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Welcome to Wharton Moneyball. Welcome to
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a full hour of sports analytics here on
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the Wharton podcast network. Kade Massie
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hosting with the whole crew. Eric Bradlo
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is here. Audi Winer is here. Sane.
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Jensen is here. Three of the four of us
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are on campus actually in offices almost
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as close as we were last week in studio.
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But we're back on Riverside coming to
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you Tuesday afternoon as we typically
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do. Show will go up on Wednesday. We are
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gonna kick things around this week. We
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are checking in with an old friend,
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maybe the original guest. OG has a
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different meaning in podcast world. The
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original guest, Neil Payne, might be the
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original guest for Wharton Moneyball.
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Neil is a frequent guest over the last
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11 plus years, but he was with us,
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probably the only guest we can name who
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was with us in the basement of the
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original building before the studio was
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built. Neil Payne was a Philadelphiaian
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at the time. He um has since moved to
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New York for a while and after that
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moved down to Arkansas
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all the while doing great sports
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analytics journalism and work and he's
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carried a water for a lot of different
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organizations. He's got a terrific
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um terrific uh I've just lost my word
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>> substack.
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>> Substack. He's got a terrific Substack
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almost daily. Neil will keep you posted
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in all kinds of interesting ways on
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sports analytics. And one of the reasons
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we reached out to Neil this week is
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because there are so many different
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topics we could talk about. It's hard to
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pick a guest and just choose one of
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them. Neil has range. Epstein would love
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Neil Payne because he's got range. He
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can talk about any sport. In fact, he
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talks about sports we may not be
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interested in talking about, but we're
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going to focus on any number of sports.
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NASCAR is what I'm referring to. Neil,
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we'll see if we can get to that.
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>> Sure.
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>> But what why don't we say hello to Neil?
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Neil, welcome to the show. Thanks for
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joining.
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Hey guys. Hey, it's great to be back.
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I'm excited to talk to the whole crew
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and uh yeah, just love hanging out with
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you guys.
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>> Well, we'll take time with you anytime.
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I think we had you in person about this
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time last year. You came through for
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maybe a conference, some little
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football, a little Shabbat. We did a
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bunch of things in that short little
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visit. I think this was about a year
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ago. So,
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>> yes, we did.
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>> We'll take you in any form we can get
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you. Glad to have a visit. Guys, Neil
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has written his Substack on any number
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of topics over the last couple weeks. We
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can just kind of pick and go. Why don't
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Why don't we pass around choice? Y'all
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choose which of the topics you want to
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hear Neil talk about and we'll see if we
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can cover four in the in the first half
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hour here. Shane Jensen, do you have one
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that you want to talk about with Neil?
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Did you have a chance to look at him
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with his I know Audi looked at a bunch.
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How about we get Audi the first choice.
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Audi, you looked at a bunch. Let's see
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what he comes up with. Audi's first.
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Well, I you know, I was reading all
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about the Rams and and I definitely was
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going to cue that one up, but I I
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definitely I just heard from Neil that
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he's got a new one, not even posted yet,
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on baseball war, and you know, I can't
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resist. So, why don't why don't I let
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Neil tick pick from those two. Yeah, I
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knew that was going to be catnip for you
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guys. So, this is a post that's going to
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come out uh the same day that the
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podcast drops, actually. So, it's it
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kind of works out that people can go
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look at it. But I saw Matt Kemp was one
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of the names on this new uh class of
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firsttime Hall of Fame ballot uh
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potential uh baseball hall of fame
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members. It's not a great group, you
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know, apologies to Cole Hamls. He's
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probably the best one on there. There's
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Ryan Brawn on there who has a lot of
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baggage. Matt Kemp, Howie Kendrick, you
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know, guys like that. So, like a lot of
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Hall of Good type of guys, guys that I
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have nostalgia for for that era of like
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around 2010 type of uh peak of guys,
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probably not Hall of Famers. Maybe you
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guys can argue with me on that. But the
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one that really
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>> So, you're saying my sons and I may be
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by ourselves in Coopertown. Like, there
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may be no active player voted on and
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maybe the veterans committee will put
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some people on, but
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>> Well, I think they'll add the remember
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these are just the newcomers. So there's
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some holdovers from previous years that
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are uh most likely going to get in. Uh
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so it's really just the um the
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newcomers, but Matt Kemp really jumped
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out to me because uh as you guys know uh
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you mentioned I lived in Philly. Uh I
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worked for Sports Reference Sean Foreman
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uh and and all the great folks there um
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out in Mount Ary and uh I remember very
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distinctly Matt Kemp being one of our
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like early war stars. So we had and I
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get into this in the piece about a
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little about the history of war, how it
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kind of came out of the a bunch of
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different streams crossing with like
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Bill James and Windshares and Pete
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Palmer with linear weights and uh
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Mitchell Lickman with like the super
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linear weights where he used uh you know
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fielding data and base running data and
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all that and uh Tango Tiger Tom Tango
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who now works for MLB. He kind of pulled
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together all of those and came up with a
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framework for war, which then you had a
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bunch of folks kind of go off and run
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and calculate it for themselves. You had
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Sean Smith who created Rally Monkey War,
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RAR, it doesn't the R doesn't stand for
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reference. It stands for rally monkey.
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Uh that he was one of the ones. And then
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Fangraphs had their own version. I think
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Dave Cameron was uh involved in David
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Appleman in making that. And so that's
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how we ended up with now we have this
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like multi-olar war world that we've
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been living in for a long time with the
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fan graphs and the baseball reference
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the dueling wars. But at the time Sean
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Smith's war was the one that we had at
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baseball reference and Matt Kemp had a
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10 war season and I it always like
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sticks out of my mind like hey man he
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was a capital G guy that year. He had an
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awesome season, but that 10- war season
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only lasted for about 7 months because
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the very next spring uh in 2012, we
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recalculated war. Uh and uh I worked
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with Sean Foreman and uh you know, kind
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of we all consulted together to come up
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with just changes in how we would
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approach the ways that it was
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calculated, including very importantly
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putting defensive runs saved as the
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fielding metric instead of total zone
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rating. And that ended up tanking Matt
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Kemp's war by a couple of wins uh at
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that time. Uh and so now he is at eight
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war. So only if you were around, you
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know, the the younger folks just
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wouldn't understand the reality of Matt
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Kemp as a 10- war player. But uh if you
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were around at the time, you had that
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picture in your mind. I did at least.
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And then now you kind of look at it,
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you're like, uh, you know, not quite as
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good. And he wasn't the only one that
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got dinged. you know, Albert Pooh's had
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didn't hadn't had a 10 more season at
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baseball reference, but he did at that
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time and uh Adrien Beltree as well. So,
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there were some seasons that just kind
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of uniformly got changed by that. But
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the only one and and I don't think it
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really matters for Matt like Matt Kemp,
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he wasn't going to make the Hall of Fame
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whether he had 10 more that season or
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eight or whatever.
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>> Just to point out, just 10 considered
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like
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>> that's like an epic season 10. Well,
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yeah, 10 war. And I looked at this for
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the piece is that everyone who had 10
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war as a batter at least in a season
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except for Al Rosen who had 10.3 war in
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1953 and then he just fell off because
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of injuries. But every other player in
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that group is either in the Hall of Fame
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right now uh tracking to be in the Hall
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of Fame Hall of Fame. So we're talking
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about like Aaron Judge, have to mention
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him obligatory every episode. uh Mike
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Trout, uh Mookie Betts had a 10- war
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season or they were left out because of
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non-performance reasons. So Barry Bonds
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and uh A-Rod and Sammy Sosa had 10 more
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seasons. So that's your group. It's like
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pretty much there's I think 56 guys and
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of those 55 are either in the Hall of
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Fame will be or left out because they
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had a scandal. That's a so it is a
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meaningful barrier.
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>> Huge. Yes.
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>> Let me ask let me ask a question. I was
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actually I was going to ask the question
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about how rare a 10 more season is, but
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you've already kind of answered that.
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Let me ask a different question. Imagine
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I asked you or Audi or anyone else that
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thinks about war measures and said,
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"Look, I don't and it relates to what
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you just said about Matt Kemp, like I
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don't care if you get someone's war that
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accurately, whatever that means, if they
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have a one or two war, who cares? But I
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really care about getting it accurately,
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whatever that means, if they're eight
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and above, because then we're talking
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about the right tail, the distribution.
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These things has high predictive and
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importance value. Would people construct
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a different war measure if what mattered
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was getting it right quote unquote in
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the far right tail of the distribution
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than if they were trying to get a
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measure that was, you know, if you'd
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like, reflective of everybody along the
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distribution of war?
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>> That's a really great question. And I
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mean, I think uh uh this gets into also
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some of like, you know, true talent
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versus uh measured performance, like
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forward-looking talent versus backwards
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looking, giving you credit for what you
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did because like Matt Kemp, he was not a
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true talent 10 war player. He's probably
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not a true talent eight war player that
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year, honestly. But the the actions that
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he did had eight war worth of value as
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best we could measure it. So, I think
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that is maybe the distinction there of
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trying to kind of pinpoint like true
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talent versus what you the worth of what
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you did. But, it's a point well taken
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because you would think if we were
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designing a war that it should apply
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equally to an average player versus a
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superstar player just in the sense of
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like this is how much these particular
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actions are worth or or not worth uh on
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the field. But at the same time, yeah,
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there's huge error bars. And I think
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that's the main thing that I wanted
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people to walk away from the story with
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is, you know, there's so much of a
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culture now especially, and you know, I
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probably contributed to that, a lot of
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people have contributed to that of
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breaking down like, you know, decimal
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like fractional differences in war or
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whatever metric you want. Uh, but I
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think pro football reference and I think
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Bill James came up with this in baseball
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even the idea of approximate value. We
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need to keep that in mind for all these
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metrics because they are still at their
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core approximate like it doesn't matter
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that it has
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>> Would you be okay if I did the following
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and you're right you could translate my
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question into is the standard error of
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war constant across the war scale and it
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probably is not. Would you be okay if I
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wanted to build a bootstrap distribution
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of performance based on uh and then I
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could simulate a gazillion seasons and
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>> No, I would not. That's a different
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issue that has to do with predictive
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accuracy.
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>> No, I wasn't doing predictive accuracy.
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I
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>> know you're trying to So, the problem
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with the standard error of war, if if
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you want to call it that, right? So,
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it's not even it's
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>> I can call it that because it's the
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standard error of estimated war. I can
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call it that.
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>> No, but you can call it anything you
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want. But when you say standard error,
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we we statisticians think sampling
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variation is as the root of uncertainty
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as the root. Where is that coming from?
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The uncertainty is not I mean it can be
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when you do predictive that's that but
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these wars that we're talking about the
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common war Aaron's 10.1 Aaron judges
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10.1 Kemp's eight or whatever those are
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historical wars that's what he did on
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the field in the past we have bias these
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are bias meth problems they're not
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uncertainty they're bias problems no my
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standard error is coming from the fact
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that Aaron Judge I'll use what Neil said
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the true score model I did not observe
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Aaron Judge or Matt Kemp or Massie for
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let me finish my for 10,000
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observations. I I observed him for some
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number of finite number of plate
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appearances. That's a it's a sample of
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plate appearances. Any model observed
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score equals true score plus error is
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going to have measurement error. And I'm
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going to adjust war for that random
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sampling. That's what I was asking Neil
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about. Well, and I think that really
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gets into when you're talking about the
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defensive metrics because we can kind of
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work out the value of the offense.
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Although even that has philosophical
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questions of like we're using sort of an
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average weight for a single, an average
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weight for double or whatever when in
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fact those had different run values
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depending on the context of the
00:11:44
situation and then you get into well
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should we use WPA or you know RE24 or
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whatever but then for the even if you're
00:11:52
taking the standard sort of like hey
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we're using generalized values for these
00:11:56
actions the defensive uh measurements
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have such uh I think error bars around
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them because They're ultimately trying
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to kind of measure a negative, measure
00:12:06
the play that wasn't made or the runs
00:12:08
that weren't scored. And so there's a
00:12:10
lot of I think uh estimation error
00:12:13
around that on defense particularly. So
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listen, I I got to jump in because Eric
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said something that has to get
00:12:19
clarified. You're talking about a war
00:12:21
that's estimating what what what Neil
00:12:23
calls true talent. True talent is
00:12:26
interesting. We can try to figure that
00:12:27
out. We can try to measure that on some
00:12:29
scale. But but most war values,
00:12:32
particularly the the ones we look at in
00:12:33
offense, are not trying to measure true
00:12:35
talent. It's trying to trying to measure
00:12:37
what you brought last year in the 650
00:12:40
observations that I saw, not what you
00:12:42
would do next year or what that's not
00:12:44
what I'm saying actually. I'm saying
00:12:46
something different, which is if you
00:12:48
know, I'll be frequentist for a second,
00:12:49
which is hard for me to do, but I'll
00:12:51
try. Which is, you know, you observed
00:12:54
this 650, but you could have observed a
00:12:57
different 650. and had to observe that
00:13:00
which is based obviously I'm going to
00:13:02
condition on the observed data that I
00:13:03
had and I could observe a different 650
00:13:06
and that would give me a different
00:13:08
estimate of war. I'm literally talking
00:13:10
about the sampling properties of the
00:13:11
estimator of war, which is the standard
00:13:14
frequentist way to think about standard
00:13:16
errors. And so that's what I'm referring
00:13:18
to. And I would guess that the standard
00:13:20
error of war or war hat if you'd like is
00:13:24
not constant along the scale. And that's
00:13:27
what I was asking Neil about. But that
00:13:29
that first I don't think we know the
00:13:31
answer to that. I would guess that it's
00:13:32
probably not. Um but secondly what
00:13:35
you're doing is using a frequencies uh
00:13:37
understanding of of standard error which
00:13:39
imagines there's some theta an unknown
00:13:41
theta and war is not an estimate of an
00:13:43
unknown thing it's unknown to us because
00:13:45
we don't know how to define it and and
00:13:47
in defensive measure we get all lost
00:13:49
>> it's a different so good by the way by
00:13:50
the way I just to our listeners out
00:13:52
there I completely agree with your
00:13:55
premise but that's a different issue I
00:13:56
would almost use the measurement term of
00:13:59
there's a construct validity issue
00:14:01
>> is we don't even know What is that thing
00:14:04
that we're measuring? Exactly.
00:14:05
>> I was saying if you could define it and
00:14:07
if you could say there's a deterministic
00:14:10
mapping between performance and that
00:14:13
measure, then you can create a sampling
00:14:15
based estimator of the uncertainty in
00:14:18
that because there's a deterministic
00:14:19
mapping and all I got to do is reshuffle
00:14:21
the observations in some way. Hey
00:14:23
fellas, we I think this is a very
00:14:25
reasonable point to wrap on because you
00:14:27
said there's a there's a construct issue
00:14:29
here which is obvious to anybody listens
00:14:31
to a single conversation with us about
00:14:32
war. So this is one of the clear things
00:14:34
we take away made salient from the first
00:14:37
observation that Kim's got knocked from
00:14:39
10 to 8. But Shane hasn't jumped in.
00:14:41
Let's give Shane a word and then let's
00:14:43
move to a different topic from Shane.
00:14:45
Well, I guess you know since we're
00:14:48
talking about things as a construct
00:14:49
anyway, I I this kind of the judge
00:14:51
discussion and u got me thinking with
00:14:53
war of course historically we've thought
00:14:55
about it like the fact that it's it's
00:14:57
pinged or normed relative to
00:14:59
replacement. I mean it makes sense for
00:15:01
like roster construction and the
00:15:03
building of a baseball team uh to do
00:15:06
things that way. Okay. But when you talk
00:15:07
about something like, you know, if
00:15:08
you're trying to measure like the
00:15:09
deviation of arian judge or something
00:15:11
like that or the MVP conversation, I
00:15:13
think it makes like something like more
00:15:14
that's like, you know, like a war above
00:15:17
like a average or something like that. I
00:15:19
I don't think replacement's the right
00:15:21
kind of quantile of the distribution or
00:15:23
whatever to compare something like that
00:15:26
to. And I think if we were doing some
00:15:28
kind of relative to average baseball
00:15:31
player, not only would that kind of just
00:15:33
shift things, but I think it might kind
00:15:34
of change the calculation a little bit,
00:15:37
right? Because you'd have a different
00:15:38
distribution of fielding and offense
00:15:40
that you'd be comparing like
00:15:41
standardizing relative to.
00:15:43
>> So that's the question is like what
00:15:44
would that do other than just rescale
00:15:46
it? Because the rescaling alone wouldn't
00:15:48
be interesting, but I but there may be
00:15:51
other knock-on consequences. So you know
00:15:54
for example um different positions might
00:15:56
the gap between players and average
00:15:59
might be different that
00:16:00
>> or or or I don't know how the fielding
00:16:01
models are like like does it it would
00:16:03
change the pool that the field that like
00:16:05
like the actual modeling components of
00:16:08
it the pool of players over which you
00:16:10
would estimate those models I think
00:16:12
would change
00:16:12
>> I want to
00:16:13
>> because you're starting to talk about
00:16:15
average price you talk about regular
00:16:16
players so now you're just kind of
00:16:18
you're almost conditioning on kind of
00:16:19
starter like you know a different kind
00:16:21
of type of player basically that you'll
00:16:23
be kind of calculating these models of.
00:16:26
>> Yeah. And the value uh I think first and
00:16:28
foremost the value of sort of like the
00:16:30
bulk of playing time would go down
00:16:33
because you'd be compared to a zero of
00:16:35
average. The whole thing the in fact in
00:16:37
Tango's model the whole thing that uh
00:16:40
differentiates wins above average versus
00:16:42
wins above replacement is literally just
00:16:44
playing time. it's giving you value for
00:16:46
how much you played because ultimately
00:16:49
uh his insight was that there is value
00:16:51
to a very slightly below average player
00:16:54
that plays a lot at a level above
00:16:57
replacement. That's the entire point of
00:17:00
war. But I think you're totally right,
00:17:02
Shane, that for purposes of asking
00:17:04
questions of like greatness, the the
00:17:07
zero baseline point should not be the
00:17:09
replacement level player because that
00:17:11
player, we know they're not great. an
00:17:13
average player is a lot better than a
00:17:14
replacement level player, but we're
00:17:16
talking wins above average would and
00:17:18
they actually have this at baseball
00:17:19
reference if you look at it. Of course,
00:17:20
it still has Aaron Judge as the leader
00:17:23
by a significant margin over Cal
00:17:25
Raleigh, but uh it is sort of a more
00:17:28
reasonable point for those conversations
00:17:29
because ultimately wins above average is
00:17:32
what you did to literally help win games
00:17:34
for your team. The average team is 500
00:17:37
and whatever you add to that gives your
00:17:39
team a winning record. But you could set
00:17:41
that baseline point anywhere. You could
00:17:43
say wins above all-star, wins above Hall
00:17:45
of Famer, and most players would be
00:17:47
drastically negative under that. But
00:17:48
that could be a cool exercise like build
00:17:51
your own replacement level.
00:17:52
>> Eric's trying to jump in, Eric.
00:17:55
>> No, I was just going to I I thought you
00:17:56
wanted to change the topic. I was just
00:17:58
raising my hand to change.
00:17:59
>> I want to let you do that. I'm going to
00:18:00
ask one. I'm always trying to get
00:18:02
calibrated myself on these things and
00:18:03
this is one of these things I am not
00:18:05
calibrated on. You talked about the
00:18:07
threshold of 10 war and we talked about
00:18:10
when hopefully we don't talk about
00:18:12
judges MVP over Rally, but let's just
00:18:14
touch on that for a moment. Um, what was
00:18:17
the war produced by those two players
00:18:19
this year? I'm curious how close Raleigh
00:18:21
got to 10.
00:18:22
>> Well, a baseball reference judge was
00:18:25
almost exactly 10. He was 9.7 and then
00:18:28
Raleigh was 7.4. Now, that could open
00:18:30
up. I know you guys have talked about
00:18:32
this. You know, I have a whole can of
00:18:33
worms where I think MVP is like a
00:18:35
narrative storytelling award and that we
00:18:38
should give credence to the the, you
00:18:40
know, unprecedented historical nature of
00:18:42
things in in ways that maybe aren't
00:18:45
reflected by a stat. You
00:18:46
>> mean Judge the first guy to win the
00:18:48
batting title and hit over 50 home runs
00:18:50
in 60 years? You are right, Neil.
00:18:52
>> Yeah. No, that's exactly what I was
00:18:54
talking about. I wasn't talking about
00:18:55
demolishing the catcher record for home
00:18:57
runs by more than a dozen.
00:18:59
>> That sucks.
00:19:01
or the switch hitter record either.
00:19:03
>> Yeah, that too. Yeah.
00:19:04
>> All right, guys. Let's go. Gradle, pick
00:19:05
us a new thread from Neil's Substack or
00:19:08
other writings.
00:19:09
>> I will. So, I noticed this actually as I
00:19:11
was preparing for the show today. It's
00:19:13
in the rundown I put and Neil noticed
00:19:15
this as well. So, two things struck me
00:19:17
about the NBA right now. I know we're
00:19:20
only roughly 15 games in, but first, OKC
00:19:24
is 14-1 with a 15.5 differential. Now,
00:19:28
that's, as we know, would break their
00:19:31
last year's record, which broke the
00:19:32
record for the highest point
00:19:34
differential ever in the history of the
00:19:36
NBA. But that's not what shocked me the
00:19:39
most. The thing that shocked me the most
00:19:41
is that there are five teams with a
00:19:42
minus 10 plus on the record. And
00:19:46
actually, I started to say to myself,
00:19:48
first, you know, OKC is going to be
00:19:50
playing those teams. you know, maybe OKC
00:19:52
breaks the record this year of 73 and N
00:19:56
on a historically set of bad teams. And
00:19:59
two, I've just never seen a distribution
00:20:01
like this. Now, it's early in the
00:20:03
season, but I mean, if a bunch of teams
00:20:06
end up with a minus 10 differential and
00:20:09
OKC breaks the record by three or four
00:20:11
points, I know running up the score on
00:20:13
these bad teams. Wow. So, that was the
00:20:15
thing that caught my eye. I know you
00:20:17
have looked at this, so I wanted your
00:20:18
thoughts on it. Yeah, you said you've
00:20:20
never seen that before. That's because
00:20:22
it's like literally never happened
00:20:24
before. And that was something that I
00:20:25
wrote about because I noticed the same
00:20:27
exact thing where it's like, okay, see,
00:20:29
they had this historic year last year.
00:20:31
Uh, but we've been having historic
00:20:33
points per game or net rating, whatever
00:20:35
you want to measure it in, differential
00:20:37
seasons in the NBA going back almost on
00:20:40
like a yearly basis recently. Like the
00:20:42
Celtics a couple years ago were in that
00:20:44
conversation. Obviously, we had those uh
00:20:46
Steph Curry, Kevin Durant uh Warrior
00:20:48
teams before that, but we uh we've kind
00:20:52
of had teams challenging this record.
00:20:54
And last year, I noticed this as well
00:20:56
because people were asking me, "Hey, do
00:20:57
you think it's more that we have good
00:20:59
teams or that we have a bunch of bad
00:21:01
teams?" And so, I looked into it and
00:21:02
when I did it last year, I found that
00:21:05
yes, there was a slight effect of the
00:21:07
really bad teams, but mostly it was just
00:21:09
Washington. They were like the team that
00:21:11
was uh substantially worse than the
00:21:13
average for their slot. Cuz I looked at
00:21:15
it by like, hey, what's the average
00:21:17
differential for like the number one
00:21:19
ranked team, the number two all the way
00:21:20
down to number 30? The Wizards were way
00:21:22
worse than the average number 30 over
00:21:24
the past, I think since the the last
00:21:26
expansion, so 05. But uh the the top
00:21:30
teams were better than their averages.
00:21:32
So I concluded that it's probably like
00:21:34
the good teams are good with a little
00:21:35
bit of bad teams are bad. This year
00:21:37
though, it does seem like it's being
00:21:39
driven mainly by the bad teams being
00:21:41
really bad. And if you look at the
00:21:43
distribution, you have this like
00:21:45
ridiculous historical uh number of teams
00:21:48
that are minus 10 or worse, like you
00:21:50
mentioned, Eric. And then you have a lot
00:21:52
of teams that are way better, especially
00:21:54
the top three, Oklahoma City, Denver,
00:21:56
and Houston.
00:21:56
>> They would all break the record.
00:21:58
>> They would break the record, right? And
00:22:00
this is through uh I didn't compare uh
00:22:03
full season from the past to the first
00:22:06
13 games this year because that would
00:22:07
kind of skew things. We were talking
00:22:09
about sampling earlier that you're going
00:22:11
to obviously have a wider distribution
00:22:13
in a smaller sample of games than an 82
00:22:16
game per team sample. So I just looked
00:22:18
at the first 13 games per team of other
00:22:20
seasons. And even compared to that, this
00:22:22
is a wildly different uh distribution.
00:22:26
Much wider. the highest standard
00:22:27
deviation of team points per game
00:22:30
differential to start a season ever, at
00:22:32
least since the the merger. I didn't
00:22:34
look before the merger, but I think that
00:22:35
that's telling uh that you have also one
00:22:38
last thing, you have a weirdly good
00:22:40
middle class of teams relative to how
00:22:42
good the middle class usually is, and
00:22:44
those teams are feeding as well on these
00:22:46
really bad teams. So, it's going to
00:22:48
completely distort what we think of as
00:22:51
sort of the benchmarks for a good team.
00:22:53
What regular season success means
00:22:55
predictively for the playoffs when you
00:22:57
get rid of the Wizards and and the
00:22:59
Pacers and the Nets and the Pelicans and
00:23:01
Kings and all these really bad teams.
00:23:03
And so, the NBA has had a legitimacy
00:23:07
problem for its regular season for a
00:23:08
very long time. And this is, I think,
00:23:11
probably shaping up to be the worst that
00:23:14
I can remember in that regard where we
00:23:16
know OKC is good, but like how good and
00:23:19
relative to other playoff teams that
00:23:21
some might be pacing themselves, some
00:23:23
might be load managing, some might be
00:23:25
injured, but getting better by the
00:23:26
playoffs. We just don't know how good
00:23:29
these teams are relative to each other.
00:23:31
And it's like this entire 82 game
00:23:33
multimonth sample going into the
00:23:35
playoffs is like I don't want to say
00:23:36
completely worthless but like it's not
00:23:39
giving us the signal that we're used to.
00:23:41
I was going to ask a question related to
00:23:43
this. So as our resident both basian and
00:23:47
probabilist theorist etc. Would you
00:23:50
expect the distribution of let's say
00:23:52
average point differential per game?
00:23:54
Could I make an argument that the
00:23:56
central limit theorem would apply
00:23:57
because it's shots and then of course
00:24:00
it's games and it's averages of games
00:24:02
and the question reason I'm asking is if
00:24:04
I don't know if Neil looked at this but
00:24:06
if I looked at the histogram of point
00:24:08
differentials would it be approximately
00:24:10
normally distributed or should it
00:24:11
conceptually therefore okay so if it is
00:24:14
can we
00:24:16
significance tests against that to see I
00:24:19
mean listen even one last part of the
00:24:21
sentence could we score seasons on how
00:24:24
non-normal they are and maybe this is
00:24:26
what Neil did and you know I I'm just
00:24:27
asking you a question from a good idea
00:24:29
actually
00:24:30
>> I'll just throw in I would guess that
00:24:31
the true talent whatever your true
00:24:33
differential is on a neutral field or
00:24:35
whatever you want to measure that is
00:24:36
probably also normal doesn't have to be
00:24:38
but I would imagine it probably is it
00:24:40
might have a spike in slab or something
00:24:42
like that you know some tail marginal
00:24:45
tail probability giving you
00:24:46
>> true talent in in in terms of the team
00:24:50
quality or individual
00:24:51
>> team quality yeah team quality Okay,
00:24:54
>> team quality. I mean, remember the teams
00:24:55
are a bunch of averages to start with,
00:24:57
right? Um, but they don't have to. And
00:24:59
then, of course, the actual what
00:25:01
actually transpires over the course of a
00:25:02
season is probably normally distributed
00:25:04
conditional on your true talent. That's
00:25:06
probably true as well. So, you're going
00:25:07
to you're going to get something that's
00:25:09
approximately, but I think you're you're
00:25:11
your your your inquisitive look there,
00:25:13
Kate, suggests that there's no reason
00:25:15
why true talent should be normally
00:25:16
distributed, right?
00:25:17
>> I just I don't think the individual
00:25:20
talent is normally distributed in the
00:25:22
professional league. So that that's that
00:25:24
>> no no no not at all but but team if you
00:25:26
look at sort of the distribution of the
00:25:28
winning percentages historically.
00:25:29
>> So what you're saying a just for all our
00:25:31
listeners out there is that you're
00:25:33
basically going to get a Gaussian a
00:25:34
normal mixture of normals which will be
00:25:36
normal. But of course if if what Kate is
00:25:39
suggesting by his expression is that the
00:25:42
distribution of team talent was skewed
00:25:44
or multimodal then you're going to get a
00:25:45
non-normal mixture of normal. They
00:25:48
provide a different distribution. That's
00:25:50
right. But either way, I I I like this
00:25:52
idea of like let's do a significance
00:25:55
test on the distribution of point
00:25:56
differentials and maybe we could score
00:25:58
seasons by how non-normal they are or
00:26:00
this we could look at the variance
00:26:02
across seasons, look for patterns and
00:26:03
stuff.
00:26:04
>> Great, great, great. But we are being
00:26:05
geeky analysts here instead of asking
00:26:07
the practical question which is why why
00:26:10
would it be that we have these tails
00:26:11
this year?
00:26:13
Well, I think one of the reasons why is
00:26:15
that you have like your usual tanking
00:26:17
teams uh that are just like perma bad
00:26:20
and have been bad. I mentioned the
00:26:22
Wizards. I mean, the Wizards just like,
00:26:24
you know, they they have not really been
00:26:26
relevant in a while. You also have teams
00:26:28
that I think, and I had an interesting
00:26:30
conversation with someone, one of my
00:26:32
commenters about this, where they were
00:26:33
like, well, did the Pelicans really like
00:26:35
try to tank by getting worse? And I
00:26:38
looked at it and I was like, well, you
00:26:39
know, they did bring in Jordan P, who
00:26:41
might be like a one-man tank uh
00:26:44
operation himself. But I think they had
00:26:47
this premise of, well, we won 49 games
00:26:49
two years ago, won 21 last year, but
00:26:52
Zion Williamson was injured and like all
00:26:54
of our guys kind of underperformed at
00:26:56
the same time. So, we're just going to
00:26:58
kind of cross our fingers. We'll make a
00:27:00
few uh superficial changes but then
00:27:02
cross our fingers and hope that on the
00:27:04
other side we can kind of regress back
00:27:06
to that 49 win mean. Now they just fired
00:27:09
their coach Willie Green which indicates
00:27:11
that and and Zion has missed like half
00:27:13
the season which indicates that they did
00:27:15
not regret the mean they regressed
00:27:16
toward was the 21 win version not the 49
00:27:19
win version. Uh so you have a few teams
00:27:21
like that. Sacramento also in that group
00:27:23
where it's almost like passive tanking
00:27:24
like they just made some bad moves and
00:27:26
they've they had some success a few
00:27:29
years ago which was rare for them and
00:27:31
then they misread that as being capable
00:27:33
of being carried forward but they didn't
00:27:34
do enough to keep the momentum going and
00:27:37
so instead they've just kind of like
00:27:38
slid into oblivion. Then I think the
00:27:40
Indiana Pacers are really interesting
00:27:42
because I think they're doing a version
00:27:44
of if you remember the 1997 9697 season
00:27:48
in the NBA. David Robinson was hurt
00:27:51
really early in the season for the San
00:27:52
Antonio Spurs. And so they just kind of
00:27:54
did like an impromptu tank. This was a
00:27:56
team that was like a perennial 50- win
00:27:59
conference finalist type of team for
00:28:01
years, but they opportunistically
00:28:03
decided like, hey, the Admiral's out.
00:28:06
We're not going to be uh worth anything
00:28:07
anyway. I think it was Greg Papovich's
00:28:09
first year as coach as well. So, they
00:28:11
were just like, "Let's do a situational
00:28:13
tank. We'll try to get as good of a
00:28:15
draft pick as possible and then we'll
00:28:17
reload next year when he's healthy and
00:28:19
we can kind of uh, you know, move
00:28:21
forward that way."
00:28:21
>> You're going to tell us this got him Tim
00:28:23
Duncan
00:28:23
>> and it ended up getting them Tim Duncan.
00:28:25
They got very lucky in the draft as
00:28:26
Celtics fans. Maybe you Shane could
00:28:28
remember this. Uh but uh they they got
00:28:31
lucky in the lottery to be able to get
00:28:33
Tim Duncan, but it was also very shrewd,
00:28:36
which I think is the right way to do
00:28:37
tanking of not this like deliberate
00:28:39
multi-year kill the fan base type of
00:28:42
like we're just this malaise sets in,
00:28:44
but situationally if you're Indiana
00:28:46
Tyres
00:28:47
>> call that they call that the process.
00:28:48
>> The process. Yes, that's what we call it
00:28:50
now. We've developed that for it. Uh
00:28:52
which, you know, that's a whole other
00:28:54
thing we could open up. But I I like
00:28:55
this Indiana approach of if you're going
00:28:57
to tank, Tyrus Hallebertton is out for
00:29:00
the year and you're coming off this
00:29:01
finals appearance, but it was a little
00:29:03
bit of a fluky finals appearance. They
00:29:05
would be one of the weirder teams to
00:29:06
have won even though they got to game
00:29:08
seven. Arguably, you know, should have
00:29:10
or could have won that game if he
00:29:11
doesn't get hurt, but they would
00:29:13
definitely be one of the more
00:29:14
mold-breaking champion teams if they had
00:29:16
won that. And so now they're just going
00:29:19
to be as bad as possible and be in the
00:29:22
hunt for there's a number of good
00:29:24
prospects in the draft next year and
00:29:26
then they'll get Hallebertton back and
00:29:28
then they'll have another guy that's you
00:29:30
know poised to to make a difference
00:29:32
coming up and they could be good again.
00:29:34
But I think it's this confluence of like
00:29:36
you're getting to tanking from like
00:29:38
multiple different uh possible ways that
00:29:40
are making them so bad. And then OKC,
00:29:43
you know, we shouldn't downplay them and
00:29:44
say they're a product of uh of these bad
00:29:47
teams. I mean, they're a really great
00:29:49
team. Uh Denver looks amazing as well.
00:29:52
Houston with Kevin Durant, they they
00:29:54
actually did make changes to improve
00:29:56
their team. Then you have other teams
00:29:57
like San Antonio, Detroit, where they're
00:29:59
kind of emerging from they did do a
00:30:02
tanking type of uh cycle, but now
00:30:04
they've got the stars out of that and
00:30:06
they're moving forward uh with that. So,
00:30:08
it's like the confluence of a bunch of
00:30:10
different like team timets syncing up at
00:30:13
the same time in the league right now to
00:30:15
produce this crazy lopsided distribution
00:30:17
of teams.
00:30:18
>> All right. Well, um, Eric, do you want
00:30:20
to jump on this before we move?
00:30:22
>> Yeah, I just had two follow-up questions
00:30:23
to that, Neil. So, when do we get to a
00:30:25
point quickly that you would, uh,
00:30:27
predict OKC may break the record of 73
00:30:30
wins? They're certainly at a pace right
00:30:31
now above it. And given the number of
00:30:33
bad teams, um, you may you may get to
00:30:36
that point. Um, and uh I don't remember
00:30:39
my second question, but that was my
00:30:41
first one. Oh, I remember my second one.
00:30:43
I'm going to give you OKC,
00:30:47
Denver, and Houston,
00:30:52
and I'll take the rest of the league.
00:30:53
How do you like that to win the finals?
00:30:56
Uh, I, you know, could I swap out uh
00:30:59
Cleveland for Houston? At least those
00:31:01
are in my model. Those are the top three
00:31:03
cuz they would have around a combined
00:31:05
60% chance. uh OKC, Cleveland, and
00:31:08
Denver. But if you're giving me the
00:31:09
Rockets, I still probably take it
00:31:10
because it would be about 55 45.
00:31:13
>> Well, I know Audi's going to take the
00:31:14
field, but you're going to take OKC,
00:31:16
Houston, and Cleveland. And all right,
00:31:18
we're so good. We'll track this.
00:31:19
>> OKC is really carrying that probability,
00:31:22
right?
00:31:22
>> It's about them at kind of a really high
00:31:26
amount. That's all OKC based. Yeah, I
00:31:30
think it's the majority OKC and then uh
00:31:33
Cleveland and Denver around double like
00:31:35
low double digits in there. Um what was
00:31:37
the first question? I forgot OKC break
00:31:39
the record. Can they get it to 73?
00:31:41
>> Oh, so I think they need a little bit
00:31:43
more. So the they're projected in my
00:31:45
model for about 65 wins right now. I
00:31:47
think their overunder at FanDuel when I
00:31:49
looked at a couple days ago was like 65
00:31:51
and a half. So that tells me they need a
00:31:53
little bit more. But probably if they
00:31:54
sustain this for another month, they
00:31:56
would be like their over under at
00:31:57
FanDuel might be above 73 wins by then.
00:32:01
>> Neil, I have one follow-up question. Um,
00:32:04
you know, we talk about owners on the
00:32:05
show a fair bit and thinking broadly
00:32:08
people appreciate more now than they
00:32:09
used to, how important owners are, but
00:32:11
probably still don't appreciate how
00:32:12
important owners are. Just observing the
00:32:14
Pelicans owner is the same as the Saints
00:32:16
owner. Am I wrong to be judgy about that
00:32:19
particular ownership group?
00:32:22
Well, in general, I think owners make a
00:32:24
huge difference in the NBA and it it has
00:32:26
to do with like setting the culture,
00:32:28
convincing players to come there, the
00:32:30
management, uh, like it just filters
00:32:32
down from the top. So, I think we had
00:32:35
always traditionally as analysts
00:32:37
undervalued the importance of the owner
00:32:39
in general. In terms of that ownership
00:32:42
group, I mean, it's tough to say when it
00:32:44
came to uh it probably they have been
00:32:47
responsible for giving Zion such a long
00:32:49
leash even when he's been out of shape,
00:32:51
even when he's been injured every single
00:32:53
year and they've just kind of pinned all
00:32:55
their hopes on him rather than trying to
00:32:58
think of like, okay, this Zion thing is
00:33:00
not working out for us and we might need
00:33:02
to think about moving on. It's like that
00:33:04
lottery with Zion was a long time ago.
00:33:06
like I was still at 538 and uh it was
00:33:09
like pre- pandemic uh I want to say. So
00:33:12
that tells you like you can get it in
00:33:13
your head that a certain player is like
00:33:15
I don't know you fall in love with them
00:33:16
as a transformational talent and
00:33:19
certainly when healthy Zion is that type
00:33:21
of player but at a certain point you
00:33:23
have to kind of understand and recognize
00:33:25
that he probably is never going to live
00:33:27
up to what you thought he was going to
00:33:29
be and then uh move on
00:33:33
>> yeah it's I think it's important to
00:33:35
recognize there's a reason for why this
00:33:37
happens in basketball and it has to do
00:33:38
with the smallness of the team and the
00:33:41
ability ability of a generational talent
00:33:43
by himself to bring you to at
00:33:46
championship or near it. And because of
00:33:49
that, an owner just thinks they could
00:33:51
they don't they could just do it right
00:33:53
with and kind of get involved and put
00:33:56
all your chips behind one or two players
00:33:57
and tell them this is who we should be
00:33:59
drafting. And
00:34:00
>> all we could do, Audi, is forecast who
00:34:01
that generational talent was going to be
00:34:03
with certainty. We'd be all set,
00:34:05
>> right? But, you know, they don't even
00:34:06
come along that much. So, I think you
00:34:08
wouldn't see it in baseball because it's
00:34:10
such a damn big organization with with
00:34:13
26 on a roster and and 200 as a minor
00:34:16
and football rosters are huge. No one
00:34:18
would have that. I mean, I shouldn't say
00:34:20
no one. People have very big egos, but I
00:34:22
would imagine that that kind of hubris
00:34:24
is ridiculous to try to imagine you can
00:34:26
manage.
00:34:26
>> Well, this is really neat. I've never
00:34:27
heard that before. And it's an
00:34:28
interaction between ownership hubris,
00:34:30
which we know to be a thing, and
00:34:32
structural differences across sports,
00:34:34
which we know to be a thing. I've just
00:34:36
never thought about the interaction
00:34:37
between those two things. I think it's a
00:34:38
really interesting observation. Why
00:34:40
don't we wrap the first half there since
00:34:43
we've been so slow to work through
00:34:44
Neil's production. We'll keep him
00:34:46
around. We'll do the second half with
00:34:48
Neil, too. So, come back and join us
00:34:50
after the break.
00:34:55
Welcome back. Welcome back to Wharton
00:34:57
Moneyball. Welcome back to the second
00:35:00
half of this week's show. talking sports
00:35:02
analytics as we always do as we have
00:35:04
been for 11 and a half years now and I
00:35:06
got the whole crew in here. Shane Jensen
00:35:07
is here. Audi Winer's here. Eric
00:35:09
Bradler's here. This is Kade Massie. We
00:35:11
are talking with our guest Neil Payne.
00:35:13
We like Neil so much. We're going to
00:35:15
keep him around for the second half of
00:35:16
the show. Also, he's so dang prolific.
00:35:19
We have to keep him around to work
00:35:20
through his stuff. By the way, I just
00:35:22
realized I I was reading Barnwell this
00:35:24
morning for the first time in a long
00:35:25
time. He's another prolific guy, right?
00:35:27
When he decides to write something, he's
00:35:29
going to write something. Those are long
00:35:30
pieces. super insightful, but really I
00:35:33
thought Neil, y'all are kind of cousins
00:35:35
in in the way you do your work. I mean,
00:35:37
you're great writers, but also good
00:35:38
analysts and you're very prolific and um
00:35:42
I was thinking about you while I was
00:35:43
reading this stuff. We um anyway, for
00:35:45
what it's worth. All right.
00:35:46
>> Good company to be in.
00:35:47
>> It's good company for sure. Okay. We
00:35:49
talked about two of Neil's recent
00:35:51
pieces. We've talked about baseball and
00:35:54
basketball. Shane is going to pick a
00:35:56
thread, then I'm going to pick a thread.
00:35:57
Shane, what do you got? Well, I wouldn't
00:35:59
mind uh talking a little bit about the
00:36:02
recent article you read on or wrote on
00:36:03
the Rams, I guess, and basically talking
00:36:06
about how it seems like I I mean, you
00:36:08
could talk a little bit about the
00:36:09
details. They're they're you know,
00:36:11
they're kind of a the number one kind of
00:36:14
team in in the foot in football kind of
00:36:16
in terms of at least balance defense and
00:36:18
offense. And it's kind of it's
00:36:19
interesting to kind of I I mean I don't
00:36:21
disagree, but one of the kind of points
00:36:23
you make I think in the article is that
00:36:25
it's not only that they're the Rams are
00:36:27
good now relative to the rest of the
00:36:28
2025 NFL, but they're good kind of
00:36:30
relative to past recent Rams kind of
00:36:33
McVey Vay Rams teams and I think about
00:36:36
those some of some of those old like
00:36:37
other Rams McVey teams where they had a
00:36:40
I feel like a lot more kind of talent
00:36:42
skill kind of people like Donald and Cup
00:36:46
and these types of players. So maybe
00:36:48
maybe talk a little bit about how how
00:36:50
the Rams are doing it. How this Rams
00:36:52
team is somehow better than those kind
00:36:53
those past teams.
00:36:55
>> Yeah. Well, I thought it was really
00:36:56
interesting. You know, the Rams uh they
00:36:58
were among the teams, you know, they
00:37:00
went to the playoffs last year, almost
00:37:01
knocked off the Eagles. A little scary
00:37:03
moment there. Uh at least speaking as a
00:37:05
Philly fan. Uh and so, um, you know,
00:37:08
they've kind of made this comeback, but
00:37:10
it's just kind of built on itself. Uh,
00:37:12
and this year you make a really strong
00:37:14
case that they've been the best team in
00:37:16
the NFL. They have the highest SRS
00:37:18
simple rating system, which is just
00:37:20
schedule adjusted point differential in
00:37:22
the league, especially after beating
00:37:24
Seattle, who's number two in the simple
00:37:26
rating system. It was not like a super
00:37:28
convincing win. it looked like it was
00:37:30
going to be early on, but just uh you
00:37:32
know the fact that they have emerged as
00:37:35
the the possible best team in the league
00:37:38
was something that I don't think people
00:37:40
would have thought just a few years ago
00:37:41
because if you think about the early
00:37:43
version of the McVey Rams, it was all
00:37:46
about almost this like I called it like
00:37:48
a get-richqu scheme type of approach to
00:37:51
team building, which was just like spend
00:37:53
a lot on stars, lock in your your
00:37:56
bigname stars if you've got them, go
00:37:58
out, get free agents, trade away all
00:38:00
your first round draft picks. They
00:38:02
famously went, I think, seven straight
00:38:03
years without a first round pick. And
00:38:06
ultimately, it was like a win now at any
00:38:09
cost type of approach. And they went to
00:38:11
the Super Bowl. And then a few years
00:38:13
later, after they uh traded from Goff to
00:38:16
Stafford, they won the Super Bowl. And
00:38:18
that seemed like that was going to be it
00:38:20
though. It was going to be like, okay,
00:38:21
you pushed all your chips into the
00:38:23
middle, you won this big bet that you
00:38:24
made, now the Bill has to come due. And
00:38:26
you know, it was a nice run. And then I
00:38:28
think they went 5 and 12 the year after
00:38:29
which was one of the biggest drop offs
00:38:30
by a defending Super Bowl champion ever.
00:38:33
But it really defied the expectations
00:38:35
that then they were able to kind of
00:38:38
retool off of that. They didn't even um
00:38:40
rebuild. They just reloaded and they
00:38:42
kept pushing forward. And so I was like
00:38:45
digging into why that happened. Some of
00:38:47
it is Matthew Stafford has been awesome
00:38:49
this season and he has been a very
00:38:51
inconsistent quarterback over the years.
00:38:53
a lot of it. You know, when he was in
00:38:55
Detroit, he had not that much supporting
00:38:57
talent aside from Calvin Johnson to kind
00:39:00
of work with, but uh he has always in my
00:39:04
mind early in his career, he had this
00:39:06
reputation of being a guy that like the
00:39:07
itest like better than the stats. Uh and
00:39:10
you'd think like he has Mahomes type
00:39:12
talent, but where's the where's the
00:39:15
production? But he in LA he has kind of
00:39:17
evolved and especially this year he has
00:39:19
been rightly considered one of the MVPs.
00:39:22
Puka Nakua helps as well. I did a little
00:39:24
thing where I did a principal component
00:39:25
analysis. You guys will like this on a
00:39:28
variety of different receiver stats
00:39:30
trying to kind of compress them down
00:39:31
into two dimensions. One of which was
00:39:33
like usage and one was efficiency. And
00:39:36
that could be efficiency on like deep as
00:39:38
a deep threat or as like an underneath
00:39:40
guy. But basically it's like how are you
00:39:42
doing your production and then how often
00:39:43
does the team call on you as like a
00:39:45
target share. And Puka showed up as
00:39:48
being one of the highest guys in terms
00:39:50
of combining efficiency mixing both the
00:39:53
attributes of an underneath guy and a
00:39:56
deep guy and then having one of the
00:39:58
highest uh target shares I think just
00:40:00
behind Jackson Smith injigba
00:40:04
uh on the season. And so, uh, I thought
00:40:07
that that's like, okay, that's an
00:40:08
underrated part of Stafford. But then
00:40:10
the main thing that I think separates
00:40:12
this Rams team is that their defense has
00:40:15
been really good, which was not a
00:40:16
hallmark of the early gooff, Todd
00:40:18
Gurley, you know, that type of Rams
00:40:20
team. Even when they went to the Super
00:40:22
Bowl, they were outclassed. Even though
00:40:24
they had Aaron Donald, they were
00:40:25
outclassed by that Bellich Patriots
00:40:27
defense in that Super Bowl. And it was
00:40:29
this idea of like, well, if you're this
00:40:30
high-flying offensive team, sometimes
00:40:33
defenses can just shut you down if you
00:40:35
don't really have that other gear. But
00:40:36
they've been one of the best defensive
00:40:38
teams in the league. And they've done it
00:40:40
almost entirely through like lower round
00:40:42
draft picks, undrafted guys, guys that
00:40:44
they got from other teams. Only two of
00:40:47
their defensive starters this year were
00:40:49
players that they drafted themselves
00:40:50
with a pick in the first two rounds. So
00:40:53
to me that's like conditional on them
00:40:56
making this turnaround despite trading
00:40:58
away all those picks and spending all
00:41:00
that money uh you know and not being
00:41:01
kind of capped out. The only way they
00:41:03
were ever going to be able to make this
00:41:05
pivot was Stafford finding the fountain
00:41:07
of youth and then discovering a bunch of
00:41:08
unsung guys. I mean Puka Nua is also a
00:41:11
fifth round draft pick and you look at
00:41:12
that guy now in his production he's like
00:41:14
he's probably the best receiver in the
00:41:15
league and you were able to get him at
00:41:16
at such a bargain. So, it's a testament
00:41:18
to the scouting that they did, the
00:41:20
coaching, the cap management, and you
00:41:23
got to get a little lucky to make to hit
00:41:24
on so many guys, but it's really worked
00:41:26
out for them so far this year. And I'll
00:41:28
just sorry again, I'll just add on top
00:41:30
of that one more thing just because you
00:41:31
talked several about them kind of
00:41:32
mortgaging their future and past drafts.
00:41:34
They actually now are a team with, I
00:41:36
think, two draft pick. They have two
00:41:37
first rounders. Yeah, they flipped it
00:41:39
and Atlanta's their their other pick.
00:41:41
And Atlanta, that draft that draft
00:41:43
slot's moving up the board as as we see
00:41:45
it is. and they also have some of the
00:41:47
most cap space for next year. So they've
00:41:48
totally flipped that identity as being
00:41:51
now they're kind of a balanced measured
00:41:53
we're being running this team
00:41:55
responsibly type of operation.
00:41:57
>> That it's interesting that they think
00:41:58
they have both gears. You know, most
00:42:00
front offices have one gear or the
00:42:01
other. And so that that's impressive,
00:42:03
but everything you said is impressive
00:42:05
about the front office. And what I the
00:42:07
main thing I'm taking away because I'm
00:42:08
kind of at a distance from this is I
00:42:11
would have thought any story about the
00:42:12
Rams would have centered around McVey,
00:42:14
but McVey is the offensive guy. And so
00:42:16
for you to say the defense has been kind
00:42:18
of a surprising and important part of
00:42:19
this is say oh well it's gonna I mean he
00:42:22
affects the whole team but his real
00:42:24
caches on the offensive side of the
00:42:25
ball. So it really puts more of the
00:42:26
premium especially based on the player
00:42:28
acquisition that you've talked about on
00:42:30
the front office. What else?
00:42:32
>> And MC,
00:42:32
>> oh, I was just going to say McVey, uh,
00:42:34
you know, one of his best qualities
00:42:36
seems to be as a manager. We've seen so
00:42:38
many times that these guys that are
00:42:40
really great tactician coordinators when
00:42:42
they become head coaches, they screw it
00:42:44
up and they can't really get it right. I
00:42:46
mean, that's like the story of a lot of
00:42:47
guys in the league. Even guys from
00:42:49
McVeyy's own pipeline uh, have have done
00:42:51
that and certainly other coaches trees.
00:42:53
Uh McVey seems to be really good at like
00:42:56
delegating to the right people, managing
00:42:58
the team, and just running it as a
00:43:00
professional operation, especially for
00:43:01
somebody so young. And he is shooting up
00:43:03
the list. I was looking at the list of
00:43:05
like career winning percentage. And it's
00:43:07
like, you know, he's I think top 30 in
00:43:09
wins above average or wins above 500 uh
00:43:12
all time in NFL history at this point.
00:43:14
So, it's sort of like pretty soon we're
00:43:16
going to look up and we're going to see
00:43:18
it's Shawn McVey is not this like
00:43:20
babyfaced kid that we think of him as,
00:43:22
but instead it's like, oh, he's like
00:43:23
literally one of the best coaches of all
00:43:25
time.
00:43:25
>> That's a neat stat you just quoted win
00:43:28
percentage, but you said wins above 500.
00:43:30
So, that combines both rate and duration
00:43:33
essentially.
00:43:34
>> Yeah, exactly. Yeah, it's like they have
00:43:35
uh it's it's like our wins above average
00:43:37
that we were talking about in baseball,
00:43:38
but for coaches um they have that at uh
00:43:40
Pro Football Reference, which I think is
00:43:41
a really cool stat because like you
00:43:43
said, you can't if you do winning
00:43:45
percentage, you have to set some kind of
00:43:46
threshold and it's like how many games
00:43:48
should be in that. So, it's a more
00:43:49
organic way to measure that.
00:43:51
>> Awesome. Eric's trying to get in. Eric?
00:43:53
>> Yeah. So, football's an interesting
00:43:56
construction in the playoffs. So, let me
00:43:58
ask you the following question, Neil.
00:44:00
Right now, I think most predictions
00:44:02
would have to have the Eagles as the one
00:44:04
seed besides strength of schedule
00:44:06
remaining. Um, they also beat the Rams,
00:44:09
which we could question. I mean, they
00:44:11
beat the Rams on a block field goal, but
00:44:12
ignoring that they beat the Rams.
00:44:15
If the Eagles played the Rams right now,
00:44:20
Eagles have home field, let's say, who
00:44:23
would be favored in that game given what
00:44:25
you just said that the Rams, By the way,
00:44:28
I looked at the It's interesting that I
00:44:29
I I saw what you were working on, but I
00:44:32
had done some stuff independently. I
00:44:34
mean, the Eagles, I understand you did
00:44:36
schedule adjusted point differential,
00:44:37
which I think is great. Eagles are only
00:44:40
33. The Rams are plus 100 this year.
00:44:42
They're not even close. How much
00:44:45
stronger are let's forget that the
00:44:47
Eagles would get a buy. Let's imagine
00:44:49
they meet in the NFC Championship game
00:44:51
like one versus two, but the Eagles are
00:44:53
at home. Are the Rams that much be what
00:44:56
would be the line? What would be the win
00:44:58
probability for the Rams in that game?
00:45:01
>> Well, so I like to look if you're
00:45:02
talking about hypothetical uh lines, I
00:45:05
like to look at Mike Buoy's
00:45:06
inpredictable. I know you guys uh know
00:45:08
that site, but they have the sort of
00:45:10
like generic points favored metric uh
00:45:12
based on the lines from recent games.
00:45:14
And the Rams are a plus 6.3. The Eagles
00:45:17
are plus 5.1. So if that was like a
00:45:19
neutral site, the Rams would be like a
00:45:21
point and a half or one one point to one
00:45:24
and a half point favorite. Uh but to
00:45:26
your point, Eric, the the stats from
00:45:29
this season would not have it anywhere
00:45:30
near that close. the the Rams would be
00:45:32
massive favorites. But I think there's
00:45:33
an element of a little of what we're
00:45:35
talking about in the NBA of this sort of
00:45:37
like teams pacing themselves. The Eagles
00:45:39
haven't revealed their true talent this
00:45:41
season. It's only been half a season. We
00:45:43
have to regress to their prior, you
00:45:46
know, all these things of like we know
00:45:47
the Eagles, the Eagles seem to win every
00:45:50
game and I watch all every Eagles game.
00:45:52
It's like some of the ugliest games
00:45:54
possible. I know some of that is like
00:45:56
the tush push and being able to just,
00:45:58
you know, you can grind out wins
00:45:59
whenever you need to, especially on like
00:46:02
closing drives and, you know, when
00:46:03
things are are tight, but the the stats
00:46:06
like SRS and even all of our predictive
00:46:09
metrics, they are geared toward blowing
00:46:11
teams out. They want to see you impress
00:46:13
in your wins. And the Eagles have been
00:46:16
highly unimpressive, but in a different
00:46:18
way than like maybe the Broncos, uh, who
00:46:20
have also been highly unimpressive in
00:46:22
their wins or the Chiefs last year where
00:46:24
theirs feel like a string of like fluky
00:46:26
circumstances just repeatedly like they
00:46:28
flipped heads 10 straight times and it
00:46:30
ca, you know, they called heads and it
00:46:31
came up heads 10 straight times. With
00:46:32
the Eagles, it does feel like almost the
00:46:34
coin is weighted. And maybe I'm biased
00:46:36
in saying that, but it does feel like
00:46:38
they know they can play a particular
00:46:39
style of game and win it if it gets to
00:46:42
be close at the end. But the question
00:46:44
is, can you really rely on that 100%.
00:46:47
And don't we need to see some level of
00:46:49
dominance at some point? I would love to
00:46:51
see some amount of dominance.
00:46:53
>> Yeah, for sure.
00:46:54
>> Start watching the Patriots, bro.
00:46:56
>> I know. That's the crazy thing. Talk
00:46:58
about regressing, you know, to their
00:47:00
prior though.
00:47:01
>> The Patriots, it's like 2015 or
00:47:04
something like that in the in the AFC.
00:47:06
>> It's remarkable.
00:47:07
>> It is crazy.
00:47:08
>> It's an interesting interesting season.
00:47:10
You know, the thing I was reading from
00:47:12
Barnwell was about the Chiefs actually,
00:47:13
and they're sitting there at 500 right
00:47:15
now. He's like, "Do we count them out?"
00:47:17
And so, he really makes cases both
00:47:18
directions on counting them out or not.
00:47:20
And I'm in I I'm in a thinking I'm not
00:47:23
counting them out. Those
00:47:24
>> No, I'm not counting them out. Better
00:47:25
they better indie at KC. They better uh
00:47:28
Indie at I'd still put them above 50% to
00:47:30
make the playoffs. Whether what happens
00:47:32
from there, I don't know.
00:47:33
>> Well, for you know, anything can happen
00:47:35
if you if you get if you get in there,
00:47:36
especially if you got somebody like
00:47:37
Mahomes. All right, last thread. I'm
00:47:39
gonna go to the Connor Baddard thread,
00:47:42
which is is this young supposed star,
00:47:45
former number one pick, finally showing
00:47:47
that he's going to be a great player.
00:47:49
So, I'm interested in Bedard in
00:47:50
particular, but I'm let's do a little
00:47:52
hockey. We've done the other three major
00:47:53
sports. Let's do the fourth. But I think
00:47:54
it generalizes to this question of when
00:47:58
do we know a young player is going to be
00:47:59
or not be what we hope him to be. So,
00:48:02
for example, JJ McCarthy with the
00:48:03
Vikings right now. Pennix just goes out
00:48:05
with a knee injury. these young
00:48:06
quarterbacks, there's always a question
00:48:07
of how much do we need to see Sam
00:48:10
Darnold? I mean, we got we thought for
00:48:11
years we had that one picked. So, this
00:48:13
is an interesting question in general in
00:48:15
sports. So, Baddard, for those of you
00:48:17
who don't know, number one pick, massive
00:48:20
attention. Blackhawks got him three
00:48:21
drafts ago. And then, you know, we have
00:48:23
another one right behind him. I I I
00:48:25
don't know if you wrote about it, but
00:48:26
Celibbrini playing his first full season
00:48:29
with the Sharks. He was consensus number
00:48:31
one. So when you get these consensus
00:48:32
number ones, when do we know whether
00:48:35
they live up to it or not?
00:48:37
>> Yeah. And that is uh something that is
00:48:39
kind of pertinent across all the
00:48:40
different sports because we had one year
00:48:42
where we had Bedard, we had Wimby in the
00:48:46
same like year. Uh a couple years
00:48:49
earlier we had Trevor Lawrence. That was
00:48:50
kind of the NFL version of that. These
00:48:52
like generational prospects. And then we
00:48:54
had Caitlyn Clark in the um WNBA as
00:48:56
well. So, it's like there's there's
00:48:58
number one picks and then there's like
00:49:00
these people that get talked about
00:49:03
generationally and that gets thrown
00:49:04
around way too much, I realize. But, it
00:49:06
is it is true that like Wimi was being
00:49:08
talked about on a totally different
00:49:10
level than even like Cooper Flag or
00:49:12
someone like that. Like there's just
00:49:14
this like there's a regular number one
00:49:16
pick and then there's transformational.
00:49:17
And Connor Bernard was talked about as
00:49:19
being transformational as well, just
00:49:21
because he had this combination of like
00:49:23
the most ridiculous shot that you've
00:49:25
ever seen in your whole life, and then
00:49:28
also this like awareness of of, you
00:49:31
know, the trademark of every great
00:49:33
center in the in hockey of like knowing
00:49:35
where everyone is, where the puck's
00:49:37
going to be, who how to set up your
00:49:38
teammates and everything like that. Uh
00:49:40
Baddard, you know, he's fine his first
00:49:42
couple years. Uh I I had kind of written
00:49:45
things that were defending him because
00:49:47
people were you know the people love to
00:49:49
start creeping in with the bus talk or
00:49:51
the kind of hey maybe we overestimated.
00:49:53
The only thing we love more than
00:49:54
building these people up is to then tear
00:49:56
them down when they don't perform like
00:49:58
immediately at the level of like one of
00:50:01
the best players in the league. Uh and
00:50:02
so I had written about like hey calm
00:50:04
down. He's still one of the best uh
00:50:07
young players like through age 20 of all
00:50:09
time. you know, maybe not quite as good
00:50:12
as Crosby was at the same age, but like
00:50:14
close or McDavid, but close to that. And
00:50:16
then this year, he has been one of the
00:50:17
best players in the league, which is why
00:50:19
I wrote this story because it's like
00:50:21
this breakout moment that I think you
00:50:23
have to have within the first handful of
00:50:25
seasons. I know it varies by sport, but
00:50:27
I talk about Trevor Lawrence. We have
00:50:30
not seen that yet from him. And I think
00:50:32
we would all have to agree that our
00:50:34
estimations of him being the next Pton
00:50:36
Manning or, you know, someone like that
00:50:38
or Andrew Luck, they're just not there.
00:50:41
Like we have to downgrade what we think
00:50:43
the rest of his career is going to look
00:50:44
like cuz he just hasn't, you know, quite
00:50:46
elevated himself to that level of like
00:50:49
he he should be where in the rankings
00:50:51
where Matthew Stafford is right now, you
00:50:53
know, if if he was that type of guy. Uh,
00:50:56
and so it's always encouraging when we
00:50:57
see like Wimi at the near the top of the
00:50:59
NBA rankings and Connor Baddard at the
00:51:02
top of the hockey rankings because then
00:51:03
you can kind of say like, okay, we're
00:51:05
probably right about these guys and
00:51:07
their generational uh potential. And
00:51:10
when I looked at this for hockey
00:51:11
especially, it seemed like probably by
00:51:13
age 20 you need to have like this bump.
00:51:17
I looked at a number of other guys like
00:51:19
Gretzky, McDavid, Crosby, Yarm, Yagger,
00:51:22
Ovuchetkin, Mariel Lemieux. All those
00:51:24
guys, a couple of them had shown signs
00:51:27
of it even before age 20, but they all
00:51:29
had a bump in their performance at age
00:51:32
20. Now, you could still make the Hall
00:51:33
of Fame without that, which I thought
00:51:35
was kind of interesting. like the this
00:51:37
is truly the definition of like value
00:51:39
above baseline Hall of Famer where it's
00:51:42
like uh if you're talking about being uh
00:51:45
on that inner circle level, you really
00:51:49
need to produce uh one of the best
00:51:51
seasons in the NHL, you know, for that
00:51:54
season by age 20. If you don't do that,
00:51:57
you're probably just you your ceiling is
00:51:59
probably just regular Hall of Famer,
00:52:01
which is like, "Oh, no. I'm only a
00:52:04
normal Hall of Famer instead of an inner
00:52:06
circle hall of famer." But it does
00:52:08
matter. It really does seem to matter.
00:52:10
>> Interesting. Eric's trying to get in.
00:52:11
Eric,
00:52:11
>> no. I was going to ask the question that
00:52:12
Neil just said, which is are what are
00:52:14
you looking for? Are you looking for
00:52:15
like an extraordinary peak performance
00:52:18
year? like if you know uh if Connor
00:52:21
Bdard had three let's call them.95
00:52:25
distribution years before age 20 be like
00:52:28
yeah he's targeting towards a regular
00:52:29
hall of famer he needs a one year or at
00:52:32
least that's a like a real n you know
00:52:36
99th percentile year for you to project
00:52:39
him as you're right generational talent.
00:52:41
So it's not it's not the summation
00:52:43
you're looking for a peak performance.
00:52:45
Yeah, a little bit. Especially since
00:52:47
like peak is kind of all you have when
00:52:49
somebody's 20, you know, especially as
00:52:51
they kind of get toward that level of
00:52:53
like the years in which we'd really
00:52:54
expect them to have at least figured out
00:52:57
what it's like to be an NHL player. You
00:52:59
give them a little grace in those first
00:53:01
few years and then it's like, okay,
00:53:03
you've been here for a while. You know
00:53:04
what this is like. Now it's time to kind
00:53:06
of take off and do that. And yeah,
00:53:08
you're right about the percentiles
00:53:09
because it's like extraordinary claims
00:53:11
require extraordinary evidence. And our
00:53:13
claim going in was that this guy is the
00:53:15
next McDavid. And so you have to provide
00:53:18
extraordinary evidence of that.
00:53:20
>> Neil, it strikes me that you're talking
00:53:23
about the sport where we see the
00:53:25
youngest player on the full professional
00:53:27
stage of the m of the four major sports.
00:53:29
So baseball is famously long pipeline.
00:53:31
Football, they just get drafted later.
00:53:33
They get thrown to the wolves mostly,
00:53:34
but they get drafted later. Basketball,
00:53:37
they they mostly get drafted at a little
00:53:39
bit later age. these high school these
00:53:41
these uh hockey players get drafted
00:53:43
basically coming out of high school. A
00:53:45
lot of them go into some kind of
00:53:46
development work. The team maintains
00:53:47
their rights but they go to college and
00:53:48
they go to one of those smaller leagues.
00:53:50
The very best players and this is my
00:53:52
question like how did they develop Bard?
00:53:54
How quickly do they put them on the ice?
00:53:55
Because it's kind of it's kind of um
00:53:58
striking to hear you say they got to do
00:53:59
it by 20s. Like my god that guy's a kid.
00:54:02
>> I know. Yeah. It's it's a little like
00:54:03
Olympic gymnast or something, right?
00:54:05
Like there's just so much younger. But I
00:54:08
do think in some ways the the hockey
00:54:10
development pipeline is a little bit
00:54:12
more accelerated like you were talking
00:54:13
about. They uh often the best ones they
00:54:16
don't go to college. Instead they go
00:54:18
through juniors and they kind of are
00:54:20
playing up uh compared with their age
00:54:22
against players that are older or better
00:54:25
or just kind of they're 16 and they're
00:54:27
going uh almost playing professional at
00:54:29
that age. Uh and and it really kind of
00:54:32
accelerates the development. And it's
00:54:34
similar to like in baseball, somebody
00:54:36
can be drafted and in, you know, the
00:54:39
summer and then like play in the major
00:54:40
leagues like that year. Uh in in
00:54:43
baseball sometimes like we've seen this
00:54:45
with pitchers. Um
00:54:47
>> rarely, right? Rarely.
00:54:48
>> Yeah. Sometimes. Oh, well, if it's like
00:54:50
a Paul Ske, who I think is sort of also
00:54:52
in that conversation as well. So, I I do
00:54:55
think that hockey kind of combines all
00:54:57
the different elements that would cause
00:54:58
you to see something at a younger age
00:55:00
than other sports like the accelerated
00:55:03
pipeline, the ability to jump right in.
00:55:05
And I'm really fascinated that the NHL,
00:55:07
you mentioned Mlin Celibbrini, Matthew
00:55:09
Schaefer as well, he was the top pick
00:55:10
for um the Islanders. He is now having
00:55:13
one of the best young seasons by a
00:55:15
defenseman I think since Bobby our which
00:55:17
really also says something. So, uh,
00:55:19
before the season in the NHL, I had done
00:55:21
this thing where I looked at the fact
00:55:23
that the average age of the league in
00:55:25
terms of just weighted by playing time
00:55:27
or or player value had gone up for seven
00:55:30
consecutive years, which uh was sort of
00:55:33
spoke to the stagnation of the league.
00:55:35
We'd also had like the same finalists in
00:55:37
multiple seasons and all these things.
00:55:38
And it was like when are we going to get
00:55:40
this this change in in the hockey right
00:55:43
now? And it seems to have come this
00:55:45
season led by Baddard, but also those
00:55:47
other guys where you're seeing a
00:55:49
disproportionate number of the top
00:55:51
players in the league in terms of like
00:55:53
value over replacement or just scoring
00:55:55
uh be the younger stars. Now, it's like
00:55:58
this next generation is finally taking
00:56:00
over the game. Now, that doesn't mean
00:56:01
that Crosby is being pushed out. The
00:56:03
Penguins have actually been amazing,
00:56:05
surprisingly, shockingly, this season.
00:56:07
uh or McDavid, you know, he's still in
00:56:09
the prime of his career, but you've got
00:56:11
these young guys kind of nipping at the
00:56:12
heels of the of the established players
00:56:14
in a way that we really had not seen in
00:56:16
a number of years in hockey.
00:56:18
>> All right, good fun. Well, listen, why
00:56:20
don't we wrap it there? We managed
00:56:22
unintentionally to cover the four major
00:56:24
North American sports in our four
00:56:26
topics. Many, many thanks to Neil Payne.
00:56:28
Neil, you can find him. We can't
00:56:30
recommend his substract strongly enough.
00:56:31
which is fantastic writing, keeping you
00:56:33
as apprised and interesting uh across
00:56:35
across the board in sports. We didn't
00:56:37
get, for example, to your piece on
00:56:39
NASCAR, which I was mocking for, but I
00:56:40
wanted to hear about because he talks
00:56:42
about tournament design essentially a
00:56:44
thematic topic for us here over the
00:56:46
years. We'll pick that up at some other
00:56:48
point. For the whole crew, Eric Bradlo
00:56:50
for Shane Jensen, for Winer, this has
00:56:51
been Kate Massie. Many thanks to Neil
00:56:53
Payne, many thanks to Dion Simpkins, D
00:56:56
Patel, Mariss, Marissa Rena, the whole
00:56:59
team. Many thanks to everybody and to
00:57:01
you guys for listening. Come back and
00:57:02
join us next time. Between now and then,
00:57:04
enjoy your sports.
