Projecting NBA Player Productivity based on Past Performance v1.0

Posted on 10/12/2010 by


Whereof what’s past is prologue; what to come,
In yours and my discharge. –The Tempest

There’s a very good reason that I spent the past few days posting and diccussing  a model t project rookie performance. I want to use the Wins Produced model to project future player productivity based on past performance. I  want to use this model to forecast team success. I want to rank players, identify future stars and identify teams that have what it takes to  contend and win.

This sound like great fun, but before we get there, I need to do some math (think of this as the show your work portion of the program).


What can I say? I couldn't afford his rates.


So in this post we debut the productivity model for players in the NBA. For newcomers go here for the Basics . Here we’ll talk about players that are already in the NBA (for rookies go here , here & here ). Let’s get to the model building.

Building the Model

This was a complicated build to say the least. I used data for every player who played more than 400 minutes from 1978 on. I am projecting ADJP48 (raw player win production per 48 minutes). I built multiple models and refined them so I would get the highest possible correlation for the last five years. The models are as follows:

  • Model #1: Last year’s ADJP48
  • Model #2: Model 1 plus Age & Position model based on % change
  • Model #3: Model 1 plus Age & Position model based on total change
  • Model #4: Weighted average of last 3 year ADJP48 with a special rule that only looks at the last season for players under 25 years of age.
  • Model #5: Model 4 plus Age & Position model based on % change
  • Model #6: Model 4 plus Age & Position model based on total change

The correlations for these models look as follows:

With model 6 being the clear winner. Now 70 %to 77% correlation doesn’t sound like a lot but a funny thing happens when I account for minutes played:

The model improves with a larger sample size (minutes). So the prediction model is more accurate for the players that will get the most minutes. So we can have some good confidence in the ability of the model to forecast future success. We will of course put the model to the test in this space. As in soon. As in probably tomorrow.


Math is awesome (image courtesy of


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