0:00

What's happening, people? Welcome back to

0:02

the show. My guest today is

0:04

Seth Stevens-Davidowitz. He's a data scientist,

0:06

economist and an author. Basketball

0:08

is one of the most popular sports

0:11

on the planet. Seth has used advanced

0:13

AI to statistically analyze everything about the

0:15

players, their backgrounds, hand span, height, first

0:17

names and more to uncover some of

0:20

the wildest trends in the game. Expect

0:23

to learn what percentage of American men

0:25

over seven feet tall are in the

0:27

NBA, why there is a huge outlier

0:29

in the most common name of all

0:31

NBA players who the best height adjusted

0:33

player of all time is, just how

0:35

important genetics are in basketball, whether the

0:37

draft is actually effective and

0:40

much more. Even if

0:42

you're not a basketball fan, this is so

0:44

fascinating. Someone that has used AI and chat

0:46

GPT and a bunch of other advanced tools

0:49

to just do the money ball of

0:51

basketball. It's really, really cool. Seth's been on the show

0:53

a bunch of times before and this is a bunch

0:55

of stats I actually dropped on Rogan's show last week.

0:57

So if you enjoyed that, you're going to enjoy this.

1:00

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Want. to miss them Ladies

4:01

and gentlemen, please welcome

4:03

Seth Stevens-Dividwits. What

4:23

percentage of seven-footers are in the

4:26

NBA? To the

4:28

best of our knowledge, it's about

4:30

one in seven, which is enormous.

4:32

Pablo Torres, the first guy who

4:34

calculated this, I've done a similar

4:36

calculation, and everyone seems to unite

4:38

around this number, around one in seven, which is

4:40

just insane. Is there any other pursuit,

4:42

glamorous pursuit, where one trait gives you a

4:44

one in seven chance of

4:47

reaching the absolute pinnacle of that field?

4:50

I don't think so. Think about all the

4:52

six in seven people that could have been

4:54

on an NBA player's wage. Yeah,

4:57

they must feel terrible. Dude, you blew it.

5:00

I guess they can...a lot of them

5:02

are probably playing abroad. They're probably our

5:04

basketball players regardless, and having fun, and making

5:06

a living playing a game, but they're not

5:09

getting the NBA wage for sure. How

5:11

rare is seven-foot height?

5:15

Being seven-foot or above is one in 650,000 height. Wow,

5:20

that is such a genetic lottery.

5:25

Yeah,

5:27

I don't think there's any other gene

5:29

that gives you such a chance of being a famous

5:33

multimillionaire. Yeah, that's

5:36

a good point. What else do you learn about height? One

5:38

of the things that's interesting about height and basketball

5:42

is each inch roughly doubles your

5:44

chances of making the

5:46

NBA throughout the height distribution. If

5:49

you're six-foot tall, you have twice the chances of

5:51

becoming an NBA player than if you're five-foot eleven.

5:53

If you're six-one, twice the chances. If you're six

5:55

feet, all the way out, if you're seven-two, you

5:57

have twice the chance of being seven feet tall.

6:00

and one, like throughout the height distribution. What

6:02

that means is just, there's this enormous

6:04

difference in probability of reaching the NBA.

6:06

We said one in seven chance. If

6:08

you're a seven footer, if you're under

6:10

five 10, which is the average height

6:12

of an American male, you have a one

6:14

in 3.8 million chance of

6:16

reaching the NBA. Like it's basically impossible.

6:18

I mean, there are exceptions. Uh, you know, I talk

6:20

a lot in the book about, uh,

6:22

one of my favorite players, mugsie bogs,

6:25

five foot three and played 14 seasons

6:27

in the NBA. Uh, so

6:29

it's not impossible, but it's pretty close

6:31

to impossible and probably not worth putting

6:33

much energy even trying. What

6:35

are the disadvantages of

6:38

being tall from a player perspective? Well,

6:41

I think there are, there

6:43

is a, if you look at the tallest humans

6:46

in history, uh, many

6:48

of them are over eight feet tall and

6:51

just about all of them, it's due to

6:53

a thyroid disease. Uh,

6:55

you literally, there's a growth hormone that

6:57

just over produces, uh, growth

7:00

or hormone is overproduced. There have been,

7:02

there has been at least one NBA player who got

7:04

to his height through a thyroid disease. That's George

7:06

Murr son. Some people might remember him. He was,

7:08

he'd be also as an actor for a little

7:10

bit. Uh, and he was literally,

7:12

it was a disease that gave him that height.

7:15

His parents were average height. And if you're, uh,

7:18

that tall just from a disease,

7:20

you're going to have all kinds of problems, a lot of the

7:22

tallest people in history, very few of the tallest people

7:24

in history even make it past the age of 40. Uh,

7:28

but I think one of the other things that's interesting is

7:30

that seven footers

7:32

are just taller NBA players in general

7:35

are just way worse athletes. Any way

7:37

we can measure it, they jump much

7:39

less high. They're much slower. They're worse

7:41

shooters. They are this kind of

7:43

surprise. Now I don't think anybody showed this before.

7:45

They're worse in the clutch. They can't handle pressure

7:47

to the same degree shorter NBA players can. And

7:50

I think the reason for this is just

7:52

because the select the advantage of being tall

7:54

is so enormous that you kind of don't

7:56

have to be as good at anything else.

8:00

You know, if you're six feet tall

8:02

and you're competing against millions of other

8:04

people for that point guard spot, you

8:06

better be an insane athlete.

8:08

You know, the six foot NBA players,

8:11

they run as fast as a sprinter.

8:13

They jump as high as a high

8:15

jumper. Uh, they shoot as

8:17

well as anybody in the world can

8:19

shoot. Uh, they can handle pressure incredibly.

8:21

They're just so good to beat out

8:24

millions of other people to that spot.

8:26

If you're seven feet tall and you're competing

8:28

with dozens of other men for your spot,

8:30

you don't have to be that good. You

8:32

just have to be one in seven. Good.

8:34

So, you know, the average seven footer, his

8:36

vertical leap is only a little bit higher

8:38

than the average person could, could achieve with

8:41

enormous practice. Uh, he's slower than

8:43

most than an average runner on a

8:45

high school track team. Uh, he shoots

8:47

worse than an average high school basketball

8:50

player. He handles pressure worse than an

8:52

average high school basketball player. He's just

8:54

not that great, but

8:56

he is really, really tall. It

8:59

begs the question. Why

9:01

are tall players so prioritized?

9:03

If, if they're less

9:06

good psychologically, cardiovascularly,

9:10

physically, whatever, why, why do they keep

9:12

getting selected? Well, because they are, it

9:16

is an advantage. Uh, they grab more rebounds, they

9:18

block more shots. I mean, the basket is up

9:20

there or maybe I'm yeah. The

9:22

basket is up there. Uh, the basket

9:24

is not in the ground. The basket is in the sky. Uh,

9:27

you know, 10 feet above the ground. And I

9:30

think when that's the case, it's a huge advantage

9:32

to be really tall, to be able to reach higher, uh,

9:35

to be able to get higher, to block shots, to grab

9:37

rebounds, to do all these things, to be able to get

9:39

your shot off without being blocked, without it being blocked. I

9:42

wondered whether you were going to have some sort

9:45

of an insight, the Moneyball style insight where you

9:47

were going to say all

9:49

of the NBA teams need to start

9:51

drafting more six foot two people because

9:53

you know, the trade-off

9:56

that you get for athleticism from

9:58

a wider pool of potential. potential

10:00

people is greater than

10:02

the advantage you get from being 7-1 or

10:04

whatever? No, I don't think that's true.

10:07

It is legitimately true

10:09

that Shaquille O'Neal dominated

10:12

the NBA for many seasons, even though I'm

10:15

a better free throw shooter than Shaquille O'Neal.

10:17

That's one of the core skills of basketball,

10:19

and I, who am not particularly good and

10:21

never play, can hit a higher percent of

10:23

my free throws than Shaquille O'Neal can. But

10:25

he legitimately, I wouldn't say to the

10:28

Lakers, hey, have you thought of taking

10:30

up Seth? He shoots free throws

10:32

better than Shaq. I legitimately think

10:34

Shaq dominated basketball, but it is

10:36

kind of a weird, unfair advantage.

10:39

It does feel almost like a little, I don't

10:41

know, as a fan of the game, it feels like

10:44

it's almost a bug in the game that height is

10:46

such an advantage. Like the

10:48

ideal sport, you shouldn't be

10:50

able to reach the top of a sport the

10:52

way George Bjorsson did through a

10:54

growth hormone disorder. It

10:58

feels like off in how an

11:01

athletic pursuit, what

11:04

it should take to reach the top of

11:06

that athletic pursuit. But yeah, I'm not telling

11:08

players, don't cut

11:11

throw LMB'd because he can't jump as

11:13

high as a six

11:16

foot player. He still does help the

11:19

team, but it definitely is true that

11:21

they are worse athletes. Yes, laughs

11:23

in thyroid disorder. All

11:29

right, so what about, are you

11:31

able to compare like for like different

11:34

players of different heights and say what

11:36

if Mugsy Bogues had

11:38

been 6'7", how good would he

11:40

have been? Yeah, I was able

11:42

to mathematically figure this out, which was the

11:44

most fun I've ever had on any study

11:46

I've ever done. You know, as a shorter

11:48

man, I'm about 5'9", I think on a good

11:50

day. So I think I kind of

11:53

did this calculation. I ranked

11:55

people, I called it Mugsy's, which

11:58

stands for metric for understanding game

12:00

given sporting individuals effectiveness and

12:03

size. And I've ranked

12:05

every player, you know, the math is in

12:07

an appendix for those who are really curious,

12:09

uh, how good they would be if they

12:11

were the same height, how many mugsies they'd

12:14

have. And number one is mugsie Bogues. Who's

12:16

just achievement is so

12:19

ridiculously insane to be an NBA

12:21

player for 14 seasons. Even

12:23

if he wasn't the greatest NBA player, he

12:25

was a decent NBA player for 14 season.

12:27

That's five foot, three inches tall. It's insane.

12:29

You know, other players, Earl Boykins and

12:32

Spud Web rank really high. Michael Jordan,

12:34

interestingly, still ranks number nine on a

12:37

height adjusted. Despite being like six six or something.

12:39

Yeah. Cause he was so, so good. So he

12:41

is legitimately one of the greatest at

12:43

his craft you've ever seen. But

12:46

if mugsie and Michael were the same height, I

12:49

think mugsie, I think it's unambiguous in the

12:51

data, the way I cut the data that

12:53

mugsie would be the more dominant player, mugsie

12:55

would be the one who would be making

12:57

the documentaries about who he'd think is the

12:59

quintessential at mastering his craft, at determination,

13:03

at work ethic and all

13:05

these other things that we now associate with Michael,

13:07

Michael had enormous talent,

13:10

enormous drive, enormous work

13:12

ethic, enormous anything.

13:14

And he also had enormous height, which, you know,

13:17

some of these other guys didn't have. What

13:20

do, like, why is it

13:22

that players come from the countries that they

13:24

do? Obviously basketball, wildly

13:27

overrepresented by the

13:29

USA. But if one

13:34

in seven people over seven

13:36

feet tall, why are

13:38

Scandinavian countries that I think have got

13:40

the tallest average height in the world,

13:43

why have we not seen loads of

13:45

Danes or Norwegians or something? Yeah.

13:47

So a big thing is a popularity

13:51

of basketball obviously, obviously

13:53

plays into how many basketball players a country

13:56

produces, and they're really only three

13:58

regions of the world. where basketball

14:01

is extraordinarily popular. The

14:04

United States where it was invented, the Baltic

14:07

States, former Yugoslavia. So if you're growing

14:09

up playing basketball, you know, the average

14:12

person, I'm sure there are countless

14:14

people around the world who, if they started

14:17

practicing when they were five, could shoot a

14:19

ball like Steph Curry or could

14:21

do, you know, everything with a basketball like

14:23

James Harden, but they never even think to

14:25

do that, they're playing soccer or they're playing

14:27

some other sports. So that's really important. There

14:30

are some subtle things that go into how,

14:33

how many basketball players the

14:35

country produces, one that I found, which I

14:37

found very, very interesting. And after you say

14:40

it, it's extremely obvious that

14:42

predicts how many basketball players the country

14:44

produces is volleyball popularity, because

14:46

there's only one other sport that

14:48

uses height the same way

14:50

basketball does, and that's volleyball. So the

14:53

average volleyball player has basically the same

14:55

body type as the average small forward

14:57

in the NBA, about six foot eight

14:59

on average, you know, recently thin, a

15:02

enormous leaper. And I didn't know this, I'm

15:04

such a, you know, an American

15:07

that I'm like, who the hell cares

15:09

about volleyball? So I, I,

15:11

excuse my naivete, but in writing this

15:13

book, I found that, you

15:16

know, in Iran, volleyball is five

15:19

times more popular than basketball. And there

15:21

are numerous countries around the world where

15:23

volleyball is more popular than basketball. Uh,

15:25

it's more popular than basketball in Brazil,

15:27

in Bulgaria and Russia and Italy and

15:29

Puerto Rico. And what you see

15:31

is in these countries where volleyball is more popular

15:33

than basketball, you see fewer, uh, NBA

15:36

players than you'd otherwise expect. Uh,

15:39

and particularly fewer forwards than you'd otherwise expect,

15:41

because a lot of these taller people, these

15:43

six foot eight, six foot nine people are

15:45

playing volleyball instead, you know, in the United

15:47

States, Carmelo Anthony and LeBron James,

15:50

when they grew their enormous height, I don't think

15:52

anybody was like, Hey, have you thought of spiking

15:54

a ball? Uh, you know, that's,

15:56

that's the dream. Uh, but the guys

15:58

who grow to be six, eight. six, nine, six,

16:00

10 in Bulgaria. The

16:03

dream is to spike a volleyball, which is

16:05

a horrible financial decision. No,

16:08

like I think I talk

16:10

about this player from Bulgaria who leaps

16:12

higher than anybody has ever been measured in

16:14

the NBA and he makes

16:16

300,000 euros a year. Which

16:19

is a great salary. That's not,

16:21

that's not terrible, but that is

16:23

so far below the NBA minimum

16:25

salary. Like someone's shoe allowance for

16:27

one week in the NBA. Yeah.

16:29

You, I, I, if

16:31

any, if any, uh, enormous men

16:34

in Bulgaria or Brazil or Iran are

16:36

listening to his podcast right now, I want

16:38

to tell you practice your free throws, not

16:41

your spiking. That's where the money is in

16:44

the world. That that's, that's the real, uh, win

16:46

I would say. Just

16:49

how genetically predisposed

16:52

or predetermined is basketball

16:55

success. Enormously basketball is

16:57

enormously genetic, more genetic than pretty much

16:59

others, any other sport we can measure.

17:01

The way to see this is

17:03

the prevalence of identical twins in

17:06

basketball. There have been a enormous number of parents,

17:08

parents of identical twins who have reached the

17:10

NBA, 11 pairs of twins have reached the NBA.

17:13

All 11 of them have been identical. And

17:16

this is not true in other sports. Uh,

17:19

more than 10% of

17:22

brother pairs of brothers in the NBA have

17:24

been identical twins way higher

17:26

than other sports. That's a

17:28

dead giveaway that genetics are

17:30

driving basketball ability because identical twins, unlike

17:33

fraternal twins or unlike other brothers share

17:35

a hundred percent of their genes, not

17:37

50% of their genes. So if one

17:39

happens to get a really good draw

17:42

of genetics, the other is going to

17:44

get that same draw. And, uh,

17:46

I did a calculation that probably more

17:48

than half of I,

17:50

if a, if a player is in the NBA

17:52

and he has an identical twin, he has

17:55

a more than 50% chance of also being

17:57

in the NBA. Uh, like if you.

18:00

that same drop genetics you're like

18:02

destined to be. There.

18:04

Are amazing player as well. Now a huge

18:06

reason for this of course is because height

18:08

is so bored and height is very genetic.

18:10

About eighty percent genetic. But. A

18:12

lot of other skills. Said are

18:14

were a lot of other traits they're

18:17

born in: Basketball had size, arm, or.

18:19

Leg Wingspan. Of. Vertical

18:21

leap sprinting speed also really,

18:23

really genetic. That's. Well, seems

18:26

like the sport designed in a

18:28

lab to rely on genetics. like

18:30

this route. It's heavily uses the

18:32

skills that are seventy eighty ninety

18:34

percent you decks and doesn't really

18:36

use the skills that are that

18:38

are twenty thirty forty percent genetic

18:40

that some other sports do. What

18:42

I did, his skills that a

18:44

twenty thirty forty percent. So.

18:46

Reaction time I handed net where

18:48

the are left ear righty is

18:51

much less genetic. I he

18:53

had die coordination much less genetic

18:55

so. Something. Like shooting in

18:57

the Elite, the Olympic sport which is

18:59

really hand eye coordination. that's not gonna

19:01

be fun orientated basketball isn't hand eye

19:03

coordination. Yeah, know if there is definitely

19:06

important importance of at hand eye coordination

19:08

for yes, relative to the other sport,

19:10

your relative to baseball for example, which

19:12

is all hand eye coordination to hit.

19:14

A bet you're all of baseball. Is.

19:17

Being. Able to. You. Know hate.

19:19

You get the swing to hit the

19:22

ball which is hand eye coordination or

19:24

a reaction time reflexes. That's right, that's

19:26

not as genetic that baseball is just

19:28

so dependent on them. Or where's basketball?

19:31

The skills that are more important? Hi

19:33

Wingspan! I you know, Vertical

19:35

Leap. Why is why you

19:38

the sun's size. So. Important.

19:40

Yeah. That actually I hadn't realized until

19:43

I wrote this book. I.

19:45

Basically, the ability to palm a ball

19:48

is. A. I'd go get my

19:50

hands in. The screen is either daily of

19:52

the palm, a ball. Now

19:54

I reveal I did not have

19:56

society a suffered a lie leonard

19:58

hands another reason. The never

20:00

mind go and the your

20:02

waving around the Donald Trump's

20:04

hands ah but I I

20:06

I. I and

20:09

being able to palm a ball is

20:11

hugely valuable to grab a rebound with

20:13

one hand, or to be able to

20:15

dribble better with the Billie Piper had

20:17

upon the ball. Really, really valuable. I.

20:20

Phil. Jackson and coached famously

20:22

coached both Michael Jordan and

20:24

Colby Bryant. And. He

20:26

was asked if you could pick one player.

20:28

Who. Would you pick? And he

20:30

said Michael Jordan because you're in had

20:33

enormous hands and Colby Bryant didn't. And

20:35

co be bright admitted the one thing

20:37

he change about. His. Body as you

20:39

wish you had bigger heads. Are. So tight

20:41

and don't in the basketball world that hands

20:43

are valuable in a lot of all time

20:46

greats. Had a dorm his hands even. For.

20:48

Their high. You know. whether it's yeah, it's or

20:50

wilts. Or. Sad how how big do

20:52

these hands get? Twelve. It's twelve

20:55

inch had. With what you know the

20:57

average about eight inches suggests very very

20:59

your voice yeah like on this is

21:01

like a foot Logs I. Yeah.

21:03

That's insane. That's insane. Yeah the pads

21:05

and you could look at pictures of

21:08

the alkali. Leonard is another player with

21:10

legendarily large. had your look at pictures

21:12

of his head, their freakish. I

21:14

had them. I It turns out

21:17

that. As and the eight

21:19

teams have known that he had size

21:21

is really important. But it doesn't

21:23

seem like they quite knew just how important it

21:25

was said. if you look at. The.

21:27

Last. Year at the abbey

21:29

a guy by they measure players' hands help that

21:32

he and with the players. And. Players

21:34

with wide he adds historically have

21:36

done better. You. Know by by

21:38

advanced metrics said you'd projects based on

21:41

there are based on their draft spot.

21:43

And. Players with your tiny hands, the

21:45

Donald Trump pads or they're just awful

21:48

players. The Us I think seventeen of

21:50

my team players. Who. had he

21:52

adds below eight inches below average upper form

21:54

below their draft spot and most of them

21:56

just couldn't even be at the a fiery

21:59

so as I think it's known that hand

22:01

size is important. I don't think

22:03

it's been appreciated just how important it is, that

22:05

it is up there with the height and the

22:07

wingspan, the skills, the traits that we know are

22:10

really, really important, the vertical leap. Are

22:12

there any other sports that you know of

22:15

that are highly genetically influenced

22:17

in the same way that volleyball and

22:19

basketball are? Track

22:21

and field seems to be very genetically

22:23

influenced, also dominated by identical twins. If

22:26

you look at track and field,

22:29

the Olympic track and field athletes, the

22:33

percent of same sex siblings that are identical

22:35

twins is up there with basketball. And

22:38

I think sprinting speed, particularly,

22:40

seems also very

22:42

genetic, that Usain Bolt

22:44

or whatever. I

22:47

want to bet on his son to be

22:49

a tremendous runner. So that

22:52

is another sport

22:54

that is highly, highly

22:56

genetic. How

22:58

important are your parents beyond

23:00

the genetics thing? Yeah, so

23:02

the average American male has a 1 in 36,000 chance

23:05

of reaching the NBA. The

23:09

average son of an NBA player has a 1

23:11

in 43 chance of reaching the

23:13

NBA. So 1 in 36. Are

23:15

you able to 1 in 36 to 1 in 40? 1

23:18

in 36,000. To 1 in 43. Are

23:20

you able to control for

23:22

the physical inheritance, like the height

23:24

and all the rest of it?

23:27

Yeah, a little bit. It's a little hard

23:30

to do. But it clearly, so that's a

23:32

744 times higher chance of reaching the NBA

23:34

than a son of an NBA player. Now,

23:36

a lot of that is genetics, but it's

23:38

pretty clear it's not all genetics. And

23:41

if you have a father who was a professional

23:43

player, was an NBA player, you're going to

23:46

get really good coaching from an early age.

23:48

And one of the things I saw in the data is

23:51

sons of NBA players on many dimensions, they

23:53

look very similar to other NBA players. They

23:57

have similar heights, they have similar weights, their stats

23:59

are pretty similar. are mostly,

24:02

but they shoot free throws extraordinarily

24:04

well. So the

24:06

average NBA player shoots free throws at a 75% clip.

24:11

Sons of NBA players shoot free throws at an 80% clip.

24:14

And that's a 5% point, it's a very big difference

24:16

in free throw shooting. And

24:20

8% of the top 50 free throw

24:22

shooters of all time have been sons of

24:24

NBA players, whereas only 2% of

24:26

NBA players more generally are sons of NBA

24:28

players. The greatest free throw

24:30

shooter of all time, Steph Curry, son

24:33

of an NBA player, Del Curry. And

24:36

you see just, you know, Devin Booker,

24:38

many, many

24:41

NBA players, Clay Thompson,

24:43

many NBA players,

24:48

extraordinary free throw shooters. One of the things

24:50

that's interesting, and okay, so why is

24:52

that? Well, form is

24:55

so important in shooting. And

24:57

if you have an NBA player for a

24:59

father, they're going to be helping you on a

25:02

form, your form from a very young

25:04

age. And

25:08

that's a huge advantage in working, working your

25:11

shot from a very young age is just

25:13

a huge advantage. One thing you see

25:15

among NBA players, it's very interesting, NBA

25:18

players, their sons, they tend to

25:20

be shorter than they were because there's regression. Regression

25:22

to the mean. Yeah. You

25:25

know, Clay Thompson's father was

25:27

a number one pick as a center.

25:29

He was six foot 10 and he

25:32

was about a 60% free throw shooter,

25:34

not extraordinary free throw shooter. Clay

25:36

Thompson's only six foot six, but he's

25:39

an 80% plus free throw shooter. So

25:41

what you see is the physical traits,

25:43

they regress to the mean, but

25:46

the shooting, which requires that early training,

25:48

the form, they're just much better at

25:50

that. So there have been many examples

25:52

of NBA players who

25:55

were power forward centers themselves and they

25:58

have sons who are shooting guards. So

26:00

they don't get quite as much of the height they

26:04

had, but they get that early training

26:06

to improve the shooting. Yeah,

26:09

very interesting. I feel like I missed my

26:12

shot because I also read that Chris

26:15

is the most common name for

26:18

black NBA players. So if only I could

26:20

have fixed the problem of not being black.

26:22

Yeah, that was your first mistake. Yeah,

26:24

well, I guess I

26:26

was not black before I was called Chris. So

26:29

yeah, maybe. So Chris

26:32

is the most common name among NBA players.

26:34

Now, why is that? That seems just like

26:36

a random piece of trivia. It

26:39

gets to a bigger question of what's

26:41

the socioeconomics of NBA players. And

26:44

for a long time,

26:46

conventional wisdom was

26:48

that the NBA

26:51

was disproportionately sampling from

26:53

people from rough backgrounds, tough backgrounds,

26:56

the ghetto, impoverished,

26:58

single parents. And

27:00

the idea behind that was if

27:03

you're, let's say, a black boy,

27:07

impoverished in the ghetto, and

27:10

you're pretty good at basketball, that

27:12

is your one chance of getting

27:14

out, escaping your hardship, escaping your

27:17

circumstance, to become an NBA great.

27:19

And you will do whatever it takes, work

27:21

as hard as is

27:24

required to reach the top of basketball.

27:26

Whereas if you're the son of a

27:29

lawyer and a doctor in the suburbs,

27:31

and yeah, you're pretty good at basketball, well,

27:35

you have so many options that

27:37

you're not going to spend day

27:39

and night practicing basketball, devoting yourself

27:42

to this pursuit. That

27:45

has never been true. There is

27:47

initially a study by Josh Dubrow and Jimmy

27:49

Adams that showed that both

27:51

among Caucasians and African Americans,

27:56

being from an upper middle class or

27:58

above family is a huge advantage

28:00

in reaching the NBA and I've

28:02

done my own study at NBA

28:04

players much less likely than the population

28:07

at large and the black NBA players much

28:09

less likely than black population at large to be born to

28:11

a single mother, to be born to a teenage mother. On

28:14

any way you can look at the data being

28:17

from a two-parent home,

28:21

upper middle class or middle class or

28:23

above huge advantage to reaching the NBA

28:25

and the most maybe interesting

28:29

data point for that is the names of NBA

28:31

players. There is a paper by

28:33

Roland Fryer and Steve Levitt that

28:35

found that among the African-American population

28:38

you can tell the demographics of someone pretty

28:40

well just based on their name and

28:43

that African-Americans

28:45

from higher socioeconomic backgrounds are more

28:48

likely to be given common names,

28:51

names that are very popular in the population at

28:53

large, African-Americans from lower

28:55

socioeconomics, from poverty, from the

28:57

ghetto, more likely to be given

29:00

rare unique names, names that nobody else is

29:02

given that year. So an example of that

29:05

I'm pretty sure is LeBron. Now

29:07

LeBron is probably a common name because you

29:09

know everybody wants to name their kids after

29:11

LeBron but when LeBron was born and he

29:14

was born to a more difficult background, a

29:16

single 16 year old mother in Akron, Ohio,

29:19

LeBron was a unique name. It wasn't

29:22

given to other people that

29:24

year. It

29:26

wasn't a name that other people had

29:28

as well and if you look at

29:31

NBA players, they're half as likely, black

29:33

NBA players are half as likely as

29:35

the black population writ large to have

29:37

unique names. They're much more likely to have

29:39

names Chris, Michael, Marcus. So in the book

29:42

I have a whole word cloud of the

29:44

names of NBA players and the most common

29:46

name by a pretty wide

29:48

margin is Chris. You know yeah,

29:51

so you think of Chris Paul or Chris

29:53

Bosh, many other examples and Chris Paul is

29:55

a great example of a player

29:57

from two parents. home,

30:01

middle class, the family joined him on

30:03

an episode of Family Feud. You

30:06

know, that's kind of where the NBA is

30:08

getting their players much

30:11

more than conventional wisdom told

30:13

us. So Michael Jordan, another example,

30:16

grew up middle class, two-parent

30:19

family in North, born in

30:21

Brooklyn, raised in North Carolina,

30:23

very stable upbringing. That's

30:26

where the NBA is getting

30:28

their talent, by and large. Now,

30:31

of course, not always, which means we have to

30:33

give that much more credit to the LeBron James

30:36

of the world, because they really did overcome a

30:38

lot. If

30:40

only he'd been five foot three as

30:42

well. Yeah. It's seven inch hands. Yeah.

30:45

Then we'd really have to give him

30:47

a lot of credit. Exactly. Yeah. Just

30:49

how dominated is the NBA

30:51

by black players or African American

30:53

heritage? Yeah. It's about

30:55

80% of American

30:59

born players are African American,

31:02

which I didn't get into. You know, some of that, I don't

31:05

go into the reasons for that, which is

31:07

probably beyond the scope of my study and, you

31:10

know, some of it is legitimately

31:13

cultural. But black

31:15

advantage, I didn't actually put this in

31:17

the book, but the black advantage in

31:20

basketball is smaller among Americans than it

31:22

is, is larger among

31:24

Americans than it is among Europeans

31:26

or people from the Caribbean or

31:29

so other regions of the world, there isn't such

31:31

a big advantage for black people.

31:33

And I think part of the reason is that basketball

31:36

is just so popular in the black

31:38

community in the United States that, you

31:41

know, if you surveys that ask whether

31:43

you're a huge basketball fan, African

31:46

Americans are about twice as likely

31:48

as other Americans to say they're

31:50

huge basketball fans. So it

31:52

is, you know, any, again, being

31:54

a big fan of the sport is

31:56

a huge advantage to recent, the top of the sport. That's

31:58

why there are so many more. players from

32:00

the United States than there are from Great Britain, for

32:03

example. You know, I

32:06

don't think, you know, most people, you'd

32:08

probably be more of an expert on this topic than I

32:11

am, but I don't

32:13

think most people growing up in London are

32:15

dreaming of being a basketball player. They're dreaming

32:17

of being, you know, they're not dreaming of

32:19

being a Chicago Bull, they're dreaming of being

32:21

an Arsenal player. And I think, you

32:23

know, at any time, any

32:25

community, whether it's a country or

32:27

a race or, you know,

32:30

anything else that

32:33

where basketball is really popular is going to produce more than

32:36

their fair share of NBA

32:38

talons. What determines who

32:40

chokes under pressure? Yeah,

32:42

so this is, so one thing

32:44

is very interesting is people

32:47

choke in basketball, I

32:50

think, more than a lot of other sports.

32:52

So you look at the average NBA player,

32:54

you compare free throws, kind of, how

32:56

they shoot free throws normally. Free throws is a great

32:58

test of choking because it's the exact

33:01

same situation throughout the game. You're shooting

33:03

from the same spot, no defenders, and

33:07

the average NBA player shoots

33:10

free throws more than one percentage point

33:12

lower in clutch moments,

33:14

five minutes or less on the

33:16

clock, game within five points

33:19

than in other times. So

33:21

the average NBA player is a choker. It's kind of

33:23

surprising because in a lot of sports, we found

33:25

that players don't choke. And the reason for that

33:27

is to reach the top of a sport. You

33:30

have to be so mentally tough. You know,

33:32

the average person, of course, is going to choke under

33:34

a pressure moment, but they're just going to be knocked

33:36

out way before they reach the top of their sport,

33:39

right? So, you know, if you can't handle

33:41

a pressure penalty kick, your

33:44

problems are going to reveal themselves in high school.

33:48

Log before, you know, you're playing in

33:50

the world copper, you know, or

33:52

whatever. And similarly, you know, studies have

33:54

shown that baseball players tend not to

33:56

choke. So why do basketball players so

33:58

consistently choke? And I think to

34:01

this Ketsil point, again, I don't love hammering

34:03

the seven footers in large part

34:05

because I feel like when a

34:07

five nine person is attacking people taller than him,

34:10

he seems like he has a horrible Napoleon complex.

34:12

So, you know, I hesitate to use my

34:14

book as just, you know, seven footers

34:16

secretly all suck and tall people suck

34:19

because there's a dangerous pattern of shorter

34:21

men doing things like this out

34:24

of their own insecurity and resentments. But

34:28

I can't lie in the data, the only

34:30

thing I could find that predict choking was

34:32

hate that taller players just

34:34

choke more. And I think the reason

34:36

for that is there's just not

34:39

much selective pressure on taller NBA

34:41

players. If you have to only have to be if

34:44

one in seven, seven footers reached

34:47

the NBA, you only have to be

34:49

have one in seven basketball ability to

34:51

reach the NBA. You don't have

34:53

to be that great at everything. You don't have

34:55

to be the world's most mentally tough person because

34:58

there just aren't enough seven footers

35:00

to choose from. And

35:03

so the average six footer in

35:05

the NBA shoots free throws exactly

35:07

the same in the

35:09

non clutch moments and clutch moments.

35:12

But the average seven footer shoots free throws

35:14

more than six percentage points worse in clutch

35:16

moments. So just an enormous tendency to choke

35:18

among the tallest NBA players. Well, didn't you

35:21

say that you wanted the NBA to have

35:23

a height cut off because you thought it

35:25

would make the game more exciting? That

35:27

was another one where I'm like, God, if I say

35:29

this, first of all, people are going to call

35:31

me a heightist or something. But

35:34

I don't know whether you can be a

35:36

heightist around the people that have got it,

35:38

that got like the advantage. Yeah. I

35:41

just I know I make clear in the book that

35:43

I don't think there actually should be a high advantage.

35:46

I think if there were a high cutoff,

35:48

if there were a high cutoff, I think it's

35:51

just a high cutoff. I think

35:53

it's all ambiguous that there would be

35:55

more talent in the game, that the shooting would

35:57

be better. The clutch shooting would be better. All

36:01

these factors would be better if there were a

36:03

high cutoff, but no, I'm not a high disc

36:05

or an anti-high disc or a reverse

36:07

high disc. A tall disc, whatever it is. A tall

36:09

disc, a tall disc short disc. And

36:12

I am a huge basketball fan and my favorite

36:14

player growing up was Patrick Ewing, who is

36:16

seven feet tall. So

36:19

they do enrich the game. Put

36:21

your tall supporting bona fides out

36:24

front to center in case anyone's

36:26

going to try and say something

36:28

mean about you. So it's at

36:30

the very end of the book,

36:32

but it seems related to this,

36:34

about childhood difficulty? Oh

36:38

yeah. So one of

36:40

the things I was interested in was whether

36:42

child difficulty predicts

36:44

your tendency to choke. It's

36:47

an interesting theory. I've heard a lot.

36:49

You know, Jimmy Butler is a classic

36:51

great clutch shooter. He's

36:54

completely unaffected by pressure moments. He's

36:56

just so good in the clutch

36:59

and Jimmy Butler had such a rough childhood. His

37:01

father abandoned the family. His mother kicked him out

37:03

of the house because she didn't like the look

37:06

of him. Like it was just a horrible

37:10

childhood. And there is a theory that

37:12

Jimmy Butler is so good

37:14

in clutch moments because he's so tough because

37:16

he's been through so much and compared his

37:18

background to someone who grew

37:21

up in the suburbs, you

37:24

know, a soccer mom and soccer,

37:26

you know, an NBA dad or

37:28

something. You know,

37:30

they can't handle what Jimmy Butler can handle.

37:33

So I actually tested this in the data

37:35

in a fun way, in the book in a fun way. There

37:39

isn't a measure, an objective measure of

37:41

how difficult was your background. So

37:43

one of the things we might get into

37:45

about this book is I heavily relied on

37:47

chat GPT in the creationist

37:50

book. And I thought chat GPT

37:52

would do a great job of

37:54

giving me an objective measure of

37:56

how difficult someone's childhood was because it

37:58

has in its data. that all this

38:00

information about all the NBA players, what

38:03

they went through in childhood. So I

38:05

asked Chat GPT to rank the background

38:07

of NBA players and it gave such

38:10

sensible answers. You know,

38:12

Jimmy Butler was ranked a nine. Kawhi

38:14

Leonard grew up in a tough neighborhood

38:16

in Compton, was ranked similarly a nine.

38:18

Alou all dang ranked very high because

38:20

he grew up in a civil war

38:22

in Sudan. Like all these different

38:24

measures, that would be hard to objectively rank. Chat

38:28

GPT gives a very sensible ranking and then

38:30

some players Dwight Howard, his

38:33

dad was a state trooper. He ranks very

38:35

low. The sons of

38:37

NBA players ranked very low. Steve

38:39

Nash, suburban family in Canada ranks

38:41

very low in difficulty of upbringing.

38:44

I had this great measure of difficulty of

38:46

upbringing and then I tested, does this predict

38:49

one's tendency to choke? And

38:51

it doesn't. It does not. God damn it.

38:53

At all. So it was a little bit of a letdown because it would

38:55

have been a cool theory if you saw it in the data. The

38:58

other thing is that it made me realize how

39:01

dangerous it is to use Chat GPT

39:04

for research because if I really wanted

39:06

to cheat, I could have just

39:08

kept on asking Chat GPT to give me

39:10

a new ranking until I had a ranking

39:12

that did predict choking. So there is a definite,

39:16

Chat GPT, as amazing as it is

39:19

as an objective coder of information

39:22

does allow for a great deal

39:24

of cherry picking if you don't feel like you

39:26

want it. Fuckerry can occur. It does. Yeah. Warren

39:30

Buffett and Paul Millsap, what did they

39:32

have in common? Yeah.

39:34

So one of my chapters called, what do

39:37

Warren Buffett and Paul Millsap have in

39:39

common? Paul Millsap's a great NBA player,

39:41

multi-time All-Star. And

39:45

Warren Buffett, as everyone knows, one of the greatest

39:47

investors of all time. What

39:50

they have in common, well,

39:53

besides being great at their craft, was

39:56

they both turned down the opportunity

39:58

to go to an elite college. to

40:00

go to a college that was less elite but

40:03

they felt more comfortable in. So

40:05

Warren Buffett started his collegiate

40:09

career at Warden,

40:11

one of the great business schools in the

40:13

world. And you'd think

40:15

someone who dreamed of seeing a businessman since

40:18

he was the age of five would

40:21

relish the opportunity to go

40:24

to Warden to learn from the greatest

40:26

business professors to have all the

40:28

great business peers. And

40:31

Buffett left Warden and

40:33

went to University of Nebraska because he wanted to

40:35

be closer to his family and

40:37

he thought the libraries were just as good anyway.

40:41

And Paul Millsap was

40:43

a top-ranked recruit, got offers

40:45

from Arizona, Louisville,

40:47

LSU, but he decided to

40:50

go to Louisiana Tech because he felt comfortable there

40:52

was close to his family. And

40:54

the chapter basically

40:57

looks at the data on whether it

40:59

matters whether you go to a good

41:01

college. So it doesn't matter. Both for a career,

41:03

the great colleges that tend to... There

41:11

are some colleges in which people who go

41:13

to them have way higher earnings.

41:16

So Harvard, Stanford, Warden, is

41:19

it a big advantage? Does

41:21

it cause you to do better to go to one of these schools?

41:23

And then in basketball, there are

41:25

certain universities, there are different universities,

41:28

but North Carolina, Kentucky, Duke,

41:30

UCLA, where players

41:33

who go there are way more

41:35

likely to become NBA players. So

41:37

is it really important to go to one of these colleges?

41:40

And I think the evidence is, my reading

41:42

of the evidence on both the real world

41:44

and the NBA is

41:48

that going to one of these elite colleges gives

41:51

you an early edge. So

41:53

if you go to Harvard

41:55

undergrad, Stanford undergrad, Ivy League,

41:57

another Ivy League undergrad... you're

42:00

more likely to get into an elite graduate

42:02

school more likely to get that first job

42:04

at mckinsey or a prestigious

42:06

firm of google. And

42:09

in basketball if you go to duke if you

42:11

go to north carolina if you go to kentucky

42:13

and more likely to be drafted. But

42:16

if you look at the long term outcome

42:18

how good you are as an nba player

42:20

eventually how much you earn over your career.

42:23

They don't seem to do that much eventually

42:25

things kinda even out and if

42:27

you know so they kinda trick people early

42:29

on they give a shine to you if

42:31

you have that gold plated resume that you

42:33

went to this elite school. You

42:36

know you can trick the world early in

42:38

your career but eventually everything is gonna even

42:40

out and that kinda happened to both buffet

42:43

and milsap where. A warren

42:45

buffet got rejected from harvard business

42:47

school because probably because they're looking at

42:49

this guy from the brass gun like you know what

42:51

we don't want someone from nebraska we get someone from

42:53

warden or one of these other elite but

42:55

i think it's pretty clear in the long run he wasn't

42:58

hurt by his nebraska education you know became

43:01

one of the wealthiest men in human history

43:03

and similarly paul milsap. Fell

43:06

to the second round perhaps because teams are

43:08

like well we don't trust the guy from

43:10

louisiana tech but in the long

43:13

run he became you know a great nba

43:15

player and all star nba player. So

43:17

it's interesting that the real world and the nba

43:19

seem to colleges seem to serve a similar function

43:22

they give you that early shine but then they

43:24

don't think the too much beyond that. How

43:27

important is going to college at all.

43:31

One thing that's very interesting in

43:34

the data is historically

43:36

nba players who didn't go to

43:38

college you know went straight out of high

43:40

school. Massively over

43:42

perform their draft spot. I

43:46

know it was a great bet now you

43:48

have to go to college for a year

43:50

so you can take advantage of this. Inefficiency

43:53

anymore but for many years

43:55

it was an extraordinary idea

43:57

to draft. A

43:59

play. straight out of high school. So

44:02

Colby Bryan, Kevin Garnett, Richard

44:04

Lewis, you know, numerous players,

44:07

Amari Stademeyer just

44:09

massively outperformed their

44:11

draft spot. I think one of

44:13

the reasons for this, my hypothesis, you know, there are

44:16

many hypotheses for these, but my hypothesis is

44:18

if you skipped

44:20

college and go on

44:22

straight to the draft, it

44:24

was such a bold move. It

44:27

was saying something about yourself and

44:29

you do yourself so well and

44:31

your capabilities that you

44:33

do something about yourself that the

44:35

rest of the world missed, that

44:39

is a huge advantage in being a basketball player.

44:41

And I think I compare that to the great

44:43

entrepreneurs. You know, if you

44:45

look at the very greatest entrepreneurs, Steve

44:48

Jobs, Bill Gates, Mark Zuckerberg,

44:50

what do many of them have in common? They

44:53

dropped out of school very quickly. And

44:55

I think if you looked at these great

44:57

entrepreneurs on paper, if you looked at Zuckerberg

44:59

and Gates, you know, you'd say, okay,

45:02

they went to a good school. They maybe had

45:04

high test scores. They were interested in computers, but

45:06

so are lots of people. But the

45:08

fact that they dropped out of school

45:10

to follow their entrepreneurial spirit, I think

45:12

was another clue that they had something

45:14

else about themselves that was so remarkable

45:17

that the, another person who had a similar

45:19

background, but wanted to stay in school, didn't

45:21

have. And I think the same among

45:24

NBA players that Kobe Bryant knew

45:26

something about himself, Amir Johnson, Richard

45:28

Lewis, they all knew something about

45:31

themselves in making that

45:33

decision to go straight to the NBA, that

45:35

the rest of the world didn't know.

45:38

And they, they're just, there was just

45:40

extraordinary inefficiency where straight

45:42

out of high school players massively have

45:44

over-performed their draft spot. Yeah. It's

45:46

so interesting the, I guess, kind of

45:48

selection effect of what's going on here. Like how much

45:50

of this is just, there's

45:52

a smaller pool that we're moving

45:54

from, or somebody has a particular

45:57

outlier, which is, it's

45:59

a commonality. between all of these

46:01

different people, right? There's a common thread

46:03

that goes between them all and yeah,

46:05

just ridiculous self-belief. I suppose probably correlates

46:07

with a ton of others. Honestly,

46:10

self-belief might correlate with VO2 max.

46:14

I would totally be open

46:16

to hearing that. Yeah,

46:18

yeah, yeah. You know,

46:20

zone two threshold and

46:22

lactate management ability are

46:25

strongly correlated with self-belief.

46:28

I totally believe

46:30

it. I think one of the frustrating things is

46:32

of writing a book about NBA basketball, when you're

46:34

an NBA basketball fan as I am, an enormous

46:37

NBA basketball fan as I am, is

46:39

it's almost impossible for the book to be

46:41

finished. Because, you know, there are

46:44

a hundred more questions I want to look at based

46:47

on these findings like that. That

46:49

would be fascinating to look at. You know, I don't know if

46:51

there's a way to measure it, but I'd love

46:53

to see, you know, to look

46:56

at that and to really understand why

46:58

hand size has been undervalued and, you know, why

47:00

high school players have over-performed and all these questions

47:03

are just like, you answer one question and if

47:05

you're a fan of the game, you have 10

47:07

more questions you want to answer based on that

47:09

question. So I had

47:11

to remind myself many times

47:14

in the process of writing this book that perfect

47:18

is the enemy of the good and that I

47:20

had to finish the project at some point. You

47:23

mentioned there about the NBA draft

47:26

and coming from an anglicized

47:29

colonial British imperialist background,

47:33

the idea of a draft for us, we don't

47:35

have it in rugby, we don't have it

47:37

in cricket, we don't have it in football and

47:40

yet it's so common in American

47:43

sports, you know. What,

47:46

how effective is it? Is it good? Is it a

47:48

good idea for basketball at least?

47:51

I mean, I think it evens

47:53

the playing field for sure. You know, there

47:55

are some sports where the

47:58

best teams are just the richest team. You

48:01

know, the, you know, the, whatever

48:03

team can spend the

48:05

most money on the best players is going to be the

48:07

best team, is going to be the best team. And

48:10

that's not really true in basketball. You know,

48:12

San Antonio was a great team

48:14

for many decades, even

48:17

though they're in one of the smallest markets. And I'm a

48:19

huge Knicks fan. We're in

48:21

the biggest market. You know, we should be able to spend

48:24

the most on players. We have the

48:26

biggest TV deal and

48:29

we can't, we've been, we're finally good

48:31

again this year, but we were terrible for

48:33

so long. And I think the

48:35

draft does serve

48:37

as an equalizer where,

48:40

you know, if you do get that number one pick and

48:42

you are able to draft

48:45

the Tim Duncan, you

48:49

know, you are going to potentially

48:51

have a great team for 10, 15 years. So

48:55

it definitely does serve the purpose of equalizing

48:57

things, I would say. And it also is

48:59

fun to try to, you

49:02

know, predict who's

49:04

going to be a good player. You know, the NBA is one

49:07

of the best leagues at predicting who's

49:09

going to be a great player. You know, if you look

49:12

at the top 10 NBA

49:14

players of all time, 60% of them,

49:17

I think were number one

49:19

picks, number

49:21

one overall. It's very predictive then. It's

49:23

very predictive. It's not true in baseball.

49:25

It's not true in football. The numbers

49:28

are much lower in those sports. Now,

49:30

of course, there are outliers. So Nicola

49:33

Djokic on Denver was the second round

49:35

pick and he's, you know, one of

49:37

the best, if not the best NBA player. So

49:40

there are exceptions, but, you

49:43

know, and there are these inefficiencies as I discussed

49:45

about in the book, you know, hand sizes improperly

49:47

taken to accounts. And

49:49

you know, another one I talk about is

49:52

standing leap versus vertical leap, which

49:54

is very interesting. If

49:58

you look at when NBA players. are

50:01

participating in the combine where they're measured on all

50:03

these traits, how tall they are, their wingspan, their

50:05

hand width. The

50:09

teams want to see how high they can jump. And

50:11

they give them two tests. The first

50:13

one is a standing

50:16

leap. You stand in place and

50:18

just, you know, without any head start, see how

50:20

high you can jump. And

50:23

then the second one is a vertical leap. You get

50:25

a running head start. It's not a full on, you

50:27

know, the whole court running head start, but you get

50:29

some head start and then see how high you can

50:32

jump. And of course, with a running head start, everybody

50:34

can jump higher. And

50:36

one thing that's very interesting is if you

50:38

see what predicts block shots or

50:41

rebounds among basketball players, it's

50:44

not the vertical leap, the running head start

50:46

leap. It's the standing leap because

50:49

a lot of basketball, you don't get a running head

50:51

start. You're boxing out a player. Yeah. You're

50:54

boxing out a player and the ball just comes and you

50:56

jump or a guy's going through the lane and you just

50:58

maybe get half a step and leap. So

51:00

if you actually look at the draft, there's

51:02

an inefficiency where players who have

51:04

a great standing leap relative to

51:06

their vertical leap are undervalued and

51:09

the players who have a great vertical leap

51:11

relative to a standing leap are overvalued. And

51:13

I think the reason for that is it's

51:16

such a sexy, shiny trait, that running head

51:18

start leap. If you can, you know,

51:20

the people there, if you can run the life of the

51:22

court and, you know, leap from the

51:24

free throw line and dunk the ball, like

51:27

that is so such an impressive athletic feat

51:29

that I think people are blinded are just

51:31

like, Oh my God, this person has to

51:33

be amazing at basketball. It's much sexier than

51:35

someone who can leap not

51:38

as high, but higher relative to

51:40

what you'd expect without a running

51:42

head start. So that's an inefficiency in

51:44

the draft. There needs to be

51:46

a, like, coolness

51:49

modifier for the

51:51

exercises. Yeah. I, you

51:53

know, I, again, once you write a book like this,

51:55

just your mind and your sports fan, I'm just a

51:58

fan of all sports. My mind just started

52:00

to. It's racing. Yeah. Does this play out in

52:02

other sports? I, you know, are cool traits, uh,

52:05

you know, overvalued in both, in

52:07

both sports. Well, I bet, I

52:09

absolutely bet that, um, Pitch

52:13

speed, like average pitch

52:15

speed in baseball is something

52:18

that is very, very highly prioritized,

52:21

you know, if you're regularly able

52:23

to hit three figures throughout multiple

52:26

innings, uh, it's

52:28

just the whole crowd, when they see

52:30

that one zero zero dot zero zero, like, oh,

52:32

like the whole crowd makes noise. Right. So it's

52:34

like, but

52:37

is that, is that the best? I

52:39

mean, there's even, I believe it. And

52:41

yeah, I believe it. And, you know,

52:44

in, in football, our football,

52:46

American football, uh, you know,

52:49

the, the speed of a player relative

52:51

of, let's say a wide receiver relative

52:54

to running good routes, you

52:56

know, a player is. It just has an incredible 40

52:59

yard dash, you know, four

53:01

two five, four three. No,

53:03

it's so impressive. So exciting. Uh,

53:05

I think they tend to be drafted maybe higher

53:08

than they should be relative to someone who runs

53:10

a four, four or five or four or five,

53:12

but, you know, really precise on those

53:14

routes, like it's just not that exciting. Uh,

53:18

but it is more important. You know, I, yeah,

53:20

you can probably go through lots of sports, uh,

53:22

where the sexy traits are overvalued. There's

53:25

a really cool YouTube video by

53:27

Total Running Productions about Su Bing

53:29

Chan. So he is the

53:31

14th fastest sprinter in the world, but

53:34

he's the only, he might

53:36

be the only non-black sprinter in the

53:38

top. The

53:41

top quite a lot, but he's the only

53:43

Asian sprinter in the top 200 or something.

53:46

And the guy's five. Eight,

53:49

I think five eight or five nine. Uh,

53:52

and when he ran at the

53:55

Tokyo 21 semi-finals,

53:58

his hundred meter time. I think

54:00

came in at like nine point eight nine

54:03

point eight one or something but

54:05

he broke the world record for the

54:07

40 meter and the 60 meter

54:10

in the hundred meter so this guy

54:12

is like an it's the video

54:14

that I'm talking about you I'll send

54:16

it to you once we're done it's

54:18

so good dude and you see this

54:20

this dude who can't get below 10

54:22

seconds he can't get below 10 seconds

54:24

he's just is this the theoretical limit

54:26

for Asian sprinters and he

54:28

changes his starting

54:31

foot and rebuilds his running rhythm from

54:33

the ground up you know when

54:35

he's been doing it for two

54:37

decades or something and it's

54:40

just awesome he's like my favorite he's

54:43

one of my favorite track and field guys now it's

54:45

short Chinese dude who's just

54:47

the most the acceleration of him

54:49

is so insane it's crazy I

54:51

think I think one of the

54:54

things you see in all sports

54:56

are these players that just

54:59

are so good at making themselves

55:01

better throughout their career and you know

55:04

you talk about wide receivers Jerry

55:06

what rice the greatest wide receiver of

55:08

all time he

55:11

didn't have the greatest natural gifts you know

55:13

he wasn't the fastest he's not the tallest

55:16

but he just would improve every

55:18

year and just so such dedication

55:20

to his craft you know tiny

55:22

improvement such a focus on

55:25

you know the routes he was running and you know

55:27

if you think of basketball Kawhi Leonard I

55:29

think fits that profile as well

55:31

maybe not the most naturally gifted player

55:35

but just year after year

55:37

improving paying attention to every

55:39

subtle little thing you know that how to

55:41

rebound better put play the angles off the

55:45

off the rim better and I

55:47

think it is fun to

55:50

watch these players even

55:52

in many ways more fun than

55:54

watching the naturally gifted

55:57

you know the most naturally gifted

55:59

players who maybe don't have to put in

56:02

quite the effort and they're, they

56:04

can frustrate you. You know, Shaquille

56:06

O'Neal, he was,

56:08

he was interviewed and they

56:11

said, Phil Jackson, your coach said

56:13

that if you just practiced hard, you

56:15

could have been MVP 10 years straight.

56:19

And you think Shaquille O'Neal would

56:21

be outraged at this statement. You know,

56:23

how could someone say that? And he

56:25

basically admitted that this was true. He's

56:28

like, yeah, I didn't love practicing. You

56:30

know, I liked my cheeseburgers in the

56:33

off season. And, but he was just

56:35

so gifted. You combine that height with

56:37

that, you know,

56:39

that foot speed and that athleticism.

56:42

It just didn't matter. But, you know, Shaq is a

56:44

little frustrated. You know, if I were a Lakers fan,

56:46

I'd be so frustrated. Like, why can't you just learn

56:48

how to shoot free throws for God's sake? Like,

56:51

whereas, you know, some of these players, the

56:53

real craftsmen who just constantly

56:57

are improving and upping their game year

56:59

after year, working at it can be

57:01

really, really fun to watch. What have

57:04

you come to believe or what are

57:06

the insights about the

57:08

role of hard work in achieving

57:11

goals? Uh,

57:14

I think it depends so much on the

57:16

pursuit. Everything

57:18

that isn't basketball or volleyball. Yeah.

57:21

I mean, if you're five,

57:23

nine and you have

57:26

small hands and you're slow and you can't jump,

57:28

like there's nothing, you got no chance.

57:30

Uh, you know, you can work as

57:33

hard as, as you want. It's not going to

57:35

help. I would say basketball, because it's

57:37

so dependent on so many traits that are

57:39

so genetic, such as height, uh,

57:42

I think hard work, it moves

57:44

the needle a little bit. And

57:46

I think, you know, there, you know, Michael,

57:48

there's a difference between, you know, Michael

57:50

Jordan is considered the greatest

57:52

of all time. And Shaq isn't probably

57:55

in large part because Michael Jordan

57:57

outworks Shaq. And, you know, if Shaq

57:59

had. outworked Michael, I think

58:01

Shaq would be the

58:03

player that is number one

58:05

on everyone's mind as the quintessential

58:07

basketball player. So

58:10

I think hard work can

58:12

take you from

58:14

Shaq to MJ,

58:17

but it's not going to take you from

58:19

Seth to Shaq. You're

58:24

moving the needle a little bit, but not

58:26

that much in basketball. But there's other pursuits where

58:29

hard work matters more. I

58:32

always suggest if you're not genetically gifted, there's

58:35

some sports, equestrian riding or skiing.

58:38

There are certain sports where I think you

58:40

really can improve your craft. You

58:44

can move the needle a lot more through hard work

58:46

than you can in a sport like basketball or sprinting

58:48

or something. All right, so we've kind

58:50

of flooded around it. Some

58:52

of the people who don't know or didn't listen

58:54

to our previous episodes and realize that you're an

58:57

ex-data scientist from Google and then you've written all

58:59

of these phenomenal books which I love. Why

59:03

you know so much

59:06

about basketball? Who

59:09

are you to write this book and how

59:11

do you happen

59:13

to have this encyclopedic x-ray

59:17

vision to be able to see what's going on inside

59:19

of the world of basketball? Well,

59:22

first of all, I'm an enormous basketball fan. I

59:25

have been since I was a little boy. I

59:28

don't think I could have written a book

59:33

like this of who becomes the best

59:35

figure skater in the world or who

59:39

becomes the best opera singer because I

59:41

definitely was relying pretty heavily on knowledge

59:45

I have from three

59:47

decades as a

59:49

passionate fandom of basketball. But

59:52

this book, I used a

59:55

new tool that I have become

59:57

obsessed with. initially

1:00:00

called code interpreter. It's now called

1:00:02

data analysis. It's from chat CPT

1:00:05

and it's basically a way to do data

1:00:07

analysis that is just completely

1:00:09

revolutionized my work stream. Uh,

1:00:12

like it's, I say it's the most amazing

1:00:14

product I've ever seen. I always need to offer

1:00:16

the caveat. I have zero affiliation with chat with

1:00:18

open AI. I feel like when I say this,

1:00:20

I sound like I'm a spokesman for

1:00:23

their, you know, pitch man for their product.

1:00:25

Uh, I, I, I, I'm not associated with

1:00:27

open AI at all. But it

1:00:30

basically, uh, data analysis, uh, code, what

1:00:32

was really called code interpreter, it

1:00:35

writes all your data analysis, your data

1:00:37

science code for you and runs it,

1:00:39

and it is just such a game changer

1:00:41

that things that used to take me four

1:00:43

months now literally take me

1:00:46

four hours or sometimes less. Like it's

1:00:48

just so wild, uh, scraping

1:00:50

data sets, cleaning data sets, merging

1:00:52

data sets, running regressions, making

1:00:55

charts. It is the most insane

1:00:57

product I've ever seen. And

1:01:01

so this book was

1:01:03

just like written in

1:01:05

like an explosion of

1:01:07

just data analysis in like a shockingly

1:01:09

short time of just all

1:01:12

day running code

1:01:14

interpreter analyses of basketball. And I was

1:01:16

having the time of my life and

1:01:18

just like, just so quickly producing these

1:01:20

charts, producing these analyses. Uh, you

1:01:22

know, I think this book, uh,

1:01:24

would have been a project of many,

1:01:27

many years, uh, without

1:01:29

code interpreter and with code interpreter was

1:01:31

a project that took basically

1:01:33

30 days. Uh, which

1:01:35

I, I initially, I was really proud of, but

1:01:38

now people are like, well, do I want to read a

1:01:40

book that only took you 30 days? Uh,

1:01:42

like, uh, but really, there's a

1:01:44

really famous, um, the

1:01:47

lecture, I guess this guy, it's a clip

1:01:50

from a, what looks like a

1:01:52

marketing class or a sales class, perhaps or something. And

1:01:55

he says, how much would you pay me if

1:01:57

you wanted me to design your new logo? I

1:02:00

say that pay you thousand dollars you're okay and

1:02:02

how much would you pay me if

1:02:04

I was able to design it in 30 minutes.

1:02:08

You can buy pay you you know like five

1:02:10

hundred dollars is like hang in a second getting

1:02:12

the service more quick. You

1:02:15

want to pay less because you think that the

1:02:17

amount of time is indicative of the amount of

1:02:19

effort which is indicative of the amount of quality.

1:02:22

Yeah it was interesting so initially the first

1:02:24

version of who makes the nba. I

1:02:27

want the marketing hooks i'm like look i wrote this book

1:02:30

in 30 days and i also show at the end i

1:02:32

am this is how i did it this how i use.

1:02:34

I check gbt to do all the analysis not

1:02:37

do the writing the writing i did all myself

1:02:39

but do the analysis. I want to

1:02:41

make the art all the art is a i generated

1:02:43

from the journey or dolly you know here's what i

1:02:45

learned along the way and in right this book. And

1:02:48

a few people were like exactly

1:02:50

like this guy said like well we don't want

1:02:53

to read a book in your 30 day vanity

1:02:55

project so now i've told that down i said

1:02:57

more that you know i also show

1:02:59

you how to use a i am. The

1:03:02

emphasizing the actual time

1:03:04

that i use but i think that's just

1:03:06

unfair to just how revolutionary chat

1:03:09

gbt is that prior to the

1:03:11

existence of code interpreter if

1:03:13

i said. I

1:03:15

wrote a book on nba

1:03:17

basketball in 30 days i

1:03:19

think people would correctly say it's a

1:03:22

piece of crap and a set vanity project i want anything

1:03:24

to do with it. What i think

1:03:26

because of code interpreter because of mid

1:03:28

journey because of dolly because of chat

1:03:30

gbt you can write a

1:03:33

book in 30 days that is a

1:03:35

real treatise on basketball with new insights

1:03:37

in the game you know many answers

1:03:39

to previously unanswered questions. I

1:03:41

think you don't realize just

1:03:43

how revolutionary is for the creative

1:03:46

process that the rules of how

1:03:48

long something should take. No

1:03:51

over the last year have completely

1:03:53

changed. It's

1:03:56

very interesting it's very interesting to see. think

1:04:00

that you've got this

1:04:02

like arbitrary link between time

1:04:04

spent and quality.

1:04:06

It would be like if you said,

1:04:10

here's some butter, but I churned

1:04:12

it myself, like with my

1:04:14

feet or something. Oh, I very much appreciate the

1:04:16

fact that you went through all of that effort

1:04:18

to give me this butter. You are okay. And

1:04:21

then that book that you see that's in front of you, it

1:04:24

was actually written by hand. It's

1:04:27

a handwritten book and then all of

1:04:29

the pages are kind of stitched and sewn together. But

1:04:32

you know, like technological progress, people are

1:04:34

very typically there's a lot of inertia to

1:04:37

people being dragged along. And

1:04:39

yeah, interesting, man. Very, very interesting.

1:04:41

I suppose what we're seeing here

1:04:43

is just like leverage at such

1:04:46

an insane level

1:04:48

of magnitude that your

1:04:50

ability to manipulate chat GPT and data analyzer

1:04:52

and to be able to spit out what you

1:04:55

needed and then to be able to put it

1:04:57

together and then to be able to use chat

1:04:59

GPT to be able to proofread the word

1:05:01

so that there wasn't any errors in it. Like

1:05:05

that is it's

1:05:08

taking a skill set, but leveraging

1:05:10

it so much way more

1:05:12

than even something like Wikipedia or a word

1:05:14

processor could do. So

1:05:16

yeah, people are just not not ready for this level of

1:05:18

exponential. Dude, I appreciate you. I think you've smashed it with

1:05:20

the book. I'm really impressed. You say at the beginning, you

1:05:22

make a joke that you're going to try and write 100

1:05:24

bucks. That is a joke, right?

1:05:26

You're not going to try and do 100 bucks. I

1:05:30

don't know. I might. I'll see.

1:05:32

I'm trying to work on the monetization of this book.

1:05:35

I'm trying to figure out like, yeah, exactly

1:05:38

how to... If I

1:05:40

get the monetization right, because I also

1:05:42

self-publish this book. Because all the publishers

1:05:44

are just like, we don't

1:05:47

know what to do with your weird 30-day projects.

1:05:50

That's not... Publishers move

1:05:53

very, very slowly. So

1:05:55

no publisher would really touch this. So I'm trying

1:05:57

to figure out the... If I got

1:05:59

the monetization... Right. I would, right. I would just keep

1:06:01

doing it. Cause the other thing is this

1:06:03

was the best month of

1:06:05

my life bar none. It was so fun.

1:06:08

Uh, in part cause I was writing about, uh,

1:06:10

you know, the NBA, I'm

1:06:12

a huge basketball fan, of course that's going to be fun

1:06:15

for someone like me, but in

1:06:17

part, because one thing I found

1:06:20

is AI just does so

1:06:22

many other things I freaking hate doing so,

1:06:25

you know, I'm a data scientist, data analysts,

1:06:28

like. A lot

1:06:30

of data science, data analysis is not

1:06:32

particularly fun. Uh, you know, uh,

1:06:36

for me, writing code, clean debugging

1:06:38

code, uh, you know, looking up

1:06:40

code, uh, you know, figuring

1:06:42

out exactly how to add something to a

1:06:44

chart in this way, it's just,

1:06:47

you know, mind numbing a lot of it. And

1:06:50

in this project that was all gone. Like

1:06:53

all I did was come up with ideas. And I'm

1:06:55

just like here at data analyst, do

1:06:57

it for me. And it was

1:06:59

just awesome. It was so fun. So it

1:07:01

was just like, it was the most fun month

1:07:03

I've had in my life. So, you know, if

1:07:05

I can get the monetization right on the book on

1:07:08

this, then I'm just, yeah, I'm

1:07:10

going. I'm bringing those together.

1:07:13

A decade of the most fun months of your

1:07:15

life back to back to back. Yeah. No, I

1:07:17

said I'd have a baseball book out by opening

1:07:19

day, then I'd have an Olympics book for the,

1:07:22

you know, by the summer, then I have an

1:07:24

NFL book for, uh, for the start

1:07:26

of that season. I just keep on going. It would just

1:07:28

be the most fun. Yeah. The most fun months of my

1:07:30

life. So, well, yeah, Seth, I appreciate you.

1:07:32

Uh, I look forward to seeing what you do next. I'll

1:07:35

bring you back on that tab. Tab

1:07:37

this chat one more time. Chris. Thanks so

1:07:39

much for having me. And congrats on the success

1:07:41

of this podcast. It's been, I, you read, I

1:07:43

remember when you first reached out to me to

1:07:46

talk about my first book, everybody lies. And

1:07:48

I think I looked you up and you're

1:07:50

like a two bit podcaster. Uh,

1:07:53

I'm like, but I'm like, yeah, it seems like a

1:07:55

nice guy. I'll do this, this little podcast and to

1:07:57

see you, uh, going

1:07:59

to the strat. has been

1:08:01

a true joy and very well deserved because you

1:08:03

have worked really hard for it. Thank

1:08:05

you man. I really appreciate that. I really really

1:08:07

do. Until next time mate, I'll catch you later on.

1:08:09

Thanks. See you Chris.