Two years ago, the now-retired Shane Battier was named by The Sporting News as the 7th smartest athlete in American pro sports. In his blurb, Battier cites “sabermetrics” as one of his off-court interests. You can imagine Battier spending his free time poring over spreadsheets while his teammates play video games or lift weights. It doesn’t get much brainier than that.
Advanced analytics are called by many different names: sabermetrics, big data, nerd stats, fancy stats, etc. They’re all labels for the same basic idea. It’s an idea that was famously profiled in books like Moneyball and The Extra 2%, both of which focus on baseball — the veritable primordial soup of advanced metrics. Bill James, who is among the pioneers of modern sports analytics, first coined the term “sabermetrics” and defined it as “the search for objective knowledge about baseball.”
As with any innovation that begets a considerable competitive advantage, the underlying idea of sabermetrics — the replacement of biased speculation with impenetrable knowledge — has been translated across sports. The National Basketball Association, for instance, rather recently entered what Battier calls in today’s featured Big Think interview “a golden age of analytics.”:
Some of the biggest (and often unfounded) criticisms of advanced stats is that they seek to quantify elements of competition that are intangible. As Battier explains in the video, most actions in basketball indeed can be quantified and the only thing advanced stats seek to offer is a detailed, empirical glimpse into the nuances of the game:
“Before I really learned analytics, I tried to guard a guy, Kobe Bryant, who in my estimation was the toughest competitor that I ever played against. And all I had to rely on was the old eyeball test scouting report. Kobe’s got a really good right hand. You have to keep him out of the painted area. He’s a great finisher. So yeah, any Joe Schmo fan could tell you those things. But after studying and going through the school of analytics, I knew exactly to a tee who Kobe Bryant was. And I knew as a defender trying to stop him Kobe’s worst-case scenario and my best-case scenario was to make him shoot a pull up jumper going to his left hand, all right.”
Basketball analytics are similar to any other kind of metrics that draw upon large swaths of data. Google Analytics, for example, draws conclusions from an analysis of every hit a website gets. With such a broad sample size, the natural and erratic statistical noise that occurs frequently in small samples is erased. Battier expounds on the above by detailing an example of how data-infused analytics allowed him to exploit a weakness in future Hall-of-Famer Kobe Bryant’s game:
“The average possession of the Los Angeles Lakers in 2008 generated 0.98 points per possession, 0.98. So you took the average possession of the Lakers. They were going to score 0.98 points every time they had a possession. And so Kobe Bryant only shot the left-handed pull up jumper at a 44 percent clip. So every time that he went left and shot that pull-up jumper he was generating 0.88 points per possession.
Well that’s a tenth of a point less than the average Laker possession. And so if I could make him do that time and time again which is a lot tougher to do than to say, I’m shaving off a tenth of a point every single time.”
Battier is such a great TV analyst because he possesses a great gift for explaining somewhat complicated concepts in general terms. Here he offers an analysis of how the objective conclusions produced via advanced metrics can influence defensive strategy. And while one-tenth of a point doesn’t sound like much, it all adds up very quickly when you account for the exploitation the strengths and weaknesses of a dozen or so players per team per game:
“In the NBA, as we all know, the margin between wins and loses is very, very thin. So those tenths of points matter. And that’s all it really is. It’s no different than playing the stock market. You’re trying to shave percentage points off your risk. And if you can accumulate enough, guess what? You’re going to do very well.”
It’s interesting that Battier compares advanced stats to playing the stock market because the recent trend in NBA, MLB, and National Hockey League front offices is to bring in new executives with experience in fields such as economics, finance, and business. In Major League Baseball, there are more Ivy Leaguers in general manager positions than former players. Professional experience and scouting aren’t necessarily being devalued. Instead they’re being merged with an understanding of the mathematical intricacies behind performance, value, and statistical evaluation.
For Battier, the shift into the golden age of analytics helped make him a better defender and guided him through a fruitful 13-year career. Sure, they didn’t always help him keep Bryant off the scoreboard, but in a game measured in inches and seconds, every bit of help is appreciated.