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Welcome, welcome to Wharton Moneyball.
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Welcome to another full hour of sports
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analytics here on the Wharton podcast
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network. This is Kade Massie hosting
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today with my longtime friend,
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colleague, collaborator, co-host Eric
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Bradlo, Audi Winer, we're expecting at
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some point in the next hour. Shane
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Jensen, we are not expecting this week.
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Our fourth colleague is out and about
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doing Shane Jensen things as he has want
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to do this time of year, but he will be
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back some combination of us are here
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almost every week of the year. And by
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that we mean I don't know 48 49 weeks of
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the year and for pushing 11 and a half
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years now. Delighted to be here on this
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Tuesday afternoon. Late summer Tuesday
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afternoon. In fact, this is the last
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Tuesday of the summer months. If you
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wrap it up in August, it is very much
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late summer. That means a few things,
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guys. That means that football's about
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to start. We're going to spend some time
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on that. It means baseball is getting
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more interesting. But it also means that
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some significant tennis is being played
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in the New York City area. And for that
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topic, we wanted to bring in Jeff
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Sackman. Some of y'all who've been
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listening know that we have Jeff on here
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a time or two a year and have for a long
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time. Sackman is as good as it gets in
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the world of tennis analytics. jumped
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off the page at us early in our time. I
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think we're reading like some of these
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economist articles without byel lines
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and like we're thinking who the heck is
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this writing all this great stuff on
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tennis analytics and we find out that
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it's Jeff Sackman. Jeff does a lot of
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stuff. He's the founder of tennis
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abstract. It's an online encyclopedia of
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sorts covering much of the history of
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tennis. But within that site he also
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writes a blog heavy top spin. Heavy top
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spin looking at players and trends. and
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that's where you see some of the stuff
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that catches our eye. We're always
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delighted for chance to talk with you,
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Jeff. Thanks for making time for us.
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>> Absolutely. Glad to be here, guys.
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>> Glad to have you. Um, in the meantime,
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while I rambled around there in the
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beginning, Audi Winer showed up. Audi is
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back. Audi is no longer on sabbatical.
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Audi's kind of supposed to be here every
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week now. It's gonna be it's gonna be
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fun to have him back around. He is the
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best traveler, I think, of the four of
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us. Audi, great to see you.
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>> Be here,
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>> Jeff. In a minute. uh you and Eric can
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dive into all things US Open, but maybe
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just as a starting place, you can tell
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us as the tournament starts, as you pull
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your head out of whatever data you've
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been looking at lately, what is on your
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mind around tennis right now? How are
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you thinking about this tournament or
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what player are you especially
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interested? Or is there any analytics
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project that's especially had you
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consumed recently?
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Well, one thing that I was looking at
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this week is the big story in men's
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tennis for I mean more than a year now
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is that we we can pretty much forget
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about the big four. We can pretty much
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like let NovakJokovic ride into the
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sunset. It's the Yanuk Center and Carlos
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Ocar show and the numbers are starting
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to put up are starting to be a bit
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mindboggling even though they are so
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young. So, one thing that I do with
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every Grand Slam is I I I throw the the
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draw and my ELO ratings into a into a
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formula and generate a pre-ournament
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forecast. And typically over the years,
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the number one seed, the top player in
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the draw, not always the same player,
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whoever the my favorite is in the in the
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draw will end up with around a 35% 40%
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chance of winning the tournament. And
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then there'll be maybe a half dozen guys
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who are in the 5 to 10% range. Now,
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coming into this tournament, Yannik
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Center was at 45%. Not the highest he's
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ever been, but that's that's very high
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for someone to be at 45%. But the
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shocking thing is not that. The shocking
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thing is that Carlos Alcarez is at 35%.
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>> So that's that's an 80% chance of these
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top two guys winning. Just I mean that
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that's before a single ball is struck.
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So your number three normally I've got a
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number three at least in double digits.
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Alexander Zerv is pulling up the number
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three spot at six whole percent which
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you know if you follow Alexander Zerv
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that might even sound generous but that
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that's the state of men's tennis right
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now. I mean even I don't think Federer
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and Nadal ever managed to pose numbers
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like that partly because like they were
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half of the big four and the rest of the
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big four was pretty good too. But I mean
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that's how dominant these two guys are
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right now. That's astounding and it's a
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great top line and it's it's exactly the
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kind of thing that we often ask about
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before a big competition. We'll ask it
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about a golf tournament. We'll ask it
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about a tennis tournament and I don't
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think we've ever heard anything quite
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that stacked up. Is
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>> just ask a followup if I could just ask
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you quickly a follow-up to that. We're
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one point away from sinner having the
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sinner slam at the French. So, um,
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how like is it even really like I'm an
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Alcarass fan just to get to make sure
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everybody knows everybody knows this. Is
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it really even the big two? I mean,
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Sinner is one point away from holding
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all four majors.
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>> Yeah, I mean, he's pretty dominant. On
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the other hand, my overall ELO ratings,
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which are not not the surface specific
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ones, they actually give Alcarez a
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slight edge right now. They they say
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Alcaras is the number one player in the
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world. not on hard courts. Sinner has
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the edge on hard courts. But what I
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always think about is
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there there's two ways to be to win a
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slam. One way is to take down the top
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dog, which is what it seems like
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someone's going to have to do. The other
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way to win a slam is let somebody else
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take down the top dog. And if uh Grigor
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Deitrov hadn't gotten injured two sets
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in his match against Wimbledon, then it
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would have been an absolute breeze for
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Alcarez to coast to that title. So
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whether it's Alexander Bublick having
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another big day like he had on grass uh
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running up to Wimbledon or whether it's
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somebody like Dimitro having a career
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best day or who knows what could happen.
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Grand slams mean you got to win seven
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matches. Sometimes in the late rounds
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it's going to be somebody like maybe
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it's Thafo in New York who's going to
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play the best tennis he's ever played.
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Sinners got to run through those
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hurdles. So if we do get to the point
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where it's a mono ono final in two weeks
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then yeah the edge goes to center. But
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there's there's just too many hurdles in
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just in the structure of tennis. There's
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too many hurdles to to annoy somebody
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the the big one unless he's unless he's
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really ahead of the pack. Uh before his
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uh his suspension earlier this year, I
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think he was getting there. Uh he posted
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a careerhigh elo rating in the 2300s,
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which is like top 20 of all time. Um he
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hasn't gotten back to that level since,
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but maybe maybe I I can give you an
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update after this tournament. We'll find
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that he is back there. I mean, he he
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could he could accomplish that, but just
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not quite yet.
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>> Jeeoff, that was getting close to one of
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the questions I was going to ask about
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the two dominating the probability of
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winning. How can you decompose that at
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least at least casually between there
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being unusually good versus the rest of
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the field being a little bit weaker? Can
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you give us some way of comparing those
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two things?
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>> That's a really, really hard question to
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answer and I've never come up with a
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satisfactory way of answering it. Partly
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because my intuition is that you
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basically never have a weak field in the
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tennis world because the tennis world is
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always getting better. I mean, you have
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the same coaches working for
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generations. You have the same
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nutritional insights, training insights,
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all that stuff is
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is gradually getting better over the
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years.
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uh the the population of players that
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the tennis world is drawing on, the
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number of countries with developmental
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programs, like every force there is is
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should be making the whether it's the
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top 100, the top 50, or even the top
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1,000 should be making it gradually a
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little bit better. So if if you agree
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with me up to that point, I mean I I
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agree you can argue some of those
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points, but if you agree with me up to
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that point, then if somebody is head and
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shoulders ahead of the pack, they're
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actually head and shoulders ahead of an
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even better pack than Federer and Nadal
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etc was or Rod Lever was 50 years ago.
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So that that's my my first level take.
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Okay.
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>> I don't know how you work out other than
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just a little bit from year to year
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whether the pack is is getting worse.
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>> So Jeeoff, let me ask you a question.
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Let's just quickly if let's imagine
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everyone gets 30 40 50 ELO points better
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um since the ELO model isn't a linear
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model would that make it so that the
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other let's call it top 100 the other 98
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players in the draw it's really 126 but
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let's say the other 98 players in the
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drawer
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does that make it less likely then that
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one person can be as dominant because
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it's a nonlinear model the top person's
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30 points better now in simply models
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just the difference in ELO ratings. So
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if everyone goes up linearly, the
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difference cancels out. But what are
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your thoughts about kind of the
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nonlinearity that you know now you have
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to beat 98 better players and how do you
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think about that?
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>> Well, I think that's exactly what we've
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seen the last decade or so in the
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women's game. Uh before Sabalinka and
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Fionek have emerged as the their own top
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two. I mean, and even EGA struggling
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this year. Um, we had a run of like 12
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slams in a row where different women won
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the title. I think I think that's the
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number. And it Yeah, you have you have
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players climbing the rankings when
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they're number 37 knocking out Fiontech
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on hardcourts or somebody beating Koko
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Goth because Koko Goth has an off day
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serving and they take advantage of that.
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Like, every single match is a contest. I
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mean, there's no there's no easy matches
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in in women's tennis anymore. There
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aren't very many easy matches in men's
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tennis. I think the only thing that's
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keeping the possibility of a top two or
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a dominant top player is just the
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structure of tennis that you don't have
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to win 58% of points to be dominant. You
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got to consistently win like 53 and a
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half%.
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>> So, if you can figure out how to do that
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um or even 52% and be better in the
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clutch. If you can figure out how to do
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that against a stronger field, then
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you'll still be dominant. I mean, not to
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say that's easy. That's not easy at all.
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But, uh but that's all you have to do.
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If if you if you had to win 58% then I
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think we would see Yeah, we would see
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that the that strength at the top start
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to weaken.
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>> Audi, why don't you jump in here?
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>> Yeah, I mean
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the observation of course that the field
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always gets better is true, but really
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that's an average observation. The
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extreme values um can have an enormous
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amount of variability and that's what
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we're discussing. And so it's not um so
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it's it's so what you're talking about
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is the difference between one and two
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and three, four, five, six, seven, not
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between what not what the whole average
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field is doing which can can continue to
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elevate yet you can still not have um
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elevation at the extremes. That's that's
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that's a kind of a different
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mathematical question that it behaves
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differently mathematically. So the and
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tennis is actually an interesting sport
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because it's one of the few one of the
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sports that has huge numbers of what I
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would call ladders of of of levels. So
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uh being on two different levels of the
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ladder means you you consistently beat
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one level below it. In in a sport like
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baseball, there aren't very many
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ladders. I mean obviously there are
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differences, but any even the worst
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team, we talked about this last week,
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even the worst team could be the best
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team on any given day. It's not even uh
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it's not it's not even that lopsided.
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But in tennis you go from like one to
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what what 20 or what rank? I mean
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typically I mean you right now we have
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the top two beating three four five with
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nearly nearly 90% probability and that
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continues to shift all the way down. You
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don't you have these you know a 15 I
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mean I it's not an it's really like an
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average but number the the fifth best
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player will beat the 10th place best
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player pretty handily. Um at least we're
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seeing that in
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>> that's just not that's not true though.
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I stop you there. You're absolutely
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right about one two against number five.
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>> The number five beats what number with
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what consistency? Like the same the same
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one two beats five at some consistency
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and five has to play X to have the same
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dominance.
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>> I see.
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>> Okay, so I'm looking at my ELO ratings
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now. Alcarz is 2270. Taylor Fritz is
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number five at 2030. So there's a 240
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point gap between number one and number
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five. go down 240 from a 2030. 2030 and
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you're at
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1790. Is my math right on that?
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>> Yep.
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>> So, for the same gap, 1790, scroll down.
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Scroll down. You get down, you're out of
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the top 50 now. Um, you're down to 53 or
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54. That's So, anybody in the top 50 has
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a better chance of beating number five
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than number five Taylor Fritz has of
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beating number one.
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And is that the kind of difference we
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typically see with a with a one, two,
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three compared to five or this is
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extraordinary? I think so.
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>> Yeah.
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>> No, I think that's I think that's pretty
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typical. I mean, you there have been
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times where one through five have been
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more tightly clustered like in the big
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four plus Vanka era. Maybe there was a
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couple years where that was the case,
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but no, I think that's um that's pretty
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close.
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>> This is an interesting general topic,
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Audi. We've talked about it here and
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there, but it's it's an interaction
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between the distribution of quality and
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the and the and the sport, the game
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design of the sport itself.
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>> And that combination gives you these
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changes and it really says a lot about
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what we should expect. Let me ask a
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different kind of question.
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>> There's a lot of points in tennis,
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right? So, um it's hard to it's what's
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actually kind of incredible that I'm
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surprised about is that five takes 50 to
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get to get that gap. By the way, I think
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Jeff, you could ask Jeff the same
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question, by the way, Audi, but say not
00:13:26
for the majors, but how would that
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change potentially for shorter matches
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like the Masters 1000, which are best of
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three? Well, the So, the the numbers I
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gave you are for best of three, but I
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think the ELO stratification is the
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same. It's just the the uh the
00:13:45
probabilities those work out to. So,
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let's see. 240 should be
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>> it'll shift both of them but it'll shift
00:13:52
them the same way. It'll sit from the
00:13:53
same amount is what you're suggesting.
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>> Yeah.
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>> The conversion of the delta into a
00:13:58
probability is going to have a different
00:13:59
constant if it's a shorter match. So
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that's what's
00:14:02
>> that's my point. Right. Exactly. That
00:14:04
was my question is that but I think
00:14:06
audience by design I mean that is you're
00:14:07
right or not by that is a design. Longer
00:14:10
matches favor the better player.
00:14:13
>> Oh yeah. So Jeff, you've been writing I
00:14:17
think you know pushing 15 years now.
00:14:19
Tennis analytics has advanced a lot in
00:14:21
that stretch of time. How would what
00:14:24
would you say is the way it's affected
00:14:26
the game if it's affected the game? So
00:14:28
if someone watched the US Open this next
00:14:31
couple of weeks and they hadn't watched
00:14:33
tennis in 10 or 15 years and they
00:14:35
remembered what style of play they saw
00:14:37
10 or 15 years ago, would they see
00:14:40
differences? Do has the analytics
00:14:42
changed the game? Do you think in some
00:14:44
way?
00:14:45
>> Um, yes and no. And I think what it has
00:14:50
what it has changed for players and
00:14:52
coaches is the awareness that the
00:14:54
average point is a short point. Um,
00:14:57
there were lots of short points before.
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I mean, if you go back 50 years, you
00:15:01
have a tour full of servant volers. Even
00:15:04
a lot of successful women were serving
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volleying. Um, which you virtually never
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see now. So, those were very short
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points. lots of serves, aces winning the
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points, returns not coming back, first
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volleys winning the point. I don't know
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what the average point point length is,
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but it's like three and a half strokes
00:15:20
even back then. Um, still now it's three
00:15:24
to four strokes is your typical point.
00:15:26
Now, that's pretty much always been
00:15:27
true. If we go back 20 years outside of
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the time frame you suggest, you get a
00:15:31
lot of big servers. You have like
00:15:32
Richard Krychek and Greg Greg Rosski and
00:15:35
Mark Filipus and those guys who are who
00:15:37
are giving you short points because
00:15:38
that's just the game they played and it
00:15:40
worked. Um what you have now instead is
00:15:43
everyone is aware of the fact that even
00:15:44
if you don't have like the Mark Philipus
00:15:47
Scut serve that you should be playing
00:15:50
with that in mind. You should be setting
00:15:52
up your serve for a plus one. You on the
00:15:55
women's side there's a huge shift
00:15:57
towards um swinging big on the return
00:15:59
just taking your chances hitting a big
00:16:01
return and maybe ending the point there.
00:16:03
Uh so what it has done statistically
00:16:06
hasn't really shortened points so much
00:16:07
as it h the game has selected for
00:16:09
players who will who are always looking
00:16:12
to end the point if you're that's one of
00:16:14
the big flaws for Alexander Zerv is he's
00:16:16
very good at hanging in long points
00:16:18
maybe as good as anybody else on tour.
00:16:20
He can keep himself alive. He can make
00:16:22
you a little uncomfortable and he he'll
00:16:23
give you these highlight reel like 4050
00:16:26
shot rallies and you know what a 40 or
00:16:29
50 shot rally gets you? it gets you one
00:16:30
point and then you're gassed after that.
00:16:33
Like it's it's not worth it. And anyway,
00:16:35
I I wrote something about this earlier
00:16:37
this year that you do that and you still
00:16:39
don't have a great chance of winning.
00:16:40
The very best players at long rallies
00:16:43
win like maybe twothirds of long
00:16:45
rallies. So if you're, you know,
00:16:47
Alexander Verv playing Riley Opelka,
00:16:49
monster server, bad groundstroke guy,
00:16:52
Riley Opela is still going to win one
00:16:53
out of three of those points. So that's
00:16:56
it's not the strategy. It's not a
00:16:57
strategy that wins tennis matches. and
00:16:59
it wasn't ever dominant, but it was
00:17:01
always present in that you'd find
00:17:03
players who who played with an eye on
00:17:06
stretching out points, defense, and
00:17:08
that's it's not gone, but especially on
00:17:11
hard courts, it's basically gone.
00:17:12
>> Okay, Jeff, I was in I happened to be in
00:17:16
France when Michael Chang had his big
00:17:18
French Open blow up in like 1989 or
00:17:21
whatever that was, and he's the first
00:17:23
person that comes to mind. It's just
00:17:25
kind of the long rally, get the ball
00:17:26
back, he's not going to win any big
00:17:28
shots, but he's going to stay on the
00:17:30
court forever. But that was 1989, and
00:17:32
even that was a kind of a short moment
00:17:34
in time.
00:17:35
>> And that was that was clay court tennis.
00:17:37
And if if you watch court tennis now,
00:17:39
you'll see some people who are still
00:17:41
playing that way. But it's fascinating
00:17:43
to watch even the the grindiest like
00:17:46
small underpowered claycourt guys. They
00:17:49
are swinging big. like they'll hit these
00:17:51
spin serves out wide to open up the
00:17:53
court and then they just wind up for the
00:17:55
biggest forehand they can hit. It won't
00:17:57
usually be a winner because you know
00:17:59
these guys are 5 foot nine and they
00:18:01
don't hit that hard. It's all just spin
00:18:02
spin spin spin spin but they're trying.
00:18:04
Like the goal is still the same. Shorten
00:18:06
points, force the guy out wide, create
00:18:08
an opening, get it over with.
00:18:10
>> Okay. All right. Well, listen, uh Eric,
00:18:13
what what are you thinking about as you
00:18:15
go into the US Open and are you going
00:18:16
out there this year? You often make it
00:18:18
out to watch a little tennis. Um, I' I'd
00:18:20
be curious to hear any specific
00:18:21
storylines you're curious about.
00:18:24
>> Well, I am going next week to the US
00:18:26
Open. I'm going on Wednesday, which is
00:18:27
Wednesday evening, which is men's and
00:18:29
women's quarterfinal, which should be
00:18:31
interesting. Um, but you know, Jeeoff,
00:18:34
I'd love your thoughts on for some
00:18:36
reason, at least at these rounds, I find
00:18:38
the women's game much more interesting.
00:18:41
I mean, the match, I'm sure you watched
00:18:42
it last night. The match between Venus
00:18:45
Williams and Mukova was incredible. I
00:18:50
mean, the fact that Venus played that
00:18:52
well at 45. Um, I mean, in that second
00:18:55
set, she looked great. How do you think
00:18:58
about the women's game right now where I
00:19:00
could literally list certainly the top
00:19:03
seven or eight players I could list at
00:19:05
least seven or eight players that could
00:19:06
win it on the women's side and it might
00:19:08
be even more than that if you include
00:19:10
Andre Evva you know certainly Rabbakina
00:19:13
can win certainly the big three of
00:19:14
Sabalena Goff and Swante I mean there's
00:19:18
at least five six seven eight 10 players
00:19:20
that could win it how it's the women's
00:19:22
game that's got me much more because of
00:19:24
the variety and diversity how do you
00:19:26
think about it.
00:19:28
>> Yeah, I I think the women's game has a
00:19:30
reputation for that variety and
00:19:32
diversity. I'm not sure whether I buy
00:19:34
that narrative anymore. I don't disagree
00:19:35
with your main point that it's more
00:19:36
interesting game. Uh like I was just uh
00:19:40
talking about with the in the broader
00:19:42
trend, points are getting shorter.
00:19:43
Someone like Mukaba is super
00:19:45
interesting. She can play an allcourt
00:19:46
game. She can grind it out from the
00:19:48
baseline. She can do a lot of different
00:19:49
things. She is a dying breed. I think uh
00:19:53
more likely you have you're going to see
00:19:56
somebody who's sort of a mini Sabalanka
00:19:58
um who will swing big on return, hit big
00:20:01
serves. There's a a lot of women like
00:20:03
that. Um I mean one
00:20:04
>> Anna Samova as Anna Samova as an
00:20:06
example.
00:20:07
>> Anna Samova is a great example. Yeah,
00:20:09
huge hitter. I mean she's a little more
00:20:11
interesting in the sense that she
00:20:12
doesn't if you saw her walking down the
00:20:14
street you wouldn't think, oh she scares
00:20:16
me the way that Sabalena might. Uh but
00:20:19
yeah, she hits a Oapeno. I mean there's
00:20:22
lot I mean there's lots of these big
00:20:24
hitter on every ball.
00:20:26
>> Yep. Absolutely. Um so the so there's
00:20:29
variety in the sense that there's some
00:20:30
women like that and there's women who
00:20:31
aren't quite as extreme. I mean
00:20:33
certainly Shantek balls in that
00:20:34
category. Uh but yeah I mean I think
00:20:38
there's it's less likely that the serve
00:20:40
itself is going to end the point. It's
00:20:42
it's more likely you're going to have a
00:20:43
player who's who's going to end it on
00:20:45
the second ball. You don't see that as
00:20:46
much in men's tennis. Whereas with
00:20:48
people like Osta Peno or Anosimova that
00:20:50
you mentioned, you are going to see
00:20:51
players who who are really geared to
00:20:53
ending the point or forcing the issue on
00:20:55
the second ball. Uh and then yeah, you
00:20:57
get you get the the Mukova, someone like
00:20:59
Koko Goff can rally with the best of
00:21:00
them. Shriant can rally with best of
00:21:02
them. So there's certainly some some
00:21:04
diversity there. What you're never going
00:21:06
to see like you'll see in the men's game
00:21:08
is you two people who are just ace ace
00:21:11
ace ace ace. And that's always been the
00:21:13
knock on the men's game even if it
00:21:14
wasn't true of everybody. uh you won't
00:21:16
get a match like that. So, you're spared
00:21:18
that in the quarterfinals. Uh you just
00:21:21
might get a lot of return winners, which
00:21:23
I'd argue maybe not much more
00:21:25
interesting, but uh but it is a
00:21:27
different strategy.
00:21:28
>> Let me ask a question. What role do you
00:21:30
think it's a follow on Kate's previous
00:21:32
question, what role do you think
00:21:34
analytics plays in tennis? And here's
00:21:35
what I mean. Like, I don't need advanced
00:21:38
analytics to go maybe I should hit more
00:21:40
balls to Federer's backhand. Like, they
00:21:43
knew that back then. And I don't need
00:21:46
advanced analytics and ball tracking to
00:21:48
know that. So let's call it, you know,
00:21:50
from a statistical perspective, let's
00:21:52
assume that they could assess main
00:21:54
effects like, wow, the first serve
00:21:56
really matters. Wow, this person's got a
00:21:58
weaker backhand. Wow, let's try to, you
00:22:00
know, hit more slices. This person has
00:22:01
trouble getting the low ball. Let's
00:22:02
assume all of that was known even in the
00:22:05
McEnroe, Borg, Connor's days and all
00:22:07
that. What impact do you think analytics
00:22:10
has had once we take away I'll call it
00:22:12
the main effects?
00:22:14
Yeah, those that's a good point. I'm I
00:22:16
am quite skeptical over the of the
00:22:18
overall influence of analytics. I find
00:22:20
it all very interesting from a a
00:22:23
postfacto kind of perspective. But but
00:22:26
yeah, if I if I were a coach, there
00:22:28
would be a lot of information I would
00:22:30
not pass on to the on to players. And
00:22:33
for as long as I've been doing this, I'
00:22:34
I've been struck by how little coaches
00:22:36
tell to players when they have the
00:22:38
opportunity. There was a span of time in
00:22:39
the women's game where they would do
00:22:41
encore coaching by having the the coach
00:22:44
come down and and talk to the player uh
00:22:46
on the sideline. So you'd get some
00:22:48
insight into that and they'd come down
00:22:50
with, you know, two really basic points
00:22:52
and like you say, everybody knows hit to
00:22:53
Feder's backhand, but the coach would
00:22:55
come down and say, you know, just focus
00:22:57
on getting your serve deep, hit to their
00:22:59
backhand. Like are you serious? Like
00:23:01
that I heard that stuff when I was
00:23:03
getting coached when I was what, like 13
00:23:05
years old. Is this really
00:23:07
>> what it is? But I mean, one perspective
00:23:09
on that is tennis is hard. I mean, it's
00:23:11
all coming at you fast. You don't have a
00:23:13
lot of time on every ball to sit there
00:23:14
and think like you're not like a
00:23:16
basketball defender who's thinking like
00:23:18
how can I edge this guy, you know, one
00:23:20
foot to the left or into this zone or
00:23:22
that zone. Like that would be nice if
00:23:24
you could do that. But if you're facing
00:23:26
a 120 mph serve in the corner, you're
00:23:28
not thinking how do I edge her back into
00:23:31
that corner. You're thinking, how the
00:23:32
heck am I going to put this in play? So
00:23:34
I think that's the issue with virtually
00:23:36
ev every question like that. Some of it
00:23:38
you can build into training and I think
00:23:39
that's what coaches do is they they
00:23:41
structure their training around
00:23:42
analytical insights but in match I think
00:23:45
you you you fight with the tools you
00:23:48
come in with.
00:23:48
>> Uh we want to go to Audi but real quick
00:23:50
followup that I was going to go that
00:23:51
direction. The self scouting has to be a
00:23:54
part of it right because even if you
00:23:55
knew you we've heard we've heard this
00:23:57
across sports actually. Even if you knew
00:24:00
that you needed to, you know, rotate
00:24:02
your hips a little bit more when you're
00:24:04
swinging a baseball bat, to be able to
00:24:06
be told precisely where you are and
00:24:08
where the average person is and where
00:24:09
the best people are, that really helps
00:24:11
on the coaching side. It's got it must
00:24:12
be the case that in tennis there's that
00:24:14
kind of self scouting and say, "Well,
00:24:16
you're much stronger in this part of the
00:24:18
court than that, or this stroke is
00:24:19
better than that, just for your own
00:24:21
development in the training regimen."
00:24:22
And then Audi's going to jump in here.
00:24:26
>> Yeah, absolutely. And and yeah, I think
00:24:28
that's where most of it's happening like
00:24:29
I say on the on the coaching side and
00:24:31
and more self- scouting because yeah,
00:24:34
you have maybe maybe one full day to
00:24:36
prepare for an opponent that your your
00:24:38
coach can can scout a player and prepare
00:24:40
a game plan. Sometimes not even a full
00:24:42
day, maybe not even a chance to practice
00:24:44
with that in mind. So I mean the self
00:24:45
scouting is certainly the the biggest
00:24:47
place where there's an opportunity to
00:24:49
exploit.
00:24:50
>> So sometimes even the most obvious
00:24:52
things are missed. So, for example, like
00:24:55
in baseball, analytics told told um most
00:24:59
base dealers to cut it to don't do it.
00:25:02
It was negative. Um and and that only
00:25:05
certain players should do it into
00:25:06
certain times. We're undoing that
00:25:07
slightly because of the way they've
00:25:09
changed the rules. Um it also showed
00:25:11
people that they're standing in the
00:25:12
wrong spot. For 100 years, they stood in
00:25:14
the wrong spot and and and the fielders
00:25:16
are now standing in the wrong spot and
00:25:18
the correct spot and it's just just
00:25:19
changed the game. So going back to
00:25:21
tennis, I've had I run um research
00:25:24
seminars atmies over the summer and
00:25:26
every now and then a student wants to do
00:25:28
a tennis project and they they ask
00:25:31
questions and it most of those have
00:25:32
fallen down pretty hard because it's
00:25:34
hard to get the data and it's and the
00:25:36
analysis is hard. But I I'll I'll throw
00:25:38
out one of them which I wonder whether
00:25:39
or not um analytics could potentially
00:25:41
answer if you had the right data. So
00:25:43
there's obviously a there's some
00:25:44
trade-off between first serve um and
00:25:48
second serve max velocity, right? Um and
00:25:51
has that been analyzed? Like if you're
00:25:54
if you treat your second serve like it's
00:25:57
a first serve, obviously giving up more
00:25:59
double faults, but maybe getting more
00:26:01
second serve aces, what's the optimum?
00:26:04
that's some seems to be something that
00:26:07
that is a hard question that that I
00:26:09
would imagine isn't known or would take
00:26:12
some serious analytics to solve. Um is
00:26:15
that a problem that is worth considering
00:26:17
because I have some students trying to
00:26:18
do this couldn't actually get the good
00:26:20
data but but um that would be like a
00:26:22
problem that I would imagine could
00:26:23
potentially have some impact or no?
00:26:25
>> Well, there's the the yeah the I mean
00:26:28
the simplest version of that question is
00:26:29
should players hit two first serves? Um,
00:26:32
and that question has that that question
00:26:34
has an answer and the answer is no. Um,
00:26:37
it's it's it's not a huge difference to
00:26:39
to the typical uh typical approach, but
00:26:42
you you'd lose maybe like 1% or one and
00:26:44
a half% of the points you win if you
00:26:45
>> Let me ask a question, Jeeoff. Would you
00:26:47
add variance, which could be good for
00:26:49
the weaker player? So, you lose mean but
00:26:51
gain variance. And I want to gain
00:26:53
variance.
00:26:55
>> Um,
00:26:56
you gain double faults. That's the
00:26:58
problem.
00:27:00
I mean,
00:27:02
aces.
00:27:03
>> Yeah, you gain aces and you gain double
00:27:04
foil. This is the if you're if you're if
00:27:06
the if you're the severe underdog, and
00:27:07
we've just learned from from our from
00:27:09
our discussion that most players are
00:27:11
severe underdogs, um they've got to take
00:27:13
chances, which means they're going to
00:27:15
lose badly a lot more often. Um but they
00:27:18
might also be more competitive a little
00:27:20
bit more often, too.
00:27:21
Yeah, I think I think that's why you see
00:27:24
players swinging away on return because
00:27:26
where you want variance is on return. On
00:27:28
the serve, at least in the men's game,
00:27:30
especially true in the women's game, but
00:27:31
more so in the men's game, even if
00:27:33
you're playing Alcarez or center, if
00:27:34
you're a pretty good player, you're
00:27:36
going to hold serve most of the time.
00:27:38
>> Um, that's not your concern. You know,
00:27:40
you can have a you can have a good day
00:27:42
and you're going to hold serve every
00:27:44
single game against Senator Alice.
00:27:46
You'll still probably lose in the tie
00:27:47
breaks, but you will you could you'll
00:27:48
hold serve most of the time on the
00:27:50
return. That's where you want to add
00:27:51
variance. If the classic example is
00:27:53
Dustin Brown knocking out Raphael Nadal
00:27:55
I think 10 years ago now. I mean he he
00:27:57
just went went crazy on the return. Uh
00:28:00
he I mean Chip in charge came in behind
00:28:03
everything he could hit big like and it
00:28:05
worked. It probably n out of 10 times.
00:28:08
This is a great this a great analytics
00:28:10
point which is you know you focus your
00:28:12
efforts and ROI on the dimensions that
00:28:14
matter and even you know it's the
00:28:17
problem that Fritz has against Alcarass
00:28:19
isn't holding serve it's just that he's
00:28:21
not going to get breakings he's not
00:28:23
going to break the serve that often and
00:28:25
so you might as well add the variance to
00:28:27
the dimension where you have more to
00:28:28
gain that's a great point great general
00:28:30
business point let me just comment
00:28:32
>> Jeff you're about to make some point
00:28:34
nine out of 10 times what they're going
00:28:35
to be humiliated
00:28:36
>> it's not going to work
00:28:37
Oh yeah, but I mean you are going to be
00:28:40
but that doesn't matter. You're I mean
00:28:41
you're going to lose anyway if you if
00:28:42
you're like Vit Copa just lost to Yannik
00:28:45
Center one and one and one. I think that
00:28:47
that was just a couple hours ago. So So
00:28:50
yeah, that's the alternative. If you go
00:28:51
in big with a wacky game plan, then
00:28:54
yeah, maybe you will knock out Yanik.
00:28:56
Maybe it's one in a thousand for Capria
00:28:58
versus Ser. But yeah, the alternative is
00:28:59
you play your game, you are gone in 80
00:29:03
minutes and you won three whole games in
00:29:05
in three sets. So yeah, I mean that's
00:29:08
not that great either. You might look
00:29:09
stupid, but if you I mean I'd rather I'd
00:29:12
rather win. But to go back to your u
00:29:14
your question auding the serves is I
00:29:17
think the hardest thing about that
00:29:18
question is knowing how practical it is.
00:29:21
So I mean what I think that presupposes
00:29:23
is that a player can have like a first
00:29:26
and a half serve.
00:29:28
>> Uh and in in theory, yes. So there's a
00:29:32
couple researchers whose names are
00:29:33
escaping me at the moment who have
00:29:35
looked at this at least from a purely
00:29:36
theoretical perspective. They looked at
00:29:38
every match and what they what they
00:29:40
basically tried to do was figure out how
00:29:42
effective each player serve was based on
00:29:45
how hard they were hitting it in that
00:29:47
match. So they would say, you know, when
00:29:49
when they were hitting at 123, they
00:29:51
would win 73%. 122 they would win, you
00:29:54
know, whatever percent and work out a
00:29:56
curve based on that relationship. So
00:29:59
they could say, you know, we don't want
00:30:00
to go all first serves, but if we could
00:30:03
go first serve and we could go 1.7
00:30:06
serve, then that's your optimum. So you
00:30:09
can you can work it out mathematically,
00:30:11
but what I don't know and what I what I
00:30:13
doubt, frankly, is that you can take
00:30:15
that information to Taylor Fritz and
00:30:17
say, "Okay, now give me your 1.7 serve."
00:30:21
>> He doesn't know what that is. He's been
00:30:22
he's been hitting the same first and
00:30:24
second serves for over a decade of his
00:30:26
life. He's perfected those deliveries.
00:30:28
So, so let me let me counter that
00:30:29
though. Taking another page from
00:30:31
baseball is they have these high uh
00:30:33
speed cameras and immediate feedback. I
00:30:35
can imagine and they've used this to to
00:30:38
essentially tailor design pitches for
00:30:40
individuals. I can imagine if you sat
00:30:42
with the with the player and say, "Okay,
00:30:44
your first serve is 125, right? And your
00:30:47
second serve is I don't know what the
00:30:48
right number is. Uh I don't even know
00:30:50
what it is. 80.
00:30:50
>> Say 105.
00:30:53
>> 100 105. I want to develop a 115 and
00:30:56
we're going to sit there with the
00:30:57
equipment until you can do it. And and
00:31:00
is that is that is that an outrageous
00:31:02
proposition? I mean, or or is it with
00:31:04
with the
00:31:05
>> Yeah,
00:31:06
>> I would say in the state world of
00:31:07
Tennessee that
00:31:08
>> I keep thinking of that Andre Agassi. I
00:31:11
don't know if it's commercial or show
00:31:12
where it's him versus Feder and he goes,
00:31:14
"Now I'm going to give you a 113. Now
00:31:16
I'm going to give you a 109." And Agassi
00:31:19
is like hitting the gun on every single
00:31:22
number he just said. Like he knows what
00:31:24
a 113 is. He knows how hard to hit. Now
00:31:26
maybe that's Agassi just fooling around
00:31:28
or cuz he's Andre Agassi and you know
00:31:30
Taylor Fritz ain't Andre Agassy.
00:31:33
>> Yeah. I mean there's only one Andre
00:31:35
Agassi for sure. I would doubt even
00:31:36
Federer could do that and Federer would
00:31:38
be the second guy I'd think of who might
00:31:39
be able to do that. Um it would be a
00:31:42
pretty out there thing to suggest to a
00:31:44
player. Um,
00:31:46
and
00:31:47
>> but it's a it's a neat idea because it
00:31:50
adopts probably the biggest revolution
00:31:52
we've seen in baseball in the last five
00:31:54
or six years. It's player development
00:31:56
using a lot of technology and it's
00:31:58
transformed some players careers. Now,
00:32:00
one question might be whether it's
00:32:02
valuable enough to be worth the effort,
00:32:05
but it's a neat hypothesis. borrowing
00:32:07
from another sport into into this sport
00:32:10
>> would be the
00:32:11
>> absolutely I mean I I would love to know
00:32:13
that someone was out there trying it. Uh
00:32:17
and maybe maybe it just needs to take a
00:32:19
little more time because I think when
00:32:20
players are really developing es
00:32:22
especially
00:32:24
well I'm not sure whether I'm going to
00:32:25
pick a gender on that but when players
00:32:27
are really developing their adult game
00:32:28
there I mean they're they're 12 or 13
00:32:30
years old. I mean there are exceptions
00:32:32
to that rule but they're figuring things
00:32:33
out when they're pretty small. So even
00:32:35
prospects we're seeing now like they
00:32:39
>> their game has been pretty much in place
00:32:40
for a long time. They're not going to
00:32:43
>> their tennis schedule doesn't allow them
00:32:44
to take 6 months and you know work out
00:32:46
their 113 serve
00:32:48
>> uh the way that a baseball player could
00:32:50
or they're certainly not going to take a
00:32:51
year off tour because you know they got
00:32:53
cut the way that a baseball player
00:32:54
might.
00:32:55
>> So it might and we might see it when you
00:32:58
know the developmental period catches up
00:33:01
with the uh with the length of time
00:33:02
we've known this stuff.
00:33:05
We're gonna need to let Jeff go. Eric is
00:33:07
asking for one more question. Eric, is
00:33:09
it gonna be a quick one? Can you jump in
00:33:10
here?
00:33:11
>> It is. It's a quick question. So,
00:33:12
Jeeoff, I would also imagine that the
00:33:14
answer has to be what was called a mix
00:33:16
strategy. And what I mean by that is if
00:33:18
I knew your second serve was always 115,
00:33:21
that isn't doing squat either. So now
00:33:23
I'd have to have the first serve. And
00:33:25
sometimes I hit it 105 in the corner.
00:33:27
Sometimes they hit it 115 cuz if
00:33:29
everybody knew Jeff Sackman was the 125
00:33:32
115 guy, they can return 115. So it
00:33:36
would have to be I mean there'd have to
00:33:38
be a mixture no matter what you do.
00:33:41
>> Yeah. And I mean to some extent there
00:33:43
already is. I mean I I was simplifying
00:33:45
to say that you know Taylor Fritz has a
00:33:47
first serve and a second serve. Taylor
00:33:48
Fritz can hit a whole ton of serves and
00:33:50
they are all pretty consistent and
00:33:51
they're all pretty reliable but it's a
00:33:54
it is still a limited number. So I mean
00:33:55
it it might be four different first
00:33:57
serves and the real magic comes from the
00:33:59
guys like Federer or Ashardy who can
00:34:01
completely disguise them. So it's like a
00:34:03
pitcher throwing four different pitchers
00:34:05
out of the same arm slot.
00:34:06
>> Uh but but that's rare and even even
00:34:11
then that that's enough variety. I mean,
00:34:12
and going back to tennis just being
00:34:14
freaking hard if you're facing Taylor
00:34:16
Fritz from the line, then if if your
00:34:19
opport your options are, you know, 125
00:34:21
that way, 118 that way, 106 with slice
00:34:25
that way. I mean, good luck, man. We we
00:34:29
don't need much strategy to just to say
00:34:31
you're probably not breaking that guy
00:34:32
today,
00:34:33
>> right? All right, Jeff. Thanks. Always a
00:34:37
always a pleasure to talk to you. um
00:34:39
wish you the best with the work that
00:34:40
you're doing and wish you um good fun
00:34:42
for the next couple weeks with the with
00:34:44
the US Open.
00:34:46
>> Thanks very much. Thanks for having me,
00:34:47
guys.
00:34:48
>> Absolutely. Jeff Sackman, the best in
00:34:50
the business in tennis analytics. You
00:34:52
can read his stuff. His blog is called
00:34:53
Heavy Top Spin and you can find it at
00:34:56
Tennis Abstract. Tennis Abstract is an
00:34:58
organization that he founded. Jeff
00:35:00
Sackman. That has been the first half of
00:35:03
Wharton Rainbow. Come back and join us
00:35:04
after the break for the second half.
00:35:07
Welcome back. Welcome back to Wharton
00:35:10
Moneyball. Welcome back to the second
00:35:12
half of this week's show. Second half of
00:35:15
a full hour of sports analytics here on
00:35:18
the Wharton podcast network. This is
00:35:21
Kade Massie hosting this week along with
00:35:23
Eric Bradlo and Audi Winer, two
00:35:25
longstanding co-hosts and colleagues of
00:35:27
mine. Shane Jensen is out this week.
00:35:29
Shane is out doing Shane things here in
00:35:32
late August. We are just off the line
00:35:35
with Jeff Sackman. Always a pleasure to
00:35:37
talk to Jeff. It's as much of a pleasure
00:35:39
to read him. If you have any interest in
00:35:40
sports analytics, you should be looking
00:35:42
at his work on tennis analytics. It's as
00:35:44
good as there is and it has been. He was
00:35:46
kind he was one of the first real sharp
00:35:49
quants in tennis. Guys, late summer, um,
00:35:53
we've got football dead ahead. We've got
00:35:55
college football week one, a full slate.
00:35:57
We'll talk a little bit about that. And
00:35:59
then we're going to have an overtime
00:36:00
segment, an extra segment with Ralph
00:36:03
Russo to talk in more depth on college
00:36:05
football after this regularly scheduled
00:36:08
show. We've got some other big stuff
00:36:09
going on though. Golf. We've been
00:36:12
talking golf off and on this summer.
00:36:15
It's a writer cup year, which makes it
00:36:17
all a little more interesting. And then
00:36:19
we've just come through the I Eric, you
00:36:21
have to remind me what they call these
00:36:22
things. The the FedEx or whatever it is
00:36:24
these days.
00:36:25
>> FedEx Cup. Y
00:36:26
>> the the FedEx Cup. But but the tour
00:36:28
championship, it's really it's not a
00:36:29
major, but it's one of the bigger non-
00:36:31
majors. And amazingly and finally, the
00:36:34
long national nightmare, long
00:36:36
international nightmare is over over
00:36:38
Tommy Fleetwood won a PGA event. Eric,
00:36:41
were you watching this thing? This was
00:36:43
the thing that they cut down to 30
00:36:44
golfers. The last round is the last
00:36:46
tournament is just 30 golfers. Fleetwood
00:36:48
got the lead, I think, on day three and
00:36:51
then he closed the day on closed the
00:36:52
deal on day four.
00:36:53
>> Yeah, I mean, I watched almost all of
00:36:55
it. Um, I thought it was great sports,
00:36:58
great TV. Um, the good news is, you
00:37:01
know, all the golfers and so, you know,
00:37:04
as you said, it is the top 30 and, uh,
00:37:07
it was a tough course, an interesting
00:37:09
course. Um, and the thing that's
00:37:11
interesting is maybe this is, you know,
00:37:13
it would be interesting to talk or, you
00:37:14
know, maybe I'll message something
00:37:16
towards Rufus because this is one of
00:37:18
those situations where I felt like if
00:37:20
you looked at Tommy Fleetwood's, whether
00:37:23
it's an ELO rating or world ranking, he
00:37:27
was probably his chances of winning were
00:37:28
probably downgraded too much because,
00:37:31
well, he had only come in second seven
00:37:34
times in the last year. All right, find
00:37:36
me another golfer other than Sheffler
00:37:39
that's had as many top five finishes as
00:37:41
Tommy Fleewood. So, because he didn't
00:37:43
get the number one, all of a sudden,
00:37:46
we're downgrading him so much. I mean,
00:37:48
to me, it was almost not certain. It's
00:37:51
never certain in golf, but I'm not
00:37:52
surprised he was in the top five. And if
00:37:54
he's in the top five, why can't he win
00:37:56
it? And so I I almost feel it was like
00:37:59
an outcome bias because he hadn't won
00:38:02
even though his world ranking was so
00:38:04
high and he was in good form that all of
00:38:07
a sudden he's not winning.
00:38:08
>> Well, that's a that's a that's great.
00:38:10
That's a great question whether people
00:38:11
overb the the story. The narrative was
00:38:14
he can't get it done. But let's be clear
00:38:15
about a couple of the stats right now.
00:38:17
Now this is post tournament, but it
00:38:19
wouldn't have been that different before
00:38:20
the tournament. Data Golf, which has the
00:38:23
most predictive publicly available world
00:38:25
rankings, much better for predicting
00:38:28
performance than the world golf
00:38:29
rankings. Data Golf has him number three
00:38:31
in the world. All right, so just behind
00:38:34
Rory, who's obviously just behind
00:38:35
Shuffer, a stroke behind Shuffler. Um,
00:38:37
the other thing is he has 30 career top
00:38:40
fives on the PGA without a win before
00:38:44
this tournament, which is the most in
00:38:46
100 years. And so that's where the
00:38:48
narrative has traction and you're
00:38:49
questioning whether okay fine but did it
00:38:52
get overb essentially. And here's my
00:38:54
empirical question for y'all. Do y'all
00:38:55
think we could model
00:38:58
whether someone plays better with the
00:39:00
lead after having won a tournament than
00:39:03
before having won a tournament or do you
00:39:05
think there's not enough data to do so?
00:39:07
This is like career level modeling as
00:39:08
opposed to you know any given
00:39:10
tournament. Do you think do you think we
00:39:12
could observe a difference? Can we test
00:39:14
and if there is a difference, find it
00:39:17
that they perform better or differently
00:39:19
with a lead after having ever clinched a
00:39:21
tournament?
00:39:23
>> Yes, I do. I mean, but it depends how
00:39:26
far you're willing to assume
00:39:27
stationarity. And I mean by, you know,
00:39:29
there's basically almost a golf tourn
00:39:31
let's say 40 weeks of the year and then
00:39:34
let's say we take the last 8 to 10 years
00:39:36
of golf tournaments. So now there's 300
00:39:38
and something observations where we've
00:39:40
had a winner. And again, if you only
00:39:42
want to model the probability of
00:39:44
winning, that's different. We could also
00:39:46
say, does the person perform better,
00:39:48
which is, you know, their world golf
00:39:50
ranking or their, you know, their data
00:39:52
golf ranking would have been the person
00:39:54
should have come in seventh. Like it
00:39:55
could be exceedences of their ranking.
00:39:57
We don't have to look just at do they
00:39:59
win more often. Do they play better once
00:40:02
they've won? Is it like a change point
00:40:04
model kind of thing? But I think the
00:40:05
answer is yes. I think there's enough
00:40:07
data. If let me just say though this is
00:40:10
probably your all other point and Audi
00:40:12
will I'm sure be happy to jump in on
00:40:13
this the effect size is probably
00:40:16
smallalish. So your question is it's not
00:40:18
can we model it of course we can model
00:40:20
it but is the effect big enough that
00:40:22
we'll see it
00:40:24
>> right I mean this goes Audi's one of
00:40:26
Audi's great lines early in our show and
00:40:28
then we kind of learned it is that he's
00:40:30
not saying that the effect doesn't exist
00:40:32
it's just too small to observe. So, he's
00:40:34
talking about a clubhouse effect, some
00:40:35
momentum. Like, sometimes these things
00:40:37
might be there, but they're just too
00:40:38
small to this.
00:40:40
>> I wasn't even thinking this is momentum,
00:40:42
but sure.
00:40:42
>> Since we're on golf, guys, since we're
00:40:44
on golf, it's time to point out that
00:40:47
even something which we know is
00:40:49
important like putting is nevertheless
00:40:53
the effect is too small to to I would
00:40:56
say know with strong statistical
00:40:58
confidence who is better and who's
00:41:00
worse.
00:41:01
That's an that's an in golf even
00:41:04
something that we can measure is is uh
00:41:06
nevertheless doesn't vary that much
00:41:08
among the players. So when you get to
00:41:10
Fleetwood in particular and you look at
00:41:12
those 30 tournaments, what did he what
00:41:14
did he come short in? I mean what was I
00:41:16
mean what aspect of his game because I
00:41:18
can imagine someone not closing because
00:41:21
you know putting just didn't come
00:41:22
through.
00:41:23
>> I love it. That's great. So this so we I
00:41:25
don't know how much we've talked about
00:41:26
this. We need to do a full segment on
00:41:28
it, but Audi has some research just over
00:41:30
the summer, I think, finally.
00:41:31
>> Yeah.
00:41:32
>> How random putting is and therefore how
00:41:35
difficult it is to say some guys are
00:41:37
actually better or worse putters. And I
00:41:39
think I mean, you could give us a quick
00:41:41
empirical observation. It's some very
00:41:43
small number of guys that you can
00:41:44
reliably say are better.
00:41:46
>> Yeah. So what so what happened with with
00:41:48
putting is that you get we get we did
00:41:50
what's called empirical bay shrinkage
00:41:52
which doesn't impose um a knowledge of a
00:41:56
standard deviation on talent but doesn't
00:41:58
we did impose a normal distribution on
00:42:00
the distribution of talent um which we
00:42:02
could we could we could argue about
00:42:04
right particularly the tales of that
00:42:05
distribution but what ends up happening
00:42:07
is that we do have in any given season
00:42:09
we we do have uh players who putt better
00:42:12
and butt worse but when we do our
00:42:13
shrinkage or empirical based shrinkage
00:42:16
those differences get really small and
00:42:19
among the golfers in any given you know
00:42:21
two or 30 hund of them we do what's
00:42:24
called an FDR which was uh we
00:42:26
essentially try to control the number of
00:42:28
falsely discovered Good.
