April 9, 2026 • 36 mins
Math is a key player in every major sport these days. Is it helping teams win, or is it making games boring to watch? Jorge talks to a "Moneyball" expert to find out.
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Speaker 1 (00:01):
Hey, welcome to Sign Stuff, a production of iHeartRadio I'm
More Hit Cham and today we're diving into the signs
of sports analytics. Can better mathematical bottles help your team win?
How exactly does it work? And is it ruining the
fun of the game for fans. We're going to be
talking to someone who ran the numbers for a Major
(00:21):
League Baseball team and who now publishes academically on the subject,
and he's going to step us through the history of
this phenomenon and how it's now spread into almost every sport,
including chess and video games. Now, I recorded part of
this episode during a visit to a sporting event, one
of the opening games for the LA Dodgers, who happen
(00:43):
to be the number one team in the world and
who have a whole staff of mathematicians working for them.
So gear up, lock in, and get ready to score
as we football tackle. The question is math We're winning
sports enjoy? Hey everyone, So I'm here at Dodger Stadium
(01:10):
for opening week to watch the LA Dodgers play the
Arizona Diamondbacks. Now, I'm not a huge baseball fan, but
from what I read, the Dodgers are favorite to win
according to the betting markets, with a probability of about
sixty seven percent, meaning that the Dodgers are favorite to win.
Of course, they have one of the best, if not
(01:32):
the best player ever. I'm talking, of course about show
Heyo Tani. Now, who actually determines that the Dodgers have
a sixty seven percent chance to win? How's that done?
Is that some guess or is there a lot of
maths behind it? That's what I want to find out.
(01:53):
But first I'm going to ask a few Dodger fans
here who they think are going to win. Who do
you think's gonna win? Dodgers or the Diamondbacks other Dodgers
for sure, Dodgers, Rogers. I think I'm sitting in a
Dodgers section. Can I ask you a question? Who do
you think is gonna win? Dodgers or Dynamits?
Speaker 2 (02:08):
The Dodgers obviously, the Dodgers for sure.
Speaker 1 (02:12):
This is kind of a silly question. Who do you
think is gonna win?
Speaker 3 (02:14):
The Dodgers for the diamonback.
Speaker 1 (02:15):
The Dodgers, of course, Dodgers.
Speaker 2 (02:19):
And this is a silly question.
Speaker 1 (02:22):
Okay, Clearly I wasn't going to get an unbiased opinion
here among the Dodger fans, so it makes sense of
all of this. I reached out to doctor Ben Baumber,
a professor of statistical and data scientists at Smith College
and the former statistical analyst for the New York Mets.
So here's my conversation with doctor Ben Bomber. Well, thank you,
(02:50):
doctor Bomber for joining us.
Speaker 2 (02:51):
Thank you so much for having me. It's a pleasure
to be here.
Speaker 1 (02:54):
You mentioned you played for the Mets.
Speaker 2 (02:56):
It works for okay, did not play for so.
Speaker 1 (03:00):
Well, sort of, you're part of the team. Come on,
you know.
Speaker 2 (03:04):
I started working for the New York Mets in two
thousand and four as a statistical analyst, and at that
time they had never had one before. I was working
on PhD in mass so I was the only person
doing it kind of at that pretty high technical level.
And I was able for a long time to do
both at the same time.
Speaker 1 (03:20):
Wow.
Speaker 2 (03:21):
But at the end of that process decided to do
something else. Instead of doing sports analytics for the Mets.
I do sports analytics for academia, the public journals. It's
a different league, Yeah, a different league exactly.
Speaker 1 (03:36):
Awesome. Can you please tell us what exactly is sports analytics?
Speaker 2 (03:41):
Sports analytics is the use of statistics and data to
think about sports, like to learn about sports, how we
might play sports better or more efficiently, or who the
better players are or who the better teams are. But
I think for centuries people have been watching sports and
trying to answer those questions for themselves. But I think
has changed when you talk about sports analytics is we're
(04:02):
actually recording the data about what's happening in those games
and them we're analyzing that data in order to inform
those questions.
Speaker 1 (04:11):
Is there a moment in history we can trace this
idea too, or what is the historical origins of this idea?
Speaker 2 (04:18):
Yeah? So actually there is a fairly specific origin story.
So you know, people have been playing sports going back
to ancient Greece or whatever. But in the United States,
like in eighteen seventy, people started playing baseball professionally. But
just imagine, like there's people playing baseball and if you
want to watch a professional baseball game, you have to
(04:39):
go to the gate, right, there's no TV. So there
was a person, a man named Henry Chadwick, had this
idea of like what if I recorded some statistics about
the game and then published it in the newspaper so
that people like, not only do they get the score,
they got a numerical summary of what happened in the game.
(05:01):
And he called it the box score. We still have
these today. You can pick up you know, USA today
and look at the box scores for the baseball games.
Speaker 1 (05:08):
Like how many runs, how many at that all of.
Speaker 2 (05:11):
That, yeah, how many runs in total? And then for
each of the players in the batting order, how many
times did they come to bat, how many hits did
they get, how many runs did they drive in? For
the pitchers, how many innings did they pitch, how many
strikeouts did they have, how many runs did they give up?
And so for those of us who are like deep
in the weeds of baseball, like you can look at
a box score and basically reconstruct the entire game, like
(05:33):
two men on and two out or whatever, and this
person grounded into a double play and that end of
the inning, and then.
Speaker 1 (05:38):
It is enough to sort of reconstruct some of the
drama of the sport. Absolutely absolutely, and they started with
baseball because I guess baseball was the as they say,
national past time. And absolutely so they started keep drugging
the box scores.
Speaker 2 (05:51):
Right again, this was happening in the eighteen seventies.
Speaker 1 (05:55):
So for the first time in known history, people started
to record data about sports events, mostly so fans could
follow along and know what happened in the game. And
this went on for over seventy years until in nineteen
forty a man named branch Ricky changed baseball and really
the world history.
Speaker 2 (06:17):
So Bran Trickey, who was the general manager of the
Brooklyn Dodgers, made like two of the more important and
long lasting contributions to the way that baseball was played
in the United States. One was he signed Jackie Robinson,
and that broke the collar barrier major league baseball.
Speaker 1 (06:34):
Wow.
Speaker 2 (06:35):
The second was that he hired a man named Alan
Roth to be the first full time statistician to work
for a Major League baseball team. Really yeah, And there's
a great article. Life Magazine did a whole spread about
Alan Roth and what he was doing with the Brooklyn
Dodgers and all these equations written on the blackboard behind him.
Speaker 1 (06:55):
What do you think with the mentality of that first
baseball owner who hired that that stition. I mean, obviously
he seemed to be sort of a groundbreaking type of
thinking person, you know, to hire Jackie Robinson, But what
do you think he was thinking at the time when
he hired the statistician, How do.
Speaker 2 (07:10):
I win more games? You know, it's really pretty simple
because fundamentally it's just about like how do we do
this better? And in sports you have very clear outcomes.
It's wins and losses. And you know, brand Shreck he believed,
I'm sure he was correct at that time that like,
one way that I can win more games is by
understanding how baseball works better so that I can find
(07:32):
players who do things that help us win, especially when
those things are maybe overlooked by these other teams who
don't have this knowledge that I have acquired or developed.
Speaker 1 (07:43):
Oh I see. He maybe asked like should I hire
this player or that player, or should we do these kinds
of places or that kind of play, And somebody told
him what to do, and he said, no, I don't
believe you, like, show me the data exactly.
Speaker 2 (07:54):
And so just to give you a simple example, if
you've got to run around first base and second base
is open, the question is, you know, how do you
get that runner on first to second. One strategy is
to bond, which means they don't swing at the ball
and Triit just kind of hold the bat there and
try to like have the ball like fall kind of
fleck in front of the catcher.
Speaker 1 (08:16):
Like they just tapped the ball. And it's almost like
a sacrifice play, right exactly.
Speaker 2 (08:20):
It's called a sacrifice bunt. And so in some ways
you've gained something because you moved that runner across, but
in other ways you've lost something because now an out
has occurred.
Speaker 1 (08:31):
Right right? Is it worth it? Is it actually a
good idea? Is the question?
Speaker 2 (08:35):
Exactly? And so up until Alan Roth, basically people had
been trying to keep track of like, well, how does
it work?
Speaker 1 (08:43):
I don't all, like, I've been watching baseball for twenty years,
and that's always a bad idea.
Speaker 2 (08:48):
Exactly anecdotal evidence, you know. So because Henry Tradwick and
other people had, you know, been collecting data about baseball
for at that point already fifty or seventy years or whatever,
but we did thousands of games, people started to sort
of pull it apart and be like, does this actually
pay off? Do we actually score more runs if we
do this relative to the times that we don't do this.
(09:11):
So this was the type of analysis that people and.
Speaker 1 (09:14):
Did that work. Did People were like, oh, my good
after that.
Speaker 2 (09:18):
I mean, the Dodgers won a lot of games. Yeah,
that's a good research question actually, But certainly the Dodgers
were a very good team through the late forties and
through the fifties. You know, fifty five the Dodgers finally
beat the Yankees, and yeah, the Dodgers today are the
dominant team in Major League Baseball for sure.
Speaker 1 (09:35):
Yeah. Yeah, so it worked back then. Did that cause
other teams to start looking at stats? Also?
Speaker 2 (09:42):
I think the short answer is it doesn't appear to
be the case. I see, there's not a lot of
historical record for people being like full time employees statistical
analysts for major League Baseball teams. Not so much between
the forties and the eighties, nineties, two thousands.
Speaker 1 (09:59):
Yeah, yes, despite the then Brooklyn Dodgers having a math
person on the team and having a winning streak, people
in sports were still not convinced. We'll get into what
that was a little later in the program. But all
of that changed with the publication of a book called Moneyball.
You might have heard of it or maybe seeing the
(10:21):
movie based on it, starring Brad Pitt. It's about a
struggling baseball team. The two thousand and two Oakland A's
who were able to get to the World Series playoffs
despite having a third of the money that other larger
teams had, and they did it by you guessed it,
using math. It all started with an amateur baseball fan
(10:42):
in Kansas named Bill James.
Speaker 2 (10:46):
Bill James is known as kind of like the godfather
of baseball analytics. Uh huh. Literally a night watchman out
a pork and beans factory in Kansas, uh huh. You
know it's a very smart guy, loved baseball, but somehow
he was the person who probably did the most to
popularize the ideas in baseball analytics. So he started looking
(11:08):
at data, and he started writing about it, and then
he started publishing these newsletters that contained like tons of ideas,
some of which became these kind of revolutionary ideas like
what it's not just the data being valuable for itself
or like in its own right, It's that the data
is a mechanism through which these very creative, intelligent people
(11:30):
were like learning new things about the game. It's those ideas,
like that's what led to moneyball. It wasn't like Billy
being woke up one day and was like, hey, we
should look at data. It's like he read Bill James,
and like Bill James was the one who was showing
sort of like what this data could be, like how
it could inform your understanding of the game.
Speaker 1 (11:49):
Okay, here's one example of a Bill James idea. For
most of baseball history, people cared about a batters RBI
or runs batted in. It's a measure of how often
a team scores a point whenever a batter goes up
to hit the ball. If you look up the list
of the people with the top career RBI numbers, you'll
(12:09):
see names like Hank Aaron, Babe Ruth, Alex Rodriguez, or
a rod Ty Cobb. You know, legends. And so the
higher your RBI, the better people thought you were, and
the more money someone would have to pay you to
be on their team. But actually, Bill James and then
the Oakland A's figure it out that's not the best
(12:30):
statistic to be focusing on because it kind of depends
on your teammates. If you have good teammates that got
on base by the time you went to bad, you're
going to have a higher RBI. But looking at the
data more closely, it turns out a better measure of
how good a batter is is something called runs created,
which is computed using a different formula that doesn't depend
(12:53):
as much on what your teammates do. And because nobody
else was looking at the statistic, the oakland As were
able to get a good team for less money. And
it really was then Moneyball, which is based on the
work in the Oakland A's that really got people thinking like,
oh my goodness, we should totally do this.
Speaker 2 (13:14):
Yeah. Things moved quickly after the publication Moneyball around the
time early aughts mid ots, when I was working for
the Mets, you know, that's when things spread throughout baseball.
So two thousand and three, two thousand and four, I
hamd full of teams have some person who's doing statistical
analysis full time. Most teams don't. But by the time
(13:34):
I left in twenty twelve, more than half the teams,
maybe three quarters of the teams had at least somebody
doing something. And now everybody and the Dodgers have like
a thirty person analytics staff, you know, with like multiple
people with PhDs and statistics and stuff like that.
Speaker 1 (13:52):
So what thirty person staff just looking at the numbers?
Speaker 2 (13:56):
Yeah?
Speaker 1 (13:57):
Wow? And part of the story here is that amount
of data that is tracked in sports has also exploded
in the last ten years. It started with fans keeping
track of more detailed baseball statistics like play by play data,
who was on base, when someone hit a home run,
and where was the ball hit. For many years, this
(14:17):
was published in books and eventually websites for basically baseball nerds.
But now it's a full blown industry. There are companies
that gather data about games and then sell it to
the teams for their analytics, and it's getting more and
more high tech. The LA Dodgers, for example, have cameras
that tract the movement of every player on the field
(14:38):
during every game, so they have data now on how
far players were when they made a good catch, or
where the shortstop should stand to have the optimal chance
of making a double play. Nowadays, teams can even buy
biomechanical data about their players, how their bodies move at
three hundred times per second when they throw a pitch,
(15:00):
sure swing their bats during live games. Also, they can
figure out how their players can play better. But here's
the question. Does all of this actually work? Is it
actually making teams better or is it that some people
say ruining the sport. When we come back, we'll answer
(15:20):
that question and we'll talk about how this push for
more math has spread to other sports like professional basketball, football,
and even chess. So stay with us. We'll be right back. Hey,
(15:52):
welcome back. So we're talking about sports analytics or how
math is being used in sports. And I'm recording this
from Dodger Stadium. It's the bottom of the fifth inning,
so about halfway through the game, and the score is tied.
The Diamondbacks took an early lead in the game, but
(16:13):
then the Dodgers had an amazing third inning with two
home runs with people on base, but then the Diamondbacks
caught up on the fourth inning. So it's a closed game,
and the under fans around me are not as confident
as they were earlier. Okay, so far, we've talked a
little bit about the history of sports analytics, how it
(16:34):
started in baseball, and now we're going to talk about
this idea of using statistics and math to play better
at games has spread to other sports, including, if you
believe it, chess and the sports. Here's doctor Ben Bauer. Okay,
(16:56):
so you're saying now it's pervasive in baseball, would you
say every team now has a sort of a statistics team.
Speaker 2 (17:03):
Yeah, for sure. Every team has a dedicated analytics presence
for sure.
Speaker 1 (17:07):
And just for baseball, does it work, Like it must
be worth it for them to hire thirty people.
Speaker 2 (17:12):
So one thing to keep in mind is that statistical
analysts make a tiny fraction of what players make, right,
And so it's like, if you can make better decisions
about even one player, well, like you can pay a
lot of analysts to help you make that better decision, right.
Speaker 1 (17:31):
I think what you're saying is that statisticians need agents, Yeah,
and I think we do. They seem very valuable exactly
free agency. But then the other part is you're working
in the zero sum system, right, because games are either
one or lost. So if my team gets better at
analytics and then we play better baseball, like, we're going
(17:51):
to win more games in the short term, But then
eventually other teams are going to figure out what we're
doing and they're going to catch up to us, and
so you have this kind of like cat and mouse game.
And you know, so when you say, like does analytics work, yes,
it works, but like there is an aspect of like
what works only works for a short period of time
(18:11):
before everyone else catches on, and then you have to
like figure out the next thing I see, And at
that point you're kind of locked into having a statistic
team because if you didn't, then you would fall behind
everyone else.
Speaker 2 (18:25):
That's exactly right, and that's certainly what happened through the
two thousands and the two thousand tens. Wow.
Speaker 1 (18:29):
Yeah, okay, so that's baseball. But now it's gone on
to other sports after the success in baseball.
Speaker 2 (18:35):
Absolutely, yeah, I would say the spread is not as
wide or as deep as it is in baseball, in
part because baseball is kind of fundamentally different than other
sports in the way that baseball has very discrete actions.
So it's just like the nature of the game is different. However,
yes it has spread to other sports, and yes NBA teams,
(18:58):
NFL teams, and NHL teams in soccer leagues in Europe,
and yes there are people doing statistical analysis for all
those teams at some level. And so in basketball you
have seen things like the way the teams are shooting
three pointers these days. I mean, when I was growing up,
our whole offensive strategy was let's get the ball into
(19:19):
the posts so that the tall players can put it
in the basket, because that's the best way for us
to score points. Right.
Speaker 1 (19:25):
Uh.
Speaker 2 (19:26):
Now, what they're doing, and this is definitely through analytics, right,
is that they realize that, well, okay, if we get
the ball down clost to the basket and somebody puts
it in, what's the expected value of that shot?
Speaker 1 (19:38):
Two points?
Speaker 2 (19:39):
It's two points. And let's say if it's close to
the basket, like I'm going to miss a couple, but
like I basically never missed, so like maybe ninety five
percent of those I make, So that's one point nine
expected points, right, Okay, But now if I shoot a
three pointer, it's for three points, so I only have
to make you know, like I f ix seventy percent,
(19:59):
that's two point one expected points.
Speaker 1 (20:01):
Uh huh.
Speaker 2 (20:02):
And these guys can make if they're open, nobody's there.
You know, they're making eighty percent of us. So a
three pointer becomes a much more attractive shot if the
probability of you're making that shot is sufficiently And so
that has absolutely changed the way that basketball's play. Teams
are now leaning more on three pointers absolutely, and that's
(20:24):
kind of like another one of those hidden moves that
was in the data, but nobody really believed because you know,
we want to see Michael Jordan's dunk the ball. Absolutely,
I think the shooting percentages have changed and that has
led to the evolution of the game that we see today.
Speaker 1 (20:41):
But I wonder if it's sort of like an arms
race there too, because now if everybody is aiming for
three pointers, then everyone's going to adapt their defense to
block those three pointers for sure.
Speaker 2 (20:52):
I mean, this is a big part of like in baseball,
how analytics is you know, ruining.
Speaker 1 (20:57):
Baseball because like, if I go to baseball, I want
to see people hitting the ball and making dramatic plays,
not like, oh, you got walked. That's not as exciting.
Speaker 2 (21:05):
Exactly, And that is more or less exactly. What has
happened over the last fifteen or twenty years is that
people like me and people who are doing jobs similar
to me sort of figured out, well, if we want
to win more games, like we need to draw more walks,
then we're not going to steal so many bases because
that turns out to be pretty risky. But it led
to a style of play that a lot of people,
(21:27):
including myself, find less interesting to.
Speaker 1 (21:30):
Watch because it's prioritizing the long term goals that the game,
not the moment to moment excitement.
Speaker 2 (21:37):
Absolutely, it's prioritizing winning the game, not making the game entertaining.
I mean, this is what people are talking about with
basketball and how it's the ruining basketball. They're talking about,
you know, instituting a four point.
Speaker 1 (21:49):
Shot like from the half court kind of.
Speaker 3 (21:51):
Yeah, something like that, Like basketball is going to become
people just standing around the middle of the court, basket
from the middle of the court.
Speaker 1 (22:02):
Oh, I see, and that's not basketball. That's if you're
a fan, you'd be like, well, it's not basketball as
we know it. Could you say anything kind of about
how it has spread to other sports?
Speaker 2 (22:20):
Yeah. One of my co authors on the paper that
we wrote a couple of years ago, Michael Lopez, was
hired by the National Football League a few years ago
to become their director of sports Analytics for the league.
And so I think you had baseball kind of like
leading the way. I think basketball came in, you know,
sort of after that, and I think the NFL husband
(22:42):
after that.
Speaker 1 (22:43):
Okay, do you have any examples of it?
Speaker 2 (22:45):
Yeah, Well, the thing that has attracted the most attention
is when to punt and when to go for it
on fourth down. So you know, in American football, yet
four downs to advance the ball ten yards, and if
you are able to do that, then you get another
to try again. But if you don't, it's the other
team's ball. And so what most football teams will do
(23:07):
is if it's fourth down and the ball is very close,
then like maybe they're going to try to get that
extra yard or two and keep going. But if not,
if it's like fourth down and eight yards to go,
then in their minds they're like, well, chances are we're
not going to make it. So if we give the
other team the ball here, they're going to be in
(23:28):
a position to maybe score against us. So what we're
going to do instead is we're going to kick the
ball as far as we can down the field, and
then even though we will have given the other team
the ball, we will have sent them all the way back,
you know, as far as we can. That's called a punt.
And then so the question becomes when should you go
for it on fourth down and when should you not?
(23:49):
And you know, so statistical analysts started looking at this,
and what they found was that generally teams were overly conservative,
that is, they did not go for it on fourth
down as often as the statistical analysis would suggest was optimal.
Speaker 1 (24:06):
I see they said it chickened out, they're trying to
go for it.
Speaker 2 (24:09):
Well, yes, so that's one of them prominent statistical analysis
contributions to football.
Speaker 1 (24:15):
I see, general teams aren't going for it more, people
are putting it less.
Speaker 2 (24:19):
Yes, there's been a movement definitely towards going for it more.
And now a lot of times they'll even have like
a little on screen graphic that's telling you, you know,
whether the team suppos to go for it or not.
Speaker 1 (24:32):
Whoa, it's part of the broadcast.
Speaker 2 (24:34):
Yeah, it's part of the broadcast.
Speaker 1 (24:35):
Like there's an analytic team saying, oh, you know, every
time it's fourth and down between these two teams, they.
Speaker 2 (24:41):
Should go for it exactly.
Speaker 1 (24:43):
And in this case, it's sort of something fans could
agree on, right, Like fans want to see more people
going for it.
Speaker 2 (24:49):
Yeah, that's a good point going forward on fourth down.
It's exciting, probably more exciting than punting on fourth down.
Don't remember exactly when this was, but sometime in the
Belichick era, but it was a big game between the
Patriots and the Colts and the Patriots had a fourth
and whatever, and they went for it and they didn't
get it, and Peyton Manning's team got the ball back
(25:09):
and then they went and scored. Oh you know, everybody
was talking about how stupid the Patriots were. And so
it was another one of these cases where a team
tried to pursue the analytically optimal strategy and a backfired
on them, and the sort of media and fan backlash
sort of overwhelmed all the times that maybe they did
(25:31):
go for it in other situations and made.
Speaker 1 (25:34):
It it's like, you can't win with sports fans, can you, Yeah, right,
right right? And you said it spread into even things
like esports and chess. Yeah.
Speaker 2 (25:44):
The concept of rating systems in chess very much goes
back many years. So things like ELO ratings were specifically
designed for chess.
Speaker 1 (25:53):
Uh the quickest side here. An ELO rating, named after
the physicists and chess player arped Elo who invented it,
basically tells you how good you are at chess. A
lot of people play chess online these days, especially young people,
and so you might have had your kids or your
younger cousins talk about their ELO rating. For example, the
(26:13):
top chess players in the world have an ELO ranking
of about twenty eight hundred, whereas the beginner would start
it zero. But here's the thing. Your ELO rating is
not just where you stand relative to other players. The
formula for it actually tells you the probability of who's
going to win between two players. So you can use
(26:34):
it for say, figuring out exactly how much to bed
on a chess championship match, or how much money to
pay a chess player to be their sponsor. And yes,
these days chess players have sponsorship deals. But the same
idea is also used in other sports like tennis.
Speaker 2 (26:52):
Like if you think about tennis versus chess from an
analytical player rating system perspective, like the same thing, right,
it's I see this person plays this person. They each
have a rating before the match, and somebody wins the match,
and then they each have a rating after the match.
What are the ramifications of the particular modeling choices that
we make when we use those rating systems?
Speaker 1 (27:12):
I see? And esports? What do you know about the
using esport? Another quickest site here in case you didn't know.
Esports or electronic sports are a thing that's where people
compete on video games. League of Legends, Valoriant, counter Strike.
These are hugely popular competitions, with millions of viewers and
(27:33):
billions of dollars in prize money.
Speaker 2 (27:36):
I think what's really interesting about esports is that in
all the things that we've talked about so far, the
game exists and then we have decided to collect data
about it. But in esports, that data is like inherently
part of the game itself because the games are happening
on the computer. Ah, so the computer is already keeping
track of all the things. You know, who's killing whom
(27:59):
and who's this portion of the board and how many WHOA.
Speaker 1 (28:02):
It's built into the game where the game itself is
tracking every possible statistic.
Speaker 2 (28:09):
That's how it works. I'm not much of any sports
participant in myself, but the papers that I read on
this subject a little better couple. Their approach is sort
of similar to what we've talked about with bisball or
any of these parts. It's sort of like what are
the best strategies for winning the game? And we've got
now millions and millions of games that people are playing
all the time, and we can analyze that data to
(28:32):
figure out, like, what are the strategies that pay off
for people. You know, which of these strategies tends to
produce the most kills or produce the most wills?
Speaker 1 (28:41):
And you know, gosh, I don't know about you, but
I can't wait for the sequel to the movie Moneyball
where it's just Brad Pitt sitting on his sofa playing
Call of Duty all day. Okay, So that's the use
of math in sports. As we mentioned, it's the kind
of thing that you need people with PhDs in statistical
science to really compete in sports. Analytics uses a wide
(29:05):
range of mathematical models, including regression models, Bayesian inference, facial
statistics in more and more of these days. AI. But
here's the big plot twist or upset to use sports lingo.
It turns out all these models are still not the
most accurate way of predicting who's going to win a game.
(29:27):
There is another indicator which at its core has almost
nothing to do with math. So when we come back,
we'll talk about what this method is, and I think
you're going to be shocked to find out what it is.
Oh and also we're going to find out if the
Dodgers won the game I went to after all, So
stay with us till the last inning. We'll be right back. Hey,
(30:05):
welcome back. I'm here watching the LA Dodgers play the
Arizona Diamondbacks and it's the top of the last inning
and the score is Dodgers five, Arizona Diamondbacks four. Dodgers
are up by one point. But we're talking about sports analytics,
or the idea of using math and science to help
(30:27):
teams win at sports, and so far we're talked about
where this idea came from and how it spread to
almost every sport. Now, I don't know who's going to win.
Anything can happen in the next few minutes. But according
to doctor Balmer, there is a way to almost perfectly predict,
at least from a statistical point of view, exactly who
has a better chance at winning. And it has almost
(30:47):
nothing to do with mathematical models. The fourth idea, what's
the fourth idea?
Speaker 2 (30:58):
Oh yeah, betting my At a certain point, you get
down to the fact that the best estimate that we
as human society have of estimating who's going to win
a particular game are the betting market oughts.
Speaker 1 (31:12):
Is that right, that's the best estimate?
Speaker 2 (31:14):
Yes, this is a result that has been corroborated time
and time again.
Speaker 1 (31:19):
But who sets those betting odds?
Speaker 2 (31:21):
People like me that work for sports gambling outfits. So
they set the lines, but then based on the money
that people bet, the lines can change, and so by
the time the game actually starts, you have a sort
of reflection of humanity's collective wisdom about who's going to
win this game.
Speaker 1 (31:39):
That blew me away. You're relying on people's intuition because
the average sports better doesn't have a math, they don't
have access to the data, They just have a feeling.
Speaker 2 (31:49):
Well, it's this notion of wisdom of the crowds.
Speaker 1 (31:51):
Uh huh.
Speaker 2 (31:52):
A lot of statistics is just like the average of
a lot of things is better than any one person's
one guess, you know, uh huh. And this is just that.
But with money at stake, which is where people tend to,
you know, use all of their collective wherewithal to make
the best estimate.
Speaker 1 (32:10):
I see, there's a lot of noise, Like you never
trust one sports betting guide to their hunch, but if
you have a million of them, then collectively they sort
of have sort of absorbed all this data in their
squishy brains and have somehow projected that into their model
that averages out and somehow that gives you an estimate
(32:32):
and you're saying that's better than anything we can come
up with. Yes, what does that mean that it's better?
Like over time, it's more accurate over time. If you
say that it's a forty seven percent chance that this
is going to happen, that it actually turns out to
be a forty seven like forty seven times out of
one hundred in the future, like they actually win, They'll
(32:54):
be right.
Speaker 2 (32:55):
It's calibrated. It's well calibrated.
Speaker 1 (32:58):
Oh the time.
Speaker 2 (33:00):
It's like, if the betting odds imply that the Cardinals
have a forty seven percent chance of beating the Padres,
then if you were to take all the games in
which the odds were the same as that game, uh huh,
the team I guess it would be the underdog in
this case, they would win forty seven percent of those games. Wo.
That's wild. It is wild. But it's also like it
(33:23):
has to be that way, because if it wasn't that way,
then you'd have a whole bunch of people who would
figure that out and they would bet on the other team,
and that would move the line right that's incredible.
Speaker 1 (33:35):
So you're almost saying that the wisdom of the crowd
is better than someone with a PhD in statistics.
Speaker 2 (33:40):
Yeah for sure. Well, because there's a bunch of people
with PH's and statistics who are part of the crowd.
Speaker 1 (33:47):
Oh, I see, that's part of it.
Speaker 2 (33:48):
It's like you got all those people, and you've got
a whole bunch of other people.
Speaker 1 (33:52):
Just fans. Fans who knows who won in the last
one hundred games?
Speaker 2 (33:56):
Maybe exactly.
Speaker 1 (33:57):
Wow, that's wild. Okay, we have only a few minutes.
I'm going to a Dodgers game this evening.
Speaker 2 (34:02):
Oh, wonderful.
Speaker 1 (34:03):
What should I look out for? What should I expect?
Speaker 2 (34:05):
I'm so excited for you. I love Dodger Stadium. It's
a great place to watch Basaka and enjoy the weather
and just enjoy the twilight.
Speaker 1 (34:13):
Well we have show hey uh badding tonight.
Speaker 2 (34:16):
Well you'll watch the greatest baseball player of all time?
Speaker 3 (34:18):
Then.
Speaker 2 (34:19):
Wow, I mean, the Dodgers are the best team in baseball.
I don't think anyone really doubts that. You know, they
have used analytics, they have gone deeper, and they have
put the resources behind it. So it's like moneyball is
sort of how do you win more games with less money?
But the Dodgers are doing moneyball with money.
Speaker 1 (34:37):
It's like money moneyball. Yeah, okay, looked at a CBS
sport says the Dodgers are favorite to win, okay, minus
two sixty six favorite on the money line.
Speaker 2 (34:49):
So, and I don't know that I'm going to be
able to do this off the top of my head,
but like, you plug that number into a fairly simple formula,
and that's going to tell you that the Dodgers have
a sixty of winning this game.
Speaker 1 (35:02):
Okay.
Speaker 2 (35:02):
And so now that gives us a sports analyst the
most accurate prediction for what's going to happen in this game.
Speaker 1 (35:11):
All right, we'll see how it pans out.
Speaker 2 (35:13):
Then, yeah, exactly, all right.
Speaker 1 (35:17):
Did the Dodgers win after all? Here's the audio of
the last few moments of the game when, Yeah, the
(35:51):
Dodgers won, just like the fans, the mathematicians, and the
betting markets predicted. Thanks for joining us, see you next
time you've been listening to science stuff. Production of iHeartRadio
written and produced by me Or hitch Ham, edited by
(36:12):
Rose Seguda, Executive producer Jerry Rowland, and audio engineer and
mixer Kasey Pegram and you can follow me on social
media to search for PhD comics and the name of
your favorite platform. Be sure to subscribe to Science Stuff
on the iHeartRadio app, Apple Podcasts, or wherever you get
your podcasts, and please tell your friends
Hey, welcome to Sign Stuff, a production of iHeartRadio I'm
More Hit Cham and today we're diving into the signs
of sports analytics. Can better mathematical bottles help your team win?
How exactly does it work? And is it ruining the
fun of the game for fans. We're going to be
talking to someone who ran the numbers for a Major
(00:21):
League Baseball team and who now publishes academically on the subject,
and he's going to step us through the history of
this phenomenon and how it's now spread into almost every sport,
including chess and video games. Now, I recorded part of
this episode during a visit to a sporting event, one
of the opening games for the LA Dodgers, who happen
(00:43):
to be the number one team in the world and
who have a whole staff of mathematicians working for them.
So gear up, lock in, and get ready to score
as we football tackle. The question is math We're winning
sports enjoy? Hey everyone, So I'm here at Dodger Stadium
(01:10):
for opening week to watch the LA Dodgers play the
Arizona Diamondbacks. Now, I'm not a huge baseball fan, but
from what I read, the Dodgers are favorite to win
according to the betting markets, with a probability of about
sixty seven percent, meaning that the Dodgers are favorite to win.
Of course, they have one of the best, if not
(01:32):
the best player ever. I'm talking, of course about show
Heyo Tani. Now, who actually determines that the Dodgers have
a sixty seven percent chance to win? How's that done?
Is that some guess or is there a lot of
maths behind it? That's what I want to find out.
(01:53):
But first I'm going to ask a few Dodger fans
here who they think are going to win. Who do
you think's gonna win? Dodgers or the Diamondbacks other Dodgers
for sure, Dodgers, Rogers. I think I'm sitting in a
Dodgers section. Can I ask you a question? Who do
you think is gonna win? Dodgers or Dynamits?
Speaker 2 (02:08):
The Dodgers obviously, the Dodgers for sure.
Speaker 1 (02:12):
This is kind of a silly question. Who do you
think is gonna win?
Speaker 3 (02:14):
The Dodgers for the diamonback.
Speaker 1 (02:15):
The Dodgers, of course, Dodgers.
Speaker 2 (02:19):
And this is a silly question.
Speaker 1 (02:22):
Okay, Clearly I wasn't going to get an unbiased opinion
here among the Dodger fans, so it makes sense of
all of this. I reached out to doctor Ben Baumber,
a professor of statistical and data scientists at Smith College
and the former statistical analyst for the New York Mets.
So here's my conversation with doctor Ben Bomber. Well, thank you,
(02:50):
doctor Bomber for joining us.
Speaker 2 (02:51):
Thank you so much for having me. It's a pleasure
to be here.
Speaker 1 (02:54):
You mentioned you played for the Mets.
Speaker 2 (02:56):
It works for okay, did not play for so.
Speaker 1 (03:00):
Well, sort of, you're part of the team. Come on,
you know.
Speaker 2 (03:04):
I started working for the New York Mets in two
thousand and four as a statistical analyst, and at that
time they had never had one before. I was working
on PhD in mass so I was the only person
doing it kind of at that pretty high technical level.
And I was able for a long time to do
both at the same time.
Speaker 1 (03:20):
Wow.
Speaker 2 (03:21):
But at the end of that process decided to do
something else. Instead of doing sports analytics for the Mets.
I do sports analytics for academia, the public journals. It's
a different league, Yeah, a different league exactly.
Speaker 1 (03:36):
Awesome. Can you please tell us what exactly is sports analytics?
Speaker 2 (03:41):
Sports analytics is the use of statistics and data to
think about sports, like to learn about sports, how we
might play sports better or more efficiently, or who the
better players are or who the better teams are. But
I think for centuries people have been watching sports and
trying to answer those questions for themselves. But I think
has changed when you talk about sports analytics is we're
(04:02):
actually recording the data about what's happening in those games
and them we're analyzing that data in order to inform
those questions.
Speaker 1 (04:11):
Is there a moment in history we can trace this
idea too, or what is the historical origins of this idea?
Speaker 2 (04:18):
Yeah? So actually there is a fairly specific origin story.
So you know, people have been playing sports going back
to ancient Greece or whatever. But in the United States,
like in eighteen seventy, people started playing baseball professionally. But
just imagine, like there's people playing baseball and if you
want to watch a professional baseball game, you have to
(04:39):
go to the gate, right, there's no TV. So there
was a person, a man named Henry Chadwick, had this
idea of like what if I recorded some statistics about
the game and then published it in the newspaper so
that people like, not only do they get the score,
they got a numerical summary of what happened in the game.
(05:01):
And he called it the box score. We still have
these today. You can pick up you know, USA today
and look at the box scores for the baseball games.
Speaker 1 (05:08):
Like how many runs, how many at that all of.
Speaker 2 (05:11):
That, yeah, how many runs in total? And then for
each of the players in the batting order, how many
times did they come to bat, how many hits did
they get, how many runs did they drive in? For
the pitchers, how many innings did they pitch, how many
strikeouts did they have, how many runs did they give up?
And so for those of us who are like deep
in the weeds of baseball, like you can look at
a box score and basically reconstruct the entire game, like
(05:33):
two men on and two out or whatever, and this
person grounded into a double play and that end of
the inning, and then.
Speaker 1 (05:38):
It is enough to sort of reconstruct some of the
drama of the sport. Absolutely absolutely, and they started with
baseball because I guess baseball was the as they say,
national past time. And absolutely so they started keep drugging
the box scores.
Speaker 2 (05:51):
Right again, this was happening in the eighteen seventies.
Speaker 1 (05:55):
So for the first time in known history, people started
to record data about sports events, mostly so fans could
follow along and know what happened in the game. And
this went on for over seventy years until in nineteen
forty a man named branch Ricky changed baseball and really
the world history.
Speaker 2 (06:17):
So Bran Trickey, who was the general manager of the
Brooklyn Dodgers, made like two of the more important and
long lasting contributions to the way that baseball was played
in the United States. One was he signed Jackie Robinson,
and that broke the collar barrier major league baseball.
Speaker 1 (06:34):
Wow.
Speaker 2 (06:35):
The second was that he hired a man named Alan
Roth to be the first full time statistician to work
for a Major League baseball team. Really yeah, And there's
a great article. Life Magazine did a whole spread about
Alan Roth and what he was doing with the Brooklyn
Dodgers and all these equations written on the blackboard behind him.
Speaker 1 (06:55):
What do you think with the mentality of that first
baseball owner who hired that that stition. I mean, obviously
he seemed to be sort of a groundbreaking type of
thinking person, you know, to hire Jackie Robinson, But what
do you think he was thinking at the time when
he hired the statistician, How do.
Speaker 2 (07:10):
I win more games? You know, it's really pretty simple
because fundamentally it's just about like how do we do
this better? And in sports you have very clear outcomes.
It's wins and losses. And you know, brand Shreck he believed,
I'm sure he was correct at that time that like,
one way that I can win more games is by
understanding how baseball works better so that I can find
(07:32):
players who do things that help us win, especially when
those things are maybe overlooked by these other teams who
don't have this knowledge that I have acquired or developed.
Speaker 1 (07:43):
Oh I see. He maybe asked like should I hire
this player or that player, or should we do these kinds
of places or that kind of play, And somebody told
him what to do, and he said, no, I don't
believe you, like, show me the data exactly.
Speaker 2 (07:54):
And so just to give you a simple example, if
you've got to run around first base and second base
is open, the question is, you know, how do you
get that runner on first to second. One strategy is
to bond, which means they don't swing at the ball
and Triit just kind of hold the bat there and
try to like have the ball like fall kind of
fleck in front of the catcher.
Speaker 1 (08:16):
Like they just tapped the ball. And it's almost like
a sacrifice play, right exactly.
Speaker 2 (08:20):
It's called a sacrifice bunt. And so in some ways
you've gained something because you moved that runner across, but
in other ways you've lost something because now an out
has occurred.
Speaker 1 (08:31):
Right right? Is it worth it? Is it actually a
good idea? Is the question?
Speaker 2 (08:35):
Exactly? And so up until Alan Roth, basically people had
been trying to keep track of like, well, how does
it work?
Speaker 1 (08:43):
I don't all, like, I've been watching baseball for twenty years,
and that's always a bad idea.
Speaker 2 (08:48):
Exactly anecdotal evidence, you know. So because Henry Tradwick and
other people had, you know, been collecting data about baseball
for at that point already fifty or seventy years or whatever,
but we did thousands of games, people started to sort
of pull it apart and be like, does this actually
pay off? Do we actually score more runs if we
do this relative to the times that we don't do this.
(09:11):
So this was the type of analysis that people and.
Speaker 1 (09:14):
Did that work. Did People were like, oh, my good
after that.
Speaker 2 (09:18):
I mean, the Dodgers won a lot of games. Yeah,
that's a good research question actually, But certainly the Dodgers
were a very good team through the late forties and
through the fifties. You know, fifty five the Dodgers finally
beat the Yankees, and yeah, the Dodgers today are the
dominant team in Major League Baseball for sure.
Speaker 1 (09:35):
Yeah. Yeah, so it worked back then. Did that cause
other teams to start looking at stats? Also?
Speaker 2 (09:42):
I think the short answer is it doesn't appear to
be the case. I see, there's not a lot of
historical record for people being like full time employees statistical
analysts for major League Baseball teams. Not so much between
the forties and the eighties, nineties, two thousands.
Speaker 1 (09:59):
Yeah, yes, despite the then Brooklyn Dodgers having a math
person on the team and having a winning streak, people
in sports were still not convinced. We'll get into what
that was a little later in the program. But all
of that changed with the publication of a book called Moneyball.
You might have heard of it or maybe seeing the
(10:21):
movie based on it, starring Brad Pitt. It's about a
struggling baseball team. The two thousand and two Oakland A's
who were able to get to the World Series playoffs
despite having a third of the money that other larger
teams had, and they did it by you guessed it,
using math. It all started with an amateur baseball fan
(10:42):
in Kansas named Bill James.
Speaker 2 (10:46):
Bill James is known as kind of like the godfather
of baseball analytics. Uh huh. Literally a night watchman out
a pork and beans factory in Kansas, uh huh. You
know it's a very smart guy, loved baseball, but somehow
he was the person who probably did the most to
popularize the ideas in baseball analytics. So he started looking
(11:08):
at data, and he started writing about it, and then
he started publishing these newsletters that contained like tons of ideas,
some of which became these kind of revolutionary ideas like
what it's not just the data being valuable for itself
or like in its own right, It's that the data
is a mechanism through which these very creative, intelligent people
(11:30):
were like learning new things about the game. It's those ideas,
like that's what led to moneyball. It wasn't like Billy
being woke up one day and was like, hey, we
should look at data. It's like he read Bill James,
and like Bill James was the one who was showing
sort of like what this data could be, like how
it could inform your understanding of the game.
Speaker 1 (11:49):
Okay, here's one example of a Bill James idea. For
most of baseball history, people cared about a batters RBI
or runs batted in. It's a measure of how often
a team scores a point whenever a batter goes up
to hit the ball. If you look up the list
of the people with the top career RBI numbers, you'll
(12:09):
see names like Hank Aaron, Babe Ruth, Alex Rodriguez, or
a rod Ty Cobb. You know, legends. And so the
higher your RBI, the better people thought you were, and
the more money someone would have to pay you to
be on their team. But actually, Bill James and then
the Oakland A's figure it out that's not the best
(12:30):
statistic to be focusing on because it kind of depends
on your teammates. If you have good teammates that got
on base by the time you went to bad, you're
going to have a higher RBI. But looking at the
data more closely, it turns out a better measure of
how good a batter is is something called runs created,
which is computed using a different formula that doesn't depend
(12:53):
as much on what your teammates do. And because nobody
else was looking at the statistic, the oakland As were
able to get a good team for less money. And
it really was then Moneyball, which is based on the
work in the Oakland A's that really got people thinking like,
oh my goodness, we should totally do this.
Speaker 2 (13:14):
Yeah. Things moved quickly after the publication Moneyball around the
time early aughts mid ots, when I was working for
the Mets, you know, that's when things spread throughout baseball.
So two thousand and three, two thousand and four, I
hamd full of teams have some person who's doing statistical
analysis full time. Most teams don't. But by the time
(13:34):
I left in twenty twelve, more than half the teams,
maybe three quarters of the teams had at least somebody
doing something. And now everybody and the Dodgers have like
a thirty person analytics staff, you know, with like multiple
people with PhDs and statistics and stuff like that.
Speaker 1 (13:52):
So what thirty person staff just looking at the numbers?
Speaker 2 (13:56):
Yeah?
Speaker 1 (13:57):
Wow? And part of the story here is that amount
of data that is tracked in sports has also exploded
in the last ten years. It started with fans keeping
track of more detailed baseball statistics like play by play data,
who was on base, when someone hit a home run,
and where was the ball hit. For many years, this
(14:17):
was published in books and eventually websites for basically baseball nerds.
But now it's a full blown industry. There are companies
that gather data about games and then sell it to
the teams for their analytics, and it's getting more and
more high tech. The LA Dodgers, for example, have cameras
that tract the movement of every player on the field
(14:38):
during every game, so they have data now on how
far players were when they made a good catch, or
where the shortstop should stand to have the optimal chance
of making a double play. Nowadays, teams can even buy
biomechanical data about their players, how their bodies move at
three hundred times per second when they throw a pitch,
(15:00):
sure swing their bats during live games. Also, they can
figure out how their players can play better. But here's
the question. Does all of this actually work? Is it
actually making teams better or is it that some people
say ruining the sport. When we come back, we'll answer
(15:20):
that question and we'll talk about how this push for
more math has spread to other sports like professional basketball, football,
and even chess. So stay with us. We'll be right back. Hey,
(15:52):
welcome back. So we're talking about sports analytics or how
math is being used in sports. And I'm recording this
from Dodger Stadium. It's the bottom of the fifth inning,
so about halfway through the game, and the score is tied.
The Diamondbacks took an early lead in the game, but
(16:13):
then the Dodgers had an amazing third inning with two
home runs with people on base, but then the Diamondbacks
caught up on the fourth inning. So it's a closed game,
and the under fans around me are not as confident
as they were earlier. Okay, so far, we've talked a
little bit about the history of sports analytics, how it
(16:34):
started in baseball, and now we're going to talk about
this idea of using statistics and math to play better
at games has spread to other sports, including, if you
believe it, chess and the sports. Here's doctor Ben Bauer. Okay,
(16:56):
so you're saying now it's pervasive in baseball, would you
say every team now has a sort of a statistics team.
Speaker 2 (17:03):
Yeah, for sure. Every team has a dedicated analytics presence
for sure.
Speaker 1 (17:07):
And just for baseball, does it work, Like it must
be worth it for them to hire thirty people.
Speaker 2 (17:12):
So one thing to keep in mind is that statistical
analysts make a tiny fraction of what players make, right,
And so it's like, if you can make better decisions
about even one player, well, like you can pay a
lot of analysts to help you make that better decision, right.
Speaker 1 (17:31):
I think what you're saying is that statisticians need agents, Yeah,
and I think we do. They seem very valuable exactly
free agency. But then the other part is you're working
in the zero sum system, right, because games are either
one or lost. So if my team gets better at
analytics and then we play better baseball, like, we're going
(17:51):
to win more games in the short term, But then
eventually other teams are going to figure out what we're
doing and they're going to catch up to us, and
so you have this kind of like cat and mouse game.
And you know, so when you say, like does analytics work, yes,
it works, but like there is an aspect of like
what works only works for a short period of time
(18:11):
before everyone else catches on, and then you have to
like figure out the next thing I see, And at
that point you're kind of locked into having a statistic
team because if you didn't, then you would fall behind
everyone else.
Speaker 2 (18:25):
That's exactly right, and that's certainly what happened through the
two thousands and the two thousand tens. Wow.
Speaker 1 (18:29):
Yeah, okay, so that's baseball. But now it's gone on
to other sports after the success in baseball.
Speaker 2 (18:35):
Absolutely, yeah, I would say the spread is not as
wide or as deep as it is in baseball, in
part because baseball is kind of fundamentally different than other
sports in the way that baseball has very discrete actions.
So it's just like the nature of the game is different. However,
yes it has spread to other sports, and yes NBA teams,
(18:58):
NFL teams, and NHL teams in soccer leagues in Europe,
and yes there are people doing statistical analysis for all
those teams at some level. And so in basketball you
have seen things like the way the teams are shooting
three pointers these days. I mean, when I was growing up,
our whole offensive strategy was let's get the ball into
(19:19):
the posts so that the tall players can put it
in the basket, because that's the best way for us
to score points. Right.
Speaker 1 (19:25):
Uh.
Speaker 2 (19:26):
Now, what they're doing, and this is definitely through analytics, right,
is that they realize that, well, okay, if we get
the ball down clost to the basket and somebody puts
it in, what's the expected value of that shot?
Speaker 1 (19:38):
Two points?
Speaker 2 (19:39):
It's two points. And let's say if it's close to
the basket, like I'm going to miss a couple, but
like I basically never missed, so like maybe ninety five
percent of those I make, So that's one point nine
expected points, right, Okay, But now if I shoot a
three pointer, it's for three points, so I only have
to make you know, like I f ix seventy percent,
(19:59):
that's two point one expected points.
Speaker 1 (20:01):
Uh huh.
Speaker 2 (20:02):
And these guys can make if they're open, nobody's there.
You know, they're making eighty percent of us. So a
three pointer becomes a much more attractive shot if the
probability of you're making that shot is sufficiently And so
that has absolutely changed the way that basketball's play. Teams
are now leaning more on three pointers absolutely, and that's
(20:24):
kind of like another one of those hidden moves that
was in the data, but nobody really believed because you know,
we want to see Michael Jordan's dunk the ball. Absolutely,
I think the shooting percentages have changed and that has
led to the evolution of the game that we see today.
Speaker 1 (20:41):
But I wonder if it's sort of like an arms
race there too, because now if everybody is aiming for
three pointers, then everyone's going to adapt their defense to
block those three pointers for sure.
Speaker 2 (20:52):
I mean, this is a big part of like in baseball,
how analytics is you know, ruining.
Speaker 1 (20:57):
Baseball because like, if I go to baseball, I want
to see people hitting the ball and making dramatic plays,
not like, oh, you got walked. That's not as exciting.
Speaker 2 (21:05):
Exactly, And that is more or less exactly. What has
happened over the last fifteen or twenty years is that
people like me and people who are doing jobs similar
to me sort of figured out, well, if we want
to win more games, like we need to draw more walks,
then we're not going to steal so many bases because
that turns out to be pretty risky. But it led
to a style of play that a lot of people,
(21:27):
including myself, find less interesting to.
Speaker 1 (21:30):
Watch because it's prioritizing the long term goals that the game,
not the moment to moment excitement.
Speaker 2 (21:37):
Absolutely, it's prioritizing winning the game, not making the game entertaining.
I mean, this is what people are talking about with
basketball and how it's the ruining basketball. They're talking about,
you know, instituting a four point.
Speaker 1 (21:49):
Shot like from the half court kind of.
Speaker 3 (21:51):
Yeah, something like that, Like basketball is going to become
people just standing around the middle of the court, basket
from the middle of the court.
Speaker 1 (22:02):
Oh, I see, and that's not basketball. That's if you're
a fan, you'd be like, well, it's not basketball as
we know it. Could you say anything kind of about
how it has spread to other sports?
Speaker 2 (22:20):
Yeah. One of my co authors on the paper that
we wrote a couple of years ago, Michael Lopez, was
hired by the National Football League a few years ago
to become their director of sports Analytics for the league.
And so I think you had baseball kind of like
leading the way. I think basketball came in, you know,
sort of after that, and I think the NFL husband
(22:42):
after that.
Speaker 1 (22:43):
Okay, do you have any examples of it?
Speaker 2 (22:45):
Yeah, Well, the thing that has attracted the most attention
is when to punt and when to go for it
on fourth down. So you know, in American football, yet
four downs to advance the ball ten yards, and if
you are able to do that, then you get another
to try again. But if you don't, it's the other
team's ball. And so what most football teams will do
(23:07):
is if it's fourth down and the ball is very close,
then like maybe they're going to try to get that
extra yard or two and keep going. But if not,
if it's like fourth down and eight yards to go,
then in their minds they're like, well, chances are we're
not going to make it. So if we give the
other team the ball here, they're going to be in
(23:28):
a position to maybe score against us. So what we're
going to do instead is we're going to kick the
ball as far as we can down the field, and
then even though we will have given the other team
the ball, we will have sent them all the way back,
you know, as far as we can. That's called a punt.
And then so the question becomes when should you go
for it on fourth down and when should you not?
(23:49):
And you know, so statistical analysts started looking at this,
and what they found was that generally teams were overly conservative,
that is, they did not go for it on fourth
down as often as the statistical analysis would suggest was optimal.
Speaker 1 (24:06):
I see they said it chickened out, they're trying to
go for it.
Speaker 2 (24:09):
Well, yes, so that's one of them prominent statistical analysis
contributions to football.
Speaker 1 (24:15):
I see, general teams aren't going for it more, people
are putting it less.
Speaker 2 (24:19):
Yes, there's been a movement definitely towards going for it more.
And now a lot of times they'll even have like
a little on screen graphic that's telling you, you know,
whether the team suppos to go for it or not.
Speaker 1 (24:32):
Whoa, it's part of the broadcast.
Speaker 2 (24:34):
Yeah, it's part of the broadcast.
Speaker 1 (24:35):
Like there's an analytic team saying, oh, you know, every
time it's fourth and down between these two teams, they.
Speaker 2 (24:41):
Should go for it exactly.
Speaker 1 (24:43):
And in this case, it's sort of something fans could
agree on, right, Like fans want to see more people
going for it.
Speaker 2 (24:49):
Yeah, that's a good point going forward on fourth down.
It's exciting, probably more exciting than punting on fourth down.
Don't remember exactly when this was, but sometime in the
Belichick era, but it was a big game between the
Patriots and the Colts and the Patriots had a fourth
and whatever, and they went for it and they didn't
get it, and Peyton Manning's team got the ball back
(25:09):
and then they went and scored. Oh you know, everybody
was talking about how stupid the Patriots were. And so
it was another one of these cases where a team
tried to pursue the analytically optimal strategy and a backfired
on them, and the sort of media and fan backlash
sort of overwhelmed all the times that maybe they did
(25:31):
go for it in other situations and made.
Speaker 1 (25:34):
It it's like, you can't win with sports fans, can you, Yeah, right,
right right? And you said it spread into even things
like esports and chess. Yeah.
Speaker 2 (25:44):
The concept of rating systems in chess very much goes
back many years. So things like ELO ratings were specifically
designed for chess.
Speaker 1 (25:53):
Uh the quickest side here. An ELO rating, named after
the physicists and chess player arped Elo who invented it,
basically tells you how good you are at chess. A
lot of people play chess online these days, especially young people,
and so you might have had your kids or your
younger cousins talk about their ELO rating. For example, the
(26:13):
top chess players in the world have an ELO ranking
of about twenty eight hundred, whereas the beginner would start
it zero. But here's the thing. Your ELO rating is
not just where you stand relative to other players. The
formula for it actually tells you the probability of who's
going to win between two players. So you can use
(26:34):
it for say, figuring out exactly how much to bed
on a chess championship match, or how much money to
pay a chess player to be their sponsor. And yes,
these days chess players have sponsorship deals. But the same
idea is also used in other sports like tennis.
Speaker 2 (26:52):
Like if you think about tennis versus chess from an
analytical player rating system perspective, like the same thing, right,
it's I see this person plays this person. They each
have a rating before the match, and somebody wins the match,
and then they each have a rating after the match.
What are the ramifications of the particular modeling choices that
we make when we use those rating systems?
Speaker 1 (27:12):
I see? And esports? What do you know about the
using esport? Another quickest site here in case you didn't know.
Esports or electronic sports are a thing that's where people
compete on video games. League of Legends, Valoriant, counter Strike.
These are hugely popular competitions, with millions of viewers and
(27:33):
billions of dollars in prize money.
Speaker 2 (27:36):
I think what's really interesting about esports is that in
all the things that we've talked about so far, the
game exists and then we have decided to collect data
about it. But in esports, that data is like inherently
part of the game itself because the games are happening
on the computer. Ah, so the computer is already keeping
track of all the things. You know, who's killing whom
(27:59):
and who's this portion of the board and how many WHOA.
Speaker 1 (28:02):
It's built into the game where the game itself is
tracking every possible statistic.
Speaker 2 (28:09):
That's how it works. I'm not much of any sports
participant in myself, but the papers that I read on
this subject a little better couple. Their approach is sort
of similar to what we've talked about with bisball or
any of these parts. It's sort of like what are
the best strategies for winning the game? And we've got
now millions and millions of games that people are playing
all the time, and we can analyze that data to
(28:32):
figure out, like, what are the strategies that pay off
for people. You know, which of these strategies tends to
produce the most kills or produce the most wills?
Speaker 1 (28:41):
And you know, gosh, I don't know about you, but
I can't wait for the sequel to the movie Moneyball
where it's just Brad Pitt sitting on his sofa playing
Call of Duty all day. Okay, So that's the use
of math in sports. As we mentioned, it's the kind
of thing that you need people with PhDs in statistical
science to really compete in sports. Analytics uses a wide
(29:05):
range of mathematical models, including regression models, Bayesian inference, facial
statistics in more and more of these days. AI. But
here's the big plot twist or upset to use sports lingo.
It turns out all these models are still not the
most accurate way of predicting who's going to win a game.
(29:27):
There is another indicator which at its core has almost
nothing to do with math. So when we come back,
we'll talk about what this method is, and I think
you're going to be shocked to find out what it is.
Oh and also we're going to find out if the
Dodgers won the game I went to after all, So
stay with us till the last inning. We'll be right back. Hey,
(30:05):
welcome back. I'm here watching the LA Dodgers play the
Arizona Diamondbacks and it's the top of the last inning
and the score is Dodgers five, Arizona Diamondbacks four. Dodgers
are up by one point. But we're talking about sports analytics,
or the idea of using math and science to help
(30:27):
teams win at sports, and so far we're talked about
where this idea came from and how it spread to
almost every sport. Now, I don't know who's going to win.
Anything can happen in the next few minutes. But according
to doctor Balmer, there is a way to almost perfectly predict,
at least from a statistical point of view, exactly who
has a better chance at winning. And it has almost
(30:47):
nothing to do with mathematical models. The fourth idea, what's
the fourth idea?
Speaker 2 (30:58):
Oh yeah, betting my At a certain point, you get
down to the fact that the best estimate that we
as human society have of estimating who's going to win
a particular game are the betting market oughts.
Speaker 1 (31:12):
Is that right, that's the best estimate?
Speaker 2 (31:14):
Yes, this is a result that has been corroborated time
and time again.
Speaker 1 (31:19):
But who sets those betting odds?
Speaker 2 (31:21):
People like me that work for sports gambling outfits. So
they set the lines, but then based on the money
that people bet, the lines can change, and so by
the time the game actually starts, you have a sort
of reflection of humanity's collective wisdom about who's going to
win this game.
Speaker 1 (31:39):
That blew me away. You're relying on people's intuition because
the average sports better doesn't have a math, they don't
have access to the data, They just have a feeling.
Speaker 2 (31:49):
Well, it's this notion of wisdom of the crowds.
Speaker 1 (31:51):
Uh huh.
Speaker 2 (31:52):
A lot of statistics is just like the average of
a lot of things is better than any one person's
one guess, you know, uh huh. And this is just that.
But with money at stake, which is where people tend to,
you know, use all of their collective wherewithal to make
the best estimate.
Speaker 1 (32:10):
I see, there's a lot of noise, Like you never
trust one sports betting guide to their hunch, but if
you have a million of them, then collectively they sort
of have sort of absorbed all this data in their
squishy brains and have somehow projected that into their model
that averages out and somehow that gives you an estimate
(32:32):
and you're saying that's better than anything we can come
up with. Yes, what does that mean that it's better?
Like over time, it's more accurate over time. If you
say that it's a forty seven percent chance that this
is going to happen, that it actually turns out to
be a forty seven like forty seven times out of
one hundred in the future, like they actually win, They'll
(32:54):
be right.
Speaker 2 (32:55):
It's calibrated. It's well calibrated.
Speaker 1 (32:58):
Oh the time.
Speaker 2 (33:00):
It's like, if the betting odds imply that the Cardinals
have a forty seven percent chance of beating the Padres,
then if you were to take all the games in
which the odds were the same as that game, uh huh,
the team I guess it would be the underdog in
this case, they would win forty seven percent of those games. Wo.
That's wild. It is wild. But it's also like it
(33:23):
has to be that way, because if it wasn't that way,
then you'd have a whole bunch of people who would
figure that out and they would bet on the other team,
and that would move the line right that's incredible.
Speaker 1 (33:35):
So you're almost saying that the wisdom of the crowd
is better than someone with a PhD in statistics.
Speaker 2 (33:40):
Yeah for sure. Well, because there's a bunch of people
with PH's and statistics who are part of the crowd.
Speaker 1 (33:47):
Oh, I see, that's part of it.
Speaker 2 (33:48):
It's like you got all those people, and you've got
a whole bunch of other people.
Speaker 1 (33:52):
Just fans. Fans who knows who won in the last
one hundred games?
Speaker 2 (33:56):
Maybe exactly.
Speaker 1 (33:57):
Wow, that's wild. Okay, we have only a few minutes.
I'm going to a Dodgers game this evening.
Speaker 2 (34:02):
Oh, wonderful.
Speaker 1 (34:03):
What should I look out for? What should I expect?
Speaker 2 (34:05):
I'm so excited for you. I love Dodger Stadium. It's
a great place to watch Basaka and enjoy the weather
and just enjoy the twilight.
Speaker 1 (34:13):
Well we have show hey uh badding tonight.
Speaker 2 (34:16):
Well you'll watch the greatest baseball player of all time?
Speaker 3 (34:18):
Then.
Speaker 2 (34:19):
Wow, I mean, the Dodgers are the best team in baseball.
I don't think anyone really doubts that. You know, they
have used analytics, they have gone deeper, and they have
put the resources behind it. So it's like moneyball is
sort of how do you win more games with less money?
But the Dodgers are doing moneyball with money.
Speaker 1 (34:37):
It's like money moneyball. Yeah, okay, looked at a CBS
sport says the Dodgers are favorite to win, okay, minus
two sixty six favorite on the money line.
Speaker 2 (34:49):
So, and I don't know that I'm going to be
able to do this off the top of my head,
but like, you plug that number into a fairly simple formula,
and that's going to tell you that the Dodgers have
a sixty of winning this game.
Speaker 1 (35:02):
Okay.
Speaker 2 (35:02):
And so now that gives us a sports analyst the
most accurate prediction for what's going to happen in this game.
Speaker 1 (35:11):
All right, we'll see how it pans out.
Speaker 2 (35:13):
Then, yeah, exactly, all right.
Speaker 1 (35:17):
Did the Dodgers win after all? Here's the audio of
the last few moments of the game when, Yeah, the
(35:51):
Dodgers won, just like the fans, the mathematicians, and the
betting markets predicted. Thanks for joining us, see you next
time you've been listening to science stuff. Production of iHeartRadio
written and produced by me Or hitch Ham, edited by
(36:12):
Rose Seguda, Executive producer Jerry Rowland, and audio engineer and
mixer Kasey Pegram and you can follow me on social
media to search for PhD comics and the name of
your favorite platform. Be sure to subscribe to Science Stuff
on the iHeartRadio app, Apple Podcasts, or wherever you get
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