HoopTransformer: Advancing NBA Offensive Play Recognition with Self-Supervised Learning from Player Trajectories.

Journal: Sports medicine (Auckland, N.Z.)
PMID: 38814566

Abstract

BACKGROUND AND OBJECTIVE: Understanding and recognizing basketball offensive set plays, which involve intricate interactions between players, have always been regarded as challenging tasks for untrained humans, not to mention machines. In this study, our objective is to propose an artificial intelligence model that can automatically recognize offensive plays using a novel self-supervised learning approach.

Authors

  • Xing Wang
    Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.
  • Zitian Tang
    Athletic Performance and Data Science Laboratory, Division of Sports Science and Physical Education, Tsinghua University, Beijing, China.
  • Jianchong Shao
    Athletic Performance and Data Science Laboratory, Division of Sports Science and Physical Education, Tsinghua University, Beijing, China.
  • Sam Robertson
    Institute for Health & Sport, Victoria University, Melbourne, VIC, Australia.
  • Miguel-Ángel Gómez
    Facultad de Ciencias de la Actividad Física y del Deporte, Universidad Politécnica de Madrid, Madrid, Spain.
  • Shaoliang Zhang
    Athletic Performance and Data Science Laboratory, Division of Sports Science and Physical Education, Tsinghua University, Beijing, China.

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