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

Page Garritt L. (), Barney Bradley J. and McGuire Aaron T.
Additional contact information
Page Garritt L.: Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile
Barney Bradley J.: Kennesaw State University, Department of Mathematics and Statistics, Kennesaw, GA, USA
McGuire Aaron T.: Capital One, VA, USA

Journal of Quantitative Analysis in Sports, 2013, vol. 9, issue 4, 337-345

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.

Keywords: Bayesian hierarchical model; Career trajectory curves; Gaussian process regression (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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DOI: 10.1515/jqas-2012-0023

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