The Undoing Project

by Michael Lewis

  His interest in sports and statistics had led him, at the age of sixteen, to pick up a book called The Bill James Historical Baseball Abstract. Bill James was then busy popularizing an approach, rooted in statistical reasoning, to thinking about baseball. With some help from the Oakland Athletics, that approach would trigger a revolution that ended with nerds running, or helping to run, virtually every team in Major League Baseball. In 1988, when he stumbled upon James’s book in a Barnes & Noble, Morey had no way of knowing that people with a gift for using numbers to predict things would overrun professional sports management and everyplace else high-stakes decisions were being made—or that basketball would be, in effect, waiting for him to grow up. He simply suspected that the established experts maybe didn’t know as much as everyone thought they did.

  That particular suspicion had been born the year before, 1987, after Sports Illustrated splashed his favorite baseball team, the Cleveland Indians, on its cover and picked them to win the World Series. “I was like, ‘This Is It!!!! The Indians have sucked for years. Now we’re going to win the World Series!’” The Indians finished that season with the worst record in the major leagues: How did that happen? “The guys they had said were going to be so good were so bad,” recalled Morey. “And that was the moment when I thought: Maybe the experts don’t know what they’re talking about.”

  Then he discovered Bill James and decided that, like Bill James, he might use numbers to make better predictions than the experts. If he could predict the future performance of professional athletes, he could build winning sports teams, and if he could build winning sports teams . . . well, that’s where Daryl Morey’s mind came to rest. All he wanted to do in life was to build winning sports teams. The question was: Who’d let him do it? In college he’d sent dozens of letters to professional sports franchises in the hope of being offered some menial job. He received not a single reply. “I didn’t have, like, any way to penetrate organized sports,” he said. “So I decided at that point that I had to be rich. If I was rich I could just buy a team and run it.”

  His parents were middle-class midwesterners. He didn’t even know any rich people. He was also a distinctly unmotivated student at Northwestern University. He nevertheless set out to make enough money to buy a professional sports team, so that he might make the decisions about who would be on it. “Every week he’d take a sheet of paper and write on top, ‘My Goals,’” recalls his then-girlfriend, Ellen, now his wife. “The biggest life goal was, ‘I’m going to someday own a professional sports team.’” “I went to business school,” said Morey, “because I thought that’s where you had to go if you wanted to get rich.” Upon leaving business school, in 2000, he interviewed with consulting firms until he found one that got paid in the shares of the companies it advised. The firm was advising Internet companies during the Internet bubble: That sounded, at the time, like a way to get rich quick. Then the bubble burst and all the shares were worthless. “It turns out it was the worst decision ever,” said Morey.

  From his stint as a consultant he learned something valuable, however. It seemed to him that a big part of a consultant’s job was to feign total certainty about uncertain things. In a job interview with McKinsey, they told him that he was not certain enough in his opinions. “And I said it was because I wasn’t certain. And they said, ‘We’re billing clients five hundred grand a year, so you have to be sure of what you are saying.’” The consulting firm that eventually hired him was forever asking him to exhibit confidence when, in his view, confidence was a sign of fraudulence. They’d asked him to forecast the price of oil for clients, for instance. “And then we would go to our clients and tell them we could predict the price of oil. No one can predict the price of oil. It was basically nonsense.”

  A lot of what people did and said when they “predicted” things, Morey now realized, was phony: pretending to know things rather than actually knowing things. There were a great many interesting questions in the world to which the only honest answer was, “It’s impossible to know for sure.” “What will the price of oil be in ten years?” was such a question. That didn’t mean you gave up trying to find an answer; you just couched that answer in probabilistic terms.

  Later, when basketball scouts came to him looking for jobs, the trait he looked for was some awareness that they were seeking answers to questions with no certain answers—that they were inherently fallible. “I always ask them, ‘Who did you miss?’” he said. Which future superstar had they written off, or which future bust had they fallen in love with? “If they don’t give me a good one, I’m like, ‘Fuck ’em.’”

  By a stroke of luck, the consulting firm Morey worked for was asked to perform some analysis for a group trying to buy the Boston Red Sox. When that group failed in its bid to buy a professional baseball team, it went out and bought a professional basketball team, the Boston Celtics. In 2001 they asked Morey to quit his job consulting and come to work for the Celtics, where “they gave me the most difficult problems to figure out.” He helped hire new management, then helped to figure out how to price tickets, and, finally, inevitably, was asked to work on the problem of whom to select in the NBA draft. “How will that nineteen-year-old perform in the NBA?” was like “Where will the price of oil be in ten years?” A perfect answer didn’t exist, but statistics could get you to some answer that was at least a bit better than simply guessing.

  Morey already had a crude statistical model to evaluate amateur players. He’d built it on his own, just for fun. In 2003 the Celtics had encouraged him to use it to pick a player at the tail end of the draft—the 56th pick, when the players seldom amount to anything. And thus Brandon Hunter, an obscure power forward out of Ohio University, became the first player picked by an equation.* Two years later Morey got a call from a headhunter who said that the Houston Rockets were looking for a new general manager. “She said they were looking for a Moneyball type,” recalled Morey.

  The Rockets’ owner, Leslie Alexander, had grown frustrated with the gut instincts of his basketball experts. “The decision making wasn’t that good,” Alexander said. “It wasn’t precise. We now have all this data. And we have computers that can analyze that data. And I wanted to use that data in a progressive way. When I hired Daryl, it was because I wanted somebody that was doing more than just looking at players in the normal way. I mean, I’m not even sure we’re playing the game the right way.” The more the players got paid, the more costly to him the sloppy decisions. He thought that Morey’s analytical approach might give him an edge in the market for high-priced talent, and he was sufficiently indifferent to public opinion to give it a whirl. (“Who cares what other people think?” says Alexander. “It’s not their team.”) In his own job interview, Morey was reassured by Alexander’s social fearlessness, and the spirit in which he operated. “He asked me, ‘What religion are you?’ I remember thinking, I don’t think you’re supposed to ask me that. I answered it vaguely, and I think I was saying my family were Episcopalians and Lutherans when he stops me and says, ‘Just tell me you don’t believe any of that shit.’”

  Alexander’s indifference to public opinion turned out to come in handy. Learning that a thirty-three-year-old geek had been hired to run the Houston Rockets, fans and basketball insiders were at best bemused and at worst hostile. The local Houston radio guys instantly gave him a nickname: Deep Blue. “There’s an intense feeling among basketball people that I don’t belong,” said Morey. “They remain silent during periods of success and pop up when they sense weakness.” In his decade in charge, the Rockets have had the third-best record of the thirty teams in the NBA, behind the San Antonio Spurs and the Dallas Mavericks, and have appeared in the playoffs more than all but four teams. They’ve never had a losing season. The people most upset by Morey’s presence had no choice at times but to go after him in moments of strength. In the spring of 2015, as the Rockets, with the second-best record in the NBA, headed into the Western Conference Finals against
the Golden State Warriors, the former NBA All-Star and current TV analyst Charles Barkley went off on a four-minute tirade about Morey during what was meant to be a halftime analysis of a game. “. . . I’m not worried about Daryl Morey. He’s one of those idiots who believe in analytics. . . . I’ve always believed analytics was crap. . . . Listen, I wouldn’t know Daryl Morey if he walked in this room right now. . . . The NBA is about talent. All these guys who run these organizations who talk about analytics, they have one thing in common: They’re a bunch of guys who ain’t never played the game, and they never got the girls in high school and they just want to get in the game.”

  There’d been a lot more stuff just like that. People who didn’t know Daryl Morey assumed that because he had set out to intellectualize basketball he must also be a know-it-all. In his approach to the world he was exactly the opposite. He had a diffidence about him—an understanding of how hard it is to know anything for sure. The closest he came to certainty was in his approach to making decisions. He never simply went with his first thought. He suggested a new definition of the nerd: a person who knows his own mind well enough to mistrust it.

  One of the first things Morey did after he arrived in Houston—and, to him, the most important—was to install his statistical model for predicting the future performance of basketball players. The model was also a tool for the acquisition of basketball knowledge. “Knowledge is literally prediction,” said Morey. “Knowledge is anything that increases your ability to predict the outcome. Literally everything you do you’re trying to predict the right thing. Most people just do it subconsciously.” A model allowed you to explore the attributes in an amateur basketball player that led to professional success, and determine how much weight should be given to each. Once you had a database of thousands of former players, you could search for more general correlations between their performance in college and their professional careers. Obviously their performance statistics told you something about them. But which ones? You might believe—many then did—that the most important thing a basketball player did was to score points. That opinion could now be tested: Did an ability to score points in college predict NBA success? No, was the short answer. From early versions of his model, Morey knew that the traditional counting statistics—points, rebounds, and assists per game—could be wildly misleading. It was possible for a player to score a lot of points and hurt his team, just as it was possible for a player to score very little and be a huge asset. “Just having the model, without any human opinion at all, forces you to ask the right questions,” said Morey. “Why is someone ranked so high by scouts when the model has him ranked low? Why is someone ranked so low by scouts when the model has him ranked high?”

  He didn’t think of his model as “the right answer” so much as “a better answer.” Nor was he so naive as to think that the model would pick players all by itself. The model obviously needed to be checked and watched—mainly because there was information that the model wouldn’t be privy to. If the player had broken his neck the night before the NBA draft, for instance, it would be nice to know. But if you had asked Daryl Morey in 2006 to choose between his model and a roomful of basketball scouts, he’d have taken his model.

  That counted as original, in 2006. Morey could see that no one else was using a model to judge basketball players—no one had bothered to acquire the information needed by any model. To get any stats at all, he’d had to send people to the offices of the National Collegiate Athletic Association (NCAA), in Indianapolis, to photocopy box scores of every college game over the past twenty years, then enter all that data by hand into his system. Any theory about basketball players had to be tested on a database of players. They now had a twenty-year history of college players. The new database allowed you to compare players to similar players from the past, and see if there were any general lessons to be learned.

  A lot of what the Houston Rockets did sounds simple and obvious now: In spirit, it is the same approach taken by algorithmic Wall Street traders, U.S. presidential campaign managers, and every company trying to use what you do on the Internet to predict what you might buy or look at. There was nothing simple or obvious about it in 2006. There was much information Morey’s model needed that simply was not available. The Rockets began to gather their own original data by measuring things on a basketball court that had previously gone unmeasured. Instead of knowing the number of rebounds a player had, for instance, they began to count the number of genuine opportunities for rebounds he’d had and, of those, how many he had snagged. They tracked the scoring in the game when a given player was on the court, compared to when he was on the bench. Points and rebounds and steals per game were not very useful; but points and rebounds and steals per minute had value. Scoring 15 points a game obviously meant less if you had played the entire game than if you had played half of it. It was also possible to back out from the box scores the pace at which various college teams played—how often they went up and down the court. Adjusting a college player’s stats for his team’s pace of play was telling. Points and rebounds meant one thing when the team took 150 shots a game and something different when it took just 75. Just adjusting for pace gave you a clearer picture of what any given player had accomplished than the conventional view did.

  The Rockets collected data on basketball players that hadn’t ever been collected before, and not just basketball data. They gathered information on the players’ lives and looked for patterns in it. Did it help a player to have two parents in his life? Was it an advantage to be left-handed? Did players with strong college coaches tend to do better in the NBA? Did it help if a player had a former NBA player in his lineage? Did it matter if he had transferred from junior college? If his college coach played zone defense? If he had played multiple positions in college? Did it matter how much weight a player could bench-press? “Almost everything we looked at was nonpredictive,” says Morey. But not everything. Rebounds per minute were useful in predicting the future success of big guys. Steals per minute told you something about the small ones. It didn’t matter so much how tall a player was as how high he could reach with his hands—his length rather than his height.

  The model’s first road test came in 2007. (The Rockets had traded their picks in 2006.) Here was the chance to test a dispassionate, unsentimental, evidence-based approach against the felt experience of an entire industry. That year, the Rockets held the 26th and the 31st picks in the NBA draft. According to Morey’s model, the odds of getting a good NBA player with those picks were, respectively, 8 percent and 5 percent. The chance of getting a starter was roughly one in a hundred. They selected Aaron Brooks and Carl Landry, both of whom became NBA starters. It was an incredibly rich haul.† “That lulled us to sleep,” said Morey. He knew that his model was, at best, only slightly less flawed than the human beings who had rendered the judgments about job applicants since time began. He knew that he suffered from a serious dearth of good data. “You have some information—but often from a single year in college. And even that has problems with it. Apart from it’s a different game, with different coaches, different levels of competition—the players are twenty years old. They don’t know who they are. So how are we supposed to?” He knew all this and yet he thought maybe they had figured something out. Then came 2008.

  That year the Rockets had the 25th pick in the draft and used it to pick a big guy from the University of Memphis named Joey Dorsey. In his job interview, Dorsey had been funny and likable and charming—he’d said when he was done playing basketball he intended to explore a second career as a porn star. After he was drafted, Dorsey was sent to Santa Cruz to play in an exhibition game against other newly drafted players. Morey went to go see him. “The first game I watch he looks terrible,” said Morey. “And I’m like, ‘Fuck!!!!’” Joey Dorsey was so bad that Daryl Morey could not believe he was watching the guy he’d drafted. Perhaps, Morey thought, he wasn’t taking the exhibition seriously. “I meet with him. We have a two-hour lunch
.” Morey gave Dorsey a long talk about the importance of playing with intensity, and making a good impression, and so on. “I think he’s going to come out the next game with his hair on fire. And he comes out and sucks the next game, too.” Fairly quickly, Morey saw he had a bigger problem than Joey Dorsey. The problem was his model. “Joey Dorsey was a model superstar. The model said that he was like a can’t-miss. His signal was super, super high.”

  That same year, the model had dismissed as unworthy of serious consideration a freshman center at Texas A&M named DeAndre Jordan. Never mind that every other team in the NBA, using more conventional scouting tools, passed him over at least once, or that Jordan wasn’t taken until 35th pick of the draft, by the Los Angeles Clippers. As quickly as Joey Dorsey established himself as a bust, DeAndre Jordan established himself as a dominant NBA center and the second-best player in the entire draft class after Russell Westbrook.‡

  This sort of thing happened every year to some NBA team, and usually to all of them. Every year there were great players the scouts missed, and every year highly regarded players went bust. Morey didn’t think his model was perfect, but he also couldn’t believe that it could be so drastically wrong. Knowledge was prediction: If you couldn’t predict such a glaringly obvious thing as the failure of Joey Dorsey or the success of DeAndre Jordan, how much did you know? His entire life had been shaped by this single, tantalizing idea: He could use numbers to make better predictions. The plausibility of that idea was now in question. “I’d missed something,” said Morey. “What I missed were the limitations of the model.”