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How the NFL Uses Data to Shape Rules and Create New Metrics

February 06, 2026 / 01:00:07

This episode of Wharton Moneyball features a discussion with Mike Lopez, senior director of football data and analytics at the NFL. Topics include the Big Data Bowl, player tracking data, and the impact of analytics on football.

Mike Lopez shares his experience with the Big Data Bowl, a competition that encourages innovative uses of football data. He explains how it has influenced both academic and practical applications in football analytics.

The conversation also covers the NFL's approach to player movement prediction and how data is used to enhance game strategies. Lopez highlights the importance of understanding player dynamics during plays.

Additionally, the episode discusses recent rule changes in the NFL, including kickoff rules and their implications for player safety and game competitiveness. Lopez shares insights on how data analytics informs these decisions.

Finally, the hosts and Lopez preview the upcoming Super Bowl, discussing team dynamics and the significance of quarterback performance in the context of the game.

TL;DR

Mike Lopez discusses NFL analytics, the Big Data Bowl, and player movement prediction in this episode of Wharton Moneyball.

Episode

1:00:07

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Welcome, welcome everybody to the

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podcast edition of Wharton Moneyball.

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This is Eric Bradlo, professor of

00:00:06

marketing, statistics, and data science

00:00:07

here at the Wharton School. Some

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combination of myself, my co-host who is

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here today, professor of statistics and

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data science Audi Winer, Kade Massie and

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Shane Jensen are here every week on

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Wharton Moneyball. Audi, first, it's

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great to see you. It's always great to

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be back with the show. 11. Oh, actually

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we're approaching 12 years of the show

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now. But one of my favorite parts of the

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show, I know it is yours, is when we get

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to interview people that actually apply

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statistics and data science in the real

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world. And today, not only is today no

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exception, but probably someone that has

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impacted you'll talk about this audi I'm

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sure, which you can talk about the big

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beta data bowl and how it's impacted you

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and your ability to train our students.

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But we're honored to be joined by

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Michael goes by Mike Lopez. Mike's the

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senior director of football data and

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analytics at the National Football

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League. Longtime friend of ours here at

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Wharton Moneyball. I actually think

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Audi, it's important. A lot of times I

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don't need to read someone's bio, but I

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think his bio is so impressive because

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of his ability to straddle academia and

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practice. I think it's important for our

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listeners to know you can have your cake

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and eat it too. So I'll say at the

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National Football League, his work

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centers on how to use data to enhance

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and better understand the game of

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football. Academically, his research is

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split between causal inference with a

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specific focus on causal inference

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methods for multiple exposures and not

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surprisingly application of statistics

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to sports. He's an associate editor of

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one of our lead journals, Journal of

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Quantitative Analysis and Sports. And

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then in the more practical side, he's

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written for 538, Sports Illustrated,

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Hockey News. Uh from 2014 to 2021, he

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worked Skidmore College first as an

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assistant professor, then as a lecturer

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and a research associate. And in 2020,

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he was named the American Statistical

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Associ statistics and sports significant

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contributor award. I think we could name

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Mike Lopez that each and every year. So

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Mike, first of all, welcome back to

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Wharton Moneyball.

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>> Thanks so much for having me. Really

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appreciate it. And that was totally

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unnecessary. Um, and

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couple of those publications are out of

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business now. So maybe I didn't do such

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a good job, but

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>> I don't know about that. 538 still in

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business. Well, sort of. Not really. No,

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I think

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>> not really. Not really. Yeah. Audi, why

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don't we start with this? Um, it's not

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on the in the list of topics that Mike

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helped to provide us, it's listed as the

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last one, but since, as you know, the

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big data bowl meant so much to my son

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and my family. Um, could you start Audi

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and just talk about what the big data

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bowl has meant for you and then it would

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be appropriate for you to ask the

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question for Mike about the 2026 one and

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what's going and the impact it's had and

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maybe tell people what it is.

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>> Okay. So I mean uh I can from our

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perspective the big data bowl you

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started it as when when Eric when your

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son Zach was a senior it was the first

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year and we weren't even a a wasabi

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hadn't even existed yet um and its

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kernel existed as a seminar that I was

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running every Tuesday evening Tuesday

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afternoon with just my great students

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who are interested in statistics and

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sports and that included Zach uh Andrew

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Castle um and some other students and

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when you had the dig data bowl they just

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jumped on it without even I didn't even

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know about it. I don't think it it it

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hit my radar. The students found out

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about it much faster than I did. And I

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remember but you the timeline was

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terrible. It was like right around

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finals week. I mean, I don't know what

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you did, but you you set it up so that

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it was right during their finals, yet

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they managed to to put together a

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winning entry. Um and uh that and it was

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a a terrific opportunity and we we've

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jumped on it and and not every year. It

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depends on what the question is. Um and

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uh and actually for us it really depends

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on how statistical it is as opposed to

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how almost uh maybe AI the question is

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or how the goal is to learn something

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about football from a statistics lens or

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maybe you're trying to do forecasting.

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So we don't always participate but we've

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had many many teams uh be finalists and

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honorable mentions and of course even

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many students who did Moneyball Academy

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with me. They went on to do Moneyball I

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mean the big data ball with their

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different schools uh that they went to

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and we've had many winners who who are

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not at Penn but also but did Moneyball

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Academy. So it's a hugely important um

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uh um enterprise and we we're delighted

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Mike to have you here and and for the

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invention of the contest.

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>> Can Yeah. Can you tell us about it and

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just tell us what made you guys started

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at the NFL? Could you tell us about the

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2026 version and we'd love to just hear,

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you know, the impact it's had on you

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personally and the NFL?

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>> Yeah, I mean in in the Field of Dreams,

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it's if you build it, they will come,

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right? In in football, it's if you have

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the data and share it, they will analyze

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it. And that's been the motto. Like we

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when I started in 2018, it was the first

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year that the teams had the player

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tracking data. And when you're going

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from traditional game stats in

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play-by-play, which has maybe 160 rows

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per game to this massive data set that

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has 300, 400,000 rows per game, um, you

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just don't quite know what to do with

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it. Uh, and traditional sports

00:04:55

hackathons up till that point had been

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sort of one night, two night sprints.

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And I mean, I had the player tracking

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data for four months on my computer and

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I couldn't get anything out of it,

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right? So, you know, what are we going

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to learn in a sprint and a sort of a one

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night or twoight thing? Probably not

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much that's usable by teams. Um, so we

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really needed four, five, six weeks at

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least. Um, apologies for putting it in

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finals week. It was a it was one of

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those things that came together. I I I'm

00:05:22

still in retrospect um wouldn't say

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lucky, but it's it's pretty amazing that

00:05:26

we sort of were able to build that um

00:05:28

without too much uh sort of um push back

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from the league. Um the league wanted

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innovation. They wanted new ideas. the

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clubs wanted it. They they sort of

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valued this experience. Um Jay Reed and

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I Jay's currently at MLS. You know, when

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we started asking the right questions at

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the league office, we we didn't get a

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lot of push back. Um the the team was

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teams were interested in it and we could

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certainly sell this as something that

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was going to help teams help the league

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office in terms of thinking about the

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rules and sort of modern metrics. Um and

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then also helping NextGen Stats. Almost

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every collaboration we have um for a

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topic comes with NextGen Stats. And and

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that sort of leads me to this year's

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topic. Uh where we're really thinking

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about predicting player movement. You

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think about when a quarterback drops

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back to pass. A lot of what we want to

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be able to do in terms of thinking about

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who that quarterback should throw to is

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thinking about if they throw to a

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certain receiver, what will happen? You

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really need to know how the defenders

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are going to move if the ball is thrown

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to a certain location in order to make

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those assessments. So it started with

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that reality and then sort of worked

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backwards. Okay, maybe we don't focus on

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the quarterback. We can do that a lot.

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Let's just focus on what the defenders

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do. What's their movement like when the

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ball's in the air? Who covers more

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space? Um, should you even be going for

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where the ball's landing or should you

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be going to try to figure out where

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you're going to tackle the player once

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the ball's caught? Um, so a lot of fun

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questions out of issues of competition,

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which is is not really surprising given

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the the effort and and the sort of

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quality of folks that are participating

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at this point. Can I ask you um so all

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these submissions come in I understand

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students there are you know winners

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finalist honorable mention kind of

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things like who gets to see the output

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of this like besides you and maybe some

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people that you're like there's some

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ones that are maybe so extraordinary you

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want to make sure it's shared throughout

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like the National Football League

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offices but like do the teams get to see

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the results of these and like if you

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could since you know we're also besides

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you know we are a business school um has

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stuff come out of the NFL big data bowl

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that you think has either changed your

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thinking, the NFL's thinking, or like,

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wow, teams are now doing something

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different. And who would have thought?

00:07:26

It just, you know, these students or I

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know there's a student division, an open

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division, but like these people don't

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even work full-time and they did

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something that kind of moved the needle

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a little little bit in the sport we all

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loved.

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>> Yeah. Yeah, I mean the NFC championship

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game back in 2020, I was sitting, you

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know, having chili or whatever, just

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like everybody else, and suddenly on the

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Leonard Fornette ran for a touchdown,

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and they put up a a thing like yards

00:07:48

over expectation, Leonard Fornette plus

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17 or plus 19, whatever the number was.

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And I'm like that that's the big data

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bowl, right? Like that we turn from the

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competition into a new metric for our um

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you know, our NGS team. And then not

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only that, they turned into sort of a

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real time way to look at running back

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effectiveness on a given play, which is

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then shared with our network partners.

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Um, we are over 20 metrics from the big

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data bowl that have been uh sort of used

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or sort of put into the NextGen stats

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ecosystem. Um, that is then shared by

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clubs. Um, as always in professional

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sports, clubs aren't exactly reaching

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out to say, "Hey, I'm using this and

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it's really helped." Um, because they

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don't want to give any type of edge that

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they found. Um but undoubtedly that it's

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being used on the club side. Um on the

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league side, I think with each passing

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year, we're looking at the tracking data

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as being able to replicate a lot of what

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exists in the football beta lexicon. Um

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it's we're able to take the things that

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scouts have been doing for years um

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turned into metrics and do it, you know,

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sort of at scale uh in ways that can

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speed up time, too. So we've used a lot

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of it for our analysis of rules changes,

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looking at the space and the speed of

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the players in the kickoff, for example,

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last year. Um and and obviously our job

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is to make the game better. We're

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pre-player agnostic, but you know, we

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also understand that our teams are using

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it, too.

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>> Um I skipped by the entire 2025 season

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and one of that's one of the things we

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wanted to talk to you about. Um we'll

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talk about the Super Bowl in just a

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second, but you you mentioned for

00:09:15

example the kickoff changes. You

00:09:17

mentioned player speed is now an issue.

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um what kind of things from uh whether

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it's metrics or now using player

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tracking data are like what are the big

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forget what the solutions are yet what

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are the questions you guys are now

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answering at the NFL I know for a large

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number of years and this will never go

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away player safety I know is at the top

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of everybody's concern and that's I

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don't imagine that's ever going to

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change but could you give us a broad

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sense of the topics to which you know

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when they come to your group the data

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and analytics group what are the kind of

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questions they're asking you guys to

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think about.

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>> So I I would say broadly and it's not

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always just us answering questions too.

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A lot of it's sort of understanding what

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data is out there and what questions

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that we know that they're sort of

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thinking in the back of their heads that

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we can use data to answer for too. Um,

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and that's kind of the fun part about

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our group is there's a lot of room for

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creativity uh to sort of think think a

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little bit.

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>> You let people know, by the way, how big

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a group are we talking about and what

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are the backgrounds of people because a

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lot of people that are listening to our

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show might say, "Huh, you know, I wonder

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when the next time Mike Lopez is going

00:10:19

to post a job at the NFL, like how group

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are we, how big a group are we talking

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about? What kind of degrees do people

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tend to have, etc." It' be good to hear

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that, too, and your thoughts about what

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kind of creativity and things you're

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able to do.

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>> Yeah. So I mean I I work on our football

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data and analytics group and at when I

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started I think I was the first person

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at the league office to code in R in any

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group anywhere. Um when I had to

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download R like it gave me a hard time

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because I don't think they knew what it

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was. Um we we are now a lot more modern.

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Um our group right now is somewhere

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between 8 and 10 depending on the time

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of year. Um that's on the football data

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and analytics side. Uh we're close with

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our health and safety group on the

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analytic side. Uh we're close with our

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broadcast um and sort of network groups.

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Um we are in the same larger data and

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analytics group as sort of a fan

00:11:03

international um sponsorship. There's

00:11:06

sort of other data and analytics groups

00:11:08

that we're we're sort of also um uh sort

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of collaborating with at different

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points. Um our job is to make the game

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better. Um you know some some of our

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folks on our team have PhDs. Some have

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um you know are really good football

00:11:21

wizards and can code a little bit in R

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Python. Um but largely we're looking at

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the competitiveness of the games. Uh the

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officiating of the games, health and

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safety, pace of play and replay is a big

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one. Uh using modern using technology uh

00:11:35

to improve the game. Um so those are the

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the the areas that we're focused on. Uh

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obviously a lot of the increases in

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accessibility of data, whether it's the

00:11:45

player tracking data that we've had now

00:11:46

the last eight years, um inevitably

00:11:49

we're going to be getting player

00:11:50

skeletal pose data, trying to think

00:11:52

about that, how to use that for either

00:11:54

improving the game, um improving the

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officiating, um thinking about how to

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use to recognize certain objective

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aspects of penalties, for example. Um

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that's the the areas that falls under

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our bucket and with each passing year,

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our group's doing more and more and

00:12:07

having more of an impact. So it's a lot

00:12:08

of fun. Um,

00:12:09

>> go ahead, please.

00:12:10

>> The, uh, the new kickoff rules, how much

00:12:12

of a role did your team play with that?

00:12:14

Did you try to forecast, for example,

00:12:16

what the impact would have been on total

00:12:17

scoring, on on strategy? Um, it just

00:12:21

seems like a totally different game in

00:12:23

some in many aspects. I mean, the the

00:12:25

kickoff and what was your role in that?

00:12:27

How do you think it went? Yeah, I mean

00:12:29

in fact like a couple weeks ago I looked

00:12:31

at our um projection from last year

00:12:34

where we projected a lot of our key

00:12:35

metrics and I sort of put a check or a

00:12:37

check minus as to whether or not we got

00:12:39

it right or wrong. Um yeah, I mean our

00:12:41

group was in charge of projecting the

00:12:43

return rate um and then conditional off

00:12:46

that what are all the things that are

00:12:47

going to happen based off that return

00:12:48

rate. Um we built a drive simulation

00:12:50

model which sort of simulated drives. We

00:12:52

wo that up into a game simulation model

00:12:54

which then simulated games. Um trying to

00:12:56

estimate the sort of overall change you

00:12:58

know holding all else equal and assuming

00:13:00

that the typical trends from 24 we're

00:13:02

going to hold again in 25. You know what

00:13:05

would we expect for scoring what we'd

00:13:06

expect for um you know we anticipated

00:13:08

that we were going to drop about 100

00:13:10

punts and sure enough we dropped about

00:13:12

100 punts. Um we anticipated we'd have a

00:13:15

little bit more scoring than we had. Um

00:13:16

but some other other things I think sort

00:13:18

of are responsible for that. um impact

00:13:21

on the total uh sort of we'll call them

00:13:24

action plays where it's not just total

00:13:26

play count which is typically what the

00:13:28

league looks at but how much action are

00:13:29

we returning to the game return yards um

00:13:32

obviously you can take a lot of those

00:13:33

things and then turn them into projected

00:13:34

injuries and things like that. So um you

00:13:37

know working with the the folks in in in

00:13:39

the the various leagues or league office

00:13:41

to to sort of come up with a

00:13:43

encompassing reflection of what that

00:13:45

impact would be. So, I have two

00:13:47

follow-up questions to Audi's questions,

00:13:48

but it's on the same basic topic, but

00:13:50

it's something Mike you just mentioned.

00:13:52

Um, you know, since Audi and I are

00:13:53

exactly the same age, graduated the same

00:13:55

time, we're, you know, in our days, you

00:13:57

had to know what I'll call mass stat,

00:13:59

you had to build, you know, let's say

00:14:01

probabilistic models, etc. Now, a lot

00:14:05

can be done by simulation. So how much

00:14:08

of the I'm just interested how much of

00:14:10

what you do is simulationbased

00:14:13

and in some sense you know let's let the

00:14:15

power of computing you know handle the

00:14:18

complex in some sense I'm not saying

00:14:20

math's not important math's always

00:14:22

important but if we can just simulate

00:14:24

because of massive computing power you

00:14:27

know bazillions of outcomes let's let

00:14:29

that decide things

00:14:31

>> yeah I mean in fact the two most recent

00:14:33

rules changes that our group has had an

00:14:36

impact on uh the kickoff being one of

00:14:38

them. And then the other one was

00:14:39

overtime. I mean, our overtime model,

00:14:41

there's there's no empirical data to

00:14:44

measure against. Um so what we did is we

00:14:45

took play data, we simulated drives, we

00:14:48

simulated what what the overtime would

00:14:49

look like. Um and then we got our

00:14:51

estimated impact of the the the sort of

00:14:53

new overtime rule. So, um I wouldn't say

00:14:56

we're always simulating, but it it it's

00:14:58

certainly a tool that we'll want and

00:15:00

depending on the question that we're

00:15:01

after. Um you know, it's it's it's

00:15:03

certainly important to to be able to

00:15:05

use.

00:15:06

>> The next question is kind of a selfish

00:15:08

question for my own knowledge and how

00:15:10

you think about this only because I was

00:15:11

just teaching it today to my MBA

00:15:13

students. I'll call it a and for our

00:15:16

listeners it'll become clear what I

00:15:17

mean, which is a multi-attribute

00:15:20

objective function. And what I mean by

00:15:21

that is you change the kickoff rule.

00:15:24

Okay, so certain things get better,

00:15:26

maybe certain things get worse. So

00:15:28

there's multiple metrics or attributes

00:15:30

you care about. And of course, any real

00:15:33

decision maker has to decide how to

00:15:35

trade off these different things. So I'm

00:15:37

not asking you for the secret decision-m

00:15:39

of how the NFL does that, but I'm just

00:15:41

asking you more broadly. I would imagine

00:15:43

any rule changes you think of, they're

00:15:46

not paro dominant in the sense that they

00:15:48

improve every single metric the NFL

00:15:50

would ever want or the teams would want.

00:15:53

I'm just interested how you approach

00:15:54

those kind of problems from a leadership

00:15:56

perspective and thinking about it.

00:15:58

>> Yeah, I mean that is exactly how we

00:16:00

approach it, right? Like so when we did

00:16:01

overtime, we had a scale and depending

00:16:04

on where you put your thumb on the scale

00:16:06

was going to pick your favorite overtime

00:16:07

format, right? Do you want equity of the

00:16:10

coin toss? Do you want the overtime to

00:16:12

end fast? Do you want it to be

00:16:14

explainable to fans quickly? Um do you

00:16:16

want it to have um uh sort of a

00:16:20

traditional kickoff and punt play?

00:16:22

Right. So depending on where you were

00:16:23

going to put your thumb on the scale

00:16:24

there, that was going to pick your

00:16:25

favorite overtime format. Um we changed

00:16:27

overtime because we wanted to sort of

00:16:30

increase the importance the decrease the

00:16:32

importance of the coin toss. We changed

00:16:34

the kickoff because we wanted um you

00:16:36

know, we found a play that had an injury

00:16:37

rate that was more in line with runs and

00:16:39

passes and as a result we wanted more

00:16:41

competitiveness. Um, so yeah, I mean I

00:16:43

think our job is to be objective and to

00:16:45

show the sort of full impact of of a

00:16:48

potential change um or modification and

00:16:50

ultimately we're after rules that will

00:16:52

benefit, you know, all five or six of

00:16:54

the elements that we're after and and

00:16:56

those are a little bit unique and and

00:16:58

there's not always easy solutions to

00:17:00

those, but that's uh ultimately what

00:17:01

we're trying to do for the game.

00:17:03

>> Well, let's talk now about the upcoming

00:17:05

game in I guess uh whatever five days,

00:17:07

the Super Bowl coming up. I'll just

00:17:09

repeat and then I'd love your thoughts

00:17:10

on this. So, we had Aaron Shatz, someone

00:17:12

I'm sure you know of and know well, uh,

00:17:14

we had him on during the season and his

00:17:16

comment was that Seattle and the Rams,

00:17:19

by every, you know, metric that he's

00:17:21

ever created, which is a lot, that these

00:17:23

were historically great teams. Like,

00:17:25

measuring in the last 50 years, like

00:17:27

Seattle and the Rams the were in the top

00:17:30

10. And then he also commented that of

00:17:32

course New England had like the third

00:17:33

easiest schedule since 1978. So, in your

00:17:37

mind, like how do you guys think about

00:17:40

the Super Bowl upcoming? Um, how do you

00:17:43

think about the teams? And, um, yeah,

00:17:47

how does the NFL think about the game in

00:17:48

general and how do you think about these

00:17:50

two teams?

00:17:51

>> Well, it's it's funny like all the

00:17:52

things that Aaron said, we don't do like

00:17:54

we don't exactly care who our best teams

00:17:57

are. Um, we don't exactly care. we do

00:17:59

care a lot about strength of schedule

00:18:01

and sort of balancing and sort of

00:18:02

understanding the impact of our schedule

00:18:04

but within a season um you know there's

00:18:06

not much we're going to be able to do

00:18:07

and so as a result there's not a ton of

00:18:09

value in it. Um I I do think it's

00:18:12

interesting this year set an NFL record

00:18:14

with I have 1,089 quarterback scrambles.

00:18:18

So that means of every NFL season in our

00:18:20

history more quarterbacks scrambled this

00:18:22

year than ever before. Um and I have New

00:18:24

England number two in scramble rate. Um,

00:18:27

so I think the the interesting part from

00:18:29

our angle in terms of the some of the

00:18:31

long-term trends of the game is that um,

00:18:33

New England has has typically made its

00:18:36

uh, impact. Um, Drake May in the AFC

00:18:39

title game was no different. Throwing

00:18:40

the ball, his numbers weren't great, but

00:18:42

his impact was running the ball. Um,

00:18:44

curious where that nets out. I know

00:18:45

Seattle plays maybe the second highest

00:18:47

zone rate in the league. Um, so we're

00:18:49

we're sort of a little bit more agnostic

00:18:51

in terms of, you know, caring which team

00:18:53

exactly is going to win. Um, obviously

00:18:55

we have to deal at the league office.

00:18:57

Sure.

00:18:57

>> Um, but I do think that, uh, the New

00:18:59

England quarterback is somewhat

00:19:00

reflective of the trends and sort of

00:19:02

where the game is going. An athletic

00:19:04

quarterback that can do a lot and when

00:19:06

he doesn't see anything downfield, isn't

00:19:07

afraid to run.

00:19:08

>> Certainly, before I get to, it's

00:19:10

certainly an interesting matchup because

00:19:11

I, you correct me if I'm wrong. I would

00:19:14

assume, but maybe I'm I have pretty good

00:19:16

knowledge of football. Um, a zonebased

00:19:18

team could be more susceptible to a

00:19:22

running quarterback. Is that in general

00:19:23

the thought? Just to make sure I'm

00:19:25

clear, I

00:19:26

>> I think the idea is that they'll prevent

00:19:28

big scrambles because they're in the

00:19:30

zone. If you go man coverage and your

00:19:31

players are staying on their players,

00:19:33

conceivably you open up big gaps,

00:19:35

>> but you could also assign somebody to

00:19:36

the quarterback, in which case maybe

00:19:38

you'll stop them right at the line of

00:19:39

scrimmage. Um, so my my sense is that

00:19:41

there'll be three, eight, eight, 10 yard

00:19:42

chunks there for Drake, but maybe not

00:19:44

the big ones. Um, but but not I'm not

00:19:47

obviously a prognosticator there.

00:19:49

>> Please, Audi. Okay. So, um this year's

00:19:51

Super Bowl, um does not feature really

00:19:54

any of the top teams that were thought

00:19:56

to be the top teams in the preseason.

00:19:58

None of them. I mean, of course, we

00:19:59

regret the the Eagles are not there, but

00:20:02

none of the top teams. Uh in fact, each

00:20:04

of them had less less than 4%

00:20:06

probability of being in the Super Bowl,

00:20:08

let alone winning it. Yet, here they

00:20:10

are. So, I just ran a quick simulation.

00:20:14

Turns out that um

00:20:16

one out of every 20 years you'll get two

00:20:18

long shots in the Super Bowl. at least

00:20:19

if you trust the preseason's odds and

00:20:21

let them run.

00:20:22

>> Um that there's any of the these two

00:20:24

specific teams is actually much smaller,

00:20:26

but that's not the right the right

00:20:28

question. So I'm going to ask somewhat

00:20:29

of a causal inference kind of question

00:20:31

to you without data. Um is there

00:20:33

anything about the way the season played

00:20:35

out or anything about the way the

00:20:38

forecasts were made preseason that

00:20:40

suggests that we were just not getting

00:20:41

it? Um and that these teams, which in

00:20:44

particular Seattle, but certainly New

00:20:45

England as well, turned out to be

00:20:47

terrific and we missed that. Is there

00:20:48

anything about the way the game has

00:20:50

changed, the u you described scrambling,

00:20:52

potentially other outcomes that have

00:20:54

made the made the the models kind of off

00:20:57

their their calibration early on and

00:21:00

that in fact it took a long time before

00:21:02

people decided that New England was any

00:21:04

good maybe because they just couldn't

00:21:05

get over Tom Brady was not leading them

00:21:07

or some sort sort of narrative. But I'm

00:21:09

very curious to know that people think

00:21:11

deeply about the game in terms of

00:21:12

structurally what you think of the fact

00:21:14

that we have two preseason long shots

00:21:16

playing each other.

00:21:17

It's certainly a trend that we at the

00:21:19

league office would be wanting to watch.

00:21:21

Um, you know, we've done a lot of stuff

00:21:23

with the equity of our schedule, right?

00:21:24

We have three standings based games

00:21:26

after the year. Um, where one seeds play

00:21:29

uh, sorry, one division number one

00:21:31

division winners play other number one

00:21:32

division winners. Um, and so

00:21:34

understanding the impact of that, I

00:21:36

believe both teams this year played the

00:21:37

NFC South, which wasn't exactly our

00:21:39

strongest division. Um, so that might

00:21:41

have played into their records, which

00:21:43

then obviously played into Seattle

00:21:45

getting the number one seed, New England

00:21:46

getting the number two seed, um, and the

00:21:48

strength of schedule thing that I know

00:21:50

Aaron's talked about. Um, we've, and

00:21:52

part of this is stuff I had done before

00:21:54

the NFL, too. But we've compared our our

00:21:56

sort of playoff format and structure to

00:21:58

other leagues and have a good sense that

00:22:00

it's a single elimination tournament. A

00:22:02

lot can happen. Um, these two teams put

00:22:04

themselves in pretty good position based

00:22:06

on the regular season. So, I don't think

00:22:08

anything necessarily that happened in

00:22:09

the postseason is that surprising. Um,

00:22:11

but to get to this point in the regular

00:22:13

season, um, you know, we've we've

00:22:15

tracked quarterback injuries and I do

00:22:17

think based on the reality that

00:22:18

quarterbacks are scrambling more often

00:22:20

and asked to I mean, we had um, our

00:22:24

quarterbacks held on to the ball an

00:22:25

average of 2.7 seconds per drop back.

00:22:28

Um, that's it doesn't sound like a lot,

00:22:31

but that's 04 more than a year before

00:22:32

that. And it's

00:22:35

20 more than it was a decade ago. So

00:22:38

0.20 times 20,000 dropbacks in a season.

00:22:41

You can do the math on how much longer

00:22:43

quarterbacks are holding on to the ball.

00:22:45

Um I don't think it's as much just the

00:22:47

quarterback wanting to hold the ball and

00:22:49

wait, wait, wait. Obviously defenses are

00:22:50

in zone. They're further back, different

00:22:52

personnel. There's just not as many

00:22:54

people open. Um so conceivably um you

00:22:57

know if you put the extra emphasis on

00:22:59

the quarterback position you know with

00:23:01

certainly the the trends of their

00:23:02

availability is something that we at the

00:23:04

league office would be wanting to follow

00:23:06

and adding noise to that right is only

00:23:08

going to um sort of increase the

00:23:10

variance in terms of who moves on. I

00:23:12

think Audi brings up Audi's point also

00:23:14

brings up an interesting question from a

00:23:16

league office point of view which is

00:23:17

let's imagine it happens for a

00:23:20

significant number of years that one or

00:23:22

both teams in the Super Bowl were you

00:23:24

know not preseason favorites. Now one is

00:23:26

the NFL could say this is a good thing

00:23:28

you know in some sense this is parody

00:23:30

this is lots of you know it's there's no

00:23:31

longer going to be the years of

00:23:33

dynasties that could be good. Um,

00:23:35

another thing you could say is, and

00:23:37

maybe this is implication, I'd love to

00:23:38

hear your thoughts too, Audi, on this.

00:23:40

Maybe our preseason models need

00:23:41

updating, and that's not the NFL's

00:23:43

business. Like, you know, it could be

00:23:44

just the models aren't great. The other

00:23:46

could be the NFL says, you know what,

00:23:48

maybe we red need to redesign the

00:23:51

postseason in some way to make it so

00:23:54

that, you know, I'll make it up, the

00:23:56

it's not as easy for someone to sneak in

00:23:59

as the seven seed, although that's not

00:24:00

what happened this year. and it's not as

00:24:02

easy for that team to make its way

00:24:04

through and make it all the way to the

00:24:06

Super Bowl. So, let me start with you,

00:24:07

Mike, and then Audi. How do you think

00:24:09

about, you know, could this be is this

00:24:12

an indictment on preseason models or

00:24:13

does it have implications for how the

00:24:15

NFL thinks about designing a postseason

00:24:17

tournament?

00:24:18

>> I do believe we are the only league that

00:24:22

after a season takes the teams that

00:24:24

finish towards the top and gives them a

00:24:25

harder schedule than the teams that

00:24:26

finish towards the bottom. I mean, most

00:24:28

of the professional leagues at this

00:24:29

point reward the teams that finish

00:24:31

poorly with the higher draft picks. Um,

00:24:33

we also reward them with easier

00:24:34

schedules. Um, and I think the longer

00:24:37

that this would play out, I do know this

00:24:39

is something the league cares a lot

00:24:40

about and the clubs particularly care a

00:24:42

lot about. Last year, we we tried to

00:24:44

push a um we found like there were a lot

00:24:47

of positives about a proposal from

00:24:49

Detroit that would have seated the

00:24:50

postseason teams based on winning

00:24:52

percentage. um the clubs pointed out

00:24:54

that it's not necessarily fair to

00:24:56

compare winning percentage alone when

00:24:58

the quality of teams that each division

00:25:00

finishes uh faces are much different.

00:25:03

And so I know the clubs are keenly aware

00:25:05

of the differences in their schedules.

00:25:07

Um and my hunch is that if if we have we

00:25:09

all we always celebrate the sort of last

00:25:11

to to first teams but the reality is for

00:25:14

every last to first team there's

00:25:15

probably a first to last team. Um and

00:25:17

being cognizant of sort of what what is

00:25:19

best for the league is something that we

00:25:20

would be tracking. AI, do you want to

00:25:23

weigh in on this top?

00:25:23

>> Yeah, sure. Well, first of all, we love

00:25:25

I mean the the imbalanced schedule, even

00:25:27

though you try to sort of caused by this

00:25:30

offers great Simpsons Paradox examples.

00:25:32

We've every year you find a team that's

00:25:34

uh that's better against good teams and

00:25:35

better against worse teams, but ends up

00:25:37

having a worse record overall. That's

00:25:39

the classic um confounding. And so, by

00:25:43

the way, might be the Tampa Bay

00:25:44

Buccaneers. My team this year, they beat

00:25:46

Seattle in Seattle this year. Yeah,

00:25:49

>> they also beat I think they I forget who

00:25:50

else they they beat the Brams I think

00:25:52

they lost to New England by three points

00:25:54

and then they couldn't beat the Saints,

00:25:56

Atlanta, the Panthers. They couldn't

00:25:59

Miami and that inconsistency is is is a

00:26:01

football issue when I'm talking about

00:26:03

teams that that beat the better teams at

00:26:06

at a they have a lower winning

00:26:07

percentage against better team because

00:26:08

they're good teams and they beat the the

00:26:10

poor team better than another team who's

00:26:12

got a yet because the team has a just

00:26:14

imbalanced schedule, their overall

00:26:16

record looks a lot better. And that was

00:26:18

why people didn't trust New England

00:26:19

because their their record was so built

00:26:22

upon beating beating up on weak teams

00:26:24

that we felt we had no no substantive ef

00:26:26

estimate of how they would do against

00:26:28

strong teams. Um, I will say that u I

00:26:30

don't think it's in in MLS, which is

00:26:32

soccer, they don't have a mis in so much

00:26:35

of an imbalanced schedule as they have

00:26:36

these other tournaments that the top

00:26:39

teams have to play, which brutalizes

00:26:41

their bodies and the travel and the

00:26:43

exhaustion of playing soccer and becomes

00:26:46

very hard to repeat because you get to

00:26:48

you get to do all these extra things

00:26:50

like um the cups they play in in in

00:26:52

South America and the and they travel

00:26:54

and that the teams that don't do well

00:26:56

don't get to perform in. that there

00:26:58

there's uh other other leagues have to

00:27:00

to deal with that. Um but I but I have

00:27:03

to say I mean I'm not sure the models

00:27:05

were wrong. Um I think that maybe people

00:27:07

just didn't believe that someone like a

00:27:10

rookie essentially a rookie quarterback

00:27:11

like Derek May not rookie but is his

00:27:13

Drake May. Drake May

00:27:14

>> Drake May is his second year or is is

00:27:16

that what he's second year and someone

00:27:18

who had who played for the Jets for so

00:27:20

long could actually be good. I mean, may

00:27:22

maybe it's our it's our na our story

00:27:24

narrative that's kind of gotten in the

00:27:26

way as a uh as a Jets fan who's who's

00:27:29

sort of given up hope um and adopted my

00:27:31

Philadelphia Eagles. Um how does Sam

00:27:34

Darnold go for so many years at the Jets

00:27:36

doing nothing and now now being two

00:27:38

years in a row um a 13 game winner? We

00:27:42

got

00:27:42

>> 14 14. So, so I I have one of the things

00:27:47

we do a lot of which I think is is

00:27:49

underutilized in football is we try to

00:27:52

understand the conditions in which teams

00:27:54

play

00:27:55

>> and in baseball every time somebody

00:27:57

talks about a game in course field

00:27:58

everybody just naturally adjusts for the

00:28:00

altitude of course field

00:28:02

>> for whatever reason we just don't do it

00:28:03

in football. Um you know and I think I

00:28:06

look at Darnold and we look at we treat

00:28:08

all quarterbacks the same etc. Um, I

00:28:11

don't if we look at all the reclamation

00:28:13

projects, the quarterbacks that have

00:28:14

gone from bad sort of situations

00:28:17

team-wise to good situations teamwise. I

00:28:20

mean, Baker Mayfield went from Cleveland

00:28:22

to Tampa. Daniel Jones from the Giants

00:28:23

to Indianapolis. Gino Smith went from

00:28:26

the Jets to Seattle. Darnold went from

00:28:28

the Jets Minnesota. All those teams went

00:28:31

from outdoor stadiums, cold conditions,

00:28:33

tough conditions to domes or indoor

00:28:35

stadiums and easier conditions. Um, when

00:28:38

we look at Drake May's two worst games

00:28:40

of the year, the last two games, one was

00:28:42

in basically a blizzard in Denver. The

00:28:44

other one was in terrible conditions in

00:28:46

New England. Not an accident that those

00:28:48

were poorly poor conditions. Um, I

00:28:51

relatedly, I mean, Drew Brees,

00:28:52

unbelievable quarterback. Are we that

00:28:54

surprised that the quarterback who set

00:28:55

the single season completion record

00:28:57

played in a dome? No, we shouldn't be,

00:28:59

right? That's where we would expect it.

00:29:00

So, um, the differences in in sort of

00:29:03

quarterback setup, um, Darnold's had,

00:29:05

you know, a tremendous, uh, turnaround

00:29:06

of his career, but I think those things

00:29:08

play play a small part of it. And, you

00:29:11

know, we're constantly trying to think

00:29:12

about the weather and the conditions of

00:29:14

the game. More and more teams are having

00:29:16

turf, so what is the impact on those? We

00:29:18

average a couple more points per game in

00:29:20

turf games than we do in outdoor games.

00:29:21

Um, and so those are the the the natural

00:29:24

sort of league office type things that

00:29:26

we get uh pretty nerdy with. Well, how

00:29:28

many turf teams are there at this point?

00:29:30

>> Uh, we're pretty I don't know the number

00:29:32

off the top of my head. I want to say

00:29:33

we're getting like 18ish. Um, and then

00:29:36

obviously like Seattle's a sort of

00:29:37

inbetweener, right? It's an outdoor one,

00:29:39

but it's it's partially closed, but

00:29:41

sometimes it's pretty bad weather there,

00:29:43

too. So, um, yeah, they're they're

00:29:45

almost like halfway in between.

00:29:47

>> So, Mike, for the last question I wanted

00:29:48

to ask you today, um, let's imagine

00:29:50

obviously we hope we have you back long

00:29:52

before a year from now, but let's

00:29:53

imagine the three of us are sitting here

00:29:54

a year from now. um what rule changes

00:29:58

are kind of on at least the that you can

00:30:01

talk about that are are on the

00:30:02

consideration set that you guys are

00:30:04

analyzing?

00:30:06

>> Yeah, I mean I my hunch is we are you

00:30:09

know our our priorities for the

00:30:11

offseason, you know, continuing to

00:30:13

figure out it refining the kickoff play.

00:30:15

Um you know ultimately league office

00:30:17

doesn't decide the rules, right? We put

00:30:18

them in you know we have conversations

00:30:21

with the clubs and the clubs if they

00:30:22

vote on a change you need 24 votes. So,

00:30:25

um I think the kickoff one and maybe

00:30:26

some tweaks around the edges, keep it

00:30:28

competitive, um but but making sure you

00:30:30

know the injury rates are low enough and

00:30:32

things like that. Um my hunch is in

00:30:35

where we are going in football is we

00:30:36

will be getting skeletal data. Um we had

00:30:38

Hawkeye in all 30 stadiums this year.

00:30:41

Their system is called Skeletrack. Um

00:30:43

the completeness of this data is is

00:30:44

fairly promising in terms of um being

00:30:46

able to show the the the joints of the

00:30:48

the players. Um and trying to think

00:30:51

about where we can use that data to

00:30:53

improve the game um is a big part of our

00:30:55

offseason. Um so for example, you know,

00:30:58

for years you've had to pay a company to

00:31:01

figure out if a player was in a

00:31:02

two-point stance or three-point stance

00:31:03

at the time of the snap. We don't need

00:31:05

to do that anymore, right? We'll just

00:31:08

pretty quickly calculate that when we

00:31:09

see the skeletal data. Um you know

00:31:12

relatedly we can calculate a lot of

00:31:13

additional football metrics with this.

00:31:15

Um we can uh recreate the officials view

00:31:17

of a play um based on the the sort of

00:31:20

where their vantage point of uh vantage

00:31:22

point of vantage point was when a a sort

00:31:24

of a defensive pass interference

00:31:26

occurred things like that. Um so I don't

00:31:28

quite know what we're going to do with

00:31:30

it. Um but I know that it's going to be

00:31:31

a bigger part of the gun in year. Hm.

00:31:34

Well, we've been fortunate uh for the

00:31:36

last half hour here on Wharton Moneyball

00:31:38

on the Wharton podcast network uh to

00:31:40

have Mike Lopez. Mike is senior director

00:31:42

of football data and analytics at the

00:31:44

NFL. Uh we've talked a lot about and

00:31:46

please please I know you will please

00:31:48

continue with the big data bowl. I don't

00:31:49

mean just for our students. I just mean

00:31:51

it's you know people love the NFL and as

00:31:54

Audi says uh they think they're learning

00:31:57

about sports and all this stuff. We're

00:31:58

teaching them statistics and data

00:32:00

science through the Trojan horse of the

00:32:01

NFL. And there is no better Trojan

00:32:03

horse. So Mike, Audi and I would like to

00:32:05

thank you for joining us today on board

00:32:07

Moneyball.

00:32:08

>> Always a pleasure. And um tell Shane and

00:32:09

Kate I miss him.

00:32:11

>> We'll we'll do. Uh thanks Mike and we'll

00:32:13

join you again right after the break.

00:32:15

>> Sounds great.

00:32:17

>> Welcome back to the Wharton podcast

00:32:19

edition of Wharton Moneyball. This is

00:32:21

Eric Bradlo. I'm here today with my

00:32:22

friend colleague uh Professor Audi

00:32:24

Winer. some combination of the two of

00:32:26

us. Kade Massie and Shane Jensen are

00:32:28

here every week on Morton Moneyball. So

00:32:30

Audi, obviously we just finished with uh

00:32:32

Mike Lopez talking about the NFL. I want

00:32:34

to talk to you about a few other things.

00:32:36

Whenever it's just you and me, I like to

00:32:37

kind of interview you about how you

00:32:39

think about various things going on in

00:32:40

the sporting world.

00:32:42

>> So I want to provide you some data

00:32:45

on tennis given the Australian Open just

00:32:49

happened. I know you know this. Carlos

00:32:51

Alcarez won the Australian. Yes,

00:32:53

>> he's not got the career grand slam at

00:32:55

the youngest age ever, age 22, two years

00:32:58

younger than Nadal was when he won it at

00:32:59

age 24.

00:33:01

>> He's also got seven Grand Slam titles,

00:33:04

which is also I'll just give you an

00:33:06

example. Feder and Djokovic had one at

00:33:09

age 22. He's got seven. Well, you know,

00:33:13

just to interrupt here, uh we knew maybe

00:33:16

earlier with Alcarez that he was going

00:33:19

to be great earlier in his life than any

00:33:21

other player. I remember. Well, how old

00:33:23

was he when we were talking about this

00:33:24

phenom coming up? 16 was he? 17.

00:33:27

>> 17. He started he didn't win a major

00:33:30

until I think he I know he's won his

00:33:31

first one in 2022. So, he's probably

00:33:34

18/19 when he won his first.

00:33:36

>> But the the reviews of the of Algaras as

00:33:39

a kid were just off the charts. The only

00:33:42

thing that I remember that's comparable

00:33:44

in terms of previews of stunning

00:33:47

greatness. So we as statistitians always

00:33:49

regress forecasts to the mean. You have

00:33:52

to that produces the best forecast. Now

00:33:55

for our listeners what that means is

00:33:56

when someone makes a forecast that's

00:33:58

really extreme there's going to be some

00:34:00

regression meaning moving it down

00:34:02

towards the average. Um and we see this

00:34:05

in lots of places. So as a as someone

00:34:07

who interacts with the media whenever I

00:34:09

hear a you know a crazy expectation you

00:34:12

always have to bring it a little bit

00:34:13

down to earth. But Alcarez has certainly

00:34:16

b born borne its way out to the fullest.

00:34:18

And the other one that I maybe I'll just

00:34:20

which I if you look back which superstar

00:34:24

forecast they're probably others um have

00:34:27

turned into absolute dead-on

00:34:30

predictions.

00:34:32

>> Well, I mean the f the two that come to

00:34:33

mind obviously is Tiger Woods and golf

00:34:36

and

00:34:36

>> certainly Tiger Woods and golf.

00:34:38

>> No no Tiger Woods in golf. LeBron James

00:34:40

in basketball. LeBron was, you know, at

00:34:42

age 15, people knew about LeBron James.

00:34:45

And he entered the NBA at age 18.

00:34:47

>> Bryce Harper, you think in in baseball?

00:34:50

>> I don't think Bryce Harper is at the

00:34:52

level obviously of a Tiger even

00:34:54

comparable.

00:34:54

>> No, he's not. But he Yeah. So, in some

00:34:56

sense, he's regressed. He's going to be

00:34:58

Hall of Fame, right? To show you, but

00:35:01

he's not going to be tier one Hall of

00:35:02

Famer.

00:35:02

>> No, he's not going to be a tier one Hall

00:35:04

of Famer. I I'd have to think about the

00:35:06

other sports in

00:35:07

>> there's one in baseball that I that I

00:35:09

think is his career is only halfway

00:35:10

over. Um it's not Aaron Judge because he

00:35:12

was never forecasted to be terrific. Um

00:35:15

>> I'm just trying to think who you're

00:35:16

thinking about. Was it Juan Sodto?

00:35:18

>> Uh no Juan Sto didn't have that.

00:35:21

>> It's it's certainly nobody predicted

00:35:23

Mike Trout until he did what he did at

00:35:24

age 19. Um Um the answer is Show Otani.

00:35:30

>> Yeah, but he didn't join the N MLB until

00:35:32

what age? What age was he when he

00:35:34

>> No, it's not. No, it was one. So,

00:35:36

usually we discount Japanese performance

00:35:38

in the Japanese leagues. And so, I

00:35:41

remember listening to Neil Payne on on

00:35:43

his show Hot Takedown talking about this

00:35:45

insane forecasting and he was of course

00:35:48

gave it a statistical spin saying you no

00:35:51

one ever lives up to the hype of being

00:35:54

top in two positions. It just doesn't

00:35:56

happen. Um, but he was forecasted to be

00:35:58

a star pitcher and a star hitter. And I

00:36:01

remember listening to this and thinking,

00:36:02

"No way. he'll end up being one and not

00:36:05

the other. Yet here he is being both.

00:36:08

And so here's my example. Uh let me so

00:36:10

let me ask you to do a little

00:36:11

forecasting here. So we just said Alcarz

00:36:14

has seven majors.

00:36:16

>> Okay?

00:36:16

>> Let's say he's basically been playing

00:36:18

since the beginning of 2022. He might

00:36:20

have started a little earlier. Let's

00:36:21

just assume as a real professional since

00:36:23

the age of 18, he's played 17 majors.

00:36:25

He's won seven. So let's assume he's

00:36:27

winning at a 40% rate right now. Right

00:36:29

now. Right now he's winning at a 40%

00:36:32

rate. Although he's won five of the last

00:36:34

nine, it's fine. If you had to project

00:36:37

out, I'm not going to ask you for his

00:36:39

career. We go out another five years.

00:36:41

Okay? So, 20 majors.

00:36:44

Is there any reason why I shouldn't

00:36:48

predict he'll have eight more, which is

00:36:50

15.

00:36:52

>> Well, you know, okay. So, uh, he's been

00:36:54

healthy, correct?

00:36:56

>> He has been healthy for I'm trying to

00:36:58

decide if he's missed any because you're

00:37:00

right. What you're pointing out also is

00:37:02

there's no doubt if Rafa Nadal hadn't

00:37:05

been injured in his career, he might

00:37:07

well have the most majors. He has 22.

00:37:09

Obviously, Jookovic has 24, but Nadal

00:37:11

missed years worth of majors. Djokovic,

00:37:14

I don't know he's ever missed one.

00:37:16

Federer wasn't that injured in his

00:37:17

career. Nadal was. Let's Yeah, you're

00:37:19

right. So, we have to take into the

00:37:20

probability, right? So, miss some,

00:37:23

>> right? So, he's uh so that your eight

00:37:25

forecast is just is just sliding over

00:37:27

the previous fraction, rolling it

00:37:29

forward. No, he's going to he's heading

00:37:30

into his prime, right? So,

00:37:31

>> he's not even No, no. Prime typically in

00:37:33

men's tennis is like 27. This is not

00:37:36

even his prime years,

00:37:37

>> right? So, by that measure, it'll go up.

00:37:39

But nevertheless, we're you got to

00:37:41

regress down slide word because he's

00:37:42

been so dominant going. It's a tr such a

00:37:45

tricky thing. Also, I what really really

00:37:47

changes the forecast is what who emerges

00:37:50

to oppose him. Right. So, right now he's

00:37:52

got sinner as his his principal um

00:37:54

opponent and and regular challenger.

00:37:57

Jookovic is probably done, right? He's

00:37:59

38 years old. I don't think we're doing

00:38:01

much of that.

00:38:02

>> Um, are there any uh any coming up and

00:38:06

coming players who can consistently

00:38:08

challenge him? How many are there?

00:38:10

>> Great question. So, I'm glad you use the

00:38:11

word consistently because let's even

00:38:13

talk about the big three era, right?

00:38:15

>> Um, Stan Marinka won three majors in

00:38:19

that period, but he couldn't

00:38:20

consistently do it. Andy Murray, some

00:38:22

people even call it the big four. Andy

00:38:24

Murray won three majors, but he couldn't

00:38:26

consistently do it. Delpatro won one

00:38:30

major.

00:38:30

>> Yeah, I remember that.

00:38:31

>> So, no, no, I'm just saying. So, are you

00:38:34

asking me right now? I don't see anybody

00:38:37

out there on the men's side.

00:38:39

>> The better opportunity. People are

00:38:40

talking about Ben Shelton or Taylor

00:38:42

Fritz or even Zarev who as you remember

00:38:45

was one game away from beating Alcarass

00:38:47

and the Australian, but consistently

00:38:50

absolutely not. And so now the challenge

00:38:53

is like it's not unreasonable to and I'm

00:38:57

even being I think conservative here.

00:39:00

Let's since they've won combined the

00:39:01

last nine majors. It's the longest

00:39:03

streak by the way in tennis history. The

00:39:05

two men have won nine the last nine

00:39:07

majors. All of 24 all of 25 and now the

00:39:10

Australian and 26. Five have been won by

00:39:13

Alcarz. Four have been won by center.

00:39:15

Okay. I think it's fair. It's not

00:39:17

unreasonable predict and center's 24.

00:39:19

It's not unreasonable predict over the

00:39:21

next four or five years combined the two

00:39:23

of them will win 75% of the majors. It

00:39:26

does not mean that there isn't one a

00:39:27

year on average that somebody else could

00:39:29

win, but right now I don't know how you

00:39:31

could predict less than that. And if

00:39:33

that's true, then 12 of the next uh 16

00:39:37

majors are going to be won by one of the

00:39:40

two of them. And even if it's a 66

00:39:42

split, that puts Alcarz at age 26 with

00:39:45

13 majors. Yeah, I'd say that's a

00:39:47

reasonable expectation quite honestly.

00:39:50

>> Yeah. So,

00:39:51

>> that's definitely I mean what I'm what's

00:39:52

amazing about tennis is that it really

00:39:55

does have this

00:39:57

uh longtailed distribution. You don't

00:40:00

that you you don't have this pileup at

00:40:02

the at the you almost like you think

00:40:04

about it is at what is the end of what a

00:40:06

human can accomplish and then you have

00:40:08

this pileup at that near that maximum.

00:40:11

Where do you see that? You see that in

00:40:12

in sprinting, right? Um you see that in

00:40:16

home run rates even. Um, but it seems

00:40:19

like in tennis the one or two or even

00:40:22

three bests just leap out way ahead of

00:40:26

everyone else. And is that due to the

00:40:28

fact that tennis is such a

00:40:30

>> a tennis match has so many

00:40:33

>> um repetitions? I think it is. I think

00:40:36

it is. You know, this is what I always

00:40:38

say. You know, it's the same thing.

00:40:39

Obviously, you know, I have a son that

00:40:40

plays squash. All three play squash, but

00:40:42

one was pretty competitive.

00:40:44

you hit like I forget the number of

00:40:46

balls, but if there's like 150 points in

00:40:49

a squash match, you might hit a thousand

00:40:50

balls in a match. You know, in tennis,

00:40:53

you know, think about just how many

00:40:55

shots are hit. And if I hit even 2% 3%

00:40:59

better than you, you add that over a

00:41:01

match and now I've hit 30, 40 balls

00:41:03

better than you, and that's going to

00:41:05

make the difference in a tennis match.

00:41:07

And even more so in the majors where

00:41:09

it's best to five. If if anything, the

00:41:12

longer the match obviously is favoring

00:41:14

the better player and so it's just

00:41:16

bigger N. There we go.

00:41:18

>> And that's that's but you'd still would

00:41:20

expect to have a little bit more

00:41:21

clumping than we do considering that

00:41:23

there should be some intra

00:41:26

interournament variation. Like what I

00:41:28

mean by that is that the center that

00:41:30

shows up to the Wimbledon might not be

00:41:31

the center who shows up to the

00:41:32

Australian Open, shows up to US Open,

00:41:35

etc. you'd expect to see enough

00:41:37

variation, which is human to do that,

00:41:40

right? Um to see that the the the

00:41:42

compression among the top, but you do

00:41:45

you don't in in in tennis, the dominant

00:41:47

players are able to just not only win,

00:41:49

but crush um say five or 10.

00:41:53

>> I only have 20. I have one other pet

00:41:56

topics related to test and I want I have

00:41:57

want to go over to MLB in a second but

00:42:00

um you know obviously

00:42:03

Jookovic is the most accomplished player

00:42:05

of all time you know he's won the most

00:42:07

majors the one the most the Masters 1000

00:42:09

ranked number one in the world the most

00:42:10

number of weeks

00:42:12

I still have a problem calling him the

00:42:15

best and let me say why or the GOAT so

00:42:17

let me just say why

00:42:18

>> so between Federer Nadal and Djokovic

00:42:23

this is a metric Maybe I've cherrypicked

00:42:25

it. He has the worst winning percentage

00:42:28

in Grand Slam finals by far. Now, he's

00:42:31

got the most, but that's cuz he was

00:42:33

never injured and he made the most

00:42:34

finals, but his winning percentage is

00:42:36

the worst in Grand Slam finals.

00:42:38

>> Yep. Yep. Back in the Okay.

00:42:40

>> He has a losing record to Nadal in

00:42:44

majors and in major finals.

00:42:46

>> Well, that's because of the Nidal's

00:42:48

dominance in the French, right?

00:42:49

>> That's I'm going to get to that.

00:42:51

>> Now, he has a better record. He has a

00:42:53

winning record against Nadal 31 and 29

00:42:56

in his career overall. Winning record

00:42:59

against Federer 27 and 23. Although you

00:43:01

might expect it to be better given he's

00:43:03

6 years younger and when they played in

00:43:05

their career. And actually if you end

00:43:07

Federer's career ended at age 37 he

00:43:09

would have had a winning record by the

00:43:10

way against Jookovic. Just so you know.

00:43:13

Um if you had to rank order the four

00:43:16

majors in tennis from most prestigious

00:43:19

to least please rank them for me.

00:43:23

uh Wimbledon US Open

00:43:26

French Australia.

00:43:28

>> I would completely agree and so would

00:43:29

everyone else. Do you know who has the

00:43:31

most Wimbledons of all time?

00:43:33

>> Roger Federer.

00:43:35

>> You know who has the most US Opens of

00:43:36

all time?

00:43:37

>> Roger Feder.

00:43:37

>> Roger Federer.

00:43:38

>> Do you know who has the most Frenches of

00:43:40

all time?

00:43:40

>> The I have to go down to the Australian

00:43:44

>> to get to Djokovic. And he's got so many

00:43:47

more. He's got 10. Matter of fact,

00:43:48

>> that's another thing about Algres.

00:43:51

No, Jookovic was 10 and0 in the

00:43:54

Australian finals.

00:43:56

Now he's 10-1. But my point is he didn't

00:44:00

even win the M. I mean,

00:44:02

>> he wasn't at work.

00:44:03

>> So, let's let's be clear here between

00:44:05

you and me and anyone else listening,

00:44:07

which hopefully you're thousands.

00:44:10

Is Feder the goat?

00:44:14

That's a tough one. It's a tough one.

00:44:16

Here's what I'll say. Um, this was

00:44:20

always my claim about Djokovic. Djokovic

00:44:22

always plays great.

00:44:25

>> Yep.

00:44:25

>> That was his great strength.

00:44:28

When he was at his best, Nadal was at

00:44:31

his best and Federer was at his best.

00:44:34

Who do I think is winning the match?

00:44:36

I'll debate Feder Nadal. It's hard for

00:44:38

me to say Djokovic because I've seen

00:44:40

Djokovic get blown out by each by those

00:44:43

players and others. Andy Murray, Winka,

00:44:45

Delpatro, blew out Djokovic when they

00:44:47

had their A+ game. I say to me, he's the

00:44:50

most accomplished tennis player of all

00:44:52

time. Is he the greatest? Was his peak

00:44:55

greatness greater than the others? No, I

00:44:58

don't think so.

00:44:59

>> That's just my opinion. That's my

00:45:01

opinion. By the way, I'm sure we could

00:45:02

look at a flawed EO rate. By if we

00:45:04

looked at flawed ELO ratings, I think he

00:45:06

does have a higher ELO rating.

00:45:08

>> Yeah, but that ELO doesn't know how to

00:45:10

rate um ma matches by with a different

00:45:12

rating system. Right. It doesn't though.

00:45:14

>> Yeah. All right. Well, that's that's

00:45:16

some tennis. So, I'm going to I know you

00:45:17

haven't looked at this. I didn't put it

00:45:18

in the spreadsheet. There's no way you

00:45:20

could see this. I'm going to give you

00:45:21

the 2025 win total for an MLB team and I

00:45:26

want you to give me your guess of what

00:45:28

the fan graph's 2026

00:45:31

win number is. Okay. You ready?

00:45:32

>> Okay. Sure.

00:45:33

>> I just picked out seven or eight. Okay.

00:45:35

Let's start with the Dodgers. The

00:45:36

Dodgers won 93 games in 2025. And I'm

00:45:38

right. They're the two-time repeating

00:45:40

champion Dodgers, right? Y.

00:45:41

>> Okay. What do you think Fan Grass has

00:45:43

them for uh 2026?

00:45:45

>> Okay, first of all, Fan isn't stupid, so

00:45:47

they regress to the mean, but I think

00:45:49

the Dodgers significantly underperformed

00:45:52

their their their payroll, their roster

00:45:55

given injuries. So, I'll bet they're at

00:45:57

96.

00:45:58

>> Well, you're off by one. 97.

00:46:00

>> Okay. That was my I was about to say

00:46:03

either one of those. Yeah.

00:46:03

>> All right. The next one.

00:46:04

>> By the way, to our listeners and

00:46:06

viewers, I am not cheating.

00:46:08

>> HE'S NOT. I have it in front of me. He

00:46:10

cannot say this.

00:46:10

>> Bring it up on my own. I'm just

00:46:12

>> Yeah, there's no but he's not. He can't

00:46:13

see. Well, we'll see if you're not

00:46:14

cheating in a second for the next one.

00:46:16

>> Yeah,

00:46:16

>> the Braves won 76 games last year.

00:46:21

>> What does Fan Graphs has them for 2026?

00:46:24

>> Uh somewhere in the low 80s, low to mid

00:46:26

80s. I'd probably say 84.

00:46:28

>> 91.

00:46:29

>> 91. What do they pick up that they think

00:46:32

that they that they're thinking?

00:46:33

>> I don't know. That's Well, that's the

00:46:34

question. I had I didn't get that far to

00:46:36

look. Um here's another couple

00:46:38

interesting ones. The Phillies won 96

00:46:40

games last year.

00:46:44

>> I would guess they're probably going to

00:46:46

regress them down to about 91 92.

00:46:48

>> 85 8 That's an enormous regression. The

00:46:51

Yankees won 94. What do you got?

00:46:55

>> Yankees haven't changed their team. Uh

00:46:58

they're going to get Garrett Kobach. Uh

00:47:00

we have some pitchers I would say around

00:47:02

93.

00:47:03

>> 87

00:47:04

>> 87. So,

00:47:06

>> the Brewers 97. Best record in baseball.

00:47:09

What do you think?

00:47:10

>> Under 90 for sure.

00:47:12

>> 82.

00:47:13

>> Yeah, of course. Yeah.

00:47:14

>> Here's the most fascinating one for me.

00:47:15

The Rockies won 43 last year.

00:47:18

>> Yeah.

00:47:19

>> What do you think they have?

00:47:20

>> 65.

00:47:21

>> 66.

00:47:22

>> Yeah.

00:47:24

>> So, the two that surprised me, I have to

00:47:25

admit, look, obviously,

00:47:27

>> I mean, I have to say Atlanta's

00:47:28

surprising me. I mean, to predict them

00:47:31

that high, that's why I gave them

00:47:32

slightly above average, right? Why would

00:47:34

you pick them to be as higher as high as

00:47:36

the Phillies?

00:47:39

>> Higher than the Phillies unless unless

00:47:41

they gained some players or unless they

00:47:43

you just felt like their true strength

00:47:45

last year based on metrics was 85 90

00:47:49

wins and they just way underperformed.

00:47:51

There'd be no other explanation. Yeah,

00:47:54

it's interesting because one of the one

00:47:55

of the one of the examples I'm doing

00:47:56

with my class is to measure

00:47:59

organizational advantage in terms of

00:48:02

wins above payroll expectation.

00:48:04

>> Oh, that's a great metric.

00:48:05

>> And uh and it's actually many people

00:48:07

have done this and they've actually done

00:48:08

it incorrectly. Um a standard way to do

00:48:11

that is to build a model that predicts

00:48:12

wins as a function of payroll and then

00:48:15

residualize each team and then average.

00:48:18

But if you do that, you don't control

00:48:19

>> say average over years.

00:48:21

>> Uh yes. So in other words, every season.

00:48:23

Yeah. So you take you take 20 years

00:48:25

worth of data, you predict the wins as a

00:48:27

function of payroll

00:48:29

>> and then you uh and standardize it

00:48:31

because money changes its value.

00:48:33

>> Um so you do whatever standard.

00:48:35

>> You're telling me that's not right.

00:48:36

>> What's that?

00:48:37

>> That's not right.

00:48:38

>> Oh no. So yeah. So if you do that,

00:48:40

>> that's what I would have done.

00:48:41

>> Yeah. The problem with that is that what

00:48:44

happens as we imagine that better

00:48:47

management gets more money.

00:48:50

Then you have a confounding. The worst

00:48:52

teams, the people, the organizations

00:48:54

that are the worst could end up with

00:48:56

very low payrolls

00:48:59

>> causally

00:49:00

>> and then and if you think causally,

00:49:01

which is essentially what we're trying

00:49:02

to get at, you're going to have a

00:49:04

confounding component. And you see this

00:49:07

in sports all the time, see this in

00:49:08

field goal kicking, right? If you look

00:49:10

if you try to modeling the conditional

00:49:12

distribution of the number of expected

00:49:14

wins given the payroll. So, so what you

00:49:17

so so the standard way of people have

00:49:19

always done this is they predict wins

00:49:21

given payroll then then you build that

00:49:23

model then you residualize for every

00:49:25

team and you take your average residual.

00:49:28

The problem with that is that if there's

00:49:30

confounding and the teams that have a

00:49:33

lot of money also have good management

00:49:34

you'll absorb the managerial effect with

00:49:38

your estimate of the payroll. I see. So

00:49:40

classic example is and by the way this

00:49:42

this undermines the Yankees for example

00:49:45

Yankees don't particularly overperform

00:49:48

their payroll but if on the other hand

00:49:50

you say to them that that high payroll

00:49:53

is given to them because they're run

00:49:55

well you can think about it like that.

00:49:57

So an alternative way to do it is you

00:49:59

residualize each team's

00:50:02

winning percentage and their payroll and

00:50:04

then you get the and then you predict

00:50:07

their residual winning percentage versus

00:50:09

their residual payroll. So the Yankees

00:50:12

are judged by how much more money

00:50:13

they're paying than what they average in

00:50:16

a given season. And you compare the

00:50:17

Yankees to how much they and that's that

00:50:20

mathematically corresponds to a fixed

00:50:22

effects model. And that's the one that I

00:50:24

come up with. And what's interesting is

00:50:26

Oakland A's are still at the top no

00:50:27

matter how you calculate it, but the

00:50:29

Yankees move into like fourth position

00:50:32

and the Atlanta Braves and the St. Louis

00:50:34

Cardinals are the two teams that are in

00:50:37

between them. So the the the actual

00:50:40

value of the and I I don't have the the

00:50:42

lecture notes. If you give me a second,

00:50:44

I'll probably be able to pull it up. But

00:50:45

at the actual value of the of the uh in

00:50:48

terms of wins above expectation after

00:50:52

adjusting properly for the team quality

00:50:54

for the Oakland A's it's around six for

00:50:56

the Yankees it's around three and a half

00:50:58

and for the Cardinals it's around

00:51:00

somewhere and and and Braves it's around

00:51:02

3 to four.

00:51:03

>> So this is some real that's some real

00:51:04

action. I mean those are that's a real

00:51:06

number of wins.

00:51:07

>> Yes. Now of course this was the A's in

00:51:09

the in the Billy Bean era when they had

00:51:11

that advantage. I I wouldn't argue that

00:51:13

they have that going forward, but that

00:51:14

might produce one of the reasons why you

00:51:16

suspect Atlanta to be good because

00:51:18

they're generally good every year. And

00:51:20

therefore, when we look back at a a a

00:51:22

poorer season that they had, that is

00:51:25

their their forecast is to regress them

00:51:28

much much much higher than 82. Um, and

00:51:31

that's maybe and maybe they've seen some

00:51:33

some action off the field that we

00:51:34

haven't that our memories don't don't

00:51:36

have and that's why they're coming up

00:51:38

with such a high number. But I am a

00:51:40

little bit surprised and that's

00:51:41

something I want to dig into.

00:51:43

>> So the last topic related to baseball

00:51:45

since you and I always like to talk Hall

00:51:46

of Fame. But just for our listeners out

00:51:48

there, by the way, one of my greatest

00:51:50

honors in a couple of weeks we're going

00:51:51

to have Josh Trowick, the president of

00:51:53

the National Baseball Hall of Fame uh on

00:51:56

our show, which will be fantastic to

00:51:57

talk to him. Uh one of the questions I'm

00:51:59

going to ask him is, and I told him I

00:52:01

was going to ask him this, like when do

00:52:02

you see a day where advanced metrics

00:52:04

dominate the plaques in the Hall of

00:52:06

Fame? like this p this person had a you

00:52:08

know I well on base percentage has been

00:52:10

on for a long time. What would be an

00:52:12

advanced metric that you would like love

00:52:13

to see on someone's plaque out of in the

00:52:15

Hall of Fame?

00:52:16

>> That's a great question which because I

00:52:18

um

00:52:19

>> uh B

00:52:22

uh I mean yes I mean for a pitcher

00:52:25

batting average on balls of play I'm not

00:52:26

really sure that um that would be it.

00:52:29

Um, I certainly wouldn't be war because

00:52:31

that is still in in the uh although I

00:52:34

will say I do like the pitcher wars much

00:52:36

better than the hitter wars. Um, I think

00:52:38

they're much much more

00:52:40

>> percentage has been on for a long time.

00:52:42

>> Percentage. OPS is a great number. OPS

00:52:45

>> ops is a single counting stat. Um, that

00:52:48

makes a lot of sense to me as as

00:52:50

something that should be. In fact, if

00:52:52

you go to a stadium now, OPS is on the

00:52:54

is on the

00:52:55

>> Oh, it's on the screen. It's absolutely

00:52:56

on the screen.

00:52:56

>> And uh that that's a great number. Um so

00:53:00

if it you have to make it simple but um

00:53:02

I probably pitching war starting pitcher

00:53:04

war uh is a great number

00:53:06

>> interesting to see

00:53:07

>> and uh it's interesting I went and spoke

00:53:09

at uh a Daniel X class in he was on our

00:53:12

show he teaches a class in um in uh in

00:53:16

baseball statistics and I I gave a to

00:53:18

talk about my grid war that I wrote with

00:53:20

Ryan Bril and right and how that really

00:53:23

teases out in players that for the Hall

00:53:26

of Fame that that that aren't um that

00:53:28

aren't uh um noticed by the the

00:53:31

conventional metrics. Actually,

00:53:32

interestingly enough, it came up two

00:53:34

pitchers rise substantially in in in

00:53:38

well, not

00:53:39

>> well, you've been talking about Kevin

00:53:40

Brown and Dave Steve forever. It's not

00:53:42

the two of them,

00:53:43

>> right? It's not the two of them, but

00:53:44

here's two here's two others that are

00:53:45

interesting. They're Yankees. Ron Gidry

00:53:48

is underrated as with Hall of Fame

00:53:50

credentials. really on and and if people

00:53:53

are talking about Pettit, Gidri is way

00:53:55

ahead of Pettit in terms of of dominance

00:53:59

during his during his peak and even

00:54:02

longevity. Um yet nobody talks about Gry

00:54:04

for the Hall of Fame. I don't think we

00:54:06

really should be talking about Pettit. I

00:54:08

mean that's he's a re but he might make

00:54:10

it because

00:54:11

>> for lots of reasons. Here's another un

00:54:14

unsung hero that was historically

00:54:17

beloved but the Saber Matricians don't

00:54:19

like him. Whitey Ford.

00:54:23

Whitey Ford is not I mean every he was I

00:54:25

mean he has the best winning percentage

00:54:27

of any pitcher with more than you know

00:54:30

>> history right in history

00:54:31

>> in history it's not even close and of

00:54:33

course everyone discounts that because

00:54:34

he played for the great Yankee teams

00:54:36

right but if you actually look at what

00:54:38

he performed as a pitcher ignoring what

00:54:41

the Yankees were able to do do for him

00:54:43

on the batting side and look at his

00:54:45

opponents and look at the the way he was

00:54:47

able to get out of difficult jams and

00:54:50

really produce his difficult opponents

00:54:53

leave them with not very many um

00:54:54

opportunities. He is an unsung player. I

00:54:57

mean, he's a genuinely a top 20 pitcher

00:55:00

of all time. Wow.

00:55:02

>> And uh most people don't think of him as

00:55:04

that.

00:55:04

>> So, just in the last one or two minutes,

00:55:06

uh any quick reactions? I don't know if

00:55:09

you and I talked about it to, you know,

00:55:11

Jeff Kent, Carlos Beltron, or Andrew

00:55:13

Jones. I mean, any excitement for you?

00:55:16

As you know, I'll be there in Coopertown

00:55:18

seeing them. any excitement that you

00:55:20

have about either of the three of them

00:55:22

being in the Hall of Fame or you like

00:55:24

but no real excitement.

00:55:26

>> No, Belchan was never much on my radar.

00:55:28

He had his great He's really one of the

00:55:29

classic um um you know postseason

00:55:33

player, never playing for my team, so I

00:55:36

don't never played that much. And Jones,

00:55:38

of course, he finished his career with

00:55:39

the Yankees and he was thoroughly

00:55:41

mediocre in those seasons. Um, and I and

00:55:44

I always thought of him as, you know, he

00:55:46

so much of his great years were in his

00:55:47

early part of his career.

00:55:50

>> And it is a saber metric. I mean,

00:55:52

frankly, he did over hit over 400 home

00:55:54

runs. Yes.

00:55:55

>> Um, and he was a center fielder and he

00:55:56

was a damn good one. Um,

00:55:58

>> damn good one.

00:55:59

>> Only like there's like three players in

00:56:01

history like him, Maize, and Griffy have

00:56:03

I know they they artificially make these

00:56:05

cut offs, but like 400 home runs and 10

00:56:07

golden gloves in center field.

00:56:08

>> Yeah. Yeah. Exactly. Um, and I remember

00:56:10

when I first, this is a point of history

00:56:12

in our work, when we first got the grant

00:56:14

from ESPN, it must be 20 years ago now

00:56:17

to do uh maybe not quite that far. It

00:56:19

was maybe 20, 2007. Um, we got this not

00:56:23

tracking data because it wasn't tracking

00:56:25

data, but it was video recorded. Human

00:56:28

beings watched plays and wrote down

00:56:30

information. We and we ESPN bought that

00:56:32

data for us and that launched my sports

00:56:35

analytics uh research line and Shane's

00:56:37

as well. And in a paper that Shane and I

00:56:40

wrote uh called SAFES uh um basically

00:56:43

spatially adjusted fielding um metric,

00:56:45

we we discovered two particular center

00:56:48

fielders who just stood out from the

00:56:49

rest and that was Andrew Jones. The

00:56:51

other was Jim Edmonds. Um

00:56:53

>> well that's that's he was certainly

00:56:55

known for that.

00:56:56

>> Yeah. Yeah. And uh andrew Jones was

00:56:59

particularly good at at at playing the

00:57:01

low ball, the ones that were they were

00:57:03

they were short. And he was just an

00:57:05

incredible center fielder. and we

00:57:07

actually valued that that um that

00:57:09

contribution.

00:57:09

>> I don't have a let me just say I don't

00:57:11

have a problem except for the sign

00:57:13

stealing stuff or the banging on the

00:57:15

trash can stuff with Beltran, but

00:57:16

whatever. Um I don't have a problem with

00:57:19

Kent, Beltron, or Jones, you know. Am I

00:57:22

excited that they're in the Hall of

00:57:24

Fame?

00:57:25

>> Hall of Famers. All three

00:57:27

>> third tier Hall of Fame.

00:57:28

>> Yeah, but they're fine. They're in

00:57:32

they're not undeserving. Are they

00:57:34

exciting? Uh, who's coming on down the

00:57:36

pike next year? We looking that far

00:57:37

ahead yet?

00:57:39

>> Any new

00:57:41

book at one point? I don't think there's

00:57:43

anybody that exciting, which is why a

00:57:44

lot of people are thinking it could be

00:57:46

Chase Utley's year next year given where

00:57:48

he's grown. Um, Andy Pettit could make

00:57:51

it. Um, you know, there's even

00:57:54

discussions, there's been a lot of

00:57:55

discussions where how can you put Utley

00:57:56

in without Jimmy Rollins in who's better

00:57:58

than him on every statistical category.

00:58:00

But either way, I think Utley and Pettit

00:58:03

may get in next year.

00:58:06

>> I think

00:58:06

>> interesting thing that it's funny

00:58:07

because uh second baseman traditionally

00:58:10

under hits the shortstop and it's not

00:58:13

because it's a more difficult position

00:58:15

and therefore you require it has to do

00:58:17

with the fact that the best athlete

00:58:19

tends to get put at shortstop. So it's

00:58:21

an interesting confounding, right? If

00:58:22

you think about confounding, right? If

00:58:23

you look at the production at which

00:58:26

position produces the worst batting

00:58:28

production historically at second base.

00:58:32

>> Yeah. Because usually, as you said, they

00:58:33

that's where they tend to move people

00:58:34

that can't field that well and you know,

00:58:36

they just want their bat in the lineup,

00:58:37

but they're not the best athlete.

00:58:39

>> Well, no, it's important. It's a middle

00:58:40

infield position, so at the major league

00:58:42

level, you need a very good second

00:58:43

baseman and that person has to be quick.

00:58:46

But they tend to as a group to under hit

00:58:50

or offensively produce far less

00:58:52

>> than the the shortstop which has to be

00:58:54

even better fielder.

00:58:56

>> So it's an interesting um correlation

00:58:59

that creates a causal misunderstanding.

00:59:01

It's a classic you know if you think

00:59:03

about it. So Utley I think is far better

00:59:07

is far more deviant as a second baseman

00:59:09

than Rollins is as a shortstop. That's a

00:59:11

And and by the way, a lot of people like

00:59:13

myself, and we'll wrap up with that, a

00:59:15

lot of people like myself believe that

00:59:17

is an important criterion for the Hall

00:59:20

of Fame. Like Jeff Kent, put him in

00:59:22

center field. Jeff Kent's not a Hall of

00:59:24

Famer. But does he have the most home

00:59:26

runs ever as a second baseman? Yep, he

00:59:29

does. That's worth something, Audi. It's

00:59:31

got to be worth something.

00:59:33

>> That's the argument right there.

00:59:34

>> That's the argument.

00:59:35

>> All right. Well, this has been one hour

00:59:37

of Wharton Moneyball. like to thank

00:59:38

again Mike Lopez, head of data science

00:59:41

for the NFL for myself, my colleague and

00:59:43

friend Winer. Uh in absentia, Kade

00:59:46

Massie and Shane Jensen. Uh it's been a

00:59:48

great hour with you here on the Wharton

00:59:50

podcast network and Wharton Moneyball

00:59:51

between now and next week. Enjoy the

00:59:53

Super Bowl. Enjoy your sports. Enjoy

00:59:55

your statistics. We'll see you next week

00:59:57

here on Wharton Moneyball.

Episode Highlights

Carlos Alcaraz's Rise
At just 22, Alcaraz has already won seven Grand Slam titles, showcasing his extraordinary talent.
“Alcarez has certainly borne its way out to the fullest.”
@ 34m 16s
February 06, 2026
Forecasting Alcaraz's Future
Experts predict Alcaraz could win 13 majors by age 26, solidifying his legacy.
“He’s not even in his prime years yet.”
@ 37m 37s
February 06, 2026
Tennis's Competitive Landscape
The sport features a unique distribution of talent, with top players consistently outperforming others.
“Tennis has this long-tailed distribution.”
@ 39m 51s
February 06, 2026
Debating the GOAT
The discussion around who is the greatest of all time in tennis remains complex and subjective.
“Is Federer the GOAT? That’s a tough one.”
@ 44m 14s
February 06, 2026
Hall of Fame Metrics Discussion
Exploring the future of advanced metrics in the Hall of Fame plaques.
“What would be an advanced metric that you would love to see on someone's plaque?”
@ 52m 06s
February 06, 2026
Underrated Pitchers
Discussing underrated pitchers like Ron Gidry and Whitey Ford for Hall of Fame consideration.
“Whitey Ford is genuinely a top 20 pitcher of all time. Wow.”
@ 55m 00s
February 06, 2026
Second Base vs. Shortstop
Analyzing the historical performance of second basemen compared to shortstops.
“Utley is far more deviant as a second baseman than Rollins is as a shortstop.”
@ 59m 07s
February 06, 2026

Episode Quotes

Alcarez has certainly borne its way out to the fullest.
How the NFL Uses Data to Shape Rules and Create New Metrics
He’s not even in his prime years yet.
How the NFL Uses Data to Shape Rules and Create New Metrics
Tennis has this long-tailed distribution.
How the NFL Uses Data to Shape Rules and Create New Metrics
Is Federer the GOAT? That’s a tough one.
How the NFL Uses Data to Shape Rules and Create New Metrics
Djokovic always plays great.
How the NFL Uses Data to Shape Rules and Create New Metrics
He's genuinely a top 20 pitcher of all time. Wow.
How the NFL Uses Data to Shape Rules and Create New Metrics

Key Moments

Alcaraz's Success34:16
Future Predictions37:37
GOAT Debate44:14
Real Wins51:06
Hall of Fame Talk51:45
Underrated Players53:48
Second Base Analysis58:40
Final Thoughts59:57

Words per Minute Over Time

Vibes Breakdown

NBA Shockwaves, Why the Chiefs Still Rank No.1, and the Power of Data

December 01, 2025

01:00:01

NBA Shockwaves, Why the Chiefs Still Rank No.1, and the Power of Data

How AI and Analytics Are Changing Quarterback Evaluation and NFL Outcomes

January 08, 2026

01:07:55

How AI and Analytics Are Changing Quarterback Evaluation and NFL Outcomes

Hockey Analytics, Simulation, and Predictive Limits

April 22, 2026

59:22

Hockey Analytics, Simulation, and Predictive Limits

How Analytics and New Rules Are Changing Baseball

March 05, 2026

01:01:15

How Analytics and New Rules Are Changing Baseball

The 2025 NFL Season Through Data: DVOA, Analytics, Awards, and Predictions

January 05, 2026

46:23

The 2025 NFL Season Through Data: DVOA, Analytics, Awards, and Predictions

Brandon Copeland on How NIL Is Reshaping the Power Structure in College Sports

August 08, 2025

52:32

Brandon Copeland on How NIL Is Reshaping the Power Structure in College Sports

How Analytics Shape NFL Team Building

March 18, 2026

46:52

How Analytics Shape NFL Team Building

NBA Analytics, Tanking, and the Future of Team Building

February 19, 2026

01:04:12

NBA Analytics, Tanking, and the Future of Team Building

Inside College Football’s Data-Driven Evolution and Decision-Making

January 22, 2026

01:10:36

Inside College Football’s Data-Driven Evolution and Decision-Making

AI in Sports – Wharton Professors Adi Wyner and Cade Massey | AI in Focus Series

November 10, 2023

27:27

AI in Sports – Wharton Professors Adi Wyner and Cade Massey | AI in Focus Series

The Math Behind Sports Rankings and Golf Analytics

May 07, 2026

01:08:01

The Math Behind Sports Rankings and Golf Analytics

Baseball Analytics, NFL Parity, and College Football Playoff Odds

November 16, 2025

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Baseball Analytics, NFL Parity, and College Football Playoff Odds