Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas - 138 | Daryl Morey on Analytics, Psychology, and Basketball

Episode Date: March 15, 2021

You might think that human beings, exhausted by competing for resources and rewards in the real world, would take it easy and stick to cooperation in their spare time. But no; we are fascinated by competition, and invent games and sports to create artificial competition just for fun. These competitions turn out to be wonderful laboratories for exploring concepts like optimization, resource allocation, strategy, and human psychology. Today's guest, Daryl Morey, is a world leader in thinking analytically about sports, as well as the relationship between impersonal data and the vagaries of human behavior. He's currently an executive in charge of the Philadelphia 76ers, but I promise you don't need to be a fan of the Sixers or of basketball or of sports in general to enjoy this wide-ranging conversation. Support Mindscape on Patreon. Daryl Morey received a bachelor's in computer science from Northwestern University, and an MBA from the MIT Sloan School of Management. He served as general manager for the Houston Rockets from 2007 to 2020, and since November 2020 has been the President for Basketball Operations for the Philadelphia 76ers. He is founder and co-chair of the annual MIT Sloan Sports Analytics Conference. He was voted NBA Executive of the Year in 2018. Philadelphia 76ers Basketball-Reference page Wikipedia Twitter

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So you turn every day into a little time for women's wellness. Vital Proteins. Stay vital. Visit VitalProtene's.com to get started. These statements have not been evaluated by the Food and Drug Administration. These products are not intended to diagnose, treat, cure, or prevent any disease, in combination with resistance exercise. Hello, everyone, and welcome to the Mindscape Podcast. I'm your host, Sean Carroll.

And as long-time listeners know, I'm a physicist, theoretical physicist. I have certain other interests, intellectually, academically, so forth. But I also have, you know, my personal hobbies and my individual and I try not to force my individual enthusiasms on the Minescape audience too much. Sometimes it leaks through. We get the occasional jazz musician or poker player, but mostly we're talking about academic type things. Today, one of the times when we're going to take a little bit of a detour, we're going to be talking about basketball and the National Basketball Association, the highest level of basketball being played out there. But I think this is a special case. If there is one person,

you would want to talk to about the idea of modern basketball to an audience or with an audience that was intellectually and analytically inclined, but not necessarily full of basketball fans. It would be today's guests. Darry is the general manager of the Philadelphia 76ers. Now, coincidentally, the Philadelphia 76ers are the best basketball team in the world. I could say this very objectively. It's not just because I grew up in Philadelphia, watching Dr. Jay and Moses Malone and Mocheeks and Bobby Jones and so forth. It's just because it's an objective fact. They don't always win.

They're sort of at the top of the heap when it comes to one's favorite basketball team, if one is just objective about it. But that's just a coincidence. Daryl was the general manager of the Houston Rockets for a long time. He just came to Philadelphia very recently. The general manager, for those of you who don't know, is kind of like the boss of the basketball team. He's one who hires the coach, drafts the players, makes trades, signs, free agent, stuff like that. The person who's ultimately responsible for assembling the basketball team. And what's special about Darryl Morey is, he, more than any other person,

has been responsible for dragging the National Basketball Association into the modern data-centered analytical age in terms of trying to understand what it means, to build a team, to best position yourself to win games and win the championships. So if it were just me talking to Darrell Mori for an hour without anyone listening in, I'd be very tempted to geek out about basketball or 76ers minutiae.

You know, should we try to land a stretch four, or is it more important to prioritize perimeter creation, things like this, who are you targeting in the trade market? But I put that aside. I put aside my impulses to do that for the greater good of the Minescape audience. And instead, what we're talking about today is the theory of being a general manager, okay? And I think that whether or not you care at all about basketball, this is an idea. that has much broader application. There's no basketball knowledge or interest required, but what we'll be talking about is a combination of science, art, and human relations.

Okay, if you're a general manager of a basketball team, you have an extremely quantifiable goal. You want to win games and ultimately win championships, okay? You know whether you have succeeded or not in ways that are highly quantifiable, as I said, much more clear cut than in many other areas of human endeavor. And you also have resources, abilities that are very quantifiable. You have players. They can score. They do score at a certain rate. They can pass. They can rebound. They can play defense, et cetera. Then the question becomes, how do you optimize? How do you give in your resources? Combine them together. There's a salary cap in the NBA, so you can only pay your players so much. You can't just outbid everyone else for the best players in the league. What are the

data that you need to take into consideration? What are the data you should ignore? What are the things you should not care too much about? How do you both correct for the biases that human beings inevitably have, but also recognize that human beings do have some intuitive knowledge of things sometimes, right? There really is human insight that needs to be taken into account. What about not just the player's ability to pass and score, but also their ability to mix, right? Their personalities. Do you need a leader? Do you want vocal players? Can you put up with a superstar who's kind of grumpy? Stuff like that, okay? So to me, this kind of, much like poker in some sense, what we have here is a toy model. What we have here is a paradigm for all sorts of optimization problems that we face in the everyday world, one where the answers ultimately are very clear cut, whether you have succeeded or not. Daryl Morey is someone who succeeded a great deal. Over his years as a general manager in Houston, they compiled the the second best overall record in the NBA, second only to the San Antonio Spurs.

Congratulations to them. And currently, as I record this intro, the Sixers are the first place team in the Eastern Conference of the NBA. So we'll see how that goes. You know, who knows what happens as the future, trundles on, injuries, all sorts of crazy things can happen

in a sport like this. You don't need to know anything about basketball or the Sixers or the current state of the NBA. We will mention Joelle Embede, Center for the Philadelphia 76ers, currently an MVP candidate. Also, Ben Simmons, point guard for the Sixers, another All-Star, and Doc Rivers, of course, who coaches the 76ers, but you don't need to know any of that stuff. The concepts we're talking about are much more general.

And in the end, just in case you want a little bit of physics in your life, we talk about dark matter. I think that one of the reasons why Daryl took time out of his busy schedule to be on the Mindscape podcast is because he wanted to talk about dark matter. He was skeptical that dark matter really exists. I gave him my sales pitch for why it does. We'll see whether or not that convinced him, whether it convinces you. Let's go.

Are you welcome to the Mindscape podcast. Thanks for all on, Sean. Appreciate it. So this is an unusual. We'd like to have some people on the podcast who are not PhDs and professors and things. So this is a wonderful addition to that tradition. But many of our audience members will not be the rabid Philadelphia 76ers fan that I am growing up, you know, worshipping Julius Irving and so forth. So why don't we start very, very basic?

What do you do? What is the day in the life of a general manager like? Are you just on the phone all the time talking about trades with other general managers? Or what is the day to day? Well, it's like most things as you move higher up, you just become more of a tax on the system. That's how I look at it. But, yeah, I mean, I started out as a computer science programmer way back when and got into basketball in 2002. Oh, I always played basketball.

into the Boston Celtics in the NBA in 2002. Sorry to hear that. And I started off doing data and coding. And, you know, now I get to do very little of that, except, you know, run the teams that do that. And, yeah, as I go on, more, my time is on the phone and meeting, you know, players and coaches and owners and fans and business people. And, you know, as you get higher up, you end up just talking more.

It's like the podcast. Like being a department chair or president of university, you know, who have to do, presumably you don't have to do fundraising, but there is fan outreach, I presume. Yeah, in fact, to the point where I'm sure you might be like this, Sean, where you just, when I get to do some stuff where I'm digging into data and solving a problem, I get so excited. Oh, yeah. And then my poor staff is like, well, just let us do it, leave us alone, you know.

So I try to do that because that's how I would be if I were then. Well, you know, and presumably the landscape has changed a little bit since nowadays people can have a video game where they play at being the general manager, right? So like everyone is an amateur who's going to give you advice on your job, I'm sure. That's right. And you'd be shocked, like the amateurs are very sophisticated. I think that's true in a lot of fields. Like there's, you know, just diehards who know the team, know the data, know everything. you know, there's a rights to Ricky Sanchez podcast with a couple diehards who are very smart.

There's, you know, message boards. And every team has these things. I was a guest on the rights to Ricky Sanchez. I've appeared on the podcast before you did, yeah. Oh, wow. So you were a pioneer. You were pro. So I was hoping I could be maybe one of the smarter ones on podcast, but now I've been us

usurped for sure by you and Joelle. Joel's been on. So Joel is shockingly smart. It would be great to have Joel join you. He's like, I mean, I shouldn't say shocking that sounds better, but he's like in, you know, just into, he's, what, polymath? Is that the word? Like, he's really smart about a lot of things and on the court as well, obviously. Right.

This is Joel Embed. We're talking about, obviously, the Philadelphia 76ers Star Center. I mean, it is interesting because some people, and I completely agree that my impression of Joel is that he's super duper smart. But he doesn't project that way, right? Like he doesn't put on affectation. I think that actually Minute Bowl was someone who was like that, a previous Philadelphia 76er's center. Everyone used to say when they talked to him, how smart he was.

I've heard that about Minutes. I mean, there's some, I mean, I've known lots of smart players firsthand, like Shane Batier. But then you have, like, Chris Bosch is like, was a coder. Sam Dallembert was, like, building his own machines before that was. thing. So, I mean, the thing is, like, if you make it to the top of your sport, whether it be player or whatever, you usually are pretty good on G. Like, you usually have pretty good pattern matching to make it that far. Yeah, there's a lot of tall people out there. Some people, but you need more than just that. So your goal, I mean, how would you say that your goal is,

as the general manager? Obviously, you want to win the championship every year, but maybe that's not plausible? Do you sort of optimize to get a chance to win a championship or long-term success? And of course, there's always a danger of being fired. It's the real world. How do you think about what your job is? Yeah, it's exactly the right question. I would say, yeah, I'd say, so the first thing you do is it's like organisms. You have to survive. So you're right. Like whether it's right or wrong, I would say most people, and I for sure focused on this early in my career a little less so later, which is nice. Try to like just make it to the next year.

Like to, and hopefully you can always align your goals with the organization. But in terms of thinking about what we're optimizing, it's pretty owner dependent. You know, I've been lucky. I've always had owners who are like just championship or busts. Like that is your goal.

But even within that, to your point, if you just focus on winning in one year, you'll create all these weird skews. So we look over about a three-year window and we're trying to optimize our championship probability over that three-year window. And we use a lot of outside measures.

We have internal measures and outside measures. Like right now, Vegas, I think, has us like about a 6% chance to win the title. And that sounds like really small, but it turns out to be pretty high. I think that's fifth or sixth in the league. Yeah, okay. And you personally, where do you put your chances?

Yeah, we have them internally higher, but that's true almost always, right? So whatever process you have, whatever team you are, if you don't think you have a higher chance than Vegas, then that means you're not actually following any sort of proprietary process. The reality is, though, everyone can't be right. So you need these outside objective measures to keep you grounded. And so if you differ a lot from Vegas, and Vegas has become pretty sophisticated with how well they measure things. Football has always been the most efficient. Basketball has become very efficient over the last 10 years. If you differ in a big way, that means you probably need to look at your own stuff.

Like, it's pretty hard to get a huge edge on Vegas. Dan, just to give the listeners who are not basketball fans a feeling for this, There's one little anecdote that I read that I thought was hilarious. Sergei Lishuk, the Ukrainian basketball player, remember his name? Yeah, Sergei Lishuk. Yeah, I think, I mean, honestly, you probably said it right. We always call him Sergei Lishuk. So the thing I read was he was drafted by the Memphis Grizzlies in 2004.

And again, for those who don't know, you draft the rights to a player. You may or may not sign them to a contract. and then you can trade the rights to sign that person to someone else. That's why the rights to Ricky Sanchez podcast has that funny name. So apparently you personally are responsible for trading the rights to Sergei Lishuk five different times, either trading for him or trading him away. He's never played in the NBA. He's already retired.

He never will. Yeah, just for the audience, it's sort of interesting. So the NBA draft is this sort of weird system that doesn't really exist in many other places. And what it is is that, Sean, you have been in the NBA draft. And really everyone listening at one point, male or female, has been in the NBA draft. At age 22, the year you turn 22, you're in the draft. And you don't have to declare or anything. You just have to be 22.

If you happen to be selected by a team, they obviously can't force you to play for the team. You have to come to a contractual agreement and they're generally prearranged, depending on where you're picked. and often those players either aren't good enough so the team never offers a contract or the player doesn't want to come, which has happened in the case of players like Sergio Yol, who we drafted in Houston. And so, yeah, long story short, you can hold these options of players to come, and they have options always have some sort of value, depending on the variance and the underlying asset.

and they get traded a lot, mostly because a lot of it's because the NBA doesn't understand that there are negative numbers in the world. No, I'm actually serious. Yeah, no, I get it. And to be fair to the NBA, this is a legal thing, apparently, too, that in a lot of contract law, they don't understand that there are things called negative assets. So even though a team might be trading a positive and a negative asset together to a team, such that it's neutral, such that they'll just take it together and they don't need to send

anything back. The NBA forces you to send something back. And so often these, these options become just the word you write such that the trade is legal, even though it doesn't make any sense. Yeah. And if I understand correctly, a negative asset isn't just a basketball player who is so bad they make you lose. It's that maybe you're contracted to pay them more than their worth. A hundred percent right. Yeah, exactly right. And to your point on everyone being GM, everyone gets this now because 2K, for example, is like, I think one of the top 10 most popular games in the world. And no one actually plays it. The game play is how the game was created, but now it's like more than half of the time spent on it is just playing GM and simulating

the games and not actually playing them. So yeah, everyone, everyone's a GM. And, Which is cool. I mean, I think it's just part of being a fan of a team now. Yeah, absolutely. All right. So moving into a little bit more of the nitty-gritty, I mean, the thing one has to talk about when you have Darryl Morey on the podcast is when you're evaluating players, there has been a seismic shift over the years from listening to the wise counsel of scouts who have been looking at these players and developing their personal opinions to a more statistical data-based approach. And you have a lot to do with that. I mean, what's your big picture? overview of where that came from and how it's going. Yeah. And so I think my job is just, it's really the same as Red Auerbach, who's a famous GM for the Celtics for many years, is, you know, help your team win and make good decisions on your three levers, draft, trade, and free agency. And how do you, and so my job is the decision-making job, more than anything else. And how do you create a consistent edge there? And it's by studying decision science. And so if you study,

Decision science, you'll see there's been a lot of research over the last 30 years, especially on, you know, how, you know, behavioral economics is a big one on all the different cognitive biases, anchoring, endowment effect, all those things. Then there's a huge set of research on combining data and human judgment to make a decision. So we try to employ all those methods to create an edge. And honestly, what we're trying to generate is a three to five percent edge, which again, doesn't sound like a lot, but over time compounds, basically. And so we use a lot of data. And with scouts, for example, we look at it as what is a scouts experience? It's a very good set of data built up over 20, 30 years of players and the patterns of the

players that succeed that are all in their head. And humans are actually really good at this. They're really good at finding patterns. They're often very too good at finding patterns, and that's often where data comes in. So data like grounds you, just like I said, look at Vegas, and compare it to your own models at all time.

If a scout or myself is very far afield from the general consensus, could be right, but you want to like ask the questions, why do you think he's not much better? And then dig into each thing. So data, like, actually forces you to ask the questions. to make your decisions more precise, essentially.

So that's, I don't know, that's a summary that helps hopefully. Yeah, it does. I mean, it raises the question. Do you do longitudinal analysis of the success of scouts? Like, are some scouts really, really good at finding the diamonds in the rough? So we do. It's tricky, though, because you have an observer effect, which I would not, not the physics observer effect.

This is the, this is the human observer effect, which is, you know, as you, as you, as you measure people, they definitely change their behavior. So I tend to be a little more of a hands off. I don't try. And you use the right word, longitudinal look over a very long period of who's successful and who isn't. The problem is we have a big windowing problem. So by the time you might have a sense that someone's 3% better or 2% worse, you know, it takes like 12 years. Like it's just, it's tough. So a lot of it, you have to use your own human. and intuition of who you think are maybe better or worse, scouts better or worse decision makers.

I wish it was all science. It's not. It's art and science like most things. That's kind of fun, though. If you have 30 minutes free, you never have to worry about a break-in at your home ever again. That's how quick and easy it is to set up a security system from SimpleSafe. It's the kind of thing that's so easy to do you can do it during a Netflix binge, watching a game, or listening to a certain podcast. I have a SimpleSafe system myself, and look, I'm a theoretical physicist. Setting up electronic systems is actually not close to my core competency,

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So go to simplysafe.com slash mindscape today to customize your system and get a free security camera. You also get a 60-day risk-free trial so there's nothing to lose. That's S-I-M-P-L-I-S-A-F-E.com and when it just comes to the players, I mean, there's been not only an increased understanding that the statistics are useful, but the kinds of statistics that we have. right? I mean, we used to keep track of how many points, how many rebounds, how many assists a player would have. These days, I mean, again, you're the expert, I'll let you say it, but we are, we collect an enormous amount of data not only at all different levels, but in the arena, there's cameras keeping track of everything. How many, what's the average speed, what's probably what the variance of the speed of a player on the court is, how many miles they go, the whole bit. Yeah, to your point, like in a lot of fields now, there's no, there's no lack of data.

data. It's really, you have more of a lack of an imagination or how to analyze the data. But so we have data from the NBA all the way down to like second, third division, Germany, that kind of a level and college and some high school of every play that's happening around the world. Right. And to your point in the NBA, we have and in the D League and then probably coming to college soon, to a few arenas in college already, 25 times a second. in three dimensions, the ball and all players on the floor. So you're really only limited by your imagination. If you want to know, was that shot open, you can look that up.

And so, yeah, we spend a, so the problem isn't the data. There's two ways to, we're trying to create an edge. So there's two ways to create an edge. You either have to have unique data, and that's hard to come by, though we have some. And then, or you have better analysts, and that's also a hard one to have a consistent advantage in over time. We used to have a huge advantage in Houston in my belief, and I think sort of borne out by the fact we never had a losing season and had the second most wins over the years.

That, a lot of those edges have eroded, and now it's like chasing. It's similar to, you know, if you were a physicist in 1880, there's probably a lot of stuff to do, and there still is a lot of stuff to do, but it's harder. it takes longer to get to the unique nuggets now in physics, I would guess, than it did in 1880 when we didn't know relativity and a whole bunch of other stuff. So, yeah, so just to put it in context, when you became the GM of the Rockets in Houston, not that many teams were devoted to this idea of collecting all the data you can, analyzing it. And so you got to be out in front a little bit there.

And by now everyone else has caught up, more or less. Yeah, absolutely. like in 2006 it was of course people knew but it was somewhat novel that shooting three-pointers and getting three points was better than shooting twos and getting twos like that it seems pretty straightforward that you want to take a shot that's worth 50% more than the other one but it turned out not to be in fact teams were actively avoiding those shots mostly because of the human how that it was introduced it was introduced to the game after a lot of the models on how to win with two-pointers came out.

And because it was introduced late, everyone thought it was a gimmick and to be avoided. And all the people who were coaches and players at the time were like, ah, that's a stupid gimmick. But they didn't realize the power of it at the time. And it took like 25, 30 years before people started using it correctly. Well, that's absolutely true. And it's a weird number. 25 or 30 years is a long time. And so to be fair, sure, a three-point shot is worth,

50% more, but you miss it more often. And there's also like these confounding things that people believed, I think it turned out not to be true, but they believed that your team was less likely to get the rebound back after a three-pointer. So you kind of had to dig into it, yeah? Correct. There was a lot of reasonable objections that could have been true just so happened that none of them were. So yeah, you could shoot them at a low enough percentage such that it's not worth it. That turned out not. It turns out people shoot eight foot shots at about the same percentage or within a few percentage

points of what they shoot 25 foot shots. So that's a counterintuitive thing. Like you wouldn't know that until you actually played. I think it's intuitive to your point that people thought, yeah, you'd get longer rebounds that the other team might get and turn into transition. That turned out not to be true. Now, of course, those happen, but they're just anecdotal. They're very small relative to the

whole set. People thought maybe an open two-pointer would be better than a fairly challenged three-pointer. That also turned out to not be true. So as you keep diving in, it just turns out that 50% more is such a huge edge that it's worth taking under most conditions. And, you know, people don't love that. But, I mean, the reality is that the three-pointer, if we were an e-sport, the three-pointer would have been nerfed a while ago.

And they would have changed it to be worth less. It would have been worth two and a half or something like that. Well, and also let's admit that there is an almost a macho component, right? You know, a lot of people in and analyzing the sport were ex-players themselves. And there's a feeling that if you bully your way down under the basket and slam dunk on someone, you've achieved more than if you've just lofted a shot from 35 feet away. That's very insightful. Yeah.

I mean, any sport with men, and that's true. I mean, there's a competitiveness and an aggressiveness that often gives you an edge. But that edge can also be your weakness. And it's absolutely true that, like, I always talk about this. There are ways that fans and coaches and GMs are comfortable with losing, and there are ways that they're uncomfortable with losing. So ways that people are very uncomfortable are losing, like in football, is if they can just run it down the middle every time, get four yards and do that every time and win, that's an uncomfortable way to lose.

But if a team, you know, makes a lot of long passes and they seem unlikely and do, then they're fine with it usually. Similar in basketball, if on a given night you take a bunch of open 18 footers and happen to miss them and lose, they're like, ah, it wasn't your night. But if the other team, like, posts you up and, you know, makes you look bad and throws it one inch from the hoop, now, now it's like our, to your point, it's a very insightful one. Now it's like our manhood is being challenged. And that's not a good way to lose. No. And it's, I think in any sport, analyze the ways that you're allowed to lose and not allowed to lose

and you learn a lot about the culture of the sport. So other than shooting for three points is even better than shooting for two points, is there anything that we've learned from this ultra-high resolution, 25 frames a second data about ball movement and things like that that just smacked you in the head that you said, oh my goodness, that's counterintuitive, but now that we think of it, oh my goodness. Yeah, there's a lot on rebounding. So to your point, a lot of the analysis on rebounding in terms of, you know, there isn't more long rebounds that that's a big one um you know there's a lot on the there's a tradeoff between obviously

basketball is one of the coolest most dynamic sports everyone plays offense and defense and it goes back and forth very rapidly i think that's one of the reasons why it's such a popular sport and so fun besides athleticism and so there's this constant tradeoff between um do you want to stop the other team from running back at you and getting an easy basket in transition or do you want to try and get an extra rebound? It turns out that problem is really complicated and really can only be done with overhead cameras because you can't look at it in the macro. You have to look at the marginal decisions of sending one more guy versus sending another guy back.

Soccer is currently dealing with this issue as well, which is, you know, one of my bugabooos is they pass back to the goalie, a super high risk move for a fairly low upside. but you can't, like, you can't use big data, like, oh, let's just count how many times you kick it backwards and see if that team wins or not. The analogy I use there is if you took the best basketball team in the world, let's say Golden State during their six-year run of being the best team in the world. And every game, Steph Curry, after he got the tip, threw it out of bounds. That was his first play of every game. Well, it would be this, like, it's amazing. that would correlate 100% with winning

because every game, they do it. They throw it straight out of bounds and they win every single game. And so teams should clearly follow this. It works. Like it happens all the time. So obviously I make a very simple,

very simple correlation causation argument, but it happens all the time. People will say, oh, the best teams don't offense the rebound. Well, that doesn't work. But they'll say the best Premier League teams pass back to the goal. Well, that tells you nothing.

It does you absolutely nothing. So until you get this better data, this over-up data, in soccer and in basketball, you can't really make these decisions. And do you go beyond the data of the players on the floor? Like, is this, are you an organization that keeps track of the player's sleep patterns and diet and does all that contribute to the data set? Yeah, absolutely. So it's been a, you know, every team's trying to find an edge wherever they can. Sleep turns out to be this tremendous edge, but it's also a tremendous one. to gain, a hard one to gain a consistent advantage.

You know, I think when you go down levels and you have more, you know, you're at a college or at a high school and you have more control. But these are professionals. These are adults playing the game. We definitely work with them when they want to be, you know, when they want to wear a sleep monitor and things like that, we do it. But we also don't, like, in fact, the CBA, not the CBA, but the players union, you know, basically has rules on how much you can instrument players, both on the court and all.

And so you do your best to get them to understand the advantage of sleep by showing them the data, which is actually overwhelming. But it's not something that we can force them into a pod in the wall once the day is over, unfortunately. Sadly, no, they still have, they still human rights, even though they're NBA players. I mean, maybe the last question on this sort of very data-focused thing is, what is the actual sort of organizational way that it happens? I mean, you have a team of people sitting in front of computers debating about what to collect, how to analyze it. Like, related to the bigger big data questions, this is a question that every field has these days. Yeah, I mean, I think, so I can just tell you how it is.

There could be a more optimal way to do it, but I can just say how we do it in most teams. Obviously, things start with the owner. Generally, there's my role to M, and then there's the coach role. And I think the best ones that I know Doc does this and I know I do this, you have like really capable people below you. And you try to put them in an environment where they're very comfortable disagreeing with you. And they're very comfortable. They basically have a lot of experience. And you're then, you're like the gate function on a multi-model where you're,

You're basically saying, okay, I'm going to wait. I'm going to wait the opinions of people who have a history of being correct, who line up with the data, things like that. And so that's basically a coach down to multiple key assistants. This is true in most even other sports NFL, especially. And then obviously, you know, their role is to get the optimal performance out of players. And then my role is to have, you know, scouts. and data and programmers make decisions on draft trade and free agency to get the best

possible players for Doc and his career to optimize. So that's that's at least how we structure things and has been successful. But for sure, I'm always open to new models. Should we, should we do a peer review process and have tenure for certain? Yeah, I mean, like you said, it takes 25 or 30 years to catch up to something obvious. And you said analogizing to physics, which is very, very interesting, like where the breakthroughs are. Presumly there are just as many or nearly as many breakthroughs yet to be made.

We just haven't thought of them, right? And you must live in fear. I know probably many physicists live in fear. Like, they're so close to finding what the breakthrough is. And then someone else comes up with it. And you're like, ah, yeah, I should have thought of that. Yeah, we don't have a timing element. Like if you're a fast follower like Microsoft, if you're the Microsoft of NBA teams, you're probably okay.

If you see a team doing it, they have to put it on the floor. And then you can aggressively follow where you can get behind our development systems of, you know, talent identification and helping them in the minor leagues, for example. So there is where you can develop a sort of a bigger longitudinal advantage. But, yeah, we thankfully don't have the physics thing where you could be working. on for Mets, you know, last theorem. And then some guy at Princeton, you know, comes up with the proof only four people understand all of a sudden in your, you know, your DOA. Everyone lives in fear of being scooped in science and academia. But so do you go so far as to use artificial intelligence, machine learning kinds of things? Like, do you ask the computer,

look for patterns that my pitiful human brain is not up to finding? Yeah. So the two big areas where you see that. One is with the overhead camera data. They do automated recognition of like pick and rolls and different isolations and different kind of actions that you have on the floor. And the other one would be in draft models. So those would be the two areas where you see. I mean, I grew up in the predictive modeling world. That was my other area of study at Northwestern and then since.

And so if you do the if you do the other basically it's all part of predictive model. I was very lucky to take a class with Jeff Hinton and Michael Jordan in the 90s, which to this day, I'm like, I wish I'd save the binder from it. It was like a 10-day, 10-day course. And the other Michael Jordan, it's not like the one you probably know. Not the one we know. Yeah. Yeah, okay. So I don't know totally your fields of study, but yeah, he's famous within the predictive modeling on the graphical model side.

and then Jeff Hinton, obviously, on the neural network side. And I still remember this day, like, they would, like, come in and say, and Michael was basically crushing Jeff Hinton and saying,

like, your neural nets are just special versions of regression and useless. And Jeff was like, I just need more processing power. And he was right. I think Jeff was more right than Michael, actually. So,

but yeah, I don't know. I just got up a whole tangent on that. No, actually, I mean, it's fascinating because I am actually interested right these days for various reasons in learning about that stuff myself. This whole, the predictive modeling idea, as far as I can tell, is here's a giant bucket of data, maybe a time stream or a series of many. What's going to happen next, right? What's the most efficient way of saying what's going to happen next? A very tricky and intellectually fascinating problem, as it turns out.

Yeah. So I worked a company called MITR with the NSA and CIA, and it was very near to the Oklahoma City. city bombing and one of the projects I got when I was there was predicting the next, you know, domestic terrorist act and using like, you know, purchases of fertilizer and, you know, messages on message boards at the time. There weren't, there was no Twitter and stuff like that. How do you, how do you predict that that's coming? And it turns out to be an incredibly hard problem. And anyone who's in predictive modeling will know this. It's called the sunspot problem. So if you try to build a model to predict something rare, unless you accurately tune it,

your model will get really good at predicting nothing's going to happen. It'll basically say, like, I'm pretty sure 99.99% nothing is about to happen. And you and that, but that's not the point. You have to tune the reward function on these predictive modeling problems. You have to tune it such that it's developing the output you want. draft picks are like this where you're generally in the draft looking for more upside. So if you don't tune your models to look for players who have a lot of upside, you'll get, unfortunately, you'll get something that's good at predicting just a, you know,

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It's the largest advantage of any team sport by a good margin. Even more than quarterback in football, that's the closest that is out there. But I haven't studied for sure. But the best analogy I give to that is in baseball, if you take, take the greatest hitter of all time, which was probably Barry Bonds at his peak. And he goes up to the plate to hit, and then he hits a home run or whatever,

and he comes back, and then he has to wait eight more times. In the NBA, the Barry Bonds, the Joelle and Bede, after he goes up and hits a whole run, can be like, I'm still the best.

I will now go up to the plate again. Yeah. And it's actually worse than that for some players to take, Take a player like Ben Simmons, for example, who's on the perimeter, a lot of teams now switch the ball screens. Now I'm getting two inside basketball. No, please. You can basically, you know, someone, your five are being guarded by another five pretty much all game and different scheme zone and man, but mostly man in the NBA.

And so someone's guarding Ben Simmons. Let's say it's a good defender. and what will happen is people switch a ball screen like a pick and roll and they can now almost choose who to guard them. So Barry Bonds in basketball can not only choose to go up every time, but select the pitcher on the other team he wants to go against it. And so you can imagine the edge that creates. And we do it not 30, 40 times a game getting up to blade. We do it 100 times. So there's a back and forth all game.

So your best players drive 90% of your value in basketball. But on the other side of that, I mean, maybe it's a tiny correction, but I remember reading a study where there is a bit of game theory and mixed strategy involved here, right? Because even if Joel Embed is the most likely person to score on your team, if the other team knew you were going to him literally 100% of the time, they could scheme against that very, very effectively. So you have to mix it up and you have to target your less good, shooters some of the time, right?

Absolutely. Yeah, there's definitely game theory. That's what makes basketball fun. I would say, though, it's a little overblown the game theory. So I call it the Luis Scola right-hand problem. So anyone who is a big basketball fan, Luis Scola was a big-time player international and a very good player in the NBA. And he, I mean, he may as well have been Jim Abbott and had one hand, like one arm.

He was that amazing. with his right hand. And no matter how many times he faked with his left, he'd never shot it with his left. He'd come back to his right. And I'm telling you, it would work 99% of the time. So it's similar. It's similar in basketball. People are like, okay, yeah, if you know you're going to Joel every time, then that's

too easy to stop. Or you know you're going to shoot a rim shutter, or three, that's too easy to stop. It turns out when you dig in and even adjust for all that, there's not as much game theory there as you'd like. I wish there's more game theory, actually. more fun yeah but joel's actually made a huge step forward this year teams are having to double him he's having his best year in the paint by a good margin and you're right he has to be able to read and make the right passes and he's he's doing that yeah no it's been fun to watch you know uh jennifer my wife

will testify that i've converted her into not just a basketball player a fan but a 76ers fan so that we were able with league pass to watch a lot more games than we used her yeah she seems like a tough one to convert. Oh yeah. She's very happy to let me know when she is not interested in what we're doing. But, uh, and now we go to see the Sixers every time they're, they play the Clippers here in L.A. So that's all so fun. We just announced we're getting fans back in Philly. Maybe L.A. will take a little on. We're 15% fans. So yeah, well, LA is especially bad. It's, it's not a great place. But hopefully, hopefully we'll get vaccinated very, very soon. Um, speaking of human beings and biases and decisions that they're making. You talked about the fact that players are human beings. It's

absolutely true. So are general managers and coaches and scouts and so forth. And one of the interesting things I've heard you remark about in the past is how easily it is that we are fooled as human beings. And in particular, the thing that struck me is the uselessness of interviewing potential draft picks ahead of time. So yeah, so I think just if you just take a step back from even the NBA. I think interviews in general have been proven to be really poor relative to track record. One of the nice things we have is we do have track record. We have them in, I would argue, a different sport, college basketball.

It seems similar, but I think it's probably enough different that it makes it challenging and hard to do. And so I would say draft interviews are very, very similar. We do our best to follow all the rules. to try to make them useful. But at least over the time I was in Houston, you could prove that our marginal ability to improve our prediction based on interviews

was near zero. And it probably was zero, but I'm saying near zero just to be saved and make myself not feel as bad about all the hours I spent. I always felt like I had to do it because, you know, what if the guy can't talk? You know, like it's just like,

there's just stuff that, the other thing that comes up in draft interviews is it is a team. And so, you know, getting along with other humans is important. So if you can get any sense of that, but even that's hard. And my general sense of why interviews are tough is we're generally interviewing 19, 20, 21-year-olds. And if you think back to yourself at 19, 20, 21, you probably didn't have much figured out.

So if we don't have, if they don't have it figured out, how are we supposed to guess what they're going to want to do? And I would say for sure the players change over time, mature, become better leaders, you know, have a complete change in approach and personality that makes a big difference. So it's very, very hard to predict. And I think I read you mentioning that you were influenced by these ideas from Conneman and Tversky about we human beings. So not just on the players' side trying to sell themselves, but on the interviewer's side,

we're very susceptible to signals that might not be relevant. There's a lot of noise because we're programmed to pick out the wrong things. We have all these biases. Well, I think it's a lot like a lot of things. People want a skill in one area to map to another area. You know, people want to think that being great at chess means you're good at other things. And I think that's been pretty well proven and not be true. And if you put a lot of weight in interviews, my sense is the research is pretty

overwhelming now that what you'll get are people who are good at interviews, which turns out to me not very relevant necessarily to whatever goal you're trying to achieve. They're good at like social cues. They're good at mirroring. They're good at like, you know, you know, saying things that are, you know, familiar. Like even before we started the call, I was like, oh, you know, I grew up like this. That's similar to you. I mean, that's just like a horrible thing that people who are good at it. do it and some of them don't even know they're good at. They're just like it's intuitive. They're good at being around people and that can be helpful that just knowing that can be a data point. I mean, it makes me ask, do you think that trying to become the best general manager you can possibly become has made you a better human being or has made you better at just social interaction with the world and analyzing things? I hope I'm a better human being. I don't know. I try to, I mean, You know, it's just, it's sort of a sport that we're not vaccinating people who are saving lives. I mean, I guess we're providing entertainment.

But I do think most people get better, you know, working with people over time. And I'm sure that's, you know, that's been myself as well. I mean, the example I always use is that you would think that physicists who studied the universe would eventually grow more humble over time. But empirically, I don't see that actually happening. So, yeah, maybe what do you expect is how it you get? What I've been shocked by physicists, and you know this way better than me, maybe you'll disagree, is they're just, they're so susceptible to the same biases of, what's the right one, you know, that their theories almost become like religion to them. Yeah. And so even though they've been trained to be able to falsify their own work, right, you're literally,

trained to do that, they're really bad about it. It becomes part of their identification of themselves versus a thing that's separate from them that they should be willing to read. They should be willing, like if you work in theory, if physicists were AI physicists, just machines, and they worked all their lives on string theory. And then someone comes along and invalidates that they should be able to be like, oh, great, we have this new thing.

it's better. I'm going to toss that out and move to the new thing. And there are people who can do that. So I think some of the very unique, you know, physicists in history could do that. But I would say most of them are human and have the same problems that anyone else has. No, very, very much so, 100%. And it reminds me I just did a podcast with Roderick Graham, who is a sociologist. And mostly we were talking about, you know, race and how African Americans are treated online. And I mentioned that it's a hot button issue. And, and And he said, oh, yeah, you probably as a physicist don't need to worry about this online. And I'm like, no, actually, if I mentioned the multiverse or the ever interpretation of quantum mechanics, emotions grow very, very heated very, very quickly. People do become both attached and anti-attached to ideas. And it is just part of being human, yeah. Well, it's also what drives them to make great discovery. So I think if we didn't have that, we probably would have not, you know, you almost need, like, obsessive people doing things. are against their own interest to create some of the great discoveries.

Yeah, no, that's absolutely true. And it's actually, there's an interesting philosophy of science question here, because I know great physicists who will articulate the idea that it is their job to be their theory's biggest advocates. And there are other people who think it's their job to be their theory's biggest critics, right? Because, you know, they want their theory to be true and they want to be as harsh on it. And I think that I can see both sides. So I think that it's just good that we have different people, coming from different attitudes because there's no right answer there.

That makes sense. I would definitely be the biggest critic one. I'm a big falsified. Like, I'm always looking for the next best thing. I'm almost too ready to jump onto the next interesting thing. Like that, I think people are just built differently.

And I hadn't thought about the two dichotomies, but you're right, both are valuable at different times for sure. You know, and speaking of which, if we human beings are, you know, bundles of heuristic pattern recognizers, and that's a big disadvantage when we can be tricked by a clever interviewee. Does it also give us some advantage?

Have you noticed there are things that a good scout or a good general manager can pick out that are just not visible there in the data? Absolutely. Yeah, we had, unfortunately, he just recently passed away. We had a great scout named BJ. And he had an advantage, he had a data advantage, and then he got to see players when they were younger because he was part of like the youth basketball circuit a little more.

So he got to see them evolve. And that gave him, I think, unique data, but he also had a unique intuition that certain guy, like I ended up leaning on him for especially players who didn't have a big college track record. because he just, again, he might have just had better data sources than me. Yeah, but he also had an intuitive feel. I couldn't prove that to you, though, but I always felt like he did.

Is there, is one of the data, one of the biases you need to worry about overvaluing your own players? I know that, you know, on the online discussion boards, we all think we would have trade our third string point guard for an all star from someone else's team and they would go for it. but that sounds like something that you got to actively work to overcome, I would think. Yeah, it's one of the major biases, the endowment effect, and anchoring are probably the two. I would say probably the three biggest biases that really mess things up are endowment, anchoring, and confirmation bias, which we've been, everyone's learning about confirmation bias because of the recent political changes in the world, like, and the fact that the media is dispersed.

But on endowment, yeah, to avoid endowment, which is overvaluing your own things, yeah, we just force ourselves to invert the trade. It turns out to be really easy. If you invert the trade and pretend you're holding the other thing, sometimes it's actually amazing. You realize that you wouldn't, while you're agonizing whether you'd do a trade, you realize if you're on the other side, you wouldn't even like put this trade as one of the possible trades. Like it's that ridiculous that you're even thinking about it. It turns out to happen all the time. And you do have to fight it. You have to fight it a lot.

I mean, can you maybe give us a little insight into the anatomy of the negotiations for a trade? Is it something where you're just always talking about possibilities with all the other teams? Or do you come with a specific thing and try to target something and hope it works? How does that go? It depends on the time period. So generally, like, there's a lot of constant conversation. all the time. And then it generally sort of bubbles up into more as you get closer to these key dates.

And just like with most negotiations, most of them don't happen until until there's a deadline. It's like everything in life. Until you have a deadline, those are bad about getting things done. So, but in negotiations, there's actually a real reason why they run to the deadline, you know, which people could go read all that research. But, and so as you get closer goes to the deadline, things get more. specific things get more transactional whereas prior to the deadline it's more conversations concepts um looking for high level fits and it's sort it's like a it's almost like a sales funnel

honestly if you're into if you're into that area of research so you have to have a lot of things in the funnel or you're going to end up with not much at the end and uh and so teams are different style some teams go for home run deals like where you just are hoping you can't a team valuing asset very differently from you. And so you don't say much. But it lowers your liquidity. You lower your chance of making a deal. Whereas I tend to be like,

I would rather be more open, be more open with information and more open to try and get things done because it allows you to work on the margins. So I'll miss like home run deals more often, but also hopefully get more done that, you know, help you do the last piece to a title team, for example. I did try to time this interview so it would not be too close to the trade deadline. I would never forgive myself if, you know, you were doing a podcast when you could have been swinging a massive deal. Yeah, I don't think Spike and Michael would forgive you if you distracted me during that.

We still have four weeks. It's the 25th. Yeah, I think we have time. Three weeks. Three weeks in a couple days. We have time. Plan B made over-the-counter emergency contraception legal more than 20 years ago. It's a safe, effective backup birth control option that helps prevent pregnancy before it starts by temporarily delaying ovulation. Plan B is the number one OBGYN recommended brand and the only one that you can find at all major retailers in all 50 U.S. states.

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and products contain aluole. See nutrition info on Hero.co for sodium and sugar content. But you mentioned, you know, there's the human side to being a GM also knowing that your players are players. And there must be some people who you just like and want to have on your team and you might have a valuation of them. And maybe that evaporates and becomes a little bit less important when you're close to the

deadline. But, you know, and also you want the people on your team to like each other, right, and to play well together. And it seems to be the Sixers are having a good time this year. And I think it's a big part of their success. Yeah, Doc River has done an amazing job. I think one thing you have to recognize if you're in my job is to like, like, you don't want people who are just like you, right? You want people who are different. Doc and I come from very different backgrounds, player versus not, you know, coach versus, but, you know, I've come to, I really, I had worked with him before in us and I really appreciate he does such a good job with you know getting everyone in the same

canoe and rowing all together and that feeling of attack like we all know we're having to win the title but there are different ways to attack it and he does a he does a fantastic job with that and um you know so it's important like I'm good at certain things doc's good at certain things elm brand who does a tremendous job with us he's so you want to have you want to have you want to know where your strengths and weaknesses are and sort of make sure they're complimented. I think we have a nice fit with that. And now I feel like I didn't answer your question. Like I just went off. Well, let me just say it in a different way. Do you ever think there would be a time where there was a deal you had in front of you where it was a close call? It wasn't like

a home run. And you would say, you know what? I'm not going to make that deal because I like this guy. This is my guy. He's on my team. Let's see if we can win with him. Yeah, that happens all the time. So I think when a team is rebuilding, which I've luckily not had to do since, since, like, 05, then, like, you shouldn't really factor that in much because you're trying to optimize your ability to win in multiple years from now. Yeah. But when you have a team like ours, which has great chemistry and I've been really happy, you know, if you see the quotes from our players and it's real, I'm there every day, you definitely have to be. careful with that because you like it's similar to like if you add you know again i don't know physics department but if you add like a bad employee it's almost like five times worse yeah than adding than adding a good one now a superstar can help but um so so with these final pieces it's a

huge factor you need to make sure you're not uh or or if you add something and it's not working you have to be able to get out of it like in some way like you have to have an exit plan so you mentioned rebuilding this is a very touchy on both sides issue in Philadelphia where under one of your predecessors, Sam Hinky, who I was a huge fan of myself and he worked with you in the past, you know, there were a couple of years where they didn't maximize the number of games they were trying to win. They were trying to maximize building assets for the future and tanking, as it's called. And there are people who just, you know, reject this philosophically. Like you should always try to win the most games this year.

Do you have feelings about this one way or the other? Yeah, no one should, that shouldn't be kind of. Like what Sam did was exactly right. And it's why we have Joel and Ben now. And I think it's, you know, the only arguments that landed against it were like ones that Philadelphia shouldn't worry about, which is there are arguments that, you know, there's some minimum efficient quality of teams. you need to present so that you're being a good partner with your other 29 owners.

But, I mean, in terms of the micro of Philadelphia, what was done was exactly right. And it's the reason why, you know, there's a lot of winning happening right now. Right. One of the big reasons. Yeah, no, so I thought that's what you'd say. I totally agree with it myself. So I know I don't want to keep you too long, but I have a couple of sort of, you know, wrapping up the podcast questions here.

if you were the boss of basketball, if you were the commissioner of the league rather than the general manager of the 76ers, would you tinker with the rules of the game? Do you think that you can make basketball a better game somehow? Absolutely, yeah. So I would do the Elam ending.

I would do, which is like, people probably have to Google that. It probably will too much. Basically, it's actually easy to try. Elim ending turns the end of a basketball game into a pickup game where you're just playing to a score versus playing to a clock.

And it turns out playing to a clock creates all these weird skews in every sport. So it's the sports that end like baseball or tennis not to a clock are better games at the end. And so the I would have one free throw for everything. Like there's no reason they have to shoot two, three free throws. You can just do one free throw for every point that you're going to get when you go. I would only allow timeouts if the ball's already dead. I don't want to stop. I don't want to stop play

if it's if it's live like people love the back and forth in the live play you don't want to stop that with time out you know those are the and I would get to more radical things those are just off the top of my head some easy

some easy ones that I would implement I mean the NBA it's famous among non-fans for the last two minutes of the game take 20 minutes to play right that's something that should be fixable somehow Our last minute, 17 in our trial game recently took 19 minutes. It was absolutely real.

We were up 17 and they cut it to 7, which was great for them. But, like, yeah, the Elam ending would fix that. And so it's just something we need to. I would get rid of jump balls and have possession arrows. I mean, there's actually a lot of easy stuff that would help the game be more. I would definitely reduce replay. That's torture. me like you just have to even after replay you're still getting things wrong so i think everyone

this should just be a disclaimer for an NBA game look we're going to do our best there's going to be a few things that are reviewed or we're going to miss things and that's okay and sorry and now watch your games it'll be more fun would you also have changes to the draft system to the lottery etc to the we have a system now where if you do badly you're rewarded by getting a high draft pick and people want to change that that one turns out to be really tricky. I actually think the commissioners balanced that pretty well now. He's flat, he's made it, he's flattened the odds at the top. And in theory, like if you grabbed an economist, they would say things like get rid of the draft. They'd do like Mike Zarin, a boss who I'd have a wheel

where they're all predetermined. The reality is it's pretty well balanced now because at the end of the day, we're an entertainment business and you need to be able to sell tickets. So the teams that are bad, need to have something sellable for them and the draft is a really good way to do that and gives them a lot of hope for the future and I think I think it's pretty well balanced now in a system that's not very easy to fix. The one thing I would do is I'd probably do one of these hybrid systems where instead of a draft order you would get you'd get like basically fake coins you'd get like Chamberlains you know like you call them Chamberlain coins like you're like a some sort of crypto Bitcoin and like whoever is the worst team would get the most,

but then they could allocate their bidding on what pick they want. I see. Okay. Right. Because if you're a rebuilding, you might, instead of having to trade back from one like Philadelphia did to get two swings at it, which actually conceptually was a reasonable trade. Obviously, some of it didn't turn out.

But conceptually it was a reasonable trade for both teams for Philly to, to, to trade up and lost in a trade back. And you could be able to do that by instead of forcing people under their slot, giving them more things to bid against a class. So there are things you could do, but you definitely want to give bad teams an advantage in the drop just so you have something to sell. This is a very, very broad philosophical question.

But once again, given that the audience here is eclectic, what is your sales pitch for sports, being good. What do you think is the value of sports? It's so artificial, right? But it's also so compelling at the same time. Well, it depends on what you have is your primary goal of life. I mean, I think the Greeks had it pretty well. The happiness is maybe the goal for each individual. And clearly, like, entertainment is part of that. So, yeah, I think it's a pretty straightforward forward mapping to a useful goal in that it makes, I think happiness is a good goal. We should have the gross happiness product, like people have said, if we can measure it,

which we can't. Well, I don't know. I mean, most teams do not win the NBA championship. Is sports a net happiness increase? It's not completely clear. Yeah, I definitely think it's a net happiness increase. So otherwise, I know people are very irrational, but they'd have to be shockingly irrational for the NBA, it would be, you know, a 10 billion-ish dollar business.

And if half of people were miserable all the time, yeah. I think people like rooting for a team. They like rooting for players. They like rooting for a process. They like rooting for, you know, Doc Rivers. They like reading. They like being part of something, a community. And I think it provides that.

All right. The last question is, is it true that you have skeptical thoughts about dark matter in the universe? Yeah, I was going to ask you this. So I guess it's trending. I'm so annoyed. I feel like, you know, I feel like everyone's discovering nine-inch nails now and I listen to their first album. So I've been saying, so there's an analyst Greg Peeam, who was a theoretical physicist who worked for us in Houston. And this was like, I don't know, 10 years ago, he works for the Clippers now.

And, you know, I was like just telling him, no, there's no way dark matter is really. Just look at the history of how knowledge is developed. And anytime we're plugging the big blank with something that you can't observe, it never turns out to be true. And it's always that our theories are off or our measurements are off or something's off. And so I had it like very likely that dark matter doesn't exist. And I guess that's trendy now I've heard. What is the latest on that? I'm curious.

You would know. Yeah, no, sorry. This is the one part of our conversation. I got to dramatically disagree here. So I don't think it's trending. There's a whole bunch of people who think about it. I mean, as we've said, you know, we think about all sorts of things, right? You can't get too emotionally attached.

And I've thought about it. I thought about getting rid of dark matter. But it doesn't work. You know, I mean, I absolutely buy the argument you just make if you had a hole and you filled it with some unknown thing. But the thing about dark matter is we have a dozen holes. And this one thing fills them all, whether it's the rotation curves of galaxies or the dynamics of clusters or the cosmic white. background or the growth of structure. One very, very simple model fits all of it.

So I'm still very, very bullish on dark matter myself. Is the analogy then more, because I mean, people say with dark matter, it's like ather in the late 1800s or whatever, but what you're arguing is that it's closer to quantum mechanics where we have something that seems to fit every observable thing, even though we don't still totally understand how quantum mechanics work, right? I'm right about that. We're still like puzzled by all that shit. You're definitely right about that.

I wrote a book about it, but I'm agreeing with you. Yes. There you go. And so, I mean, okay, but so that's your, that makes, that makes sense, but I still feel like there's some, you know, theory that could overlay and explain all that too, potentially, right? But you're saying the odds are getting low that maybe that's the case at this point. I think, I think actually it's a very interesting. history of science question because, you know, in the 80s or 90s, your point of view that dark matter is a temporary holding place for a more deep understanding would have been 100% respectable.

And again, like, I definitely thought about it myself. But I don't think that we as cosmologists have quite conveyed to the public the extent to which that changed to when we really started observing the cosmic microwave background in detail. You could make predictions. If dark matter exists, the CMB will look. a certain way, if it doesn't exist, it will look another way, and it came bang in on what was predicted by dark matter in a way that it's almost impossible. I never want to say impossible, but it's almost impossible to reproduce that success in a model where you just change gravity

without dark matter. So you're saying something important that I didn't know. I didn't know about the CMB stuff. Yeah. That a prediction was made prior to knowing the result, and then it turned out to fit the theory. And so that's important. I mean, that's what happened with quantum mechanics. Like a long time, if I'm right, you tell me, right? Where the theory kept predicting things that kept being true over and over. And even though we didn't quite know the full mechanism, although we know it much better

now, to your point, that it's, so what is the best candidate? Is it like these, like these huge, like, remnants of the, Big Bang? What is the best candidate at this point? Well, that's, I think the totally fair worry is that for a long time, we had a favorite candidate, the weekly interacting mass of particles, right? We have the weak interaction of particle physics, the wimps, right. We have the weak interactions of particle physics. It's very easy to invent a new particle that is not electrically charged, but does feel the weak interactions. it very naturally would have the right abundance in the universe to be the dark matter,

but we could have found it by now. And still, the chances that we would have had it by now are maybe like 60%. It's not like 99.9%. But we had a good chance of finding it by now and we haven't. So at some point, your credences have got to go lower. And to me, that doesn't decrease my credence in dark matter that much. But the more esoteric candidates like Axions are a famous, candidate. They're a very, very light particle, lighter than a neutrino, basically, but that could

sort of be left over from the Big Bang in interesting ways. And so if it's not a wimp, if the dark matter exists, but it's not a wimp, then there's dozens of other candidates that is hard to find it. Does it interact with anything? Does it glens? Does it gravitationally lens stuff? What is it like? Oh, absolutely. Yeah, I mean, that's the one thing we know about dark matter, that it has a gravitational effect. That's why we know it's there. That's why we were led to believe it's there. And in fact, these days, we literally map it. You know, we know where it is through gravitational lensing mostly. So you get these gorgeous images, you know, three-dimensional reconstructions of the dark matter density, even though we've never detected the particle that it actually is.

Still feels like it's going to be something totally we don't. I just like, like, it feels like, Yeah, my gut tilts, but of course, I just have my gut, and your gut's better than mine. It's more experienced. One of the reasons why I feel justified in being so confident about this is that I would love it if I were wrong. Like, no more would be happier than I would be if there was something interesting going on with gravity that tricked us into thinking that there's darkmen. We don't totally understand gravity, right? So that's another thing, right? We don't totally understand anything.

We actually understand gravity on scales of the solar system. larger pretty well. We are able to imagine other things. I've written papers imagining other things. But, you know, I think Einstein more or less nailed that on the, astrophysical scales, gravity is pretty well under control. Gotcha. I do.

I think it's just the simulation people up, effing with us. They're just like, they're just like, oh, they're looking again. They're trying to see if they can find the dark matter. We'll put it in there. That is, you know, I literally, when we were looking for the Higgs boson, back before the Large Hadron Collider, turned on. I literally had a theory that every place we looked, like there was a

probability of seeing the Higgs boson with different masses, and whenever we looked and didn't find it, it just moved up. And you can invent physics theories along those lines. But I think for the dark matter, it's just we haven't been clever enough yet. We don't have enough direct evidence. Why did we stop the Collider in Texas? That would have been awesome. Like, I'm so annoyed that that killed.

It would have been not only earlier, but it was a more powerful accelerator. than the large had... Are we getting that or are we still? It feels like it's set science back like 25 years. The United States has entirely abandoned even the attempt to build world-class particle accelerators. So right now the debate on what the next one will be is between China and Europe. I remember Biden making this a big part of his platform.

I'm surprised. The physics community doesn't know about it. Yeah, no, it's it's it's it's they're expensive. You know, and I get that, but we build them once every 20 or 30 years. I think it's worth the expense myself. Oh, it's easily worth the expense. Yeah. All my friends are like, now telling me it's stupid to go to Mars.

And I'm like, yeah, it is. But like, why wouldn't we? We'll learn something, right? It'll be interesting. It's part of a portfolio. You've got to do some crazy things. You got to do some ambitious things because you don't know. You know, I had Martin Rees, famous theoretical astrophysicist on the podcast.

and he points out that people were worried when we turned on the large Hadron Collider, would it destroy the Earth? And he was literally, even though he was British, he was called before Congress to testify. And he said, you know, as a careful scientist, I can't promise you it won't destroy the Earth. But I can't promise you it won't be free energy and cure all the diseases we have either. Like those are fringe things that we can't care about. We can't control those. Like, let's look at the likely thing in this particular case. Well, politics and media do really bad with orders of the matter.

magnitude. So yeah, they're like, yes, it's possible that this could happen, but it's also possible that a plane could crash into my building right now. And they can't distinguish that fear from one that's 10%. Like we just do really bad between 10%, 1%, 0.1%, we're really terrible at that. Yeah. Yeah. There is some, some bias. I think it, I don't know if it has a name or not, but the only percentages that people believe in are zero, 50, and 100 as far as I know, right? I think Nate Silver is a good friend who's talked about that quite a bit. But here's the bottom line. I say we start off talking about championship odds in Vegas.

What is the current betting odds on them finding dark matter in the next five years? There must be somewhere you can wager on this. Yeah, no, I'm sure there are. And if you would ask me five years ago, I would have said it's over 50, 50% chance that we would have found it by now. That's why that does raise some skepticism, I think. But so if it's not a weekly interacting massive particle, then there's still wonderful evidence that dark matter exists,

but none of the other candidates are as easily searchable. So we might be stuck for the rest of our lifetimes. Right, right. Well, maybe I can be on in five years we can talk. I'm glad you guys didn't call it the gimp, the gravity. No, there's a whole bunch of, a whole bunch of bad puns. In physics. And on that note,

Darry, thanks so much for being on the Mindscape podcast. This was great. Thanks so much, John. Appreciate it.