A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance.
Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player's performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players' skills to the team's success at running different plays, can be used to automatically learn players' skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players' respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player's interactions with a given lineup based only on his performance with a different lineup.
Fine Theoretical Physics Institute, University of Minnesota, Minneapolis, 55455 MN, United States of America; Massachusetts Institute of Technology, Cambridge, 02139 MA, United States of America.
Brian
B
Skinner
Department of Computer Science, University of North Carolina at Chapel Hill, 27599 NC, United States of America; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, 55455 MN, United States of America.
Stephen J
SJ
Guy
D000328:Adult; D054874:Athletic Performance / Q000706:statistics & numerical data*; D001490:Basketball / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005544:Forecasting; D006126:Group Processes; D006801:Humans; D008297:Male; D009048:Motor Skills; D016571:Neural Networks, Computer; D011361:Professional Competence; D055815:Young Adult