Improving Prediction of Pass Receivable Players in Basketball Simulation-Based Approach with Kinetic Models

Recently, a tool has been developed to reproduce basketball games by visualizing players and ball positions data in 3D space. This tool can also display predictive information of pass receivable players to understand the tactics in the games effectively based on real game data. In this paper, we propose an improved method to predict such pass receivable players considering the characteristics of basketball plays. In this method, kinetic models for ball and player movements are used to simulate a large number of virtual passes with parameters estimated from the players and ball positions data. The pass receivable players are predicted based on the number of receivable virtual passes for each player. For an initial validation, this method was applied to the players and ball positions data included in the APIDIS basketball dataset, and the result was compared with that of a conventional method based on players' adjacency information.