Recently, our colleague Ken Pomeroy posted something interesting that I'm not sure drew sufficient attention. In discussing why the NCAA Tournament Selection Committee has shown little interest in replacing the antiquated RPI metric with something more accurate, Pomeroy broke down the descending incentives various groups have for adopting statistical analysis, ranking coaches at the top and the selection committee at the very bottom, with fans and the mainstream media somewhere in between.
"My point today is that it's foolish to expect the committee to readily adopt a new methodology," wrote Pomeroy. "When one looks at how tempo-free stats have been accepted by various groups involved in the game (coaches, fans, media, and the committee), there's a connection between those that embraced it first and their incentive to do so."
As the NBA statistical analysis community prepares to gather for the fifth annual MIT Sloan Sports Analytics Conference this Friday and Saturday in Boston, it seems worth pondering how Pomeroy's hierarchy translates to the professional game. In the NBA, we draw a distinction between front offices, who select players, and coaches, who utilize them. (This breakdown is strikingly similar to the one described in the opening of Law and Order.) Because most of the research done using advanced statistics in basketball has focused on evaluating players, front offices rank at the very top of the NBA pyramid. That position is confirmed by how many teams now employ someone tasked with performing statistical analysis--at least 17 in all.
Coaches are a more interesting topic. The same incentives in terms of scouting opponents and optimizing player usage that have made college coaches turn to tempo-free stats exist in the NBA, but with one important difference. Much of this data has existed for years and has been generated by video coordinators, so coaches think of it as "scouting," not "statistics." Most every NBA coach considers, say, the percentage a player shoots going to his left or his right, but they don't think of this as making them statistics users. Because similar data is more difficult to come by in college hoops, young coaches have to seek it out, often on sites like this one, and they get exposed to statistical theory in the process.
Incentives for fans are presumably reasonably similar in both college and the NBA. Because opinions about teams are irrelevant at the professional level--the New York Knicks get the same seed in the Eastern Conference no matter what we think of their performance--rating teams tends to be more of an academic or a predictive exercise than it is for our college peers, but at the same time the standardized schedules make it easier to compare players and teams on a level playing field.
One curious distinction between the pro and college games is how much more readily the media has embraced statistical analysis of the NBA. I don't want to overstate this point; you're still far more likely to see teams ranked on the basis of points per game than per-possession ratings, surely. Nonetheless, every major site covering the NBA has at least one person who tends to approach things from an analytical perspective, and ESPN Insider's John Hollinger has no college equivalent in terms of an analyst who has made a career almost entirely out of using the numbers. (Even Sports Illustrated's incomparable Luke Winn, as handy as he is with statistics, comes from a traditional background.)
I'm not sure exactly why the media would have greater incentive to use analytics in the NBA. Perhaps it is simply the fact that at the professional level, the shortcomings of traditional stats are often so obvious that mainstream writers are driven to find something better. I tend to think that's what has happened with ESPN's ubiquitous Bill Simmons, who has proclaimed his newfound affinity for certain statistics like usage rate and peppered his recent column on aging with Win Shares and PER.
As intriguing as I find the discussion of incentives, I don't think it completely explains the rate at which groups embrace statistical analysis. To me, there's a second side of the coin--the extent to which each group is entrenched in its position. This factor allows us to compare how statistical analysis has evolved across sports. After all, by this point it seems obvious to note that the sport with the greatest incentives for using analytics is baseball, yet the process by which sabermetrics became accepted was far slower than the growth of the APBRmetrics community has been in basketball. Defensiveness seems like the obvious explanation for this discrepancy.
Basketball analysts have had it easier for a variety of reasons. I suspect that the NBA is a little more open to change and new ideas by its very nature than baseball, the sport most rooted in tradition. APBRmetrics also benefited from coming of age right at the same time sabermetrics was breaking through. The publication of Moneyball tipped off both sports to the incentives to using statistics, since Michael Lewis' bestseller was read by curious owners around the NBA.
The biggest factor of all, in my opinion, seems a bit counterintuitive at first. To me, the very limitations of statistical analysis in basketball ("the perfect futility of basketball analytics," as Rob Mahoney termed it in a recent piece on the New York Times' Off the Dribble blog) make it more palatable to insiders. With a few exceptions at the extremes, the vast majority of NBA analysts acknowledge not only the need for scouting but its superiority in many areas, primarily at the defensive end of the court. This has limited the culture war between scouts and analysts that briefly broke out in baseball after Moneyball but now seems to have faded into the rear-view mirror.
Statistical analysis isn't for everyone, and we are still years--decades, perhaps--from the day when every NBA team integrates numbers into the decision-making process. Even the GMs who aren't sold, however, tend to be careful with sharing that opinion. Being anti-stats in the NBA no longer seems to hold much cachet.
To the extent that stubbornness tends to slow down the enlightened self-interest that causes various groups to utilize statistical analysis, there is an important implication that applies to both the college and basketball statistical communities. Telling people how foolish they are for ignoring the numbers is a poor way to change their minds. Instead, adding a healthy dose of humility and acknowledging both the weaknesses and the strengths of a statistical approach tends, over time, to have more success.
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Kevin Pelton is an author of Basketball Prospectus.
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