WEBVTT - Is Math Ruining Sports?

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<v Speaker 1>Hey, welcome to Sign Stuff, a production of iHeartRadio I'm

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<v Speaker 1>More Hit Cham and today we're diving into the signs

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<v Speaker 1>of sports analytics. Can better mathematical bottles help your team win?

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<v Speaker 1>How exactly does it work? And is it ruining the

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<v Speaker 1>fun of the game for fans. We're going to be

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<v Speaker 1>talking to someone who ran the numbers for a Major

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<v Speaker 1>League Baseball team and who now publishes academically on the subject,

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<v Speaker 1>and he's going to step us through the history of

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<v Speaker 1>this phenomenon and how it's now spread into almost every sport,

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<v Speaker 1>including chess and video games. Now, I recorded part of

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<v Speaker 1>this episode during a visit to a sporting event, one

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<v Speaker 1>of the opening games for the LA Dodgers, who happen

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<v Speaker 1>to be the number one team in the world and

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<v Speaker 1>who have a whole staff of mathematicians working for them.

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<v Speaker 1>So gear up, lock in, and get ready to score

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<v Speaker 1>as we football tackle. The question is math We're winning

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<v Speaker 1>sports enjoy? Hey everyone, So I'm here at Dodger Stadium

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<v Speaker 1>for opening week to watch the LA Dodgers play the

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<v Speaker 1>Arizona Diamondbacks. Now, I'm not a huge baseball fan, but

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<v Speaker 1>from what I read, the Dodgers are favorite to win

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<v Speaker 1>according to the betting markets, with a probability of about

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<v Speaker 1>sixty seven percent, meaning that the Dodgers are favorite to win.

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<v Speaker 1>Of course, they have one of the best, if not

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<v Speaker 1>the best player ever. I'm talking, of course about show

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<v Speaker 1>Heyo Tani. Now, who actually determines that the Dodgers have

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<v Speaker 1>a sixty seven percent chance to win? How's that done?

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<v Speaker 1>Is that some guess or is there a lot of

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<v Speaker 1>maths behind it? That's what I want to find out.

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<v Speaker 1>But first I'm going to ask a few Dodger fans

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<v Speaker 1>here who they think are going to win. Who do

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<v Speaker 1>you think's gonna win? Dodgers or the Diamondbacks other Dodgers

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<v Speaker 1>for sure, Dodgers, Rogers. I think I'm sitting in a

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<v Speaker 1>Dodgers section. Can I ask you a question? Who do

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<v Speaker 1>you think is gonna win? Dodgers or Dynamits?

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<v Speaker 2>The Dodgers obviously, the Dodgers for sure.

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<v Speaker 1>This is kind of a silly question. Who do you

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<v Speaker 1>think is gonna win?

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<v Speaker 3>The Dodgers for the diamonback.

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<v Speaker 1>The Dodgers, of course, Dodgers.

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<v Speaker 2>And this is a silly question.

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<v Speaker 1>Okay, Clearly I wasn't going to get an unbiased opinion

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<v Speaker 1>here among the Dodger fans, so it makes sense of

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<v Speaker 1>all of this. I reached out to doctor Ben Baumber,

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<v Speaker 1>a professor of statistical and data scientists at Smith College

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<v Speaker 1>and the former statistical analyst for the New York Mets.

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<v Speaker 1>So here's my conversation with doctor Ben Bomber. Well, thank you,

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<v Speaker 1>doctor Bomber for joining us.

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<v Speaker 2>Thank you so much for having me. It's a pleasure

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<v Speaker 2>to be here.

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<v Speaker 1>You mentioned you played for the Mets.

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<v Speaker 2>It works for okay, did not play for so.

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<v Speaker 1>Well, sort of, you're part of the team. Come on,

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<v Speaker 1>you know.

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<v Speaker 2>I started working for the New York Mets in two

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<v Speaker 2>thousand and four as a statistical analyst, and at that

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<v Speaker 2>time they had never had one before. I was working

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<v Speaker 2>on PhD in mass so I was the only person

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<v Speaker 2>doing it kind of at that pretty high technical level.

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<v Speaker 2>And I was able for a long time to do

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<v Speaker 2>both at the same time.

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<v Speaker 1>Wow.

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<v Speaker 2>But at the end of that process decided to do

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<v Speaker 2>something else. Instead of doing sports analytics for the Mets.

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<v Speaker 2>I do sports analytics for academia, the public journals. It's

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<v Speaker 2>a different league, Yeah, a different league exactly.

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<v Speaker 1>Awesome. Can you please tell us what exactly is sports analytics?

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<v Speaker 2>Sports analytics is the use of statistics and data to

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<v Speaker 2>think about sports, like to learn about sports, how we

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<v Speaker 2>might play sports better or more efficiently, or who the

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<v Speaker 2>better players are or who the better teams are. But

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<v Speaker 2>I think for centuries people have been watching sports and

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<v Speaker 2>trying to answer those questions for themselves. But I think

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<v Speaker 2>has changed when you talk about sports analytics is we're

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<v Speaker 2>actually recording the data about what's happening in those games

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<v Speaker 2>and them we're analyzing that data in order to inform

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<v Speaker 2>those questions.

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<v Speaker 1>Is there a moment in history we can trace this

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<v Speaker 1>idea too, or what is the historical origins of this idea?

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<v Speaker 2>Yeah? So actually there is a fairly specific origin story.

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<v Speaker 2>So you know, people have been playing sports going back

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<v Speaker 2>to ancient Greece or whatever. But in the United States,

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<v Speaker 2>like in eighteen seventy, people started playing baseball professionally. But

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<v Speaker 2>just imagine, like there's people playing baseball and if you

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<v Speaker 2>want to watch a professional baseball game, you have to

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<v Speaker 2>go to the gate, right, there's no TV. So there

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<v Speaker 2>was a person, a man named Henry Chadwick, had this

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<v Speaker 2>idea of like what if I recorded some statistics about

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<v Speaker 2>the game and then published it in the newspaper so

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<v Speaker 2>that people like, not only do they get the score,

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<v Speaker 2>they got a numerical summary of what happened in the game.

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<v Speaker 2>And he called it the box score. We still have

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<v Speaker 2>these today. You can pick up you know, USA today

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<v Speaker 2>and look at the box scores for the baseball games.

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<v Speaker 1>Like how many runs, how many at that all of.

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<v Speaker 2>That, yeah, how many runs in total? And then for

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<v Speaker 2>each of the players in the batting order, how many

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<v Speaker 2>times did they come to bat, how many hits did

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<v Speaker 2>they get, how many runs did they drive in? For

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<v Speaker 2>the pitchers, how many innings did they pitch, how many

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<v Speaker 2>strikeouts did they have, how many runs did they give up?

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<v Speaker 2>And so for those of us who are like deep

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<v Speaker 2>in the weeds of baseball, like you can look at

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<v Speaker 2>a box score and basically reconstruct the entire game, like

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<v Speaker 2>two men on and two out or whatever, and this

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<v Speaker 2>person grounded into a double play and that end of

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<v Speaker 2>the inning, and then.

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<v Speaker 1>It is enough to sort of reconstruct some of the

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<v Speaker 1>drama of the sport. Absolutely absolutely, and they started with

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<v Speaker 1>baseball because I guess baseball was the as they say,

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<v Speaker 1>national past time. And absolutely so they started keep drugging

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<v Speaker 1>the box scores.

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<v Speaker 2>Right again, this was happening in the eighteen seventies.

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<v Speaker 1>So for the first time in known history, people started

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<v Speaker 1>to record data about sports events, mostly so fans could

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<v Speaker 1>follow along and know what happened in the game. And

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<v Speaker 1>this went on for over seventy years until in nineteen

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<v Speaker 1>forty a man named branch Ricky changed baseball and really

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<v Speaker 1>the world history.

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<v Speaker 2>So Bran Trickey, who was the general manager of the

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<v Speaker 2>Brooklyn Dodgers, made like two of the more important and

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<v Speaker 2>long lasting contributions to the way that baseball was played

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<v Speaker 2>in the United States. One was he signed Jackie Robinson,

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<v Speaker 2>and that broke the collar barrier major league baseball.

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<v Speaker 1>Wow.

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<v Speaker 2>The second was that he hired a man named Alan

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<v Speaker 2>Roth to be the first full time statistician to work

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<v Speaker 2>for a Major League baseball team. Really yeah, And there's

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<v Speaker 2>a great article. Life Magazine did a whole spread about

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<v Speaker 2>Alan Roth and what he was doing with the Brooklyn

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<v Speaker 2>Dodgers and all these equations written on the blackboard behind him.

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<v Speaker 1>What do you think with the mentality of that first

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<v Speaker 1>baseball owner who hired that that stition. I mean, obviously

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<v Speaker 1>he seemed to be sort of a groundbreaking type of

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<v Speaker 1>thinking person, you know, to hire Jackie Robinson, But what

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<v Speaker 1>do you think he was thinking at the time when

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<v Speaker 1>he hired the statistician, How do.

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<v Speaker 2>I win more games? You know, it's really pretty simple

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<v Speaker 2>because fundamentally it's just about like how do we do

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<v Speaker 2>this better? And in sports you have very clear outcomes.

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<v Speaker 2>It's wins and losses. And you know, brand Shreck he believed,

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<v Speaker 2>I'm sure he was correct at that time that like,

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<v Speaker 2>one way that I can win more games is by

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<v Speaker 2>understanding how baseball works better so that I can find

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<v Speaker 2>players who do things that help us win, especially when

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<v Speaker 2>those things are maybe overlooked by these other teams who

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<v Speaker 2>don't have this knowledge that I have acquired or developed.

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<v Speaker 1>Oh I see. He maybe asked like should I hire

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<v Speaker 1>this player or that player, or should we do these kinds

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<v Speaker 1>of places or that kind of play, And somebody told

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<v Speaker 1>him what to do, and he said, no, I don't

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<v Speaker 1>believe you, like, show me the data exactly.

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<v Speaker 2>And so just to give you a simple example, if

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<v Speaker 2>you've got to run around first base and second base

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<v Speaker 2>is open, the question is, you know, how do you

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<v Speaker 2>get that runner on first to second. One strategy is

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<v Speaker 2>to bond, which means they don't swing at the ball

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<v Speaker 2>and Triit just kind of hold the bat there and

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<v Speaker 2>try to like have the ball like fall kind of

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<v Speaker 2>fleck in front of the catcher.

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<v Speaker 1>Like they just tapped the ball. And it's almost like

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<v Speaker 1>a sacrifice play, right exactly.

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<v Speaker 2>It's called a sacrifice bunt. And so in some ways

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<v Speaker 2>you've gained something because you moved that runner across, but

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<v Speaker 2>in other ways you've lost something because now an out

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<v Speaker 2>has occurred.

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<v Speaker 1>Right right? Is it worth it? Is it actually a

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<v Speaker 1>good idea? Is the question?

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<v Speaker 2>Exactly? And so up until Alan Roth, basically people had

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<v Speaker 2>been trying to keep track of like, well, how does

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<v Speaker 2>it work?

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<v Speaker 1>I don't all, like, I've been watching baseball for twenty years,

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<v Speaker 1>and that's always a bad idea.

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<v Speaker 2>Exactly anecdotal evidence, you know. So because Henry Tradwick and

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<v Speaker 2>other people had, you know, been collecting data about baseball

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<v Speaker 2>for at that point already fifty or seventy years or whatever,

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<v Speaker 2>but we did thousands of games, people started to sort

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<v Speaker 2>of pull it apart and be like, does this actually

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<v Speaker 2>pay off? Do we actually score more runs if we

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<v Speaker 2>do this relative to the times that we don't do this.

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<v Speaker 2>So this was the type of analysis that people and.

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<v Speaker 1>Did that work. Did People were like, oh, my good

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<v Speaker 1>after that.

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<v Speaker 2>I mean, the Dodgers won a lot of games. Yeah,

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<v Speaker 2>that's a good research question actually, But certainly the Dodgers

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<v Speaker 2>were a very good team through the late forties and

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<v Speaker 2>through the fifties. You know, fifty five the Dodgers finally

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<v Speaker 2>beat the Yankees, and yeah, the Dodgers today are the

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<v Speaker 2>dominant team in Major League Baseball for sure.

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<v Speaker 1>Yeah. Yeah, so it worked back then. Did that cause

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<v Speaker 1>other teams to start looking at stats? Also?

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<v Speaker 2>I think the short answer is it doesn't appear to

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<v Speaker 2>be the case. I see, there's not a lot of

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<v Speaker 2>historical record for people being like full time employees statistical

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<v Speaker 2>analysts for major League Baseball teams. Not so much between

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<v Speaker 2>the forties and the eighties, nineties, two thousands.

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<v Speaker 1>Yeah, yes, despite the then Brooklyn Dodgers having a math

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<v Speaker 1>person on the team and having a winning streak, people

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<v Speaker 1>in sports were still not convinced. We'll get into what

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<v Speaker 1>that was a little later in the program. But all

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<v Speaker 1>of that changed with the publication of a book called Moneyball.

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<v Speaker 1>You might have heard of it or maybe seeing the

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<v Speaker 1>movie based on it, starring Brad Pitt. It's about a

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<v Speaker 1>struggling baseball team. The two thousand and two Oakland A's

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<v Speaker 1>who were able to get to the World Series playoffs

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<v Speaker 1>despite having a third of the money that other larger

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<v Speaker 1>teams had, and they did it by you guessed it,

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<v Speaker 1>using math. It all started with an amateur baseball fan

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<v Speaker 1>in Kansas named Bill James.

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<v Speaker 2>Bill James is known as kind of like the godfather

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<v Speaker 2>of baseball analytics. Uh huh. Literally a night watchman out

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<v Speaker 2>a pork and beans factory in Kansas, uh huh. You

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<v Speaker 2>know it's a very smart guy, loved baseball, but somehow

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<v Speaker 2>he was the person who probably did the most to

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<v Speaker 2>popularize the ideas in baseball analytics. So he started looking

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<v Speaker 2>at data, and he started writing about it, and then

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<v Speaker 2>he started publishing these newsletters that contained like tons of ideas,

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<v Speaker 2>some of which became these kind of revolutionary ideas like

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<v Speaker 2>what it's not just the data being valuable for itself

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<v Speaker 2>or like in its own right, It's that the data

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<v Speaker 2>is a mechanism through which these very creative, intelligent people

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<v Speaker 2>were like learning new things about the game. It's those ideas,

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<v Speaker 2>like that's what led to moneyball. It wasn't like Billy

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<v Speaker 2>being woke up one day and was like, hey, we

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<v Speaker 2>should look at data. It's like he read Bill James,

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<v Speaker 2>and like Bill James was the one who was showing

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<v Speaker 2>sort of like what this data could be, like how

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<v Speaker 2>it could inform your understanding of the game.

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<v Speaker 1>Okay, here's one example of a Bill James idea. For

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<v Speaker 1>most of baseball history, people cared about a batters RBI

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<v Speaker 1>or runs batted in. It's a measure of how often

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<v Speaker 1>a team scores a point whenever a batter goes up

0:12:03.679 --> 0:12:05.960
<v Speaker 1>to hit the ball. If you look up the list

0:12:06.000 --> 0:12:09.320
<v Speaker 1>of the people with the top career RBI numbers, you'll

0:12:09.320 --> 0:12:13.520
<v Speaker 1>see names like Hank Aaron, Babe Ruth, Alex Rodriguez, or

0:12:13.640 --> 0:12:17.559
<v Speaker 1>a rod Ty Cobb. You know, legends. And so the

0:12:17.640 --> 0:12:21.200
<v Speaker 1>higher your RBI, the better people thought you were, and

0:12:21.240 --> 0:12:23.880
<v Speaker 1>the more money someone would have to pay you to

0:12:23.960 --> 0:12:27.800
<v Speaker 1>be on their team. But actually, Bill James and then

0:12:27.840 --> 0:12:30.760
<v Speaker 1>the Oakland A's figure it out that's not the best

0:12:30.800 --> 0:12:34.440
<v Speaker 1>statistic to be focusing on because it kind of depends

0:12:34.520 --> 0:12:37.800
<v Speaker 1>on your teammates. If you have good teammates that got

0:12:37.840 --> 0:12:39.880
<v Speaker 1>on base by the time you went to bad, you're

0:12:39.920 --> 0:12:43.080
<v Speaker 1>going to have a higher RBI. But looking at the

0:12:43.160 --> 0:12:46.280
<v Speaker 1>data more closely, it turns out a better measure of

0:12:46.320 --> 0:12:50.199
<v Speaker 1>how good a batter is is something called runs created,

0:12:50.480 --> 0:12:53.840
<v Speaker 1>which is computed using a different formula that doesn't depend

0:12:53.840 --> 0:12:57.600
<v Speaker 1>as much on what your teammates do. And because nobody

0:12:57.640 --> 0:13:00.480
<v Speaker 1>else was looking at the statistic, the oakland As were

0:13:00.480 --> 0:13:05.800
<v Speaker 1>able to get a good team for less money. And

0:13:05.880 --> 0:13:08.640
<v Speaker 1>it really was then Moneyball, which is based on the

0:13:08.679 --> 0:13:11.760
<v Speaker 1>work in the Oakland A's that really got people thinking like,

0:13:11.960 --> 0:13:13.880
<v Speaker 1>oh my goodness, we should totally do this.

0:13:14.360 --> 0:13:18.400
<v Speaker 2>Yeah. Things moved quickly after the publication Moneyball around the

0:13:18.440 --> 0:13:21.720
<v Speaker 2>time early aughts mid ots, when I was working for

0:13:21.760 --> 0:13:24.520
<v Speaker 2>the Mets, you know, that's when things spread throughout baseball.

0:13:24.679 --> 0:13:27.000
<v Speaker 2>So two thousand and three, two thousand and four, I

0:13:27.080 --> 0:13:30.959
<v Speaker 2>hamd full of teams have some person who's doing statistical

0:13:31.000 --> 0:13:34.680
<v Speaker 2>analysis full time. Most teams don't. But by the time

0:13:34.720 --> 0:13:38.400
<v Speaker 2>I left in twenty twelve, more than half the teams,

0:13:38.400 --> 0:13:41.120
<v Speaker 2>maybe three quarters of the teams had at least somebody

0:13:41.240 --> 0:13:46.000
<v Speaker 2>doing something. And now everybody and the Dodgers have like

0:13:46.240 --> 0:13:50.040
<v Speaker 2>a thirty person analytics staff, you know, with like multiple

0:13:50.080 --> 0:13:52.600
<v Speaker 2>people with PhDs and statistics and stuff like that.

0:13:52.640 --> 0:13:56.439
<v Speaker 1>So what thirty person staff just looking at the numbers?

0:13:56.559 --> 0:13:57.439
<v Speaker 2>Yeah?

0:13:57.520 --> 0:14:01.440
<v Speaker 1>Wow? And part of the story here is that amount

0:14:01.480 --> 0:14:04.880
<v Speaker 1>of data that is tracked in sports has also exploded

0:14:05.000 --> 0:14:08.120
<v Speaker 1>in the last ten years. It started with fans keeping

0:14:08.160 --> 0:14:11.880
<v Speaker 1>track of more detailed baseball statistics like play by play data,

0:14:12.200 --> 0:14:14.360
<v Speaker 1>who was on base, when someone hit a home run,

0:14:14.400 --> 0:14:17.520
<v Speaker 1>and where was the ball hit. For many years, this

0:14:17.679 --> 0:14:22.320
<v Speaker 1>was published in books and eventually websites for basically baseball nerds.

0:14:22.680 --> 0:14:26.160
<v Speaker 1>But now it's a full blown industry. There are companies

0:14:26.200 --> 0:14:28.760
<v Speaker 1>that gather data about games and then sell it to

0:14:28.800 --> 0:14:32.080
<v Speaker 1>the teams for their analytics, and it's getting more and

0:14:32.200 --> 0:14:36.120
<v Speaker 1>more high tech. The LA Dodgers, for example, have cameras

0:14:36.160 --> 0:14:38.800
<v Speaker 1>that tract the movement of every player on the field

0:14:38.880 --> 0:14:41.840
<v Speaker 1>during every game, so they have data now on how

0:14:41.880 --> 0:14:44.280
<v Speaker 1>far players were when they made a good catch, or

0:14:44.480 --> 0:14:47.720
<v Speaker 1>where the shortstop should stand to have the optimal chance

0:14:47.800 --> 0:14:52.320
<v Speaker 1>of making a double play. Nowadays, teams can even buy

0:14:52.520 --> 0:14:57.320
<v Speaker 1>biomechanical data about their players, how their bodies move at

0:14:57.400 --> 0:14:59.920
<v Speaker 1>three hundred times per second when they throw a pitch,

0:15:00.080 --> 0:15:04.520
<v Speaker 1>sure swing their bats during live games. Also, they can

0:15:04.560 --> 0:15:08.160
<v Speaker 1>figure out how their players can play better. But here's

0:15:08.160 --> 0:15:12.080
<v Speaker 1>the question. Does all of this actually work? Is it

0:15:12.160 --> 0:15:15.680
<v Speaker 1>actually making teams better or is it that some people

0:15:15.720 --> 0:15:20.320
<v Speaker 1>say ruining the sport. When we come back, we'll answer

0:15:20.320 --> 0:15:23.080
<v Speaker 1>that question and we'll talk about how this push for

0:15:23.200 --> 0:15:29.280
<v Speaker 1>more math has spread to other sports like professional basketball, football,

0:15:29.520 --> 0:15:52.560
<v Speaker 1>and even chess. So stay with us. We'll be right back. Hey,

0:15:52.600 --> 0:15:56.520
<v Speaker 1>welcome back. So we're talking about sports analytics or how

0:15:56.600 --> 0:15:59.360
<v Speaker 1>math is being used in sports. And I'm recording this

0:15:59.400 --> 0:16:06.640
<v Speaker 1>from Dodger Stadium. It's the bottom of the fifth inning,

0:16:07.120 --> 0:16:10.360
<v Speaker 1>so about halfway through the game, and the score is tied.

0:16:10.720 --> 0:16:13.600
<v Speaker 1>The Diamondbacks took an early lead in the game, but

0:16:13.680 --> 0:16:16.400
<v Speaker 1>then the Dodgers had an amazing third inning with two

0:16:16.440 --> 0:16:20.280
<v Speaker 1>home runs with people on base, but then the Diamondbacks

0:16:20.280 --> 0:16:23.720
<v Speaker 1>caught up on the fourth inning. So it's a closed game,

0:16:24.160 --> 0:16:27.200
<v Speaker 1>and the under fans around me are not as confident

0:16:27.240 --> 0:16:31.680
<v Speaker 1>as they were earlier. Okay, so far, we've talked a

0:16:31.680 --> 0:16:34.560
<v Speaker 1>little bit about the history of sports analytics, how it

0:16:34.640 --> 0:16:37.520
<v Speaker 1>started in baseball, and now we're going to talk about

0:16:37.520 --> 0:16:41.920
<v Speaker 1>this idea of using statistics and math to play better

0:16:41.960 --> 0:16:45.880
<v Speaker 1>at games has spread to other sports, including, if you

0:16:45.960 --> 0:16:56.480
<v Speaker 1>believe it, chess and the sports. Here's doctor Ben Bauer. Okay,

0:16:56.640 --> 0:16:59.800
<v Speaker 1>so you're saying now it's pervasive in baseball, would you

0:16:59.840 --> 0:17:02.760
<v Speaker 1>say every team now has a sort of a statistics team.

0:17:03.000 --> 0:17:06.640
<v Speaker 2>Yeah, for sure. Every team has a dedicated analytics presence

0:17:06.720 --> 0:17:07.399
<v Speaker 2>for sure.

0:17:07.359 --> 0:17:10.120
<v Speaker 1>And just for baseball, does it work, Like it must

0:17:10.200 --> 0:17:12.040
<v Speaker 1>be worth it for them to hire thirty people.

0:17:12.240 --> 0:17:14.760
<v Speaker 2>So one thing to keep in mind is that statistical

0:17:14.760 --> 0:17:18.040
<v Speaker 2>analysts make a tiny fraction of what players make, right,

0:17:18.359 --> 0:17:21.720
<v Speaker 2>And so it's like, if you can make better decisions

0:17:22.400 --> 0:17:26.280
<v Speaker 2>about even one player, well, like you can pay a

0:17:26.280 --> 0:17:28.920
<v Speaker 2>lot of analysts to help you make that better decision, right.

0:17:31.000 --> 0:17:34.840
<v Speaker 1>I think what you're saying is that statisticians need agents, Yeah,

0:17:35.040 --> 0:17:38.560
<v Speaker 1>and I think we do. They seem very valuable exactly

0:17:39.200 --> 0:17:42.200
<v Speaker 1>free agency. But then the other part is you're working

0:17:42.200 --> 0:17:45.640
<v Speaker 1>in the zero sum system, right, because games are either

0:17:45.680 --> 0:17:48.720
<v Speaker 1>one or lost. So if my team gets better at

0:17:48.760 --> 0:17:51.520
<v Speaker 1>analytics and then we play better baseball, like, we're going

0:17:51.560 --> 0:17:54.000
<v Speaker 1>to win more games in the short term, But then

0:17:54.080 --> 0:17:56.400
<v Speaker 1>eventually other teams are going to figure out what we're

0:17:56.440 --> 0:17:58.679
<v Speaker 1>doing and they're going to catch up to us, and

0:17:58.720 --> 0:18:00.960
<v Speaker 1>so you have this kind of like cat and mouse game.

0:18:01.760 --> 0:18:05.520
<v Speaker 1>And you know, so when you say, like does analytics work, yes,

0:18:05.600 --> 0:18:08.679
<v Speaker 1>it works, but like there is an aspect of like

0:18:08.880 --> 0:18:11.520
<v Speaker 1>what works only works for a short period of time

0:18:11.720 --> 0:18:15.600
<v Speaker 1>before everyone else catches on, and then you have to

0:18:15.920 --> 0:18:18.160
<v Speaker 1>like figure out the next thing I see, And at

0:18:18.160 --> 0:18:21.480
<v Speaker 1>that point you're kind of locked into having a statistic

0:18:21.520 --> 0:18:24.159
<v Speaker 1>team because if you didn't, then you would fall behind

0:18:24.240 --> 0:18:24.840
<v Speaker 1>everyone else.

0:18:25.040 --> 0:18:27.760
<v Speaker 2>That's exactly right, and that's certainly what happened through the

0:18:27.800 --> 0:18:29.920
<v Speaker 2>two thousands and the two thousand tens. Wow.

0:18:29.960 --> 0:18:32.480
<v Speaker 1>Yeah, okay, so that's baseball. But now it's gone on

0:18:32.560 --> 0:18:35.200
<v Speaker 1>to other sports after the success in baseball.

0:18:35.320 --> 0:18:38.399
<v Speaker 2>Absolutely, yeah, I would say the spread is not as

0:18:38.640 --> 0:18:41.480
<v Speaker 2>wide or as deep as it is in baseball, in

0:18:41.560 --> 0:18:46.280
<v Speaker 2>part because baseball is kind of fundamentally different than other

0:18:46.359 --> 0:18:50.520
<v Speaker 2>sports in the way that baseball has very discrete actions.

0:18:50.760 --> 0:18:54.560
<v Speaker 2>So it's just like the nature of the game is different. However,

0:18:54.960 --> 0:18:58.440
<v Speaker 2>yes it has spread to other sports, and yes NBA teams,

0:18:58.600 --> 0:19:01.719
<v Speaker 2>NFL teams, and NHL teams in soccer leagues in Europe,

0:19:01.720 --> 0:19:05.560
<v Speaker 2>and yes there are people doing statistical analysis for all

0:19:05.600 --> 0:19:09.240
<v Speaker 2>those teams at some level. And so in basketball you

0:19:09.359 --> 0:19:12.520
<v Speaker 2>have seen things like the way the teams are shooting

0:19:12.560 --> 0:19:15.440
<v Speaker 2>three pointers these days. I mean, when I was growing up,

0:19:15.880 --> 0:19:18.960
<v Speaker 2>our whole offensive strategy was let's get the ball into

0:19:19.000 --> 0:19:21.760
<v Speaker 2>the posts so that the tall players can put it

0:19:21.800 --> 0:19:24.600
<v Speaker 2>in the basket, because that's the best way for us

0:19:24.640 --> 0:19:25.720
<v Speaker 2>to score points. Right.

0:19:25.920 --> 0:19:26.040
<v Speaker 1>Uh.

0:19:26.840 --> 0:19:30.960
<v Speaker 2>Now, what they're doing, and this is definitely through analytics, right,

0:19:31.160 --> 0:19:33.800
<v Speaker 2>is that they realize that, well, okay, if we get

0:19:33.800 --> 0:19:36.000
<v Speaker 2>the ball down clost to the basket and somebody puts

0:19:36.000 --> 0:19:38.359
<v Speaker 2>it in, what's the expected value of that shot?

0:19:38.560 --> 0:19:39.240
<v Speaker 1>Two points?

0:19:39.280 --> 0:19:41.720
<v Speaker 2>It's two points. And let's say if it's close to

0:19:41.760 --> 0:19:43.600
<v Speaker 2>the basket, like I'm going to miss a couple, but

0:19:43.680 --> 0:19:46.360
<v Speaker 2>like I basically never missed, so like maybe ninety five

0:19:46.400 --> 0:19:49.399
<v Speaker 2>percent of those I make, So that's one point nine

0:19:49.640 --> 0:19:53.240
<v Speaker 2>expected points, right, Okay, But now if I shoot a

0:19:53.280 --> 0:19:56.479
<v Speaker 2>three pointer, it's for three points, so I only have

0:19:56.560 --> 0:19:59.160
<v Speaker 2>to make you know, like I f ix seventy percent,

0:19:59.440 --> 0:20:01.360
<v Speaker 2>that's two point one expected points.

0:20:01.560 --> 0:20:02.400
<v Speaker 1>Uh huh.

0:20:02.480 --> 0:20:06.040
<v Speaker 2>And these guys can make if they're open, nobody's there.

0:20:06.160 --> 0:20:09.000
<v Speaker 2>You know, they're making eighty percent of us. So a

0:20:09.040 --> 0:20:13.520
<v Speaker 2>three pointer becomes a much more attractive shot if the

0:20:13.600 --> 0:20:17.879
<v Speaker 2>probability of you're making that shot is sufficiently And so

0:20:17.960 --> 0:20:21.280
<v Speaker 2>that has absolutely changed the way that basketball's play. Teams

0:20:21.280 --> 0:20:24.680
<v Speaker 2>are now leaning more on three pointers absolutely, and that's

0:20:24.760 --> 0:20:27.359
<v Speaker 2>kind of like another one of those hidden moves that

0:20:27.520 --> 0:20:30.480
<v Speaker 2>was in the data, but nobody really believed because you know,

0:20:30.520 --> 0:20:34.680
<v Speaker 2>we want to see Michael Jordan's dunk the ball. Absolutely,

0:20:35.000 --> 0:20:38.200
<v Speaker 2>I think the shooting percentages have changed and that has

0:20:38.320 --> 0:20:41.320
<v Speaker 2>led to the evolution of the game that we see today.

0:20:41.480 --> 0:20:43.600
<v Speaker 1>But I wonder if it's sort of like an arms

0:20:43.720 --> 0:20:47.240
<v Speaker 1>race there too, because now if everybody is aiming for

0:20:47.280 --> 0:20:50.879
<v Speaker 1>three pointers, then everyone's going to adapt their defense to

0:20:50.960 --> 0:20:52.680
<v Speaker 1>block those three pointers for sure.

0:20:52.800 --> 0:20:55.000
<v Speaker 2>I mean, this is a big part of like in baseball,

0:20:55.119 --> 0:20:57.960
<v Speaker 2>how analytics is you know, ruining.

0:20:57.600 --> 0:21:00.600
<v Speaker 1>Baseball because like, if I go to baseball, I want

0:21:00.600 --> 0:21:03.320
<v Speaker 1>to see people hitting the ball and making dramatic plays,

0:21:03.400 --> 0:21:06.200
<v Speaker 1>not like, oh, you got walked. That's not as exciting.

0:21:05.920 --> 0:21:08.719
<v Speaker 2>Exactly, And that is more or less exactly. What has

0:21:08.760 --> 0:21:11.440
<v Speaker 2>happened over the last fifteen or twenty years is that

0:21:11.560 --> 0:21:14.119
<v Speaker 2>people like me and people who are doing jobs similar

0:21:14.160 --> 0:21:16.720
<v Speaker 2>to me sort of figured out, well, if we want

0:21:16.720 --> 0:21:19.480
<v Speaker 2>to win more games, like we need to draw more walks,

0:21:19.560 --> 0:21:21.800
<v Speaker 2>then we're not going to steal so many bases because

0:21:21.840 --> 0:21:24.520
<v Speaker 2>that turns out to be pretty risky. But it led

0:21:24.560 --> 0:21:27.520
<v Speaker 2>to a style of play that a lot of people,

0:21:27.560 --> 0:21:30.640
<v Speaker 2>including myself, find less interesting to.

0:21:30.600 --> 0:21:34.480
<v Speaker 1>Watch because it's prioritizing the long term goals that the game,

0:21:34.840 --> 0:21:36.879
<v Speaker 1>not the moment to moment excitement.

0:21:37.000 --> 0:21:42.320
<v Speaker 2>Absolutely, it's prioritizing winning the game, not making the game entertaining.

0:21:43.160 --> 0:21:45.080
<v Speaker 2>I mean, this is what people are talking about with

0:21:45.160 --> 0:21:48.000
<v Speaker 2>basketball and how it's the ruining basketball. They're talking about,

0:21:48.040 --> 0:21:49.600
<v Speaker 2>you know, instituting a four point.

0:21:49.359 --> 0:21:51.320
<v Speaker 1>Shot like from the half court kind of.

0:21:51.359 --> 0:21:56.280
<v Speaker 3>Yeah, something like that, Like basketball is going to become

0:21:56.359 --> 0:22:00.439
<v Speaker 3>people just standing around the middle of the court, basket

0:22:00.520 --> 0:22:01.879
<v Speaker 3>from the middle of the court.

0:22:02.600 --> 0:22:05.640
<v Speaker 1>Oh, I see, and that's not basketball. That's if you're

0:22:05.640 --> 0:22:08.880
<v Speaker 1>a fan, you'd be like, well, it's not basketball as

0:22:08.880 --> 0:22:17.919
<v Speaker 1>we know it. Could you say anything kind of about

0:22:17.920 --> 0:22:19.840
<v Speaker 1>how it has spread to other sports?

0:22:20.080 --> 0:22:23.520
<v Speaker 2>Yeah. One of my co authors on the paper that

0:22:23.560 --> 0:22:26.000
<v Speaker 2>we wrote a couple of years ago, Michael Lopez, was

0:22:26.080 --> 0:22:29.000
<v Speaker 2>hired by the National Football League a few years ago

0:22:29.080 --> 0:22:32.720
<v Speaker 2>to become their director of sports Analytics for the league.

0:22:33.040 --> 0:22:35.280
<v Speaker 2>And so I think you had baseball kind of like

0:22:35.359 --> 0:22:38.600
<v Speaker 2>leading the way. I think basketball came in, you know,

0:22:38.960 --> 0:22:41.800
<v Speaker 2>sort of after that, and I think the NFL husband

0:22:42.000 --> 0:22:42.680
<v Speaker 2>after that.

0:22:43.080 --> 0:22:44.879
<v Speaker 1>Okay, do you have any examples of it?

0:22:45.080 --> 0:22:47.320
<v Speaker 2>Yeah, Well, the thing that has attracted the most attention

0:22:47.560 --> 0:22:50.119
<v Speaker 2>is when to punt and when to go for it

0:22:50.160 --> 0:22:53.720
<v Speaker 2>on fourth down. So you know, in American football, yet

0:22:53.760 --> 0:22:57.200
<v Speaker 2>four downs to advance the ball ten yards, and if

0:22:57.200 --> 0:22:59.240
<v Speaker 2>you are able to do that, then you get another

0:23:00.800 --> 0:23:03.280
<v Speaker 2>to try again. But if you don't, it's the other

0:23:03.320 --> 0:23:07.280
<v Speaker 2>team's ball. And so what most football teams will do

0:23:07.520 --> 0:23:11.880
<v Speaker 2>is if it's fourth down and the ball is very close,

0:23:12.240 --> 0:23:13.960
<v Speaker 2>then like maybe they're going to try to get that

0:23:14.040 --> 0:23:16.639
<v Speaker 2>extra yard or two and keep going. But if not,

0:23:17.040 --> 0:23:19.920
<v Speaker 2>if it's like fourth down and eight yards to go,

0:23:20.359 --> 0:23:23.440
<v Speaker 2>then in their minds they're like, well, chances are we're

0:23:23.440 --> 0:23:25.800
<v Speaker 2>not going to make it. So if we give the

0:23:25.800 --> 0:23:28.679
<v Speaker 2>other team the ball here, they're going to be in

0:23:28.720 --> 0:23:31.800
<v Speaker 2>a position to maybe score against us. So what we're

0:23:31.840 --> 0:23:33.240
<v Speaker 2>going to do instead is we're going to kick the

0:23:33.280 --> 0:23:35.720
<v Speaker 2>ball as far as we can down the field, and

0:23:35.840 --> 0:23:38.440
<v Speaker 2>then even though we will have given the other team

0:23:38.520 --> 0:23:40.879
<v Speaker 2>the ball, we will have sent them all the way back,

0:23:41.000 --> 0:23:43.240
<v Speaker 2>you know, as far as we can. That's called a punt.

0:23:43.440 --> 0:23:46.760
<v Speaker 2>And then so the question becomes when should you go

0:23:46.800 --> 0:23:49.000
<v Speaker 2>for it on fourth down and when should you not?

0:23:49.920 --> 0:23:53.480
<v Speaker 2>And you know, so statistical analysts started looking at this,

0:23:53.760 --> 0:23:58.520
<v Speaker 2>and what they found was that generally teams were overly conservative,

0:23:58.880 --> 0:24:01.560
<v Speaker 2>that is, they did not go for it on fourth

0:24:01.600 --> 0:24:06.160
<v Speaker 2>down as often as the statistical analysis would suggest was optimal.

0:24:06.320 --> 0:24:08.560
<v Speaker 1>I see they said it chickened out, they're trying to

0:24:08.560 --> 0:24:08.960
<v Speaker 1>go for it.

0:24:09.000 --> 0:24:14.280
<v Speaker 2>Well, yes, so that's one of them prominent statistical analysis

0:24:14.359 --> 0:24:15.600
<v Speaker 2>contributions to football.

0:24:15.800 --> 0:24:18.400
<v Speaker 1>I see, general teams aren't going for it more, people

0:24:18.440 --> 0:24:19.720
<v Speaker 1>are putting it less.

0:24:19.880 --> 0:24:23.240
<v Speaker 2>Yes, there's been a movement definitely towards going for it more.

0:24:23.280 --> 0:24:26.080
<v Speaker 2>And now a lot of times they'll even have like

0:24:26.359 --> 0:24:29.800
<v Speaker 2>a little on screen graphic that's telling you, you know,

0:24:30.119 --> 0:24:32.239
<v Speaker 2>whether the team suppos to go for it or not.

0:24:32.560 --> 0:24:34.240
<v Speaker 1>Whoa, it's part of the broadcast.

0:24:34.320 --> 0:24:35.440
<v Speaker 2>Yeah, it's part of the broadcast.

0:24:35.600 --> 0:24:38.720
<v Speaker 1>Like there's an analytic team saying, oh, you know, every

0:24:38.720 --> 0:24:42.000
<v Speaker 1>time it's fourth and down between these two teams, they.

0:24:41.840 --> 0:24:43.280
<v Speaker 2>Should go for it exactly.

0:24:43.480 --> 0:24:46.000
<v Speaker 1>And in this case, it's sort of something fans could

0:24:46.040 --> 0:24:48.520
<v Speaker 1>agree on, right, Like fans want to see more people

0:24:48.640 --> 0:24:49.239
<v Speaker 1>going for it.

0:24:49.359 --> 0:24:51.920
<v Speaker 2>Yeah, that's a good point going forward on fourth down.

0:24:51.960 --> 0:24:55.360
<v Speaker 2>It's exciting, probably more exciting than punting on fourth down.

0:24:55.880 --> 0:24:58.720
<v Speaker 2>Don't remember exactly when this was, but sometime in the

0:24:58.840 --> 0:25:01.240
<v Speaker 2>Belichick era, but it was a big game between the

0:25:01.240 --> 0:25:04.679
<v Speaker 2>Patriots and the Colts and the Patriots had a fourth

0:25:04.720 --> 0:25:07.280
<v Speaker 2>and whatever, and they went for it and they didn't

0:25:07.280 --> 0:25:09.600
<v Speaker 2>get it, and Peyton Manning's team got the ball back

0:25:09.640 --> 0:25:13.600
<v Speaker 2>and then they went and scored. Oh you know, everybody

0:25:13.760 --> 0:25:18.399
<v Speaker 2>was talking about how stupid the Patriots were. And so

0:25:18.440 --> 0:25:20.840
<v Speaker 2>it was another one of these cases where a team

0:25:21.080 --> 0:25:25.639
<v Speaker 2>tried to pursue the analytically optimal strategy and a backfired

0:25:25.680 --> 0:25:28.520
<v Speaker 2>on them, and the sort of media and fan backlash

0:25:28.680 --> 0:25:31.920
<v Speaker 2>sort of overwhelmed all the times that maybe they did

0:25:31.960 --> 0:25:34.400
<v Speaker 2>go for it in other situations and made.

0:25:34.200 --> 0:25:38.040
<v Speaker 1>It it's like, you can't win with sports fans, can you, Yeah, right,

0:25:38.119 --> 0:25:41.639
<v Speaker 1>right right? And you said it spread into even things

0:25:41.680 --> 0:25:44.120
<v Speaker 1>like esports and chess. Yeah.

0:25:44.320 --> 0:25:47.720
<v Speaker 2>The concept of rating systems in chess very much goes

0:25:47.800 --> 0:25:51.560
<v Speaker 2>back many years. So things like ELO ratings were specifically

0:25:51.640 --> 0:25:52.840
<v Speaker 2>designed for chess.

0:25:53.040 --> 0:25:57.080
<v Speaker 1>Uh the quickest side here. An ELO rating, named after

0:25:57.119 --> 0:26:00.520
<v Speaker 1>the physicists and chess player arped Elo who invented it,

0:26:00.560 --> 0:26:03.960
<v Speaker 1>basically tells you how good you are at chess. A

0:26:04.040 --> 0:26:07.280
<v Speaker 1>lot of people play chess online these days, especially young people,

0:26:07.520 --> 0:26:09.840
<v Speaker 1>and so you might have had your kids or your

0:26:09.920 --> 0:26:13.760
<v Speaker 1>younger cousins talk about their ELO rating. For example, the

0:26:13.800 --> 0:26:16.639
<v Speaker 1>top chess players in the world have an ELO ranking

0:26:16.760 --> 0:26:20.040
<v Speaker 1>of about twenty eight hundred, whereas the beginner would start

0:26:20.080 --> 0:26:23.600
<v Speaker 1>it zero. But here's the thing. Your ELO rating is

0:26:23.640 --> 0:26:26.879
<v Speaker 1>not just where you stand relative to other players. The

0:26:27.000 --> 0:26:31.080
<v Speaker 1>formula for it actually tells you the probability of who's

0:26:31.119 --> 0:26:34.199
<v Speaker 1>going to win between two players. So you can use

0:26:34.240 --> 0:26:37.560
<v Speaker 1>it for say, figuring out exactly how much to bed

0:26:37.720 --> 0:26:40.800
<v Speaker 1>on a chess championship match, or how much money to

0:26:40.840 --> 0:26:44.119
<v Speaker 1>pay a chess player to be their sponsor. And yes,

0:26:44.240 --> 0:26:47.720
<v Speaker 1>these days chess players have sponsorship deals. But the same

0:26:47.760 --> 0:26:51.200
<v Speaker 1>idea is also used in other sports like tennis.

0:26:52.640 --> 0:26:55.600
<v Speaker 2>Like if you think about tennis versus chess from an

0:26:55.640 --> 0:26:59.240
<v Speaker 2>analytical player rating system perspective, like the same thing, right,

0:26:59.320 --> 0:27:02.160
<v Speaker 2>it's I see this person plays this person. They each

0:27:02.200 --> 0:27:04.760
<v Speaker 2>have a rating before the match, and somebody wins the match,

0:27:04.800 --> 0:27:06.439
<v Speaker 2>and then they each have a rating after the match.

0:27:06.840 --> 0:27:10.000
<v Speaker 2>What are the ramifications of the particular modeling choices that

0:27:10.040 --> 0:27:12.520
<v Speaker 2>we make when we use those rating systems?

0:27:12.880 --> 0:27:15.359
<v Speaker 1>I see? And esports? What do you know about the

0:27:15.440 --> 0:27:19.080
<v Speaker 1>using esport? Another quickest site here in case you didn't know.

0:27:19.480 --> 0:27:24.080
<v Speaker 1>Esports or electronic sports are a thing that's where people

0:27:24.119 --> 0:27:28.679
<v Speaker 1>compete on video games. League of Legends, Valoriant, counter Strike.

0:27:29.080 --> 0:27:33.000
<v Speaker 1>These are hugely popular competitions, with millions of viewers and

0:27:33.440 --> 0:27:35.720
<v Speaker 1>billions of dollars in prize money.

0:27:36.640 --> 0:27:39.760
<v Speaker 2>I think what's really interesting about esports is that in

0:27:39.880 --> 0:27:42.199
<v Speaker 2>all the things that we've talked about so far, the

0:27:42.240 --> 0:27:45.480
<v Speaker 2>game exists and then we have decided to collect data

0:27:45.520 --> 0:27:49.160
<v Speaker 2>about it. But in esports, that data is like inherently

0:27:49.320 --> 0:27:52.240
<v Speaker 2>part of the game itself because the games are happening

0:27:52.280 --> 0:27:56.200
<v Speaker 2>on the computer. Ah, so the computer is already keeping

0:27:56.359 --> 0:27:59.080
<v Speaker 2>track of all the things. You know, who's killing whom

0:27:59.119 --> 0:28:02.760
<v Speaker 2>and who's this portion of the board and how many WHOA.

0:28:02.800 --> 0:28:07.359
<v Speaker 1>It's built into the game where the game itself is

0:28:07.480 --> 0:28:09.600
<v Speaker 1>tracking every possible statistic.

0:28:09.920 --> 0:28:12.600
<v Speaker 2>That's how it works. I'm not much of any sports

0:28:12.720 --> 0:28:15.280
<v Speaker 2>participant in myself, but the papers that I read on

0:28:15.320 --> 0:28:18.040
<v Speaker 2>this subject a little better couple. Their approach is sort

0:28:18.080 --> 0:28:20.399
<v Speaker 2>of similar to what we've talked about with bisball or

0:28:20.440 --> 0:28:22.080
<v Speaker 2>any of these parts. It's sort of like what are

0:28:22.080 --> 0:28:25.520
<v Speaker 2>the best strategies for winning the game? And we've got

0:28:25.640 --> 0:28:29.400
<v Speaker 2>now millions and millions of games that people are playing

0:28:29.640 --> 0:28:32.800
<v Speaker 2>all the time, and we can analyze that data to

0:28:32.880 --> 0:28:35.320
<v Speaker 2>figure out, like, what are the strategies that pay off

0:28:35.359 --> 0:28:38.440
<v Speaker 2>for people. You know, which of these strategies tends to

0:28:38.720 --> 0:28:41.240
<v Speaker 2>produce the most kills or produce the most wills?

0:28:41.320 --> 0:28:44.320
<v Speaker 1>And you know, gosh, I don't know about you, but

0:28:44.560 --> 0:28:46.840
<v Speaker 1>I can't wait for the sequel to the movie Moneyball

0:28:47.200 --> 0:28:49.760
<v Speaker 1>where it's just Brad Pitt sitting on his sofa playing

0:28:49.840 --> 0:28:54.840
<v Speaker 1>Call of Duty all day. Okay, So that's the use

0:28:54.880 --> 0:28:58.080
<v Speaker 1>of math in sports. As we mentioned, it's the kind

0:28:58.120 --> 0:29:00.920
<v Speaker 1>of thing that you need people with PhDs in statistical

0:29:00.960 --> 0:29:05.040
<v Speaker 1>science to really compete in sports. Analytics uses a wide

0:29:05.120 --> 0:29:10.640
<v Speaker 1>range of mathematical models, including regression models, Bayesian inference, facial

0:29:10.720 --> 0:29:14.760
<v Speaker 1>statistics in more and more of these days. AI. But

0:29:14.880 --> 0:29:18.600
<v Speaker 1>here's the big plot twist or upset to use sports lingo.

0:29:19.000 --> 0:29:22.160
<v Speaker 1>It turns out all these models are still not the

0:29:22.200 --> 0:29:26.400
<v Speaker 1>most accurate way of predicting who's going to win a game.

0:29:27.120 --> 0:29:31.200
<v Speaker 1>There is another indicator which at its core has almost

0:29:31.240 --> 0:29:34.680
<v Speaker 1>nothing to do with math. So when we come back,

0:29:34.840 --> 0:29:37.600
<v Speaker 1>we'll talk about what this method is, and I think

0:29:37.640 --> 0:29:39.960
<v Speaker 1>you're going to be shocked to find out what it is.

0:29:40.520 --> 0:29:42.040
<v Speaker 1>Oh and also we're going to find out if the

0:29:42.040 --> 0:29:44.959
<v Speaker 1>Dodgers won the game I went to after all, So

0:29:45.080 --> 0:30:05.840
<v Speaker 1>stay with us till the last inning. We'll be right back. Hey,

0:30:05.880 --> 0:30:08.680
<v Speaker 1>welcome back. I'm here watching the LA Dodgers play the

0:30:08.720 --> 0:30:12.080
<v Speaker 1>Arizona Diamondbacks and it's the top of the last inning

0:30:12.400 --> 0:30:17.080
<v Speaker 1>and the score is Dodgers five, Arizona Diamondbacks four. Dodgers

0:30:17.080 --> 0:30:24.760
<v Speaker 1>are up by one point. But we're talking about sports analytics,

0:30:24.880 --> 0:30:27.080
<v Speaker 1>or the idea of using math and science to help

0:30:27.120 --> 0:30:29.840
<v Speaker 1>teams win at sports, and so far we're talked about

0:30:29.880 --> 0:30:32.240
<v Speaker 1>where this idea came from and how it spread to

0:30:32.280 --> 0:30:34.680
<v Speaker 1>almost every sport. Now, I don't know who's going to win.

0:30:34.960 --> 0:30:37.479
<v Speaker 1>Anything can happen in the next few minutes. But according

0:30:37.520 --> 0:30:41.200
<v Speaker 1>to doctor Balmer, there is a way to almost perfectly predict,

0:30:41.400 --> 0:30:44.280
<v Speaker 1>at least from a statistical point of view, exactly who

0:30:44.400 --> 0:30:47.400
<v Speaker 1>has a better chance at winning. And it has almost

0:30:47.440 --> 0:30:57.920
<v Speaker 1>nothing to do with mathematical models. The fourth idea, what's

0:30:57.960 --> 0:30:58.640
<v Speaker 1>the fourth idea?

0:30:58.880 --> 0:31:02.040
<v Speaker 2>Oh yeah, betting my At a certain point, you get

0:31:02.040 --> 0:31:05.120
<v Speaker 2>down to the fact that the best estimate that we

0:31:05.440 --> 0:31:09.640
<v Speaker 2>as human society have of estimating who's going to win

0:31:09.680 --> 0:31:12.000
<v Speaker 2>a particular game are the betting market oughts.

0:31:12.440 --> 0:31:14.640
<v Speaker 1>Is that right, that's the best estimate?

0:31:14.880 --> 0:31:17.880
<v Speaker 2>Yes, this is a result that has been corroborated time

0:31:17.920 --> 0:31:18.600
<v Speaker 2>and time again.

0:31:19.120 --> 0:31:20.960
<v Speaker 1>But who sets those betting odds?

0:31:21.040 --> 0:31:24.520
<v Speaker 2>People like me that work for sports gambling outfits. So

0:31:24.560 --> 0:31:27.600
<v Speaker 2>they set the lines, but then based on the money

0:31:27.640 --> 0:31:31.320
<v Speaker 2>that people bet, the lines can change, and so by

0:31:31.360 --> 0:31:34.400
<v Speaker 2>the time the game actually starts, you have a sort

0:31:34.400 --> 0:31:38.040
<v Speaker 2>of reflection of humanity's collective wisdom about who's going to

0:31:38.120 --> 0:31:38.680
<v Speaker 2>win this game.

0:31:39.200 --> 0:31:43.280
<v Speaker 1>That blew me away. You're relying on people's intuition because

0:31:43.440 --> 0:31:46.600
<v Speaker 1>the average sports better doesn't have a math, they don't

0:31:46.640 --> 0:31:48.760
<v Speaker 1>have access to the data, They just have a feeling.

0:31:49.080 --> 0:31:51.160
<v Speaker 2>Well, it's this notion of wisdom of the crowds.

0:31:51.240 --> 0:31:51.760
<v Speaker 1>Uh huh.

0:31:52.000 --> 0:31:54.960
<v Speaker 2>A lot of statistics is just like the average of

0:31:55.000 --> 0:31:58.440
<v Speaker 2>a lot of things is better than any one person's

0:31:58.480 --> 0:32:01.560
<v Speaker 2>one guess, you know, uh huh. And this is just that.

0:32:02.000 --> 0:32:04.880
<v Speaker 2>But with money at stake, which is where people tend to,

0:32:05.760 --> 0:32:09.520
<v Speaker 2>you know, use all of their collective wherewithal to make

0:32:09.520 --> 0:32:10.360
<v Speaker 2>the best estimate.

0:32:10.720 --> 0:32:12.640
<v Speaker 1>I see, there's a lot of noise, Like you never

0:32:12.720 --> 0:32:17.080
<v Speaker 1>trust one sports betting guide to their hunch, but if

0:32:17.120 --> 0:32:20.720
<v Speaker 1>you have a million of them, then collectively they sort

0:32:20.720 --> 0:32:23.480
<v Speaker 1>of have sort of absorbed all this data in their

0:32:23.520 --> 0:32:28.360
<v Speaker 1>squishy brains and have somehow projected that into their model

0:32:28.480 --> 0:32:31.240
<v Speaker 1>that averages out and somehow that gives you an estimate

0:32:32.040 --> 0:32:34.400
<v Speaker 1>and you're saying that's better than anything we can come

0:32:34.480 --> 0:32:39.800
<v Speaker 1>up with. Yes, what does that mean that it's better?

0:32:40.120 --> 0:32:43.440
<v Speaker 1>Like over time, it's more accurate over time. If you

0:32:43.560 --> 0:32:46.120
<v Speaker 1>say that it's a forty seven percent chance that this

0:32:46.200 --> 0:32:48.280
<v Speaker 1>is going to happen, that it actually turns out to

0:32:48.280 --> 0:32:51.600
<v Speaker 1>be a forty seven like forty seven times out of

0:32:51.640 --> 0:32:54.760
<v Speaker 1>one hundred in the future, like they actually win, They'll

0:32:54.760 --> 0:32:55.160
<v Speaker 1>be right.

0:32:55.720 --> 0:32:57.840
<v Speaker 2>It's calibrated. It's well calibrated.

0:32:58.080 --> 0:33:00.000
<v Speaker 1>Oh the time.

0:33:00.080 --> 0:33:03.280
<v Speaker 2>It's like, if the betting odds imply that the Cardinals

0:33:03.280 --> 0:33:06.080
<v Speaker 2>have a forty seven percent chance of beating the Padres,

0:33:06.240 --> 0:33:09.040
<v Speaker 2>then if you were to take all the games in

0:33:09.120 --> 0:33:12.960
<v Speaker 2>which the odds were the same as that game, uh huh,

0:33:13.000 --> 0:33:14.840
<v Speaker 2>the team I guess it would be the underdog in

0:33:14.880 --> 0:33:18.080
<v Speaker 2>this case, they would win forty seven percent of those games. Wo.

0:33:19.440 --> 0:33:23.280
<v Speaker 2>That's wild. It is wild. But it's also like it

0:33:23.360 --> 0:33:25.520
<v Speaker 2>has to be that way, because if it wasn't that way,

0:33:25.720 --> 0:33:28.000
<v Speaker 2>then you'd have a whole bunch of people who would

0:33:28.000 --> 0:33:30.880
<v Speaker 2>figure that out and they would bet on the other team,

0:33:31.360 --> 0:33:35.240
<v Speaker 2>and that would move the line right that's incredible.

0:33:35.400 --> 0:33:37.320
<v Speaker 1>So you're almost saying that the wisdom of the crowd

0:33:37.360 --> 0:33:40.480
<v Speaker 1>is better than someone with a PhD in statistics.

0:33:40.560 --> 0:33:44.240
<v Speaker 2>Yeah for sure. Well, because there's a bunch of people

0:33:44.240 --> 0:33:46.720
<v Speaker 2>with PH's and statistics who are part of the crowd.

0:33:47.040 --> 0:33:48.320
<v Speaker 1>Oh, I see, that's part of it.

0:33:48.320 --> 0:33:51.080
<v Speaker 2>It's like you got all those people, and you've got

0:33:51.120 --> 0:33:52.520
<v Speaker 2>a whole bunch of other people.

0:33:52.440 --> 0:33:55.080
<v Speaker 1>Just fans. Fans who knows who won in the last

0:33:55.160 --> 0:33:56.000
<v Speaker 1>one hundred games?

0:33:56.040 --> 0:33:57.120
<v Speaker 2>Maybe exactly.

0:33:57.280 --> 0:34:00.560
<v Speaker 1>Wow, that's wild. Okay, we have only a few minutes.

0:34:00.680 --> 0:34:02.479
<v Speaker 1>I'm going to a Dodgers game this evening.

0:34:02.760 --> 0:34:03.400
<v Speaker 2>Oh, wonderful.

0:34:03.440 --> 0:34:05.400
<v Speaker 1>What should I look out for? What should I expect?

0:34:05.600 --> 0:34:08.399
<v Speaker 2>I'm so excited for you. I love Dodger Stadium. It's

0:34:08.440 --> 0:34:11.080
<v Speaker 2>a great place to watch Basaka and enjoy the weather

0:34:11.600 --> 0:34:13.480
<v Speaker 2>and just enjoy the twilight.

0:34:13.800 --> 0:34:16.640
<v Speaker 1>Well we have show hey uh badding tonight.

0:34:16.400 --> 0:34:18.719
<v Speaker 2>Well you'll watch the greatest baseball player of all time?

0:34:18.760 --> 0:34:18.960
<v Speaker 3>Then.

0:34:19.040 --> 0:34:21.520
<v Speaker 2>Wow, I mean, the Dodgers are the best team in baseball.

0:34:21.560 --> 0:34:24.040
<v Speaker 2>I don't think anyone really doubts that. You know, they

0:34:24.120 --> 0:34:27.600
<v Speaker 2>have used analytics, they have gone deeper, and they have

0:34:27.719 --> 0:34:30.920
<v Speaker 2>put the resources behind it. So it's like moneyball is

0:34:31.320 --> 0:34:33.520
<v Speaker 2>sort of how do you win more games with less money?

0:34:33.600 --> 0:34:36.080
<v Speaker 2>But the Dodgers are doing moneyball with money.

0:34:37.440 --> 0:34:42.080
<v Speaker 1>It's like money moneyball. Yeah, okay, looked at a CBS

0:34:42.120 --> 0:34:46.920
<v Speaker 1>sport says the Dodgers are favorite to win, okay, minus

0:34:47.040 --> 0:34:49.400
<v Speaker 1>two sixty six favorite on the money line.

0:34:49.600 --> 0:34:51.239
<v Speaker 2>So, and I don't know that I'm going to be

0:34:51.239 --> 0:34:52.759
<v Speaker 2>able to do this off the top of my head,

0:34:52.760 --> 0:34:56.680
<v Speaker 2>but like, you plug that number into a fairly simple formula,

0:34:57.160 --> 0:34:59.239
<v Speaker 2>and that's going to tell you that the Dodgers have

0:34:59.360 --> 0:35:01.920
<v Speaker 2>a sixty of winning this game.

0:35:02.360 --> 0:35:02.760
<v Speaker 1>Okay.

0:35:02.840 --> 0:35:06.439
<v Speaker 2>And so now that gives us a sports analyst the

0:35:06.480 --> 0:35:09.640
<v Speaker 2>most accurate prediction for what's going to happen in this game.

0:35:11.960 --> 0:35:13.359
<v Speaker 1>All right, we'll see how it pans out.

0:35:13.400 --> 0:35:17.680
<v Speaker 2>Then, yeah, exactly, all right.

0:35:17.560 --> 0:35:20.360
<v Speaker 1>Did the Dodgers win after all? Here's the audio of

0:35:20.360 --> 0:35:51.480
<v Speaker 1>the last few moments of the game when, Yeah, the

0:35:51.560 --> 0:35:55.360
<v Speaker 1>Dodgers won, just like the fans, the mathematicians, and the

0:35:55.360 --> 0:35:59.759
<v Speaker 1>betting markets predicted. Thanks for joining us, see you next

0:35:59.800 --> 0:36:08.280
<v Speaker 1>time you've been listening to science stuff. Production of iHeartRadio

0:36:09.040 --> 0:36:12.000
<v Speaker 1>written and produced by me Or hitch Ham, edited by

0:36:12.080 --> 0:36:15.960
<v Speaker 1>Rose Seguda, Executive producer Jerry Rowland, and audio engineer and

0:36:16.000 --> 0:36:19.080
<v Speaker 1>mixer Kasey Pegram and you can follow me on social

0:36:19.120 --> 0:36:22.120
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0:36:22.160 --> 0:36:25.080
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