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Effect of position, usage rate, and per game minutes played on NBA player production curves

  • Garritt L. Page EMAIL logo , Bradley J. Barney and Aaron T. McGuire
Published/Copyright: October 14, 2013

Abstract

In this paper, we model a basketball player’s on-court production as a function of the percentiles corresponding to the number of games played. A player’s production curve is flexibly estimated using Gaussian process regression. The hierarchical structure of the model allows us to borrow strength across players who play the same position and have similar usage rates and play a similar number of minutes per game. From the results of the modeling, we discuss questions regarding the relative deterioration of production for each of the different player positions. Learning how minutes played and usage rate affect a player’s career production curve should prove to be useful to NBA decision makers.


Corresponding author: Garritt L. Page, Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile, e-mail:

The authors would like to thank the editor, associate editor, and anonymous referees for comments that improved the manuscript. The first author’s work was partially funded by grant FONDECYT 11121131

Appendix A

MCMC iterate history plots

We provide a few arbitrarily selected history plots of MCMC iterates (see Figures 6 and 7). The first set corresponds to the first entry of μ(·)j for the twelve groups, and the second set corresponds to ρj for the twelve groups. The general indication these plots provide is that the MC chains have reached their equilibrium states.

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Published Online: 2013-10-14
Published in Print: 2013-12-01

©2013 by Walter de Gruyter Berlin Boston

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