Watching NBA highlights last night on NBATV, I stumbled across this video story on the 2010 MIT Sloan Sports Analytics Conference. Daryl Morey, GM of the Houston Rockets, helped establish the conference (now in its 4th year) that includes a variety of owners (Mark Cuban, Robert Kraft), GMs, coaches, and statistical assistants. Panels cut across a variety of on-the-field analysis (basketball analytics, baseball analytics, limits of analytics) as well as off-the-field issues (attendance, expansion, and others).
Data-oriented sports analytics now appears to be in mid-stream when viewed along Zvi Griliches S-Curve for the spread of innovations. When Moneyball appeared in 2003, it chronicled some of the earliest innovators. In looking over the conference panels and the NBA’s website articles on the nature and uses of “analytics” in their area, the fingerprints of the Moneyball-“metric” approaches are explicit. Of course, a generation or more earlier, the contributions in Sabermetrics as well as in sports economics-statistics like that of Rotemberg, Scully, Quirk-Fort, and others began plowing this soil, even if those works don’t receive much explicit attention. After all, Bill Walsh may receive most “mentions” for modern passing schemes while Coryell and, even earlier, Sid Gillman did much of the early development.
With that said, there are a lot of sports-focused “metrics” out there with little or no connection to economics or economists. That’s healthy. Statisticians with different training bring their own comparative advantages to the table. In looking over Chance or the American Statistician, it’s clear that statisticians (math stats types) tend to focus much more heavily on distributional issues than economists. Others, like the StatCube outfit mentioned in the NBA article, specialize in the collection and management of data.
Do economists have a niche even beyond academics and in the “engineering” type data analytics discussed at the conference? I think so. The background of economists gives us some advantages at disentangling (identifying) specific relationships, thinking in terms of omitted variables, and other model-building skills. (See 2009 TSE piece on ECONFL.) Like other data-oriented disciplines, econ also contributes by raising awareness among undegrads (like Bill Belichick and Jim Schwartz) for what data analysis can do.