Gelf Magazine - Looking over the overlooked

Books | Sports

February 28, 2011

The Stephen J. Dubner of Sports

Jon Wertheim played the sportswriter role in his new book, 'Freakonomics' for the athletic set. He tells Gelf what he'd like to analyze for the sequel.

Mickey Lambert

It is arguable that, in the corporate-sponsored arena of public sports debate, there are only straw men at the table—or at least that the voices most frequently quoted have the least-useful things to say about where statistical analysis fits into the games we love. To date, much of the internet traffic on the topic has read more like a hackneyed jocks vs. nerds standoff than like an informed discussion that advances the collective understanding or contributes to winning more games. More importantly, because the issue falls along the typical fault lines of mainstream media vs. new media, old guard vs. new guard, the end result has been—at risk of mixing metaphors—a zero-sum border war instead of a rising tide to lift all boats.

Jon Wertheim. Photo by Michael Lebrecht.
"There's some resistance around analytics taking the humanity and the excitement out of sports, and making it feel more like homework."

Jon Wertheim. Photo by Michael Lebrecht.



Amidst this dishearteningly low signal-to-noise ratio, there are signs of hope that more generative approaches are in the offing. Moneyball sparked the initial conversation that changed how the mainstream sports world viewed statistical analysis by cloaking it in the compelling siren song of competitive advantage. And Jon Wertheim's and Tobias Moskowitz's recently released Scorecasting: The Hidden Influences Behind How Sports Are Played and Games Are Won uses a behavioral-economics perspective in an attempt to lend useful insight into why in-game strategy so frequently flies in the face of what the numbers tell us. In examining a variety of sports phenomena, from referees' whistle-swallowing to the role of home-field advantage in game outcomes, the authors seek to expose the places where gut instinct and cold calculation collide.

In addition to serving as a preeminent voice covering professional tennis, NBA, and a range of social and business issues in sports as a senior writer for Sports Illustrated, Wertheim also has penned several previous books, including Strokes of Genius, about the classic Roger Federer-Rafael Nadal 2008 Wimbledon final (Gelf interviewed him about the book); Blood in the Cage, a profile of the rise of mixed martial arts (Gelf interview); and Running the Table, about a pool hustler (Gelf interview). Wertheim appeared at Gelf's Varsity Letters sports reading event for all three of those books. This time, Gelf interviewed Wertheim about the book's roots in his childhood friendship with University of Chicago economics professor Moskowitz, how audiences outside of the sports world relate to its Freakonomics-style analysis, and areas for future examination. The following interview has been edited for clarity.

Gelf Magazine: Where does this book, in your opinion, fall in terms of the larger context of the Joe Morgan-to-Jonah Keri spectrum that seems to have evolved in recent years around the role of statistical analysis in sport?

Jon Wertheim: Our take, essentially, is that it's a false dichotomy. On one hand, you would be an idiot to ignore freely available data that could help you win, and certainly couldn't hurt you. But we're also dealing with human beings, and there's a lot of stuff that is not quantifiable, so there's a role for human intuition and gut instinct, too. Mostly, we wanted to provide a framework for thinking about things a little bit differently, and to broaden the appeal beyond sports fans and statheads, who probably have their own issues with the book, as well.

Gelf Magazine: Who is your intended audience for the book? How do you see other potential audiences reacting to it? I saw the Wired interview, which seemed to be directed at a non-sports-fan readership.

Jon Wertheim: We were actually going for a more Wall Street-type crowd, the same kinds of readers who read Moneyball even if they weren't huge baseball fans. We wanted people who weren't necessarily as conversant with sports to get something out of the book; we didn't want it to be too esoteric. We're hoping to appeal to people who like thinking about issues from new and different angles. The sports crowd has definitely gone for it, but that was not our sole intended audience.

Gelf Magazine: Many of the general conclusions reached in the book have been covered, in one way or another, by various sports bloggers—the idea of the "hot hand" as a function of random luck rather than reliable production, for example. What is the book bringing to the already-existing body of work on these types of data-interpretation issues, and did you look at what was out there in the blogoverse in the course of your research?

Jon Wertheim: We were definitely operating from the Freakonomics model—Toby is a colleague of Steven Levitt, and we were interested in that synthesis of original research and meta-analysis of existing models. We liked the approach of using the arena of sports as a laboratory for examining different variables—there's a lot of great stuff there in terms of the salary cap, rosters from year to year, and the like that really make it a great environment for this kind of work. We wanted to answer the "OK, then what?" questions that stem from some of the existing work, and take another step with what was already out there.

Gelf Magazine: Freakonomics also took on a kind of "translation" function for its readers, most of whom would not be caught reading original research or perusing the academic studies on which many of its chapters were based. Did you have a similar goal for Scorecasting?

Jon Wertheim: To some extent, sure—there's a real readability role to be played in making data usable and interesting for mainstream audiences. People get very caught up in academic terminology and inscrutable formulas, because they're difficult to interpret. We wanted to make this relevant and readable for non-academic audiences.

Gelf Magazine: In the process of collecting and analyzing data, what did your research methodology look like? Did you have a hypothesis going into a particular chapter (e.g. the chapter on whether it makes more sense to sit a player in foul trouble), or did you take a more open approach? How, if at all, do you think that impacted the outcomes?

Jon Wertheim: We took a pretty open approach—we weren't trying to come to a foregone conclusion, and our first few hypotheses were actually wrong, like around the reasons why home-field advantage exists. I had the good fortune of being the sportswriter as opposed to the data guy, and so I got to play devil's advocate with the data analysis and make sure that different variables were being accounted for—like, when we were working on the chapter about the Cubs being cursed, there were a lot of questions to be asked to most effectively interpret the data and come up with reasons for their unluckiness. It helps that both Toby and I are sports fans and have business backgrounds—there was good overlap there, but we tended to stay in our roles and stick to our strengths.

Gelf Magazine: What got left out of the book? Did you study any phenomena where the data was too murky to make a cogent point, or where the results were not going to make for a captivating read? To what extent, if at all, was the content affected by the limitations of the medium?

Jon Wertheim: We didn't take out anything because we didn't think it would sell books or because it didn't pan out with our hypothesis. The things that got left out were cut more because we couldn't really isolate which variables were impacting the data, or there was no effective way to track the data we needed. There were plenty of fun discussions that we couldn't include because there would be no way to test our assertions.

Gelf Magazine: Given a sometimes overwhelming raft of data that calls "tried-and-true" practices into question, is sports' embrace of tradition the biggest obstacle to the kind of evolutionary change that is suggested in the book?

Jon Wertheim: Yes, I think that's absolutely the case: There are so many cases where the conventional wisdom, however wrong or misguided, has been petrified through repeated exposure. None of us grew up managing hedge funds or real-estate development; we grew up in Little League, and we've internalized some of this stuff since we were old enough to swing a bat. Sports are such a visceral, primal pursuit, and that can really run against the kind of calculation that data analysis includes—it can feel like applying analytics to jazz. A friend of mine is in analytics for an MLB team, and says that it feels like he's saying everything in a vacuum, like shouting into the void. There's some notion that using data is a good idea, but they don't want to change their actual practices at all.

Gelf Magazine: There's also the escapism and entertainment that sports provide—some respite from a world that wants us to have all the answers.

Jon Wertheim: Yeah—it's not like they're televising options trading on Sunday afternoons. There's some resistance there around analytics taking the humanity and the excitement out of sports, and making it feel more like homework. There's still the idea of the new-media guard as bloggers sitting at home in their underwear playing computer quarterback, wanting to automate everything.

Gelf Magazine: In that vein, there are already signs of automation in terms of the roles of human referees in officiating games. In tennis, Hawk-Eye really has been incorporated into the fabric and flow of the match, and there's less room for the kind of home-field advantage and its impact on officiating that you find in other sports. What's the right balance between Hawk-Eye and Mike Carey?

Jon Wertheim: It really depends on the sport—there's no way that a machine could capture everything that's going on in a basketball game, for example, and there are too many calls that are really judgment-related. You'll always need human referees to some extent, but in general, I think that these technologies—Hawk-Eye and instant replay and others—are helpful to their sports, and are only going to get better in terms of figuring out how they can best be used. The biggest critique is really that Hawk-Eye takes some of the emotion out of match play—players will generally get upset, challenge the call, and then live with whatever happens, so you don't get the McEnroe-style tirades (Serena Williams's outburst at the US Open notwithstanding).

Gelf Magazine: Contrast the long arm of the sports establishment with the more recent development of technology like Pitch F/X, which uncover data goldmines for folks like you and Mr. Moskowitz to play with. Do you think access to that data will be negatively impacted to the extent that it's controlled by the leagues, who are not invested in dismantling the status quo?

Jon Wertheim: No—I think the opposite, actually. There's some chance that enough people look at how robust the F/X data is and begin to question the utility of home-plate umpires, or something along those lines, but I think that if anything, this kind of data will become more accessible as people keep using it and writing about it.

Gelf Magazine: Given the limitations of any data in controlling for the infinite number of random variables, how is data analysis best balanced with more qualitative/anecdotal/situational information to make the best choices at any given moment? What do you do with this information?

Jon Wertheim: Essentially, the role of data is to provide information to arm yourself for making better decisions based on something in addition to your gut instinct or your emotions. If you're four-for-four from the field, your gut is going to tell you to take a difficult shot, since you've got the "hot hand." Maybe you're on the court, and you've got that difficult shot—you might still take it, but you might think twice, as well. Neither your gut instinct nor your conversance with data alone mean you're going to be successful. Look at Tony La Russa: Even though he had a good idea about using effective pitchers in higher-leverage situations rather than just at the end of games, the implementation of that idea was a dud. There are so many variables that you can't account for in any given situation, and they are just as random as anything happening on the field; having the data at least gives you some backing.

Gelf Magazine: You mentioned in a Q&A on the NYT Freakonomics blog that you and Tobias Moskowitz would be studying other phenomena going forward—in particular, evaluating the predictive ability of NFL combine events. Are there other examples of things you're looking at that you could share?

Jon Wertheim: In a perfect world, we'd do a sequel—there are a ton of things we'd like to look at more closely. The NFL combine has long been an interest of mine. It may as well be like American Gladiators; I'm not sure how you can predict anything about in-game performance by having a guy run in a straight line with no pads on. We'd also like to take a closer look at some of the stuff around blocked shots, trying to come up with some better metrics to evaluate their role. Baseball is somewhat ahead of the curve on this in terms of devising new ways of measuring the value of particular metrics.

Listen to Jon Wertheim's essay about sports and losing, read by Gelf's Michael Gluckstadt at Varsity Letters on Feb. 3, 2011

Mickey Lambert

Mickey Lambert is a sports fan and occasional writer who lives in Brooklyn.







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Article by Mickey Lambert

Mickey Lambert is a sports fan and occasional writer who lives in Brooklyn.

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