Ever since Wilt Chamberlain’s dominant career yielded only two championships, certain NBA players have been criticized and even demonized by the perception that they care only for their own statistics instead of making their teammates better and winning. Some hoops historians have argued that these players focused on individual accomplishments like points, assists or rebounds, instead of doing “the little things” that help teams win, whatever those are. This narrative is supported by today’s advanced statistic gurus who point out how other metrics like TS% and PER better represent a player’s impact. The consensus seems to be that great production in traditional statistics doesn’t necessarily yield success, but what if a player was solely motivated by advanced statistics?
Permit me a personal anecdote:
Last weekend, in a cold, unevenly lit gym full of unevenly skilled hoopers, a confluence of terrible opposition and random luck allowed me to score 18 points on only 5 FG attempts (I padded the stats with some late freethrows). But what was really odd was that I found myself consciously factoring advanced statistical measurements into my play.
(Important note: my four made shots came between committing a number of basketball atrocities: throwing passes over teammates’ heads, dribbling off of knees (mine and my opponents), and allowing some unconscionable blow-bys to a 5-11 230lb Bobby Moynihan impersonator. Suffice to say, it was not a perfect game by any standard.)
Like any neverwas, knocking down my first couple three pointers usually turns on the “shoot like you’ve got NBA Jam On Fire power” switch in my delusional brain. But instead of jacking up my next five touches like I was J.R. Smith on amphetamines, I only attempted one shot, a breakaway layup, the rest of the half.
Why? Because Henry Abbott’s contrarian posts on whether “being hot” exists were running laps in my cerebellum: “Your next shot is statistically likely to be a bad one, don’t succumb to your inefficient urges!”
In this limited example, my understanding of research-proven trends materially altered play. Instead of launching away, I clumsily shot-faked in hopes of penetrating the zone and picked up a couple assists. In doing so, I finished with an unexpected TS% of 211%, but had my desire to preserve my advanced statistical superiority been a detriment to my team’s chances? Should I have been greedier and risked shooting 6 for 10 if I was in a good rhythm? Was it selfish of me to do what I did to avoid playing selfishly? Or maybe this was an example of the best of economic imperatives: I was selfishly motivated to play unselfishly.
Anyways, this moment of dubious glory in Bethesda, MD got me thinking about the role that advanced statistics could play in the future of NBA business and playing practices.
Dave J. Berri makes the argument that the amount of points a player scores is the best predictor of how much a player will be paid and whether he will be selected for an All-Star team, but that a great scorer does not a winner make. Thus, players are motivated to shoot and score, even if at a low percentage, by millions of dollars. Financially shrewd players appropriately view their careers as a business, invest in the correct skills and development and hope to make as much money as they can, just like every other working stiff. In the present, that means doing things to get on the court (which often involves adding value in ways other than scoring), then scoring as much as possible once given the chance.
But the NBA talent market is changing. Ten years ago, it’s hard to imagine the Bulls hesitating to trade Joakim Noah and anyone other than Derrick Rose in exchange for the exquisite scoring talents of Carmelo Anthony. I don’t have intimate knowledge of why the trade didn’t go down, but I know there was significant outcry among writers and commentators, even a little squawking on this blog, aimed at proving why Carmelo Anthony’s rare ability to put the ball in the bucket was not worth Noah’s rebounds, hustle and toughness.
It’s hard to speculate on the tangible effect that advanced statistics will have on NBA players, but I would suggest that any widespread changes in player evaluation standards will be paralleled by changes in player compensation practices—and that players will alter their play to be more marketable, better paid and more successful. By following their economic incentives, by selfishly seeking their advanced stats, players might also end up playing better, more efficient basketball. For fans, this is all good.
I don’t mean to say that players are motivated only by money and don’t care about winning, that organizations solely care about the bottom line, or that a league-wide embrace of advanced statistics will revolutionize the game. Just that while I was playing, I heard a bunch of writers who will never see me play chirping about efficiency and playing style. Imagine if it had been an extra $5million on my mind.
Addendum: It should be noted that all teams use statistics to greater or lesser effect, and that many coaches do a great job of informing players of their opponents FG% in different areas of the floor to make defenses more efficient. The initial idea for this post was to illustrate how knowledge of statistical data, like that hitting a few shots in a row made me more likely to take a bad one, could positively impact play.