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Welcome, welcome to Wharton Moneyball.
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Welcome to a full hour of sports
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analytics here on the Wharton podcast
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network. This is Kade Massie hosting
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this week with my two longtime
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collaborators, friends, colleagues here
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at Wharton, Eric Bradlo, and Audi Winer.
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Our fourth co-host, Shane Jensen, is out
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this week. He's teaching. He'll be back.
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He and Audi are juggling teaching
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schedules this quarter, but we've got
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three of us in here for the duration. We
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are coming up on 12 years. We've been
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doing this virtually every week uh for
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12 years. Some combination of us in
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here. We're going to hit our anniversary
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in March. We're recording on Tuesday
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afternoon as we typically do. Show will
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go up on Wednesday. We are going to run
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our regular format this week, which
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means in the first half the show, bring
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a guest in here who's already here. And
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second half, we'll do open lines, kick
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things around a little bit, see what the
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boys have been thinking about over the
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last week. Guest this week, longtime
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friend of the show, old friend of the
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show. I don't mean old man, I mean
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oldtime friend, Ben Alamar. You probably
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know him from his involvement in sports
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analytics. Really one of the first guys
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in working in the NBA. One must have
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been one of the very first full-time
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guys in the NBA. He's worked with the
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Spurs, the Cavs. It's been a good
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stretch at ESPN working with people like
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Dean Oliver building out really kind of
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building out the sports analytics prof
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platform there at ESPN internally and
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externally that has had a profound
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influence on the world of sports. He
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teaches in various places. He's teaching
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right now at the University of Texas.
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He's taught in universities around the
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country teaching a sports analytics
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class. Has a has a book out on sports
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analytics done well enough to be in
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second edition. and Ben Alamar, good to
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see you. Thanks for making time for us.
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>> Oh, pleasure to be here again. Uh, it's
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been uh, you know, as I say, old oldtime
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uh, participant here, but always glad to
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join in the discussion.
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>> Thanks, man. Good to see you. Um,
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fellas, uh, Audi is going to be up at
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the MIT conference in a couple of weeks,
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the Sloan MIT Sloan Sports Analytics
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Conference, 20th annual.
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Those guys run up there every year. Audi
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will be up there. Ben's Ben's a a
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frequent host of some platform event
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session. Ben's got a session this spring
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on academic collaborations, I believe,
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or academic research. So, uh, do y'all
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need to cross paths while they're
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running around Boston?
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>> I hope I'll be able to attend.
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>> Yeah, absolutely.
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>> We're slowly tearing our attention away
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from the football world and casting
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around for other things to um to to
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attend to. Um Audi Audi of course is
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already excited about whatever meager
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offerings baseball has right now.
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They're they're up and running, but
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really there are two sports underway and
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we're trying to get deeper on both of
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them. Setting aside for the moment
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Olympics, um basketball and hockey and
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you you've been you're you're you're
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good across all sports, but I think
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you're especially deep in basketball.
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So, can you help us get to up to speed
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on the NBA and what do you as you look
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around, as you dig in, as you pay
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attention, what do you think's
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interested in the NBA this year?
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>> Yeah, I mean, the the NBA has been
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really pretty exciting this year. I
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think, you know, there's always some
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some issues folks have and uh with with
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tanking and such and we'll get to that
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at some point, but um I think what's
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great about this year is we're seeing,
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you know, for one, the Spurs, the rise
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of women really starting to fulfill the
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promise that everybody had expected.
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We're seeing some real surprises as well
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that you have these the young Detroit
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team um really leading the league now in
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a lot of ways and um uh in a great
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position to make a a strong run in in
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the postseason. And you also have these
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this other surprise the Boston Celtics
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who you know during the offseason they
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were you know tearing their team down
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you know sent a lot of salary off a lot
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of good players off um but have been
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able to put together a a really strong
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team that is competing well in in the
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east uh which not as strong as the west
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but the still uh they've been against
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New York and and Detroit they've been
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really good uh despite not having uh
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Jason Tatum uh and the idea that Tatum
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is getting ready to perhaps join them
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for a playoff run makes that a really
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scary team when you know nobody was
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really counting on the Celtics. They saw
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them as much like the Pacers sort of
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taking a gap year here while they their
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their best player was on the bench. Uh
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>> then can I jump in there real quick? Um
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the four franchises that you just
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pointed out I you pointed out San
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Antonio actually maybe you didn't point
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out Oklahoma City but I can't help but
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think about Oklahoma City. So you
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didn't. So, San Antonio,
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Detroit, and Boston. San Antonio and
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Boston, I think anybody would call two
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of the bestrun franchises in the NBA.
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Famously good front offices. And of
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course, OKC is floating around in the
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background as well. I don't know
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anything about Detroit's front office.
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Are they thought of in the are they
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thought of as an upand cominging? What
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is it that's going on around there that
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allowed them to kind of surprise with a
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young team here? Yeah. So they're so
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they a couple years ago uh they brought
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in uh Tjan Langden and um uh uh Michael
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Blackstone. Uh both the guys have been
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they they were working together in New
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Orleans before. Uh I worked with with
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Blackstone in Cleveland. Um he's really
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sort of a negotiation salary cap expert.
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Trejan's just been um you know come in
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and really worked his way up in the
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league in the front office is very well
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respected generally speaking and they've
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done you know they they had some young
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talent there already when they got there
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uh that the old regime had put in place
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but they also had a lot of you know
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contracts that they needed to move and
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and turn into valuable assets which they
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they they've done a great job of
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combining sort of creating uh a platform
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for their young players to uh
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demonstrate that they they can uh play
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while uh not um you know uh by the same
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time adding some some valuable pieces
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that can help them uh you know win games
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as those young guys develop and get a
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little bit more experience. So, uh, I
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think they've done a really nice job
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over the last couple years, uh, you
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know, transitioning that team from what
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it was, which was, you know, u sort of
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mired in, uh, not being able to move
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forward too much. Uh, created some
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flexibility for themselves and really
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has, um, you know, obviously, uh, put
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together a really talented roster that
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that's doing a great job.
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>> Cool to hear. Um, Trajan Lane done uh
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Duke Colomb. Yeah, see the guy played
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Alaska, drifted down from Alaska, played
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ball at Duke for a while.
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>> That's perfect.
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>> Interesting. I I don't Has anyone ever
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tried to map the influence from various
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collegiate programs into the NBA? Some
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They've got a number of former players
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and successful front offices, don't
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they?
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>> Yeah, Duke. Duke certainly does. Um, but
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uh it's it's hard to track. So, the
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front office guys are so varied in in in
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their experiences. I mean, you take San
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Sam Prey, he is, you know, from D3
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school at uh and and
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>> you know, has obviously is really
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thought of as the top the best run
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franchise right now in the league. So,
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>> Right. Right. Was he I assume he was
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there when you were there back in the
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early 2000s. So, how early in Sam's
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regime was your time with him?
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>> Well, so Sam got hired in SE you know,
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it was the SuperSonics at the time. uh
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he got hired um and then you know his
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his first decision was to draft Kevin
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Durant. Um and then that he hired me. So
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it was a really big summer for the
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uh
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we we we got going. So yeah, I started
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working with Sam right after his first
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draft. Um
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>> okay,
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>> you know, suffered through a couple of
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tough years as we were building that
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team, but um turned it turned it around
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with some brilliant draft work from Sam.
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This
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>> this is a a big a bigger question than
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we have time for, but I'm curious to get
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just a first taste from you on this.
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What do you think Sam had to learn? Like
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what are some of the fir what what's one
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of the first things he had to learn as
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he moved into that position? Presumably
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he kept on learning for a long time.
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He's gotten so good at it, but what's
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something that you saw him work through
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and you think he learned early on in his
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time?
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>> Um so I think that you know one of the
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things was um sort of how to you know
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use it. He had come from the Spurs where
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you they were one of the early teams
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that had done anything in this in the
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area of data analytics u and he knew
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that he really wanted it but he didn't
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really um have a you know full grasp of
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what was possible or how to incorporate
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it you know really into decision-m uh
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you know how to trust and then but you
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know he's he's so smart he's really good
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at just asking questions and asking
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questions which is sort of the key piece
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that you need in that position to to to
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grow and learn how to utilize things
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well and so he really developed from
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sort of like, all right, here's a
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spreadsheet that we can provide for you
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every, you know, week that's updated to
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really understanding how to build a
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system within the team, uh, and not just
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a one-off guy, but like a how do we have
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this all this information really
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permeate through uh, everything that
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we're we're working on.
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>> Yeah, that's so interesting. I think
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Eric was trying to jump in.
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>> No, no. I wanted to go back just for a
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second to yourself comment. I'm just
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wondering, Ben, if you think that
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>> um in some sense, if they were in the
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West,
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>> I think you would agree the Celtics
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would not be the favorites in the West.
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>> Oh, no.
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>> But the fact that they're in the East
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also, which gives them, let's call it, a
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round or two to get Tatum back in the
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flow.
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>> Yeah.
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>> You could make an argument that they
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could be higher than Let me make an
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argument. You just tell me I'm wrong.
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>> Sure.
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>> They're high because of the weakness of
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the East.
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>> Yeah. the openness of the East. They're
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higher than any team in the West because
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in the West it could be the Spurs, could
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be the Nuggets, it could be the Thunder,
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>> but out of the East, they might be the
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favorite with Tatum back and so they
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have a higher probability of making it
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to the finals even though they have a
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lower probability of of winning the
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finals. What What do you is if you had
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to guess right now, would they be your
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favorite based on that argument or is
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that not right?
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>> Uh I I I I think it's it's it's a it's a
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fair argument. I'm not sure that they're
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the total favorites just because we
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don't know what Tatum is like. Is he
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coming back and how he meshes with how
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the team is playing this season? Uh, you
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know, Jaylen Brown there has been the
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guy making that team work. And while
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Brown and Tatum have obviously played
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together a lot, not in this particular
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situation and not in the case where
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Jaylen's gotten the the taste of really
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being the lead of the team. And so how
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those guys as Tatum works his way back,
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you know, he's not gonna he's gonna be
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he's way back into basketball shape,
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got, you know, get everything going
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again. Um having that their how they
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work together in this new uh you know,
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takes some time. Um I mean we
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just when players get traded that it
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takes time for them to settle into the
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new environment. Um, I think Tatum will
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take some time to settle into these this
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new Celtics environment in similar ways.
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Um, if and they figure that stuff out
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quickly, then yeah, they're probably
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favorites. Um, but, uh, you know,
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Detroit and New York are are are still
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really good. They will give them a
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strong run for money in the playoffs.
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But um certainly not the stumbling
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blocks that uh exists in the west- w
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with OKC San Antonio Denver as
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>> Ben let's talk about the west. You
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mentioned women coming on the team
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really coming on. OKC was such a force
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last year and they seem just stocked for
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years to come. How does that rivalry
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shape up and and why is it if if San
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Antonio is kind of surprisingly
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competitive in that rivalry? Why is
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that? Yeah, it's it's really interesting
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because the um the Spurs are are are you
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know look like Thunder killers right
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now. They are the ones who are have
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figured out how to beat them. Uh they
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you know they've won they've handed the
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Thunder four of their 13 losses uh this
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season. Um you know so clearly they've
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got something going there. Um and so you
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know it you can see the impact in a
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couple of ways. Um, first, you know, on
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the offense, like the Thunder overall
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have the fourth ranked offense in the
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league when in terms of efficiency. Um,
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but against the Spurs, they would be
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like the 26th ranked offense. You know,
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they, you know, dropping from 120 points
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per possession to 112. Um, they they
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shoot less efficiently. Uh, they get
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fewer fouls. They just they don't
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>> Okay, but does doesn't everybody look
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worse against the Spurs? Are they not
00:12:35
just that that
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>> the Thunder are so good that I mean it's
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a dramatic drop off, right? Like they
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they they I mean this is a team that we
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expect to uh you know potentially be a
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perennial champion, you know, and really
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put together a a run here. And you know,
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they're only you know scoring like again
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like the um the 26th best offense in the
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league when they're playing the Spurs.
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>> Yeah. They're not regressing to the
00:13:00
mean. They're reg they're regressing
00:13:02
beyond the mean to the bottom.
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It's polar opposite what's going on
00:13:05
there.
00:13:05
>> Let me ask him, Ben, how much weight do
00:13:07
you put in the regular season record of
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the Thunder right now? Actually, by the
00:13:10
way, they have 14 losses. I didn't even
00:13:12
realize this. The Pistons now have the
00:13:14
best record in basketball. I actually
00:13:16
did not know that the Thunder had uh
00:13:18
passed them for the best record. Is it
00:13:20
does it mean anything to the Thunder? I
00:13:22
mean, yeah, they'd like to hold off the
00:13:24
Spurs. Trust me, they'd love to have
00:13:25
homecourt advantage in that round. Yeah,
00:13:27
that does mean something. But whether
00:13:29
they win 60 games, 63 games, 58 games,
00:13:34
you know, does it really mean anything?
00:13:36
>> Um, it's it's I think it it depends a
00:13:39
little bit. I think as early on the
00:13:41
season when this the Thunder were just
00:13:43
like they were on pace for, you know, 70
00:13:45
plus games.
00:13:46
>> Yeah.
00:13:47
>> Like then then it was like, well against
00:13:49
that, by the way,
00:13:51
>> what
00:13:51
>> I bet it against that
00:13:55
mean is a powerful force.
00:13:57
>> It is. They're really good though. Um,
00:13:59
but as like at that point there there is
00:14:02
a you know uh a decision you have to
00:14:04
make as a team like do we care about
00:14:06
this? Should we fundamentally care about
00:14:08
doing this? Um you know a few years ago
00:14:10
the Warriors decided that they did and
00:14:11
they went for it uh hard. Um the this
00:14:15
year the the Thunder you know they've
00:14:17
>> I think they've had enough injuries and
00:14:19
things that uh you guys have missed
00:14:21
games where it became clear like that's
00:14:23
not going to be uh going to happen for
00:14:26
them this year. They're not going after
00:14:27
a win record this year. So, generally,
00:14:29
no, they want the they want the one
00:14:30
seat. They'll they'll play for it. They
00:14:32
don't they don't like the Spurs. So,
00:14:35
they there is a you know, a a rivalry
00:14:37
there was fun to see. It makes the
00:14:39
games, you know, gives it a little extra
00:14:41
edge because they clearly don't like
00:14:42
each other. Um so, you know, in general,
00:14:45
yeah, they don't care between
00:14:48
that's not an issue. One seed and Spurs
00:14:52
do matter to them.
00:14:55
So I on the other end of the spectrum,
00:14:58
we're hearing talk about tanking. I
00:15:00
think the NBA just fined. Was it Utah?
00:15:02
Somebody got fined for taking out
00:15:04
players in a competitive game.
00:15:07
>> It it you know, it's been a few years
00:15:09
since everybody was up in arms about
00:15:11
tanking with the NBA, but it seems to be
00:15:12
back kind of in force. What's going on?
00:15:15
And what do you think, Ben, could be
00:15:16
done about it, if anything?
00:15:18
>> Yeah. So, I think this what two two
00:15:21
dynamics have sort of led to the the the
00:15:23
level of tang we're seeing right now,
00:15:25
which is, you know, the Jazz not playing
00:15:27
their starters in the fourth quarter.
00:15:30
Um, you know, like that's that's that's
00:15:32
beyond some of the things that we've
00:15:34
seen in the past in terms of tang like
00:15:35
that's pretty uh extreme uh situation.
00:15:38
>> Well, Ben, let me jump in real quickly.
00:15:40
This is maybe cheap, but I don't know
00:15:43
what Danny Ang's role is out there in
00:15:45
Utah. You probably have. Can you tell us
00:15:46
something about the role? When Danny
00:15:47
Ames was a player, he would take any
00:15:49
edge he could find. It wouldn't be
00:15:51
surprising at his organization would be
00:15:53
Am I Is that unfair to say? Probably
00:15:55
unfair.
00:15:56
>> Oh, I just want to interject. As a
00:15:57
Knicks fan growing up, you put me with
00:15:59
Larry Bird, Kevin McCale, Parish, and
00:16:02
DJ, and I'm a winner, too. Sorry.
00:16:04
>> Oh, no.
00:16:06
>> I'm not I'm not And so, I'm not giving
00:16:09
Danny A credit for anything on that. But
00:16:11
Ben, please go ahead. I Danny H like
00:16:14
he's he has regardless of how you think
00:16:16
about him as a player as an executive
00:16:20
he what he did in Boston
00:16:21
>> you cut you cut out there for just a
00:16:23
second Danny A as executive what
00:16:25
>> as executive has been incredibly
00:16:26
successful you know he built the team in
00:16:28
Boston did an excellent job you know
00:16:31
bringing talent in there has won
00:16:32
championships as an executive um moved
00:16:35
to you know to Utah now has this a
00:16:37
similar role there uh you know sort of
00:16:40
re trying to build that team and he is
00:16:42
you
00:16:42
believes and and that it's hard for Utah
00:16:45
to acquire players other than through
00:16:47
the draft to high level top level
00:16:49
players other than through the draft and
00:16:50
so he has to feel like um you know we
00:16:54
have to do it through the draft and tang
00:16:55
is the way to do it and so
00:16:57
>> is that is that because people won't
00:16:58
come out to Utah is that
00:17:00
>> that's that's that's the you know one of
00:17:01
the concerns is that you free agents
00:17:03
don't look at at Salt Lake City as a uh
00:17:06
destination um both from desiraability
00:17:10
of the city and also just from the the
00:17:11
the the sponsorship opportunities are
00:17:13
not the same as they are in places like
00:17:15
LA and New York. So small markets are
00:17:17
always at this kind of disadvantage. Um
00:17:19
and Utah I think is sort of an extra
00:17:22
disadvantage
00:17:23
>> but um
00:17:25
so
00:17:25
>> I mean it sounds like you can't win at
00:17:27
all out there. I mean winning only
00:17:29
through the I mean how long do you get
00:17:30
to hold on to a player? Usually they're
00:17:32
not
00:17:32
>> they have to free a player once you have
00:17:34
a player through the the way that you
00:17:36
know through a few cont through the
00:17:37
first contract you're the one who gets
00:17:39
to pay them the most afterwards. So you
00:17:41
have a chance to to what OKC is doing
00:17:43
right now. Uh I mean that's how they
00:17:45
they they through the through a one very
00:17:48
smart trade which helped them accumulate
00:17:49
a lot of draft assets and some other
00:17:51
trades even more draft assets. They've
00:17:53
you know really built a uh a juggernaut
00:17:56
without free agents. Um they've done it
00:17:59
through you know they acquired Shay
00:18:00
through a trade. Everything else is is
00:18:02
draft. Um,
00:18:04
>> but everybody would have said, everybody
00:18:05
thought Prey was greatly disadvantaged
00:18:07
operating out of Oklahoma City for the
00:18:09
same,
00:18:09
>> but also they ended up with Shay. I
00:18:10
mean, almost that's crazy, right?
00:18:14
>> Yeah. Yeah. They the the Clippers were
00:18:16
put in a position by by Quai Leonard
00:18:18
where they basically had to acquire Paul
00:18:20
George if they wanted Quai. And so uh
00:18:24
you know Sam knew that he had that the
00:18:26
leverage to do that and and he insisted
00:18:28
on including Shay in that trade. now
00:18:30
should clearly exceed the expectations
00:18:32
of everybody. Uh but yeah, he's been you
00:18:35
know uh like that that that was been a
00:18:37
real thing. Um but overall in terms of
00:18:40
tanking like you know we've had a
00:18:42
flattening of the the odds in the in the
00:18:44
lottery. So you know that if you have
00:18:46
the worst record in the the league you
00:18:48
know you don't it's hard your odds of
00:18:50
getting the number one pick are are are
00:18:51
less than they used to be. And so you so
00:18:54
more teams have, you know, in that, you
00:18:56
know, one to six range have a shot at a
00:18:58
top four pick, uh, a reasonable shot.
00:19:02
And this particular draft coming up is
00:19:03
thought to be one of a a really, really
00:19:05
strong draft with at least three guys
00:19:07
who are thought to be significant, you
00:19:10
know, uh, franchise level players in in
00:19:12
the future. And so you have those two
00:19:15
dynamics coming together to really push
00:19:17
teams incentives to want to tank and
00:19:21
make sure they're in that bottom six,
00:19:23
bottom eight at least to have a
00:19:24
reasonable shot at keeping at getting
00:19:26
one of those guys uh in the draft. Ben,
00:19:29
has there been any evidence that you've
00:19:31
seen or looked at that suggests that um
00:19:35
like I understand like you know, let's
00:19:36
say obviously LeBron James was number
00:19:38
one and there were a lot of great
00:19:39
players number one, but also there were
00:19:41
some number one players that didn't work
00:19:42
out as well. Like
00:19:45
>> how much information is there really to
00:19:47
say, well, number three is so much
00:19:50
better than number seven or eight. Is
00:19:52
there any evidence to suggest that's
00:19:53
really true?
00:19:54
>> Oh, yeah, for sure. I mean we you can
00:19:56
look at the the at draft you know uh you
00:19:58
know uh uh win probability added over by
00:20:02
draft's position and there's a real
00:20:03
curve there's a definitely a decline
00:20:05
curve now that's that how steep we that
00:20:10
curve is varies significantly by the
00:20:12
draft um because as you point out like I
00:20:15
was in Cleveland when we drafted Anthony
00:20:17
Bennett number one overall like and if
00:20:20
you don't know who Anthony Bennett is
00:20:21
it's not your fault
00:20:24
it like that that happens like there are
00:20:26
busts. It's not a but um some drafts are
00:20:29
stocked with players that everybody
00:20:32
believes great and some most of them a
00:20:35
lot often turn out to be great.
00:20:37
Sometimes it's stocked with players that
00:20:39
people think are going to be great and
00:20:40
they're fine. Andrew Wiggins is a great
00:20:42
example of that. Um, and then you have
00:20:45
guys that we totally miss on, uh, like
00:20:46
Giannis, you know, was not a, you know,
00:20:49
when we were thinking in that, you know,
00:20:51
2013 14 draft, um, you know, uh, I guess
00:20:55
2013 draft, Giannis wasn't on our board
00:20:58
for one of the top five with the top
00:20:59
pick. Um, because we just didn't have
00:21:02
the information we needed to to to
00:21:04
believe he was at that level. But, you
00:21:06
know, obviously he was the by far the
00:21:08
best player in that draft
00:21:09
>> because international scouting wasn't
00:21:11
then fully developed. Is that is that
00:21:13
what was going on?
00:21:14
>> Yeah. And you know, we we had some
00:21:16
international scouting, but we did not
00:21:17
have good international data uh at that.
00:21:20
>> Okay.
00:21:21
>> Yeah.
00:21:21
>> Okay. So, Ben, you said this thing. You
00:21:23
said the odds have been flattened at the
00:21:26
top of the lottery, and that was by
00:21:27
design. And the design was to decrease
00:21:29
the incentive to have the top pick. But
00:21:31
you're saying ironically in a deep
00:21:34
draft, it gives exactly the wrong
00:21:36
incentives because if you can get into
00:21:38
the top three or four, then you're have
00:21:41
e even chances at relatively even
00:21:43
chances at a at one of the great
00:21:45
players. Is that is that the
00:21:47
>> more So it means that if you're you know
00:21:49
you know if you finish with the fifth,
00:21:51
sixth, seventh record, you now your odds
00:21:54
of getting that first pick are higher
00:21:55
than they used to be. So, you just want
00:21:57
to get down into that range. You don't
00:21:59
have to get down to the bottom, but you
00:22:00
want to get down that range to have a
00:22:02
reasonable shot at that pick.
00:22:04
>> Um,
00:22:04
>> well, so these guys in the NBA front
00:22:06
office are having a tough time, you
00:22:08
know, tweaking these numbers exactly uh
00:22:11
in the way that would give the
00:22:12
incentives to play throughout the
00:22:13
season. What do you think if if they
00:22:16
they've made their best effort to get
00:22:18
those the slope of those odds right,
00:22:19
it's still not working. What other
00:22:21
levers can they pull? Well, I mean the
00:22:23
so the there are there's one very clear
00:22:26
and then there's a second even more
00:22:27
radical way to do this. The the the
00:22:30
obvious way to do this to eliminate this
00:22:32
the the problem the incentive problem
00:22:33
here is to just eliminate the draft. Um
00:22:36
and you you have to you let all the
00:22:39
players coming in are they're all free
00:22:40
agents and you go sign teams that want
00:22:42
them can go sign them. Uh and you
00:22:44
eliminate the rookie scale contracts so
00:22:47
players you know so teams are allowed to
00:22:49
differentiate their offers. You'd have
00:22:50
to have a hard cap if you did that,
00:22:52
right?
00:22:53
>> Well, you you you can have a hard cap
00:22:55
and not um and not have a rookie scale.
00:22:58
Like that's fine. There's no reason
00:22:59
those two things don't have to go
00:23:00
together,
00:23:01
>> right?
00:23:01
>> Um and so you can still have the hard
00:23:03
cap and uh it, you know, unlikely that
00:23:07
the owners would get rid of that, but
00:23:08
the the rookie scale um which you know
00:23:11
would eliminate the use the value of the
00:23:13
going to the free agency. Once you have
00:23:15
you eliminate the rookie scale, then
00:23:17
teens can go and say, "All right, yeah,
00:23:18
I'm Utah. Uh, you may not want to come
00:23:20
here, but I'm going to offer you a
00:23:22
million dollars more than LA is going
00:23:24
to." So now I there there's way to
00:23:27
differentiate my offer from other teams.
00:23:29
Uh, you know, or, you know, I'm going to
00:23:31
book, you know, invest in you to be the,
00:23:34
you know, the franchise, the center of
00:23:35
my franchise, whereas you if you go to
00:23:37
OKC, yeah, you're gonna you're going to
00:23:38
be on the team, but, you know, you're
00:23:39
not going to be uh, you know, getting a
00:23:41
lot of playing time right away. So,
00:23:43
>> but Ben, in general that it is true
00:23:46
though that all the regional effects we
00:23:49
were talking about before would would
00:23:50
just be exacerbated, would they not?
00:23:52
Given that it's now now they have to
00:23:54
compete also for the rookies in the same
00:23:56
way that they're disadvantaged when
00:23:57
competing for free agents
00:23:59
>> to to some extent except that uh one
00:24:01
there are only so much so many uh uh of
00:24:05
these guys that teams are going to
00:24:06
invest significantly in. So, if LA
00:24:08
decides that they want player X uh and
00:24:10
and player Y, then that's it. Like,
00:24:13
they're going to go after those guys,
00:24:14
they're not going to bid on every single
00:24:17
player that in in the draft. Um they
00:24:19
can't. And or if you take a team that
00:24:21
like, you know, if Boston is really
00:24:22
good, uh then they're not going to spend
00:24:26
a lot on rookies, no matter how much
00:24:27
people want to go there. They're just
00:24:28
not going to because it's not worth it
00:24:30
to them. They need to save their cap for
00:24:31
their existing star players.
00:24:34
>> Um
00:24:35
>> that makes that that makes sense. But
00:24:36
also you get the you get the
00:24:38
intertemporal um effects as well. I
00:24:40
think I think because you're saying some
00:24:42
drafts are much deeper than others. So
00:24:44
this upcoming draft anticipated being
00:24:45
deep if in this in the world you're
00:24:47
describing in the current world because
00:24:49
of the rookie cap the the slots are kind
00:24:51
of fixed. The same amount is going to be
00:24:54
spent on the top 10 players regardless
00:24:55
of whether they're great or whether
00:24:56
they're lousy.
00:24:57
>> Exactly.
00:24:58
>> In the world you're describing some
00:25:00
years could be a lot more rookie pay
00:25:02
essentially than others according to
00:25:04
what that's interesting. That alone
00:25:05
sounds like a big inefficiency though.
00:25:07
>> So, but let me ask you a question. Let's
00:25:08
imagine you're the you're the NBA.
00:25:10
>> Yeah.
00:25:11
>> And Ben Alamar works for the NBA and
00:25:13
suggests eliminating the draft.
00:25:16
>> Yeah.
00:25:16
>> Is there any way you could kind of
00:25:20
simulate or have a prediction of what
00:25:23
might happen if you got rid of the draft
00:25:26
given it's never happened before? And so
00:25:28
I'm just wondering like could all hell
00:25:30
break loose? Like how would you even
00:25:32
think about evaluating a concept like
00:25:34
that? Yeah, I so I mean I think that
00:25:36
what would happen and this is you know
00:25:37
anytime there's a significant rule
00:25:39
change or you know this the the the uh
00:25:42
the cap rules change significantly for
00:25:44
whatever reason the smart teams figure
00:25:46
it out first. They figure out their
00:25:48
strategy they they have a clear strategy
00:25:50
that they execute around whatever the
00:25:52
new rules are. Um and the teams that
00:25:54
aren't as as sophisticated or you know
00:25:56
as as smart in the front office you know
00:25:58
they sort of like assume it's going to
00:26:00
work exactly like free agency works now
00:26:02
for example. Um and and you know
00:26:04
whatever the dynamics are they might
00:26:06
there there's going to be some
00:26:07
interesting dynamics. We don't really
00:26:09
know what they are. The closest thing we
00:26:11
have to this would be you know NIL and
00:26:12
college basketball right now. Um where
00:26:15
you know we are not seeing you know all
00:26:17
the players flocking to you know the
00:26:18
biggest cities. They're flocking to the
00:26:20
teams that are going to pay them. Um
00:26:22
there there's there's no cap there. you
00:26:24
know, all so it's it it's it's a little
00:26:26
bit different obviously, but um you
00:26:29
know, team reputation, strength of the
00:26:31
organization,
00:26:32
>> all that opportunity for the player, all
00:26:34
those things are going to matter in that
00:26:36
world. Um whereas, you know, right now
00:26:38
in the draft, they don't because the
00:26:39
player gets no choice.
00:26:41
>> By the way, guys, quick quick aside, uh
00:26:43
in the WNBA, they've got this craziness
00:26:46
that's about to happen about something
00:26:48
that's never happened before. They're
00:26:49
negotiating a new CBA right now. And for
00:26:52
years, the agents have known that
00:26:54
they're going to have a new CBA this
00:26:55
year. So, they've expired a lot of
00:26:57
contracts now.
00:26:58
>> And so, basically, almost every player
00:27:01
in the league is a free agent. As soon
00:27:03
as they close the CBA, it's just going
00:27:05
to be a free-for-all. It's a complete
00:27:06
free-for-all. And just as Ben says, the
00:27:08
advantage goes to the sharper teams, the
00:27:10
teams that have been thinking about this
00:27:11
ahead of time. No one knows exactly
00:27:13
what's going to happen, but can you
00:27:14
prepare yourself for all the
00:27:16
contingencies so that you have an
00:27:17
advantage in this new world?
00:27:18
>> Ben, can I just have a counterargument
00:27:20
to yours? So, let's suppose I'm a player
00:27:22
where I certainly care about money. I
00:27:23
mean, no doubt players care about money
00:27:24
deeply,
00:27:25
>> but they also care about winning.
00:27:27
>> So, if there's no draft and I'm I don't
00:27:30
even know is one of the three players
00:27:31
one of the boozers. I don't even know. I
00:27:33
know they is is are they one of the
00:27:34
players that you're talking about that's
00:27:36
coming out?
00:27:36
>> No, he's they've fallen out of the top
00:27:38
three. They're they're they're part of
00:27:39
the high quality of this draft, but
00:27:41
they're not top they're not top.
00:27:42
>> So, let's just say one of the top three
00:27:44
players says, you know, I care about
00:27:46
money, but I do really care about
00:27:48
winning. You know what? I'm gonna go to
00:27:49
OKC, San Antonio, those are my teams
00:27:53
that I'm gonna go Denver. That's it.
00:27:55
Boston, maybe Detroit, New York. Six.
00:27:56
Those are the six. Other 26 teams don't
00:27:59
even bother contacting me. Why wouldn't
00:28:02
the rich get richer under this scenario
00:28:04
where you I'll just call it it's a
00:28:06
multi- objective function where there's
00:28:09
some weight on winning and that's going
00:28:11
to force possibly the over team the
00:28:14
other teams to overpay
00:28:16
>> in uncertainty which could actually lead
00:28:19
to their demise.
00:28:20
>> Yeah. So I I mean so there there a
00:28:22
couple things there. One you've just
00:28:24
named OKC and San Antonio is getting you
00:28:26
know the rich getting richer which is
00:28:28
true but they're also pretty small
00:28:30
market teams. So the small market big
00:28:32
market thing you know we it seems like
00:28:34
that that's counter. The other sort of
00:28:36
part of that is like yeah if you're not
00:28:40
if if you know winning is if if we
00:28:42
assume that consistent winning is a
00:28:44
product of you know a strong smart
00:28:46
organization
00:28:48
um then that gives you know smart
00:28:52
organizations
00:28:53
an advantage that players will want to
00:28:57
go to those kinds of organization. Of
00:28:59
course, if you're if you're not a smart
00:29:01
good organization that can't figure out
00:29:03
how to win, you're going to be at a
00:29:05
disadvantage. And the only way to to to
00:29:06
do it is you had to spend more. That's
00:29:09
the way to do it. Now, I while money
00:29:12
matters, winning matters, opportunity
00:29:14
matters also for these guys because
00:29:16
particularly the guys who are haven't,
00:29:18
you know, proven may not have be off the
00:29:21
huge rookie uh offers. um they're going
00:29:24
to have to prove themselves. And so if
00:29:25
they don't have opportunity to play
00:29:26
going to OKC, Boston, uh you know,
00:29:29
Denver, like if they're not going to
00:29:31
have an opportunity to play there, then
00:29:33
they're not going to have an opportunity
00:29:33
to get their next contract, you know,
00:29:36
right? And brilliant.
00:29:37
>> So lots of dimensions going to come into
00:29:39
the position.
00:29:41
>> Let me AI AI has a question. I have a
00:29:43
followup on this topic as well. Audi, so
00:29:45
in the draft um you you're talking about
00:29:49
uh or replacing it or something similar.
00:29:52
How much does the quality drop from the
00:29:55
top three, top four and and that must
00:29:58
make an enormous difference on this kind
00:30:00
of calculation? So I mean this is
00:30:04
something that that uh we we've always
00:30:06
talked about a moneyball. Um, and
00:30:08
basketball is clearly the most rapidly
00:30:10
decaying function among all sports. And
00:30:14
I'm not sure it's it actually is because
00:30:16
they really get that much worse or just
00:30:18
because a team only has five players and
00:30:21
there's only one ball. And
00:30:22
>> by the way, a do you mean a priori a
00:30:24
posterior?
00:30:26
>> Uh, I mean, okay. I mean a priori. I
00:30:30
mean basically I've just it's a great
00:30:33
question but I'm I'm essentially asking
00:30:35
can you really build a team
00:30:38
>> without having regular access to the top
00:30:41
X where X is what three five nine 20 um
00:30:45
generally my understanding about the
00:30:47
draft is after the first round is just
00:30:49
it's just you're just nothing there
00:30:51
maybe you get a you know a generation
00:30:53
you get a player of any value every now
00:30:55
and then but
00:30:56
>> can you really build it without access
00:30:58
to the real
00:31:00
Um, I mean, you you can build a team
00:31:02
without access to the top, but you're
00:31:04
going
00:31:04
>> winning team, right?
00:31:05
>> You can build a team without access to
00:31:07
the very top of the draft, provided that
00:31:10
you have other avenues to acquire, you
00:31:12
know, high level talent. um you know,
00:31:16
you can't uh if if you don't if you
00:31:19
can't bring players in through free
00:31:20
agency or trade that are of that
00:31:23
caliber, the most likely place to get
00:31:25
those kinds of players is at the very
00:31:27
top of the draft. They're not they don't
00:31:30
all exist there. You can get them like
00:31:31
you go was, you know, and he's the
00:31:35
exception that proves the rule on some
00:31:36
level. I mean, you know, these these
00:31:38
players do exist and they happen, but
00:31:41
they tend to be in the at least in the
00:31:43
top 10 of the draft, uh, if not top
00:31:46
five. Uh, and so
00:31:49
that doesn't it's really hard to compete
00:31:50
without a couple of guys that are that
00:31:52
that good.
00:31:54
>> Guys, let me real quick observation and
00:31:56
then we need to change to a final
00:31:59
question for Ben before we go, but I
00:32:00
just want to observe a specific thing
00:32:03
about what Ben said before. If you did
00:32:04
go to the system, the the advantage to
00:32:07
the sharp clubs essentially, if you
00:32:09
think about it right now, the challenge
00:32:10
with a draft primarily
00:32:13
is to order the players. And if you go
00:32:16
to free agency model, the challenge is
00:32:18
to value every player. And the second
00:32:22
task is much harder than the first task.
00:32:24
And so that the advantage you if you're
00:32:26
a sharp club, you want a more
00:32:27
complicated, more challenging task
00:32:29
because you can separate more. And so
00:32:31
you might claim that that would be an
00:32:33
advantage to the Sharp Club. Um Ben, I I
00:32:36
saw you recently and you mentioned that
00:32:38
you've taken up a a cause. You're you're
00:32:41
evangelical about a new cause. Yes.
00:32:44
>> That I think we'd probably be
00:32:45
sympathetic to. So tell tell us about
00:32:46
this cause. Now Audi, you this is going
00:32:49
to tickle you, I think.
00:32:50
>> Okay.
00:32:52
It it is my very strong belief that uh
00:32:54
that Dean Oliver belongs in the
00:32:56
Basketball Hall of Fame. Uh I think I
00:32:59
think the case is a slam dunk no
00:33:01
problem. Uh his qualifications and
00:33:04
background, his impact on the game are
00:33:07
so deep and obvious that um he he
00:33:10
absolutely uh belongs in in the
00:33:12
Basketball Hall of Fame. Um Dean
00:33:14
obviously wrote basketball on paper.
00:33:16
>> So hold on, Ben. Hold on. First tell us
00:33:18
by what avenue do you get him in here?
00:33:20
We think about Will Chamberlain and
00:33:21
Magic Johnson and Dean Oliver. Come on.
00:33:24
>> Yeah.
00:33:24
>> Yeah. So, Dino, so there's a there's a a
00:33:27
category in the Hall of Fame of
00:33:28
contributors, people who have had an
00:33:30
positive impact on the game. Um, you
00:33:32
know, and so those are the the that's
00:33:34
the avenue the the the lane in which
00:33:36
Dean gets added to the Hall of Fame.
00:33:38
>> And who are some examples from that?
00:33:40
>> I mean, there there there are variety of
00:33:42
guys, but uh Dell Harris, Rod Thorne,
00:33:46
Rick Weltz, Doug Collins. Um, so these
00:33:49
are and so you can get in multiple
00:33:51
times. So, you know, uh, Doug Collins
00:33:53
can get in as a coach but also as a a
00:33:55
contributor through his media work or,
00:33:57
you know, and Del Harris has been a real
00:33:59
ambassador for the game overseas, for
00:34:01
example. Um, uh, Rick Weltz has been
00:34:04
really innovative on the business side
00:34:06
of of basketball and really contributes
00:34:08
to the game. Um, Dean obviously has
00:34:10
contributed to how teams think about
00:34:13
basketball and really changed that
00:34:15
significantly. Um, so that that's why as
00:34:18
a contributor, he really belongs in the
00:34:20
Hall of Fame. So, so Ben, tell us. So,
00:34:22
you're you're we're analysts and so we
00:34:24
think Dean Dean speaks our language and
00:34:27
so yes, he helps us understand it. He
00:34:28
gives us a lens to understand the game.
00:34:30
You're from that same world. You have a
00:34:32
PhD in economics. What's the evidence
00:34:34
that you would muster for his having
00:34:37
changed the way actual basketball people
00:34:40
who run teams and play the game think
00:34:42
differently because of Dean?
00:34:44
>> Um the the whole concept of of
00:34:47
efficiency and the four factors of
00:34:49
basketball uh are have permeated
00:34:52
throughout the league. So Dean brought
00:34:53
these concepts in basketball on paper
00:34:55
which is uh to me the the starting point
00:34:58
of modern basketball analytics uh when
00:35:00
he published that book. Um and so that
00:35:03
that I mean you find that book on on
00:35:05
most you know in most front office on a
00:35:09
bookshelf somewhere uh at least one copy
00:35:11
in every team building somewhere um and
00:35:15
but so if you take the the four factors
00:35:18
for example which is sort of a you know
00:35:19
clear way to think about a team uh you
00:35:22
have their shooting efficiency, rebound
00:35:24
efficiency, turnovers and ability to get
00:35:26
to the line. These are the four factors
00:35:28
uh that that we can measure and you know
00:35:30
we know that we take those how a team
00:35:32
performs on those things on offense and
00:35:34
defense
00:35:35
90 plus percent correlation to to their
00:35:38
point differential.
00:35:39
>> Let me just point out because we I do
00:35:41
this with my my my senior uh students. I
00:35:44
teach a sports analytics uh capstone and
00:35:47
it's uh remarkable because it works
00:35:50
almost as well basically as well as
00:35:52
point differential
00:35:54
>> just to show you. So, in other words,
00:35:55
you're basically taking a bunch of
00:35:56
peripherals and you're using them to to
00:35:59
predict winning percentage and it does
00:36:01
it as well as the score differential.
00:36:04
What does this well, Audi? Not quite. It
00:36:06
can't be exactly as well, but really
00:36:09
incredibly close. I mean, in fairness,
00:36:12
shooting efficiency is one of them. And
00:36:14
of course, that that's that's the
00:36:15
biggest factor. And it's not like and
00:36:17
that doesn't that has a lot to do with
00:36:18
your score differential.
00:36:20
>> So, real quickly to remind us what
00:36:22
shooting efficiency is. And you
00:36:24
mentioned efficiency right off the top
00:36:25
and if you want to give Dean credit for
00:36:26
one thing it's probably this notion of
00:36:28
efficiency but to remind us what it is.
00:36:29
>> So efficiency overall is points per
00:36:32
possession. That's how we think about
00:36:33
and that that is a a a paradigm shift in
00:36:37
in basketball significantly. And
00:36:39
shooting efficiency typically we think
00:36:41
about effective field goal percentage
00:36:43
which says all right we're going to give
00:36:44
you more credit for making a three-point
00:36:47
shot than a two-point shot because it's
00:36:50
harder to make and it's worth more. Um
00:36:53
that's the the effective field goal
00:36:54
percentage basically, you know, weights
00:36:56
twos and threes more reasonably uh and
00:36:59
gives us a better sense of how well a
00:37:01
team is shooting given these two
00:37:03
different kinds of shots.
00:37:05
>> Well, Ben, by the way, if it makes you
00:37:06
feel any better, I asked the Oracle chat
00:37:08
GPT what his odds are of making the Hall
00:37:11
of Fame
00:37:12
>> and it said actually it's pretty
00:37:15
moderate. It gave it 40% chance. Now,
00:37:18
>> I could ask it on what basis and all
00:37:20
kinds of things, but it said he's an
00:37:22
extraordinarily strong contributor, but
00:37:25
in a what people consider a niche area,
00:37:27
but either way,
00:37:28
>> but but your your Oracle doesn't know
00:37:30
that Ben Alamar is on the job. It hasn't
00:37:32
picked up yet on the fact that there's
00:37:34
an evangelist out there. Who do you have
00:37:35
to convince, Ben? And how and how long a
00:37:37
campaign are you geared up to wage?
00:37:40
>> Yeah, so I'm I'm going to keep fighting
00:37:41
this one until uh I get uh get some
00:37:44
traction. I fir I first rolled this out
00:37:45
at Sloan a couple of years ago in a
00:37:47
history of basketball analytics panel uh
00:37:49
that Dean was on, but I asked Mike
00:37:50
Zaren, you know, when we were going to
00:37:52
get Dean in the Hall of Fame. Um and and
00:37:54
Xarin uh acknowledged that that he
00:37:57
belongs there. Uh
00:37:58
>> Xarn front office with the Boston
00:38:00
Celtics.
00:38:01
>> Absolutely. Yes.
00:38:03
>> And call the Bill James of basketball.
00:38:06
So
00:38:06
>> is Bill Bill James is not in the in the
00:38:08
the baseball hall of fame? There's no
00:38:10
avenue for that, right?
00:38:11
>> Yeah. I that that's you know
00:38:14
>> if this avenue existed in baseball
00:38:16
>> I don't know how you do it but I don't
00:38:18
>> he could be I mean Bill James would he
00:38:20
be cons I guess he wouldn't be
00:38:21
considered a writer at all because there
00:38:22
was obviously a writer's
00:38:24
>> I mean he's he he was a writer obviously
00:38:26
>> he should be considered a writer and he
00:38:28
should be in the Hall of Fame.
00:38:29
Absolutely why like they've got if
00:38:31
there's not a reason for Bill for Bill
00:38:32
James be in the baseball hall of fame
00:38:34
they got to figure that out because
00:38:35
that's that doesn't seem right. By the
00:38:37
way, just so you know, um I asked Jot
00:38:39
GPT if Ben Alamar really pushes for it.
00:38:42
You added 10 to 15%.
00:38:45
>> I if I'm Ben, I'm not
00:38:48
>> here's a better question, Eric. We're
00:38:49
out of control.
00:38:50
>> I'm going to ask it
00:38:52
pushes for it. Is that
00:38:53
>> That's what I'm about to do. A I'm not
00:38:56
going to use me. I'm gonna use me. If
00:38:57
Eric Bradloan says, "All right, now
00:38:59
we're down to 25."
00:39:00
>> Yeah, we're losing. We're losing points.
00:39:02
>> But Ben, you're worth 10 out of it. Hey,
00:39:04
hey, I want
00:39:05
>> value ad.
00:39:06
>> We are getting the uh baseball hall of
00:39:10
fame, the head of the baseball hall of
00:39:12
fame on the show in the next month and
00:39:15
we're going to start the Bill James
00:39:17
we're going to start the Bill James
00:39:18
campaign.
00:39:18
>> There you go. Somebody needs to lead
00:39:20
that charge. That's the I I I can't take
00:39:22
that one on, but I I'll uh
00:39:24
>> actually, let me just say, Kade, I will
00:39:26
ask um I will we're having Josh Trowick,
00:39:29
who's the president of the National
00:39:30
Baseball Hall of Fame, on next week,
00:39:32
Ben, and I'm going to ask him, do you
00:39:34
ever see a path where someone that's
00:39:36
trains the game of baseball through
00:39:38
analytics? Like, you could argue, why
00:39:40
not Billy Bean at some level, too? I
00:39:42
mean, why doesn't he get why isn't he in
00:39:44
the Baseball Hall of Fame?
00:39:45
>> What are you talking about? How about
00:39:46
Theo Epstein?
00:39:48
>> Yeah. Well, that's another name.
00:39:50
>> Championships, right? Theo will get
00:39:52
there, right? So, I mean, I to me I I
00:39:56
mean, those guys like there's clear ways
00:39:58
for them to do it and and the the impact
00:40:00
that they had are sort of advancing, you
00:40:02
know, using these things, you know,
00:40:04
think about the front office in a new
00:40:06
and interesting way with the tools. But,
00:40:08
you know, Bill James created the tools
00:40:10
like Dean Oliver changed the way we, you
00:40:12
know, make decisions in basketball. Um,
00:40:14
and and that's
00:40:16
>> it's a great cause. It's a great cause.
00:40:17
Keep beating that drum. We'll we'll
00:40:19
start we'll start going along with you.
00:40:20
Ben, we've kept you longer than we meant
00:40:22
to. Thank you very much. Always a
00:40:24
delight to talk to you.
00:40:25
>> My pleasure. Thanks, guys.
00:40:27
>> Ben Alamar, longtime sports analytics.
00:40:30
He has got a book on sports analytics.
00:40:32
He is teaching a program on teach on
00:40:34
sports analytics at the University of
00:40:36
Texas at the moment. He will be at the
00:40:39
Sloan Sports Analytics Conference
00:40:41
hosting a panel in just about a month's
00:40:43
time. All right, guys. That has been the
00:40:45
first half of Orton Moneyball. Come back
00:40:47
and join us after the break. Welcome
00:40:49
back.
00:40:51
Welcome back to Wharton Moneyball.
00:40:53
Welcome to the second half or you might
00:40:55
say the last quarter. We went long that
00:40:57
first first half with Ben Alamar.
00:41:00
Delightful conversation. Could have gone
00:41:01
on for a while. Ben's always a fun
00:41:04
conversation. We're we got to go about
00:41:06
15 minutes. Audi has to get the
00:41:07
classroom. So, let's do a quick What
00:41:10
caught your eye. There are obviously
00:41:13
interesting things floating around. I
00:41:14
think Audi might want to jump in here
00:41:15
first. gentlemen around the world.
00:41:17
>> I will jump in because because it's
00:41:19
something that I've studied in general
00:41:20
though not in specifics. It's the it's
00:41:22
the judging fiasco in skating at the
00:41:25
Olympics.
00:41:26
>> Okay.
00:41:27
>> And uh so I won't fill in all the
00:41:29
details but suffice it to say the French
00:41:31
judge um gave a very high score to the
00:41:34
French competitors and importantly gave
00:41:37
an extremely low score to the American
00:41:39
competitors and it essentially
00:41:41
completely reversed the order of the the
00:41:44
competition. Which competition was this?
00:41:45
>> And of course, this was the this is the
00:41:48
doubles the mixed doubles skating
00:41:50
competition. Um I forget exactly which
00:41:53
version. And as you know, Shane's not
00:41:55
here to scream and yell, but he'll he'll
00:41:57
tell you that real sports aren't
00:41:59
subjective, right? Um but of course,
00:42:01
skating is a real sport and it's has
00:42:03
judges and they have a format and
00:42:06
typically you have a set of judges. each
00:42:08
each make a score and then there's a
00:42:10
then there's a a compet some sort of
00:42:12
summary of the usually
00:42:14
>> don't they trimmed uh
00:42:16
>> No, I know they do that in diving. Um
00:42:18
I'm not sure they do it in in skating.
00:42:20
>> I thought they did it in skating. I
00:42:21
thought they
00:42:22
>> I think I think this I I I think this
00:42:24
might be a niche skating competition,
00:42:27
not the main skating competition. And I
00:42:29
feel like they've really ironed out the
00:42:30
main skating competition in a way that I
00:42:33
mean I can tell you that uh
00:42:34
>> it's like ice dancing or something
00:42:36
instead of the actual Well, I think the
00:42:38
ice danders would disagree whether or
00:42:39
not it's niche or not. May not be the
00:42:42
historically the most famous of the
00:42:44
>> But Audi, there were full-on like
00:42:47
communist block almost battles over this
00:42:50
back years ago. I mean, the whole the
00:42:52
whole meme of the Russian judge comes
00:42:54
from that, right?
00:42:55
>> Yeah. But this they ironed it out in
00:42:56
some way. So, this must be
00:42:58
>> Well, clearly they haven't because
00:42:59
there's a controversy over this and I
00:43:01
haven't looked at the data. But one of
00:43:03
the things that I think as a
00:43:03
statistician is interesting is the idea
00:43:06
that you can essentially
00:43:08
um establish
00:43:10
an enormous bias you um I don't know
00:43:12
what you want to call it other than that
00:43:14
a if without having much data um if the
00:43:18
delta is ex is exceptionally large
00:43:21
relative to historical standards and I
00:43:23
believe that is what happened here. I
00:43:25
see. Well, let me just say I did look.
00:43:27
They do prune. They prune the highest
00:43:28
and the lowest. There's nine judges for
00:43:30
both technical and whatever they call
00:43:32
the other one aesthetics or whatever.
00:43:34
They do prune the highest and lowest.
00:43:36
So, one judge, even a French judge on a
00:43:39
French team could not actually lift
00:43:41
them, although it does remove margin of
00:43:44
error if you want. It means, you know,
00:43:46
that means the second score is going to
00:43:49
count in some way much more than
00:43:51
otherwise. So I'm not
00:43:52
>> what would have been the max now becomes
00:43:53
the second and that gets
00:43:55
>> that's what I'm saying the distribution
00:43:57
and score is different than the max.
00:43:59
>> You're saying the delta you're saying
00:44:00
between the top rated and lowest rating
00:44:02
is that the delta you're talking about
00:44:03
what delta
00:44:04
>> delta delta is between the top and the
00:44:07
bottom compared to the the overall panel
00:44:09
average
00:44:10
>> of the delta the panel average
00:44:12
>> that's what I would call the delta. So
00:44:13
there's a, you know, one of the one of
00:44:15
the exercises that I've done with
00:44:16
students is you can find a judge that
00:44:19
only has two or three uh measurements
00:44:22
where the he has a nationality match
00:44:25
with his compatriate during the
00:44:27
competition. And you can still
00:44:29
nevertheless demonstrate pretty clearly
00:44:31
that there is bias because he had he
00:44:34
might have judged 40 or 50 skaters or
00:44:37
divers or whatever competition you're
00:44:38
looking at and his two highest
00:44:40
differentials or delta are for when he
00:44:42
made the match and that just has
00:44:44
astoundingly small probability.
00:44:46
>> Why don't they just knock out the judges
00:44:48
rating their own nation? Why is that?
00:44:51
That would be an interesting Well, you
00:44:53
can't that easily because the nations
00:44:56
with the largest competitors, United
00:44:58
States or China, France, whatever it is,
00:45:00
probably have more than one. And that
00:45:02
would would um
00:45:04
>> Well, don't give them more than one.
00:45:05
There's plenty of nations with figure
00:45:07
skating going on to have one per
00:45:09
>> right from different
00:45:11
>> Anyway, it's not done. At least not yet.
00:45:13
>> Interesting.
00:45:15
>> All right, Eric, what you got?
00:45:16
>> Well, I got a brief one and then I I
00:45:18
still want to emphasize again. I'm a big
00:45:21
fan because I used to ski a lot. I'm a
00:45:23
big fan of skiing and I want to say it
00:45:25
again. So now Michaela Shiffren, the
00:45:28
most celebrated women's downhill skier
00:45:31
in the history of skiing. Matter of
00:45:32
fact, of male or female, is now 0 and8
00:45:37
in her last eight Olympic races. And I
00:45:39
just want to be clear, I don't mean 0
00:45:40
and8 not winning the gold. I mean 0 and8
00:45:43
not even medlin. And so, look, I the
00:45:47
good news is, let me say for her, she
00:45:50
has two golds and a silver from the
00:45:52
first three races she ever did in the
00:45:54
Olympics. That's great. That's an
00:45:57
extraordinarily accomplished career, but
00:46:00
it's starting to get to the point where
00:46:02
it is going to detract from her overall
00:46:06
accomplishments
00:46:07
as a skier. It's just going to This is
00:46:10
about to be She has one race left. It's
00:46:12
about to be the third straight Olympics
00:46:14
where she will not win a medal. And
00:46:16
>> so so in bet so the race is tomorrow's
00:46:19
Wednesday slalom I believe her best
00:46:23
>> her best event. So what is your bet?
00:46:26
What probability do you give that she
00:46:27
medals?
00:46:31
>> I would say
00:46:34
one/3.
00:46:36
>> Okay. So it's distinctly below what her
00:46:39
world ranking would suggest. below what
00:46:41
her world ranking is, but still high
00:46:43
enough that reflects that that is her
00:46:46
best event and she's the best slalom
00:46:48
women's slalom racer of all time.
00:46:51
>> Uh, do you go you want you want above or
00:46:53
below 33%.
00:46:54
Who's you want to be above
00:46:56
>> winning? Is this winning or meddling
00:46:58
meddling? Mattling
00:46:59
>> meddling. I want above it.
00:47:00
>> I want to I want above it, too. So, you
00:47:02
and I neither one are going as far to
00:47:04
the to the
00:47:05
>> I'm not Well, you know, we are not
00:47:07
momentum guys.
00:47:08
>> I'm a momentum person. I mean, let's
00:47:10
just get a meme going here.
00:47:11
>> Yeah, let's go. But let me talk about
00:47:13
the topic I really wanted to talk about.
00:47:14
>> So, no, no, hold on. We're real quickly
00:47:16
on this point. Um, you guys are hardcore
00:47:19
statisticians.
00:47:20
Do you believe
00:47:22
people that practice like performance
00:47:24
coaches are a thing? Do you think that's
00:47:26
a real Do you think they can Do you
00:47:28
think they can substantively shift an
00:47:30
athletes performance? A performance
00:47:31
coach, a mental coach?
00:47:35
God, you know what? I have nothing
00:47:37
substantive to say on that on that
00:47:39
statement.
00:47:40
>> I I I don't know.
00:47:42
>> Well done, Audi. Well done. That's good
00:47:43
awareness.
00:47:46
>> Do I think a performance coach can help
00:47:48
someone perform at their maximum
00:47:52
>> given also understanding there is
00:47:54
interday variation and all of those
00:47:56
things? Yes, I do. I I I do. I believe I
00:48:00
didn't say that they can make them
00:48:01
necessarily perform better, but can they
00:48:04
make someone perform near their maximum
00:48:07
maybe more consistently? Yes,
00:48:09
>> that's that's yeah, that that's that's
00:48:11
really well defined. You you
00:48:12
operationalized it better.
00:48:13
>> Yes, I think we should we should find we
00:48:16
should find someone to come on and talk
00:48:17
to us about that. You're hearing lots of
00:48:19
between what's happened with Shiffron
00:48:21
and the figure skater Ilia, what's his
00:48:23
name? There's lots of talking about
00:48:25
that. Eric, I've got to sneak mine in
00:48:27
here before Audi goes away. Okay. So, if
00:48:28
you want to linger for a minute, I'll
00:48:29
get your second one. Let me let me put
00:48:31
one in there that I that that I have for
00:48:33
y'all in particular.
00:48:34
>> I want to hire y'all. Y'all y'all do
00:48:36
speaking, y'all do statistical
00:48:38
consulting on occasion. I want to hire
00:48:39
y'all. I run the Olympics and I'm
00:48:41
worried about the the I think it's been
00:48:44
kind of willy-nilly uh when events when
00:48:46
when we get multiple versions of the
00:48:48
same event and it's not clear to me why
00:48:51
we should have multiple versions or
00:48:52
maybe we should have more, maybe we
00:48:54
should have fewer. So I want to know
00:48:55
like is four the right number of
00:48:57
downhill events to have. We have a
00:48:59
downhill on one end, we have slalom on
00:49:02
the other, but now we have a giant
00:49:03
slalom, we have a super G. So we have
00:49:05
four variations and I don't know I run
00:49:07
the Olympics. Is four the right number?
00:49:09
I mean is four too much? Should it be
00:49:11
five? Okay, different var different
00:49:13
question. Speed skating and this is this
00:49:16
goes back to something Audi's complained
00:49:17
about in the in the summer Olympics. How
00:49:19
many different events there are with
00:49:20
swimming. So speed skating, what is the
00:49:22
right number of links to have? Not
00:49:26
forget short track, just traditional
00:49:27
speed skating. There's 500, there's
00:49:29
thousand, there's 1500, 5,000, 10,000.
00:49:32
And I I run the Olympics. I don't know.
00:49:34
Are these speed skaters giving us all
00:49:36
these extra races because they want more
00:49:37
medals? Is it does it make sense? Or
00:49:39
maybe we need even more. Maybe we need a
00:49:41
750 meter. I need y'all as statisticians
00:49:44
to tell me how I should think about
00:49:45
this,
00:49:45
>> bro. So I can start with one way to
00:49:49
think about it which which uh I know has
00:49:51
is similar in track and it's certainly
00:49:53
true in swimming is that there's a huge
00:49:55
difference between the training the body
00:49:58
type the skill of being distance to
00:50:00
sprint and and the middle is also
00:50:04
essentially its own it its own
00:50:06
centroidid now and the reason why that's
00:50:09
fascinating is that basically you have
00:50:11
the elite in each of those three
00:50:13
categories and they're almost always
00:50:14
distinctly different people except for
00:50:17
an occasional superstar can dominate in
00:50:21
two over you know two adjacent uh
00:50:24
lengths. So in swimming you can have
00:50:26
someone who's good at the 400 and maybe
00:50:28
the 800
00:50:29
>> Katy Leiddki
00:50:30
>> Katy Leiddki is the only one who really
00:50:32
comes to mind. Um, for example, uh, even
00:50:35
in
00:50:35
>> Okay, so now I I understand the theory.
00:50:38
>> I've got a
00:50:39
>> Okay, I understand that I I understand
00:50:40
the
00:50:41
>> I have a different I'm sorry. Let me
00:50:42
just jump in quickly about different
00:50:44
measure. A different measure would be
00:50:46
let's imagine you could measure ability
00:50:49
at different
00:50:51
>> uh lengths. And let's imagine I'm just
00:50:54
trying to operationalize this. Let's
00:50:56
imagine the correlation between theta j
00:50:59
and theta j + one where j indicates the
00:51:02
length of the race was extraordinarily
00:51:04
high. So that in some sense you might as
00:51:07
well call it one winner because the
00:51:10
ability is so correlated between the to
00:51:13
me that would suggest a redundancy in
00:51:16
races.
00:51:17
>> So your what you did with your theta was
00:51:19
the underlying ability it's theta j is
00:51:22
that right? So it's all the different
00:51:23
thetas for the the
00:51:26
>> it's your theta was the underlying
00:51:28
ability. You're going to estimate the
00:51:30
underlying ability, but it's theta i.
00:51:31
You're going to do underlying thetas for
00:51:33
all the different skiers. And then
00:51:35
you're going to do it for each of the
00:51:36
different races. And you're going to ask
00:51:38
how correlated the theta i's are across
00:51:41
J's. So for for each skier,
00:51:45
what their theta in an event, how is it
00:51:47
correlate with their theta in another
00:51:48
event? Do I have that right? That's
00:51:50
that's a very clear
00:51:52
That's what that's what my suggestion
00:51:54
was.
00:51:55
>> Okay. Okay. So So take take downhill. So
00:52:00
um Audi, are you happy with that? And by
00:52:02
the way, what what what would your
00:52:03
criteria be? How how how would we know
00:52:06
what's high or what's low? Like take the
00:52:07
four downhill events and so how are we
00:52:10
going to judge whether that's do we
00:52:12
really need Super G or is it like you
00:52:14
know the giant slalom is enough? That's
00:52:17
the middle race. Super G was I think the
00:52:18
last last comer. I'm just statistically
00:52:22
or
00:52:23
>> so now you want me to come up with a
00:52:24
cutoff score you're saying?
00:52:26
>> Well, I just want I'm asking Audi, I
00:52:27
like the way you did that, but I want to
00:52:29
know Audi, are you satisfied with that?
00:52:30
I thought there might be a clust is
00:52:32
clustering any different really than
00:52:35
what Eric just suggested.
00:52:38
>> I don't think so. I mean, it's
00:52:40
complicated issue because there's also
00:52:41
the marketing side of this. people want
00:52:42
to see more events because it's just
00:52:44
more fun, more opportunity
00:52:45
>> and that really giant giantly competes.
00:52:47
I mean I mean speed skating you might
00:52:48
say is over is overly overly divided too
00:52:52
many because think about Eric Heiden I
00:52:53
think he won seven gold medals in um
00:52:56
>> but he was making that many
00:52:59
>> that was 46 years ago and one of the
00:53:00
things they have said about this
00:53:02
Wisconsin kid is that he was the first
00:53:03
only the second ever to win both the 500
00:53:06
and the 1000. So that's Eric's
00:53:08
correlation not being very high the 500
00:53:10
and 1000. So apparently that's a
00:53:13
distinction between the short and the
00:53:14
middle that's substantive. Okay, I'll
00:53:16
let that go. I want to get your quick
00:53:17
thoughts and we should probably cut odd
00:53:20
out while
00:53:22
>> Eric is going to give us another what he
00:53:23
really want to talk about.
00:53:24
>> Stay and I'll talk to you next week.
00:53:26
Have fun in class.
00:53:27
>> So I just had one more. So this number
00:53:30
this statistic has been on my mind for
00:53:32
weeks now because it's been true. So
00:53:34
I'll have you guess for a second. So the
00:53:36
Lakers by the way this year it's in the
00:53:37
NBA of course have a very good record.
00:53:39
They're 33 and 21. I think we'd agree
00:53:41
that's a good record, right?
00:53:43
>> Good.
00:53:44
>> It's good. I didn't say it's great. I
00:53:45
said it's good. It's a good record.
00:53:47
They're actually winning I mean 54
00:53:49
games. Uh they're winning well more than
00:53:52
60% of their games. So they're playing
00:53:54
well.
00:53:54
>> Do you know what their point
00:53:55
differential is, Kate?
00:53:57
>> I'm guessing not very much.
00:53:58
>> It's zero.
00:53:59
>> This Okay.
00:54:01
>> So I compare that to teams around them
00:54:05
like the Nuggets,
00:54:07
the Rockets,
00:54:08
etc. their differential is like plus
00:54:11
five. So over 50s something games those
00:54:15
teams have more than a 300 like a 300
00:54:17
point differential and the Lakers have
00:54:19
zero.
00:54:20
>> So you're saying the average the average
00:54:22
differential is.5.
00:54:23
>> Yeah. The average per game. The average
00:54:24
Yeah. per game.
00:54:26
>> Well, this is this is Pythagorean. This
00:54:28
is the Pythagorean.
00:54:28
>> That's what I'm saying. This to me seems
00:54:30
like a a significant deviation from
00:54:32
Pythagorean where literally they're at
00:54:35
zero, but somehow whether you could
00:54:38
argue they're I mean there's two things
00:54:40
that could cause us, right? They're
00:54:41
winning a lot of close games and LeBron
00:54:43
James is going to help you do that
00:54:45
>> or they're getting blown out a lot in
00:54:48
games, which is when they lose, they
00:54:51
lose real bad. And by the way, that's
00:54:52
cuz Luca hasn't played a lot of games.
00:54:54
LeBron's injured. But all I'm commenting
00:54:57
on is this is something actually next
00:54:59
time I speak to you know Ben Alamar or
00:55:03
you know Dean Oliver or somebody else on
00:55:06
I'm going to I I'm going to ask him like
00:55:07
how much does because again OKC let me
00:55:10
just say by the way despite them having
00:55:12
played 500 ball over the last 20 games.
00:55:14
If we look over the almost 60 games of
00:55:16
the season so far the season ended right
00:55:18
now they would break the record their
00:55:20
own record of last year for the greatest
00:55:22
point differential in NBA history.
00:55:25
per game. So at some level that has to
00:55:28
say something. Just in the same way to
00:55:30
me the Lakers being at zero means
00:55:33
>> they're really 500ish but they just
00:55:36
happen to be 33 and 21 unless someone
00:55:39
tells me otherwise.
00:55:41
>> One one version of this question would
00:55:43
be is the Pythagorean theorem which
00:55:46
comes out of Bill James. We talked about
00:55:47
Bill in the first half the show is a
00:55:49
Bill James thing but then it gets
00:55:50
applied across all sports. It's
00:55:51
basically just asking
00:55:53
>> to what extent it's saying in a big
00:55:55
sample your point differential is going
00:55:57
to relate to your win record. And so we
00:56:00
we kind of know whether someone's
00:56:02
underperforming or under overperforming
00:56:04
their um their um point differential. Um
00:56:08
here's one version of the question,
00:56:09
Eric. Might it mean less in basketball
00:56:12
than other sports? I guess that's you've
00:56:14
already hypothesized on why the Lakers
00:56:16
might
00:56:17
have a worse differential than their
00:56:19
record would suggest because they've got
00:56:20
a couple of players that are key and
00:56:22
those players aren't playing as
00:56:24
regularly and so they have these
00:56:25
disproportionate negative point
00:56:27
differentials when those guys are out.
00:56:29
Might that be just kind of a hallmark of
00:56:31
basketball these the load issues and the
00:56:34
impact of a single player and stars?
00:56:37
might all that suggest that Pythagorean
00:56:39
would be less predicted than
00:56:40
>> that's by the way that's an answerable
00:56:43
uh empirical question and it's a good
00:56:44
one. It's also one related to the topic
00:56:47
we talked to Ben Alamar about in the
00:56:48
first half. You you have a league with
00:56:51
tanking and a team with resting players.
00:56:54
You even mentioned resting players and
00:56:56
you're you're going to get situations
00:56:58
like here's here's you know here's an
00:57:00
argument. What we're observing for the
00:57:02
Lakers is a mixture distribution between
00:57:04
the 80% of games they're trying, the 20%
00:57:08
of games they're not. That leads to a
00:57:11
breakage of the Pythagorean formula and
00:57:14
it leads to a point differential of
00:57:16
zero, but a massively winning record.
00:57:19
>> Okay? And that that to me suggests that
00:57:22
baseball I mean basketball
00:57:25
needs these mixture distributions more
00:57:27
than other sports do. Like you're not
00:57:30
gonna get discreetly different
00:57:32
distributions of team performance in the
00:57:34
NFL like you will in basketball and
00:57:37
you're certainly not going to get it in
00:57:38
baseball either, right? So mixed
00:57:40
distribut.
00:57:44
>> Okay. So if that's the case then for
00:57:47
sure it will be less meaningful a
00:57:50
Pythagorean. It's almost like you need
00:57:52
you need to have a mixture of your
00:57:53
Pythagoreans.
00:57:54
>> I order I like it actually. you in some
00:57:57
sense you could fit you could imagine
00:57:59
fitting a latent class or a mixture
00:58:00
model unless you want to say for
00:58:02
basketball you can kind of tell based on
00:58:04
who they play which are the real games
00:58:06
and which are the tank games you then
00:58:08
fit a model separately or fit the
00:58:11
formula separately to those two and then
00:58:13
see what would happen. I think that
00:58:14
would be a that would be something
00:58:16
interesting.
00:58:17
>> Well, it calls to mind uh when Nate
00:58:20
Silver was publishing at 538, they had a
00:58:22
basketball model. He probably still
00:58:23
publishes a basketball model, but they
00:58:25
had um a basketball power ranking and
00:58:28
they had two versions of it, Eric. They
00:58:30
had regular season and playoff. And they
00:58:32
were re that's that's essentially the
00:58:34
mixture distribution we're talking
00:58:35
about. They're recognizing that there
00:58:37
are there's a full strength team in the
00:58:39
NBA and then there's some non full
00:58:42
strength team and you need to understand
00:58:44
them and model them separately.
00:58:45
>> And just I know we're going to run out
00:58:46
of time, but just one last thing to give
00:58:48
a shout out for your Texas boy Scotty
00:58:50
Sheffller. I mean this I don't care that
00:58:54
he didn't win. It was great theater.
00:58:57
This is the greatest in golf since Tiger
00:58:59
Woods in like 2001. He's now got 18 top
00:59:02
10s in a row. I mean, come on.
00:59:05
>> I mean, unbelievable. And you know, um,
00:59:10
and it's amazing how great a golfer this
00:59:13
man is. I just I and I was very happy to
00:59:16
see Colin Maro win his first tournament
00:59:18
in almost three years. you know, he won
00:59:20
>> by age 24 and hasn't even won since then
00:59:23
essentially. I was thrilled to see more
00:59:25
win. But watching Sheffller blow three
00:59:28
putts 5 feet or less and still shot a 63
00:59:31
by the way, including making three
00:59:33
Eagles. I don't think in my whole life
00:59:35
of watching golf I've ever seen a golfer
00:59:37
make three Eagles before in one single
00:59:39
round of golf. I'm sure it's happened,
00:59:41
but I'm just commenting that
00:59:43
>> I think we we should appreciate Scotty
00:59:46
Sheffller for who he is. And I'm gonna
00:59:48
tell you, he's he's not Tiger Woods yet.
00:59:51
Not even close. Let him do it for
00:59:52
another five years. But I have no reason
00:59:54
to believe he won't do it for another
00:59:56
five years. And look, I don't know if
00:59:58
he's getting to 15 majors. That's not
01:00:00
fair. But he could easily get into the
01:00:02
Gary Player, uh Tom Watson, you know,
01:00:06
that that league of 8 to 10 majors. And
01:00:09
I'm going to tell you something, this is
01:00:12
a top 10 golfer of all time. He's great.
01:00:15
that I I don't I don't disagree with any
01:00:17
of that, Eric. It reminds me it makes me
01:00:19
wonder about what other comparisons we
01:00:22
can come up with. Um like we let's let's
01:00:24
let's move beyond Tiger because Tiger is
01:00:27
well within our adult memory and we
01:00:29
lived that we lived it. We understand
01:00:31
what that's like. There were a handful
01:00:33
of great golfers in our lifetime, but we
01:00:35
probably weren't paying enough attention
01:00:36
and the metrics weren't there yet. And
01:00:38
then there are a number of them before
01:00:39
our lifetime. If what's if you go back
01:00:42
and ask what's the best five-year run of
01:00:44
golf in PGA history, like nine of the
01:00:47
top 10 are Tiger. So, let's not do it
01:00:49
that way. Let's do it. Let's do it at a
01:00:51
golfer level and ask peak five years of
01:00:56
the best 10 or 12 golfers in the history
01:00:58
of golf and ask what does Scotty need to
01:01:02
do or where to pick three years. Pick
01:01:05
three years like Nicholas at his best,
01:01:07
Watson at his best. How is Sheffer
01:01:09
comparing to that? And let's let's let's
01:01:12
stack up the historical golfers again.
01:01:14
Tiger's going to be number one. And if
01:01:15
we let him have multiple observations,
01:01:17
he'd be the top 10. Yeah. But let's not
01:01:19
do that. And let's just ask because
01:01:20
we're not I want the next comparison.
01:01:22
>> Yeah. I I'm going to give you that. But
01:01:23
just one last thing. I think the thing
01:01:24
that's amazing about Schoffler, besides
01:01:26
he has 20 wins already in his career, he
01:01:28
had his first one only four years ago.
01:01:30
So let's just be clear. He's had a
01:01:33
four-year run. That's incredible. I
01:01:35
think it's fair to compare him to the
01:01:37
three-ear stretch. Let's talk about
01:01:38
recent golf. The four the 3 to four year
01:01:41
stretch that Rory Maroy had when he
01:01:43
piled up all those majors but hadn't
01:01:46
still yet won the Masters. Remember he
01:01:48
went 11 years without winning a major
01:01:50
between ages 21 and 24. Rory Maroy
01:01:53
cleaned up. Let's even say Jordan.
01:01:55
>> How many wins How many wins did he have?
01:01:57
>> That I don't know. But I'm guessing
01:01:59
didn't come close to it's not close to
01:02:01
20. He might have had as many majors but
01:02:04
I'm not even sure of that.
01:02:05
>> No, no, we want we want the other as
01:02:06
well. Well, this is a question like
01:02:07
what's the criteria?
01:02:08
>> Yeah, but you ask who are the other
01:02:10
stretches? I would compare it to Rory
01:02:12
Mroy during that stretch. Jordan speed
01:02:14
that stretch. Those would be the
01:02:15
>> he's going to be speed. No, no, it's
01:02:17
going to be he's going to beat those
01:02:18
guys. So, but it's it's not Tiger, but
01:02:20
it's I want to know how he compares to
01:02:22
Nicholas's best to Palmer's best and
01:02:25
going further back like Hogan's best
01:02:27
like what?
01:02:29
>> I have that for you and I will announce
01:02:32
it. Not next week. Maybe I will in the
01:02:34
open segment, but I'm so excited about
01:02:35
the Baseball Hall of Fame guy next week.
01:02:37
But I will I will produce that analysis
01:02:40
on request.
01:02:41
>> Let's have a moment on the criteria and
01:02:43
then we'll let it go.
01:02:45
>> Um we've already decided it can't just
01:02:46
be majors and it can't just be wins. It
01:02:49
could be wins. It could be number of top
01:02:51
tens, but we like wins and that's what
01:02:53
they care about is wins. Um I don't
01:02:57
know. We could have we could have the
01:02:58
actual, you know, um uh scoring average
01:03:02
below the PGA average.
01:03:05
>> We're going to need multiple dimensions.
01:03:07
>> Yeah.
01:03:08
>> Um wins would be the easiest like the
01:03:11
the probability of winning like the like
01:03:13
the
01:03:14
six month probability of winning. Um
01:03:18
something like that. But anyway, fun
01:03:21
conversation. We need to push beyond the
01:03:23
tiger comparison. Um, I just want to
01:03:25
know how and we have to we can't do it
01:03:28
from memory is one of the things that my
01:03:30
sense is. So, let's let's ask about
01:03:32
Nicholas and Watson in their prime and a
01:03:34
few others like that. Okay, why don't we
01:03:37
wrap it there? That has been a full more
01:03:40
than a full hour of sports analytics
01:03:42
here on Wharton Moneyball for the whole
01:03:43
team. Eric Bradloow who's been in here
01:03:45
for the whole run. Audi Winer was here
01:03:46
for almost all of it. Shane Jensen in
01:03:48
Absentia, our friend and associate
01:03:52
producer Dion Simkins who makes things
01:03:54
happen around here, Marissa Raina, our
01:03:56
producer, and Deep Patel, the boss lady.
01:03:59
Many thanks to you guys for listening as
01:04:01
well. Come back and join us next time
01:04:02
between now and then. Enjoy your sports.
