Tuesday, March 23, 2010
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.
Monday, September 21, 2009
The process begins with information-gathering. For seven years, the R[ugby] FU has been logging every injury in the professional game in an effort to gather enough data to identify trends reliably. The England and Wales Cricket Board and the English Institute of Sport, which looks after Olympians, now have similar rolling audits, although they are at an earlier stage of development.My hunch is that the big EPL clubs know this, but keep their information private.
The numbers are already being crunched – and proving useful in planning which treatments might best serve not just the player, but the team. A recent study of hamstring injuries found that every new hamstring injury costs the team an average of 14 playing days; an average recurrence costs 25 days. Furthermore, almost all the recurrences took place during matches in the first month after return, and after an hour of play. It quickly became clear that players who had sustained hamstring injuries should be replaced after an hour during their first few games back. Moreover, the two clearest risk factors for hamstring injury were age and a previous injury, and players who performed specific strengthening exercises reduced the incidence, severity and recurrence of hamstring injuries.
Labels: statistical innovation
Thursday, July 16, 2009
But, I'm a skeptic on this one. My prediction is that this new data will perhaps be an aid in player development -- i.e. to improve positioning in the field, baserunning skill, etc. But I don't expect it will do much to isolate differences in "baseball glovework," and what little it does achieve on this score won't matter very much.
A number of years ago, Jahn Hakes and I decided to enter the quest to measure the missing elements of baseball productivity. We initially focused on fielding ability, and like most others, we failed in our quest. Why? Some people believe that statistical measures of fielding are very poor, and are of little help in distinguishing excellence from competence in this skill. I think that's right. But there is another factor: as much as baseball aficionados (like me) appreciate the fielding skills displayed by MLB's best fielders, differences in fielding ability are not a big factor in determining who wins and loses a baseball game.
Bill James' approach to Win Shares provides a useful benchmark for assessing the value of this new data with regard to the issue of fielding. James asserted that 1/2 the game is offense, 1/2 is defense, and of the defense part, 2/3 is determined by pitching. Now this was merely asserted and not analyzed, but various analyses by others are consistent with the emphasis on pitching as the dominant defensive factor. Taking the win share allocation as given, then all of the effort that goes in to measuring fielding ability can at best capture 16.7% (.5/3) of the variation in game outcomes. And how much of the variation around average fielding ability (the competent ballplayer) cannot be discerned with the naked eye? My hunch is that the scouts will beat the statheads on this one, even with newly improved data.
Despite my skepticism, I recommend a trip to Schwarz' article, if only to the view video clip which shows a replay with locational data overlaid on the field. It's pretty cool stuff.
Sunday, February 15, 2009
That's a small slice of an intriguing story; this is Lewis at his best. Thanks to Al Roth for sending the link. Roth's post at Market Design identifies the principal-agent problem as key to the conflict between individual and team productivity in basketball. Lewis' account also illustrates how innovative managers like Daryl Morey can mitigate the problem. While Lewis focuses mostly on Battier, the athlete and the person, the economic punch line seems to me to reside in Morey, the general manager. As Roth points out, "basketball contracts may change" as a result of his innovations. Great stuff.
One well-known statistic the Rockets’ front office pays attention to is plus-minus, which simply measures what happens to the score when any given player is on the court. In its crude form, plus-minus is hardly perfect: a player who finds himself on the same team with the world’s four best basketball players, and who plays only when they do, will have a plus-minus that looks pretty good, even if it says little about his play. Morey says that he and his staff can adjust for these potential distortions — though he is coy about how they do it — and render plus-minus a useful measure of a player’s effect on a basketball game. A good player might be a plus 3 — that is, his team averages 3 points more per game than its opponent when he is on the floor. In his best season, the superstar point guard Steve Nash was a plus 14.5. At the time of the Lakers game, Battier was a plus 10, which put him in the company of Dwight Howard and Kevin Garnett, both perennial All-Stars. For his career he’s a plus 6. “Plus 6 is enormous,” Morey says. “It’s the difference between 41 wins and 60 wins.” He names a few other players who were a plus 6 last season: Vince Carter, Carmelo Anthony, Tracy McGrady.
***Daryl Morey, as quoted by Michael Lewis