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.
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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.
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0:00:00.3 Sean Carroll: 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 my personal hobbies and my individual enthusiasms. I try not to force my individual enthusiasms on the Mindscape 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 gonna take a little bit of a detour, we’re gonna 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 guest.
0:01:00.3 SC: Daryl Morey 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. J and Moses Malone and Mo Cheeks 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 the one who hires the coach, drafts the players, makes trades, signs free agents, stuff like that, the person who’s ultimately responsible for assembling the basketball team.
0:01:48.8 SC: And what’s special about Daryl 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 Daryl Morey for an hour without anyone listening in, I’d be very tempted to geek out about basketball or 76ers, minutiae. 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 Mindscape audience, and instead, what we’re talking about today is the theory of being a general manager. 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. If you’re a general manager of a basketball team, you have an extremely quantifiable goal.
0:03:01.2 SC: You want to win games and ultimately win championships. 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, etcetera. Then the question becomes: How do you optimize? How do you, given 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 their personalities? Do you need a leader? Do you want vocal players? Can you put up with the superstar who’s kinda grumpy? Stuff like that.
0:04:14.6 SC: 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’s succeeded a great deal over his years as a general manager in Houston. They compiled 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. Who knows what happens as the future trundles on. Injuries, all sorts of crazy things can happen in a sport like this.
0:05:01.8 SC: You don’t need to know anything about basketball, or the Sixers, or the current state of the NBA. We will mention Joel Embiid, center for the 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.
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0:05:55.2 SC: Daryl Morey, welcome to the Mindscape Podcast.
0:05:57.8 Daryl Morey: Thanks for having me on, Sean. Appreciate it.
0:06:00.4 SC: So this is an unusual. We 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 not many of our… Or many of our audience members will not be the rabid Philadelphia 76ers fan that I am, growing up worshipping Julius Erving 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?
0:06:29.8 DM: 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 started out as a computer science programmer way back when and got into basketball in 2002. Oh, I always played basketball, but I got into the Boston Celtics in the NBA in 2002.
0:06:51.8 SC: Sorry to hear that.
0:06:52.2 DM: And I started off doing data and coding and now, I get to do very little of that, except run the teams that do that. And yeah, as I go on, more of my time is on the phone and meeting players and coaches and owners and fans and business people. And as you get higher up, you end up just talking more like this podcast.
0:07:15.5 SC: It’s like, yeah, like being a department chair or president of university who have to do… Presumably, you don’t have to do fundraising, but there is fan outreach, I presume.
0:07:24.8 DM: 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.
0:07:35.9 SC: Oh, yeah.
0:07:36.2 DM: And then my poor staff is like, “Well, just let us do it. Leave us alone.” [chuckle] So I try to do that ’cause that’s how I would be if I were them.
0:07:46.0 SC: Well, 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, so everyone is an amateur who’s gonna give you advice on your job, I’m sure.
0:08:00.5 DM: That’s right, and I’d be… You’d be shocked, the amateurs are very sophisticated. I think that’s true in a lot of fields. There’s just diehards who know the team, know the data, know everything. There’s a Rights to Ricky Sanchez podcast with a couple diehards who are very smart. There’s message boards, and every team has these things.
0:08:23.0 SC: I was a guest on the Rights to Ricky Sanchez. I’ve appeared on the podcast before you did, yeah. [chuckle]
0:08:27.0 DM: Oh, wow! So you’re a pioneer, you’re a pro. So yeah, I was hoping I could be maybe one of the smarter ones on the podcast, but now, I’ve been usurped, for sure, by you and Joel. Joel has been on, he’s… So Joel’s shockingly smart. It would be great to have Joel join you. He’s like, I shouldn’t say shocking, that sounds bad, but he’s just into… He’s, what, polymath? Is that the word?
0:08:53.0 SC: Yup, yeah.
0:08:54.5 DM: He’s really smart about a lot of things and on the court as well, obviously.
0:09:00.0 SC: Right, this is Joel Embiid we’re talking about, obviously, the Philadelphia 76ers star center. It is interesting because some people, and I completely agree that my impression of Joel is that he’s super duper smart, but yeah, but he doesn’t project that way. He doesn’t put on affectation. I think that actually Manute Bol was someone who was like that, a previous Philadelphia 76ers center. Everyone used to say, when they talked to him, how smart he was.
0:09:24.0 DM: I’ve heard that about Manute. There’s some, I’ve known lots of smart players first-hand like Shane Battier, but then, you have Chris Bosh was a coder, Sam Dalembert was building his own machines before that was a thing. So the thing is, if you make it to the top of your sport, whether it be player or whatever, you usually are pretty good on G. You usually have pretty good pattern-matching to make it that far, so.
0:09:55.5 SC: Yeah, there’s a lot of tall people out there, or some people, tall people. But you need more than just that. So your goal… How would you say that your goal is as the general manager? Obviously, you wanna 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 the danger of being fired, it’s the real world. How do you think about what your job is?
0:10:20.0 SC: 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, 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 just make it to the next year 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. I’ve been lucky I’ve always had owners who are like, “Just championship or bust, 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 to… We have internal measures and outside measures, we… Right now, Vegas, I think, has us at about a 6% chance to win the title, and that sounds really small, but it turns out to be pretty high, that’s… I think that’s fifth or sixth in the league.
0:11:30.8 SC: Yeah, okay. And you, personally, where do you put your chances?
0:11:36.6 DM: Yeah, we have them internally higher, but that’s true almost always. 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 is… Football’s always been the most efficient. Basketball’s 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. It’s pretty hard to get a huge edge on Vegas.
0:12:27.2 SC: Then just to give the listeners who are not basketball fans a feeling for this, there is one little anecdote that I read that I thought was hilarious. SerguéI Lischuk, the best Ukrainian basketball player, remember his name? [chuckle]
0:12:39.9 DM: Yeah, SerguéI Lischuk. Yeah, I think… I honestly, you probably said it right, we always called him SerguéI Lischuk, so that’s, yeah.
0:12:47.5 SC: 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.
0:13:05.0 DM: Correct, yup.
0:13:05.2 SC: So apparently, you personally are responsible for trading the rights to SerguéI Lischuk five different times, either trading for him or trading him away. And he’s never played in the NBA, and he’s already retired, he never will.
0:13:18.7 DM: Yeah, just for the audience, it’s sort of interesting. So the NBA draft is a 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 turned 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 pre-arranged depending on where you’re picked. And often, those players either are good enough so the team never offers a contract or the player doesn’t wanna come, which has happened in the case of players like Sergio Llull who we drafted in Houston. And so, yeah, long story short, you can hold these options of players to come and they have… All 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.
0:14:33.2 SC: Yeah, no, I get it.
0:14:36.0 DM: 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 options become just the word you write such that the trade is legal even though it doesn’t make any sense, yeah.
0:15:11.0 SC: 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 they’re worth.
0:15:22.7 DM: 100% right, yeah, exactly right. And to your point on everyone being GM, everyone gets this now ’cause 2K, for example, is, I think, one of the top 10 most popular games in the world. And no one actually plays it, no one plays the game. The gameplay is how the game was created but now, it’s 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’s a GM, which is cool. I think it’s just part of being a fan of the team now.
0:15:55.9 SC: Yeah, absolutely. Alright, so moving into a little bit more the nitty-gritty, the thing one has to talk about when you have Daryl 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. What’s your big picture overview of where that came from and how it’s going?
0:16:25.5 DM: 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 help your team win and make the 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 behavioral economics is a big one and all of the different cognitive biases, anchoring, endowment effect, all those things. Then there’s a huge set of research on combining data and human judgement 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 3%-5% edge, which again, doesn’t sound like a lot but over time, compounds, basically.
0:17:28.0 DM: And so that, and so we use a lot of data. And the scouts, for example, we look at it as, “What is a scout’s 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 are often very too good at finding patterns, and that’s often where data comes in. So data grounds you. Just like I said, look at Vegas, then compare it to your own models the whole time. If a scout or myself is very far afield from the general consensus, it could be right, but you wanna ask the questions, “Why do you think he’s not much better?” And then dig into each thing. So data actually forces you to ask the questions to make your decisions more precise, essentially. So that’s, I don’t know if that’s a summary that helps, hopefully.
0:18:27.0 SC: Yeah, it does. It raises the question, do you do longitudinal analysis of the success of scouts? Are some scouts really, really good at finding the diamonds in the rough?
0:18:37.3 DM: So we do. It’s tricky though because you have an observer effect, which… Not the physics observer effect. This is the human observer effect which is, as you measure people, they definitely change their behavior. So I tend to be a little more of the hands-off. I don’t try… And you used 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, it takes like 12 years. It’s tough. So a lot of it, you have to use your own human 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.
0:19:31.1 SC: That’s fun though. And when it does come to the players, there’s been not only an increased understanding that the statistics are useful but the kinds of statistics that we have. We used to keep track of how many points, how many rebounds, how many assists a player would have. These days, again, you’re the expert, I’ll let you say it, but we collect an enormous amount of data not only at all different levels but in the arena, there’s cameras keeping track of everything. What’s the average speed, what’s probably the variance of the speed of a player on the court is, how many miles they go, the whole bit.
0:20:07.2 DM: To your point, like in a lot of fields now, there’s no… There’s no lack of 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 second, third division, Germany, that kind of a level and college and some high school of every play that’s happening around the world. And to your point on the NBA we have… And in the D-League, and then probably coming to college, soon to a few areas 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 wanna know, was that shot open you can look that up. So the problem isn’t the data, there’s two ways… We’re trying to create an edge, 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. Now, we used to have a huge advantage in Houston, in my belief, and I think sort of born out by the fact we never had a losing season and had the second most wins over the years, that…
0:21:25.3 DM: A lot of the ledges have eroded, and now it’s like chasing… It’s similar to 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, that it did in 1880 when we didn’t know relativity and a whole bunch of our stuff. It was awesome.
0:21:50.0 SC: 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.
0:22:07.3 DM: Yeah, absolutely. Like in 2006, it was… Of course, people knew, but it was somewhat novel that shooting three-pointers and getting three points is better than shooting twos and getting twos. That… It seems pretty straightforward that you wanna 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, “Oh, 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.
0:23:00.5 SC: 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 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 kinda had to dig into it, yeah?
0:23:23.6 DM: 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… Now, 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 counter-intuitive thing. You wouldn’t know that until you actually played. I think it’s intuitive to your point that people thought, Yeah, you 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 to pointer. That also turned out to not be true. So it’s just 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 people don’t love that, but I mean, the reality is that the three-pointer if it was… If we were an esport, the three-pointer would have been nerfed a while ago. They would have changed it to be worth less. It would have been worth two and a half or something like that.
0:24:39.8 SC: Well, and also, let’s admit that there is an almost a macho component, a lot of people in and analyzing the sport where X 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 just lofted a shot from 35 feet away.
0:24:58.5 DM: That’s very insightful. Yeah, then any sport with men, and that’s true, there’s a competitiveness and aggressiveness that often gives you an edge, but that edge can also be your weakness, and it’s absolutely true that… 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 over losing like in football is if they could 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 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 posts you up and makes you look bad and rose it for… One inch from the hoop, 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. 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.
0:26:26.0 SC: 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 is just… That just smacked you in the head that you said, “Oh my goodness, that’s counter-intuitive, but now that we think of it, oh my goodness”?
0:26:44.8 DM: Yeah, there’s a lot on rebounding. So to your point a lot of the analysis on rebounding in terms of there isn’t more long rebounds, that’s a big one. There’s a lot on… There’s a trade-off between… Obviously, basketball is one of the coolest, most dynamic sports. Everyone puts 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 trade-off between, do you wanna stop the other team from running back at you and getting an easy basket and transition or do you wanna 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 on the macro. You have to look at the marginal decisions of sending one more guy versus sending another guy back.
0:27:41.7 DM: Soccer is currently dealing with this issue as well, which is one of my bugaboos, is they pass back to the goal. It’s a super high-risk move for a fairly low upside. But 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. 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, it happens all the time. So obviously, I make a very simple correlation and causation argument but it happens all the time. People will say, “Oh, the best team’s doing offensive rebound.” Well, that doesn’t work. People will say, “The best premier league teams pass back to the goalie.” That tells you nothing. It tells you absolutely nothing. So until you get this better data that’s overhead data, in soccer and in basketball, you can’t really make these decisions.
0:29:00.6 SC: And do you go beyond the data of the players on the floor? Are you an organization that keeps track of the players’ sleep patterns and diet and does all that contribute to the data set?
0:29:12.4 DM: Yeah, absolutely. So it’s been… Every team’s trying to find an edge wherever they can. Sleep turns out to be this tremendous edge but it’s also a hard one to gain a consistent advantage. I think when you go down levels and you have more… You’re out of college or the high school and you have more control, but these are professionals, these are adults playing the game, we definitely work with them when they wanna be… When they wanna wear a sleep monitor and things like that, we do it but we also don’t… In fact, the CBA… Not the CBA but the Players’ Union basically has rules on how much you can instrument players both on the court and off. 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.
0:30:14.3 SC: Sadly. They still have human rights even though they’re NBA players. Maybe the last question on this very data-focused thing is, what is the actual organizational way that it happens? Do you have a team of people sitting in front of computers debating about what to collect, how to analyze it? Related to the bigger big data questions, this is a question that every field has these days.
0:30:41.3 DM: 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. So obviously, things start with the honor. Generally, there’s, my role, GM, 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 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 like the gate function on a multi-model or you’re basically saying, “Okay, I’m gonna weigh the opinions of people who have a history of being correct, who line up with the data and things like that.” And so that’s the model. There’s basically a coach, down to multiple key assistants. This is true in most, even other sports, NFL especially. And then obviously, their role is to get the optimal performance of the players and then my role is to have scouts and data, and programmers make decisions on draft, trade and pre-agency to get the best possible players for Doc and his crew to optimize. So that’s at least how we structure things and it has been successful but for sure, I’m always open to new models if there would be. Should we do a peer review process and have tenure? For sure.
0:32:16.7 SC: Yeah, 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, where the breakthroughs are. Presumably, there are just as many or nearly as many breakthroughs yet to be made, we just haven’t thought of them. 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, “Oh yeah, I should have thought of that.” [chuckle]
0:32:44.0 DM: Yeah, we don’t have a timing element. 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 or you can get behind our development systems of talent identification and helping them in the minor leagues, for example. So there is where you can develop a bigger longitudinal advantage but yeah, we thankfully don’t have the physics thing where you could be working on for Mets last year and then some guy at Princeton comes up with approval only four people understand all of a sudden, in your DLA…
0:33:26.9 SC: 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? Do you ask the computer, “Look for patterns that my pitiful human brain is not up to finding?”
0:33:42.8 DM: Yeah, so the two big areas where you see that, one, is with the overhead camera data, they do automated recognition of 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 grew up in the predictive modeling world, that was my other area of study at Northwestern, in that sense, and so if you do the other… Basically, it’s all part of a predictive model. I was very lucky to take a class with Geoff Hinton and Michael Jordan, in the 90s, which to this day, I’m like, “I wish that I’d saved the binder from it,” it was a 10-day course. The other Michael Jordan, it’s not the one you probably know.
0:34:30.2 SC: Not the one we know. Yeah.
0:34:31.9 DM: Yeah, yeah. Okay, I don’t know totally, your fields of study, but he’s famous within the predictive modeling on the graphical model side, and then Geoff Hinton, obviously, on the neural network side. And I still remember to this day, they would come in and say… And Michael was basically crushing Geoff Hinton and saying, “Your neural nets are just special versions of regression and useless,” and Geoff was like, “I just need more processing power.” And he was right. I think Geoff was more right than Michael, actually. But yeah, I don’t even… I just off got a whole tangent on that.
0:35:11.2 SC: No, actually, it’s fascinating, ’cause I am actually interested… Write these days for various reasons and 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 gonna happen next? What’s the most efficient way of saying, what’s gonna happen next?” A very tricky and intellectually fascinating problem, as it turns out.
0:35:33.9 DM: Yeah. So I worked at a company called MITRE, with the NSA and CIA, and it was very near to the Oklahoma city bombing, and one of the projects I got when I was there, was predicting the next domestic terrorist act, and using purchases of fertilizer and messages on message boards at the time, there was no Twitter and stuff like that. How do you predict that that’s coming? And it turns out to be an incredibly hard problem, and anyone who’s in predictive modelling 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 gonna happen.
[overlapping conversation]
0:36:22.9 DM: I’m pretty sure 99.999%, nothing is about to happen. 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 upsides. 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 slightly above replacement player.
0:37:00.0 SC: Right, and you’re really looking for… ’cause I think the NBA is a game where there’s only five players on the floor, at any one time, for your team, having the one best player is an enormous advantage, I think a much bigger advantage than in baseball, football, or anything like that.
0:37:13.6 DM: It’s the largest advantage of any teams sport, by a good margin, even more than a 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 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 Joel Embiid, after he goes up and hits a home run, can be like, “I’m still the best. I will now go up to the plate again.” And it’s actually worse than that, for some players. So 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 too inside basketball…
0:38:09.5 SC: No, please.
0:38:10.3 DM: You could basically… Someone… Your five are being guarded by another five, pretty much all game, in different schemes, 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’ll happen is, people will 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. And so, you can imagine the edge that creates. And we do it not 30, 40 times a game, getting up to play, 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.
0:38:58.3 SC: But on the other side of that, 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. Because even if Joel Embiid 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?
0:39:25.2 DM: Absolutely. Yeah, there’s definitely game theory, and 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 in international and a very good player in the NBA, and he may as well have been Jim Abbott and had one hand, one arm. He was that amazing with his right hand, and no matter how many times he faked with his left, he never shot 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 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 gonna shoot a rim shot or a three, that’s too easy to stop. It turns out that 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 was more game theory, actually.
0:40:21.1 SC: It’d be more fun, yeah.
0:40:22.3 DM: Joel has actually made a huge step forward this year. Teams are having to double up, 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 as he’s doing that.
0:40:36.3 SC: Yeah, it’s been fun to watch. Jennifer, my wife, will testify that I’ve converted her into not just a basketball fan, but a 76ers fan. So that…
0:40:45.7 DM: Really?
0:40:46.3 SC: We were able with League pass to watch a lot more games than we used to, yeah.
0:40:48.8 DM: She seems like a tough one to convert…
0:40:51.7 SC: Oh yeah, she’s very happy to let me know when she’s not interested in what we’re doing.
[laughter]
0:40:57.1 SC: But and now we go to see the Sixers every time they play the Clippers here in LA, so that’s also fun.
0:41:02.1 DM: Well, we’re getting… We just announced we’re getting fans back in Philly, maybe LA we’ll take a little one… We’re 15%, so…
0:41:07.6 SC: Well, LA is especially bad, it’s not a great place, but hopefully we’ll get vaccinated very, very soon. 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.
0:41:42.2 DM: 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 a 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 safe and make myself not feel as bad about all the hours I spent. I always felt like I had to do it because what if the guy can’t talk? 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 getting along with other humans is important.
0:42:53.3 DM: 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 they don’t have it figured out, how are we supposed to guess what they’re gonna wanna do? And I would say for sure the players change over time. Mature, become better leaders, have a complete change and approach and personality that makes a big difference, so it’s very, very hard to predict.
0:43:29.0 SC: And I think I read you mentioning that you were influenced by these ideas from Kahneman and Tversky about how we human beings, so not just on the player side trying to sell themselves, but on the interviewers side, we’re very susceptible to signals that might not be relevant. There’s a lot of noise ’cause we’re programmed to pick out the wrong thing, so we have all these biases.
0:43:51.6 DM: Well, I think it’s a lot… Like a lot of things. People want a skill in one area to map to another area. People wanna think that being great at chess means you’re good at other things, and I think that’s been pretty well proven to not be true. And if you put a lot of weight in interviews, that 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 be not very relevant necessarily to whatever goal you’re trying to achieve. They’re good at social cues, they’re good at mirroring, they’re good at saying things that are familiar. Even before we started the call, I was like, “Oh, I grew up like this. That’s similar to you,” that’s just a normal…
0:44:39.7 SC: There you go, yeah. Human…
0:44:41.9 DM: Thing that people who are good at it, do it, and some of them don’t even know they’re good at it, 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.
0:44:52.6 SC: 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?
0:45:07.5 DM: I hope I’m a better human being, I don’t know, I try to. It’s a sort of a sport that we’re not vaccinating people or saving lives. I guess we’re providing entertainment, but I do think most people get better working with people over time, and I’m sure that’s been myself as well.
0:45:31.7 SC: The example I always use is that you would think that physicists who study the universe would eventually grow more humble over time, but empirically, I don’t see that actually happening [chuckle], so yeah, maybe what you’d expect, you already got.
0:45:42.9 DM: What I’ve been shocked with businesses, 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? That their theories almost become like religion to them. And so even though they’ve been trained to be able to falsify their own work, you’re literally trained to do that, they’re really bad about [chuckle].. It becomes part of their identification of themselves versus a thing that’s separate from them that they should be willing to… They should be willing like if you were… 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 it, they should be able to be like, “Oh great, we have this new thing, it’s better, I’m gonna 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 physicists in history could do that, but I would say most of them are human and have the same problems that anyone else has.
0:46:58.1 SC: 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 race and how African-Americans are treated online, and I mentioned that it’s a hot button issue. 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 Everett 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.
0:47:30.4 DM: Well, it’s also what drives them to make great discoveries, so I think if we didn’t have that, we probably would have not… You almost need obsessive people doing things that are against their own interest to create some of the great discoveries.
0:47:46.8 SC: Yeah, no, that’s absolutely true. But 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 theories’ biggest advocates. And there are other people who think it’s their job to be their theories biggest critics, right? ‘Cause they want their theory to be true and they wanna 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, ’cause there’s no right answer there.
0:48:14.9 DM: That makes sense. I would definitely be the biggest critic one. I’m a big falsify… I’m always looking for the next best thing. I’m almost too ready to jump on to the next interesting thing.
0:48:29.2 SC: Yeah. That balances support.
0:48:29.8 DM: 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.
0:48:39.6 SC: And speaking of which, if we human beings are 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?
0:49:03.3 DM: Absolutely. Yeah, we had a… 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, ’cause he was part of the youth basketball circuit a little more. So he got to see them evolve and that gave him… I think you need data, but he also had a unique intuition that certain guy… 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, but he also had an intuitive feel. I couldn’t prove that to you though, but I always felt like he did.
[chuckle]
0:49:53.9 SC: Is one of the biases you need to worry about overvaluing your own players? I know that on the online discussion boards, we all think we would’ve 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 gotta actively work to overcome, I would think.
0:50:13.6 DM: Yeah, it’s one of the major biases, the endowment effects and anchoring, are probably the two. But I would say that 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 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 do a trade, you realize if you’re on the other side, you wouldn’t even put this trade as one of the possible trades. It’s that ridiculous, they were 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.
0:51:14.6 SC: 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?
0:51:30.9 DM: It depends on the time period. So generally, 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 there’s a deadline, just like everything in life until you have a deadline [0:51:53.5] ____ about getting things done. But in negotiations, there’s actually a real reason why they run to the deadlines, which people could go read all that research, and so as you get closer and closer to the deadline, things get more specific, things get more transactional, whereas prior to the deadline, it’s more conversations, concepts. Looking for high level fits, and it’s sort of… It’s like a… It’s almost like a sales funnel, honestly, if you’re into that area of the research, so you have to have a lot of things in the funnel or you’re gonna end up with that much at the end.
0:52:29.2 DM: And so teams have different styles, some teams go for home run deals like where you’re just are hoping you catch a team valuing an 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 margin. So I’ll miss home run deals more often, but I’ll also hopefully get more done that will help you do the last piece to a title team, for example.
0:53:14.5 SC: 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 were doing a podcast when you could have been swinging a massive deal. [chuckle]
0:53:24.0 DM: Yeah, I know. I don’t think Spike and Michael are gonna forgive you if you distracted me doing them.
0:53:29.4 SC: They would not. There’s a million people who would never forgive me.
0:53:32.0 DM: But we still have left, four weeks. It’s funny ’cause it’s not…
0:53:34.8 DM: Yeah, I think we have time.
0:53:36.2 DM: Three weeks, three weeks and a couple of days.
0:53:37.4 SC: We have time. But you mentioned… 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… And also, you want the people on your team to like each other and to play well together, and it seems to me the Sixers are having a good time this year, and I think that’s a big part of their success.
0:54:08.6 DM: Yeah, Doc Grover has done an amazing job, I think. One thing you have to recognize if you’re in my job is to 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, coach versus… But I’ve come to… I had worked with him before in Boston, and I really appreciate… He does such a good job with getting everyone in the same canoe and rowing all together, and that feeling of attack. We all know we’re having to win the title, but there are different ways to attack it, and he does a fantastic job with that and… So, it’s important like, I’m good at certain things, Doc’s good at certain things, Al Brant who does a tremendous job with us, he’s… So you wanna know where your strengths and weaknesses are and 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, I just went off…
0:55:09.7 SC: 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 gonna 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?”
0:55:26.1 DM: Yeah, that happens all the time. So I think when a team is rebuilding, which I’ve luckily not had to do since like ’05, then you shouldn’t really factor that in much ’cause you’re trying to optimize your ability to win in multiple years from now.
0:55:45.6 SC: Yeah.
0:55:45.9 DM: But when you have a team like ours, which has great chemistry, and I’ve been really happy if you see the quotes from our players and it’s real in there every day, you definitely have to be careful with that ’cause it’s similar to like… If you add… I don’t know, physics department, but if you had a bad employee, it’s almost like five times worse than adding a good one. Now a superstar can help, but… So with these final pieces, it’s a huge factor, you need to make sure you’re not… Or if you add something and it’s not working out to be able to get out of it… Like in some way, you have to have an exit plan.
0:56:25.7 SC: But you mentioned re-building, obviously this is a very touchy on both sides issue in Philadelphia where under one of your predecessors, Sam Hanky, who I was a huge fan of myself, and he worked with you in the past, 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 reject this philosophically, you should always try to win the most games this year. Do you have feelings about this one way or the other?
0:57:00.6 DM: Yeah, that shouldn’t be counted like… What Sam did was exactly right. And it’s why we have Joe Allen Ben now. And I think it’s the only arguments that landed against it were ones that Philadelphia shouldn’t worry about, which is, there are arguments that there’s some minimum efficient quality of team you need to present so that you’re being a good partner with the other 29 owners. But in terms of the micro of Philadelphia, what was done was exactly right, and it’s the reason why there’s a lot of winning happening right now, or one of the big reasons.
0:57:43.9 SC: Yeah, so I thought that’s what you’d say, I totally agree with it myself. So, I know I don’t wanna keep you too long, but I have a couple of sort of 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?
0:58:07.4 DM: Absolutely, yeah. So, I would do the Elam Ending, I would do, which is like… People probably have to Google that.
0:58:16.2 SC: You can explain that.
0:58:17.2 DM: It’s actually easy to say, Elam Ending turns the end of a basketball game to a pick-up 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 that’s the sports that add like baseball or tennis, not to a clock, are better games at the end. And so the Elam Ending switches the end of the… I would have one free throw for everything, there’s no reason you have to shoot two, three, we can just do one free throw for every point that you’re gonna get when you go. I would only allow time outs if the ball’s already dead, I don’t wanna stop, I don’t wanna stop play if it’s live. People love the back and forth and the line of play, you don’t wanna stop that with time outs. Those are the… I would get to more radical things, but those are just off the top of my head, some easy ones that I would…
0:59:13.2 SC: The NBA, it’s famous among non-fans for the last two minutes of the game, take 20 minutes to play, it’s something that should be fixable somehow.
0:59:22.2 DM: Our last minute 1:17 in our trial game recently took 19 minutes. It was absolutely real. We were up 17 and they cut it to seven, which was great for them, but the Elam Ending would fix that. And so it’s just something we need to… I would get rid of jump balls and have position arrows, there’s actually a lot of easy stuff that would help, to help the game be more… I would definitely reduce replay, that’s what… 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 gonna do our best. There’s gonna be a few things in a review, we’re gonna miss things, and that’s okay, and sorry. And now watch your game, it’ll be more fun.”
1:00:10.7 SC: Would you also have changes to the draft system, to the lottery, etcetera, to the… We have a system now where if you do badly, you’re rewarded by getting a high draft pick and people wanna change that.
1:00:21.1 DM: That one turns out to be really tricky, I actually think the Commissioners balanced that pretty well now. He’s made it… He was fighting 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 [1:00:36.5] ____ and have a wheel where they’re all pre-determined. 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, you 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 it’s pretty well-balanced now in a system that’s not very easy to fix. But one thing I would do is I’d probably do one of these hybrid systems where instead of a draft order you would get basically fake coins, you’d get Chamberlains, we found Chamberlain Coins, they’re like some sort of crypto bitcoin and like whoever’s the worst team would get the most, but then they could allocate their bidding on what pick they want.
1:01:30.2 SC: Oh, I see. Okay, good. Yeah.
1:01:31.9 DM: Right, because if you are 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 trade up and lost on a trade back. And you could be able to do that by instead of forcing people into their slot, giving them more things to bid against a class. So there are things you can do, but you definitely wanna give bad teams an advantage in the draft just so you have something to sell.
1:02:12.6 SC: 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, but it’s also so compelling at the same time.
1:02:29.0 DM: Well, it depends on what you have as your primary goal of life. I think the Greeks had it pretty well. Happiness is maybe the goal for each individual and clearly entertainment is part of that, so I think it’s a pretty straightforward mapping to a useful goal and that it makes… I think happiness is a good goal. We should have the gross happiness product like people have said if we could measure it, which we can’t.
1:02:58.0 SC: Well, I don’t know, most teams do not win the NBA championship. Is sports in that happiness increase or it’s not completely clear?
1:03:05.1 DM: Oh, right ’cause you’re… Yeah, I definitely think it’s in that happiness increase. Otherwise, I know people are very irrational, but they’d have to be shockingly irrational for the NBA to be a $10 billion-ish business if half the people were miserable all the time. I think people like rooting for a team, they like rooting for players, they like rooting for a process, they like rooting for Doc Rivers, they like rooting… They like being part of something, a community, and I think it provides that.
1:03:38.6 SC: Alright, last question is, is it true that you have skeptical thoughts about dark matter in the universe?
1:03:44.7 DM: Yeah, I was gonna ask you this. So I guess it’s trendy now, I’m so annoyed, I feel like everyone’s discovering Nine Inch Nails now and I listened to their first album, so I’ve been saying… So there’s an analyst, Greg Keem who is a theoretical physicist who worked for us in Houston and this was, I don’t know, 10 years ago, he works for the Clippers now, and I was just telling him, “No, there’s no way dark matter is real. Just look at the history of how knowledge is developed at any time. 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 very likely the dark matter doesn’t exist. I guess that’s trendy now I’ve heard… What is the latest on that? I’m curious, you would know.
1:04:35.8 SC: Yeah, no, sorry, this is the one part of our conversation I gotta dramatically disagree here. So I don’t think it’s trending, there’s a whole bunch of people who think about it, as we’ve said, we think about all sorts of things, 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. I absolutely buy the argument you’d 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 microwave 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.
1:05:20.3 DM: Is the analogy then more… ‘Cause people say with dark matter it’s like aether 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 puzzled by all that shit?
1:05:42.9 DM: You’re definitely right about that. I wrote a book about it, but I’m agreeing with you, yes. [chuckle]
1:05:47.0 DM: There you go. And so… Okay, so that’s your… That makes sense, but I still feel like there’s some theory that could overlay and explain all that too potentially, but you’re saying the odds are getting low, that maybe that’s the case of this.
1:06:08.3 SC: I think actually it’s a very interesting history of science question because 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, 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 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 banging on what was predicted by dark matter in a way that it’s almost impossible, I never wanna say impossible, but it’s almost impossible to reproduce that success in a model where you just change gravity without dark matter.
1:06:57.4 DM: Oh, so you’re saying something important that I didn’t know. I didn’t know about the CMB stuff, that a prediction was made prior to knowing the result, and then it turned out to fit the theory. And so that’s important. That’s what happened with quantum mechanics, so you wanna tell me if I’m right, you’d 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 huge remnants of the Big Bang? What is the best candidate at this point?
1:07:42.3 SC: Well, that’s I think the totally fair worry is that for a long time, we had a favorite candidate, the weakly interacting massive particles, we have the weak interaction of particle physics.
1:07:54.0 DM: The WIMPs.
1:07:54.3 SC: 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 if we… It’s like, it’s still, the chances that we would have found 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 gotta 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 they 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 and it’s rectifying…
1:08:50.1 DM: Does it interact with anything? Does it lens? Does it gravitationally lens stuff? Or does it like… What is it…
1:08:55.2 SC: Electrically. Yeah, 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, we know where it is through gravitational lensing mostly, so you get these gorgeous images, three-dimensional reconstructions of the dark matter density, even though we’ve never detected the particle that it actually is.
1:09:21.4 DM: Still feels like it’s gotta be something totally we don’t… I just like… It feels like, my gut tells me something, but of course, I just have my gut and your gut’s better than mine, it’s more experienced.
1:09:36.4 SC: 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 one would be happier than I would be if there was something interesting going on with gravity that’s tricked us into thinking that there was dark matter.
1:09:52.5 DM: Right. But we don’t even totally understand gravity, so that’s another thing.
1:09:54.4 SC: We don’t totally understand anything. We actually understand gravity on scales of the solar system and larger, pretty well. We are able to imagine other things. I’ve written papers, imagining other things, but I think Einstein more or less nailed that on the astrophysical scales, gravity is pretty well under control.
1:10:12.2 DM: Gotcha. I think it’s just a simulation people [1:10:15.9] ____ they’re just like… “Well, they’re looking again, they’re trying to see if they can find the dark matter. Well, we’ll put it in there.” [chuckle]
1:10:23.6 SC: That is… 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 [chuckle] and you could 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 to get there.
1:10:47.0 DM: Why did we stop the collider in Texas? That would have been awesome. I’m so annoyed that that was killed back…
1:10:55.0 SC: Yeah. It would have been not only earlier, but it was a more powerful accelerator than the Large Hadron Collider.
1:11:01.1 DM: Right. Are we getting that? Are we still… It feels like it set science back like 25 years.
1:11:05.9 SC: 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.
1:11:19.3 DM: I remember Biden making this a big part of his platform. I’m surprised. [chuckle]
1:11:22.6 SC: The physics community doesn’t know about it. Yeah, no, they’re expensive. And I get that, but we build them once every 20 or 30 years. I think it’s worth the expense myself.
1:11:33.3 DM: Well, it’s easily worth the expense, yeah. And all my friends are like, now, telling me it’s stupid to go to Mars, and I’m like, “Yeah, it is, but why wouldn’t we? We’ll learn something. It’ll be interesting.”
1:11:47.5 SC: It’s part of a portfolio. You gotta do some crazy things, you gotta do some ambitious things, ’cause you don’t 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’s British, he was called before Congress to testify. And he said, “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.” But those are fringe things that we can’t care about, we can’t control those, let’s look at the likely thing in this particular case.
1:12:23.6 DM: Well, politics and media do really bad with orders of magnitude, so yeah. Yes, it’s possible that this could happen, but it’s also possible a plane could crash into my building right now. And they can’t distinguish that fear from one, that’s 10%. We just do really bad between 10% and 1%. But 1% is a really terrible thought.
1:12:47.6 SC: Yeah, no, there is 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. [chuckle]
1:12:55.9 DM: I think Nate Silver has a good friend that has talked about that quite a bit. But here’s the bottom line, I say, Look, we start off talking about championship bouts 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.
1:13:16.3 SC: Yeah, no, I’m sure there are. And if you had asked me five years ago, I would have said it’s over 50% chance that we would have found it by now. That’s why that does raise some skepticism, I think. So if it’s not a weakly 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 [chuckle] with thinking there is dark matter, but not the skeptics.
1:13:43.6 DM: Right, right. Well, maybe I can be on in five years and we can talk. I’m glad you guys didn’t call it the GIMP. The gravity…
1:13:47.4 SC: No, there’s a whole bunch of…
1:13:49.1 DM: [1:13:49.2] ____.
[chuckle]
1:13:49.3 SC: A whole bunch of bad puns in physics, so and on that note, Daryl Morey, thanks so much for being on the Mindscape Podcast. This was great.
1:13:57.6 DM: Thanks so much, Sean. Appreciate it.
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