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.
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