Learning Stochastic Models for Basketball Substitutions from Play-by-Play Data

Abstract

Using play-by-play data from all 2014-15 regular season NBA games, we build a generative model that accounts for substitutions of one lineup by another together with the plus/minus rate of each lineup. The substitution model consists of a continuous-time Markov chain with transition rates inferred from data. We compare different linear and nonlinear regression techniques for constructing the lineup plus/minus rate model. We use our model to simulate the NBA playoffs; the test error rate computed in this way is 20%, meaning that we correctly predict the winners of 12 of the 15 playoff series. Finally, we outline several ways in which the model can be improved.

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Cite this paper

@inproceedings{Bhat2015LearningSM, title={Learning Stochastic Models for Basketball Substitutions from Play-by-Play Data}, author={Harish S. Bhat and Li-Hsuan Huang and Sebastian Rodriguez}, booktitle={MLSA@PKDD/ECML}, year={2015} }