The Annals of Applied Statistics

A continuous-time stochastic block model for basketball networks

Lu Xin, Mu Zhu, and Hugh Chipman

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Abstract

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.

Article information

Source
Ann. Appl. Stat., Volume 11, Number 2 (2017), 553-597.

Dates
Received: June 2015
Revised: July 2016
First available in Project Euclid: 20 July 2017

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1500537715

Digital Object Identifier
doi:10.1214/16-AOAS993

Mathematical Reviews number (MathSciNet)
MR3693538

Zentralblatt MATH identifier
06775884

Keywords
Clustering transactional network Markov chain EM algorithm Gibbs sampling basketball analytics social network

Citation

Xin, Lu; Zhu, Mu; Chipman, Hugh. A continuous-time stochastic block model for basketball networks. Ann. Appl. Stat. 11 (2017), no. 2, 553--597. doi:10.1214/16-AOAS993. https://projecteuclid.org/euclid.aoas/1500537715


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