Open Access
June 2017 A continuous-time stochastic block model for basketball networks
Lu Xin, Mu Zhu, Hugh Chipman
Ann. Appl. Stat. 11(2): 553-597 (June 2017). DOI: 10.1214/16-AOAS993


For professional basketball, finding valuable and suitable players is the key to building a winning team. To deal with such challenges, basketball managers, scouts and coaches are increasingly turning to analytics. Objective evaluation of players and teams has always been the top goal of basketball analytics. Typical statistical analytics mainly focuses on the box score and has developed various metrics. In spite of the more and more advanced methods, metrics built upon box score statistics provide limited information about how players interact with each other. Two players with similar box scores may deliver distinct team plays. Thus professional basketball scouts have to watch real games to evaluate players. Live scouting is effective, but suffers from inefficiency and subjectivity. In this paper, we go beyond the static box score and model basketball games as dynamic networks. The proposed continuous-time stochastic block model clusters the players according to their playing style and performance. The model provides cluster-specific estimates of the effectiveness of players at scoring, rebounding, stealing, etc., and also captures player interaction patterns within and between clusters. By clustering similar players together, the model can help basketball scouts to narrow down the search space. Moreover, the model is able to reveal the subtle differences in the offensive strategies of different teams. An application to NBA basketball games illustrates the performance of the model.


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Lu Xin. Mu Zhu. Hugh Chipman. "A continuous-time stochastic block model for basketball networks." Ann. Appl. Stat. 11 (2) 553 - 597, June 2017.


Received: 1 June 2015; Revised: 1 July 2016; Published: June 2017
First available in Project Euclid: 20 July 2017

zbMATH: 06775884
MathSciNet: MR3693538
Digital Object Identifier: 10.1214/16-AOAS993

Keywords: basketball analytics , clustering , EM algorithm , Gibbs sampling , Markov chain , Social network , transactional network

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.11 • No. 2 • June 2017
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