Explanation of the Box Score Prediction System (BSPS)

by Jon Nichols

*UPDATE: Euroleague statistics are also now included in BSPS: For more information, go to:


One of the hardest things to project is a college player’s performance in the NBA.  So many factors come into play, many of which can’t be measured.  However, I have attempted to solve this problem.  Using regression analysis, I have developed the Box Score Prediction System (BSPS). 

The system works on the basis of a player’s statistical performance at the college level.  It also takes into account a player’s height, weight, and NCAA experience.

With BSPS, I can input the various data about a player and it will shoot out the projected NBA numbers.  Each NBA box score statistic is calculated based on some combination of the above variables and different coefficients.  Certain NBA stats, such as rebounds, can be predicted using many different variables.

The adjusted R^2 values (go to http://en.wikipedia.org/wiki/Coefficient_of_determination for an explanation) for the NBA stats I project are as follows:

Points: 0.4557
Field Goal Attempts: 0.4666
Field Goal Percentage: 0.5879
Three Point Attempts: 0.6765
Three Point Percentage: 0.7972
Free Throw Attempts: 0.3874
Free Throw Percentage: 0.8052
Rebounds: 0.8927
Assists: 0.887
Steals: 0.5904
Blocks: 0.9314
Turnovers: 0.5639
Personal Fouls: 0.5629

As you can see, we can project with the most certainty blocks, rebounds, assists, free throw percentage, and three-point percentage.  Predictions regarding free throw attempts, points, field goal attempts, turnovers, and fouls are the most questionable. 

At this time I’m not willing to give out the exact formulas I use, but in the future I will be predicting various college players’ future performances in the NBA using their college stats.

BSPS has the following limitations:

  • Player statistics are not adjusted for strength of schedule.  Just like with any other statistic, you have to keep context in mind when you look at the results.  A guy who has lit up poor competition may project to be better than he actually will do.
  • Most of the R^2 values are not extremely high, which just confirms common sense that a lot more goes into NBA success besides college success.  Athleticism, game IQ, work ethic, etc. all have an effect.
  • The study only includes NBA players that have “made it.”  Players that fizzled out or were never good enough to get drafted in the first place aren’t included.  Therefore, these numbers are more useful when projecting the guys who are likely to have an NBA future.

This system is still a work in progress and tweaks will inevitably be made in the future.  Also, I may include projections of advanced stats such as Composite Score.  If you have any comments or suggestions, please send them to [email protected]





Copyright © 2009 Basketball-Statistics.com