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

• URL: http://arxiv.org/abs/2303.09715v1
• Date: Fri, 17 Mar 2023 01:13:18 GMT
• Title: Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling
• Authors: Alejandro Rodriguez Pascual, Ishan Mehta, Muhammad Khan, Frank Rodriz and Rose Yu
• Abstract summary: Basketball is dictated by many variables, such as playstyle and game dynamics. It is crucial that the performance profiles can reflect the diverse playstyles. We present a tool that can visualize player performance profiles in a timely manner.
• Score: 70.54015529131325
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Understanding player shooting profiles is an essential part of basketball analysis: knowing where certain opposing players like to shoot from can help coaches neutralize offensive gameplans from their opponents; understanding where their players are most comfortable can lead them to developing more effective offensive strategies. An automatic tool that can provide these performance profiles in a timely manner can become invaluable for coaches to maximize both the effectiveness of their game plan as well as the time dedicated to practice and other related activities. Additionally, basketball is dictated by many variables, such as playstyle and game dynamics, that can change the flow of the game and, by extension, player performance profiles. It is crucial that the performance profiles can reflect the diverse playstyles, as well as the fast-changing dynamics of the game. 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. Our approach generates interpretable heatmaps that allow us to identify and analyze how non-spatial factors, such as game dynamics or playstyle, affect player performance profiles.

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