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Welcome, welcome to Wharton Moneyball.
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Welcome to a full hour of sports analytics
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here on the Wharton Podcast Network.
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This is Cade Massey hosting this week at
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the moment with my longtime friend and collaborator,
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Eric Bradlow.
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Shane Jensen is out this week, sadly, tragically,
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but Audie Weiner will join us just a
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little bit late.
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Audie is in transit at the moment.
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Some combination, as you guys know, some combination
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of us are here almost every week of
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the year and have been for more than
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12 years now talking sports analytics with you
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guys.
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We are going to run our usual format
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this week.
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We're gonna run a guest in the first
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half hour and then we'll do open lines.
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In the second half hour, me, Audie, and
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Eric will kick around a few things.
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I am piped in from Austin, Texas.
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Eric is from Huntsman Hall, West Philly, on
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campus.
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Mark, I'm assuming it's New York.
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I don't know that for a fact.
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Mark Brody is our guest in the first
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half of the show.
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He is a longtime friend of the show
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and we're always delighted to get time with
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you.
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Mark, thanks for making time for us.
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Thanks for having me.
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And yes, I'm in New York, New York,
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and looking out over the Hudson River, which
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you can't see in my background because it's
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the way I'm looking, not behind me.
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Right.
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Mark, as some of you guys know, is
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at Columbia, the Columbia Business School there in
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New York.
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He is the Carson Family Professor of Business
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there.
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He came out of the quant finance world,
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but he is best known in the sports
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analytics world for his contributions to golf.
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He flat out invented the concept of strokes
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gained, which is just kind of ridiculous.
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It's one of the great sports analytics stories
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of our lifetime and continues to be engaged
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in the sport at multiple levels.
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He has a great book, Every Shot Counts.
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We'd strongly recommend his book, Every Shot Counts.
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He consults to professional golfers and he has
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been involved with revising the ranking systems of
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various associations, including the World Golf Ranking System,
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which is like the biggie, but also NCAA
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ranking system.
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So something we wanna talk about at some
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point with Mark is that, that's a fun
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topic for us.
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But maybe we'll start, Mark, with just, this
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is kind of, in some ways, peak golf
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season.
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We're a few weeks post-masters, we're a
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week before the PGA, which is gonna be
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in the Philly area.
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The majors are all lined up now, one
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a month, which is fun these days.
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What is on your mind, what is on
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your docket right now in the world of
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golf?
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Well, I would say that I don't really
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follow golf as closely as some fans do,
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certainly not as closely as Rufus Peabody does.
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But I look at more kind of longer
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-term problems and actually my day-to-day
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stuff recently has been with college golf rankings
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and professional disc golf rankings.
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And this is the ranking season for college
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golf because, like the March Madness basketball tournament,
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the main use of college rankings, besides bragging
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rights throughout the year, is determining which teams
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go to regionals.
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And that's run by the NCAA.
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And then from regionals, you go to the
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nationals and then you crown the national champion.
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So it wasn't clear to me how this
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ecosystem worked until getting into the rankings, but
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the colleges, universities themselves choose their schedule and
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teams run their events.
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And it's kind of in coordination with the
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NCAA, but the NCAA isn't running all of
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these events.
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So we're keeping track of all that's going
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on and then ranking the teams throughout the
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year.
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And then at this point in the year,
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then the qualifiers for regionals are set.
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So there's some interesting problems there in the
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rankings.
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There's some interesting problems also, which is more
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operations research, I guess, than sports analytics, which
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is how do you assign the teams that
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you have selected into the six regions?
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Okay.
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So that comes up in many sports, but
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I was involved in it this year in
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golf.
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This sounds like a ball, frankly.
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Let me say, interesting that you mentioned Rufus
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at the top there.
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I happen to know that Rufus and Mark
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occasionally play golf in the New York area.
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So these guys are friends.
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But yeah, Rufus, not a trivial part of
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his living is made watching golf, paying attention
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to golf very closely.
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But one of the first things we ever
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did together when he was shortly after he
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was at Yale, the Yale women's golf coach
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at the time asked us if we could
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help develop a ranking system for women's golf,
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because they felt they were disadvantaged being in
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the Northeast, playing different tournaments, playing a different
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time of year.
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They thought whoever the cabal was in Oklahoma
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that ran the college golf rankings, maybe they're
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still the cabal.
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Maybe you're in with the cabal now, Mark.
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I don't know.
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But that coach was not happy with it.
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And we dabbled a little bit.
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It was good fun.
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But I could imagine that's great fun to
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get into.
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It is absolutely great fun.
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And I would argue that the previous rankings
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did appear to be biased in certain ways
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that if you played a stronger schedule, that
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you would be ranked higher for an equal
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level of performance.
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But that requires some analytics in order to
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prove that.
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Right.
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So I've heard a lot from coaches, but
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then when I develop this new ranking system,
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which has been in place for, this is
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the third year, I then hear questions from
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coaches.
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And I realized that just because people are
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criticizing the ranking system, doesn't mean they're right.
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You really need to go into the analysis
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to figure out back then, was Penn, was
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it biased against teams in the Northeast or
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not, just because...
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And that's another thing that's almost as important
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as the analytics is being able to convey
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the underlying rationale and to communicate why it's
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fair and to convince people that it is
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fair.
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Because I don't know how many times coaches
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have come up with the question to me,
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we have beaten this other team twice in
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the season.
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By that means they played in the same
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event and they finished higher in the final
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leaderboard.
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We've beaten them twice, therefore we should be
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ranked higher than them, but we're not.
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Your system is messed up.
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And so it's like, well, how do you
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answer that question?
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And the most direct way I could find
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to answer that question was to show cycles.
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So I found in the data, just looking
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at wins and losses, team A has beat
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team B two or three times.
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Team B has beat team C two or
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three times.
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And if you argue that A has to
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be ranked better than B and B should
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be ranked better than C, then A should
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be ranked better than C, but C beat
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A two or three times total.
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And then you realize you can't use that
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logic.
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It's completely impossible to have a ranking system
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that would satisfy that criterion.
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Right, so I think it's the NFL that
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by some point in the season every year,
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the circle's complete, that everybody has beat somebody
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and transitive property takes us all the way
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around.
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Eric has a question.
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So I just want to say real quickly,
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Eric, you don't know, like you probably don't
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quite understand that he's hinting at the greatest
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playoff tournament format in sports, you know, the
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one I get fired up about every May
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and June, the college golf tournament.
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Mark, I don't know if you've heard me
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rant, every year I make the guys pay
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attention because they play this, you know, three
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or four or five rounds of stroke play
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to identify the 16 or whatever it is,
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teams that play match.
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And it's just fricking brilliant in my opinion,
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but let's leave it there for now.
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Eric wants to jump in with a question.
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No, I just had a clarification question, Mark.
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Are the rankings, like literally it's a deterministic
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function on who qualifies based on the rankings
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or it's used like in college football, like
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it's an input to some subjective committee.
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It's a clarification question.
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I'm just asking which one is it?
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It's more the latter.
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It's an input, not a rule, but in
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practice, more of the divisions follow the ranking
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exactly than don't, but where it comes up
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is usually in borderline cases, when you draw
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the line and you take a look at
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the teams right around that line and somebody
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will make an argument.
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And I think it could be a valid
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argument that the rankings aren't perfect.
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And there might be some reason to take
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the team that's ranked one out rather than
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one in, you know, across the line.
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But also when you do that, then the
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committee that makes the ultimate decision needs to
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justify it because it's the team that got
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kicked out that was inside the bubble is
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like, oh, well, you better have a good
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reason for not letting us get in.
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But I assume Mark that the distribution of
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team, let's call them strengths or rankings is
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what we classically see that there's greater separation
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out in the right tail, but that right
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near the cut score, there tends to be
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bunching.
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And therefore there's a significant number of teams
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that could argue, I should be above the
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cut line or not.
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I assume that that follows for rankings as
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well.
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I think it happens in just about every
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stat that I've looked at, including rankings.
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So you're exactly right there.
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Mark, let's talk about, well, first one more
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thing on the college thing.
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You said it's interesting how they assign them
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to regions.
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Yes.
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And you said this is kind of an
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OR question, but what are the considerations?
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So you said there's six regions.
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What are the considerations?
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Yeah.
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So once you decide on the 72 teams
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and it's not in rank order because the
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conference champions get in.
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So Columbia women got in this year because
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they won the Ivy league championship.
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And so conference champions get a free pass
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into the regionals.
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And when they're not ranked in the top
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72, that means there's teams that are ranked
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higher that get in place of other teams.
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But once you have the 72 teams, the
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six regional hosts, the six locations have been
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determined years in advance.
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So now the question is, how do you
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put those 72 teams and assign them to
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six regions?
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And the multiple- Why don't we listen
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to, we're all math people here.
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Is there an objective function, literally a mathematical
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objective function that's maximized by that assignment?
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Except it's an OR problem, there has to
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be an objective function, right?
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Well, the problem is there's three or four
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objectives.
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Good, good.
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And the question is, how do you weight
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them?
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But by far the number one objective is
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to make sure that the top six seeds,
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the top six ranked teams, which would be
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the number one seeds in each region have
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the easiest and an equal shot of getting
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through.
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In other words, you wanna make sure there's
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regional balance.
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You don't wanna put a bunch of strong
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teams in one region and a bunch of
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weak teams in another.
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So the number one criteria is so that
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the number one seeds have the best chance
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and a nearly equal chance of getting through
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and the same with the two seeds and
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three, four, five seeds.
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And even, so that's one objective, sort of
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this regional balance in terms of strength of
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each region.
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The other that I found out that they
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care about is they don't want one region
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to be overloaded with a bunch of teams
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from one conference.
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So it seems like it's a repeat of
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a conference championship and they have to duke
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it out somehow.
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Somebody is gonna get left out just because
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they happen to be in the same region
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as same conference.
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Well, Mark, let me ask you a question.
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If I gave you the 72 teams, suppose
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we all agree, I'm not saying we're agreeing
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on what those teams are, but I give
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you the list of 72 teams.
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Would you write down the criterion?
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Like you just mentioned, the top team should
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have the easiest path.
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We don't want too many teams in the
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same.
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Would some algorithm now just pump out those
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72 broken into the six or would there
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still be subjectivity there?
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So yes, actually to both because the last
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one is travel.
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So I put that in as an objective,
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but it's less important than I had thought
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it would be.
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But I put it into an optimization problem
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and it will spit out an assignment.
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And then they will look at that assignment
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in more detail and find reasons for doing
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swaps.
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And because there are many, many assignments of
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these 72 to six regions that are nearly
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comparable, this to me is a way of
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choosing among many good alternatives, but the algorithmic
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viewpoint gets you to a really good starting
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place, which is very hard to do manually,
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which is what they were doing before.
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So what the standard practice is called the
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S-curve, which is teams one through six
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go into these regions and seven goes where
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the six and then you go around.
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Right, right, right.
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So we made the algorithm so that it
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starts with the S-curve and then when
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it does swaps, it preserves seeds so that
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only two seeds are swapped or three seeds
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are swapped or four seeds.
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So in some sense, the S-curve isn't
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completely blown apart, which again would be harder
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to justify or explain.
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This is a great example of man-machine
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collaboration and set the board with the algorithm
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and then tweak as you need to tweak,
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but you're going to do so much better
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as a baseline if it's set with, look,
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it's not Mark Brody deciding what the weights
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are.
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You had a long consultation about what those
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weights were before you ran the optimization.
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Adi Weiner has joined the show and he
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has a question from his office on a
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campus at University of Pennsylvania.
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Yeah, great to see you, Mark.
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I just want to, just only one question
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based on my partial understanding of the conversation.
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This seems like a job for AI, can
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it do it?
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At least get you at right towards a
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decent set of solutions pretty quickly?
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I think AI is good at some things,
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but not this.
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No.
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It's like saying, let's give a traveling salesman
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problem to AI and expect to get a
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better solution than our current algorithms?
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No.
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It isn't.
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I would expect you to use the current
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algorithm.
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That's basically what I would expect.
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Oh, sure, sure.
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Yeah, so yeah, if you think of it
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that way, but if you don't give AI
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enough guidance and it goes to try and
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figure this out on its own, it's not
00:15:01
going to come up with a good solution.
00:15:03
Right.
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And Mark, you just mentioned something that some
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of our audience will understand and some won't.
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And it's worth pausing and explaining what is
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the traveling salesman problem?
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Sure, so the traveling salesman problem is one
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of the most famous OR problems.
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And it says, if you have a list
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of cities, think of a bunch of cities
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in the United States and you want a
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salesman to go on a tour, visit each
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of those cities in some order, and you
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want to minimize the total travel that they
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take.
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And it turns out if you have 20
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cities, then there's 20 factorial, which is a
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large, large number, possible orderings of these traveling
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salesman tours.
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And it's very, very difficult to find the
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optimal solution.
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Well, 20 is pretty small now, so that
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can be done.
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But it's a problem that's in a class
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of very hard problems.
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It means as the number of cities increases,
00:16:01
it becomes harder and harder to find the
00:16:03
solution.
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But for any kind of reasonable set of
00:16:08
cities that we have today, like the 72
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college teams to assign them, that can be
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done relatively very fast on modern computers.
00:16:18
And Mark's making a general point that I
00:16:20
think Adi set up quite intentionally, which is
00:16:22
whenever we have well-established models and algorithms
00:16:27
for a solution, then they do most of
00:16:30
the work.
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And unless they're fed it directly, AI wouldn't
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come up, because that's a well-honed, decades
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-long optimized model.
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Okay, let's jump to the pros, because you're
00:16:42
also involved with the World Golf Rankings.
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And now we have a different set of
00:16:47
problems, because this is, I was talking with
00:16:51
Rufus about this just a few weeks ago,
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actually.
00:16:54
World Golf Rankings versus like Data Golf Rankings.
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It's a little bit like the college football
00:17:00
playoff debate that happens every season between best
00:17:04
versus most deserving.
00:17:06
And the best is the predictive model, that's
00:17:08
kind of the Data Golf model.
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If you're gonna bet who's gonna win this
00:17:11
weekend, you want a model like that.
00:17:13
Versus most deserving, which is what I take
00:17:16
to be the World Golf Rankings.
00:17:18
So I'm sure there's best in there, but
00:17:19
you're also trying to reward accomplishment.
00:17:22
So it's more backward, it's at least some
00:17:24
backward looking, whereas the best, quote best, is
00:17:27
gonna be strictly forward looking.
00:17:30
So that's the setup.
00:17:32
And then can you talk to us about
00:17:33
how y'all decide, talk about weights.
00:17:36
I mean, you gotta decide what weight to
00:17:38
put on different levels of accomplishment in order
00:17:40
to fix these World Golf Rankings.
00:17:41
And importantly, Mark, you weren't, you know, this
00:17:43
thing existed many years before you got involved.
00:17:45
So what are the changes since you've been
00:17:46
involved?
00:17:48
So Cade, you hit on the point that
00:17:51
I think many people when they look at
00:17:54
rankings don't realize that the objective could be
00:17:57
predictive, forward looking, or it could be backward
00:18:01
looking reward for performance.
00:18:04
And it's easier to see the difference in
00:18:07
the official World Golf Rankings if you just
00:18:10
used a who's the best, what's the most
00:18:13
predictive, then you wouldn't give any special treatment
00:18:19
for majors beyond that they have a strong
00:18:22
field.
00:18:23
You wouldn't give any special reward for winning
00:18:26
by- Winning, yeah.
00:18:27
One shot for winning, period.
00:18:29
Because it could be, you know, you won
00:18:31
in a playoff and you didn't differentiate yourself
00:18:33
from the other two or three players that
00:18:35
were in the playoff, whereas a predictive system,
00:18:38
one stroke matters, not very much, but in
00:18:41
the official World Golf Rankings, winning means a
00:18:44
lot.
00:18:45
And winning majors means a lot more.
00:18:49
So the board of the official World Golf
00:18:52
Rankings and the way it has been set
00:18:54
up for, you know, since the 1980s when
00:18:56
it first started, is that wins are rewarded
00:19:01
a lot, heavily, like the ratio is 100
00:19:04
for first, 60 for second, 40 for third.
00:19:07
And that decline, that exponential decline really says
00:19:10
one stroke at the top matters a huge
00:19:12
amount.
00:19:14
Missing the cut, whether you miss the cut
00:19:15
by one stroke or 20 strokes, you get
00:19:17
zero points, which in a predictive sense clearly
00:19:21
isn't gonna be good.
00:19:22
So the official World Golf Rankings objective is
00:19:25
clearly not to be predictive.
00:19:27
It's a reward for performance, kind of backward
00:19:30
looking, but it's of course related to skill
00:19:33
because the better players win more often.
00:19:37
But the biggest change was trying to make
00:19:41
it unbiased within this reward for performance system.
00:19:45
So you wanna make sure if you're giving
00:19:47
points out this way, that a strong field
00:19:50
gets the right total number of points compared
00:19:54
to a weaker field.
00:19:55
So you can still have the same exponential
00:19:57
payoffs, but the main question then is, how
00:20:00
many points do you give to a strong
00:20:02
field versus a weak field event or between
00:20:04
a PGA Tour event and the Hero World
00:20:08
Challenge, which is 20 players or between that
00:20:11
and some Asian Tour event?
00:20:14
So that's- How do you do that?
00:20:16
How do you do that?
00:20:16
Let me clarify the question first.
00:20:18
You're saying it's always gonna have this steep
00:20:20
decay, say 160, 30.
00:20:22
So 1.6.3, that ratio off the
00:20:25
top three.
00:20:26
But for a minor tour event, it might
00:20:30
just be at a base of 10 points.
00:20:32
Versus a PGA event might be, I don't
00:20:34
know, 50, and then a major might be
00:20:36
100 or whatever.
00:20:37
What's the ratio of those things?
00:20:38
Yeah, we of course know, Mark, I don't
00:20:40
know if your system relates this at all,
00:20:42
but it's not principled really in any way.
00:20:44
We know how they do it in tennis,
00:20:46
for example, which is exactly that.
00:20:48
It's 2,000 for the majors, 1,000
00:20:50
for the Masters 1,000, which is why
00:20:52
they call them that.
00:20:54
You know, ATP 500, there's 500 points, 250.
00:20:57
So how is it done in golf?
00:20:59
And is it related at all to this
00:21:01
kind of two for one system, two for
00:21:03
one ratio system?
00:21:04
Definitely not.
00:21:06
And that was the problem that that's kind
00:21:08
of arbitrary and that leads to biases.
00:21:12
And there were biases that you could easily
00:21:15
see in the old system.
00:21:17
And so the question is, what's the principled
00:21:18
way to do it?
00:21:20
And the idea is you take an unbiased
00:21:23
measure of skill, in this case, a strokes
00:21:25
gained measure, and you actually have a curve
00:21:30
which converts that into kind of points or
00:21:34
endowments that the players bring to an event.
00:21:38
And that endowment, you know, weaker players that
00:21:41
have, you know, worse strokes gain bring fewer
00:21:44
points to an event and stronger players bring
00:21:46
more.
00:21:47
And that total endowment of the players in
00:21:50
the field is the number of points that
00:21:52
are redistributed.
00:21:52
And so then you get on average in
00:21:57
any field, the players don't move up or
00:21:59
down because their points that they bring to
00:22:02
the event are just redistributed.
00:22:04
So the players that do well are better
00:22:07
than the points they brought, move up in
00:22:10
the rankings, the players that do worse move
00:22:12
down.
00:22:12
And that's- It's a standard system.
00:22:15
It's a standard of any ranking system ought
00:22:17
to be that way that if your expected
00:22:19
performance, if your performance matches your expected performance,
00:22:22
you don't change the rankings, right?
00:22:25
And if you outperform your expectation due to
00:22:28
the rankings, then you should move up or
00:22:30
move down.
00:22:31
So it's a very- So Bradley Terry
00:22:32
is optimized using the EM algorithm and you
00:22:35
just, if you're at your expectation, you don't
00:22:38
change.
00:22:39
Well, so this is, it's lovely because you
00:22:40
said principled and I was kind of like,
00:22:42
well, I want to see this because it
00:22:44
wasn't clear to me how you're going to
00:22:45
come up with a principle for these ratios.
00:22:46
How off was it before, Mark?
00:22:48
Without throwing anybody under the bus or too
00:22:50
much shade, like how big an adjustment was
00:22:53
that?
00:22:53
Not just that, good, Kate.
00:22:54
How the hell did Mark get them to
00:22:55
actually adopt that system?
00:22:57
I want to hear that part of it
00:22:58
too.
00:22:59
Yeah, yeah, right.
00:22:59
Well, there's two parts of that question.
00:23:02
So one is, I mean, we kind of
00:23:05
showed with Dick Rendleman, a colleague of mine,
00:23:08
that there is great bias, but it's easier
00:23:10
anecdotally to see that you could have a
00:23:13
particular player, in this case, say from the
00:23:16
Japanese tour, and they used to have these
00:23:18
World Golf Champion events where they'd bring the
00:23:22
top ranked players, top 72 or 75, and
00:23:27
invariably, players from some of these lesser tours
00:23:31
that happened to just make it into the
00:23:33
top 75 would be destroyed in the tournament
00:23:39
by many, many strokes.
00:23:41
And you could see that they always, when
00:23:43
you got them together in head-to-head
00:23:45
competition with the other players, you could see
00:23:50
that their ranks were misaligned, were out of
00:23:54
whack.
00:23:56
So to answer Eric's question, how did this
00:23:59
happen?
00:24:00
Well, Dick Rendleman and I wrote this paper,
00:24:02
and like academics, we thought this would affect
00:24:05
some change, but no, unlike, you know, we
00:24:10
showed drive for show, putt for dough is
00:24:12
not right.
00:24:13
Here, when we showed the official World Golf
00:24:15
rankings in the past iteration were biased, people
00:24:17
said, yeah, we knew that.
00:24:19
It was like, but then how do you
00:24:21
get it to be fixed?
00:24:23
And basically they got a, the official World
00:24:28
Golf Rankings Board decided that they needed a
00:24:32
mission statement, and the mission statement was to
00:24:35
fairly rank golfers, not to just reward certain
00:24:38
tours for one reason or the other.
00:24:41
And they did an analysis, independent of what
00:24:47
Dick Rendleman and I had done, five other
00:24:49
groups did independent analyses that came to the
00:24:51
same conclusion that the rankings as currently they
00:24:56
stood at the time were biased.
00:24:59
And so now they had a mission statement,
00:25:00
they want unbiased rankings, and they have independent
00:25:03
academic expertise saying they're biased, so what's the
00:25:08
fix?
00:25:08
And that's what led to the change.
00:25:11
It's interesting, so then there's an, so let
00:25:13
me just check.
00:25:15
I just want to think about this.
00:25:16
So if I'm a good player, I want
00:25:20
Sheffler or potentially Sheffler or McElroy, well, when
00:25:25
they're not in the field, there's less than
00:25:27
total points brought to that event, which means
00:25:30
there's less, I'll call it bonus for winning.
00:25:34
So beating them, I mean, it's what you'd
00:25:35
want.
00:25:35
You'd want that to be true, but of
00:25:37
course your probability of winning goes down.
00:25:40
So have you ever thought of the problem
00:25:41
of like, if I could, if I had,
00:25:44
if I'm a, let's say I'm the number
00:25:45
10 player in the world, okay?
00:25:47
I don't even know who that is, but
00:25:48
let's, I could make up a name, but
00:25:50
let's say, I don't know, whoever.
00:25:51
Let's say I'm the number 10 player in
00:25:53
the world.
00:25:53
The number 10 player in the world golf
00:25:55
rankings is Xander Shuffley.
00:25:57
Okay, that's a great name.
00:25:59
So I'm Xander Shuffley.
00:26:01
Should I want McElroy and Sheffler not to
00:26:06
show up?
00:26:06
Now, the bad news is they're not, those
00:26:08
points aren't coming their way, but of course
00:26:10
my probability of being higher up the exponential
00:26:12
curve goes up.
00:26:14
So which, have you thought about the problem
00:26:16
of optimal field depending on where I am
00:26:19
in the rankings?
00:26:20
Absolutely.
00:26:20
So this was designed so that, that question
00:26:26
that you just asked, you're indifferent between whether
00:26:28
Scotty and Rory show up or not if
00:26:30
you're Xander Shuffley.
00:26:32
Right, right.
00:26:32
Beautiful.
00:26:33
Take it, but if you take it to
00:26:35
an extreme and you say, we're going to
00:26:39
give Eric Bradlow a sponsor's exemption into a
00:26:42
no-cut signature event, okay?
00:26:47
Yeah.
00:26:48
You would be, you would not be in
00:26:49
the top 10,000 of the official world
00:26:52
golf ranking, yet because it's a no-cut
00:26:54
event, you might get a few points and
00:26:58
that would be unfair.
00:26:59
So it works for almost all events, but
00:27:02
not if you take a large, large range
00:27:05
from, the Scotty Shufflers of the world to
00:27:08
the 10,000th ranked player in the world.
00:27:10
So your point is academically, it's perfectly correct.
00:27:14
In practice, you don't have that large a
00:27:17
dispersion in the quality of the field.
00:27:19
I'm sorry, but just to be clarified, does
00:27:20
that mean that an expected value, it's sort
00:27:23
of balanced?
00:27:24
It's indifferent?
00:27:25
Yes.
00:27:26
Okay, but not necessarily, but you could have
00:27:28
other loss functions that could be optimized differently.
00:27:31
On average, when you, when players, professionals play
00:27:35
in events, they neither go up nor down
00:27:38
in the rankings, unless like I said, you're
00:27:41
Eric Bradlow on a sponsor's exemption, then on
00:27:44
average, you're going to go up.
00:27:46
All right, Mark, one, I'm curious, especially now
00:27:50
that this has been jiggered a little bit,
00:27:51
when it comes to like writer cup selection.
00:27:53
Yep.
00:27:55
How did the world golf rankings play into
00:27:58
that versus predictive models like data golfs?
00:28:02
And some of it, are the captains restricted
00:28:04
or some guys automatically qualify, but that's a
00:28:07
world golf rankings automatic qualification.
00:28:09
And then, but they've scrunched that down a
00:28:12
little bit to give the captains more discretion.
00:28:14
Do you have any sense of whether captains
00:28:16
just want the predictive or do they put
00:28:18
a little value in the most deserving?
00:28:21
Cause they might think that it captures an
00:28:23
ability to win under pressure counting.
00:28:25
So they've changed from like 10 out of
00:28:27
12 or automatic qualifiers now, like six out
00:28:30
of 12.
00:28:31
So the captains tend to have six discretionary
00:28:34
picks.
00:28:34
And I would say most of them are
00:28:37
based on predictive and looking forward.
00:28:40
And then the question is, well, something like
00:28:44
strokes gained or the data golf rankings, whatever,
00:28:47
don't take into account necessarily course fit, recent
00:28:51
play.
00:28:52
And the question is, how do you weight
00:28:53
those factors?
00:28:55
And so you're still trying to make it
00:28:58
predictive, but you're including additional features that aren't
00:29:02
necessarily in the raw numbers.
00:29:04
Okay.
00:29:05
Okay.
00:29:06
Mark, we are coming up on the time
00:29:08
we'll need to let you go, but I
00:29:09
do want to ask one kind of general
00:29:11
question about you and your experience with golf
00:29:13
analytics.
00:29:13
When you created this concept of strokes gained,
00:29:16
you probably could have only fantasized that it
00:29:19
would be as impactful as it has been.
00:29:21
But I'm curious years on now, what is
00:29:25
one place it's gone or something that's come
00:29:27
from it that has surprised you or that
00:29:29
you never would have anticipated?
00:29:31
Because it strikes me that it's this tool,
00:29:33
it's a method really, and any good method
00:29:36
is going to find new and unexpected uses.
00:29:39
So I'm just curious your experience with it.
00:29:40
Where has it gone that you might not
00:29:42
have anticipated?
00:29:43
Well, I did not anticipate that most professional
00:29:48
players and coaches and college coaches and college
00:29:52
players would be looking at this, but for
00:29:55
the most part they are.
00:29:56
I didn't really anticipate that it would form
00:29:59
the underpinnings of an official world golf ranking.
00:30:03
And I didn't really anticipate that many betters
00:30:07
like Rufus would also be using this as
00:30:10
step one in their betting system.
00:30:13
So it has had a lot more uses
00:30:16
than I ever imagined.
00:30:18
And also surprising when I started teaching the
00:30:21
sports analytics course at the Columbia Business School
00:30:25
is the strokes gain concept is now in
00:30:28
most modern stats that are coming out.
00:30:31
So rushing yards over expected and many other
00:30:35
kind of new stats in football and many
00:30:38
other sports have this idea of performance over
00:30:41
expectation.
00:30:43
And that's basically what strokes gained is for
00:30:45
golf.
00:30:46
Well, it's XG in golf and in soccer.
00:30:49
Expected goals in soccer, expected goals in hockey.
00:30:52
Guys, it's April or May, it's hockey.
00:30:55
Come on now, Eric.
00:30:56
I just have to ask Mark one question.
00:30:59
When I think, and this is one academic
00:31:01
to another.
00:31:02
When I think of some of the things
00:31:04
that I'm most quote unquote famous for in
00:31:06
academia, it's none of the things that are
00:31:07
particularly the most technical of my work.
00:31:10
So can you just give us a sense
00:31:11
briefly, like I assume strokes gained is not
00:31:15
the most technical thing you've ever written.
00:31:17
And so does it bother you?
00:31:19
It doesn't bother me at all.
00:31:20
I'll just tell you.
00:31:21
But does it bother you at all that
00:31:23
the thing you may become most famous, or
00:31:25
you are most famous, I don't know, is
00:31:26
or the most cited for is something that
00:31:28
isn't as technical as like your training?
00:31:32
No, it doesn't bother me because as academics,
00:31:34
I think we try and come up with
00:31:37
ideas that have an impact.
00:31:40
And this is an idea that's had way
00:31:42
wider an impact, but not on academics, but
00:31:45
on the real world, at least as far
00:31:47
as the golf world or the sports world
00:31:49
is concerned.
00:31:50
So I'm not embarrassed at all.
00:31:53
And I, you know, in many of my
00:31:54
talks, it said, you know, how, who would
00:31:58
have guessed that you could get so much
00:32:00
mileage out of a concept which is subtracting
00:32:02
two numbers?
00:32:05
Right.
00:32:06
Well, there you go.
00:32:07
That's a good counsel.
00:32:08
That's good counsel to us and some of
00:32:10
our listeners that it doesn't have to be
00:32:11
the absolutely most technically proficient thing to make
00:32:14
a contribution.
00:32:15
Why don't we leave it there?
00:32:16
Mark, greatly appreciate your being with us.
00:32:19
Always enjoy the chance to visit with you.
00:32:22
Thanks so much, Cade and Eric and Adi.
00:32:24
It's always a pleasure.
00:32:25
Thank you.
00:32:26
Mark Brody, Carson Family Professor of Business at
00:32:28
the Columbia Business School, the creator, literally the
00:32:31
creator of Strokes Gained and a longtime friend
00:32:34
of Wharton Moneyball.
00:32:36
Come back and join us after the break.
00:32:38
We have another half hour still ahead.
00:32:41
Welcome back.
00:32:43
Welcome back to Wharton Moneyball.
00:32:45
Welcome to the second half of this week's
00:32:46
show.
00:32:47
Tuesday afternoon, as it usually is when we're
00:32:50
recording, show will go up bright and early
00:32:52
Wednesday.
00:32:52
I think Dion gets this thing going early
00:32:54
Wednesday.
00:32:56
Eric Bradlow's in here with me.
00:32:57
This is Cade.
00:32:58
Adi Weiner's in here now, rolled in to
00:33:01
his office about halfway through the first segment.
00:33:03
Shane Jensen out and about doing Shane Jensen
00:33:05
things.
00:33:05
He'll be back.
00:33:06
I think he's blood on a bloody committee
00:33:07
right now, the poor guy.
00:33:09
He'll be back just off the line with
00:33:12
Mark Brody.
00:33:12
He always enjoyed talking Mark.
00:33:15
I didn't know he'd done all those golf
00:33:17
rankings things.
00:33:17
That was good fun.
00:33:18
And that's a neat little extension of his
00:33:20
Strokes Gained.
00:33:20
And tournament design as a result of it.
00:33:23
Yeah, it affects tournament design.
00:33:25
That's great.
00:33:26
We have a handful of topics to hit,
00:33:30
some big, some small, but I'd mentioned last
00:33:32
week on the show that he had done
00:33:34
a little work on the ABS.
00:33:36
Original primary research that Adi is prone to
00:33:39
do on occasion just kind of spins it
00:33:40
off.
00:33:41
Some of us eat lunch kind of naturally,
00:33:43
some of us drink coffee, some of us
00:33:44
go for walks.
00:33:45
And Adi just spins off original primary research
00:33:47
on things like the challenges in baseball.
00:33:50
So what do you got, Adi?
00:33:52
Yeah, so it's really, it's all new.
00:33:54
So we're really interested in trying to figure
00:33:56
things out.
00:33:57
And lots of analysis are being done.
00:34:00
In fact, the MLB has its own website
00:34:02
devoted to challenges created by, as usual, Tom
00:34:06
Tango, who is not a real name.
00:34:08
That's a real person, that's not a real
00:34:09
name.
00:34:11
And he's looking at essentially challenge rates and
00:34:15
success rates, but he kind of creates his
00:34:17
own denominator of challengeable pitches, and he's kind
00:34:20
of have his own thing.
00:34:21
So what I ask the question is, think
00:34:24
about a universe where every wrong play was
00:34:28
turned to the right one.
00:34:30
Okay, good.
00:34:30
And now accumulate all the, what I call
00:34:34
run expectancy flip, caused by flipping the wrong
00:34:37
play to the right play.
00:34:39
We're just talking about called balls and strikes
00:34:40
now.
00:34:41
Exactly.
00:34:42
And only when they're challenged.
00:34:44
Well, no, no, no.
00:34:45
No, no, no.
00:34:46
All of them.
00:34:46
All of them, right?
00:34:47
And so every team will have a run,
00:34:50
a defensive set of run expectancy they could
00:34:52
reclaim by challenging, and offensive run expectancy that
00:34:56
they can reclaim by challenging.
00:34:58
So why I wanted to ask the question,
00:35:00
how much of that pile, if you will,
00:35:03
that's available to them, are they reclaiming?
00:35:05
Okay, first, great, cool.
00:35:07
But give us some context.
00:35:08
How big is that pile?
00:35:09
And how's it distributed?
00:35:11
Well, it's actually pretty big.
00:35:13
It balances out.
00:35:14
So your opponent is missing or is being
00:35:18
denied about the same amount that you are.
00:35:20
So it's not like, it's a bias.
00:35:22
But if you can recover more than your
00:35:24
opponent, that's actually an advantage, substantial.
00:35:27
Per game, it's getting close to about a
00:35:30
quarter to a half a run.
00:35:31
All right.
00:35:32
So just remind me, per team?
00:35:37
Yeah, per team.
00:35:38
It balances out.
00:35:39
So it's equal, so it's not like one
00:35:41
side is advantage.
00:35:41
That's like 5% of the number of
00:35:44
runs that are scored.
00:35:45
So that's a lot.
00:35:46
It's a little less than that, but yes,
00:35:48
it's a lot, yeah.
00:35:49
Okay, and just correct me if I'm wrong.
00:35:52
How many, before I answer your question on
00:35:53
percent, can you just remind me of how
00:35:56
many challenges I get and when, if I
00:35:58
get it right?
00:35:59
Because it matters.
00:36:00
Because if I could infinitely challenge, and that's
00:36:02
a different thing, but obviously I cannot.
00:36:04
Obviously, you'll get all of it if you
00:36:05
can infinitely challenge.
00:36:07
Right.
00:36:07
You just keep challenging until they make it
00:36:09
right.
00:36:09
And at that point, you give up the
00:36:10
system and you just let the robots determine
00:36:13
all the value.
00:36:14
But that's not the system.
00:36:15
The current system allows you two.
00:36:18
And every time if you make a mistake,
00:36:20
your amount is decremented.
00:36:22
So you can do as many as you
00:36:23
can until you hit two errors.
00:36:26
So it's a classic.
00:36:27
So that's a wonderful system.
00:36:28
It is a good system.
00:36:29
Because it actually is a lot of fun
00:36:30
to sort of think about from a operational,
00:36:34
strategic perspective, what should the teams do?
00:36:37
And the players are kind of working them
00:36:39
out.
00:36:40
Now we've learned a few things.
00:36:41
We've learned that catchers are much better than
00:36:43
hitters and pitchers.
00:36:45
Catchers are best at this.
00:36:46
They're the best view.
00:36:48
They also don't have the emotion.
00:36:49
Jazz Chisholm is the worst.
00:36:52
He has challenged pitches right down the middle.
00:36:54
And he's already an internet meme for like
00:36:56
looking at the camera going, oh, what am
00:37:00
I doing?
00:37:00
Most players- Catchers have caught a million
00:37:04
pitches.
00:37:05
And so they're calibrated for this.
00:37:06
Exactly.
00:37:07
But they make errors as well.
00:37:09
The catcher error rate is around 60%.
00:37:11
And the position player- Zero?
00:37:14
No, no, sorry, getting backward.
00:37:15
Success rate is around 60%.
00:37:17
And the position players are in the mid
00:37:19
40s.
00:37:19
Well, that already gives us some information.
00:37:22
So because look, the two factors that help
00:37:24
me determine reclaim percentage is the hit rate
00:37:29
or how successful am I when I challenge,
00:37:32
but also the distribution of when I ask
00:37:34
for it, because I want to ask for
00:37:36
it, for example, if it's bases loaded and
00:37:38
it's three, two count and a marginal pitch
00:37:40
comes in, I should challenge that pitch because
00:37:43
that's potentially worth a lot in terms of
00:37:46
expected number of runs.
00:37:48
Absolutely.
00:37:49
So people have recognized that your decision to
00:37:53
challenge depends on two factors, your probability that
00:37:56
you're right, which is how bad the call
00:37:59
was.
00:38:00
And of course, the run expectancy that would
00:38:02
flip.
00:38:02
Didn't I just say that?
00:38:03
Exactly right.
00:38:04
And people have recognized that, but people have
00:38:06
not.
00:38:06
There's no algorithm that anyone has made as
00:38:08
yet.
00:38:09
And I'm working, our team is working to
00:38:11
create that.
00:38:12
We don't know the algorithm right now because
00:38:14
there's a problem.
00:38:15
You only get a resource of two.
00:38:16
So you have to husband that resource and
00:38:19
think about it properly.
00:38:20
So the question that I went out to
00:38:22
answer and did answer is a very simple
00:38:25
one, which is what percentage of the run
00:38:28
expectancy that is out there for you to
00:38:31
reclaim are you actually getting?
00:38:33
And among the amount that you're missing, are
00:38:36
you missing it because you're running out of
00:38:38
challenges and therefore you can't challenge?
00:38:40
That's really horrifying, by the way, if you're
00:38:41
watching baseball and it's the eighth inning and
00:38:43
there's a strikeout and it's a bad call
00:38:45
and you're out of challenges that you're pulling
00:38:47
your hair out.
00:38:48
So how much of the missing challenge is
00:38:51
cost is, missing RE is caused by lacking
00:38:54
the ability to challenge because you're all out.
00:38:57
And how much is it missed because you're
00:38:58
just not challenging?
00:38:59
Can I ask you a clarifying question?
00:39:01
Sure.
00:39:02
Why you decided to study runs as opposed
00:39:04
to win probability?
00:39:07
Eric Bradlow with the home run question.
00:39:10
The answer is it's a lot more stable
00:39:14
and it doesn't depend on context and it'll
00:39:18
be the same no matter what inning you're
00:39:20
in.
00:39:22
And it is just, and there's a lot
00:39:23
more data on it that's reliable.
00:39:26
So win probabilities can barely move with a
00:39:29
strike in the first inning, but a run
00:39:32
expectancy is a measurable and determined quantity.
00:39:35
So it really is a statistics argument.
00:39:37
Yeah, the only reason I'm asking because I
00:39:39
think it's a good question, I'm glad you
00:39:40
thought of it, obviously, but also the reason
00:39:43
why you might see a percentage lower than
00:39:46
one might expect is because you've created a
00:39:50
metric, but the team, I'm being generous, the
00:39:54
team may be trying to reclaim win probability,
00:39:56
not runs.
00:39:57
And therefore that's why you see a lower
00:39:59
percentage in your metric than you might expect.
00:40:02
Actually, real quickly, you could imagine psychologically regret
00:40:06
is more closely associated to win probability than
00:40:08
runs.
00:40:09
Even though I 100% agree with what
00:40:11
Adi's saying and doing.
00:40:12
And by the way, that's something Adi has
00:40:14
said over the years about expected points versus
00:40:17
win probability in different sports.
00:40:19
So I kind of knew where he was
00:40:20
coming from, but you could imagine regret, like
00:40:22
even the hair pulling out in the eighth
00:40:23
inning, that's a little bit closer.
00:40:25
All right, do you want my answer, Adi,
00:40:26
on the percent?
00:40:26
Let me just before, just to respond on
00:40:28
that as well, like in fourth down decision
00:40:31
-making, the right thing to do is win
00:40:32
probability.
00:40:34
In decision-making in football, everything should be
00:40:36
win probability, but a lot of decisions are
00:40:38
made on expected points.
00:40:39
And one of the reasons for that is
00:40:41
there's just many more independent possessions.
00:40:44
Possessions are valued on the point basis.
00:40:47
Win probabilities are all related, right?
00:40:49
So you have much more data to accurately
00:40:51
estimate things when you're dealing with possessions and
00:40:53
the points rather than win probability.
00:40:55
And so we know much more about run
00:40:57
expectancy than we know about win probability.
00:41:00
And so we have to recognize the limits
00:41:01
of our data.
00:41:02
So also one of the things that I
00:41:03
also tell my students, start with the easy
00:41:06
thing, for God's sakes.
00:41:08
No, I don't.
00:41:09
All right, so you want to make some
00:41:11
guesses?
00:41:12
What percentage of the available RE is being
00:41:14
reclaimed?
00:41:15
I got it.
00:41:16
I understand the question.
00:41:17
And give me a defense and an offensive
00:41:19
number.
00:41:21
Well, clearly we know that defensive is going
00:41:24
to get more because catchers are better.
00:41:26
Yeah.
00:41:26
I don't have a calculated answer, but my
00:41:29
intuition is like just a very small fraction.
00:41:31
I don't know, 10%, 15%.
00:41:33
Eric?
00:41:33
I was going to guess a third.
00:41:36
Okay, so you're both in the ballpark.
00:41:38
It is small.
00:41:40
And no one is at a third, but
00:41:42
defense is around 27% and offense is
00:41:46
just around 19.
00:41:47
And that's only through April 29th when I
00:41:49
ran the data.
00:41:51
Eric, we should have asked if we could
00:41:52
pool our guesses and we would have hit
00:41:54
it.
00:41:54
You would have nailed it.
00:41:55
Yeah, exactly.
00:41:56
Model average.
00:41:57
That would have been the right thing to
00:41:58
do as you guys all know.
00:42:00
Ensemble, we just have to demonstrate it.
00:42:01
Now the real question is, the second question,
00:42:03
which I know the answer to, is how
00:42:05
much of the missed challenge, which is roughly
00:42:07
between 75% to 80%, how much of
00:42:10
that missed challenges are caused by not having
00:42:13
challenges?
00:42:17
Small number.
00:42:20
Quarter to a third.
00:42:21
I'm going to guess more than that.
00:42:23
I'm not going to, I was going to
00:42:24
say half.
00:42:25
Now we're going to average.
00:42:27
Which side?
00:42:28
That doesn't matter.
00:42:29
Eric's half is this.
00:42:30
So how much is lost of the remaining
00:42:32
chunk that we're missing?
00:42:34
X, take out the ones you're getting.
00:42:36
Yeah, if I had more challenges, I could
00:42:38
regain half of that.
00:42:40
That's my thought.
00:42:40
Okay, so I'm not asking the question, that's
00:42:43
a different, that's a totally different question.
00:42:44
If I had three challenges, how much can
00:42:46
I regain?
00:42:47
I'm asking you just an observational question of
00:42:49
the challenge run expectancy that you have not
00:42:52
reclaimed, how much of it is missed during
00:42:56
the periods of time where you have no
00:42:57
challenges?
00:42:58
Yeah, yeah.
00:43:00
Then it's small.
00:43:01
Then I'm going to go back to what
00:43:02
Kate's saying.
00:43:03
I'm going to go with a quarter.
00:43:07
It's actually much smaller than that.
00:43:09
Much smaller.
00:43:10
They are barely missing any challenge because they
00:43:14
don't have it.
00:43:15
It's about 5%.
00:43:16
So they happen to pick the right number
00:43:18
of attempts too?
00:43:19
No, I just think they're underusing their challenges.
00:43:21
That's my guess.
00:43:24
They're underusing their challenges.
00:43:25
That's another explanation.
00:43:26
I got plenty of challenges because I'm not
00:43:28
using them.
00:43:29
They're just not using them.
00:43:29
And when they use them, but they don't
00:43:31
use them enough.
00:43:32
I think the teams, once they blow one,
00:43:35
are just under, I just think they underuse
00:43:37
them.
00:43:38
And particularly later in the game, I think
00:43:40
they're underusing them.
00:43:41
Seventh innings, eighth inning, and the ninth.
00:43:44
I mean, come on.
00:43:46
Just use the goddamn challenge.
00:43:48
Is this the same as like sitting your
00:43:50
guy with two fouls in the first half?
00:43:52
Yes.
00:43:53
You're trying to preserve something and you're being
00:43:56
too conservative about the preservation.
00:43:57
Yep.
00:43:58
Or it's like, you're starting actually to see
00:44:00
Adi's point actually in the NBA now.
00:44:02
Like you're starting to see at the end
00:44:03
of games, like you know it was a
00:44:06
foul, but what the hell?
00:44:08
Let's challenge this thing and see what happens.
00:44:10
There's 15 seconds on the clock.
00:44:11
And you know what?
00:44:12
We're going to lose the game if this
00:44:14
thing isn't overturned.
00:44:15
There's no cost for me to see.
00:44:16
You know, maybe some gets in the ref's
00:44:18
eye and he says, no, that's not a
00:44:20
foul.
00:44:20
You know, who knows?
00:44:22
Actually, I'm starting to see it in the
00:44:24
playoffs.
00:44:24
I've seen ridiculously bad challenges in high leverage
00:44:29
situations.
00:44:30
Are there no penalties for over?
00:44:32
You get a certain number.
00:44:33
But it's just the end of the game.
00:44:35
You're going to lose the game if this
00:44:36
isn't reversed.
00:44:38
Give it a shot.
00:44:39
So that's the real question.
00:44:40
So the answer, the unknown now is what's
00:44:43
the right policy.
00:44:45
Right.
00:44:45
Yeah, right.
00:44:46
Don't know it.
00:44:47
Are you observing different teams following different policies?
00:44:51
Is there much heterogeneity?
00:44:52
Is there already learning?
00:44:53
Oh, Adi could have been talking about two
00:44:55
different things, Adi.
00:44:56
Do you mean MLB's policy?
00:44:58
Or do you mean what Cade was talking
00:44:59
about, which is the team policy?
00:45:01
I'm not changing MLB.
00:45:03
Oh, okay.
00:45:03
By the way, so I'm able to rank
00:45:05
teams by their ability to recover challenges.
00:45:08
And actually Philadelphia is very good on the
00:45:09
offensive side.
00:45:10
They've been recovering a lot of challenge.
00:45:12
They may be doing that because they're actually
00:45:13
challenging a lot.
00:45:15
And in fact, a lot of, I mean,
00:45:16
this gets to the psychology.
00:45:18
When you challenge and are wrong, they often
00:45:21
feel the heat for that.
00:45:22
And I think in a way that's probably
00:45:24
not deserved.
00:45:25
A good challenging team should be not ending
00:45:29
the game with challenges.
00:45:30
That's, you screwed up if you did that.
00:45:32
Which means that you have to bear the
00:45:33
brunt of what might be considered silly challenge.
00:45:36
Can you tell us the probability?
00:45:38
If I have on the Y-axis, the
00:45:40
probability of successful challenge, and on the X
00:45:43
-axis, the distance from the strike zone, what
00:45:45
does that curve look like?
00:45:47
I actually have, so I don't have that
00:45:50
number because it's very correlated with that.
00:45:55
But I'll start with the first number, which
00:45:57
is, I can tell you the probability of
00:45:59
an umpire error as a function of the
00:46:00
distance from the strike zone.
00:46:01
Okay.
00:46:02
I fit that curve.
00:46:04
And by the time you get to three
00:46:05
inches, the probability is nearly zero.
00:46:08
Yeah.
00:46:09
And it interpolates fairly linearly.
00:46:12
What is the diameter of a baseball?
00:46:15
Just under three inches.
00:46:16
All right, so it's basically, once it gets
00:46:18
to more than a baseball outside of the
00:46:21
zone.
00:46:21
They tend to be very, very good.
00:46:23
That's good.
00:46:24
I'm glad, thank you.
00:46:24
The umpires, by the way, the umpires are
00:46:26
awesome.
00:46:27
Yeah.
00:46:28
I mean, we laugh at them and think,
00:46:30
oh, 92.5 is their success rate.
00:46:33
Do you have any idea how many pitchers
00:46:35
are right on the border?
00:46:36
These are professional pitchers we're talking about.
00:46:39
They throw it on the corners and on
00:46:41
the edges.
00:46:42
And when you're on the edges, their probabilities
00:46:44
are roughly 50%.
00:46:45
I mean, it's very hard to get right.
00:46:47
The trouble is, and this is one of
00:46:49
the reasons the challenge system is so great,
00:46:51
is that there are outliers.
00:46:52
I mean, there are human beings.
00:46:53
And the outlier, if you can zap those,
00:46:56
then you've made a lot of progress.
00:46:57
Why don't we let that sit there for
00:46:59
now?
00:46:59
This sounds like an ongoing project.
00:47:01
I'll be able to come back to us
00:47:02
on some things.
00:47:03
I have some other, I'll do some other
00:47:05
baseball trivia for y'all.
00:47:06
Trivia is the wrong word.
00:47:07
I'll put you through your paces a little
00:47:08
bit.
00:47:09
Did y'all happen to see this Wall
00:47:10
Street Journal article about the Brewers the other
00:47:12
day?
00:47:13
They looked at how over-performing they are
00:47:15
over the years.
00:47:16
So, FanGraph's preseason predictions for number of wins
00:47:20
over an 11-year period of time, 2015
00:47:23
through 2025.
00:47:24
So 11 seasons.
00:47:26
The Brewers have been the most outperforming of
00:47:28
the FanGraph's expectations.
00:47:29
And you can go to like Pakoda, you
00:47:31
can go to somebody else's system.
00:47:32
They're also the top there.
00:47:34
What do you think the average number of
00:47:36
wins above expectation the Brewers have achieved over
00:47:40
the last 11 years?
00:47:42
Five.
00:47:44
So what is the, in wins?
00:47:47
How far in wins above expectation?
00:47:49
Observe your wins minus FanGraph's expected wins.
00:47:53
Sum it over 11 years and divide by
00:47:54
11.
00:47:55
Yeah, and I've guessed five.
00:47:57
I would guess three.
00:47:59
Seven and a half.
00:48:01
What?
00:48:02
Right?
00:48:03
And that's 11 years set.
00:48:04
Dodgers are five, 5.1. Astros are 4
00:48:07
.8. You're Yankees, who seem like underachievers to
00:48:10
me, are actually four.
00:48:11
But the Brewers are 7.6. And-
00:48:15
Does that say more about their model than
00:48:16
anything else?
00:48:18
Well, the key, I think the article, is
00:48:20
it Jared Diamond?
00:48:21
He's always doing these kinds of articles.
00:48:23
I'm not sure if it's him.
00:48:23
Let me look real quick.
00:48:25
Yeah, Jared Diamond, as a matter of fact.
00:48:26
This is May 4, so yesterday.
00:48:30
They say it's development.
00:48:32
You know, this is a small market team,
00:48:33
small budget team.
00:48:35
And they say they are turning, making players
00:48:39
better more than they're expected to and more
00:48:42
than other teams are.
00:48:43
And if that's true, I just- They're
00:48:45
also relying heavily on youngsters because if they're
00:48:48
not youngsters, they would be priced into the
00:48:51
preseason expectation.
00:48:52
So if I'm getting this right- Unless
00:48:54
they're just turning them into, they take a
00:48:56
veteran and they make them better than they
00:48:58
were before in some way.
00:48:59
I mean, this was the story for the
00:49:00
Astros all those years.
00:49:01
Like Astros were first into development.
00:49:03
Now we talk about baseball being this way.
00:49:04
But apparently, seemingly, at least by this article,
00:49:09
they're ahead of the league on that.
00:49:10
But Adi's got a good point in that,
00:49:12
like when residuals like this tend to be
00:49:14
persistent, any good statistician would go back to
00:49:18
the model and say, I'm missing something here,
00:49:21
you know?
00:49:21
But what if it's a team effect?
00:49:22
And then you're just going to put your
00:49:23
thumb on the scale and have a real
00:49:25
Brewer's Fixed Effect?
00:49:26
That's not the way you want to go.
00:49:28
That's a big number.
00:49:28
That's seven and a half.
00:49:29
That's a big number.
00:49:30
Right?
00:49:31
Yeah, no, because listen, they don't tell us
00:49:32
what their forecasting system is.
00:49:34
Yeah.
00:49:35
I don't know what that is.
00:49:36
That is a very big number.
00:49:37
And it is time for us to just
00:49:39
shout out a former student of ours, Eric
00:49:40
Babitz, who is the Director of Player Acquisition
00:49:43
for the Brewers and has been there since
00:49:44
he graduated five, six years ago.
00:49:47
At the Brewers?
00:49:48
At the Brewers.
00:49:49
Oh, how about that?
00:49:50
Well, heck, let's get him on the show
00:49:51
and find out whether Jared Diamond's article is
00:49:53
actually capturing what's going on.
00:49:54
I mean, he's right in the middle of
00:49:55
that stuff.
00:49:56
Because he's charged with finding people that aren't
00:49:59
going to break the bank because they're a
00:50:00
small budget team, and yet they believe they
00:50:02
can make better.
00:50:03
That's his charge.
00:50:04
That's his charge.
00:50:05
That's interesting.
00:50:06
Okay, let me give you one other baseball
00:50:07
question.
00:50:09
I don't know how I wasn't calibrated for
00:50:11
this, but top, so the women's softball season
00:50:14
is winding down.
00:50:15
Their postseason is great fun.
00:50:18
Top shortstop in the country, Issa Torres, Florida
00:50:22
State.
00:50:22
She's got a big bat, she makes offensive
00:50:24
contributions, but she's also great defensively.
00:50:27
What do you think, just let's say, I
00:50:30
don't know if this is true, but let's
00:50:31
say she has the top fielding percentage in
00:50:33
the country.
00:50:34
What do you think her field, a shortstop,
00:50:35
women's college softball shortstop, what do you think
00:50:39
the top fielding percentage would be?
00:50:41
And I'll give you the number of, well,
00:50:42
you might even guess, what's the number of
00:50:43
chances?
00:50:44
There's a simple thing to guess.
00:50:45
How many games are there in a season?
00:50:46
30 or so?
00:50:47
About 30, yeah.
00:50:48
And I would guess chances are probably in
00:50:50
the, there's so many strikeouts, probably between six
00:50:54
or seven chances a game.
00:50:56
So I'd go for about 150, so.
00:50:59
You're dead on, 142.
00:51:00
I thought it'd be higher, 142.
00:51:02
So Issa Torres, 142 chances this season, led
00:51:05
the country in fielding percentage.
00:51:06
What do you think it was?
00:51:09
So I bet she got about 138 of
00:51:11
those 143, would be my guess.
00:51:14
So that's 97, 98%.
00:51:15
That's what I was, I was gonna guess
00:51:17
somewhere between 95 to 98%.
00:51:19
What's your upper limit?
00:51:20
Well.
00:51:22
100.
00:51:25
Well, I know that's your true max, but
00:51:27
we do agree.
00:51:28
She's 100%, she had no errors this year.
00:51:30
She had no errors, that's amazing.
00:51:32
Playing shortstop.
00:51:33
Now, let me ask you a question about
00:51:34
that.
00:51:35
So let's suppose you have a known statistic.
00:51:38
So let's imagine there's a play that happens
00:51:41
now or whatever during the season.
00:51:43
And the scoring person says, you know what,
00:51:46
let's tilt this to the no side.
00:51:50
First baseman should have scooped that ball.
00:51:52
Yeah, exactly.
00:51:53
That was not a bad throw.
00:51:55
Okay, that is wonderfully cynical, Eric.
00:51:58
Well done.
00:51:59
Okay.
00:52:00
Let's do one last one.
00:52:02
And this is more involved.
00:52:03
And this is the paper, y'all saw
00:52:04
that Dylan Weawad.
00:52:05
Dylan Weawad is with Slack.
00:52:07
He's a quantitative researcher with Slack.
00:52:09
He is a social psychologist out of Simon
00:52:12
Fraser, making him Canadian.
00:52:16
And he did, Northwestern has had, Northwestern, Kellogg's
00:52:19
Graduate School of Business at Northwestern, as Eric
00:52:21
knows anyway, has had postdocs for years.
00:52:24
Great postdoc program.
00:52:24
He did that.
00:52:25
And then he's moved on to Slack.
00:52:27
But he's rediscovering his love of hockey and
00:52:30
is running some quantitative analysis.
00:52:31
And so I wanna ask y'all.
00:52:32
So he sent us this paper and he's
00:52:34
a published academic.
00:52:35
And so when he writes a paper, he's
00:52:37
gonna write it soundly like an academic.
00:52:39
So it's this impressive, only 15 pages, but
00:52:42
very strong methodologically.
00:52:43
I wanna ask y'all, because he takes
00:52:45
on an interesting challenge.
00:52:46
He goes to measure depth in hockey.
00:52:50
And one of the reasons he sent it
00:52:52
to us was that we talked to Eric
00:52:54
Tolsky a few weeks ago, the general manager
00:52:57
of the Carolina Hurricanes.
00:52:58
And Tolsky's whole thing is basically depth.
00:53:01
They've built their whole roster to be deep,
00:53:04
like less top-heavy and just deep.
00:53:06
All four front lines, all three back lines,
00:53:08
whatever.
00:53:09
They're gonna have guys that can play all
00:53:10
the way.
00:53:10
Is there an agreed upon measure?
00:53:12
Like you can imagine like entropy of skillset.
00:53:15
You can imagine max minus min.
00:53:16
You can imagine taking lines and looking at
00:53:19
the summation of some metric and looking at
00:53:22
lowering the variance.
00:53:23
Is there a well, I'm saying, is there
00:53:25
a, no, okay.
00:53:26
But you mean something like this.
00:53:27
But no, but you mean something like this
00:53:29
when you say depth.
00:53:30
If we take Dylan to have done a
00:53:33
thorough lit review, and I don't know that
00:53:35
that was his objective, so we don't know,
00:53:36
but he's acting as if he's creating this.
00:53:39
I suspect teams, some teams have this.
00:53:41
Some teams, certainly Tolsky has done this.
00:53:43
But as far as we know, there's no
00:53:45
accepted measure.
00:53:46
So I'm asking y'all how you go
00:53:48
about doing it.
00:53:49
Just a neat little paper about Dylan.
00:53:51
I just came up with three measures right
00:53:53
there.
00:53:54
I just, so you have some strength parameter
00:53:57
for a player.
00:53:58
And so depth would be, well, I mean,
00:54:01
it depends on meaning across the whole team,
00:54:03
or you could mean across lines.
00:54:05
You could, matter of fact, around the team.
00:54:08
Well, you know, there's classic measures of, you
00:54:10
know, variance is one.
00:54:12
You could have some sort of- Just
00:54:14
look at the difference.
00:54:16
How much, what's the rate of scoring per
00:54:17
minute among each line and look at the,
00:54:21
how that erodes from, look for big drop
00:54:23
in three and four in some way.
00:54:25
So you want to use scoring, Adi, as
00:54:27
opposed to like some sort of strength measure.
00:54:29
Well, you can make a composite measure because
00:54:31
there's obviously offense and defense.
00:54:33
But I think the lines are different.
00:54:34
I mean, they're misaligned, right?
00:54:36
You're out of my pen here, folks.
00:54:38
No, no, no, but let's- No, no,
00:54:40
let's- But isn't there offensive players and
00:54:42
defensive players- No, no, but this-
00:54:43
But no, you're asking a good question.
00:54:45
Suppose- Let's say, let's, straight offense.
00:54:46
We're going to play- Yeah, so I
00:54:48
want to look at your rate of scoring
00:54:51
and then compare that across the lines, either
00:54:53
per possession per minute, probably per minute.
00:54:55
Okay.
00:54:55
And then there's also a power play issue
00:54:57
because I think you bring in your top
00:54:58
line.
00:54:59
You could do that, yes.
00:55:00
You could do power plays, sure.
00:55:02
Okay, so let me just walk you through
00:55:04
a few of the steps that he does
00:55:05
because I think it's thoughtful and interesting.
00:55:07
Let me say it's relevant also because he
00:55:09
ran the numbers on this year's NHL and
00:55:12
Carolina is like a standard deviation beyond everybody
00:55:15
else in this measure.
00:55:16
And in his analysis, it shows that it's
00:55:19
predictive.
00:55:20
It makes contribution above baselines on the likelihood
00:55:25
of a team winning.
00:55:26
Okay, so that's how you're controlling for a
00:55:28
bunch of other things that you'd expect.
00:55:30
It's there's incremental contribution.
00:55:32
That's right.
00:55:32
Can I just shout out to, yeah, go
00:55:35
ahead, finish.
00:55:35
Well, I'm going to give you a little
00:55:37
bit of a, I'm going to give you
00:55:37
a couple of steps on methodology because I
00:55:39
thought they were nice.
00:55:41
First, it's at the player level as opposed
00:55:42
to the line level.
00:55:43
So he'll take a stat like ice time
00:55:47
or closer to what you might care about,
00:55:49
like XG, expected goals.
00:55:51
He looked at also things like assist and
00:55:53
shots on goal, but it ends up with
00:55:55
a suite of these statistics.
00:55:57
And in each case, he asked, he asked
00:56:00
what's the compliment of the Gini coefficient.
00:56:02
So y'all know the Gini coefficient is
00:56:04
this IO kind of, it's a concentration measure.
00:56:08
And so he's asking how concentrated is the
00:56:11
ice time or shots on goal or expected
00:56:13
goals among the players or the offensive player
00:56:16
or the skaters.
00:56:16
It's all obviously defensive players can have XG
00:56:20
as well.
00:56:22
And so that's one, that's the basic measure,
00:56:25
but then he models the whole thing as
00:56:27
a latent variable.
00:56:28
And so I think this is something that
00:56:29
we ought to listen to.
00:56:31
I think there's a nice contribution because he's
00:56:32
saying, look, it's not one thing.
00:56:35
XG, like the compliment of the Gini coefficient
00:56:37
of XG for a team is an imperfect
00:56:40
measure of depth.
00:56:42
But if we get three or four of
00:56:43
these things and ask, is there a latent
00:56:45
variable?
00:56:47
I'd say this is basic measurement science.
00:56:49
You don't measure a construct with one thing.
00:56:51
You use a bunch of manifest variables that
00:56:54
represent some underlying basic concept, which is why
00:56:59
I was thinking of some sort of strength
00:57:00
model.
00:57:01
You could call that latent thing a strength
00:57:03
thing, which is manifested itself in different observables.
00:57:09
I'm excited to look at his paper, Cade.
00:57:11
Thanks for bringing it to our attention.
00:57:13
I will just point out that that was
00:57:15
the qualitative or quantitative or qualitative question that
00:57:18
we asked our high school students in our
00:57:19
competition.
00:57:20
Is that right?
00:57:21
They were asked to essentially evaluate whether depth,
00:57:24
we didn't call it that, we call it
00:57:26
line variance, how that affected winning.
00:57:29
And that was the final question for their
00:57:33
high school students.
00:57:35
And we didn't- Maybe I should just
00:57:35
say a few words, what you're talking, I
00:57:37
mean, I obviously know what you're talking about.
00:57:39
You should just say a few words.
00:57:40
What is the Wharton High School Data Science
00:57:42
Competition?
00:57:42
So Wharton Analytics and AI at Wharton, we
00:57:45
sponsored a major competition for high school students
00:57:48
and we created a fake hockey league.
00:57:52
They played each other all season.
00:57:54
And then we asked the students to forecast
00:57:55
the outcomes of the playoffs.
00:57:58
And that was all very forecasty and very
00:58:01
rigorous and qualitative.
00:58:03
But then we had them ask an open
00:58:05
-ended question, like a hockey question, which is,
00:58:07
what is the relationship between having a depth,
00:58:12
which is the right word for it, and
00:58:14
winning?
00:58:15
And we asked them to study that.
00:58:16
And it was actually very hard because there
00:58:20
was a very strong confounder, which is the
00:58:23
top lines also play against the top defenses.
00:58:27
And so you have to control for the
00:58:30
quality of the defense that they play against
00:58:32
to measure the quality of your top line.
00:58:34
And if you didn't do that, you thought
00:58:36
that your underlying, your lower lines were better
00:58:39
than they were because they were playing against
00:58:42
weaker defenses.
00:58:43
And I will tell you that one team
00:58:45
managed to do that out of the 700
00:58:49
submissions.
00:58:50
Well, I know who it was because they
00:58:51
presented actually at the Wharton AI and Analytics
00:58:54
Board Meeting.
00:58:54
I assume you mean the Pingree team.
00:58:55
Is that the team that won?
00:58:56
They were the team that won.
00:58:58
I'm not sure they're the team that figured
00:58:59
that out because it was so esoteric that
00:59:03
there were so many components to the program.
00:59:06
All right, guys.
00:59:06
So we'll let that one go.
00:59:07
If anyone's interested in chasing it down, Dylan's
00:59:10
website is hockeydecoded.com, hockeydecoded.com.
00:59:15
Thanks, Dylan, for sending that to us.
00:59:17
And listen, guys, we're always interested in the
00:59:19
work.
00:59:19
And to be fair, the more put together
00:59:22
the work is when you send it to
00:59:23
us, the better, but we'll take work in
00:59:25
progress.
00:59:26
We'll take data.
00:59:26
We'll take websites.
00:59:27
And Dylan, maybe he got lucky.
00:59:30
He got me in a moment where I
00:59:31
could pause and read the paper, but we
00:59:33
love that you guys send us stuff on
00:59:35
occasion and we learn from it.
00:59:36
So please keep it up.
00:59:39
Fellas, what else around the world of sports
00:59:42
right now before we have to run?
00:59:44
Well, just one thing for me.
00:59:46
I always love talking about tennis, but Yannick
00:59:49
Sinner has done something no one's ever done.
00:59:51
He's won five straight Masters 1000 titles.
00:59:55
And no, not Federer, not Djokovic, not Nadal
00:59:59
has ever done that.
01:00:00
And my prediction is, I don't think, unless
01:00:02
he gets injured, I don't think he's going
01:00:04
to lose unless, until Alcaraz comes back.
01:00:09
I mean, the gap between him and everybody
01:00:12
else, look, he just played Zverev, who's number
01:00:15
three in the world.
01:00:16
He beat him 6-1, 6-2.
01:00:19
It's the fourth straight match.
01:00:21
He hasn't given up more than six games
01:00:23
to Zverev.
01:00:23
And that's the number three player in the
01:00:25
world, okay?
01:00:26
And Djokovic is injured right now.
01:00:28
Maybe Djokovic beats him on a bad day
01:00:30
for Sinner, but the gap is so large
01:00:33
that he may win every tournament he competes
01:00:37
in, assuming he stays healthy right now.
01:00:39
I don't see any reason why he wouldn't
01:00:41
be.
01:00:41
I have to start looking at the betting
01:00:43
odds, but you have to put him at
01:00:44
minus 400 minimum, minimum for any tournament he
01:00:49
plays right now.
01:00:50
That takes the fun out of it, doesn't
01:00:51
it?
01:00:52
That's a little too heavy.
01:00:53
It does.
01:00:53
How long is Alcaraz out, you said?
01:00:55
It's hard to know.
01:00:56
I mean, he's not playing the French, which
01:00:58
is upcoming.
01:00:59
They're not at the French yet.
01:01:00
They still have, I forget which one's next,
01:01:02
the Italian Masters or whatever.
01:01:04
But there's one more tournament and then the
01:01:05
French, and Alcaraz is already saying, because of
01:01:07
his wrist, he's not playing the French.
01:01:10
So, yeah.
01:01:12
So that's what caught my eye.
01:01:14
All right.
01:01:16
We should note that two Philadelphia franchises are
01:01:19
in the second round of the playoffs.
01:01:20
That's not nothing.
01:01:21
How long has it been?
01:01:22
How long has it been since that happened?
01:01:23
Both Flyers and the Sixers are into the
01:01:24
second round?
01:01:25
That's pretty good.
01:01:27
They're both getting whipped right now in that
01:01:29
second round, but presumably the Sixers will bounce
01:01:32
back.
01:01:32
I assume that that Knicks first game was
01:01:34
just because of how rough that Sixers-
01:01:36
Well, the Knicks, by the way, have set
01:01:37
up, I mean, whatever this is worth, it's
01:01:39
a small sample.
01:01:39
The Knicks have now set a playoff record.
01:01:41
They've won four, three straight, but they've won
01:01:44
four straight, I think, playoff games now by
01:01:46
25 points or more, which has never been
01:01:48
done before.
01:01:49
So the Knicks are on a heater.
01:01:50
They were down 2-1, just remember, mind
01:01:52
everyone, they were down 2-1 to the
01:01:54
Hawks.
01:01:54
They beat them three straight by plus 25.
01:01:57
They beat the Sixers by 39 last night.
01:02:00
So whatever heater they're on, they're on a
01:02:03
wrecking crew mission right now.
01:02:05
And you're right, the Sixers may bounce back
01:02:07
and lose by 15.
01:02:09
Now, one trick, concern potentially, is that they
01:02:13
were winning, and then the team starts to
01:02:14
try to get desperate, and then it ends
01:02:16
up creating a gigantic margin if it doesn't
01:02:18
work out.
01:02:19
They were beat pretty early on this game.
01:02:21
Yeah, I have to tell you, I was
01:02:22
in- It cuts out pretty quick.
01:02:23
I was in Penn Station when the Knicks
01:02:25
were playing their last game of their first
01:02:26
series, and at the bar in Penn Station,
01:02:30
they had the game going on, and I
01:02:31
turned over and looked, and I saw the
01:02:32
score, and I'm like, that can't be real.
01:02:34
Right, it was like 90 to 30 or
01:02:36
something.
01:02:36
No, it was an unbelievably unreal score.
01:02:39
But there's this thing in tennis that I
01:02:41
think is known, that tennis players, if they
01:02:45
get behind in a set, sometimes just say,
01:02:47
I'm gonna lose this set, I'm gonna conserve
01:02:48
energy.
01:02:49
And I think this happens in basketball series
01:02:52
as well, that sometimes a team will get
01:02:53
sufficiently behind, they're like, we're gonna conserve energy
01:02:56
this time, we're gonna come back- Well,
01:02:57
Cade, I mean, let's remember, the Sixers played
01:03:00
seven games.
01:03:00
They ended on Saturday.
01:03:04
So they had to fly down from Boston.
01:03:06
By the time they was blown out, I
01:03:08
know for a fact, Embiid, Paul George, Tyrese
01:03:10
Maxey, they were all pulled in the early
01:03:12
to mid part of the third quarter, the
01:03:14
third quarter, because they're playing again tomorrow.
01:03:17
There's no, I mean, I'm saying there's no
01:03:18
rest.
01:03:19
There's one day rest.
01:03:20
You obviously, the game's over.
01:03:22
Tank this game and go for the next
01:03:23
one.
01:03:24
I'm worried about that in the Eastern NHL
01:03:28
playoffs, the Canes are up on the Flyers
01:03:30
2-0.
01:03:31
They're big favorites anyway, they might sweep them.
01:03:33
The team I'm pulling for, the Sabres, haven't
01:03:36
yet started their series against the Habs.
01:03:39
And so it could be that the Canes
01:03:41
get out and have like a 10-day
01:03:43
break before- Get rusty.
01:03:46
Then you hope for us, that's exactly right.
01:03:47
All right.
01:03:48
Let me give you one other thing, guys,
01:03:49
before we go.
01:03:50
The NHL lottery, draft lottery is today.
01:03:55
So there's a few wrinkles here that are
01:03:57
just a little bit interesting.
01:03:58
But by the way, the presumed number one
01:04:00
pick, it's not 100% sure, but the
01:04:01
presumed number one pick is that kid that's
01:04:04
at Penn State.
01:04:05
He kind of unexpectedly went to Penn State
01:04:07
when Penn State was practically just an intramural
01:04:09
team still, Gavin McKenna.
01:04:11
And by the way, he's like six foot
01:04:13
170.
01:04:14
It's just amazing to me that a guy
01:04:16
on the hockey ice, six foot 170, which
01:04:18
is a very average size guy, could be
01:04:20
that good.
01:04:21
But setting that aside, the odds, the top
01:04:24
odds for the worst performing team last year,
01:04:26
the Vancouver Canucks, it's 25 and a half
01:04:29
percent, 25% chance.
01:04:30
It's pretty, pretty good.
01:04:32
And then it drops to Blackhawks are 13,
01:04:34
Rangers are 11.
01:04:35
And here's something for us to pull for
01:04:37
tonight.
01:04:37
In absentia, Shane Jensen's Calgary Flames are the
01:04:41
fourth highest probability of getting the number one
01:04:43
pick, nine and a half percent.
01:04:44
So we should all be pulling for the
01:04:47
Flames.
01:04:47
They haven't done quite as much flattening as
01:04:50
like the NBA has done with the draft
01:04:53
lottery, et cetera.
01:04:54
Or where they haven't even, there's tiers.
01:04:56
Like bottom four in the NBA are all
01:04:58
at the same percentage now, so in other
01:05:00
words, you can tank, but there's no reason
01:05:02
to tank from third to second.
01:05:04
They haven't, I don't think they've done that
01:05:06
yet, but they're talking about making that last
01:05:08
change she just said.
01:05:09
But there's another wrinkle in here that I've
01:05:10
never seen before.
01:05:11
We talk about tournament design, but here's lottery
01:05:12
design.
01:05:13
Of course, we're hearing a lot about it
01:05:14
from the NBA, but I think this is
01:05:16
just gonna exacerbate what you just said.
01:05:17
They have separate draws for the number one
01:05:20
and the number two pick.
01:05:22
I think that means in the NBA, they'll
01:05:25
count down like 10, nine, eight, seven to
01:05:28
one.
01:05:28
And so it's one draw all the way
01:05:30
through.
01:05:31
I think that means they're gonna choose a
01:05:33
ball for number one, and they're not just
01:05:36
gonna do a full, well, would it make
01:05:39
any difference?
01:05:40
And they're gonna, that team will obviously be
01:05:42
removed for the next draw.
01:05:46
Why would it be any different?
01:05:46
That team's probability should be, that probability, let's
01:05:50
say it's 25.5%, that probability should be
01:05:52
distributed equally.
01:05:53
In other words, it doesn't violate what we
01:05:56
call the IIA property, meaning the remaining 74
01:06:00
.5% probability still has the same ratio
01:06:03
amongst all the other teams.
01:06:05
So there should be no impact.
01:06:07
Yeah, so why would it matter?
01:06:09
Maybe I have it, maybe I get, so
01:06:11
I don't know.
01:06:11
I don't know what they're doing and what
01:06:13
they're doing wrong.
01:06:13
That must not be what they're doing.
01:06:15
Anyway, I would just wanna, something for a
01:06:16
future show, but I have a group of
01:06:18
students from my Moneyball Academy over the summer.
01:06:20
They studied the rate at which value is
01:06:24
lost as you go down the draft curve
01:06:27
for all sports.
01:06:28
And hockey surprisingly gets the incredible amount of
01:06:31
value from the top of the draft, and
01:06:33
it drops quickly, but way more than other
01:06:36
sports.
01:06:36
I was surprised.
01:06:38
I didn't realize how predictable a top-ranked
01:06:41
hockey player is in terms of value.
01:06:43
That's a lot.
01:06:44
The difference in predictability is what you're talking
01:06:46
about, though, the drop.
01:06:47
Yeah, but even, it starts high, and then
01:06:49
it stays high and it goes down, but
01:06:52
it's amazing.
01:06:53
So the metric they used was all-star
01:06:56
appearances, and they are a lot at the
01:07:00
top of the draft.
01:07:01
Interesting, and did you have any sense of
01:07:03
where the other sports come in?
01:07:04
Yes, we have that.
01:07:05
We'll have to talk about that at a
01:07:07
later date, because it's a great little study.
01:07:10
Well, it's surprising given the long development time
01:07:12
on most hockey players.
01:07:16
It's much more like, it's in the direction
01:07:17
of baseball as opposed to like a football
01:07:19
or basketball.
01:07:20
Okay, guys, that's a pretty solid show.
01:07:23
Well, well over an hour here.
01:07:25
It feels like we haven't talked in a
01:07:26
while, and we did some today.
01:07:28
We'll let it go there.
01:07:30
For the whole team here, Eric Bradlow and
01:07:31
Adi Weiner joining me, Shane Jensen in absentia,
01:07:35
Dion Simpkins, the big boss behind the scenes
01:07:38
making this thing happen for more than 12
01:07:39
years now, Marissa Reyna, our producer, and Deep
01:07:43
Patel, boss lady, keeping us all in line.
01:07:46
Thanks to that whole team.
01:07:47
Thank you guys for listening.
01:07:48
Come back and join us next time.
01:07:49
Between now and then, enjoy your sports.
