Latest / The Favorites Sports Betting Podcast / Analytics vs Expertise with Author Chris Jones

Transcript

Analytics vs Expertise with Author Chris Jones · The Favorites Sports Betting Podcast

0:00
Speaker 1: The Favorite podcast is presented by FanDuel Sports Book. There's
0:03
Speaker 1: no better place to make every moment more during the
0:05
Speaker 1: Super Bowl than with FanDuel, and new users can bet
0:08
Speaker 1: five dollars to win two hundred and eighty dollars in
0:11
Speaker 1: cash on either team to win. I love FanDuel because
0:13
Speaker 1: it offers great promos for both new users and existing
0:16
Speaker 1: users like me with an app that's safe, secure, and
0:19
Speaker 1: easy to use. Plus, I love combining multiple bets from
0:22
Speaker 1: the same game to build same game parlays. So if
0:27
Speaker 1: you're new, just download the fandel sports Book app to
0:29
Speaker 1: get started and sign up with promo code Favorites so
0:32
Speaker 1: they know I sent you. You You must be one and
0:34
Speaker 1: over in present in Arizona, Colorado, Connecticut, Indiana, Michigan, New Jersey,
0:38
Speaker 1: New York, Tennessee, Virginia, West Virginia, or Louisiana. New users
0:42
Speaker 1: only ten dollar first deposit required, must wager in designated
0:46
Speaker 1: offer market max bonus two hundred and eighty dollars bonus
0:49
Speaker 1: for Tennessee users fulfilled in side credit within seventy two hours.
0:51
Speaker 1: Tennessee side credit expires fourteen days after received. Restrictions apply
0:57
Speaker 1: Sea terms at sports book Dot fandel dot com gamboom
0:59
Speaker 1: problem called one eight hundred next step or text next
1:02
Speaker 1: step to five three three two in Arizona one eight
1:05
Speaker 1: hundred gambler or visit FanDuel dot com slash r G
1:08
Speaker 1: in Colorado, Indiana, New Jersey. In Virginia one eight seven
1:12
Speaker 1: eight nine seven seven seven seven, or visit CCPG dot
1:15
Speaker 1: org slash chat in Connecticut one eight seven zero seven
1:19
Speaker 1: seven for confidential help in Michigan one eight seven seven
1:22
Speaker 1: seven seven zero s t o P in Louisiana called
1:26
Speaker 1: the Tennessee Redline one eight hundred eight eight nine nine
1:28
Speaker 1: seven eight nine, or visit one eight hundred gambler dot
1:31
Speaker 1: net in West Virginia. Welcome to the Favorites the podcast
1:42
Speaker 1: from the Volume and Podcast Network. I am Chad Mellman,
1:45
Speaker 1: Chief Content Officer of the Action Network. We have a
1:48
Speaker 1: very special guest today. You could argue that my guest
1:51
Speaker 1: today is the greatest magazine writer of his generation. He's
1:58
Speaker 1: written for The Atlantic, He's written for Esquire, for which
2:01
Speaker 1: he wrote multiple stories that won national magazine Awards. The
2:07
Speaker 1: very first columnist I hired when I took over as
2:11
Speaker 1: editor in chief DSP in the magazine. He wrote some
2:13
Speaker 1: of the best stories we did at the magazine, and
2:15
Speaker 1: sort of the two thousand tens. He has written a
2:18
Speaker 1: new book that tries to undo everything that is important
2:23
Speaker 1: to me. It's called The Eye Test, A case for
2:27
Speaker 1: human creativity in the age of analytics. I can't think
2:32
Speaker 1: of a writer more equipped to try to tear down
2:37
Speaker 1: the way the world is going the Mr Chris Jones,
2:41
Speaker 1: how are you, buddy, dude? That was the nicest introduction
2:46
Speaker 1: of all time. You've earned it. We could talk about
2:49
Speaker 1: the amazing stories you wrote for the magazine. I will say,
2:53
Speaker 1: and I've said this to you before, there's a there's
2:55
Speaker 1: a seminal magazine piece that Right Thompson wrote about Michael
2:59
Speaker 1: Jordan and turning fifty. And I think it was two
3:03
Speaker 1: thousand twelve is when it came out. Maybe and um,
3:07
Speaker 1: maybe it was two thousand eleven, I can't remember exactly.
3:10
Speaker 1: That same year you wrote a story about a Japanese
3:15
Speaker 1: high school baseball pitcher that I just thought was better
3:18
Speaker 1: than writes Michael Jordan's story. It was like, you know,
3:22
Speaker 1: when right like is in a subject, he can like
3:26
Speaker 1: write you epic, epic stories. And this was a beautiful,
3:30
Speaker 1: amazing story that it was so inside, one of the
3:33
Speaker 1: people who was harder to get inside than anyone. But
3:37
Speaker 1: there was something about the Japanese baseball story that to me,
3:39
Speaker 1: and you should explain to people what it was. It
3:41
Speaker 1: actually speaks to a lot of what your book is about,
3:44
Speaker 1: which was so clean and interesting and dramatic in very
3:50
Speaker 1: subtle ways. I just loved it. Tell people what that
3:53
Speaker 1: was about, and then we can get into the book.
3:55
Speaker 1: Having enjoyed this conversation. So far, it's been good for
3:57
Speaker 1: my ego. Um. The story was about out a young
4:01
Speaker 1: Japanese high school picture expected to do great things. And
4:05
Speaker 1: in Japan there's a high school of tournament coation that happens.
4:09
Speaker 1: There's two spring and fall, but the Spring is the
4:11
Speaker 1: big one every district in Japan. Since team was astrate
4:15
Speaker 1: to me as sort of the Super Bowl plus Marty
4:17
Speaker 1: Gras plus July four. It's like the biggest that the
4:21
Speaker 1: country stops to watch this tournament and these kids play
4:24
Speaker 1: each other, and that that picture, you know, there's his
4:27
Speaker 1: team kept winning and he threw I think it was
4:29
Speaker 1: seven hundred and seventy two pitches in five days. Um
4:34
Speaker 1: and his arm fell off in the final basically and
4:37
Speaker 1: his career didn't really become what it was expected to become.
4:42
Speaker 1: And it was sort of an exploration of how the
4:44
Speaker 1: Japanese approach baseball, which is a very different way than Americans.
4:48
Speaker 1: It's a lot less individualistic, and that what he did
4:51
Speaker 1: was sort of sacrifice. It was he was helping his
4:53
Speaker 1: brothers try to win this tournament, and he had no
4:56
Speaker 1: regrets even though he lost, you know, potentially hundreds of
4:59
Speaker 1: mill in some of dollars, and that's just for me.
5:02
Speaker 1: It was. It was just such a different perspective on baseball.
5:04
Speaker 1: It was It was a really great experience. So thank
5:07
Speaker 1: you for your kind words about the story. But as
5:09
Speaker 1: an experience, as a selfish sort of travel and cultural experience,
5:13
Speaker 1: it was fantastic. I think about it all the time. Yeah,
5:16
Speaker 1: we really did you right on that one. You're really dead? Yeah, yeah,
5:20
Speaker 1: spectacularly right. T J. Quinn. I don't know if you remember.
5:22
Speaker 1: T J. Quinn joined me to do the TV side
5:25
Speaker 1: of things, and I was a little offside about it
5:27
Speaker 1: at first, but then I realized the TV side of
5:29
Speaker 1: things lived a lot better um, and so it was
5:32
Speaker 1: even that was great. I got some move hotels and yeah,
5:34
Speaker 1: it was fantastic. All right, So you wrote this book,
5:36
Speaker 1: The I Test, The Case for Human Creativity in the
5:39
Speaker 1: Age of Analytics. It's out now. I've read it. It's
5:42
Speaker 1: really good. Thank you. You would think it's counter to
5:45
Speaker 1: have someone like you on a podcast that is a
5:47
Speaker 1: betting podcast in which analytics are the core of what
5:51
Speaker 1: we do and at Action Network, analytics drive our entire engine, right.
5:54
Speaker 1: We are constantly on the lookout for the number, the metrics,
5:59
Speaker 1: the edge edge, lots of times driven by models and algorithms,
6:03
Speaker 1: which you lay waste too in your book, And yet
6:08
Speaker 1: I found it fascinating because it does speak to a
6:13
Speaker 1: lot of things that matter when you are trying to
6:17
Speaker 1: make decisions and why you can't just rely on analytics,
6:23
Speaker 1: including the Super Bowl that we just saw between the
6:26
Speaker 1: Bengals and the Rams. My co host Simon Hunter was
6:29
Speaker 1: a professional better. He and I in the podcast earlier
6:32
Speaker 1: this week talked about the need to have feel the
6:36
Speaker 1: eye test elements of what the Bengals did all season
6:39
Speaker 1: and incorporating that into our numbers. So my question for
6:42
Speaker 1: you is why do you hate analytics so much and
6:45
Speaker 1: the people who use them. I don't. I don't. This
6:49
Speaker 1: is the thing the book is going to get cast
6:52
Speaker 1: is like the anti money ball, and it's it's not
6:56
Speaker 1: that at all. What I'm trying to say is that
6:58
Speaker 1: data is useful. Models are useful. Algorithms in certain situations
7:02
Speaker 1: are useful, but they're not the entire answer to many questions,
7:07
Speaker 1: particularly in complicated situations. So they're pretty good at that.
7:11
Speaker 1: You know, I get the use of statistics in baseball
7:14
Speaker 1: when you're deciding whether to match up a particular picture
7:18
Speaker 1: with a particular hitter, totally understood. When things get more complicated,
7:22
Speaker 1: like when you're trying to dissect a hockey game, for instance,
7:26
Speaker 1: five guys playing together against five other guys, lots of
7:29
Speaker 1: moving parts, they can also be helpful, but maybe maybe
7:33
Speaker 1: don't give you the entire answer, and something like sports
7:36
Speaker 1: betting what I would argue. And by the way, I
7:39
Speaker 1: regret very deeply not having an entire chapter about gambling,
7:42
Speaker 1: which I should have done. Um, But the the idea
7:47
Speaker 1: that you can rely entirely on models, I don't see
7:52
Speaker 1: what your edges. Then unless your model just happens to
7:56
Speaker 1: be better than any other model on the planet, or
7:58
Speaker 1: your access to numbers is better, or your understanding of
8:02
Speaker 1: numbers is better. But if you're if you're not number
8:06
Speaker 1: one at that, and I feel like you must need
8:08
Speaker 1: some ten or that little flivver that that comes from
8:15
Speaker 1: field or knowledge or experience or or not. GOT. I
8:19
Speaker 1: wouldn't advocate FORGOT. For me, it's like experience wisdom, Like
8:24
Speaker 1: what's the value of that? And so you have your
8:27
Speaker 1: model and you have I'm assuming you plug in all
8:29
Speaker 1: sorts of numbers, but I'm assuming other people have those
8:32
Speaker 1: numbers as well. What's the difference? What's that? What's the
8:35
Speaker 1: competitive advantage of doing it exactly the same way as
8:38
Speaker 1: anybody else? Well, you're right, I mean your argument really
8:43
Speaker 1: is that the fallibility of the models that everybody uses.
8:47
Speaker 1: And like I said, you know, for the Super Bowl,
8:51
Speaker 1: I relied there were three experts at Action Network who
8:57
Speaker 1: we were podcasting with, who were doing a lot of shows,
8:59
Speaker 1: and they're a lot of experts at Action Network who
9:01
Speaker 1: are amazing, but three people who build ostensibly three different
9:06
Speaker 1: models coming from three entirely different approaches. The fallibility in
9:12
Speaker 1: the model is that there is a human element that
9:14
Speaker 1: builds the model. With any of these things, they're sort
9:18
Speaker 1: of treated as these objectives like hugely rational, like they
9:22
Speaker 1: were found in nature and we build the models, so
9:25
Speaker 1: the algorithms and the models contain all the bias that
9:29
Speaker 1: the people who make them contain. So if I'm building
9:32
Speaker 1: a model and I think something is particularly important, it
9:34
Speaker 1: needs to be waited a certain way. I'm adding a
9:36
Speaker 1: human input into what is allegedly an objective model, and
9:41
Speaker 1: it's it's that's not wrong. I mean, it's just I
9:44
Speaker 1: think it's important for people to be aware that numbers
9:48
Speaker 1: can be used as subjective instruments just as much as
9:51
Speaker 1: stories can, or just as much as gut can't. You know,
9:54
Speaker 1: It's it's it's this idea that math is black or
9:57
Speaker 1: white that sort of bothers me. It's it's it's not
10:00
Speaker 1: necessarily like statistics are used to lie all the time.
10:03
Speaker 1: I can massage numbers to make a certain case if
10:05
Speaker 1: I want to, or the opposite case if I want to.
10:08
Speaker 1: And what you're describing is three different people have all
10:11
Speaker 1: the same numbers, but they approached them with different um
10:14
Speaker 1: weights and measures, and that again, I think that's okay.
10:17
Speaker 1: But then if you're picking between those three models, like
10:19
Speaker 1: how do you decide what's the right model? That's that
10:21
Speaker 1: then you're sort of employing these other things. I think
10:24
Speaker 1: that I'm talking about, which is the best way I
10:28
Speaker 1: can describe. Stole this out of the New Yorker. Um
10:30
Speaker 1: the New Yorker, a guy named Burkhardt Builder wrote a
10:33
Speaker 1: story about a carpenter named Mark Ellison who was using
10:38
Speaker 1: a table saw to make a curved cut. Now, if
10:41
Speaker 1: you know anything about carpentry, table saws are designed to
10:43
Speaker 1: make straight cuts. That is the purpose of a table saw.
10:46
Speaker 1: Mark Allison, this master carpenter was using a table saw
10:49
Speaker 1: to make this intricate curve cut. While talking to Burkhardt
10:53
Speaker 1: Builder and an apprentice, he wasn't really paying attention to
10:56
Speaker 1: what he was doing, and Burkhard Builders like, how do
10:58
Speaker 1: you not have to focus on what you're doing? And
11:01
Speaker 1: the term that they came up with together was embodied analysis,
11:06
Speaker 1: which is like almost like muscle memory, but intellectual. And
11:11
Speaker 1: that that is the case that make is that you
11:14
Speaker 1: want to be the person who has the best embodied analysis.
11:18
Speaker 1: The carpenter used the example of Roberto Clemente how he
11:22
Speaker 1: would turn his back on a on a fly ball
11:25
Speaker 1: run to the spot where he knew the ball was coming,
11:28
Speaker 1: turn around and catching, and that sort of deep understanding
11:33
Speaker 1: of a situation has a value. It doesn't matter, uh
11:36
Speaker 1: that a model um might run slightly countered your your thinking.
11:41
Speaker 1: They're like, the model has is a worthy metric and
11:45
Speaker 1: your experience is a worthy metric. And what I'm saying
11:49
Speaker 1: is the best decision making comes with a delicate, nuanced
11:53
Speaker 1: combination of those two things. It's not a black or
11:56
Speaker 1: white way of thinking. And I used to be you
11:57
Speaker 1: know this about me. I used to be a very
11:59
Speaker 1: black and white thing like this is right, and this
12:01
Speaker 1: is wrong, this is universal and this is not the
12:04
Speaker 1: world doesn't work like that, and and for me, there's
12:07
Speaker 1: an advantage to being really smart and good about football
12:10
Speaker 1: is a great example, Like those models, did they incorporate
12:15
Speaker 1: anything about stress? Like like the super Bowl is a
12:19
Speaker 1: different game. So any game analysis that you've done, like
12:24
Speaker 1: did you look at which guys are going to perform
12:27
Speaker 1: under pressure or not? That to me is an element
12:30
Speaker 1: that you need to think about. But did the models
12:32
Speaker 1: think about that? I don't know. Well, a couple of things.
12:35
Speaker 1: On that one you would say you were a black
12:39
Speaker 1: and white thinker. I would just say you were judging too.
12:46
Speaker 1: I think I think what you're talking about is the
12:50
Speaker 1: best model ends up becoming a combination of learned experience
12:56
Speaker 1: and data. Right, It's like, and you wrote about this
12:59
Speaker 1: in the Look, it's more than trust your gut, um,
13:02
Speaker 1: it's really maybe trust your eyes. Right obviously, you know
13:06
Speaker 1: if if if you are if, if you know what
13:09
Speaker 1: you're talking about, lots of opinions are not valid. They
13:13
Speaker 1: are dumb, and my gut would be useless in a
13:16
Speaker 1: lot of situations because I don't have any knowledge, you know,
13:20
Speaker 1: to to back up my my feeling. What I'm saying
13:24
Speaker 1: is if you have that experience, if you have watched
13:27
Speaker 1: a thousand football games, and if you've been on a
13:29
Speaker 1: thousand football games, that has value. That knowledge has value.
13:34
Speaker 1: And I think we're we sort of live in a
13:36
Speaker 1: time when that human element is sort of dismissed as
13:40
Speaker 1: bad almost like it's it's dismissed as a rational or subjective.
13:44
Speaker 1: There's all these words that we use to describe that
13:46
Speaker 1: sort of that little eggs that that humans bring. The
13:49
Speaker 1: things I'm saying, I'm saying that has some worth. It's
13:53
Speaker 1: it's it's and especially in the situation like you're talking about,
13:56
Speaker 1: where everyone's got the same numbers, like what's going to
13:58
Speaker 1: separate why there's no business book? I could write where
14:01
Speaker 1: I go. You know what, the best thing you can
14:03
Speaker 1: do is follow the crowd. Just do what everyone else
14:06
Speaker 1: is doing. That's the right thing. Like you would think
14:08
Speaker 1: that was insane, but that's the argument that we have
14:10
Speaker 1: about analytics all the time, that it's the one right
14:13
Speaker 1: tool and will always find the right answer. It's it's nonsense,
14:19
Speaker 1: missing football. Well, you can still turn every Thursday into
14:23
Speaker 1: pay day with NBA on t n T on fan
14:26
Speaker 1: Duel sports Book. It doesn't matter if you would to
14:29
Speaker 1: lose fanduels giving all customers ten dollars back every Thursday
14:33
Speaker 1: in site credit. Just bet ten dollars or more on
14:36
Speaker 1: the same game parlay on any NBA on t NT game.
14:40
Speaker 1: Same game parlays like you combine the money, line points
14:42
Speaker 1: by player props, and more all into one wager. I
14:46
Speaker 1: like same game parlay bets because there are so many
14:49
Speaker 1: NBA markets to bet. I can root from my favorite
14:52
Speaker 1: players like Julius Randall, and it's the perfect way to
14:56
Speaker 1: turn a small bet into a big time score and
15:00
Speaker 1: gonna lose your guaranteed to get ten dollars added to
15:03
Speaker 1: your account. Betting on the NBA FANDOL sports Book is
15:05
Speaker 1: great because it's easy for me to find my bets
15:07
Speaker 1: and they have great live betting options. So get ten
15:10
Speaker 1: dollars back every Thursday in site credit winter lose with
15:14
Speaker 1: TNT Thursdays. If you're new to Fando, will just sign
15:17
Speaker 1: up with Promo code Favorites to make every moment more
15:20
Speaker 1: this NBA season. That's Promo Code Favorites exclusively on the
15:26
Speaker 1: FANDO sports Book app. You must be and over and
15:30
Speaker 1: president Arizona, Colorado, Connecticut, Indiana, Louisiana, Permitted parishes only, Michigan,
15:38
Speaker 1: New Jersey, New York, Tennessee, Virginia or West Virginia. Refund
15:41
Speaker 1: issued as non withdrawal side credit that expires in seven days.
15:45
Speaker 1: Max refund ten dollars. Restrictions apply see terms at sports
15:49
Speaker 1: book dot fandel dot com. Same game parlay available to
15:53
Speaker 1: multiple sports in all states on mobile and web. Gambling
15:57
Speaker 1: problem called one next up or text next step to
16:01
Speaker 1: five three three F two in Arizona one eight hundred
16:04
Speaker 1: gambler or visit vanduel dot com, slash RG in Colorado, Indiana,
16:09
Speaker 1: New Jersey. In Virginia one eight eight seven eight nine
16:12
Speaker 1: seven seven seven seven or visit CCPG dot org slash
16:16
Speaker 1: chat in Connecticut one eight hundred to seven zero seven
16:20
Speaker 1: one one seven for confidential health in Michigan called the
16:23
Speaker 1: Tennessee Red Line one eight hundred eight eight nine nine
16:25
Speaker 1: seven eight nine, or visit one eight hundred gambler dot
16:29
Speaker 1: net in West Virginia. You know, it's interesting I mentioned
16:36
Speaker 1: the Bengals Ram super Bowl. That's a big football game
16:39
Speaker 1: that happens here in the United States. I know the
16:42
Speaker 1: Gray Cup is Mike, Yeah, so it's like the Gray Cup.
16:45
Speaker 1: I guess it's a little bit. It's a little bit
16:48
Speaker 1: like a combination of sort of the Gray Cup and uh,
16:52
Speaker 1: the World Juniors Finals, maybe the Tournament of Hearts curling exactly.
17:01
Speaker 1: The thing that there were so many people who were
17:03
Speaker 1: betting the Rams. The Rams got the propondents of money.
17:06
Speaker 1: There were a lot of professional betters who liked the Rams.
17:09
Speaker 1: The Rams were opened his three and three and a
17:11
Speaker 1: half point favorites, It went up to four, It got
17:13
Speaker 1: up to four and a half and five in some places,
17:16
Speaker 1: and everyone was saying, the reason like the Rams is
17:19
Speaker 1: because the Bengals wins, we're flukey and there were too
17:25
Speaker 1: many numbers that we're sort of violating their principles and
17:32
Speaker 1: what their models said, and in every matchup, the Rams
17:36
Speaker 1: win head to head. And there were a lot of us,
17:39
Speaker 1: myself included Simon Hunter, who's professional better, Chris RayBan, Shaan Kroner,
17:44
Speaker 1: a lot of Action Network experts who watched these games
17:49
Speaker 1: and thought, sure, but if it's a fluke that happens
17:53
Speaker 1: twenty out of times over the course of the season,
17:57
Speaker 1: it's not a fluke, it's how they play. And if
18:00
Speaker 1: the Bengals only lost three games by more than three points,
18:04
Speaker 1: there's something happening within the context of those games that
18:07
Speaker 1: is immeasurable in the overall stats they have to account
18:11
Speaker 1: for some way. And I think that's a lot of
18:13
Speaker 1: what you're arguing. That's we're watching the game matters like stats.
18:17
Speaker 1: Stats can give you if you there's a the other
18:22
Speaker 1: football soccer Liverpool were built largely by two people who
18:28
Speaker 1: approached the game very differently. One they have a physicist
18:32
Speaker 1: named Dr Ian Graham, who literally does not watch the game.
18:36
Speaker 1: He strictly uses stats. He maintains this database of a
18:41
Speaker 1: hundred thousand players. He thinks that watching film or watching
18:45
Speaker 1: the game itself biases him. He doesn't want that. He's
18:49
Speaker 1: a very It's as sure and analytical approach as you
18:52
Speaker 1: can get. But you marry that or you marry that
18:56
Speaker 1: with Jurgen Klopp, who's the manager who as this. He
19:01
Speaker 1: played the game, he has an instinctual understanding of soccer.
19:05
Speaker 1: He has a fluid sort of slashing style. He's brought
19:09
Speaker 1: sort of the speed metal approach to the game, this
19:11
Speaker 1: really human element to it. And both of those things
19:15
Speaker 1: combined allowed them to win their first title in thirty years.
19:19
Speaker 1: One or the other I don't think would have done it.
19:22
Speaker 1: You're gonna needed that sort of sober second thought that
19:24
Speaker 1: the analytics gave him. The analytical mind needed that little
19:27
Speaker 1: what you're talking about, Well, if the numbers if the
19:30
Speaker 1: numbers on the Bengals seemed like flukes and yet there
19:34
Speaker 1: was you know how what you said twenty out of like,
19:37
Speaker 1: then it's not a fluke. At some point it becomes
19:39
Speaker 1: not an accident. Is how they play, And that you
19:42
Speaker 1: only get I think from sort of watching the games.
19:45
Speaker 1: Like statistics are are are can be indicative, but they
19:48
Speaker 1: don't tell the whole story. Possession is another great stat
19:51
Speaker 1: soccer that is used all the time. But there's such
19:54
Speaker 1: a thing is useless possession, Like when you're just passing
19:57
Speaker 1: it around in the midfield is not particularly construct Fruitful
20:01
Speaker 1: possession is a very valuable thing, but that requires some
20:05
Speaker 1: watching of the game to go, oh, that they're actually dangerous.
20:08
Speaker 1: It's not useless possession, it's dangerous possession. And that's that's
20:11
Speaker 1: what I'm talking about. Is that sort of well you're
20:13
Speaker 1: talking about is exactly right, and as it turns out,
20:18
Speaker 1: what was right. So the Bengals, if you got the Bengals,
20:21
Speaker 1: it was the right call. I bet the Bengals. Um lies,
20:26
Speaker 1: damn lies and statistics is what I keep thinking about
20:28
Speaker 1: whenever you're talking about sort of how statistics can be
20:31
Speaker 1: used to manipulate things. Also, you mentioned Liverpool. Uh, you'll
20:35
Speaker 1: be glad to know that I was in l A
20:37
Speaker 1: last week. On my flight out and on my flight back.
20:40
Speaker 1: H the two books I was reading were The Eye
20:43
Speaker 1: Test by Chris Jones and Lyrics by a famous Liverpool
20:47
Speaker 1: Lean named Paul McCartney. And I spent more time reading
20:51
Speaker 1: The Eye Test by Chris Jones than Lyrics by Paul McCartney. Um,
20:56
Speaker 1: even though even though I do take issue with something
21:00
Speaker 1: in the book, So, uh, this is the whole point.
21:07
Speaker 1: It's the whole point. You talked about Michael Lewis uh
21:10
Speaker 1: and Moneyball and sort of the impact that that Moneyball
21:12
Speaker 1: has had on this sort of concept, right, And for
21:16
Speaker 1: me like this, what Michael Lewis did is the epicenter
21:21
Speaker 1: of our business action network doesn't exist if Michael Lewis
21:24
Speaker 1: isn't writing about moneyball, if he's not capturing how people
21:31
Speaker 1: are thinking opportunistically about being fans of sports, If the
21:35
Speaker 1: Internet doesn't exist, if there's not a democratization of data,
21:39
Speaker 1: if if there's not a perfect storm of all these things,
21:42
Speaker 1: then all of a sudden, gambling doesn't become as widespread,
21:44
Speaker 1: it doesn't become as accessible, it doesn't become something that
21:47
Speaker 1: ultimately is legalized, you know, in many, many states, and
21:51
Speaker 1: continues to be legalized in more states. Um, you're right
21:55
Speaker 1: about the movie. I think the scene that you hate
21:58
Speaker 1: the most in the movie is my favorite scene, which
22:00
Speaker 1: is when God, Yeah, when Brad Pitt is sitting around
22:03
Speaker 1: with his scouts and they're all saying, we've got to
22:08
Speaker 1: you know, they're talking about he's got a good looking girlfriend,
22:11
Speaker 1: I love his swing, he's a five to a player,
22:12
Speaker 1: and Brad Pitt is saying, no, we don't have to
22:15
Speaker 1: replace Jason Giandi, we have to replace this number of hits,
22:20
Speaker 1: this number of runs, this number of times a guy
22:22
Speaker 1: gets on base, and let's do that anyway we can.
22:26
Speaker 1: That's how I think about building a team. And you
22:29
Speaker 1: hated that part of the part of the movie. I
22:32
Speaker 1: hated how the Scouts are portrayed. They were pertraders, like
22:35
Speaker 1: bumbling idiots, uh death. They all at hearing aids, they
22:40
Speaker 1: were all gray haired, they were combative, who's fabio like
22:44
Speaker 1: just they were portrayed as complete morons, which is what
22:48
Speaker 1: the analytics movement has done to like institutional knowledge. Like
22:52
Speaker 1: if you if you dare say analytics, but you immediately
22:57
Speaker 1: become moron. You're a BlooDye, you're a heathen, you'r you
23:02
Speaker 1: believe in fairies. Like it's it's it's an extremely negative
23:06
Speaker 1: like I've been attacked for what I think by analytics
23:09
Speaker 1: people and it's aggressive. It's like, and I'm the scouts.
23:14
Speaker 1: Those scouts found Roberto Clemented, The scouts found Derek Jeter,
23:19
Speaker 1: those scouts found all the great baby baseball players that
23:23
Speaker 1: we all grew up loving. That's not an accident. They
23:25
Speaker 1: weren't lucky. They knew something about baseball was their knowledge, uh, infallible. No,
23:32
Speaker 1: analytics taught us some really important things like clucke shitting,
23:36
Speaker 1: Like I I agree that cluck shitting doesn't exist. I
23:38
Speaker 1: agree that the sacrifice bunt is kind of dumb. You
23:41
Speaker 1: shouldn't do it. Those are things that analytics taught us.
23:44
Speaker 1: But all that knowledge, all the stuff that those guys
23:48
Speaker 1: new in their bones, has the value. So to make
23:52
Speaker 1: them seem like absolute morons I thought was like an injustice.
23:55
Speaker 1: So some very smart people and and and we could
24:00
Speaker 1: use more smart people. I just don't understand why you
24:02
Speaker 1: would ever limit your perspective to the point where it's like, well,
24:06
Speaker 1: I know you've seen the game for forty years. I
24:08
Speaker 1: don't care what you think. That's that's not that's that's
24:11
Speaker 1: that's ridiculous to me. That's like and we have this
24:14
Speaker 1: this this, Uh, there's the strain of anti expertise that's
24:17
Speaker 1: kind of crapt into society. That's really damaged it. Guess
24:21
Speaker 1: to me, like, I'm I'm getting worked up. My voice
24:23
Speaker 1: is about to crack. Um you know you see it
24:26
Speaker 1: with the COVID debates. Oh, I don't trust the experts
24:29
Speaker 1: because I have access to a computer. Well, they had
24:31
Speaker 1: access to forty years of knowledge, like, let's rely on
24:34
Speaker 1: that maybe a little bit. It's just that's why I
24:36
Speaker 1: didn't like that scene. It just treated them like morons,
24:38
Speaker 1: but not morals. They were just we're right about everything,
24:41
Speaker 1: but neither is analytics. It's like, I don't care why
24:43
Speaker 1: we're arguing. One has to be right and the other
24:46
Speaker 1: has to be wrong. They can both be right, and
24:48
Speaker 1: they're especially right when they work together to find the
24:50
Speaker 1: best answer. So a couple of years last was it
24:53
Speaker 1: last summer, I don't remember it was right now, it
24:56
Speaker 1: was the summer or twenty when the pandemic was sort
24:59
Speaker 1: of in full swing. I wrote a bunch of stories
25:02
Speaker 1: like this series of stories for Action Network that really
25:06
Speaker 1: traced the modern movement of sports betting. Started with UM
25:12
Speaker 1: this guy Charles McNeil who invented the point spread, and
25:15
Speaker 1: he was a mathematician and a trader in Chicago in
25:17
Speaker 1: the nineteen thirties, and then went to the development of
25:21
Speaker 1: sort of um more analytically driven models. And a guy
25:25
Speaker 1: named Roxy Roxboro who, by the way, UH was a
25:30
Speaker 1: son of Vancouver and was running a sports book and
25:33
Speaker 1: sort of some of the Wild West oil trading offices
25:39
Speaker 1: in Vancouver in the early seventies kind of got run
25:42
Speaker 1: out of town um, and he moved to Vegas and
25:45
Speaker 1: was betting and sort of developed this this concept that
25:49
Speaker 1: he could be a bookmaker to everybody because he was
25:52
Speaker 1: better at running the numbers, and so people started buying
25:56
Speaker 1: his numbers on a consultancy basis. So he was the
25:59
Speaker 1: bookmaker who was feeding the stardust, and he was feeding
26:01
Speaker 1: books and renals and all that kind of stuff. And
26:04
Speaker 1: then ended with a guy who was five years old
26:09
Speaker 1: and a genius math genius who studied you know, computer
26:12
Speaker 1: science at usc and got into bitcoin and you know,
26:17
Speaker 1: made millions of dollars betting on sports, and then moved
26:19
Speaker 1: to Costa Rican is now moving markets, right, And I
26:23
Speaker 1: spoke to Michael Lewis about it, UM, and I asked
26:26
Speaker 1: him like, and you comment about this, Is it better
26:33
Speaker 1: that people can relinquished responsibility for for their decisions because
26:38
Speaker 1: they've got a model and you have a line. I've
26:41
Speaker 1: become a professional header. Welcome to my kingdom of doubt.
26:48
Speaker 1: That's why you make the models so we don't have doubt,
26:50
Speaker 1: and so we can say we have a handrail on
26:52
Speaker 1: the roller coaster to hold onto I guess your argument,
26:56
Speaker 1: Michael Lewis was like, no, it's great we have these.
26:58
Speaker 1: I'm okay with people really relinquishing responsibility. The models do
27:02
Speaker 1: the work. You would disagree, Well, I would say the
27:05
Speaker 1: models give us an illusion of certainty that we find comforting,
27:09
Speaker 1: but it actually doesn't really exist. And we saw that
27:12
Speaker 1: with COVID all the time. Because the models predicted certain outcomes,
27:18
Speaker 1: you interfere with the process in various ways, positively or negatively,
27:23
Speaker 1: those outcomes change, like the models. You know, I I
27:28
Speaker 1: go back to and this was in money Ball too,
27:30
Speaker 1: like and you go to a ninety eightercent chance because well,
27:33
Speaker 1: that's gonna happen. But two out of one out of
27:37
Speaker 1: fifty that happens not infrequently. That's one out of how
27:43
Speaker 1: many times in poker that the one card you don't
27:46
Speaker 1: want comes on the river? It happens. And so we
27:48
Speaker 1: have this like the models give us some I don't
27:52
Speaker 1: know what you want to call it, but it's like this,
27:54
Speaker 1: this this idea that life is less chaotic than it
27:57
Speaker 1: really is. And I get the appeal of that, Like
28:00
Speaker 1: we want to feel like we're in control, Like we
28:02
Speaker 1: want to feel like we're masters of our own faith.
28:06
Speaker 1: I don't believe in not planning like I plan. I
28:10
Speaker 1: also don't believe in ignoring data. I'm not anti science,
28:13
Speaker 1: but the idea that if this outcome seems likely, then
28:17
Speaker 1: it's going to happen is really dangerous because then you
28:21
Speaker 1: don't prepare for the other thing. This is Trump winning
28:25
Speaker 1: the election, you know, like major consequential things happen all
28:29
Speaker 1: the time, where we think, well, that's not likely, and
28:32
Speaker 1: it's unlikely things happen, and so that that's certainty is
28:36
Speaker 1: like it's a false sense of security. That again I
28:40
Speaker 1: get we want the comfort because life seems really chaotic
28:42
Speaker 1: and difficult sometimes, but it's not real. I think it
28:46
Speaker 1: gives you. It lulls you into this sense that that um,
28:52
Speaker 1: that things are gonna go the way you think they're
28:53
Speaker 1: gonna go. And my life has taught me that sometimes
28:56
Speaker 1: you get plunged in the mouth and then what really happened?
28:59
Speaker 1: Then then you've you decide who you are, Like those
29:01
Speaker 1: are the moments when And the idea of abdicating responsibility
29:04
Speaker 1: in the numbers, I mean, why do we want anyone
29:07
Speaker 1: to advocate responsibility seems hugely unethical. Well, I don't know
29:10
Speaker 1: the machine told me to do it. How how mental
29:12
Speaker 1: would that make you? If someone said if you went
29:14
Speaker 1: to the bank and you said, well, there's this problem
29:16
Speaker 1: and they were going, well, the machine decided that was right.
29:19
Speaker 1: Would make you insane? You like, tell the machine that
29:23
Speaker 1: is wrong? You know it's so this is this is
29:26
Speaker 1: a larger debate about the path we're on as a
29:29
Speaker 1: society and if it's good or bad to be so
29:32
Speaker 1: removed from each other. And I think it's ultimately bad.
29:36
Speaker 1: I don't know, brother, there are days I'd love to
29:37
Speaker 1: addicate responsibility completely. People love it. That's that's why. Because
29:42
Speaker 1: you can say, well, the model told me to do it,
29:44
Speaker 1: and you obeyed the math, and the math told me.
29:46
Speaker 1: I get the idea that you wanted to outside of
29:48
Speaker 1: your yourself. But it's like it's it's I don't know,
29:52
Speaker 1: it's not right. It's that you can tell yourself whatever,
29:54
Speaker 1: lie do you want to tell yourself. But I mean,
29:56
Speaker 1: look at the game last night, Like that game, if
29:58
Speaker 1: we really want to break it down on you, analyze
30:01
Speaker 1: the hell out of it in advance. Other people did
30:03
Speaker 1: as well. That game really came down to two officiating
30:06
Speaker 1: calls like that, that element it exists, so you could
30:13
Speaker 1: plan all you want. Can you plan for officials suddenly
30:17
Speaker 1: deciding to not make a call that was pretty agree
30:19
Speaker 1: us and then to make a call that seems soft,
30:21
Speaker 1: Like those are the that's the that's that game, and
30:25
Speaker 1: it's like, so everything you put into it came down
30:27
Speaker 1: to that. That's hard to think about it that way
30:29
Speaker 1: because you want to think, oh, no, I knew what
30:31
Speaker 1: I was doing, and I bet the bangals because I
30:33
Speaker 1: did some smart analysis and you did. But the ultimately
30:36
Speaker 1: the score line was dictated a lot by two calls
30:39
Speaker 1: that were pretty random. Uh, you're right. That's also something
30:44
Speaker 1: that we talked about it in our podcast earlier in
30:47
Speaker 1: the week, is that at the end of the day,
30:49
Speaker 1: the officials had a much bigger impact on this game
30:52
Speaker 1: than they should have. But listen, I I actually love
30:56
Speaker 1: everything you're sort of advocating for in the book. I
30:58
Speaker 1: think for anyone who listens to this podcast, which tends
31:00
Speaker 1: to be people who are looking for answers, looking for
31:02
Speaker 1: ways to mitigate risk, looking for models and predictions that
31:08
Speaker 1: help them find a three percent edge instead of being
31:12
Speaker 1: but being in one of the best sort of moments
31:18
Speaker 1: in the book for me, and this is what I
31:20
Speaker 1: want you to answer before we get out of here
31:22
Speaker 1: is you talked about how the best time for people
31:27
Speaker 1: to shine is during life storms, right, the idea that
31:31
Speaker 1: extreme moments are when people are going to outshine the
31:36
Speaker 1: machines because of their imagination. Give me an example that
31:41
Speaker 1: you can think of from the sports world when that's
31:46
Speaker 1: been true. Oh gosh, sports. So in the book, I
31:51
Speaker 1: talked about you know, weather, literally climate because the models
31:55
Speaker 1: that we use the forecast weather are less and less
32:00
Speaker 1: um good at predicting extreme weather because it's the models
32:05
Speaker 1: I've never seen it before. Um so, like the data
32:09
Speaker 1: mining or you know, it's basically relies on the future
32:13
Speaker 1: behaving something like the past, and then climate it doesn't.
32:17
Speaker 1: The same thing happened, you know, there was wildfires in
32:21
Speaker 1: California and iPhones weren't capturing how red the skies were,
32:25
Speaker 1: and people thought it was like this tech conspiracy to
32:27
Speaker 1: make it seem less bad than it actually was. And
32:29
Speaker 1: it's just because the algorithm that operates your camera just
32:33
Speaker 1: didn't understand what it was seeing and it was like, well,
32:35
Speaker 1: those skies can't be that red. I'm going to correct it,
32:38
Speaker 1: you know, because it wasn't looking at a volcano it
32:40
Speaker 1: was trained to know that volcanoes are red. Those guys
32:42
Speaker 1: generally aren't that red, and so it was fixing a
32:44
Speaker 1: problem that that was in fact reality. I'm trying to
32:48
Speaker 1: come up with a good sports example where it's like
32:50
Speaker 1: where the past did not behave Well, look, you had
32:55
Speaker 1: one in the book. It might not be about imagination
32:57
Speaker 1: and pressure, but the whole zero experience I thought was
33:03
Speaker 1: a great reflection of lack of imagination of what could
33:07
Speaker 1: happen not you know, I was trying to come up
33:12
Speaker 1: with a possible Barry Zito was a negative one. Yeah,
33:15
Speaker 1: because statistically Barry Zito seemed like a lock when when
33:18
Speaker 1: the Giants signed him, Yeah he was. I know, wins
33:22
Speaker 1: and losses are sort of fraught, but he was and
33:24
Speaker 1: eleven when the a score more than three runs. Uh,
33:28
Speaker 1: he'd never had an injury. He projected to be better
33:34
Speaker 1: than Hall of Fame pictures and almost every category if
33:37
Speaker 1: you follow the stats. Uh. And what this was This
33:41
Speaker 1: was one of the times when I really started thinking
33:43
Speaker 1: about what I believe in because I would have these
33:46
Speaker 1: arguments with people about Barry Zito, who I knew as
33:48
Speaker 1: a human being that spent a lot of time with him,
33:52
Speaker 1: and I think he was damaged by the contract itself
33:55
Speaker 1: when he signed with the Giants record contract, and I
33:58
Speaker 1: think Barry it was a pretty sensitive soul, just just
34:04
Speaker 1: took on too much weight and it screwed up how
34:06
Speaker 1: he picked. Because the way he picked, he talked about
34:10
Speaker 1: being an instrument that the universe played. You just let
34:12
Speaker 1: it flow through you, you know that flow state, and
34:15
Speaker 1: you you don't think about what you're doing. He talked
34:18
Speaker 1: about being consciously something, you know, working in your subconc
34:21
Speaker 1: consciously working in your subconscious like a like flipping a
34:24
Speaker 1: switch and just turning everything off. And the contract made
34:28
Speaker 1: it impossible for him to do that because now there's
34:30
Speaker 1: all this outside influence and pressure and analytics. Guys were like, well, no,
34:34
Speaker 1: there's more foul territory in Oakland than San Francisco, so
34:38
Speaker 1: we didn't get those foul ball outs. I'm like, that's
34:41
Speaker 1: not That can't be the whole answer to why berries
34:43
Speaker 1: you don't fell off a cliff as soon as he
34:45
Speaker 1: moved over to the Giants, Like, foul territory at home
34:48
Speaker 1: is one small factor, and it's just the idea that
34:51
Speaker 1: he that the contract might damage him never entered the
34:54
Speaker 1: conversation and never entered the conversation from the giant's perspective,
34:58
Speaker 1: It never entered berries thinking until that happened, his agents thinking,
35:01
Speaker 1: of course, he wants the biggest contract. Like when Barry
35:04
Speaker 1: found out about the contract, he was out with a
35:06
Speaker 1: friend having sushi and sashimi and he choked, you know,
35:10
Speaker 1: Scott Boris text them the numbers and and and he
35:13
Speaker 1: choked on his fish. And I'm like, that matters, That matters,
35:18
Speaker 1: And so I think when you're signing that's a great example. Actually,
35:21
Speaker 1: when you're signing a player, like will this contract change
35:25
Speaker 1: how he plays? Will it add pressure to him? Or
35:28
Speaker 1: will it make him rise to greatness? And that the
35:30
Speaker 1: numbers won't help you there. That's when you come down
35:33
Speaker 1: and talking to someone and go on, what kind of
35:35
Speaker 1: person is this? Is he gonna rise to the occasion
35:37
Speaker 1: or is he gonna choke? And that's, you know, a
35:40
Speaker 1: super interesting debate that I think you can't really answer
35:45
Speaker 1: with a spreadsheet. That comes down to being in a
35:48
Speaker 1: room with somebody or watching how they play, or watching
35:51
Speaker 1: them in moments of stress. And it's it's that for me,
35:54
Speaker 1: is that like that three percent you're talking about, that
35:56
Speaker 1: that and of course retroactively you can go. Of course Barrios,
36:00
Speaker 1: it was gonna fall off a cliff. Of course that
36:02
Speaker 1: contract was gonna screw with his head. I mean, but
36:05
Speaker 1: we didn't see it at the time, and that's that's Yeah,
36:07
Speaker 1: that is a good example of negat I'm trying to
36:08
Speaker 1: come up with a positive one where where you know,
36:13
Speaker 1: like Bryson the Shambo and golf is really interesting to
36:15
Speaker 1: me because he's playing in a different way and they
36:18
Speaker 1: keep trying to tweak the game to mass with him
36:21
Speaker 1: and um, and it's it's like they don't want what
36:24
Speaker 1: he's doing, and that that sort of give and take,
36:28
Speaker 1: like Canny survived golf screwing with him is sort of
36:31
Speaker 1: a really interesting thing for me to watch because he's
36:34
Speaker 1: pure analytics. Basically, Yeah, you're right about this book, like
36:38
Speaker 1: he is going to completely reimagine and we've talked about
36:41
Speaker 1: this in action. Jason Sobel has written about it really well,
36:44
Speaker 1: like completely reimagine how he's going to swing the club
36:49
Speaker 1: and what it is important to him, which is basically
36:53
Speaker 1: hit the ball as far as you fucking can. Yeah,
36:56
Speaker 1: he's changed what Matt, how what matters. It's not your store,
37:00
Speaker 1: it's your boss. Speed is your swing. Speed is your
37:02
Speaker 1: and and golf doesn't like it. And so you know,
37:06
Speaker 1: there's an interesting thing happening with Bryson right now. We're golf.
37:10
Speaker 1: The golfers themselves voted to ban green reading books, like
37:14
Speaker 1: the books that had these beautiful laser red maps of
37:17
Speaker 1: every green they played and they were perfectly legal. Bryson
37:20
Speaker 1: used them all the time to figure out lines, because
37:24
Speaker 1: Bryson was good at Bryson has a literal chart that
37:28
Speaker 1: he consults. This is the this is the distance, this
37:31
Speaker 1: is the slope, this is the blind, this is how
37:33
Speaker 1: hard I hit it. And he knows, you know, he
37:36
Speaker 1: can control how hard he hits it. But the line
37:38
Speaker 1: reading was a big advantage to him. It helped them,
37:41
Speaker 1: and they took it away, and they took it away.
37:44
Speaker 1: It's like a pretty purposeful rebuke of how he plays
37:47
Speaker 1: the game. Like, yeah, they took it away from everybody,
37:49
Speaker 1: but it's it's really gonna mess with Bryson, like I
37:52
Speaker 1: think you could. This year is going to be really
37:54
Speaker 1: interesting to watch it because you know, he built this
37:57
Speaker 1: system that now no longer can be used. And that's life.
38:01
Speaker 1: That's life. This is Rice is also in a way
38:04
Speaker 1: the argument against pure analytics because I can always mess
38:07
Speaker 1: with the system a little bit and then what now?
38:11
Speaker 1: And that's I guess the book. The book is basically
38:14
Speaker 1: how do you succeed when you get asked what now?
38:17
Speaker 1: And for me, the answer isn't always numbers. Sometimes you
38:21
Speaker 1: just gotta be an awesome person. That's a lovely sentiment
38:25
Speaker 1: to end it on. Chris, I hope the book is hopeful.
38:30
Speaker 1: I hope it is. Hopefully it's totally people. People should
38:35
Speaker 1: read it. It's a great book because there is like
38:39
Speaker 1: not even in sports, right you mentioned in polling in
38:45
Speaker 1: COVID in the way the economy is being discussed in sports.
38:50
Speaker 1: That really is the reason we want to be here
38:52
Speaker 1: and the thing that we want to escape from the
38:55
Speaker 1: COVID and the economy and the politics and the environment, um,
39:00
Speaker 1: all of which are sort of discussed in the book. Uh.
39:04
Speaker 1: Like this helps us find clarity when we're looking for
39:08
Speaker 1: new ways to be fans. And for that reason alone,
39:13
Speaker 1: I would say everyone go by the eye test. A
39:18
Speaker 1: Case for Human Creativity. Emphasis on the Creativity in an
39:23
Speaker 1: Age of Analytics by multiple National Magazine Award winner Chris Jones.
39:30
Speaker 1: Thank you, chat that's lovely to hear your boys, and
39:32
Speaker 1: thank you for your earlier patriot Nation in my career.
39:34
Speaker 1: No matter to love whatever. Man, you don't even mention
39:36
Speaker 1: it in the book. It's like kind of weird it
39:40
Speaker 1: did it didn't matter. It helped a lot of my thinking,
39:43
Speaker 1: helped a lot of my thinking. Okay, thanks, all right.
39:47
Speaker 1: I want to thank Chris Jones, brilliant writer, wrote an
39:50
Speaker 1: amazing book called The Eye Test. Also want to remind
39:55
Speaker 1: everybody I talked a little bit about this in the
39:57
Speaker 1: podcast on Monday, the Super Bowl review. Sign and I
40:00
Speaker 1: have gotten a lot of questions about how we'll handle
40:03
Speaker 1: the show now that NFL season is over. Don't worry.
40:06
Speaker 1: We will still be here every Tuesday and Thursday. We'll
40:10
Speaker 1: cover everything that matters in the NFL off season, the combine,
40:14
Speaker 1: the draft, free agency, all of it. It is a huge,
40:18
Speaker 1: huge part of what we're going to continue to do.
40:20
Speaker 1: We'll also cover NFL betting one of one educational stuff.
40:23
Speaker 1: We'll talk about how Simon uses gametape, how he builds
40:26
Speaker 1: his models. We finally have time for all that stuff. Honestly,
40:30
Speaker 1: the podcast got so big so fast. We were always
40:33
Speaker 1: in the midst of covering games and talking about games,
40:35
Speaker 1: and people have a ton of questions, so we want
40:36
Speaker 1: to get to those, but the gambling season is never over.
40:39
Speaker 1: That's why we're going to talk about all the stuff
40:41
Speaker 1: we love betting on between now in the summer, including
40:43
Speaker 1: NBA March Madness, Oscars Masters, all that good stuff. Stay tuned,
40:49
Speaker 1: it's going to be a ton of fun between now
40:53
Speaker 1: an NFL training camp. I love you, no