NBA Analytics Is About People, People!
The Porziņģis story, and the rewards of working with a star receptive to basketball numbers
So much of basketball analytics has been about measuring how good players are. So little of what has been public basketball analytics has been about making players better.
The last two pieces I’ve done here have been about that latter component — identifying ways to make players better. In particular, this piece on the Pistons’ talented young backcourt and this one on Austin Reaves identify specific statistical goals for each player. With these targets in mind, we can paint a picture around the kind of development that maximizes who the players will be in the future.
These goals can provide guidance for coaches and players, regardless of what drills they run.
I have been fortunate to be around NBA coaches and players for years. Thursday, some of the work that I did with Kristaps Porziņģis in Washington was brought up again in this ESPN story by Ramona Shelburne.
Our work together was even more detailed than my pieces at 🏀 5x5, naturally, but the work was guided by the same kind of analysis. Which can be summed up this way: What are the key things that would make the player better?
This piece by Dean Oliver — author of Basketball on Paper and the upcoming Basketball Beyond Paper — is part of our series on making NBA analytics easy.
Most importantly, Porziņģis was actively interested in how analytics worked and how it could help him. He wanted the long-term value of what we were doing expressed to him — the shot quality if he posted up, or the value of putting the ball on the floor in a drive. He wanted data from me to support what he was working on. I wasn’t going to make up statistics to support some preconceived notion, and he trusted that.
Trust is a big thing in developing players. Many of the coaches that I worked with were particularly good at building trust in their own way. That built the enthusiasm of the players to follow through on that trust by working hard and listening. So that enthusiasm fed back to building more trust.
Trust and enthusiasm represent the kind of less-analytical concepts that can and should work with numbers to guide a team. Blending those together is how you go from “on paper, this player should be better” to “on paper and on the court, this player is clearly better.”
Modern basketball analytics isn’t just about measuring how good players are. It has a lot more tools at its disposal, enough to work seamlessly at the coaching level. (And that is part of what is going into my book that will be out in the fall.)
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