Speaker 1: Hey, welcome to Sign Stuff, a production of iHeartRadio I'm
Speaker 1: More Hit Cham and today we're diving into the signs
Speaker 1: of sports analytics. Can better mathematical bottles help your team win?
Speaker 1: How exactly does it work? And is it ruining the
Speaker 1: fun of the game for fans. We're going to be
Speaker 1: talking to someone who ran the numbers for a Major
Speaker 1: League Baseball team and who now publishes academically on the subject,
Speaker 1: and he's going to step us through the history of
Speaker 1: this phenomenon and how it's now spread into almost every sport,
Speaker 1: including chess and video games. Now, I recorded part of
Speaker 1: this episode during a visit to a sporting event, one
Speaker 1: of the opening games for the LA Dodgers, who happen
Speaker 1: to be the number one team in the world and
Speaker 1: who have a whole staff of mathematicians working for them.
Speaker 1: So gear up, lock in, and get ready to score
Speaker 1: as we football tackle. The question is math We're winning
Speaker 1: sports enjoy? Hey everyone, So I'm here at Dodger Stadium
Speaker 1: for opening week to watch the LA Dodgers play the
Speaker 1: Arizona Diamondbacks. Now, I'm not a huge baseball fan, but
Speaker 1: from what I read, the Dodgers are favorite to win
Speaker 1: according to the betting markets, with a probability of about
Speaker 1: sixty seven percent, meaning that the Dodgers are favorite to win.
Speaker 1: Of course, they have one of the best, if not
Speaker 1: the best player ever. I'm talking, of course about show
Speaker 1: Heyo Tani. Now, who actually determines that the Dodgers have
Speaker 1: a sixty seven percent chance to win? How's that done?
Speaker 1: Is that some guess or is there a lot of
Speaker 1: maths behind it? That's what I want to find out.
Speaker 1: But first I'm going to ask a few Dodger fans
Speaker 1: here who they think are going to win. Who do
Speaker 1: you think's gonna win? Dodgers or the Diamondbacks other Dodgers
Speaker 1: for sure, Dodgers, Rogers. I think I'm sitting in a
Speaker 1: Dodgers section. Can I ask you a question? Who do
Speaker 1: you think is gonna win? Dodgers or Dynamits?
Speaker 2: The Dodgers obviously, the Dodgers for sure.
Speaker 1: This is kind of a silly question. Who do you
Speaker 1: think is gonna win?
Speaker 3: The Dodgers for the diamonback.
Speaker 1: The Dodgers, of course, Dodgers.
Speaker 2: And this is a silly question.
Speaker 1: Okay, Clearly I wasn't going to get an unbiased opinion
Speaker 1: here among the Dodger fans, so it makes sense of
Speaker 1: all of this. I reached out to doctor Ben Baumber,
Speaker 1: a professor of statistical and data scientists at Smith College
Speaker 1: and the former statistical analyst for the New York Mets.
Speaker 1: So here's my conversation with doctor Ben Bomber. Well, thank you,
Speaker 1: doctor Bomber for joining us.
Speaker 2: Thank you so much for having me. It's a pleasure
Speaker 2: to be here.
Speaker 1: You mentioned you played for the Mets.
Speaker 2: It works for okay, did not play for so.
Speaker 1: Well, sort of, you're part of the team. Come on,
Speaker 1: you know.
Speaker 2: I started working for the New York Mets in two
Speaker 2: thousand and four as a statistical analyst, and at that
Speaker 2: time they had never had one before. I was working
Speaker 2: on PhD in mass so I was the only person
Speaker 2: doing it kind of at that pretty high technical level.
Speaker 2: And I was able for a long time to do
Speaker 2: both at the same time.
Speaker 1: Wow.
Speaker 2: But at the end of that process decided to do
Speaker 2: something else. Instead of doing sports analytics for the Mets.
Speaker 2: I do sports analytics for academia, the public journals. It's
Speaker 2: a different league, Yeah, a different league exactly.
Speaker 1: Awesome. Can you please tell us what exactly is sports analytics?
Speaker 2: Sports analytics is the use of statistics and data to
Speaker 2: think about sports, like to learn about sports, how we
Speaker 2: might play sports better or more efficiently, or who the
Speaker 2: better players are or who the better teams are. But
Speaker 2: I think for centuries people have been watching sports and
Speaker 2: trying to answer those questions for themselves. But I think
Speaker 2: has changed when you talk about sports analytics is we're
Speaker 2: actually recording the data about what's happening in those games
Speaker 2: and them we're analyzing that data in order to inform
Speaker 2: those questions.
Speaker 1: Is there a moment in history we can trace this
Speaker 1: idea too, or what is the historical origins of this idea?
Speaker 2: Yeah? So actually there is a fairly specific origin story.
Speaker 2: So you know, people have been playing sports going back
Speaker 2: to ancient Greece or whatever. But in the United States,
Speaker 2: like in eighteen seventy, people started playing baseball professionally. But
Speaker 2: just imagine, like there's people playing baseball and if you
Speaker 2: want to watch a professional baseball game, you have to
Speaker 2: go to the gate, right, there's no TV. So there
Speaker 2: was a person, a man named Henry Chadwick, had this
Speaker 2: idea of like what if I recorded some statistics about
Speaker 2: the game and then published it in the newspaper so
Speaker 2: that people like, not only do they get the score,
Speaker 2: they got a numerical summary of what happened in the game.
Speaker 2: And he called it the box score. We still have
Speaker 2: these today. You can pick up you know, USA today
Speaker 2: and look at the box scores for the baseball games.
Speaker 1: Like how many runs, how many at that all of.
Speaker 2: That, yeah, how many runs in total? And then for
Speaker 2: each of the players in the batting order, how many
Speaker 2: times did they come to bat, how many hits did
Speaker 2: they get, how many runs did they drive in? For
Speaker 2: the pitchers, how many innings did they pitch, how many
Speaker 2: strikeouts did they have, how many runs did they give up?
Speaker 2: And so for those of us who are like deep
Speaker 2: in the weeds of baseball, like you can look at
Speaker 2: a box score and basically reconstruct the entire game, like
Speaker 2: two men on and two out or whatever, and this
Speaker 2: person grounded into a double play and that end of
Speaker 2: the inning, and then.
Speaker 1: It is enough to sort of reconstruct some of the
Speaker 1: drama of the sport. Absolutely absolutely, and they started with
Speaker 1: baseball because I guess baseball was the as they say,
Speaker 1: national past time. And absolutely so they started keep drugging
Speaker 1: the box scores.
Speaker 2: Right again, this was happening in the eighteen seventies.
Speaker 1: So for the first time in known history, people started
Speaker 1: to record data about sports events, mostly so fans could
Speaker 1: follow along and know what happened in the game. And
Speaker 1: this went on for over seventy years until in nineteen
Speaker 1: forty a man named branch Ricky changed baseball and really
Speaker 1: the world history.
Speaker 2: So Bran Trickey, who was the general manager of the
Speaker 2: Brooklyn Dodgers, made like two of the more important and
Speaker 2: long lasting contributions to the way that baseball was played
Speaker 2: in the United States. One was he signed Jackie Robinson,
Speaker 2: and that broke the collar barrier major league baseball.
Speaker 1: Wow.
Speaker 2: The second was that he hired a man named Alan
Speaker 2: Roth to be the first full time statistician to work
Speaker 2: for a Major League baseball team. Really yeah, And there's
Speaker 2: a great article. Life Magazine did a whole spread about
Speaker 2: Alan Roth and what he was doing with the Brooklyn
Speaker 2: Dodgers and all these equations written on the blackboard behind him.
Speaker 1: What do you think with the mentality of that first
Speaker 1: baseball owner who hired that that stition. I mean, obviously
Speaker 1: he seemed to be sort of a groundbreaking type of
Speaker 1: thinking person, you know, to hire Jackie Robinson, But what
Speaker 1: do you think he was thinking at the time when
Speaker 1: he hired the statistician, How do.
Speaker 2: I win more games? You know, it's really pretty simple
Speaker 2: because fundamentally it's just about like how do we do
Speaker 2: this better? And in sports you have very clear outcomes.
Speaker 2: It's wins and losses. And you know, brand Shreck he believed,
Speaker 2: I'm sure he was correct at that time that like,
Speaker 2: one way that I can win more games is by
Speaker 2: understanding how baseball works better so that I can find
Speaker 2: players who do things that help us win, especially when
Speaker 2: those things are maybe overlooked by these other teams who
Speaker 2: don't have this knowledge that I have acquired or developed.
Speaker 1: Oh I see. He maybe asked like should I hire
Speaker 1: this player or that player, or should we do these kinds
Speaker 1: of places or that kind of play, And somebody told
Speaker 1: him what to do, and he said, no, I don't
Speaker 1: believe you, like, show me the data exactly.
Speaker 2: And so just to give you a simple example, if
Speaker 2: you've got to run around first base and second base
Speaker 2: is open, the question is, you know, how do you
Speaker 2: get that runner on first to second. One strategy is
Speaker 2: to bond, which means they don't swing at the ball
Speaker 2: and Triit just kind of hold the bat there and
Speaker 2: try to like have the ball like fall kind of
Speaker 2: fleck in front of the catcher.
Speaker 1: Like they just tapped the ball. And it's almost like
Speaker 1: a sacrifice play, right exactly.
Speaker 2: It's called a sacrifice bunt. And so in some ways
Speaker 2: you've gained something because you moved that runner across, but
Speaker 2: in other ways you've lost something because now an out
Speaker 2: has occurred.
Speaker 1: Right right? Is it worth it? Is it actually a
Speaker 1: good idea? Is the question?
Speaker 2: Exactly? And so up until Alan Roth, basically people had
Speaker 2: been trying to keep track of like, well, how does
Speaker 2: it work?
Speaker 1: I don't all, like, I've been watching baseball for twenty years,
Speaker 1: and that's always a bad idea.
Speaker 2: Exactly anecdotal evidence, you know. So because Henry Tradwick and
Speaker 2: other people had, you know, been collecting data about baseball
Speaker 2: for at that point already fifty or seventy years or whatever,
Speaker 2: but we did thousands of games, people started to sort
Speaker 2: of pull it apart and be like, does this actually
Speaker 2: pay off? Do we actually score more runs if we
Speaker 2: do this relative to the times that we don't do this.
Speaker 2: So this was the type of analysis that people and.
Speaker 1: Did that work. Did People were like, oh, my good
Speaker 1: after that.
Speaker 2: I mean, the Dodgers won a lot of games. Yeah,
Speaker 2: that's a good research question actually, But certainly the Dodgers
Speaker 2: were a very good team through the late forties and
Speaker 2: through the fifties. You know, fifty five the Dodgers finally
Speaker 2: beat the Yankees, and yeah, the Dodgers today are the
Speaker 2: dominant team in Major League Baseball for sure.
Speaker 1: Yeah. Yeah, so it worked back then. Did that cause
Speaker 1: other teams to start looking at stats? Also?
Speaker 2: I think the short answer is it doesn't appear to
Speaker 2: be the case. I see, there's not a lot of
Speaker 2: historical record for people being like full time employees statistical
Speaker 2: analysts for major League Baseball teams. Not so much between
Speaker 2: the forties and the eighties, nineties, two thousands.
Speaker 1: Yeah, yes, despite the then Brooklyn Dodgers having a math
Speaker 1: person on the team and having a winning streak, people
Speaker 1: in sports were still not convinced. We'll get into what
Speaker 1: that was a little later in the program. But all
Speaker 1: of that changed with the publication of a book called Moneyball.
Speaker 1: You might have heard of it or maybe seeing the
Speaker 1: movie based on it, starring Brad Pitt. It's about a
Speaker 1: struggling baseball team. The two thousand and two Oakland A's
Speaker 1: who were able to get to the World Series playoffs
Speaker 1: despite having a third of the money that other larger
Speaker 1: teams had, and they did it by you guessed it,
Speaker 1: using math. It all started with an amateur baseball fan
Speaker 1: in Kansas named Bill James.
Speaker 2: Bill James is known as kind of like the godfather
Speaker 2: of baseball analytics. Uh huh. Literally a night watchman out
Speaker 2: a pork and beans factory in Kansas, uh huh. You
Speaker 2: know it's a very smart guy, loved baseball, but somehow
Speaker 2: he was the person who probably did the most to
Speaker 2: popularize the ideas in baseball analytics. So he started looking
Speaker 2: at data, and he started writing about it, and then
Speaker 2: he started publishing these newsletters that contained like tons of ideas,
Speaker 2: some of which became these kind of revolutionary ideas like
Speaker 2: what it's not just the data being valuable for itself
Speaker 2: or like in its own right, It's that the data
Speaker 2: is a mechanism through which these very creative, intelligent people
Speaker 2: were like learning new things about the game. It's those ideas,
Speaker 2: like that's what led to moneyball. It wasn't like Billy
Speaker 2: being woke up one day and was like, hey, we
Speaker 2: should look at data. It's like he read Bill James,
Speaker 2: and like Bill James was the one who was showing
Speaker 2: sort of like what this data could be, like how
Speaker 2: it could inform your understanding of the game.
Speaker 1: Okay, here's one example of a Bill James idea. For
Speaker 1: most of baseball history, people cared about a batters RBI
Speaker 1: or runs batted in. It's a measure of how often
Speaker 1: a team scores a point whenever a batter goes up
Speaker 1: to hit the ball. If you look up the list
Speaker 1: of the people with the top career RBI numbers, you'll
Speaker 1: see names like Hank Aaron, Babe Ruth, Alex Rodriguez, or
Speaker 1: a rod Ty Cobb. You know, legends. And so the
Speaker 1: higher your RBI, the better people thought you were, and
Speaker 1: the more money someone would have to pay you to
Speaker 1: be on their team. But actually, Bill James and then
Speaker 1: the Oakland A's figure it out that's not the best
Speaker 1: statistic to be focusing on because it kind of depends
Speaker 1: on your teammates. If you have good teammates that got
Speaker 1: on base by the time you went to bad, you're
Speaker 1: going to have a higher RBI. But looking at the
Speaker 1: data more closely, it turns out a better measure of
Speaker 1: how good a batter is is something called runs created,
Speaker 1: which is computed using a different formula that doesn't depend
Speaker 1: as much on what your teammates do. And because nobody
Speaker 1: else was looking at the statistic, the oakland As were
Speaker 1: able to get a good team for less money. And
Speaker 1: it really was then Moneyball, which is based on the
Speaker 1: work in the Oakland A's that really got people thinking like,
Speaker 1: oh my goodness, we should totally do this.
Speaker 2: Yeah. Things moved quickly after the publication Moneyball around the
Speaker 2: time early aughts mid ots, when I was working for
Speaker 2: the Mets, you know, that's when things spread throughout baseball.
Speaker 2: So two thousand and three, two thousand and four, I
Speaker 2: hamd full of teams have some person who's doing statistical
Speaker 2: analysis full time. Most teams don't. But by the time
Speaker 2: I left in twenty twelve, more than half the teams,
Speaker 2: maybe three quarters of the teams had at least somebody
Speaker 2: doing something. And now everybody and the Dodgers have like
Speaker 2: a thirty person analytics staff, you know, with like multiple
Speaker 2: people with PhDs and statistics and stuff like that.
Speaker 1: So what thirty person staff just looking at the numbers?
Speaker 2: Yeah?
Speaker 1: Wow? And part of the story here is that amount
Speaker 1: of data that is tracked in sports has also exploded
Speaker 1: in the last ten years. It started with fans keeping
Speaker 1: track of more detailed baseball statistics like play by play data,
Speaker 1: who was on base, when someone hit a home run,
Speaker 1: and where was the ball hit. For many years, this
Speaker 1: was published in books and eventually websites for basically baseball nerds.
Speaker 1: But now it's a full blown industry. There are companies
Speaker 1: that gather data about games and then sell it to
Speaker 1: the teams for their analytics, and it's getting more and
Speaker 1: more high tech. The LA Dodgers, for example, have cameras
Speaker 1: that tract the movement of every player on the field
Speaker 1: during every game, so they have data now on how
Speaker 1: far players were when they made a good catch, or
Speaker 1: where the shortstop should stand to have the optimal chance
Speaker 1: of making a double play. Nowadays, teams can even buy
Speaker 1: biomechanical data about their players, how their bodies move at
Speaker 1: three hundred times per second when they throw a pitch,
Speaker 1: sure swing their bats during live games. Also, they can
Speaker 1: figure out how their players can play better. But here's
Speaker 1: the question. Does all of this actually work? Is it
Speaker 1: actually making teams better or is it that some people
Speaker 1: say ruining the sport. When we come back, we'll answer
Speaker 1: that question and we'll talk about how this push for
Speaker 1: more math has spread to other sports like professional basketball, football,
Speaker 1: and even chess. So stay with us. We'll be right back. Hey,
Speaker 1: welcome back. So we're talking about sports analytics or how
Speaker 1: math is being used in sports. And I'm recording this
Speaker 1: from Dodger Stadium. It's the bottom of the fifth inning,
Speaker 1: so about halfway through the game, and the score is tied.
Speaker 1: The Diamondbacks took an early lead in the game, but
Speaker 1: then the Dodgers had an amazing third inning with two
Speaker 1: home runs with people on base, but then the Diamondbacks
Speaker 1: caught up on the fourth inning. So it's a closed game,
Speaker 1: and the under fans around me are not as confident
Speaker 1: as they were earlier. Okay, so far, we've talked a
Speaker 1: little bit about the history of sports analytics, how it
Speaker 1: started in baseball, and now we're going to talk about
Speaker 1: this idea of using statistics and math to play better
Speaker 1: at games has spread to other sports, including, if you
Speaker 1: believe it, chess and the sports. Here's doctor Ben Bauer. Okay,
Speaker 1: so you're saying now it's pervasive in baseball, would you
Speaker 1: say every team now has a sort of a statistics team.
Speaker 2: Yeah, for sure. Every team has a dedicated analytics presence
Speaker 2: for sure.
Speaker 1: And just for baseball, does it work, Like it must
Speaker 1: be worth it for them to hire thirty people.
Speaker 2: So one thing to keep in mind is that statistical
Speaker 2: analysts make a tiny fraction of what players make, right,
Speaker 2: And so it's like, if you can make better decisions
Speaker 2: about even one player, well, like you can pay a
Speaker 2: lot of analysts to help you make that better decision, right.
Speaker 1: I think what you're saying is that statisticians need agents, Yeah,
Speaker 1: and I think we do. They seem very valuable exactly
Speaker 1: free agency. But then the other part is you're working
Speaker 1: in the zero sum system, right, because games are either
Speaker 1: one or lost. So if my team gets better at
Speaker 1: analytics and then we play better baseball, like, we're going
Speaker 1: to win more games in the short term, But then
Speaker 1: eventually other teams are going to figure out what we're
Speaker 1: doing and they're going to catch up to us, and
Speaker 1: so you have this kind of like cat and mouse game.
Speaker 1: And you know, so when you say, like does analytics work, yes,
Speaker 1: it works, but like there is an aspect of like
Speaker 1: what works only works for a short period of time
Speaker 1: before everyone else catches on, and then you have to
Speaker 1: like figure out the next thing I see, And at
Speaker 1: that point you're kind of locked into having a statistic
Speaker 1: team because if you didn't, then you would fall behind
Speaker 1: everyone else.
Speaker 2: That's exactly right, and that's certainly what happened through the
Speaker 2: two thousands and the two thousand tens. Wow.
Speaker 1: Yeah, okay, so that's baseball. But now it's gone on
Speaker 1: to other sports after the success in baseball.
Speaker 2: Absolutely, yeah, I would say the spread is not as
Speaker 2: wide or as deep as it is in baseball, in
Speaker 2: part because baseball is kind of fundamentally different than other
Speaker 2: sports in the way that baseball has very discrete actions.
Speaker 2: So it's just like the nature of the game is different. However,
Speaker 2: yes it has spread to other sports, and yes NBA teams,
Speaker 2: NFL teams, and NHL teams in soccer leagues in Europe,
Speaker 2: and yes there are people doing statistical analysis for all
Speaker 2: those teams at some level. And so in basketball you
Speaker 2: have seen things like the way the teams are shooting
Speaker 2: three pointers these days. I mean, when I was growing up,
Speaker 2: our whole offensive strategy was let's get the ball into
Speaker 2: the posts so that the tall players can put it
Speaker 2: in the basket, because that's the best way for us
Speaker 2: to score points. Right.
Speaker 1: Uh.
Speaker 2: Now, what they're doing, and this is definitely through analytics, right,
Speaker 2: is that they realize that, well, okay, if we get
Speaker 2: the ball down clost to the basket and somebody puts
Speaker 2: it in, what's the expected value of that shot?
Speaker 1: Two points?
Speaker 2: It's two points. And let's say if it's close to
Speaker 2: the basket, like I'm going to miss a couple, but
Speaker 2: like I basically never missed, so like maybe ninety five
Speaker 2: percent of those I make, So that's one point nine
Speaker 2: expected points, right, Okay, But now if I shoot a
Speaker 2: three pointer, it's for three points, so I only have
Speaker 2: to make you know, like I f ix seventy percent,
Speaker 2: that's two point one expected points.
Speaker 1: Uh huh.
Speaker 2: And these guys can make if they're open, nobody's there.
Speaker 2: You know, they're making eighty percent of us. So a
Speaker 2: three pointer becomes a much more attractive shot if the
Speaker 2: probability of you're making that shot is sufficiently And so
Speaker 2: that has absolutely changed the way that basketball's play. Teams
Speaker 2: are now leaning more on three pointers absolutely, and that's
Speaker 2: kind of like another one of those hidden moves that
Speaker 2: was in the data, but nobody really believed because you know,
Speaker 2: we want to see Michael Jordan's dunk the ball. Absolutely,
Speaker 2: I think the shooting percentages have changed and that has
Speaker 2: led to the evolution of the game that we see today.
Speaker 1: But I wonder if it's sort of like an arms
Speaker 1: race there too, because now if everybody is aiming for
Speaker 1: three pointers, then everyone's going to adapt their defense to
Speaker 1: block those three pointers for sure.
Speaker 2: I mean, this is a big part of like in baseball,
Speaker 2: how analytics is you know, ruining.
Speaker 1: Baseball because like, if I go to baseball, I want
Speaker 1: to see people hitting the ball and making dramatic plays,
Speaker 1: not like, oh, you got walked. That's not as exciting.
Speaker 2: Exactly, And that is more or less exactly. What has
Speaker 2: happened over the last fifteen or twenty years is that
Speaker 2: people like me and people who are doing jobs similar
Speaker 2: to me sort of figured out, well, if we want
Speaker 2: to win more games, like we need to draw more walks,
Speaker 2: then we're not going to steal so many bases because
Speaker 2: that turns out to be pretty risky. But it led
Speaker 2: to a style of play that a lot of people,
Speaker 2: including myself, find less interesting to.
Speaker 1: Watch because it's prioritizing the long term goals that the game,
Speaker 1: not the moment to moment excitement.
Speaker 2: Absolutely, it's prioritizing winning the game, not making the game entertaining.
Speaker 2: I mean, this is what people are talking about with
Speaker 2: basketball and how it's the ruining basketball. They're talking about,
Speaker 2: you know, instituting a four point.
Speaker 1: Shot like from the half court kind of.
Speaker 3: Yeah, something like that, Like basketball is going to become
Speaker 3: people just standing around the middle of the court, basket
Speaker 3: from the middle of the court.
Speaker 1: Oh, I see, and that's not basketball. That's if you're
Speaker 1: a fan, you'd be like, well, it's not basketball as
Speaker 1: we know it. Could you say anything kind of about
Speaker 1: how it has spread to other sports?
Speaker 2: Yeah. One of my co authors on the paper that
Speaker 2: we wrote a couple of years ago, Michael Lopez, was
Speaker 2: hired by the National Football League a few years ago
Speaker 2: to become their director of sports Analytics for the league.
Speaker 2: And so I think you had baseball kind of like
Speaker 2: leading the way. I think basketball came in, you know,
Speaker 2: sort of after that, and I think the NFL husband
Speaker 2: after that.
Speaker 1: Okay, do you have any examples of it?
Speaker 2: Yeah, Well, the thing that has attracted the most attention
Speaker 2: is when to punt and when to go for it
Speaker 2: on fourth down. So you know, in American football, yet
Speaker 2: four downs to advance the ball ten yards, and if
Speaker 2: you are able to do that, then you get another
Speaker 2: to try again. But if you don't, it's the other
Speaker 2: team's ball. And so what most football teams will do
Speaker 2: is if it's fourth down and the ball is very close,
Speaker 2: then like maybe they're going to try to get that
Speaker 2: extra yard or two and keep going. But if not,
Speaker 2: if it's like fourth down and eight yards to go,
Speaker 2: then in their minds they're like, well, chances are we're
Speaker 2: not going to make it. So if we give the
Speaker 2: other team the ball here, they're going to be in
Speaker 2: a position to maybe score against us. So what we're
Speaker 2: going to do instead is we're going to kick the
Speaker 2: ball as far as we can down the field, and
Speaker 2: then even though we will have given the other team
Speaker 2: the ball, we will have sent them all the way back,
Speaker 2: you know, as far as we can. That's called a punt.
Speaker 2: And then so the question becomes when should you go
Speaker 2: for it on fourth down and when should you not?
Speaker 2: And you know, so statistical analysts started looking at this,
Speaker 2: and what they found was that generally teams were overly conservative,
Speaker 2: that is, they did not go for it on fourth
Speaker 2: down as often as the statistical analysis would suggest was optimal.
Speaker 1: I see they said it chickened out, they're trying to
Speaker 1: go for it.
Speaker 2: Well, yes, so that's one of them prominent statistical analysis
Speaker 2: contributions to football.
Speaker 1: I see, general teams aren't going for it more, people
Speaker 1: are putting it less.
Speaker 2: Yes, there's been a movement definitely towards going for it more.
Speaker 2: And now a lot of times they'll even have like
Speaker 2: a little on screen graphic that's telling you, you know,
Speaker 2: whether the team suppos to go for it or not.
Speaker 1: Whoa, it's part of the broadcast.
Speaker 2: Yeah, it's part of the broadcast.
Speaker 1: Like there's an analytic team saying, oh, you know, every
Speaker 1: time it's fourth and down between these two teams, they.
Speaker 2: Should go for it exactly.
Speaker 1: And in this case, it's sort of something fans could
Speaker 1: agree on, right, Like fans want to see more people
Speaker 1: going for it.
Speaker 2: Yeah, that's a good point going forward on fourth down.
Speaker 2: It's exciting, probably more exciting than punting on fourth down.
Speaker 2: Don't remember exactly when this was, but sometime in the
Speaker 2: Belichick era, but it was a big game between the
Speaker 2: Patriots and the Colts and the Patriots had a fourth
Speaker 2: and whatever, and they went for it and they didn't
Speaker 2: get it, and Peyton Manning's team got the ball back
Speaker 2: and then they went and scored. Oh you know, everybody
Speaker 2: was talking about how stupid the Patriots were. And so
Speaker 2: it was another one of these cases where a team
Speaker 2: tried to pursue the analytically optimal strategy and a backfired
Speaker 2: on them, and the sort of media and fan backlash
Speaker 2: sort of overwhelmed all the times that maybe they did
Speaker 2: go for it in other situations and made.
Speaker 1: It it's like, you can't win with sports fans, can you, Yeah, right,
Speaker 1: right right? And you said it spread into even things
Speaker 1: like esports and chess. Yeah.
Speaker 2: The concept of rating systems in chess very much goes
Speaker 2: back many years. So things like ELO ratings were specifically
Speaker 2: designed for chess.
Speaker 1: Uh the quickest side here. An ELO rating, named after
Speaker 1: the physicists and chess player arped Elo who invented it,
Speaker 1: basically tells you how good you are at chess. A
Speaker 1: lot of people play chess online these days, especially young people,
Speaker 1: and so you might have had your kids or your
Speaker 1: younger cousins talk about their ELO rating. For example, the
Speaker 1: top chess players in the world have an ELO ranking
Speaker 1: of about twenty eight hundred, whereas the beginner would start
Speaker 1: it zero. But here's the thing. Your ELO rating is
Speaker 1: not just where you stand relative to other players. The
Speaker 1: formula for it actually tells you the probability of who's
Speaker 1: going to win between two players. So you can use
Speaker 1: it for say, figuring out exactly how much to bed
Speaker 1: on a chess championship match, or how much money to
Speaker 1: pay a chess player to be their sponsor. And yes,
Speaker 1: these days chess players have sponsorship deals. But the same
Speaker 1: idea is also used in other sports like tennis.
Speaker 2: Like if you think about tennis versus chess from an
Speaker 2: analytical player rating system perspective, like the same thing, right,
Speaker 2: it's I see this person plays this person. They each
Speaker 2: have a rating before the match, and somebody wins the match,
Speaker 2: and then they each have a rating after the match.
Speaker 2: What are the ramifications of the particular modeling choices that
Speaker 2: we make when we use those rating systems?
Speaker 1: I see? And esports? What do you know about the
Speaker 1: using esport? Another quickest site here in case you didn't know.
Speaker 1: Esports or electronic sports are a thing that's where people
Speaker 1: compete on video games. League of Legends, Valoriant, counter Strike.
Speaker 1: These are hugely popular competitions, with millions of viewers and
Speaker 1: billions of dollars in prize money.
Speaker 2: I think what's really interesting about esports is that in
Speaker 2: all the things that we've talked about so far, the
Speaker 2: game exists and then we have decided to collect data
Speaker 2: about it. But in esports, that data is like inherently
Speaker 2: part of the game itself because the games are happening
Speaker 2: on the computer. Ah, so the computer is already keeping
Speaker 2: track of all the things. You know, who's killing whom
Speaker 2: and who's this portion of the board and how many WHOA.
Speaker 1: It's built into the game where the game itself is
Speaker 1: tracking every possible statistic.
Speaker 2: That's how it works. I'm not much of any sports
Speaker 2: participant in myself, but the papers that I read on
Speaker 2: this subject a little better couple. Their approach is sort
Speaker 2: of similar to what we've talked about with bisball or
Speaker 2: any of these parts. It's sort of like what are
Speaker 2: the best strategies for winning the game? And we've got
Speaker 2: now millions and millions of games that people are playing
Speaker 2: all the time, and we can analyze that data to
Speaker 2: figure out, like, what are the strategies that pay off
Speaker 2: for people. You know, which of these strategies tends to
Speaker 2: produce the most kills or produce the most wills?
Speaker 1: And you know, gosh, I don't know about you, but
Speaker 1: I can't wait for the sequel to the movie Moneyball
Speaker 1: where it's just Brad Pitt sitting on his sofa playing
Speaker 1: Call of Duty all day. Okay, So that's the use
Speaker 1: of math in sports. As we mentioned, it's the kind
Speaker 1: of thing that you need people with PhDs in statistical
Speaker 1: science to really compete in sports. Analytics uses a wide
Speaker 1: range of mathematical models, including regression models, Bayesian inference, facial
Speaker 1: statistics in more and more of these days. AI. But
Speaker 1: here's the big plot twist or upset to use sports lingo.
Speaker 1: It turns out all these models are still not the
Speaker 1: most accurate way of predicting who's going to win a game.
Speaker 1: There is another indicator which at its core has almost
Speaker 1: nothing to do with math. So when we come back,
Speaker 1: we'll talk about what this method is, and I think
Speaker 1: you're going to be shocked to find out what it is.
Speaker 1: Oh and also we're going to find out if the
Speaker 1: Dodgers won the game I went to after all, So
Speaker 1: stay with us till the last inning. We'll be right back. Hey,
Speaker 1: welcome back. I'm here watching the LA Dodgers play the
Speaker 1: Arizona Diamondbacks and it's the top of the last inning
Speaker 1: and the score is Dodgers five, Arizona Diamondbacks four. Dodgers
Speaker 1: are up by one point. But we're talking about sports analytics,
Speaker 1: or the idea of using math and science to help
Speaker 1: teams win at sports, and so far we're talked about
Speaker 1: where this idea came from and how it spread to
Speaker 1: almost every sport. Now, I don't know who's going to win.
Speaker 1: Anything can happen in the next few minutes. But according
Speaker 1: to doctor Balmer, there is a way to almost perfectly predict,
Speaker 1: at least from a statistical point of view, exactly who
Speaker 1: has a better chance at winning. And it has almost
Speaker 1: nothing to do with mathematical models. The fourth idea, what's
Speaker 1: the fourth idea?
Speaker 2: Oh yeah, betting my At a certain point, you get
Speaker 2: down to the fact that the best estimate that we
Speaker 2: as human society have of estimating who's going to win
Speaker 2: a particular game are the betting market oughts.
Speaker 1: Is that right, that's the best estimate?
Speaker 2: Yes, this is a result that has been corroborated time
Speaker 2: and time again.
Speaker 1: But who sets those betting odds?
Speaker 2: People like me that work for sports gambling outfits. So
Speaker 2: they set the lines, but then based on the money
Speaker 2: that people bet, the lines can change, and so by
Speaker 2: the time the game actually starts, you have a sort
Speaker 2: of reflection of humanity's collective wisdom about who's going to
Speaker 2: win this game.
Speaker 1: That blew me away. You're relying on people's intuition because
Speaker 1: the average sports better doesn't have a math, they don't
Speaker 1: have access to the data, They just have a feeling.
Speaker 2: Well, it's this notion of wisdom of the crowds.
Speaker 1: Uh huh.
Speaker 2: A lot of statistics is just like the average of
Speaker 2: a lot of things is better than any one person's
Speaker 2: one guess, you know, uh huh. And this is just that.
Speaker 2: But with money at stake, which is where people tend to,
Speaker 2: you know, use all of their collective wherewithal to make
Speaker 2: the best estimate.
Speaker 1: I see, there's a lot of noise, Like you never
Speaker 1: trust one sports betting guide to their hunch, but if
Speaker 1: you have a million of them, then collectively they sort
Speaker 1: of have sort of absorbed all this data in their
Speaker 1: squishy brains and have somehow projected that into their model
Speaker 1: that averages out and somehow that gives you an estimate
Speaker 1: and you're saying that's better than anything we can come
Speaker 1: up with. Yes, what does that mean that it's better?
Speaker 1: Like over time, it's more accurate over time. If you
Speaker 1: say that it's a forty seven percent chance that this
Speaker 1: is going to happen, that it actually turns out to
Speaker 1: be a forty seven like forty seven times out of
Speaker 1: one hundred in the future, like they actually win, They'll
Speaker 1: be right.
Speaker 2: It's calibrated. It's well calibrated.
Speaker 1: Oh the time.
Speaker 2: It's like, if the betting odds imply that the Cardinals
Speaker 2: have a forty seven percent chance of beating the Padres,
Speaker 2: then if you were to take all the games in
Speaker 2: which the odds were the same as that game, uh huh,
Speaker 2: the team I guess it would be the underdog in
Speaker 2: this case, they would win forty seven percent of those games. Wo.
Speaker 2: That's wild. It is wild. But it's also like it
Speaker 2: has to be that way, because if it wasn't that way,
Speaker 2: then you'd have a whole bunch of people who would
Speaker 2: figure that out and they would bet on the other team,
Speaker 2: and that would move the line right that's incredible.
Speaker 1: So you're almost saying that the wisdom of the crowd
Speaker 1: is better than someone with a PhD in statistics.
Speaker 2: Yeah for sure. Well, because there's a bunch of people
Speaker 2: with PH's and statistics who are part of the crowd.
Speaker 1: Oh, I see, that's part of it.
Speaker 2: It's like you got all those people, and you've got
Speaker 2: a whole bunch of other people.
Speaker 1: Just fans. Fans who knows who won in the last
Speaker 1: one hundred games?
Speaker 2: Maybe exactly.
Speaker 1: Wow, that's wild. Okay, we have only a few minutes.
Speaker 1: I'm going to a Dodgers game this evening.
Speaker 2: Oh, wonderful.
Speaker 1: What should I look out for? What should I expect?
Speaker 2: I'm so excited for you. I love Dodger Stadium. It's
Speaker 2: a great place to watch Basaka and enjoy the weather
Speaker 2: and just enjoy the twilight.
Speaker 1: Well we have show hey uh badding tonight.
Speaker 2: Well you'll watch the greatest baseball player of all time?
Speaker 3: Then.
Speaker 2: Wow, I mean, the Dodgers are the best team in baseball.
Speaker 2: I don't think anyone really doubts that. You know, they
Speaker 2: have used analytics, they have gone deeper, and they have
Speaker 2: put the resources behind it. So it's like moneyball is
Speaker 2: sort of how do you win more games with less money?
Speaker 2: But the Dodgers are doing moneyball with money.
Speaker 1: It's like money moneyball. Yeah, okay, looked at a CBS
Speaker 1: sport says the Dodgers are favorite to win, okay, minus
Speaker 1: two sixty six favorite on the money line.
Speaker 2: So, and I don't know that I'm going to be
Speaker 2: able to do this off the top of my head,
Speaker 2: but like, you plug that number into a fairly simple formula,
Speaker 2: and that's going to tell you that the Dodgers have
Speaker 2: a sixty of winning this game.
Speaker 1: Okay.
Speaker 2: And so now that gives us a sports analyst the
Speaker 2: most accurate prediction for what's going to happen in this game.
Speaker 1: All right, we'll see how it pans out.
Speaker 2: Then, yeah, exactly, all right.
Speaker 1: Did the Dodgers win after all? Here's the audio of
Speaker 1: the last few moments of the game when, Yeah, the
Speaker 1: Dodgers won, just like the fans, the mathematicians, and the
Speaker 1: betting markets predicted. Thanks for joining us, see you next
Speaker 1: time you've been listening to science stuff. Production of iHeartRadio
Speaker 1: written and produced by me Or hitch Ham, edited by
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