Abstract:
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For professional basketball, finding valuable and suitable players is the key to build a winning team, but this is never an easy task. Objective evaluation of players and teams has always been the top goal of basketball analytics. Traditional statistical analytics mainly focuses on the individual box score and has developed various metrics. However, such metrics provide limited information about how players interact with each other. Two players with similar box scores may deliver distinct team plays. In this talk, 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 play styles and performances. Not only can the model evaluate how good players from different clusters are at scoring, rebounding, stealing, etc, but it also captures the interaction patterns between players within and cross clusters. 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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