Understanding why shooters shoot - An AI-powered engine for basketball performance profiling

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Alejandro Rodriguez Pascal, Ishan Mehta, Muhammad Khan, Rose Yu, Frank Rodriz


In professional basketball, it is crucial for the coaching staff of a team to analyze an opposing team and develop an effective strategy. Understanding player shooting profiles is an essential part of this analysis: knowing where certain opposing players like to shoot from can help coaches neutralize offensive gameplans from their opponents, while understanding where their players are most comfortable can lead them to developing more effective offensive strategies. We present a tool that can visualize player performance profiles in a timely manner while taking into account factors such as play-style and game dynamics, generating interpretable heatmaps that allow us to identify and analyze how these non-spatial factors affect the performance profiles. Our methods provide an effective and efficient tool that can provide insight into how certain players and teams play, without requiring the time-consuming process of reviewing hours of film, and could potentially be applied to other sports with adaptations.