Shane Battier is not as into advanced statistical data as previously thought

Eric Freeman
Ball Don't Lie

No basketball player has been more closely identified with the advanced statistics than veteran forward Shane Battier. Due in large part to a February 2009 New York Times Magazine profile by superstar writer Michael Lewis, Battier has typically been seen as the rare player willing to put aside conventional basketball wisdom and accept various lesson available via statistical study. He appears to know more about random variance, numerical data on player trends, and assorted other ideas than his peers. Statistically minded fans can be forgiven for assuming Battier is on their side.

It's something of a surprise, then, to learn that Battier is not quite so optimistic about the potential effects of this data as many analysts. In an interview with Couper Moorhead for, he explains his approach to the newest forms of data, including the extra-fancy SportVU tracking system:

How do you feel about SportVu now that every team has it? Has anyone talked about it among the players?

No. I don’t think guys would understand what it is. It puts a premium on computer programmers. It’s a premium on guys, engineers, computer scientists, guys who can evaluate data and make it adjustable for scouts and make it adjustable for directors of personnel. It’ll be fascinating. I still think it’ll be a while before you understand what makes Tony Parker different from John Wall in the open court. You’ll have the data, or closeout speed or thing that you measure . . . it’s really infinite, the things that you can measure. It will take a while to trickle down to how players learn the game. Guys have a pretty good understanding of what’s a good shot and what’s a bad shot now, but no one is teaching that in youth basketball. They’re still teaching the same old. It’s going to be a while until that trickles down to the grass roots level and a coach understands this is what you need to do to make it to the next level.

Do you think at this point it’ll be important for players to educate themselves on the new stuff? To understand how the league perceives them.

It’s an edge and players always look for an edge. Be it they work a little harder in the weight room to get a little stronger, whether they take 100 extra jumpers a day to get an edge on their jump shot… It’s just another edge, another way to get ahead of the competition. But obviously you can make more money the more edges you have.

It’ll take time for someone to take the data and make it digestible for players to understand, ‘OK, this is what I really need to work on.’ The game is not changing. It doesn’t change the way it’s understood, described and analyzed. The game is still going to be the same, it’s just going to be a different nuance.

Is there anything that you want to know about yourself that the new data could tell you?

Nope. I think it will be awesome when I retire, whenever that is, when I step away, to look at the numbers and see how I ranked, but I’m psycho enough to where that will cloud the way I play. That makes it less instinctual, to be honest with you. I rationally understand what’s good for me, obviously the threes, the paint shots, and I stay away from corner twos like they’re the plague, but I don’t want to know anything else.

I don’t want to know. I don’t want to know my weaknesses. I mean, I know what my weaknesses are, obviously, but I want to be as instinctual as possible, while still keeping the rational edge that keeps me a player in this league.

What Battier is describing, effectively, is an important distinction between the way that the data says the game should be played and the way that players have learned how to play the game effectively over years in the sport. As he notes in his answer to the third question in this excerpt, getting too deep into the data will make it impossible for him to play the game on the instinctual level necessary for success at this level. He needs very general instructions, not minute facts about the best way to play.

Battier is self-aware enough to understand this point, which is why he rightfully notes that these gains mean more to analysts than to players and coaches. The key here, as with every bit of potentially revolutionary information, is to find a way to communicate it in a way that doesn't shock people or confuse the logically correct answer with the one that works best for all involved parties.

At the moment, the most crucial step for statistical analysts is admitting that producing and interpreting the data for themselves is only one part of the solution. Logic is rarely enough to convince a wary party of the worthiness of a point.