Playing the numbers

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Thank you, Camp Young Judaea, for assigning two sports-crazed 12-year-olds to bunk seven in 1984. These two onetime bunkmates, despite divergent career paths, have remained lifelong friends—and have now written a book on behavioral economics in sports that I had trouble putting down. Tobias Moskowitz is a finance professor at the University of Chicago, while L. Jon Wertheim ’93 is a senior writer for Sports Illustrated. Following in the same journalist-scholar coauthorship model of the phenomenally successful Freakonomics franchise, Moskowitz and Wertheim have found a way to convey fascinating social science results through easily accessible storytelling. Scorecasting is not a very compelling or descriptive title, but if you are looking to buy a present for a sports fan, you can’t go wrong with this book. Indeed, its super-short chapters are the perfect length to fill the commercial breaks while watching your favorite team.

The book is chock-full of surprising findings across all of the major professional sports organizations. You’ll learn that the Chicago Cubs never win the World Series in part because their fans are so long-suffering. You see, the demand for tickets at Wrigley Field is relatively insensitive to the team’s win/loss percentage, so management has less reason to invest in making the team a contender. If there were more fair-weather fans, there might be more fair weather.

But what makes the findings in Scorecasting so memorable is how the authors tie common sports phenomena to a few recurring cognitive biases. One of book’s central investigations is an attempt to explain the marked home court advantage that can be seen across a wide variety of sports. Moskowitz and Wertheim systematically examine a variety of traditional and not-so-traditional hypotheses. For example, they reject the possibility that athletes actually perform better at home by looking at isolated behavior. Turns out the percentages of successful free throws and field goals are the same when athletes are playing at home as when playing away.

Instead, the authors find support for the idea that referees favor the home team because of what psychologists call “social influence.” According to this theory, referees are subtly influenced by being in a stadium with thousands of people who want a particular result—and call the games accordingly.

I was initially skeptical, but the book shows that the degree of the referee bias is itself a function of the size of the crowd. When there are fewer people in the stands, the data show, there is less of a home field advantage. The trend is easiest to track in baseball, where an “electronic eye” shows the size of the effective strike zone for the home and away teams—and how much they can differ. There are exceptions: in the NBA, schedulers are actually responsible for about a fifth of the home court bias; “visiting teams play the vast majority of back-to-back games,” so the away team is more likely to be tuckered out. Professional leagues could lean against the home field advantage, but they have good reason not to—because ticketholders will pay more to see their team win at home.

Another big theme of the book is that mental framing matters. Players and referees behave differently when they frame a decision as a potential gain rather than a potential loss. Tiger Woods putts more aggressively when he is putting for par (a loss if he misses) than when he is putting for birdie (a gain if he makes). Controlling for the distance and the ball placement—and a host of other possible influences—the authors show that Woods is not only more likely to make a par putt, but when he misses the shots are more evenly divided between long and short. On similar birdie putts, he is more likely to be short. Even though a stroke is a stroke is a stroke, Woods treats them differently because of the gain/loss frame.

The authors show the same effect in baseball with regard to 3–2 pitches. You might think that there is no natural gain or loss frame for how a pitcher or a hitter would think about a full count. But the authors inventively restrict their attention to full counts that had started as either 3–0 or 0–2. If you are a pitcher and had initially gone down 3–0, then battled back to 3–2, you are likely to frame the next pitch as a potential gain. Conversely, if you had started out 0–2 and then thrown three balls in succession, you’re more likely to think of the next pitch as a potential loss. Can you guess how the frame affects the behavior of pitchers?

The book also sparkles when it comes to suggesting solutions to problems that I didn’t know even existed. We learn that in NFL overtime games, the team that wins the possession-deciding coin flip has a 61 percent chance of winning the game. Instead of a coin flip, the authors suggest, coaches should submit bids for where they would be willing to start on the field. The team that was willing to start closest to its own end zone would get to start with the ball in overtime first. I love the idea of replacing the arbitrary advantage of the coin flip with an interesting strategic choice for the likes of New England Patriots coach Bill Belichick to stew over.

In other areas, I wish the book had called for more coin flipping. The book whetted my appetite for evidence-based conclusions about sports performance—and crunching historical data sets can only take us so far. Evidence-based medicine would never operate in the absence of randomized trials, but to date there has never been a randomized trial of sports strategy. We could test, for example, whether a statistician could call better pitches than a team’s pitching coach: just allow a statistical algorithm to control in a randomly selected sample of at-bats. The absence of randomized trials suggests to me that there’s still a lot of low-hanging empirical fruit. But all in all, Scorecasting does a great job in showing how much players, coaches, and fans can learn from diving into the data.