towards preseason rankings. And that um, you tended to over overvalue the people the
00:19:49
teams that had really good seasons. And um, and one way to to account for that was just regress them slightly to
00:19:56
their preseason expectations. Is that still true? Yeah, it is. You do see that effect. Um,
00:20:02
so, you know, in my system, I you know, my preseason ratings are pretty much washed out by now and like
00:20:08
I really wanted maximum accuracy. They They wouldn't, but there's sort of a
00:20:12
perception issue with, you know, the fact that my ratings are used on the NCAA team sheets for selection and
00:20:17
things like that that, you know, you probably don't want preseason ratings influencing them, but
00:20:21
um, that is a factor. I mean, one of the one of my favorite stats, which may or may not come into play this year, but
00:20:26
uh, teams that are teams that end up being one or two seeds in the NCAA tournament, uh, if they were not
00:20:32
ranked in the preseason AP poll, uh, there's like 38-ish, I think, of these cases over the past
00:20:40
40 years, none of them have made the final four. Wow. Which you normally expect these are one
00:20:45
or two seeds, right? You normally expect like eight or nine of them to make the final
00:20:49
four, but so, it's like definitely a significant trend and like I said, I don't think it'll happen this year.
00:20:53
Nebraska's the only possible case if they end up as a two seed, but uh, that's, you know, really an example
00:20:57
where like, yeah, clearly like teams have like overshot their preseason expectations and it it does, you know,
00:21:04
you do see some regression when it comes to the NCAA tournament. With strength of schedule
00:21:09
kind of is is that part of that story as well or is it more just um what what do you think is is is kind
00:21:16
of driving that? No, I think it's just what we're we're talking about, right?
00:21:20
Like you you know if you had you know, you want to get perfect information on a team, right? Going into
00:21:25
the tournament. And perfect information is not solely based on what they did that season. Like the NCAA basketball
00:21:30
season short, you know, it's only 30 33 games before you get to the tournament.
00:21:34
A lot of those games are mismatches, you know. Yeah, I guess that's what I was going
00:21:38
with with kind of very uneven schedule, I guess, yeah. Yeah, so you know, you maybe have like 20 to 25 games of
00:21:43
meaningful information and uh there's still meaningful information in the preseason, Paul. So yeah, you
00:21:49
definitely uh you know, want to include that when you're making projections. So
00:21:52
can I haven't been following it as closely as I normally do. I don't know
00:21:55
why cuz I love college basketball. There is an undefeated team, is there not? Yeah, it's really one of the best
00:22:02
stories of of all time. Tell her this So for those of us that don't follow, like
00:22:06
what the what conference are they in? How likely are they to win their conference? And how far do you project
00:22:12
them to go in the NCAA tournament? I would imagine not very far, but what do you see for Miami of Ohio? Yeah, so
00:22:19
they're in the Mid-American Conference and uh you know, notoriously didn't play
00:22:23
a very difficult non-conference schedule. Like most teams from weaker conferences, they end up playing you
00:22:28
know, one of the top teams on the road just because they actually get, you know, a a nice check from that team for
00:22:32
doing so. Um but Miami of Ohio did not play any of those teams. Debatable whether that was by choice or
00:22:39
not. Like they'll tell you they were they were not able to schedule those teams, you know, under the terms they
00:22:43
wanted. But uh anyway, they ended up, you know, running through their entire schedule undefeated, which uh
00:22:48
you you know, people are saying it's entirely schedule-based, but still really hard to do that even playing a
00:22:52
weak schedule. They're ranked 90th in my predictive rating, so uh Yeah, so you know, if they played in a
00:22:59
power league, they probably would have taken quite a few beatings over the course of the season playing the way
00:23:03
they did. Like you look at teams at the bottom of the Big East, right? And they're ranked in the 90s. Like, you
00:23:07
know, that's about what what they'd equate to. They're oddly like playing
00:23:11
getting ready to play their conference tournament. They're not even favored to
00:23:14
win their conference tournament as an undefeated team. Akron lost one game in conference play to Miami at Miami, but
00:23:21
overall has, you know, better point differential metrics and that's kind of carrying the day in in these
00:23:25
predictions. But So, if they go undefeated going into the tournament, are they like
00:23:30
>> a I I don't see how they they have to be a top eight seed, right? If they go
00:23:35
undefeated. I don't even know if they're guaranteed to make the tournament.
00:23:38
>> if they win their conference tournament, by definition, they have to cuz there's
00:23:41
an automatic bid. But what Ken's saying is if they lose, let's say in the
00:23:45
semi-finals or something of their they could be whatever 33 and one and be left out. Yeah, so if they they it's very
00:23:53
unlikely they'd be left out based on what we know about how teams are selected and the metrics that are being
00:23:57
used now. Um so they're going to get in. But yeah, seeding-wise, even if they win their
00:24:02
conference tournament, I eight would be pretty optimistic, I feel like. They could be an eight, but yeah,
00:24:07
probably more in that 9 10 11 range. Just because their schedule I mean, they have one of the five weakest schedules
00:24:11
in the country, which just put it this way, like no team that's earned an at-large selection has ever had a
00:24:15
schedule remotely that weak. So, they are they're just like such a wild team.
00:24:20
I mean, it's first of all, amazing they went undefeated. They had so many close
00:24:22
calls and every time, you know, they pull it out. Um but they've also like, you know, have this kind of
00:24:28
Frankenstein-like profile that nobody has ever seen before. It's just a it's
00:24:32
just a wild wild story. So, let me ask you another question about the tournament. One of the like if
00:24:36
you think about the whether it's the four factors and that each of the teams
00:24:39
having four factors or maybe it's eight factors, whatever it is. Um one of the
00:24:43
things you hear all the time at the time of the tournament is matchups. Do matchups matter that much? Like I
00:24:51
tend to think like they're talked they make great stories. And they're over they're talked about a
00:24:57
lot. But I you know, whether you want to call them interaction effects, if you got like the you know, the loyal and
00:25:02
maramounts against the slow down teams, someone's got to win and pose their will
00:25:07
on the style of the game. How much do matchups matter or when you're making predictions, are there matchup variables
00:25:15
in there that move the predictions what we would call a significant effect size?
00:25:21
Right. So, there's Yeah, in my predictions there's no matchup effects. Like historically when I've looked at
00:25:26
this or other people have looked at this, it's been extremely challenging to
00:25:30
find any like specific matchup effects where for instance like you know, a good offensive rebounding team is playing a
00:25:36
bad defensive rebounding team. Like might there be some sort of extra advantage there like that's an example.
00:25:41
But the trying to find those like combinations has been pretty much impossible. Like you watch
00:25:47
if you watch the tournament and watch the commentary, like almost everybody will talk about matchups. They'll talk
00:25:51
about that pace effect that you're talking about when you have two contrasting paces and well, it's going
00:25:55
to be really important that one team controls the pace in this game. But man, when you look at it historically,
00:25:59
there's almost nothing to that and sometimes I just feel like you know, analysts feel like they sound
00:26:04
smart talking about matchups and stuff. But in the reality is like it's just it's I'm not saying that those
00:26:10
effects don't exist, but it's been a very challenging to tease that out of
00:26:15
the data. By the way, I we as at Wharton Moneyball, myself, Audi, Shane, Tate is
00:26:21
not here today, but he we all appreciate this. I we all like the way you phrased
00:26:25
it. You didn't say these effects don't exist. You just said it's hard to detect
00:26:30
them. It either means a couple things. One is there's not tons and tons of data
00:26:34
to do so or the effects are small and therefore it's hard to detect small effects. And
00:26:40
so if they exist, they might exist on a you know, I always call it a second or third effect size level as opposed to a
00:26:47
primary one. If I can interject, it's like it could be also that these effects
00:26:51
are are kind of real at that season that time, but they're ephemeral. They're not
00:26:55
like consistent over year to year that like they'd show up kind of in a historical analysis. This
00:27:01
discussion's kind of making me think about how we retrospectively every year
00:27:04
in baseball we're like, "Oh, well, relief pitching is what wins in playoffs." Because, you know, that's
00:27:09
what you know, won the previous year and stuff like that. I think there's a lot
00:27:13
of narrative forming that after retrospective narrative forming. Because again, as again, as your point yeah,
00:27:19
that you know, the people we're analyzing it, especially analyzing in real time have to talk about something.
00:27:24
Uh and I I kind of wonder how much of these are sort of us we we kind of wrap up particular analysis or narrative into
00:27:30
what's currently happening, how consistent that is you know, year to year, next year, 10
00:27:35
years from now. >> Good point. >> I don't I don't think there's much to
00:27:37
that. Yeah, I mean, you like the one thing for me is, you know, I'll I'll talk to like
00:27:43
coaches for like pretty successful coaches and they'll they'll mention, you
00:27:46
know, "Oh, this game's a favorable match-up for us." And they'll explain
00:27:49
why and so, you know, you you have to respect that knowledge. And I think possibly like to the extent match-ups
00:27:55
exist, you know, it's it's probably one of those things that swings like your
00:27:58
expected outcome by a point or two or something like that. Like so, that's you
00:28:03
know, that's going to be something that's really hard to detect in in specific situations. But uh
00:28:08
I guess the the key point is you know, match-ups don't turn a 10-point underdog
00:28:11
into a favorite. Like I think we can safely say that. Right. Can you tell us so, before I ask uh a last few questions
00:28:17
about the future and what you're working on now, um what do you see happening in
00:28:21
this year's NCAA tournament? Like are there a couple teams you really like based on your model? Who are those
00:28:28
teams? Who is doing better than let's say either the betting markets or you know, either coaches rankings? Uh how do
00:28:35
you see things going this year? Well, uh the story is really going to be about the the top teams. Like last year
00:28:41
we had a notoriously uh upset-free almost tournament. Uh the top teams were rated exceptionally strong
00:28:50
heading into the tournament and it turned out that it played out that way. Like the top
00:28:54
eight teams in my rating heading into the tournament ended up in the Elite Eight and the top four teams ended up in
00:28:59
the Final Four. So, it was kind of a predictable tournament from that standpoint and
00:29:04
it's a similar structure this year. Like the top four teams are are really strong. You know, Duke, Arizona,
00:29:09
Michigan, and Florida are separating themselves from the rest of the country. I doubt all four of
00:29:15
those teams get to the Final Four. Like I feel like last year was an anomaly on some level, but we are seeing the
00:29:19
structure of college basketball change a bit where there is more separation now between the
00:29:25
teams at the you know, at the top of the rankings and some of the teams that we >> Why do you think that is? Why do you
00:29:32
think there's a more separation? So, really in recent years uh player rules about player movement have
00:29:39
been relaxed uh tremendously and you know, it used to be it was difficult for players to to change programs. Uh often
00:29:47
times they'd have to sit out a year which would discourage them from doing that, but those restrictions have been
00:29:51
removed and so now uh teams can aggregate talent really quickly. You know, if they get a star player on their
00:29:58
roster in the off season, well like that might attract three or four other really
00:30:01
good players from other schools to join them. And so, I think the aggregation of
00:30:04
talent is just more efficient at these top programs and it uh just makes them uh you know, able to produce more
00:30:08
dominant teams than than we used to see. So, a lot of this a lot of obviously the
00:30:12
loosening of the transfer uh rules is a big thing, but also the NIL money. I mean, it's uh the top college players, I
00:30:19
mean, they only a few of them go to the to the NBA and because because there's just aren't
00:30:26
that many openings in the NBA. It it seems that with collegiate money you can make almost a career or at least
00:30:33
you want to stay in the NBA in a way that you never used to. And it just that have any um direct or important impact
00:30:40
on the fact that now the top four, eight, even really are substantially better than the rest of the field
00:30:46
because of that. Is Is there Are you detecting that? Yeah, I think you can say that. Like
00:30:51
certainly still for the top players it makes sense to to go pro. I mean ultimately when you get your second NBA
00:30:56
contract you're going to make more money than you can make in college. So you
00:30:59
want to get that clock started. Yeah, but certainly for like the top, you know, five to 10 players like
00:31:06
they're almost certainly Um but beyond that, you know, like Michigan has Yaxel Landeborg, right? He
00:31:12
could have been a first round draft pick last year but he chose to stay in college for another year and presumably
00:31:17
got, you know, compensated pretty well by Michigan. Moussa Johnson, another player on Michigan who um
00:31:23
you know, kind of falls into that boat. Uh so yeah, so I'd say I'd say for like
00:31:27
players outside the top 15 or so, yeah, they're more likely to to stick around
00:31:31
than they used to be and uh there's no doubt college basketball more talented than it's ever been. Throw in
00:31:36
the fact by the way that international players now who have like professional experience are allowed to play college
00:31:42
basketball. G League players who are not who have not signed a contract with an NBA team are allowed to play college
00:31:47
basketball. So uh everybody's looking for a competitive advantage in finding
00:31:51
these guys and it's it's uh you know, made made the college talent pool, you
00:31:55
know, better than it's ever been. So let me just ask you a couple uh complete conclusion questions if you'd
00:32:01
like. Um what has changed in your mind? Like what do you think you've learned
00:32:06
about college basketball analytics over the last 20 years? Like what has been the biggest learning for you?
00:32:12
Yeah, that's a good question. I There's obviously a lot of things and uh I'm
00:32:16
blanking on uh on all of them but uh Yeah, I mean, you know, the main thing is like,
00:32:23
you know, there was a time when I started where it was kind of fun to, you know,
00:32:30
mock conventional wisdom and you know, you learn over time that like conventional wisdom is
00:32:37
you know, right more often than not. Like it's not always right. And there are certainly things that I feel like
00:32:43
biases that coaches still have. But over time like as you know, a new generation
00:32:46
of coaches have have come along and kind of grown up with this stuff. You see some of these
00:32:51
you know, old school fallacies go away. But you know, there's no question like
00:32:55
there are things that that coaches do and and that you know, are on gut instinct or whatever that you know, when
00:33:02
you start to analyze them, you might think they're incorrect. But you know, obviously there's there's some
00:33:08
wisdom there that that coaches have acquired over the years even if it's just ignoring data and and and using
00:33:12
their gut instinct. So, let me ask you um one last question. So, I always talk about the following uh
00:33:20
uh two things. Um it's a kind of a two-part question. Um and Shane and Audie are going to love
00:33:25
this cuz I'm going to use my favorite word is momentum. How much do you think momentum
00:33:31
matters going into the NCAA tournament? Like let's imagine Duke for example,
00:33:36
I'll make it up, loses in the second round of the ACC tournament or Michigan
00:33:41
loses in the first or second round of the Big Ten tournament. Does that change anything for you? Now, of course, we
00:33:47
have to It has to do something. In other words, it is recent more than past. We all Anybody's model counts the current
00:33:53
power more than the past. But are you a big believer in momentum or that doesn't
00:33:58
mean that much to you? Oh, I think uh once you uh account for what you're talking about, like
00:34:04
obviously there is more value in more recent data points. Um once you account for that, like
00:34:12
beyond that, I I don't have any particular uh use for momentum. I mean, that's one thing
00:34:19
You've got You're You're joining the Shane and Audie camp. They don't believe
00:34:22
in momentum, too. Once you account for all these other factors, of course. But yes.
00:34:26
Yeah, so I mean, there's obviously you Yeah. Um I mean, there's all sorts of examples, you know,
00:34:32
of momentum fooling you. Like momentum exists until it doesn't, basically. You
00:34:36
know, it's it's not very predictable on a a player or team level. Uh you know, I
00:34:41
always try to make the point that like all data matters, okay? Like the games at the beginning of the season matter.
00:34:47
Like there's it's kind of becoming vogue now for people to do analysis and really
00:34:51
like split the season into like small samples and make teams look favorable, you know,
00:34:55
they'll find that that a team played a terrible game like on January 12th and
00:34:58
it's like, well, since January 12th, this has been the best team in the country, you know, and it's like, well,
00:35:02
that game on January 12th actually mattered and you do need to consider >> [laughter]
00:35:06
>> you need to consider it. And uh so so I would be like obviously teams that
00:35:10
are playing better now than they played in November, that matters. Like they're
00:35:13
they're a different team in that sense, but still those games in November uh
00:35:17
are still data points that uh should be considered and uh you know, they they are considered in my model.
00:35:23
Yeah, Shane and I and probably 99% of the rest of the planet would rather call it non-stationarity. Maybe they're just
00:35:28
a better team or playing better. Has nothing to do with momentum. This should These are still college kids. I mean,
00:35:34
maybe they're not the college kids that we used to think of in the back in the
00:35:38
day because they're so professionalized, which means that the learning curve has
00:35:42
got to be still fairly steep for young folks. So, there's nothing to prevent
00:35:46
that the idea that momentum is really learning and some teams are getting better because they're working to work
00:35:53
together better together better. And I think in basketball in particular, there's a strong sense of of um I don't
00:36:00
want to call it esprit de corps, but I don't know. When two players work together, their team works together,
00:36:05
they get better at anticipating each other. And I can imagine that momentum really appears that way um in so far as
00:36:11
it is non-stationary. Maybe it's definitional, yeah, cuz I like everything you describe, I would just
00:36:16
describe classify as non-stationarity. Yes. And momentum is more like the psychological
00:36:23
>> like like like non-stationarity is like a a a in some latent process that's, you
00:36:29
know, of of how they're playing the game. Momentum is like some extra juice because of the outcome at the outcome
00:36:37
level. They keep winning and therefore I don't know I don't know if that's a way of
00:36:41
deconfounding these two ideas, but >> one more I agree longer term and state-like latent state-like and I
00:36:47
consider the other one localized and based on outcomes. I think that's fair.
00:36:53
>> correlation in the recent recent residual is how you define momentum or
00:36:56
something like that. >> I would agree. Well, Ken on behalf of myself, Adi Wyner, Shane
00:37:01
Jensen, as always, we'd like to thank you for joining us here on Wharton Moneyball. You can catch Ken on his
00:37:07
website kenpalm.com. He's again college basketball statistician and the founder
00:37:12
of Analytics website kenpalm.com. Ken, thank you again for joining us this week on Wharton Moneyball.
00:37:17
All right, thanks everybody. Appreciate it. Welcome back to Wharton Moneyball here
00:37:21
on the Wharton Podcast Network. This is Eric Bradlow, Professor of Marketing, Statistics and Data Science and I'm
00:37:26
joined today by my two co-hosts, Adi Wyner and Shane Jensen, both professors of Statistics and Data Science. Some
00:37:32
combination of the three of us and Cade Massey are here every week on Wharton Moneyball. So guys, we just finished
00:37:38
with Ken Pomeroy talking about college basketball. Obviously, there's a lot of
00:37:42
else lot of else going on in the statistics and sports world. So we'll do our normal second half of the show where
00:37:48
we say what caught your eye. Shane, I'll start with you. Anything in particular?
00:37:52
Prob- It's probably going to be about the World Baseball Classic, but either
00:37:54
way >> It is. It is. Yeah, I woke up I woke up a little bit early this morning and
00:37:59
tuned tuned into the end of the Chechnya-Japan match going on this morning. The starting pitcher for
00:38:07
Chechnya, this guy Andrei Satoria, held Japan scoreless for 4.2 innings pitched. This guy was already I I I knew
00:38:17
I'd remember this guy. This guy in 2023 the World Baseball Classic struck out
00:38:21
like Shohei and a bunch of Japanese pitchers as well. I just think it's kind
00:38:25
of notable cuz he tops out at 80 mph. So, he struck out like he went through the
00:38:32
Team Japan lineup for almost like a five innings. >> he change speeds or does he just have
00:38:37
good location? Changeup and I you know, I yeah, he's got a changeup. He's got trickery. He's
00:38:45
got trickery. He's like I don't know. I don't know what what the analog
00:38:50
would be, but it's pretty no I mean really what's driving this is unfamiliarity, right? This guy pops out
00:38:55
of nowhere for every four three years, but but I think it's just notable. It is
00:39:00
pretty fantastic little story that this guy can kind of has dominated Japan twice now. Um
00:39:06
you know, and obviously it's not not not something where he could go in and pitch
00:39:10
in the I don't think he's really a major league prospect despite uh Well, as
00:39:13
you're pointing out and then I want to say one other thing about the baseball
00:39:16
classic before turning it over to Audi. Um you pointed out something. Are there some pitchers that could be effective
00:39:25
once, maybe two starts in the major leagues? Yeah, cuz they're not used to you. They're not familiar with you. But
00:39:32
over a 162 game season, over 30 starts, everything, especially now in the world of AI, everyone's going to track
00:39:41
everything, everything's going to be motion, they're going to know all the
00:39:44
pitches you throw, they're going to know the locations and the zones you throw.
00:39:48
You would think that might be done in the world of baseball classic also, but maybe not as much as it would be over a
00:39:53
long season with >> No, yeah, I mean I I mean even before AI, I think the hitters had RI, real
00:39:58
intelligence, where they would just kind of keep like like experience the same pitcher many many times and kind of I
00:40:04
think there's a familiarity that even like pre-analytics meant that I think every pitcher coming
00:40:09
into the major leagues would have that kind of curve of their stuff probably seeming unfamiliar at first and then
00:40:14
hitters adjusting to it and the kind of back and forth. I just When you only When you top out at 80 miles per hour,
00:40:19
the sub straight to kind of have that back and forth and keep the hitters guessing, I think is probably limited
00:40:25
over like a 162 game season. That probably is something you can only pull off in a in a in a short tournament. But
00:40:31
it's still an amazing story. The USA right now has, according to the betting
00:40:35
odds, a 50% chance of winning. I'm ignoring the vig a little bit, but they're about a minus 105. Isn't that
00:40:41
nuts? >> That's nuts. Absolutely nuts. I mean, it's sort of like I mean, you posted
00:40:45
some odds too of like kind of like the um kind of the odds for like a Major League
00:40:52
for the World Series The World Series odds going are also nuts. I think it's
00:40:55
like some kind of weird like unfamiliarity bias or something like that. Like the fact that
00:41:01
you know, Japan I I mean, you know, the US has only won like like one out of four of these things that's even been
00:41:06
held. And I don't even know if they're, you know, Japan may be a bit more
00:41:12
kind of well-put-together team. I I'm not even sure like they they they probably should be favored.
00:41:17
But, you know, to kind of give them half the probability is >> Minus 105. Not that that
00:41:21
>> To give them half the probability is disrespectful. It's disrespectful to Japan and to the
00:41:26
Dominican Republic and all the other teams that could kind of in a one-game playoff
00:41:30
>> A one-game? Isn't it one game? Isn't it one Well, yeah, each game each
00:41:35
round is one game. Yeah. Oh, come on. Then Right. Right. I mean, you know, it's like I might make an argument to
00:41:40
the following. The Jets could go on a run. I mean, they're out now, but exaggerate this, but to say
00:41:45
I might put the 2025-24 Los Angeles Dodgers in there, and I'm not sure you should take them against
00:41:53
the rest of the field in a one-game playoff at this round. Maybe. Maybe that would be fine to do so. But just think
00:41:59
about if there's, you know, if there's a quarterfinal round, semifinal round,
00:42:02
final round, they have to be at over 80% probability to win each of those games,
00:42:06
which maybe they are cuz of, you know, But I don't know. Are the Well, that makes a the
00:42:11
I would have the Japan team and half of the American team, you put them together
00:42:14
and you do have the Dodgers. >> is that's what I was going to say. All
00:42:17
right, so never mind. That was my [laughter] question. I mean, one of the things that makes it
00:42:21
tricky is how much of the pitchers are going to go, right? I mean, schemes >> Yeah. and uh schemes
00:42:26
>> pitched like one game against Great Britain and he's out. Yeah, oh well,
00:42:29
that and he's now rethinking it. I mean, the thing is this is all preseason for
00:42:33
the American team and and the Dominican I mean, and it's so it's hard to really know. I'm I
00:42:38
think one of Mine is one of our Maybe the betting odds, but they cannot be the probability. No way. That is sounds like
00:42:45
a betting opportunity in my in my estimate. And I don't usually you know, jump towards identifying
00:42:51
those, but I it really sounds like a betting Let's think about We always talk
00:42:55
about some batters, pitchers get off to slow starts. If we track these players through the first whatever 40, 50 games
00:43:05
of the Major League of season this season. What expectation do you have? Do these people get off to a faster start
00:43:12
than normal? >> That's what I would guess, but do you think that's actually going to be true?
00:43:16
Yeah, but I on the other side of it like you'd also want to track like longevity cuz they're
00:43:20
playing like more intense like, you know, like you know, I mean, Aaron Judge is probably going to have an amazing
00:43:25
season again, but he's he's getting in like very like kind of regular season
00:43:28
at-bats right now and that probably that to me says somebody like him is even more likely to hit the ground running
00:43:34
have like an incredible sort of sprint like May, June. But come September, October like does that, you know, cuz
00:43:42
already wear and tear we can see affecting players when they play up the regular season. Does this have any kind
00:43:49
of influence on that? >> It's always exciting to see all these players come play for different teams,
00:43:53
but I just want to point out It's nice to be able to Aaron Judge cheer for Aaron Judge Yeah, well, Aaron Aaron
00:43:58
Judge of course is is the world's greatest hitter right now, but Bobby Witt Jr. That guy can field. Yeah, and
00:44:05
all that and defense. It's [laughter] incredible. Yeah, yeah. It's like like
00:44:09
Judge, you're like he's the best like you have to I mean, he's the best in the
00:44:13
game, but you know, But Bobby Witt, not all that and defense. >> bad for the guy to have to have to be
00:44:18
playing in the same league and the same prime as Aaron Judge and comes from MVP voting, but it just to
00:44:25
you just really see it. I mean, I have to say some of our earliest work Shane my probably my first paper published
00:44:30
paper in baseball was a paper we wrote years ago. The field and one that's still my favorite paper. Yeah. And and
00:44:36
you just sometimes the eye test I mean, I wish we we we knew like the the probability that ball being and he did
00:44:42
two of them being caught, but it's just so it's remarkable how he how he got to
00:44:47
the ball then managed to sort of leap up in a sort of acrobatic motion. He's on
00:44:52
his feet and he's throwing 90 mile an hour first base. I mean, WHAT? >> [laughter]
00:44:57
>> JUST JUST it's just it's it's such a the the beauty of the baseball is so much in
00:45:03
the in those kinds of moments. And then of course, the walk-off home runs. When you see these sort of these national
00:45:08
teams have a walk-off and they celebrate the way the way they do is just it's just a pure
00:45:14
joy and it's wonderful to participate in. Just to be clear by the way to what
00:45:18
Shane said earlier, I also posted in our rundown the World Series odds and I agree it's equally shocking in the
00:45:26
sense that the next closest We all agree the Dodgers should be the favorite, of course. Of course.
00:45:30
>> But they're five times the odds of every other team. Like that's the gap. That
00:45:37
just can't be it's it's unreasonable. And again, you you see that who who they faced off
00:45:43
against not even in that top seven or eight or six or seven. It's ridiculous.
00:45:47
Those odds. >> Yeah, so that I I just think you know, just some of our fans they're plus 210
00:45:51
at the moment. The Yankees are plus 1,000, the Mariners plus 1,200, the Mets plus 1,300. We're not saying they
00:45:57
shouldn't be the favorite. Maybe even two to one. All right, maybe you could
00:46:00
stretch it and say three to one. Well, that's the thing. I I guess it's almost
00:46:03
like a a meta this season question of like what is the most dominant a baseball team can
00:46:09
be? Like, you know, like, you know, I I I like in in in in in in this day and age. I mean, obviously what the Yankees
00:46:14
did back in the '30s was as dominant as any baseball team could be, but you can no longer be that
00:46:20
dominant. Like like in our modern game, like what's what's the most you'd ever
00:46:24
kind of advantage >> look at this, but I should have. If I went back to 2000 and the Yankees were
00:46:30
going for a three-peat back then, they had already won three of four. What were their preseason betting odds? And it
00:46:37
might have been similar. I And let me just be clear. In terms of their uh gap between them and the second most
00:46:45
favorite team. It'll be It would be interesting to see that. I will take a look and I will post something at W
00:46:50
Money Ball. Um so, let me just say what caught my eye. So, obviously guys, you know I'm a huge tennis fan.
00:46:56
And you know, there's definitely the big two. No doubt about it. I'm starting to think Alcaraz is now one
00:47:03
and Sinner is 1A. This Alcaraz is just incredible. I want to say it a couple of things again.
00:47:12
He's 22 years old. Okay? He's got the same number of majors as John McEnroe won.
00:47:19
That's seven. He's got a career Grand Slam. The youngest to do that. He's currently started the season with
00:47:27
14 straight wins. By the way, the record, if you want to know what the record is, this is the
00:47:32
most impressive thing I've ever heard. It's Djokovic. Take a guess how many
00:47:36
matches to to start the season Djokovic won. In 2011, by the way. 30? 41. He won 41 consecutive matches until
00:47:49
Federer actually beat him at the French. So, he went the entire season until June
00:47:55
without losing a match to start the season. He's won 31 straight matches on outdoor
00:48:02
hard courts. So, the hard court season. Outdoor. Now, Bublik sinner does have an
00:48:06
advantage on inner indoor hard courts. It's unclear why. He's won five straight
00:48:11
titles on outdoor hard courts. He has to be the favorite now of every tournament
00:48:16
he goes into. And I just think, you know, this is something I'm going to talk about as um
00:48:22
Audi knows we're doing a if you'd like a webinar on Friday with a bunch I'm not
00:48:26
going to ruin it for our webinar listeners who might be listening to this as well.
00:48:30
But, I've asked Chat GPT for a prediction interval for Carlos Alcaraz and how many majors he's going to end up
00:48:37
with in a 95% interval and I'll tell you how it does it on Friday. And for those
00:48:40
listeners, we're going to post that on the Wharton podcast network as well. Um let me just say it's hard to imagine
00:48:48
it's hard to come up with any reasonable prediction where he's not at least
00:48:51
greater than Pete Sampras. In other words, the I would say, you know, assuming he stays healthy enough the
00:48:57
lower bound I have with Sampras won 14 majors. He's 22 with seven majors. It's
00:49:03
hard to imagine a scenario where he doesn't I'm not saying he's going to get
00:49:06
to Djokovic 24. I'm not going to say he's going to break his record. That's
00:49:10
still 18 more majors. That's a lot of majors. That's a lot of majors. But,
00:49:15
it's hard to imagine a scenario where he doesn't get to 15 which makes him then
00:49:20
the winningest player of all time except for the big three. And I think that's
00:49:24
his lower And that Yeah, well, I mean, it's not really cuz you have to have put cuz you
00:49:30
you say it's hard to imagine he could have a catastrophic injury. And again,
00:49:33
I'm not wish I would never wish that on anybody. But, that's got to be like a small
00:49:39
probability P multiplying all this. And if he, you know, I mean, if he basically
00:49:45
he could never win again type of thing. And so, I think I think that type of thing I
00:49:49
mean, we can't we tend not to kind of think about those sort of rare events type things and I I don't I don't think
00:49:55
that will happen obviously. I think it's a very low probability but that's the
00:49:58
type of thing worth it. That's the scenario you where you would imagine him not breaking even So so let me let me
00:50:05
respond to that. I think that the the You have to consider it'll fall off quickly. Not all tennis players have a
00:50:11
long careers. Um some of I mean back in the day they fell off really quickly all the time.
00:50:15
>> Audi, he doesn't need one. He doesn't need a long career. He's won all he's
00:50:18
won a half of the last major of the last year. I understand that. eight more is he like age-wise
00:50:26
if you compared him historically would he even be at like kind of the peak? He wouldn't be at the peak.
00:50:31
>> No, 25 is the peak. 25 like 27's the peak for men's tennis. Yeah, but guys
00:50:35
like Borg and McEnroe they were kind of done by 27. They were done by Actually, Borg actually retired at 26. McEnroe
00:50:42
never won a major past the age of 25. Right, so those those career trajectories are possible. Um injury is
00:50:49
possible although I don't know how common that is in in tennis career >> Very
00:50:53
injuries are are not that rare but like career-ending injuries are very rare. I guess
00:51:00
probably injury slash mileage is really what I'm kind of talking about. I think
00:51:03
really what's more more of a the real wild card is who develops to offer him incredible That's a good
00:51:09
question. That's really I mean so the the big three had the big three. They had to go against each other. They were
00:51:15
slightly shifted but Number one, you pointed out Audi why a lot of people right now right now By the way, you guys
00:51:21
remember this maybe don't remember cuz I've been following it. There was the
00:51:24
big two. As a matter of fact, the record for the most consecutive majors between
00:51:29
two players is Federer and Nadal, 11 straight. Sinner and Alcaraz are in now nine
00:51:35
straight major finals just the two of them. That was until Djokovic came along. So look, if it stays the big two
00:51:43
he's got a very strong chance of winning the most majors. >> Yes. Cuz if it comes to big three, that
00:51:50
changes or four, that changes the math entirely. Mhm. That's all. That's all I was going to
00:51:56
say, you know. >> think that's the biggest wild card in terms of prediction in the future. Among
00:52:01
them all the wild cards we've talked about, that's the biggest uncertainty,
00:52:03
whether he'll have to deal with two, one, or no can can challenge. >> fascinating sport because I mean, Eric,
00:52:10
for what how old were you like what what how how much of your life have you not convinced yourselves that you're
00:52:18
watching the greatest tennis player of all time? Like like for almost the entire adult life
00:52:24
you probably were watching somebody and you're like, "That's the greatest tennis
00:52:27
player of all time." >> Federer to like Well, I'll even go before
00:52:31
>> Nadal to Djokovic, maybe somebody even before Federer. Yeah, it was never in
00:52:35
the Borg, McEnroe, Connors era, Lendl because they were all I mean, Borg was better
00:52:41
but they all beat each other enough that it it's clear Borg was slightly the
00:52:47
best, but they each won seven, nine, 10, 11 majors. So, there was a there was a great strength there. Yeah, so I guess
00:52:54
it's since Federer basically, right? Well, then Sampras came along and Sampras was just better. I mean, Agassi
00:53:01
was a close second and they they you know, Agassi and and Sampras didn't have
00:53:05
that unbalanced a record against each other, but Sampras was just the best. He was the best of his generation. And then
00:53:14
of course there was the big three and that's been the rest of my adult life.
00:53:19
>> know, it's just fascinating that it's been like, you know, that's like a
00:53:21
30-year period or something like that where at the time you thought you were watching the greatest
00:53:28
tennis player of all time. Yeah. No, I agree. No, no, I'm I agree with Audi though. I think if he doesn't get
00:53:35
to, let's say, better than Sampras, 15 plus, the most likely reason is two or
00:53:41
three other players come up and then he wins his share but he wins one of four, one of five. So maybe of the next 30
00:53:47
majors he wins five or six which is no slouch. That's nothing to be ashamed of
00:53:51
but he's 30 and he wakes up and he's got 12 majors not 18 to 20 majors and that
00:53:56
could absolutely happen. So guys maybe one last thing I wanted to talk about um something's happened in golf the last
00:54:04
couple weeks and I just want to put it into comparison. So Shane Lowry and I know his last name is Berger maybe
00:54:12
Justin Berger you know they've lost the last two tournaments in golf leading three strokes with three to
00:54:19
play. Now I the reason I like to point this out besides that's hard to do as a pro.
00:54:28
Tiger Woods is 51 and two leading not with three to play the whole fourth round to play.
00:54:38
And so these seem like extraordinarily rare events that we've seen the last two
00:54:44
weeks where essentially it's been a collapse by the leader going into the last couple holes and
00:54:50
literally it wasn't that the other guys eagled and birdied. Both these guys made
00:54:54
double bogeys, bogey like they literally played the last three holes in plus three and the other person played it in
00:54:59
minus one where you know it's the old expression you know Ben Hogan said yeah
00:55:03
all I got to do is win the Masters is par the last 11 holes. Let's see you par
00:55:06
the last 11 holes on the Masters. It's not that simple but this wasn't that
00:55:11
hard. So I would just want to point out Yeah. that I think we're seeing something in golf where some players
00:55:19
just you know let's not take for granted players that just win at this incredible
00:55:25
pace leading into the final round. It's not that simple. >> Oh no I I feel like that's one of the
00:55:30
most unusual ways and I mean Tiger was unusual in a lot of different ways but I think that was one of his most unusual
00:55:35
things is that kind of like lack of like, you know, not even not even a random amount of collapses or whatever
00:55:41
in the last round. I think that's really kind of what you're talking about. I
00:55:43
don't know if they do any kind of kind of collapse metric where it's like cuz
00:55:47
you got you know you can imagine like some stati- like kind of like, you know, like how often
00:55:53
is how often do how often does it happen that a a golfer drops three shots in three holes straight? I mean, that
00:56:00
probably the you know, all happens kind of throughout the week like >> think I think what what what Shane is
00:56:06
asking for is a baseline. Yeah, yeah, like like what you know, how Like obviously we treat ones at the end
00:56:12
of the last round kind of specially because they usually have more consequence or at least we notice if
00:56:16
they have more consequence. But like I kind of wonder what the baseline rate of that kind And I guess the the other
00:56:21
question is not only the baseline, but what you point out, Eric, is it's happening because the the the uh the
00:56:27
player in the lead blows it. Uh I wonder whether how often it it happens because
00:56:31
of or some combination thereof. They blow it and they do really well. Those are great questions. Um but that just
00:56:36
does lead to my question, my I guess my final thought here, um how um in which sports does psychology
00:56:44
really play an important role? Um cracking under pressure. We know we see it in penalty kicks in soccer in soccer.
00:56:52
And we I've seen it at second base in Yankee Stadium enough times. >> [laughter]
00:56:56
>> You know, where they get the yips, right? And they can't throw anymore. Uh
00:57:00
we've seen that happen. But how and Not to be up there. Golf golf I think what I'm thinking about sports
00:57:06
that have a lot of it, it's kind of ones that almost do have that pause and action where you can kind of have you
00:57:12
know, like baseball I think is you know, the ideal substrate for cuz you have very high leverage leverage
00:57:16
events and there's also like the you like you really get in your own head because you know, there's like this long
00:57:22
stop and action between kind of Yeah, the only thing opportunities. I agree, Shane. The only thing I would add to
00:57:27
what Audi said is that let's remember though the two players I'm talking about
00:57:33
were It's Daniel Berger, by the way. The The two players I'm talking about were
00:57:38
the best players for 69 holes. So, that's the other thing you have to condition on. You can't just look at any
00:57:44
three-hole stretch and say, "Well, the guy's plus three." That happens all the
00:57:47
time in golf. Yeah, but these were the best players for 69 holes. And then all of a sudden, the last three? So, that's
00:57:54
my point. >> No, and I I I would I would the base rate would be to sort of see if there's sort of like,
00:57:59
you know, if you kind of had the base rate of like any three-hole window, and then you start looking at, "Oh, well,
00:58:05
what happens now if we condition on Is there a different kind of rate for that last day or that last afternoon?" That I
00:58:12
think That would be the kind of interesting comparison. Well, guys, it's been college basketball to in World
00:58:17
Baseball Classic. We talked some tennis. We talked some golf. But, this is what we do on Wharton Moneyball. So, on
00:58:22
behalf of myself, my colleague and friend Adi Wyner, my colleague and friend Shane Jensen, some combination of
00:58:27
the three of us and Tae Massie here every week on Wharton Moneyball. On behalf of our sound engineer and
00:58:32
producer today, Aaron Tran, on behalf of Dee Patel and Marissa Reno, we'd like to
00:58:37
thank you for joining us here on the Wharton Podcast Network. Between now and next week, enjoy your sports, enjoy your
00:58:42
statistics. We'll see you next week here on Wharton Moneyball.