HoopTransformer: Advancing NBA Offensive Play Recognition with Self-Supervised Learning from Player Trajectories.
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 intelli...
| Publicado en: | Sports Medicine Vol. 54; no. 10; pp. 2663 - 2674 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180214537&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180214537 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01121642 C5H jtl: Sports Medicine issn: 01121642 maglogo: N pubinfo: dt: Oct2024 vid: 54 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 180214537 177551408 180214537 180214537 10.1007/s40279-024-02030-3 180214537 ppf: 2663 ppct: 11 formats: tig: atl: HoopTransformer: Advancing NBA Offensive Play Recognition with Self-Supervised Learning from Player Trajectories. aug: au: Wang, Xing Tang, Zitian Shao, Jianchong Robertson, Sam Gómez, Miguel-Ángel Zhang, Shaoliang affil: https://ror.org/03n6nwv02 Facultad de Ciencias de la Actividad Física y del Deporte, Universidad Politécnica de Madrid, Madrid, Spain sug: subj: Athletic Performance Evaluation Artificial Intelligence Support Vector Machine Basketball Physiology Task Performance and Analysis Human Machine Learning Descriptive Statistics Biomechanics Software Design Attention Computer Simulation Videorecording ab: 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. Methods: The dataset was collected by SportVU from 632 games during the 2015–2016 season of the National Basketball Association (NBA), with a total of 90,524 possessions. A multi-agent motion prediction pretraining model was built on the basis of axial-attention transformer and trained with different masking strategies: motion prediction (MP), motion reconstruction (MR), and MP + MR joint strategy. A downstream play-level classification task and similarity search were used to evaluate the models' performance. Results: The results showed that the MP + MR joint masking strategy maximized the ability of the model compared with individual masking strategies. For the classification task, the joint strategy achieved a top-1 accuracy of 81.5% and top-3 accuracy of 97.5%. In the similarity search evaluation, the joint strategy attained a top-5 accuracy of 76% and top-10 accuracy of 59%. Additionally, with the same MP + MR joint masking strategy, our HoopTransformer model outperformed the two baseline models in the classification task and similarity search. Conclusion: This study presents a self-supervised learning model and demonstrates the effectiveness and potential of the model in accurately comprehending and capturing player movements and complex interactions during offensive plays. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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