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Do you think that we're going to see the first generation of players who dive into their own data?
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>> If we're moving toward kind of this democratization of information and intelligence, then I hope that is where it goes is where the players really understand how they can benefit from it.
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>> How do I make sure I'm not captive to the metrics that everybody else is benchmarking against?
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>> No problem can withstand the assault of sustained thinking.
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And I love that because it goes kind of like a core principle of mine, which is just solve the next problem.
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Just solve the next problem.
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Welcome to Infinite Loops. My name is Jimmy Sony.
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I am the editor-in chief of Infinite Books and I am guest hosting for Jim Ashanazy.
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My guest today is Ryan Rush.
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Ryan spent nearly a decade with the Phoenix Suns where he started as an intern and left as the assistant general manager and VP of basketball operations running everything from roster strategy and data science to scouting and health and performance and everything in between.
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Ryan Rash, welcome to Infinite Loops. Thank you. Thanks for having me. Of course.
1:00
So, just so listeners have a bit of context and background, you and I have been friends for a number of years, but when I met you, you were the vice president of basketball operations for the Phoenix Suns, the assistant general manager.
1:13
Um, and you reported directly to the head of the Phoenix Suns and you kind of gave me and a number of other people we were with like behind the scenes view of the Suns organization and how it operates.
1:26
>> Um, but just so people listening have context, you >> you first of all, you were in NBA, you were in an NBA front office at like a ridiculously young age.
1:33
Um, you were chief of staff to the head of the organization when the Suns went to the finals and when they won 64 games. franchise record. >> Yeah.
1:44
Um and then and you've become this kind of you're like one of the go-to people on sports and data and where they intersect and now sports data and AI and where they intersect and right now you are at 1949 Capital Partners and you're leading the firm's work on data and AI.
2:00
Um so the the thing that interests me most I mean among other things is you know you're not 6'4 right?
2:06
How do you So, so walk us through kind of like your your early life.
2:13
You were you were a military brat, but I want to I want to go from to before you get to the NBA, let's figure out how you got to the NBA.
2:19
So, you started out in life and you're a military, but you're moving around a lot.
2:22
So, walk us through kind of where did you grow up? How'd you live?
2:25
>> Uh, son of two Air Force parents.
2:25
Um, we lived in seven places before the age of 18 years old. Wow.
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Which I always kind of surprise myself when I say that.
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So born in New Jersey, lived on effectively every coast.
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Went to high school in Germany.
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So I went overseas for high school.
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It was a military base on Rammstein Air Base.
2:42
So still kind of an American experience there, but we did live in a German village.
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And so it was, you know, a bit of a international time.
2:51
Uh came back and moved to Texas to go to Baylor University where I had family in Texas in Florida from my parents' sides.
2:59
And Baylor was the uh first school to accept me.
3:02
and I'm not the most patient person and so the fact that they did accept me so quickly did help um while I was waiting on other schools did enough research into Baylor their political science program which was my first I would say passion and first love growing up was political science probably from being around the air force and the military and and learning that side of it and uh enrolled in the man what would that have been the fall of 2010. >> Got it.
3:28
And uh I was a campus tour guide my sophomore year.
3:31
It's still very good at walking backward and talking.
3:34
And um I had this weird desire to go into sports journalism >> which I don't >> Were you a sports fan growing up?
3:43
Like >> I was a huge NFL fan and I was a big basketball fan. >> Okay.
3:48
>> And but it's hard when you're a military kid cuz you don't really have a home.
3:50
So whenever I'm asked, "Oh, where are you from?"
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I have to give this whole long explanation around it.
3:54
And uh um when you don't have like a hometown, it's hard to like really pinpoint what you're a fan of.
4:02
And I got into basketball when we lived in Florida.
4:04
So I became a Miami Heat fan at the time right around when Dwayne Wade was drafted, which was then ironic that my future boss uh James Jones was on those Miami Heat championship teams. >> Yeah.
4:15
>> And uh my dad was a big Pittsburgh Steelers fan growing.
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So I grew up as a big Pittsburgh Steelers fan and I loved watching sport.
4:24
I loved the competition of it, the game of it, and I had no, as you mentioned, I'm not 6'4, and so I had no uh I'm deeply I have no coordination, no hand.
4:35
To >> be clear, Monsy Bogues like broke the mold, right?
4:37
Like every when I was growing up, cuz I was short and I love basketball.
4:40
So in the in the brief period in which I held these, you know, uh dashed dreams of becoming a basketball player, I would just turn to Mugsy B like Mugsy made it work. >> There you go. That's all you need.
4:49
you just need determination and grit and hope that your jump shot go in.
4:54
>> Um and I I for a very very short period decided to double major in journalism which I quit and then decided one day it's like I am just going to cold email Scott Drew who is the head coach of Baylor men's basketball still to this day. >> Yeah.
5:11
>> And uh testament to him that he responded right away.
5:13
So right after my summer my after my sophomore year I sent a cold email to Scott said I respect your program.
5:19
I admire what you're building.
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If there's ever anything I can do to help, let me know.
5:22
And he replied that same day, if I'm remembering correctly, and he said, "Come be a student manager." Wow. And I said, "Yes."
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I had no idea what it meant. >> Right.
5:32
>> And um I show up maybe two or three days later >> and I am an equipment manager folding towels and doing laundry for Baylor men's basketball. >> Wow.
5:42
>> And it's very uh they just throw you right in.
5:45
And so you're in the locker room like getting everything ready for the guys before practice, after practice for games.
5:50
You're on the court um helping, you know, rebound and pass balls and those kinds of things.
5:56
I was so bad at passing a basketball when I strugg I was so bad that Scott Drew called me into his office.
6:01
He probably remembers this.
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And he taught me how to throw a chess pass with spin on it.
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Like how embarrassing is that?
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>> So So wait, just so I have this I have this clear.
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you you were you're there and you're passing basket.
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Did did the players say something like did they saw how bad this is coach Drew? Yeah. This is coach Drew.
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This is why he's a national championship running coach cuz he sees these things and he fixes them. >> Right.
6:27
>> Even in his student manager. >> Exactly. Exactly.
6:28
And that um I think it says a lot about him, but it also says I guess it's like an early lesson. Yeah.
6:35
like the nature of detail in the sport and winning these things because if you throw a knuckle ball, if you throw a ball without any spin, you can break someone's finger.
6:41
You can jam their finger, right?
6:42
God forbid it's on a shooting hand. >> Yeah.
6:45
>> Um >> Wait, is that true? Sorry.
6:46
Is that true in So you're saying in basket knuckle balls or baseball is a baseball term, right? But you >> It is. Yeah. So a ball with no spin.
6:51
So like, so I wasn't putting any spin on my chest b I was just literally chucking heaving the ball.
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>> I was accurate like accuracy was not the issue.
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This is this is actually getting spin and rotation on the ball.
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Um, which the best shooters can tell you about because you have to put backspin on the ball in order to actually guide it to the target. Then you're launching.
7:12
Don't ask me about shooting. I'm not >> not yet. Okay.
7:17
>> So, we just out of curiosity, you know, if we if we take a step back in in the story, >> you I mean, I want to go a bunch of different places, but let let's talk about living in seven different places before the age of 18. Yeah. >> Right.
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because that like that that is not an experience that that I had.
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not an experience that that I had. I'm assuming actually most listeners are pretty they're they're pretty like geog geographically as a kid you you know your parents do tend to try to want to stay in one place right by design your parents can't right so what what was it
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like being the new kid all the time right like was that I mean I'm sure this shape your life I'm asking how did they shape your life >> I think the the most obvious is what I've already mentioned where you don't have a sense of home in the traditional way that people think about that where I'm just now learning. So, we still have
8:03
So, we still have a house in Phoenix. Love Phoenix.
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Um, very actually attached to that to that house.
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And that is the first kind of time that I've felt that I can call a place home and that has been a very kind of transformative experience for me.
8:19
>> The other aspects of it, um, you become very adaptable and you become very malleable kind of you can enter any space and be comfortable.
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So, I'm also an only child, so I have no brothers or sisters.
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This was just me and my parents moving around the country and then over to Germany >> and you you kind of you're able to talk to people and you're able to introduce yourself maybe not so comfortably when you're 12. >> Yeah.
8:44
>> But you know you you start you get reps at it, right?
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is I think probably the more important thing is I was forced into situations where I had to get reps at introducing myself and telling people and showing people who I am as I'm also trying to figure that out throughout the rest of my life, right? Or growing up rather.
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>> And uh it does leave you with kind of a a few things.
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One of them is I'm always moving like not >> moving homes necessarily now, but I am always constantly in a new place.
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So it's very hard for me to stay still. >> Yeah.
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This is why sports was so good for me, right?
9:17
Because you're on two week east coast road trips and you're playing in Toronto and then tomorrow you have a game in Philly and you're just kind of moving around a lot. >> Yeah.
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>> Um >> and then it also leaves you just with this sense of confidence if you if you're kind of maturing into that space where you can just enter spaces and be okay in a way that maybe somebody who hasn't had that experience just needs more reps at it. >> Yeah.
9:43
So this this ties to my next question which connects to the time with Baylor and with coach Drew.
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>> You know there are a lot of people who watch sports as casual fans or as kids kind of admiring athletes from afar >> and there's a certain impenetrability to the world of sports.
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you think, well, if I don't have that level of talent, there's no way for me to get involved in sports, right?
10:07
And so for you, you do this extraordinary thing, which is like as a sophomore, you take a gig that puts you at the very very very bottom rung of the organization.
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When you sent that email, like if you take if you can think back that far, like that's a pretty extraordinary like I I'll give you an example.
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I'm suddenly sitting here so jealous that you did that because I went to Duke and basketball is a religion.
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>> They have a whole manager program >> and I would have been like I would have been applying if I known that was even a possibility, right?
10:37
But I I never knew it was a possibility.
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>> And so my question is like how did you was it was it in that moment was it a desire to be closer to sports or was it just kind of on a whim like what motivates you to do that because it really did shape the rest of your trajectory? >> It did.
10:50
I think it's I think it's all of those things that you're saying where it's it the idea came to me because my boss actually at the campus tour guide center. >> Yeah.
11:02
>> Um she had a a Baylor basketball poster on her wall in her office and we would always talk about the games and Baylor basketball.
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It's it's an excellent program. They're still excellent.
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And this was kind of the first period of of like Scott's excellence was those like 2010 to 2014 really right when I was there. >> Yeah. >> Um those years.
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And so we >> that was a nice subtle way of being like, you know, I might have had something to do.
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>> Well, they won the national >> once I fixed those passes.
11:25
I was passing out to the players, everything got better.
11:31
>> Um, and she uh we would just talk about it so much and I think I maybe just said off hand like I wonder how you even begin to get involved in that and she was like just email Scott Drew >> Huh.
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>> or something like that. >> Right.
11:42
>> And this was it's so common place now.
11:45
if you want to reach someone, it's their company name, their first initial, last name, or their first name at companyame. com or whatever it is.
11:53
>> I think that that was still a bit of a foreign concept back in whatever it was, 2012 maybe.
11:57
>> And so I was probably very nervous to send it. >> Yeah.
12:02
But it also, like you said, it sparked all of this.
12:05
And it's why I I have this rule that if if someone reaches out to me, Yeah.
12:11
and wants advice on how to get into sport, >> then I will at least like talk to them one time so that we can go through this process of, hey, you're in college, you want to figure out how to do this, >> you need to find the director of basketball operations for your team.
12:25
Send them an email saying that you want to be a student manager and ask if they have that program.
12:29
>> And the reason I do that is because I didn't have that. did it.
12:33
>> And um it's cool now because there's actually a few people around the league that were undergrads that reached out that became managers, Tennessee and other schools that are now in the NBA because of it.
12:45
>> But that is I mean I >> I I think I'm I'm a bit of a venturer now is how I can describe it.
12:49
And >> in order to do that well, you just have to have the agency send the email, right? Just send the email.
12:57
If they don't respond, everything's okay.
12:59
All right, find the next thing, right? And then keep going. >> That's great.
13:03
Did Okay, so you you know, you did mention this, but Baylor was a serious and it's still a serious program.
13:09
I mean, you went to the I think it was a 2021 championship, right?
13:13
>> Yes, it was co it was the COVID tournament cuz I did not get to go unfortunately. >> Oh, okay.
13:17
Um, you know, they during your tenure they they were they were really starting their their climb.
13:21
What So what do you what did you see in that culture?
13:25
like what was it that made Baylor stand out from other comparable programs?
13:31
>> I will say one of the things that I understand now that I did not understand then was the coaches always shouldering the blame for the players. Always >> interesting.
13:42
>> And behind closed doors, you know, publicly as well. That one's more obvious.
13:47
A lot of coaches do that, but even behind closed doors, >> like a lot of the conversations would center around, >> okay, if this player is turning the ball over, how do we teach them not to do that?
13:58
>> How do we put them in a position not to do that?
14:01
>> And I think that that's notable because obviously sport is extremely emotional. It is very volatile.
14:08
>> You're going to lose three games in a row at some point. >> Yeah.
14:11
>> And you have to really have this stability in order to ride that.
14:13
And uh the easy thing to do there is to get all pissed off and start blaming the players.
14:21
Say that you need better players, you need more talent, whatever.
14:24
And maybe that is objectively true if you are going up against a Duke that has the top five guys in the class, like whatever.
14:31
Like you still need to figure out how to beat them, >> right?
14:33
>> But to see a coaching staff led by Scott Drew, Jerome Tang, a lot of the folks that are still there like shoulder that blame and then they would always tell they would tell me, they would be like, "It's our fault." Wow.
14:42
And I would say, I don't understand this.
14:44
We're not the ones turning the ball over because I was so like petulant back then. And then I get it now.
14:51
Like having gone through everything that we went through at the Suns and everything that's come after like I understand now that when you ask about the culture of that space, it is you need to create a teaching culture like an actual learning culture where somebody can make a mistake like turning the ball over or missing a shot and then know that it's not going to result in punishment.
15:10
It's going to result in a lesson.
15:11
Maybe that lesson is hard, right?
15:13
It's tough love, but there is going to be a lesson involved in it.
15:16
>> Can I can I ask you this?
15:16
This question is it's a it's a bit more just personal perspective.
15:20
But, >> you know, when you're a college student, sometimes you have a a a too healthy sense of your own importance, right?
15:28
And and I got to believe like you're a very smart guy and you're studying political science, you know, and you're at Baylor and you know, you have a good head about you and now you are folding towels, right?
15:41
Like how did you put your ego to the side like you know and like was it was it a rude awakening?
15:49
Like you you get in, you think student manager is going to do all kinds of interesting things and now you're folding towels and you're you're learning how to pass better.
15:57
>> What what was that like?
15:57
Like is there a part of this that was just you already knew what you were getting into or was it now the proximity to the game was the payoff.
16:03
Like it was just you wanted to be a part of this and you figured this was the way to do it.
16:07
>> Uh I did not know what I was getting myself into.
16:08
It's funny that you asked that because I still like one of my my fondest memories is on Sundays at Baylor.
16:16
People are at church and Ryan was cleaning the gym. >> Yeah.
16:19
I was cleaning the practice gym and I loved it cuz you had to get this big pole out, put towels on it and like drag this pole all the way across the course in order to clean it >> and it was just a very cathartic like >> meditative >> meditative.
16:31
That's exactly what it was.
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You would turn on a podcast or music or something and you would just do it and then you would clean the gym.
16:37
>> And I I don't remember having kind of an adverse reaction to any of it.
16:41
Although I probably did at some point years when I was in >> when I was head manager my senior year, right?
16:48
Did I act probably some way where it was like, you know, now this is beneath me.
16:52
The young manager can do it probably. >> Yeah.
16:55
>> Um but what you learn in doing it genuinely when you are the person putting out the loops that have all of the jerseys and the shorts and the socks and everything on the player's chair, it's behind a locked door. So you're not doing it.
17:07
Player can't get dressed. Player can't practice. >> Okay.
17:11
>> And so you're able to actually experience like the operational chain of how we go from this nice idea.
17:15
All right, let's run a basketball team to know these are all of the dirty steps and the details that it takes to get it done. >> Yeah.
17:25
>> And I think that that was a huge payoff for me.
17:26
Um especially my last year at the Suns when I did run the daily operations of of the franchise. >> Yeah.
17:32
>> Where it was like I knew what our excuse me, I knew what our equipment manager was going through. >> Okay.
17:38
where he's dragging these trunks around constantly, him and his assistant, and how do we make that how do we lighten that lobe for these people who are extremely important to get this done?
17:49
>> So, at some point during your tenure at Baylor, you become the guy who has the data, who has the numbers.
17:55
So, >> that was chapter two. >> Yeah.
17:59
In in excruciating detail, describe like I you know, spare no spare no story there.
18:04
How do you go from towels to >> Yeah.
18:07
analytics and spreadsheets and you're you're helping the coach think differently about the game.
18:11
When does that transition start to happen?
18:15
>> So, I was head manager my senior year >> and political science was my passion. Basketball was my hobby.
18:19
Like that was kind of the differentiation that I put there.
18:23
That's really the only two things I had outside of friend group obviously, but >> we would be at the gym till 3:00 a. m.
18:28
scouting the next opponent like at crazy hours. >> Wow.
18:31
>> It's still even in the NBA crazy hours. Um, and I graduated.
18:34
I went to go get a PhD in political science at the University of Missouri.
18:40
There was a professor there that I wanted to study under for American political development.
18:46
>> And um, around was it my sophomore?
18:46
It was either my sophomore or my junior year of undergrad, I was introduced to data science and statistics through econometrics and political science.
18:56
And when I kind of dug deeper into that world and I realized, oh, my brain is actually extremely logical. Yeah.
19:04
And like coding came naturally to me, data came naturally to me. I still love theory.
19:10
I still love philosophy, liberal arts, all of those things for sure.
19:14
>> How do we blend these two things?
19:14
I go to Missou for a year and um I I we're working on these papers and this research and I just have this this feeling that's like this is just not it for me.
19:27
this is not right for me.
19:29
Different than when I, you know, quit campus tour guide to go to basketball.
19:32
This was like, no, this is fundamentally not the place for me. >> Yeah.
19:36
>> And I think uh what I ultimately settled on, which is kind of a a principle that I now carry throughout life, is I don't want to study problems.
19:44
I want to actually solve the problem.
19:46
Now, that requires you to study them in order to solve them. >> Yeah.
19:50
>> But we're not going to actually solve it if we are just kind of writing papers and going about our day.
19:54
Hey, I want to actually do I want to do all that and then I want to do it to see if we're right or not about it.
20:01
>> And um I decided that I was going to leave the program.
20:04
So, I quit >> and I did not know what I was going to do.
20:08
>> I had absolutely no idea.
20:08
It is probably the scariest period of my life >> because I just had no concept of what else I could be doing, >> right?
20:18
>> And I happened to go to the Big 12 tournament, which is the conference that Baylor >> I think they're still in.
20:23
Yeah, I can't keep up with the compound. >> I know. Neither can I.
20:26
>> Um, >> it seems to change on a dime. >> It's absurd. >> It's like wild.
20:31
>> Um, and I went to the Big 12 tournament, spent a few days with the team again and realized, okay, I think I just missed the team environment.
20:40
>> So, I called Scott Drew again and he answered again and and I said, I don't know what I'm going to do, but do you have any advice for me?
20:45
And he was like, well, I just got done talking to Tomo at Michigan State, >> who's a good friend of his. >> Yeah. and >> legendary coach. >> Legendary. Yes. Yeah.
20:54
And he just hired someone to do data science and statistics for them.
20:59
Can you come do it for me? >> I said, "Sure." >> That's amazing.
21:06
>> And >> that was amazing.
21:08
>> I I probably a month leading up to Well, first we had to get me into a grad program.
21:13
Uh cuz this was very late.
21:13
It was like April at this time.
21:15
Thankfully, that all worked out.
21:18
>> And >> why did you you had to get new grad program?
21:20
Because he couldn't hire you unless you were a student.
21:21
I was going to be a grad assistant.
21:22
Yeah, I was going back as a grad assistant. Got it.
21:25
Um, so you can there's like the student manager program for undergrads.
21:26
There are grad assistants.
21:28
Typically grad assistants are going to follow the coaching route. Okay.
21:31
Although more and more common it's video analytics and whatnot as part of it.
21:35
Um, and I guess GM now for these colleges.
21:39
>> Um, and so I I go back to Baylor in whatever was that >> 2015. No, 20 >> 2015. Spring of 2015. >> Yeah.
21:50
And coach Drew was like, "I want lineup data." Okay.
21:56
And line of data did not exist at this time.
21:58
Like there was no there was no easy accessible like public form of lineup data that you could also trust.
22:04
You know, it was a whole kind of amorphous concept still.
22:07
>> And >> and just for context, like I remember I think Moneyball came out in 2003.
22:09
The movie is like 2011 like that.
22:12
So we're not in the we're still we're post Moneyball but we are pre like the digitization of everything sports especially probably at that level >> and it also it it started in baseball.
22:25
So Moneyball is the baseball book obviously and then it kind of slowly made its way across football and soccer and you know obviously lacrosse and those other ones and then basketball was also really starting to take off.
22:35
Daryl my popularizes, you know, threes and layups only with Mory Ball and that's kind of the launching of the analytics movement in the NBA at like a popular level. Yeah.
22:46
>> And Moneyball kind of joins along those lines.
22:49
So the mid2010s was I would say really where >> teams and people took it more seriously. Okay. In basketball. >> Yeah. >> Um in Yeah.
22:59
And so there there was just no lineup data for college basketball that we could rely on.
23:02
And I to this day, I don't know how I had this idea, but in in basketball, all sports, you have the playbyplay. >> Yeah.
23:12
>> That says, okay, you know, your point guard is subbing in, your shooting guard is subbing out, point guard made a two-point shot, assisted by your small forward, along those lines in text. >> Yes. >> Prei. >> Yeah. Prei.
23:22
Well, early days of machine learning and and Alex Net and those things.
23:28
>> And I scraped those playbyplays. >> Wow.
23:31
And from the text, I turned it into a box score.
23:35
>> And so we could build the And I'll never forget this.
23:37
This all hinged on one thing being the two things being true.
23:40
The sub in sub outs had to be on the playby-play. >> Yeah.
23:43
>> Or else this wouldn't work, >> right?
23:44
>> And the starters, there had to be a connotation of who started the game. >> Huh.
23:49
>> So typically that would be the first five players listed. Yeah.
23:52
>> Not always, but thank god there was an asterisk next to start.
23:57
>> Just so I have this right, so I'm understanding this correctly.
23:58
There was no box score data y >> for Baylor or maybe even men's college basketball at circa whatever year this is 2015.
24:09
>> 2015 nobody is tabulating these and putting them on ESPN. >> Mhm. >> Wow. >> Now individuals. Yes. Yeah.
24:16
>> Like you can go to college basketball reference amazing website.
24:17
They have the individual box scores and whatnot cuz that comes from the scorers table the statistics from the game. >> Yeah. >> This was no. Okay.
24:25
Now we want to know fiveman combinations.
24:26
So for those who don't know, a lineup would be your point guard, shooting guard, small forward, power forward, center, fiveman combinations.
24:32
How are they performing together? >> Got it.
24:36
>> What is I'm not going to say it's like a brand new concept, >> right?
24:39
>> Getting that data at scale so we can have it for Baylor and our opponents. >> Yeah.
24:44
>> Was a novel problem to solve. >> Okay.
24:47
So you scrape the data and you build something to scrape the data.
24:48
And then what happens after that?
24:51
>> We converted the uh the text into the box score. Yeah.
24:53
So point guard made, two-point shot, that becomes two points.
24:57
Two point field goal attempt, two point field goal make.
24:58
So each of the little uh traits that you can cons the variables that would make up that action, you can now register and log it in your box score.
25:08
>> And then you sum it all up and you aggregate by lineup >> and then you you know I think and this is what AI I think is teaching so many of us is once you have the data extracted and structured, you can do so many different cuts of it.
25:21
And so now we have fiveman, fourman, threeman, twoman.
25:26
Like what about these different combinations of guys?
25:27
Like it just kind of exploded in that way and coach was happy with it.
25:31
Um and it started just kind of influencing rotations and substitution patterns and those kinds of things.
25:38
>> So yeah, this is where I want to go a bit deeper, right?
25:40
Because I mean famously the scenes in the movie in Moneyball, but then also as described in the book is there's a graph versus host problem, right?
25:46
Like you have a a person who's very very smart with this kind of stuff and there's an allergic reaction from people who believe it's going to it's going to sully the sanctity of sports, right?
25:55
You're you're going to you're going to you're going to add an impurity which is like logic and reason in something that ought to be >> uh subject to to competition and fate and individual effort and all these other kind of more call it more ethereal things, right?
26:10
And into that you're introducing like logic and and regression analyses and and actually looking at data.
26:16
Did you face any of that when you were starting to pull these things together? Like were you sheepish? Was the coach resistant?
26:21
Was any was there any part of the organization that was like Ryan, we love this stuff, but we know how to play the game and you don't you couldn't even throw a pass until he coached you to throw the pass better.
26:30
>> Maybe that's why I worked.
26:30
This coach knew that I wasn't even I wasn't trying to front as though I knew how to do these things.
26:35
Um >> I definitely have run into that resistance. Yes.
26:39
But I've also been extremely fortunate that the the principles that I have worked for.
26:43
So Scott Rue, James Jones, these are people who um uh are very rational thinkers to begin with. Yeah.
26:52
And so they want this data, they want this information.
26:54
And so that helps number one.
26:56
But >> what it does teach you is this incredible lesson of like and I think that this is important for data folks and maybe more like rationally minded folks is there is a difference between being effective and being right.
27:06
being effective and being right. M >> and this is something that I had to learn at the Suns and it came from kind of exactly what you're describing which is if sport is this constrained bounded game that we have all of the rules for we know exactly how it's structured there is a very clear scoring and reward system we can apply game theory we can optimize it for strategic and tactical
27:27
choice if that's true then why are we not doing >> right >> and the answer in my mind always came back to what I called the communication problem which is we we are fundamentally lacking the ability to communicate why
27:40
these insights matter to help you make better coaching decisions or you make better playing decisions or you make better executive level decisions like who to put on the roster and who to take off, >> right? >> And I think there are there are a host
27:51
>> And I think there are there are a host of ways to solve that.
27:53
The language that you choose, the tone in which you speak to people, like the more maybe ephemeral concepts that are qualitative, right?
28:01
>> Maybe more quant driven folks don't naturally move towards. >> Yeah.
28:04
Um but I think also and this is this is where I hope it's it goes in sport is team building is a beautiful beautiful art >> and a beautiful science like there's really a blending of it for to do it well. Yeah.
28:20
It it's the cultural aspects that you asked about earlier as well as if you think about kind of a 2 by two grid when you're making team building decisions whether it's a roster or your staff you know for a job somewhere else you have to focus on the person and you have to focus on the professional.
28:38
>> So the instead of person for sports it'd be a player.
28:40
So player and professional.
28:43
>> You have to focus on the league's perception.
28:46
So kind of the in a vacuum perception. >> Yeah.
28:48
And you have to focus on for my team, what is that going to be?
28:50
So this player, player X, might be an excellent three-point shooter in the league standards. Absolutely excellent.
28:57
But they can only get their threes off of very specific actions.
29:03
>> Those actions are not good for our team. >> Why?
29:05
>> Because we don't have the person to create those actions. >> Yeah.
29:08
>> And so I objectively believe that you are an excellent three-point shooter, >> but you're not going to help our team right now.
29:13
>> Like you have to kind of hold this balance.
29:15
And that's a very human thing is to hold these these uh conflicting and competing views in your mind.
29:22
>> And I do think that where data helps is like quantifying and telling that story of player, professional, person, professional.
29:29
How are they viewed kind of in a vacuum within the within their entire cohort, right?
29:34
>> And then how are they viewed for our team specifically under our conditions, >> right?
29:37
And if we can get the data with the help of AI, if we can get the data more down that more human story, >> Yeah.
29:45
>> then I think it becomes more easy to apply it. Got it.
29:47
>> And once it becomes easier to apply it, it's more helpful, right?
29:49
And once it's helpful, it's valued.
29:53
>> What's what's an example from your days at Baylor where because you're you're really creating this stuff from whole cloth, right?
29:58
And there's not anybody feeding you this.
30:00
You don't have APIs you're plugging into.
30:02
You're doing this largely on your own, I assume.
30:03
What's an example of something that shifted because of something you found?
30:08
It can be small, it can be big, but like what's an example of when you had an insight and you said this is I've I've generated new knowledge here and then something shifted in the organization as a result >> at Baylor specifically. >> Yeah.
30:23
>> I think one of them we had a forward um who absolutely excellent scorer. >> Yeah. >> Absolutely excellent.
30:28
68 69, you know, very very smooth scoring the ball and he didn't get a ton of minutes and part of that was because like he wasn't the hardest worker, did not kind of present as, you know, like a cultural anchor of the team, those kinds of things.
30:45
And I'm not saying that that is not a deserved reputation or not, but when you looked at the numbers and you said, "Okay, when this player is on the floor like per minute, the impact that this guy is having is actually extremely high."
30:58
Okay, >> how do we fix these things that are going wrong so that maybe he shows up on time >> or how do we put a better environment around him where he's playing with certain guys who are going to get the most out of him >> because his per minute like efficiency is so high.
31:12
Let's let's not turn a blind eye, but let's let's focus on fixing those things that we really dislike that we want to vent about. Yeah.
31:20
>> So that we can unlock this for the team. Got it.
31:22
So that we can unlock this scoring prowess for the team. Cuz it's really easy.
31:26
Again, sports is extremely emotional.
31:27
It's really easy to be focused on we just lost.
31:30
You were late to shoot around.
31:32
You're one of the reasons that we lost.
31:34
I'm not saying that's not true, >> right?
31:37
>> But the payoff of unlocking that, >> we can now see what that looks like. >> Got it.
31:42
>> And that's where, you know, we talk I think that data is a mechanism to create focus. Yeah.
31:46
>> That helps you focus on if we can fix this set of issues, we unlock this.
31:51
>> That's really interesting.
31:51
Did um was this common in college basketball at the time?
31:55
Like I know Tom, you said Tom Mizo is what turned Scott Drew onto it.
31:57
Had it become common place for coaches to hire somebody on their team to do this kind of data science?
32:05
>> I wouldn't say it was common.
32:05
I think coach Drew was definitely ahead of his time.
32:09
Tom Mizo ahead of his time.
32:09
Ken Pomemeroy, who's still a big name in college basketball data, had been around for a few years and he was kind of the one that was known. But this was pre NIL.
32:20
So this was pre all of the money like I >> you want to talk about pre NIL I got in trouble by the NCAA for buying honey roasted peanut butter. >> What?
32:31
>> Because of that story I had no idea.
32:33
>> So the NCAA was extremely strict. >> Yeah. >> PreNIL. >> Yeah.
32:38
>> And the meals what classified as a meal was extremely strict as well. >> Uhhuh.
32:43
>> And so we could not provide food under certain conditions to guys because then it would be considered a meal.
32:47
If they get a meal, it's an impermissible benefit. >> Wow.
32:51
>> Oh, it was a whole bureaucracy.
32:51
I mean, it still is a bureaucracy, >> right?
32:53
But on a different level.
32:55
>> And so, look, according to Baylor Compliance, adding honey to peanut butter >> Uhhuh.
33:02
>> made it a meal at that time.
33:04
>> Adding honey to peanut butter that a player was going to eat >> Yes.
33:07
>> made what you did impermissible rules. >> Wow. >> It's absurd. >> Wow. >> Absolutely crazy. >> That is bananas. >> It is.
33:16
And you talk about, you know, you go back to your previous question about data logic entering sport and the human component.
33:22
The bureaucracy of it all is what removes the human component.
33:26
Whereas >> the player needs this not like >> let's put aside the fact that people just love peanut butter.
33:32
>> Like it's amazing and I can't have it in the house.
33:36
>> Players need this to recover.
33:38
>> They need the sugar, they need the protein, they need the fat, >> right?
33:40
So you are instead focused on this arbitrary definition of impermissible benefit instead of what's best for the players. >> Right?
33:46
That's what's actually removing >> right.
33:51
>> You know, it's a bit of a detour, but I am curious because there are very strong opinions on what the NIL changes have done to college sports.
33:58
And I'm not well versed enough.
34:01
So, if you could give a little bit of a of a of a brief on what has changed, but my my own sense as a fan from afar is that today's particularly college basketball because that's the one I follow, but today's collegiate athletic environment feels like the wild west.
34:16
Like there was just a lawsuit in Colorado that allowed an additional year of eligibility for a bunch of NCAA players and now all of a sudden men's college basketball for the year ahead is it's scrambled.
34:26
You don't know what's going to happen.
34:28
You don't know who's going to come back, how they can come back, how long they can stay.
34:32
If you get an appeals court to say one thing, it might change the decision.
34:35
This could rise to the level of the Supreme Court.
34:36
What do you make as everybody who's been there?
34:38
What do you make of contemporary college athletics?
34:42
>> I find it very hard to keep up with for all of the reasons that you're describing, which is there are there are institutions involved now that we would have never considered.
34:50
have never considered. like at our time we're not thinking about okay what is what is the Supreme Court going to decide on this case and will that affect whether my player is eligible or not >> right >> the rules there were very clear and I think we appreciated the time
35:05
>> I I think that the positive side of it is that these these players who have this unbelievable gift and these incredible talents both skill-wise physically athletically all of it they are now receiving compensation for these things I think that that is like an
35:18
economic benefit and a good thing that is occurring where it is gone poorly is I think most importantly where it's gone poorly is the the continuity of teams >> and transfers were a problem pre-NIL right so NIL is name image likeness where you are now paid for your name image likeness and there's a host of
35:40
rules around how you can receive your money um but it has led to NIL plus what they call the transfer portal in college >> where players can now basically leave teams in schools whenever they want to put your name into the transfer portal >> sometimes in an effort to maximize your NIL. >> Yeah. >> Yeah.
35:57
>> Um it has led to rapid turnover teams >> and makes the coaches and the now college general managers jobs harder.
36:07
That job did not exist when I was in college.
36:09
>> There was a college GM.
36:10
>> There was no college GM when I was there.
36:12
>> Um we were just scraping playbyplay. >> Exactly.
36:17
>> Doing in the stone age. >> Exactly. Exactly.
36:18
>> Exactly. Exactly. And uh the the turnover of these teams is just so high now where players are leaving going to the transfer portal maybe trying to maximize their cash whatever whatever they deem the most important and you lose like Duke does lose this period of
36:34
where you guys had Christian Lakeman for years or yeah Julio Okafer and those guys only stay Ty Jones only stayed for for one season but you still had those those faces on the team that you recognized year over year over year Baylor was the exact same way right >> you don't really have a lot of that anymore. And I think that that loses
36:49
And I think that that loses again back to the human side of sport that loses the when everybody's kind of in the foxhole together playing the zero sum game every night. It's win or lose. >> Yeah.
37:01
>> Yeah. That is where those bonds are really formed where I can still call the coaching staff at Baylor or the gas and managers at Baylor or some of the players at Baylor and we'll talk about you know what the what the 20 whatever it was 16 sweet 16 at Madison Square Garden or South Carolina completely dismantled us like we still you know
37:21
talk about that right >> and you just don't have those those memories or those camaraderie I I do have one question on this one it's interesting I you know I think about this just as a casual fan, but also it it it kind of the challenge here is on the one hand there was there were always going to be people who were going to be one and done, right? Who were going to
37:39
Who were going to come in Yep.
37:41
>> light college basketball on fire, do their year and then go off and play in the NBA.
37:45
And even before that, you could skip college altogether and go straight to the NBA the way Kobe and others have done.
37:50
At the same time, you now have money that's available to you to be paid to stay in college, right?
37:55
So, isn't the argument against what you just said? Well, yes, it's true.
38:00
People can job hop effectively or college hop, right?
38:03
And go from one school to another using a transfer portal to maximize their winnings and earnings.
38:10
>> But on the other hand, they're actually getting paid while they're in school and thus can stay in school longer, right?
38:17
And then it's on the program and on the coach and on the university to make that both worth their while financially and make sure that they feel like it's a place where they they they they want to call home for x number of years. Right.
38:29
So isn't there a little bit of this actually took some of the safety valve uh it created the safety valve for the pressure to leave for the league like too early or skip school or like like to be a one and done because now you could be a two or three and done, right?
38:41
Or a four and done and still get paid for your time.
38:44
I'm not well versed enough.
38:46
But does that argument have any have any weight?
38:49
>> I I want to say yes, more so in football.
38:52
>> Like I don't know exactly what that would look like, but anecdotally it feels like I've seen more of those stories of someone choosing to stay in college football. >> Yeah.
39:01
>> In the NIL money is larger in college football than it is college basketball unless you are at a Duke, right, >> or a Kentucky.
39:06
Um >> so I think it's more true in football.
39:09
I could be wrong on the basketball side of things.
39:13
I I don't know if it has yet reached the NBA side where Yeah.
39:16
like >> the the contracts in the NBA are fundamentally different than the NFL, than the Major League Baseball.
39:23
Each of these leagues has a very different economic structure. >> Yeah.
39:28
>> The NBA is extremely player friendly. Okay.
39:30
>> Very, very strong players association.
39:32
Um especially back in the 2010s when they were negotiating those really core CBAs.
39:38
>> And the players will get if they're drafted in the first round, two years guaranteed two-year team option.
39:42
But you usually will get all four years guaranteed at some point.
39:47
>> And then from there, if you're a good player, you're making 20 plus million dollars a year now. >> Wow.
39:51
>> So the payoff for college basketball may not be there yet.
39:53
Or I do think your your argument has a lot of merit is that the onus is now on program building, right?
40:00
You need to you need to build a program, right?
40:02
>> Which is where the Balor and the Dukes and these other schools who have done it are benefiting where like these brandame institutions who have built actual programs.
40:09
There's now a value on that.
40:11
There's a premium on that, >> right?
40:12
At some point this will all be as things always are regulated to some degree.
40:17
There will be more rules in place about how much you can pay under what conditions like some kind of salary cap system. Yeah.
40:21
So I do think that there is a premium that's now placed on building an actual program.
40:26
So if you do have one of these brands like a Baylor or a Duke, there is a lot of value there.
40:31
>> Um once we have some form of a salary cap system where I think that that'll happen at some point in the future. >> Yeah.
40:38
marry those constraints with what you've described of that program culture building.
40:43
Now you've got a more professional professional sport environment there where how do you build an Oklahoma City Thunder in college basketball?
40:52
How do you build a Pittsburgh Steelers in college basketball?
40:57
>> You kind of need both the economic constraints and the recognition of how valuable those programs are, >> right?
41:03
Ironically, these deals could lead to people like completing their degrees, right?
41:06
Like the great irony of this might be that extra score is the student athlete, right?
41:11
Like it really could because you think about it at some point all other things being equal, if there is a salary cap system and you are constrained on the economic side, then you would just have you know you would just all schools would probably reach like the theoretical limit of whatever those caps are, then program design and call it like student retention matter a great deal.
41:31
And if you are a coach that has a history of winning and producing NBA level talent and a culture that people actually want to be a part of that and a and and you are at a school where people want to stay to finish their degrees, you you as a player, you'd have serious if you're if you're not a superstar, if you're not a one and done, you'd have a serious reason to stick around four years and earn a degree. Kevin sent alignment. That's right. >> Yeah, I like that.
41:54
>> And and and I don't know, I'm I'm I'm speaking about it in a total vacuum.
41:56
I don't know how these decisions actually happen, but it seems to me to be one of the kind of I know that there's been a lot of coverage on it and a lot of back and forth on it, but actually I have found this to be the one of the most interesting things to be happening in college sports that is an economics question and a psychology question and a culture question, not even a what does the law say and doesn't say.
42:16
Because right now, what's what's interesting, the law isn't clear.
42:19
Congress is being petitioned 20 different ways on it.
42:21
It may ri there are already cases underway.
42:25
So, it is interesting to me to think about what is a player who's good but not good enough to leave and like kind of likes where they're at but sees that the grass might be greener on the other side, right?
42:34
Like I I wonder how people are thinking about this.
42:37
I I do think that you're you're probably on to something there where it is >> incentive alignment will happen at some point it feels like in this space >> and it probably does benefit everybody in the end.
42:49
I think right now, you know, it's already so hard to recruit in college sports to begin with. >> Yeah.
42:55
>> That it has made the jobs of these head coaches infinitely harder. Yeah.
42:59
>> Only because there aren't the economic constraints that are specific right now.
43:04
>> Um >> and because it's made it so hard, that's probably where some of the negative Yeah.
43:08
like publicity and whatnot comes in.
43:11
>> I It is interesting though to think like we're sitting here today and we don't bat an eye on college basketball players or football players making millions of dollars, >> right?
43:18
And that was I mean >> unheard of just a few years.
43:21
>> Absolutely unheard of.
43:21
Recruiting visits had very strict budgets and you can only do certain things. >> Yeah, it is. It's wild. >> It's wild.
43:27
It's also I remember when I was a freshman at Duke >> when by the time I got to my junior year senior year they did a deal with Apple where all the freshmen at Duke for a given year got iPods.
43:37
And I remember being so mad that I had missed the free iPod window.
43:43
If I was a college athlete who finished up at Duke after these after these deals went into effect, so the deals weren't around when I was around, I'd be livid.
43:52
I would be livid because the size is actually significant life-changing amounts of money for these players. >> It really is.
43:58
And that's why the transfer portal is so active, right? >> It really is. >> Right.
44:02
So, so now we go, you're at Baylor and you're you're doing you're a grad assistant and you are furnishing analytics and insights for the team.
44:09
How long are you at Baylor?
44:12
and tell us how the story gets from there to the Phoenix Suns.
44:16
>> I was I would be at Baylor for 2 years in chapter 2 postPHD.
44:18
The summer after the first year, I got an internship with the Phoenix Suns's analytics department >> on the basketball operation side.
44:26
So, every NBA team, every professional sports team more or less, is split between uh sport operation and business operation.
44:34
So, business operation would be tickets, sponsorship, sales, media, kind of everything you'd think under the business banner.
44:40
And then the sport operation is building and managing and operating the team. >> Yeah.
44:45
>> Um I was sent by a classmate this analytics internship in Phoenix.
44:52
>> I did not have a plan about what I was going to do and I told myself probably the only path forward is in the NBA because again it's not a huge thing in college basketball.
45:02
Scott, Coach Drew would eventually like consider bringing on this position, but he would also push me out and say, "You have to go to the NBA." >> Oh, wow.
45:11
When that opportunity presented itself.
45:12
Um, which I'm very grateful for because it would have been easy to stay. >> Yeah.
45:17
>> Um, and I got this internship.
45:22
>> So, how did that Well, walk us walk us through it.
45:23
How you know, sometimes these internships don't just happen.
45:24
Did you Did you just apply?
45:26
You applied and you interviewed? >> I applied. >> That was it.
45:29
applied and interviewed and um I I I think you know again it's funny it comes back to the communication side of it I think. >> Yeah.
45:38
>> Where when I was interviewing with the individual who would hire me the the then director of analytics of the Suns >> there was a question around um so the Phoenix Suns were really bad at this time.
45:49
So one of the worst teams in the league.
45:50
there was a playoff drought and uh the league was just starting to play faster, shoot more threes, have more space and the Suns were not doing these things and the Suns were still playing two centers at the time and I was asked like what would you look at to show the coaching staff not to play two centers?
46:12
Very interesting question, >> very specific question. >> Yeah.
46:15
And I'm sitting there just thinking about it and I said, 'Is the transition defense really bad?
46:22
>> And the response was, you're the first person to answer that >> and it is.
46:28
>> And I said, well, we can just show them that like where do we rank transition defense?
46:31
This combination when they're together is very poor. >> Yeah.
46:36
>> But it has to come back to the why.
46:36
And well, the why is that, you know, it's it's very difficult to play two centers because they're, you know, the tallest players typically on the floor, the heaviest players on the floor, and they're usually not the fastest.
46:46
They're usually the slowest >> and if you're playing both in a league that is increasing in pace and speed. >> Yeah.
46:53
>> And they're just going to outrun you at some point.
46:55
Like that's what's going to happen.
46:57
And the data was showing that.
46:59
And so >> my understanding is that that answer helped get that internship.
47:04
And um I was very grateful to get the call.
47:07
>> Just just out of curiosity, they had not discovered this on their they you didn't discover it.
47:10
You knew the answer because that was a very >> Well, I mean it was a guess. >> Yeah.
47:15
>> Uh cuz I didn't see the data.
47:15
I didn't have anything in front of me.
47:16
I was just I was proposed the question. Yeah.
47:19
>> And they had the they had the data. They knew the answer. Got it.
47:22
>> Um I think what they were looking for, which is a very interesting data interview topic, is can this person creatively identify something? Yeah.
47:32
>> Can you think I guess outside the box of whatever the I don't know what other answer they're right then, >> right?
47:38
>> Um but I get the internship and I do was it 3 months maybe three or four months over the summer in the NBA.
47:43
So the NBA calendar is July 1 to June 30 every year.
47:48
So the salary cap resets on July 1.
47:50
So that's when you can start signing players again.
47:52
That's why the draft is at the very end of June.
47:53
It's the very last thing.
47:55
It's the finals in the draft at the very end of the NBA calendar.
47:57
And I started, I believe, right before the draft.
48:04
Yeah, it was right before the draft.
48:07
I did a little bit of work on that. Yeah.
48:10
>> Where it's just basic, hey, >> interpret these statistics for these draft prospects.
48:15
>> This would have been the summer that the Suns drafted Maris Chris and Dragon Bender at four and eight in the lottery.
48:21
Um, that led into summer league, I'm sorry, free agency where we're just doing again what would you value like this player's contract at based on what they've done in the past.
48:31
>> And I was just sitting in my cubicle which was >> just doing that work, >> just doing that work by myself.
48:36
>> Um, >> and went to summer league, had a good time.
48:39
August is completely dead and that was the end of the internship.
48:43
>> And I was fortunate enough that they kept my database access on.
48:45
So for whatever reason they decided that that was worth it and I just kept producing work when I went back to Baylor. Oh wow.
48:55
>> So I would I was still working at Baylor and I was still doing like little side projects.
48:58
Oh that's great for Suns Analytics.
49:00
>> Eventually building out the web application that they used in the war room which is where the draft is held in Phoenix. >> Okay. >> In 2016, no 2017.
49:10
And probably because I just kept doing work.
49:13
They're like we just feel bad.
49:15
Yeah, we know we have to hire this guy.
49:17
Um, and they hired me as the coaching analytics person, which effectively meant that I was hired to sit with the coaches, sit on the on the bench during games and interpret statistics and data for the coaching staff.
49:28
So, be that communication bridge that looks at the numbers and then is able to relay what exactly is going on. Got it.
49:34
When out of curiosity, because this >> having somebody like you in the background makes a lot of sense to me.
49:44
uh game's done, the week is long, there's no nothing happening at in you, your day job is looking at the analytics and helping to come up with insights.
49:52
>> What what does someone like you you do in that role when you're on the bench next to the coaches?
49:56
Are they asking you specific questions like or is it is it just you know what, Ryan?
50:00
Yeah, this makes you feel real good. We'll have you here.
50:04
It's really just window dressing.
50:06
You're going to have a clipboard.
50:06
It's not going to have anything on it, but when the camera pans to you, you'll have a clipboard >> there. Sure.
50:11
I mean, there are clips of me clapping on my clipboard.
50:12
Um, it's I laugh because it's a little bit of a loaded question in that I don't there is no purpose like they and you kind of realize it too when you're in the moment where it's like, all right, it's first quarter, team's taken eight shots. >> Yeah.
50:32
>> Smallest sample size you could possibly imagine, >> right?
50:35
>> I I can tell you where it may go. >> Yeah.
50:38
If we shoot enough shots, it may look like this.
50:40
and right >> regression to mean may occur.
50:43
>> There's a lot of qualitative maze and like we're kind of theoretically thinking these things.
50:49
>> Yes, >> it's not helpful in game. Like it's really not.
50:51
And it goes back to it goes back to something that I used to talk about with one of our coaches at Baylor, Coach Tang, >> where we would talk about what is the best way to prepare for a game. >> Yeah.
51:02
>> Because I think maybe 538 back in the day did a study of how many decisions an NBA coach makes in a game. Some absurd number.
51:09
exactly what you would think.
51:11
And it's just you can't no human can process all of this information in real time, manage the players, manage the emotion of it all, manage your staff.
51:20
Like it's there is so much going on in the moment.
51:26
>> And um what we eventually came up with was like you have to have plan A, which is we're going in with this style of play.
51:32
We're going to do this offensively, defensively.
51:33
Our team knows how to do these things.
51:35
And then you have to have plan B, which is if plan A fails, we're moving to plan B.
51:39
And then if you get to plan C, guys, like we're probably done. We're probably losing.
51:47
>> And um I mean on the data side of it, it goes back to like whether it's qualitative or quantitative information, your preparation needs to be there >> pregame. Yeah.
51:57
Before that ball goes up at 7:07 p. m. >> Yeah.
52:01
>> You need to be prepared, right?
52:01
Like you need to have a coach or someone behind you that taps you on the shoulder and says, "Hey, our goal was, you know, a three-point attempt rate of 40% and we're only at 35%." >> Right.
52:14
>> And but then a good coach, their next question will be, "Okay, what's wrong?" >> Right.
52:18
>> Like why are we so low? >> Yeah.
52:20
>> And and you're saying this is happening at halftime or well after the game is over or you're saying after the game's over before the next game to understand what went on.
52:29
>> I think that you have to prepare definitely before. No doubt.
52:30
I think at halftime you have to have a very structured process that you go through to basically you have like 15 minutes. >> Yeah.
52:38
>> Some percentage of those minutes must be devoted to talking to your guys.
52:39
That's the most important thing.
52:41
Some percentage at the beginning must be devoted to okay let's review our strategy and our tactics and our style of play.
52:47
>> Yeah >> that is there's a lot of value that can be done there if you can really refine that process.
52:51
Obviously the pregame prep is extremely valuable. Right.
52:53
Postgame debrief hit or hit or miss because it's so emotional after a game.
53:00
Like you are coming off of an adrenaline high whether you win or you lose. Everyone is. >> Yeah.
53:05
>> So it's really not the best time to chat. >> Yeah. Right. After your game.
53:10
>> Um >> but >> as evidenced by any number of press conferences that go so badly arrive. Exactly. Right.
53:16
That that you really and and actually it explains the the monotone monoselabic responses that a lot of players give to questions. Right.
53:23
There's no room for creativity after that kind of thing.
53:25
>> There's not like you you're emotionally depleted, >> right?
53:28
>> Um and there's just you don't you don't want to talk.
53:30
You certainly don't want to talk in front of the camera, >> right?
53:33
>> And uh >> it's the it's the in the- moment data assistance that I think is have literally having done the job.
53:42
>> I don't think that there's a lot of value that it can create because what you should be focusing on instead is training your coaches and the people making decisions in game how to think this way. >> Yeah.
53:51
So instead of me being like, "Hey, we're not shooting enough threes, your coach who is in charge."
53:57
>> Get back to your cubicle, Ryan. >> Exactly.
53:58
Instead of Instead of saying that, you have a coach who says, "Hey, my job is the offense.
54:02
I know what our threes should look like.
54:05
I know how they're generated.
54:06
I know what the quality looks like.
54:09
>> We are not getting enough offball screen threes, right? We're just not."
54:12
>> And that is useful information that helps the coach and helps the players.
54:15
Whereas, hey, we're not shooting enough threes is not useful information.
54:17
That's that's a fun fact. >> Got it. Great.
54:20
This is a super fun fact.
54:22
When you come into the organization, you know, as you mentioned, the Suns were coming off a bad stretch and there was a leadership change.
54:27
There was also an ownership change.
54:28
Can you talk a little bit about what it was like um being a part of because so you finished up another year at Baylor, you're doing all this side hustle work for the Suns. That is what it was.
54:38
>> They hire they hire you based on the strength of that side hustle work and then you join them.
54:44
>> But you're in the middle of chaos, right?
54:46
I mean, there was it was a the GM changed and the owners changed. >> Changes constantly.
54:49
Yeah, >> I can tell you that it was the exact opposite of what Baylor was, which I mean, Scott Drew is still the head coach at Baylor.
54:56
He's been there for >> I mean, my goodness, 25 years now. >> Wow.
55:00
>> And Phoenix was on I believe it was the second coach in two years and it would go on to be the third and then the fourth.
55:08
Like there was a whole host of change on a coaching side.
55:09
We did go through a uh basketball operations executive change.
55:14
So, our general manager changed when I started full-time.
55:17
James Jones started, who was the player that I mentioned earlier from the Miami Heat.
55:22
Um, he had just retired, seven straight NBA Finals appearances, played with LeBron James on all of those teams, and LeBron publicly called him the greatest teammate of all time. >> Wow.
55:33
>> And I can now attest to why. I love James dearly.
55:37
>> And he starts full-time the time that I start full-time.
55:40
He starts in the role that I would finish in.
55:42
So, there's a bit of a like a a book end here. >> Yeah.
55:46
and he's coming from winning championships.
55:48
I'm coming from Baylor basketball where we're second weekend NCAA tournament teams.
55:52
We didn't win the Big 12 yet, but we were in the championship game in the tournament quite a bit.
55:58
>> And you go to losing, >> right? >> You go to music. >> Yeah.
56:04
>> And it is culture shock. >> Yeah.
56:06
>> James, I believe in his 17ear career, however long it was, missed the playoffs once.
56:10
I'm coming from spoils and riches at Baylor. >> Yeah.
56:14
and we just kind of bonded over this >> wow >> this weird uh culture at least. >> Yeah.
56:22
>> And there's really no other way to say it.
56:23
And um I give him so much credit in that he had the perspective when he took over that it's very easy.
56:34
You hire a GM, you hold the press conference, we're going to compete for championships.
56:38
We're going to play hard.
56:39
We're going to do all these things. >> So easy.
56:42
Like you can win a press.
56:42
It's very easy to win a press conference.
56:45
It's not easy to win the game, right?
56:46
Like those two very different things.
56:48
>> And James came in and his very first strategy.
56:50
Well, on day one, he did, you know, change over quite a bit of the the staff. >> Yeah.
56:56
>> Which I think was necessary at the time.
56:58
And we became a very small lean operation.
57:00
I call us now the startup sons for that period of time.
57:04
>> Why were you not part of the changeover?
57:06
Cuz you came in under the old old management. What was it? Yeah.
57:07
So, what how did you manage to stick around?
57:09
I I mean truthfully I didn't have an office.
57:13
I didn't have a desk and James being who James is told me like sit in my sit on the other side of my desk and work. Okay.
57:21
>> And so we just had a really strong relationship from that.
57:22
He's also a very rational thinker. >> Okay.
57:25
>> Um he loves data, he loves technology, >> but he also understands that human component to such a degree as well.
57:30
And >> um he's an unbelievable teacher I think too.
57:35
And if you're willing to learn from him Yeah.
57:36
and you can become one of his people, >> right?
57:40
>> Um, but the first strategy that he implemented was raising the floor.
57:46
>> It's not championship ceiling, championship expectations.
57:48
It was no guys, we're going to raise the floor.
57:51
The floor is way too low, like the competitive floor, the talent floor.
57:53
And this is not just players.
57:56
And this is the beauty of having a former player as your lead executive. Yeah.
58:00
which I think is so important is the the individual who has been in those championship games, who has won the title, who has made the threes, who has, you know, defended the best players in the league, right?
58:12
>> Who understands what these players are going through, >> they will hold the rest of the organization >> to that same standard that the players will hold themselves to.
58:20
>> And it's so important.
58:22
>> And he literally he was like, "It's not sexy, but raise the floor."
58:26
>> So, how do you do that?
58:26
Like what are some of the key both the data and analytics components in your job but then also just organizationwide. How did that happen?
58:33
>> You you look at age first and Suns were extremely young. >> Yeah.
58:37
>> It's very popular in the NBA to tank.
58:40
This is another huge thing in sports is if we lose then we'll get a higher draft pick.
58:45
If we get a higher draft pick we will maximize our chances of getting an all-star or franchise player. >> Yeah. >> Not incorrect.
58:50
You know, if you get the number one pick in the draft, then yeah, you'll have a much higher chance of drafting an all-star franchise player from that class than anyone who comes after you.
58:59
That's basic logic, right?
59:02
What the tanking debate fundamentally misses because the Suns were publicly tanking when we were there at the very early days and we went the exact opposite is uh they were missing the that the the the tanking proponents I should say >> are missing the again we come back to this the human factor. >> Yeah.
59:23
>> Of sports is an extremely unique environment.
59:25
It is the only publicly played zero sum game in the world. Huh?
59:29
>> I mean, literally everything's public and there will be a winner, there will be a loser. >> Yeah.
59:33
>> And it is also time bound.
59:33
So every year you get one year and then we're resetting. Go again.
59:38
>> There's no rollover effect.
59:38
You don't have to carry things over any of that.
59:42
>> When you think about that on a macro level with games, it's true.
59:44
But on a micro level as well, where these players like we're drafting the replacements, >> right?
59:50
It's crazy when you think about like the the cycle of this >> and it's arguably like deeply unfair as well where it's like someone who is an NBA player will get pushed out because we're bringing in this 18-year-old high potential. >> Yeah.
1:00:04
>> And it was when we talked about like James raising the floor.
1:00:07
It was finding the value in the um uh the older guy again like finding the value in the four-year player like Cam Johnson who we drafted at 11.
1:00:18
uh James' first draft and we got panned for it. Right.
1:00:22
Cam has since gone on to have an incredible career. He's a great person. He's a great player. Like fantastic. >> Yeah.
1:00:30
>> Um and it's things like uh like I mentioned the age.
1:00:35
It's also things like teaching the front office staff what the player is going through when you talk about tanking >> because they hear you want to draft my replacement. >> Oh, interesting.
1:00:47
You want to bring in the guy who's going to take my job away >> or the coach here's >> you want me to lose so that you can fire me, >> right?
1:00:55
>> And then we can continue, >> right?
1:00:58
>> And you just can't like it's so hard to win a championship to begin with.
1:01:00
Like your odds are so infantestally small >> to do this to begin with >> that if you don't realize like the consequence of these strategic choices that you're making. >> Yes.
1:01:11
>> You don't have a chance. >> Right.
1:01:12
And this is why like Oklahoma City for example executed a tanking um process extremely well.
1:01:21
>> I don't think that they ever publicly called it that. Of course not.
1:01:23
>> I mean this is one of the this is one of the curious things about tanking is you can't say you're tanking tank get in trouble. >> Yeah.
1:01:29
>> Um the integrity of the game but Oklahoma City they did an amazing job of keeping their culture so strong and I give Sam Prey all the credit to that.
1:01:36
He is a fantastic executive. Absolutely fantastic.
1:01:41
>> And they built this, you know, this this program where it's, hey, we all know what we're going to do for the next couple of years >> and then we hope that we'll be champions on the other end of it. >> Yeah.
1:01:51
>> And I think if you do it in that deliberate manner, right, then you definitely increase your chances of success. >> Yeah.
1:01:57
>> It's when you do it in kind of the manner that's >> I think we're supposed to do this because >> we're told that you should tank if you're not good because you don't want to be in the middle, right?
1:02:05
like you know it it comes down to like you understand the strategic consequ the consequences of the strategic decisions that you're made interesting.
1:02:13
that you're made interesting. So when you're when you're in the day-to-day of this, are you like is it how is I guess how is the sun's job different than the Baylor job when you got to that gra you're you're in both cases you're looking at analytics you're looking at
1:02:28
data how were what were those two environments what were the differences between those two environments >> at face value the roles were very different post James taking over the suns >> so that was when I became his chief of staff director of strategy Um he would then go on to win the 21 executive of the year. I believe it was 2021. And so I believe it was 2021.
1:02:47
And so the role was more expansive for sure.
1:02:52
>> Uh helping out in operations, salary cap if that was needed at the time, traveling with the team quite a bit to basically be a front office presence for them if something happened. >> Yeah.
1:03:03
Um whereas at you know it's funny now that I say it it sounds a little bit closer to the manager role kind of like the catchall situation and just being around to do these things that need to get done. >> Yeah.
1:03:16
>> Uh whereas the the Baylor data roles and even those early Suns data roles were very >> as you would think they are behind a computer looking at numbers looking at spreadsheets.
1:03:25
Uh thankfully at that time in the NBA well that's another difference is the NBA had a much richer data set. >> Yeah.
1:03:31
>> Yeah. So optical tracking data came online right around the time that I was an intern company called Sport View which had installed cameras in all of the arenas and they could track I think it was 24 frames a second from six cameras at that time the XY coordinates of the players the referees and the ball
1:03:49
>> and now it's XYZ coordinates with Hawkeye which is the new camera provider >> and so we were extremely data rich and so we would spend a lot of time >> kind of playing in that sandbox of the XY coordinates >> right And then as um the role increased in scope, it was less of that hands-on coding and more of program building. Yeah, I think is the way to think about
1:04:09
Yeah, I think is the way to think about it.
1:04:11
And >> I kind of bounced around.
1:04:14
Uh I was very lucky that James trusted me so much and over the course of my time at the Suns under James led or managed everything except players and coaches in basketball operations.
1:04:24
So uh scouting at one point for a couple of years, health and performance for a year, operations, strategy, so data science, salary cap, those things. >> Yeah.
1:04:35
>> So you go from Baylor and doing the data stuff to the Suns at a period of real transition for the Suns like what are what is different about the job that you were doing in one you know in both cases you're working with data and you're trying to find insights that are going to help the team.
1:04:50
What are the differences between the role in the NBA and the role in college?
1:04:53
the it kind of at the top level the MBA is a much richer data set.
1:04:58
So when I joined the Suns this was right around when optical tracking was taking off.
1:05:04
So optical tracking was introduced to the NBA through a company called Sportview which installed I believe it was six cameras in the arena the the rafters of the arena at 24 frames a second where we could track the XY coordinates of every player, the ball and the referees.
1:05:18
And uh now they can do the XYZ coordinates with the latest technology which is pretty cool.
1:05:24
And so we would spend just quite a bit of time in those early Sun's days uh not dissimilar to what we did at Baylor where it's like okay we have our hands on all this data now let's build something and the the optical tracking data could produce really cool new metrics like okay let's measure how far the screen defender is from the screener and does that matter if our center is 6 feet behind the screen versus 2 feet behind the screen. Yeah.
1:05:52
>> Why does this question matter?
1:05:52
Well, at that time, if you're trying to invite the mid-range shot and take away threes, which was the popular style of defensive play back then as teams are shooting more threes, then you want to know, can my 7 foot center play at the level of the screen?
1:06:08
Does he need to play back?
1:06:09
If we do play back, are they going to pull up? Is he going to contest?
1:06:11
And there's a whole kind of like chain of thought that goes into that that's really interesting.
1:06:16
Um the role itself though it it it grew very quickly uh under James and I'm I'm really lucky that he trusted me as much as he did >> and I event I eventually would go on to lead or manage every team within basketball operations outside of the players and the coaches directly.
1:06:38
>> So health and performance for a year which was athletic training, strength conditioning, nutrition, sports science, those kinds of things.
1:06:44
So, keeping the players healthy, getting them stronger, helping them play better from a physical athletic standpoint.
1:06:49
Uh, scouting, which is obvious, professional scouting, amateur scouting, so college basketball, data science, uh, the salary cap side of things as well, which is super interesting.
1:06:59
Um, kind of ran the whole gamut there.
1:07:02
And then eventually my last my last year ran operations. Yeah.
1:07:06
>> Um, which was the true day-to-day, right, >> of being in it.
1:07:08
>> of being in it. So, I want to I want to dive in because and we'll link to it in the show notes, but there was this really interesting interview you did about sports and analytics and AI and and you talk a little bit about what happened when data first arrived at the
1:07:23
NBA where you finally have optical tracking data and then as I understand it, a a company came around and built dashboards and for the first time I guess in the league's history, this data that you know would have had to been cobbled together and you know hacked together before is now easily visible to teams. Um, talk a little bit about that
1:07:40
Um, talk a little bit about that moment, what happened and kind of what you saw as that as that happened.
1:07:44
Yeah, the when when sport view came in and they introduced optical tracking data, it delivered as all nent forms of data deliver which is you know structured to some degree but you need to know how to write SQL and you need to know how to write Python or R at a base in order to actually interact with these numbers and make them mean anything of relevance. >> Yeah.
1:08:09
And uh you know around the mid2010s too in the data science community the tech community machine learning is really starting to take off.
1:08:19
>> So Facebook algorithms Alex Net mentioned previously you know >> it's insane to talk about it now but like Alph Go like the things that would then go on to truly change the world. >> Yeah.
1:08:32
>> Are are coming into the public conscious and a company called Second Spectrum and they did a great job with it.
1:08:36
They they solved the problem of needing to have technical coding chops to interact with this optical tracking data by building machine learning algorithms to track the actions of these coordinates.
1:08:52
>> So instead of me having to now figure out, okay, a middle pick and roll occurred at 7 minutes and 15 seconds in the second quarter by our players. >> Yeah.
1:09:00
Second Spectrum built the algorithms, the machine learning algorithms to actually identify this and then from there we could split that and filter it into a host of different metrics whether it's on the left side of the court, the right side of the court.
1:09:12
Basically, you're able to summarize and assess the game at a much more granular level and they tied film to it.
1:09:16
So that was also a huge innovative side of it that again goes towards solving the communication problem.
1:09:23
So now coach sees my middle pick and roll is 1.
1:09:26
20 two zero points for possession and then here's the 30 clips of our guys doing it.
1:09:29
You can reinforce, okay, >> why is it 1. 2?
1:09:34
Let's watch the film to find out, >> right? >> And super helpful.
1:09:40
>> Yeah, extremely useful, very advantageous when you are one of the few teams that has it.
1:09:46
>> Ah, >> but as with all things, it becomes democratized and everybody has access to this data and this information.
1:09:51
And something that started happening my last few years in the league was this constant conversation around is everyone playing the same way?
1:09:59
Is there a homogeneous style of play? Yeah.
1:10:02
Is everyone running middle pick and roll >> trying to shoot threes >> and either switching ball screens or playing in the deep drop on ball screens?
1:10:11
Where is the innovation in our style of play? >> Right.
1:10:14
>> And I think it's a fascinating conversation. >> Yeah.
1:10:16
>> And there's been some studies that say it is happening, some studies that say it isn't happening.
1:10:19
This is one of those topics where it's if people are talking about it, if the perception is that it's happening, then it's probably important to figure out why that perception exists. >> Yeah.
1:10:31
>> And it occurred to me one day that we really did all have access to similar data, right?
1:10:37
>> And the same data, >> right?
1:10:38
>> And you know, you think a program would get, you know, ridiculed if they didn't shoot, you know, 30 35 43s.
1:10:45
you know, your coach doesn't have good shot selection, blah blah blah, what what kind of behavior does that drive, right?
1:10:54
That the coach now is like, "Okay, what does the data say I should do?" >> Yeah.
1:10:58
>> But if you're basing it off of just what a dashboard says, you're looking at lagging indicators.
1:11:01
You're looking at what others are doing to win. Yeah.
1:11:05
>> You're not drilling down, drilling down, drilling down and saying, "No, what is it for our team?" Back to that 2 by 2.
1:11:10
What is it for our team's context for how we win? >> Interesting.
1:11:13
And so when you have this kind of democratized dashboard instead of proprietary questions, proprietary data or proprietary metrics built on publicly available data, right, is really the way to do it with the optical tracking data, >> you do end up in this kind of path dependency where yeah, okay, middle pick and roll seems to be the uh most efficient action. >> Yeah.
1:11:34
>> Switching ball screens is extremely efficient as well. >> Yeah.
1:11:38
>> And then it's kind of this race toward some kind of false optimization in a way.
1:11:42
the opposite problem as what you would have as what many people read in in the Moneyball book, right?
1:11:46
So on the one end of the spectrum, you like ah data is useless, right?
1:11:49
We we that's that's over and done with.
1:11:52
Most team owners at this point are going to emphasize the numbers, right?
1:11:54
Um now you go to the other place where because the numbers are the same numbers everybody else is looking at, you get like a homogeneized style of play, right?
1:12:04
Or you're all doing the same things because the numbers suggest the same things.
1:12:07
So that's that's that's an interesting thing to square whether you're a GM or a coach to say to yourself, okay, how do I make sure I'm not I'm not captive to the metrics that everybody else is benchmarking against?
1:12:20
How did you think about that internally?
1:12:22
Like how did you make the most use of the dashboards that you know a company provided while not then allowing it to drive the same conclusions that everybody else was going to drive so you're basically playing the same game as everybody else?
1:12:36
I think it I think it follows a path of what you kind of described where the the information that's readily available is great for education.
1:12:43
>> It's great to tell you what is going on.
1:12:45
It's great to tell you what what happened.
1:12:46
It's great to tell you okay under these conditions this existed. >> Yeah.
1:12:51
>> It does not tell you moving forward if if we face those conditions again will this outcome still occur?
1:12:57
And you know, I think if I could go back, then I would probably emphasize this point stronger with the analytics team and I would have us just spend more time asking better questions >> to get us back into the mindset of XY coordinates.
1:13:17
How do we build the model that shows whether our center should be six feet or two feet or five feet?
1:13:22
Can we get that on what's available? Yes.
1:13:27
But this is different >> because of how we teach our defense.
1:13:31
Like if we are able to define our shift to defense, our helpline defense, and we know where these guys are supposed to stand. >> Yeah.
1:13:38
>> Then we should also be able to code out that logic and get those metrics that are more stuns proprietary or more team proprietary than just taking what is available on the dashboard because they don't know our defense, right?
1:13:49
Nor should they, >> right?
1:13:51
And this again it comes back to I think like data is not a holy grail like it is not a panacea for all of this stuff.
1:13:59
It is another area where you can find competitive advantage.
1:14:02
If you know how to use it just like any other tool as cliche as that argument is yes it is a tool. Yeah.
1:14:08
You know how to use it then you can find advantage with it.
1:14:09
And if you do talk to these these leagues where it has become kind of more pervasive, like baseball for example, it's really hard to find the next differentiation lane >> of data in baseball because there's been 20 years tons of money and you've had an owner class in Major League Baseball that comes from these more rational worlds that use data in their private equity shops or what whatever they're doing in their business life.
1:14:37
And so they've almost like optimized it to the point where if you don't have it at an A+ level to begin with, like you're just behind, right?
1:14:46
>> You're not even talking about eking out advantage at that point.
1:14:48
You're just like we have it's a model that I would use at the Suns >> was like what is the negligence line?
1:14:54
>> Like if we are below this negligence line, then we must focus everything on getting above it. >> Yeah.
1:14:59
>> And if we are above it, now we can start talking about okay, what are the cool unique ways that we can differentiate ourselves from this.
1:15:04
So you think about if we talk about basketball again for example >> style of play.
1:15:10
So style of play is just literally how are you going to play offense?
1:15:14
How are you going to play defense?
1:15:15
What are you going to focus on?
1:15:18
>> Basketball at the end of the day just like all sports is a possession game.
1:15:21
>> You want as many shots on goal as you can get.
1:15:23
Feal attempts plus free throw attempts.
1:15:25
You want that number to be higher than your opponent. Not in a reckless way.
1:15:29
Like don't cross half court and just throw the ball up unless you're Steph Curry.
1:15:32
But you don't want you want to just maximize that number. Yeah.
1:15:35
>> So, what are the ways to do that?
1:15:35
Well, rebounding is one of those ways.
1:15:37
And turnovers is one of those ways as well.
1:15:41
So, either you don't turn it over, you get your opponent to turn it over. >> Yeah.
1:15:45
>> Or you grab the ball offensively and defensively so your opponent cannot grab the ball. It's really that simple.
1:15:51
>> If you don't have rebounding and turnovers dialed in, like if you're not top 15 in those areas, negligence line would say focus on that.
1:16:00
focus on defensive rebounding and how you're going to get better from a roster perspective, from a coaching perspective, from a runoff office perspective.
1:16:08
Culturally, how are we going to become a better rebounding team? >> Interesting.
1:16:13
>> Before you try to be like, let's get cute by shooting more threes like this.
1:16:18
>> You have to get above the negligence line for your style of play. Interesting.
1:16:20
It's I I think that that is kind of a helpful model to think about >> all of these things being tools, when do we start making strategic choices that genuinely differentiate us instead of just having fun with >> Right. fun with data. >> Yeah. Exactly. >> Yeah.
1:16:36
And then and did you find that in a way what you're describing is is is the opposite of what the fiercest critics about data in sports would have said which is they're like it's going to make the game boring.
1:16:46
It's going to give the name game over to Egghehead.
1:16:47
it's going to be, you know, bean counters making all the decisions.
1:16:50
What you're actually saying is no, like the more information you have, if you're a bad team, you're going to you're going to lapse into homogeneity anyway, right?
1:16:59
But actually, having more information gives you an ability to tailor your particular style of play to your particular opponent or to the group you have.
1:17:06
And there's room for a ton of innovation even when every movement they're making has been dissected to the nines.
1:17:14
>> I that's what I believe. >> Okay.
1:17:15
And I think that that is, you know, if humans are just modeling the behavior of other people at the end of the day, like teams do the same thing, programs, institutions, they all do the same thing.
1:17:24
So Golden State is going to win all these championships.
1:17:26
How do we play more like Golden State? How do we get smaller?
1:17:29
Because they're playing small ball. >> Yeah.
1:17:32
>> Well, one of the one of the the the most important factors of their small ball is they have Draymond Green, >> right?
1:17:38
>> Who is arguably the smartest defensive player in NBA history. >> Yeah.
1:17:42
like someone who just redefined what it meant to be a small ball center, >> right?
1:17:46
>> And if you don't have Draymond Green, >> yeah, >> probably not the path for you to go down, >> right?
1:17:52
>> And so again, if we return to this like negligence line model, >> basketball is played in a vertical plane. >> Yeah.
1:17:59
>> The scoring goal is 10 ft above the ground.
1:18:01
It's not like soccer where it's like low to the ground.
1:18:03
You can kick the ball in there.
1:18:05
>> Height and size and jumping ability will always have some level of advantage. >> Yeah.
1:18:10
So you can't build a small team in pursuit of playing small ball, >> right?
1:18:15
>> You're below the negligence line here, >> right?
1:18:17
>> Unless you have that defining factor, the Draymond Green type person. >> Gotcha.
1:18:22
>> And it is exactly what you said where it's okay, if we can get all of this information, >> as soon as we establish our floor and our foundation.
1:18:28
Again, it goes back to James' model of raising the floor. >> Yeah.
1:18:32
>> If we can establish a really high floor here where all of our fundamentals are taken care of, best the best coaches will preach fundamentals.
1:18:37
M >> read any John Wooden quote out there.
1:18:41
He's only he's only talking about fundamentals and like first principles like you see this in the best tech founders all of this. >> Yeah.
1:18:47
>> Where it is um uh this like set the floor really high. Yeah.
1:18:53
>> Get it there and then you can start like branching off and doing these things where >> you have Steph Curry who is fundamentally the greatest shooter of all time, >> right?
1:19:02
>> You have Draymond in his role, Clay in his role, Andre Godala in his role.
1:19:06
Okay, now we can start Yeah.
1:19:06
you know, bringing in these people who can differentiate from there. >> Gotcha.
1:19:12
Or these styles that can differentiate.
1:19:14
>> So, you transitioned at some point from um the Suns to your role now in private equity.
1:19:21
>> Walk us through kind of that latest iteration in your career.
1:19:23
Like >> you asked earlier about in ownership change. >> Yeah.
1:19:28
>> And I I like to joke that my NBA bingo card was full except for winning a championship, which is the worst part.
1:19:36
>> We got We got to >> We got to the finals.
1:19:37
>> We got to the finals. I did lose um unfortunately and I did go through an ownership change as well and part of that ownership change I got to meet Justin Ishbia >> who is the founder of Shore Capital Partners and I spent a lot of time with Justin and with Shore from 2023 when he
1:19:57
and his brother uh bought the team, his brother Matt bought the team uh through May of 25 when I did leave Phoenix And I came to just really appreciate how Shore was built and how Shore was operating and um extremely process driven, very data heavy, very much a very much a sports culture, right? >> In that sports are very much a part of
1:20:21
>> In that sports are very much a part of what they're doing dayto-day. >> Yeah.
1:20:25
>> And I learned so much from them in such a short period of time that >> nine years in the NBA is a lifetime.
1:20:30
We had done the whole you know start from the bottom get to the top client again.
1:20:37
I think one of the through lines of my career has been like incessant problem solving.
1:20:41
solving. So it is like just solve the next problem in front of you and uh I just wanted a new problem like I wanted a new challenge and >> I was very fortunate that Justin gave me an opportunity to join Shore for a year and then recently moving over into 1949
1:20:57
Capital um which is uh also part of Justin's Justin's world and it's been a fascinating kind of it's been a fascinating look at the difference between zero sum nature of reports in non-zero sum nature of everything else, >> right? >> And that's exactly what I was going to
1:21:15
>> And that's exactly what I was going to ask you, which is in in your prior world, there are win winners and there are losers.
1:21:21
There are wins and losses.
1:21:22
And now you're in a world, private equity, where there's still winners and losers, but it's it's not zero sum.
1:21:28
There are partnerships to be made.
1:21:28
There are deals to be made.
1:21:30
>> How have you thought about that transition from one to the other?
1:21:33
>> I would tell you that I miss the games. >> Yeah.
1:21:36
in in the in the sense of there's nothing quite like being a part of a winning team in sports like the ride of that. >> Yeah.
1:21:44
>> The opposite is also true.
1:21:44
It's extremely hard to be part of a losing team, >> right?
1:21:49
>> Um >> but what I have really enjoyed about kind of the nonzero sum world is what you mentioned which is like you get to finally live out what game theory would tell you which is cooperation will often times lead to the best outcome. >> Yeah.
1:22:01
And it's you get to take these lessons from sport, about team building, about setting and raising the floor, about how do different personalities work with each other in the best way and try to form those partnerships and get the most and the best out of like the unit instead of just the individual person.
1:22:19
>> And uh I'm also very fortunate that you know Shore and Justin are very data driven and very AI focused as well.
1:22:26
And so the overlap has been that Shore and Justin in that environment is very data heavy. >> Yeah.
1:22:36
>> And very AI forward I would say for sure.
1:22:39
Um Shore started an AI team very early I think it was early 2025 that they started hiring AI specific people to go into the portfolio companies and implement these solutions which is incredible to think about how early that was.
1:22:54
Um, and it was it was having the opportunity to to go to an environment that is that forward-looking. Yeah.
1:23:01
That excited me and helping them roll out a lot of their AI enterprise tools over the last year uh has been an unbelievable learning experience.
1:23:11
Like tech deployments are one thing, but rolling out frontier models to an entire private equity firm is a whole another beast.
1:23:17
and learned a ton there and um have since gone deeper on the AI space and the sports space.
1:23:27
>> Do you and and if you you know you sort of like there's that line like prediction is always hard especially about the future.
1:23:33
>> Um but I'm curious what you make of the the the a you know AI as it has been adopted in sports like where are we at?
1:23:43
Are we are we early innings? Is it far enough along?
1:23:46
Because a lot of the things you're talking about with like saber metrics and and all this, did that pave the way for AI use?
1:23:51
Is it is it still kind of where the rest of the world is, which is we're figuring ourselves out in this moment, right?
1:23:57
Like what is what is the interaction or intersection between sports and AI in 2026?
1:24:04
>> From what I've seen, absolutely early innings and absolutely still in this formation of where are we like what exactly is the impact that this is going to have?
1:24:14
So not not dissimilar to what is going on in every other industry.
1:24:16
And I think when you when you zoom back out and you say okay who is using AI the best?
1:24:24
What can we learn from them?
1:24:24
At least in my experience it's been if you think about AI transformation as operations transformation.
1:24:32
>> So operations being how do we take this unbelievable amount of information that we are receiving synthesize it and then make a decision on it. >> Yeah.
1:24:40
And that's can happen at a very small level such as how are you going to stock the snack bar at your office all the way up to a very very very high level of where are we going to move this capital so that we can invest it.
1:24:49
uh if you think about it in that way then you can start to identify places where having this I don't want to call it a logic machine but this this ability to extract structure and synthesize information in pattern match at a scale that we have just never seen.
1:25:08
Once you can once you can start finding like the the links in your chain of operations, be like, "Okay, this looks like data extraction for the snack room because we need to have a better sense of how much money we're spending on whatever, you know, yogurt or whatever structured in that way and then AI can help you there."
1:25:30
>> Um, I I think that sports is still trying to figure out what that operations chain looks like, right?
1:25:34
And do I think that there are coders out there who are using codecs and cloud code? Of course.
1:25:40
And they absolutely should be doing that.
1:25:43
>> How do we now take that capacity and get us back to getting our hands deep in that raw optical tracking data so that we can build new novel metrics so that we can just answer more novel questions.
1:25:54
just answer more novel questions. like my whole my whole thing on tanking where >> you know I think that there is probably a paper waiting to be written by someone who can examine you know the kind of perverse incentives of tanking as well that were you if you were only considering it from the perspective of the general manager trying to acquire an
1:26:15
all-star yeah sure you might have found like an equilibrium there when you consider it from the perspective of players and coaches and owners who need to look good in the community >> maybe it's a very different calculus >> there's not enough time or attention within a sports team to do that because it moves so fast because we are on a clock every year it restarts. >> But now that you have AI,
1:26:33
>> But now that you have AI, >> like a good ownership group should be asking, okay, you think that we should tank.
1:26:40
>> Model that for me, >> right?
1:26:41
>> Like actually run through all of the different scenarios of where this could go well and where this could go wrong. >> Yeah.
1:26:48
>> All the way down to when you think that we're going to be contending for the number one pick, who's in that class >> and are they Victor Wimbeyama?
1:26:53
levels of generational talent, >> right?
1:26:58
>> Because that is a different proposition than, you know, if you're just some kind of run-of-the-mill class, >> right?
1:27:04
>> But it I my hope is that that is where AI and sports will get back to is now that we have the the ability at a much lower cost to ask these deeper questions instead of just trying to win the game in front of us. >> Yeah.
1:27:18
>> We can just produce more novel insights and more interesting ideas.
1:27:22
>> It's one thing to be in the front office and doing this.
1:27:23
One of the things that AI has done is it's democratized the ability to ask these questions without knowing how to code in Python or in R whatever.
1:27:32
>> Do you think that we're going to f see the first generation of players who dive into their own data or into sports data to to understand the game?
1:27:40
>> I you know it's funny we had uh we traded for Chris Paul. >> Yeah.
1:27:45
and he was a heavy user of the second spectrum >> and I was very impressed by that when I saw that.
1:27:54
And it it makes sense when you think about how Chris plays and I have an immense amount of respect for him.
1:27:58
He was incredible for us and the some of the best players.
1:28:04
You think about James and his career.
1:28:06
These players understand themselves.
1:28:08
Yeah, >> they understand truly their strengths and their weaknesses and they understand what to focus on in practice, what to focus on in warm-ups.
1:28:17
It is very dialed in to a degree that it it teaches you the importance of specificity and detail when you watch how these best guys work >> and data can help them with that.
1:28:26
Yeah, it absolutely can help them with that where it's not, you know, it's not so much, okay, the front office may use it against me in contract negotiations or somebody's forcing somebody like Ryan that kid Ryan shows up with his glasses and whatever.
1:28:41
He's got some he's got his spreadsheet and he's got his clipboard that has nothing on it, but we get we let him hang out at the game.
1:28:47
He's telling me something about my spacing when I'm doing screen.
1:28:51
This is like the you know >> Yeah.
1:28:54
>> It's more that the player could own this conversation. >> Exactly.
1:28:56
And I think that that's what if we're moving toward kind of this democratization of information and intelligence then I hope that is where it goes. >> Yeah.
1:29:03
>> Is where the players really understand how they can benefit from it. >> Right.
1:29:06
So if we take ourselves outside of the work context and we're I want to be respectful of your time.
1:29:10
I want to wrap up when you when you're thinking about AI right now.
1:29:13
What are your what have you personally been most impressed by? Right?
1:29:17
Because you you came of age and in a profession and and in day-to-day work where you were taking huge amounts of data and trying to ring insights out of them. Yeah.
1:29:25
So, what is your personal like what's most exciting to you or what tools do you use like how what's your day-to-day interaction with AI like?
1:29:33
>> I don't know if this will be an exciting answer, but I think the thing that I am most interested in right now is using these coding agents with my workspace.
1:29:46
>> Workspace I would have previously called a working directory.
1:29:49
>> Yeah, >> someone can call it a repo, whatever they want to call it.
1:29:52
But having my workspace now be designed not for me to read. Yeah.
1:29:58
>> But for agents to read, for agents to navigate. >> Yeah.
1:30:02
>> It's been such an interesting problem to solve. Okay.
1:30:04
Ryan is not navigating this as much as he otherwise would have been like in a little Dropbox setup, >> right?
1:30:12
>> Ryan is building agents who need to read the other the work of the other agents and what they're producing and what they're writing to this workspace. >> Yeah.
1:30:20
And that is two very different things. >> Yeah.
1:30:23
And I have torn it down or last month, I think I've torn it down four times and then rebuilt it.
1:30:28
Uh I have more copies of a working directory than I ever thought I would have.
1:30:36
>> Um but I think that that I think that that speaks to kind of the the the age that we're entering also, which is >> you do have to change your perspective. Yeah.
1:30:45
Of how you used to approach work fundamentally, >> right?
1:30:48
>> And I think it's maybe a better manager of people to be honest because you have to be so much more specific in what you're looking for and asking for.
1:30:56
Maybe not five years from now when these models are even better. Yeah.
1:31:01
>> But uh again, not an exciting answer, but I think like re redesigning what your literal workspace digitally looks like. Fascinating.
1:31:11
>> Um >> and then dayto-day it's really become an operating system where >> I'm I love to write.
1:31:17
Um, so yes, I will still open up Word or whatever, but I don't really type my writing anymore.
1:31:22
I will handr write writing. >> Yeah.
1:31:27
>> And then I will build like work product writing. >> Oh wow.
1:31:30
>> With codeex or with cloud code or something like that.
1:31:32
So >> it's very interesting where these worlds are diverging where professionally I am living very much in these tools as an operating system and in my personal life and going back to like this analog the old school. Yeah. Back to analog. >> Very interesting.
1:31:46
I think ad divergence is only going to become sharper, right?
1:31:49
I think it's going to be you have this world that you step into that's almost quasi like the matrix, but unlike the matrix, you can pull the cord out of the back ex and you do daily because it's the only way to stay sane, >> right?
1:31:59
I think it's the reason we talked about this a little bit earlier before we were recording, but it's the reason live events like the premium in live events is going to go up.
1:32:05
I think I think the it's it's ironically the race to the new in the in AI is going to race many people back to the old, right?
1:32:12
And like the things that may have been underindexed in the social media era, I think because of AI are going to be overindexed in the next wave because I think what's going to happen you're just going to be too over you're going to be too digitally plugged in.
1:32:27
>> And the the relief that you feel when you're not right the relief I feel now when I read a book is unlike it the it only rivals what it felt like to read as a kid.
1:32:36
And I think that's entirely because so much of my day is spent in different AI modes that reading a book feels like Christmas.
1:32:43
Like it just is it's incredible to have the analog experience again.
1:32:47
>> I completely agree with that.
1:32:47
I mean, I'm sitting here wearing a $20 Casio watch from Amazon because I did not want a smartwatch, right?
1:32:55
>> I did not want to wear it and be constantly hooked into these AI tools. >> Yeah.
1:32:59
And it is it's interesting because I think I could not have vocalized this in sport but I can vocalize it now which is I love any work where you have to perform.
1:33:10
M >> sports is that where there's a performance every night, hospitality, restaurants, hotels, you have to be on, you have to perform.
1:33:17
Broadway here in New York where we're recording this, you have to be on.
1:33:21
And >> I think that that element of someone is going to rehearse, rehearse, rehearse, practice, practice, practice, and then the curtain is going to go up, the ball is going to go up, the restaurant's going to open.
1:33:34
What an unbelievable like thrill that was going to be for I mean for people to attend it to see it to see that level of talent but then to also to do it do that work. >> Yeah.
1:33:44
>> Um I don't think I could have explained that when I was in sports but I feel that way now. >> Right.
1:33:48
I think the premium on it goes up because because few thing fewer things will become unpredictable.
1:33:54
>> I my my theory of the case would be that because you don't know the outcome of how the night's going to go in a show or how the game's going to go. >> Yes.
1:34:01
And AI can give you very good predictions about most everything else, right?
1:34:06
Like it's going to work from the available set of human data and restrictions and come back with an answer.
1:34:10
The sheer unpredictability of some of these things is what's going to drive the the desire for them. Right. >> Yeah.
1:34:18
>> Randomness is good for us, right? It turns out, >> right? Exactly.
1:34:20
Um, what are some of the And I promise we'll finish here, but I because we do this for another three hours.
1:34:26
This is basically like what we do anyway now.
1:34:28
What are some of the um like the the books or podcasts or kind of other media or intellectual influences that this year have helped you particularly as you've made the transition from kind of sports to um to private equity.
1:34:44
>> I read a lot so I absolutely love to read like you whenever I am reading I am extremely happy.
1:34:48
um from a fiction perspective, a mutual Tyler, a mutual friend of ours um recommended on his website when Dan Simmons uh passed away the Hyperion series.
1:34:59
And so I did read Hyperion and then I am in the fourth book now.
1:35:02
And it's fascinating to read that to go back and read some of the other science fiction classics and see how they talked about AI at that time. deeply philosophical.
1:35:15
Not an accident that Silicon Valley in the tech space right now is has that slant and that bend to it as well.
1:35:21
But I think that that has helped me in a way contextualize maybe society like where not so much where it's going and like oh no things are going to go so poorly but where things are like culturally with has been really interesting.
1:35:35
Um, I'm I am still a huge fan of memoirs and biographies as you know.
1:35:46
>> So I I gave your book out to many people at the Suns.
1:35:48
Jimmy came and invited and saw stacks of the founders.
1:35:50
I would give out to to the team.
1:35:52
Um, and I and I continue to find myself drawn to those stories more and more. I think I don't know.
1:36:01
I'm kind of processing this now as we're talking about it, but I think it's like reading these stories of these people who accomplished such unbelievable things.
1:36:10
>> Um, pre-Ire like how did they think about these problems before we had the ability to like drill down or synthesize all of these random things?
1:36:23
Like how did how did Steve Jobs approach that?
1:36:25
How did um you know past presidents, how did historical figures like solve their problems in an era where it was just much more difficult to get you know to see the board clearly >> uh with the information that you had access to. >> Yeah.
1:36:42
>> Um what I do really enjoy is the kind of the podcast and the media landscape that this industry has birthed >> with shows like TBPN. >> Yeah.
1:36:53
and just how fun people how much fun people are having that space >> or even things like the Doorcash podcast. Yeah.
1:37:00
podcast. Yeah. or it's no we're going to go super deep on these kind of 10 years ago we would have been like what are we all talking about but now that we have direct application to KB caches in our life like I do want to watch a whiteboard session
1:37:15
>> right >> um it's just it's at the point now where I wish I wish I had two more hours in a day >> same >> so that I could read or listen to a show or any of that >> more yeah because the issue is that a whole new category of media has has opened up. >> Exactly. >> Exactly.
1:37:33
>> It was so different from the media I had when I was growing up and it's so rich.
1:37:37
It's so protein rich >> and I just don't have the time and I do not know from where I will find the time. Exactly. >> Right.
1:37:43
And and the truth is it's like I don't know if you felt this way, but I know people have their different opinions about AI.
1:37:48
I have found it to be just the most exciting thing to me since I kind of like first got on a dialup internet and have to make sure my parents didn't pick up the phone by accident and interrupt that phone signal so I could interrupt the internet.
1:37:59
so I could interrupt the internet. It's that exciting like because I think it especially if you're in any kind of creative space or any any kind of space where there's a lot of flux like what you're in where things can go one way or they can go another AI just it opens up the universe of possible questions you
1:38:14
could ask agree >> and begin thinking about in a way that I have never seen like I find myself being what it's done for me in addition to I think making me a better manager it's also made me ask better questions now because that is just good training for the LLM it's good training for life
1:38:29
>> that completely agree with all that there is like a default optimism I think that I have on these things recognizing the risks that do exist obviously but >> I I think the story of humanity and society is one of progress and you know when you really look back at all the technological change and the progress
1:38:45
and the way that we spoke about those things at the time there are a lot of overlaps there um >> I just feel so lucky to be in this I you tell me when I was like like you said I was I mean I consider myself lucky being probably the last generation of millennial that did experience the analog to digital shift. So I did have a
1:39:03
So I did have a CD player despite how young I look. I did have a CD player.
1:39:07
Um I also remember Napster. >> Yep.
1:39:12
>> That was the whole ball game.
1:39:12
Finding out ways to download those MP3 files.
1:39:16
iPod with the click wheel, >> right?
1:39:18
>> Uh God, we're really dating ourselves here. This funny I know.
1:39:20
Um, I had the funniest the funniest interactions I have on these is I have an 11-year-old and she doesn't realize the digital bounty that she's been given, right? She has no idea.
1:39:30
So, I remember one day we were talking and we were she was like we were watching one of our shows we like and she said, "Oh, let's go to season 2 episode 3."
1:39:37
And I stopped in that moment.
1:39:39
I said, "Do you know that when I was young and I wanted to watch a show like Sonic the Hedgehog, I had to wake up like every Saturday to make sure that I was in front of the TV when that show came on, otherwise I would miss it."
1:39:47
And you have the ability to just pick a season. this is extraordinary.
1:39:51
And she looks at me and perfectly seriously with like true pity in her eyes, she goes, "That is so sad."
1:40:03
Like felt deeply bad for me that the the challenge of my life was not being able to pick the season and pick the episode. >> Amazing.
1:40:11
>> Next time you got to show her how to record it using a VCR, but there has to be something on the DHS tape.
1:40:16
Y >> So she has to erase that first >> and then she has to record it.
1:40:21
>> Oh my god, >> that is too funny.
1:40:23
>> So one of the ways we like to finish out Infinite Loops is >> Ryan, you're emperor.
1:40:26
You're named Emperor for a day and you get to basically incept two ideas, messages, themes, concepts, quotes into all everyone's mind and humanity.
1:40:36
So they'll wake up and have these two ideas in their head.
1:40:40
Uh what two ideas or thoughts would you want people to carry carry forward into their lives?
1:40:45
There was a quote in my office in Phoenix.
1:40:47
I don't know if he actually said or wrote it, >> but it is from Vololta, >> right?
1:40:56
>> Voltater gets a lot of credit for things he didn't say, >> but you know what? Like, good for him. >> Yeah. Right.
1:41:00
Like, >> don't hate the play or hate the game.
1:41:01
I mean, maybe that was Volta that said that.
1:41:04
>> He is living big in the age of AI.
1:41:04
And uh he he allegedly said uh no problem can withstand the assault of sustained thinking.
1:41:12
And I love that because it goes kind of it's like a core principle of mine which is just solve the next problem.
1:41:18
Just solve the next problem.
1:41:20
And if you do that then you'll win the game or you know it's you think about basketball it's you're the defense is going to change.
1:41:27
We'll solve for that defense because guess what?
1:41:29
They're going to change again and then you got to solve for that one and then just keep solving. >> Yeah.
1:41:34
And I think that if it also goes back to the to the default optimism point where it is if we believe that we can solve these problems and we can get around this, we can get around the risks that these you know AI tools may or may not pose for example.
1:41:47
>> Um I think that that would be one and then the second and again it goes back to I think a realization that I've only had recently which is try to do work that has to face some form of reality.
1:42:00
some form of reality. M >> and what I mean by that is you're an author, you write a ton, you have to publish at some point >> like >> uh you have to stop editing and send that book out so that I can read it and give it to my team >> u that is facing reality like sports is that every single night you're a sum someone's going to win someone's going to lose what we talked about earlier with the restaurant to the Broadway show
1:42:24
like >> let people experience the work that you're creating have the confidence for that >> so that You can yeah you can get the feedback from it but also what you'll find is when you get when you find your work and when you find your people to do this work it is so unbelievably fulfilling >> to do that thing together with those people to put that thing out into the world to actually change something or to introduce something to society. I think
1:42:48
I think that that's so fulfilling and it's probably why I like reading biographies and memoirs too.
1:42:54
So >> that's a wonderful place to end it.
1:42:58
Ryan, thank you so much for coming out to Infinite Loops. Thank you.