research-article

How Is the Stroke? Inferring Shot Influence in Badminton Matches via Long Short-term Dependencies

Authors:

Teng-Fong Chan,

Chih-Chuan Wang,

Yao-Chung FanAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 1

Article No.: 7, Pages 1 - 22

https://doi.org/10.1145/3551391

Published: 09 November 2022 Publication History

Abstract

Identifying significant shots in a rally is important for evaluating players’ performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data has remained untouched. In this article, we introduce a badminton language to fully describe the process of the shot, and we propose a deep-learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result. Our model incorporates an attention mechanism to enable the transparency between the action sequence and the rally result, which is essential for badminton experts to gain interpretable predictions. Experimental evaluation based on a real-world dataset demonstrates that our proposed model outperforms the strong baselines. We also conducted case studies to show the ability to enhance players’ decision-making confidence and to provide advanced insights for coaching, which benefits the badminton analysis community and bridges the gap between the field of badminton and computer science.

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Cited By

Rahman MKhaliq LPiper TGeyer HEquey TBaume NAikin RMaass WLarson K(2024)SACNNProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/665(6017-6025)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/665
Wang KWang WHsieh PPeng W(2024)Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian MotionMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_22(348-364)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70381-2_22
Feng YSun H(2023)Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer MethodInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.33436518:1(1-17)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.4018/IJITWE.334365
Show More Cited By

Index Terms

How Is the Stroke? Inferring Shot Influence in Badminton Matches via Long Short-term Dependencies
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 1

February 2023

487 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3570136

Editor:
Huan Liu
Arizona State University, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 09 November 2022

Online AM: 23 September 2022

Accepted: 19 July 2022

Revised: 10 June 2022

Received: 25 October 2021

Published in TIST Volume 14, Issue 1

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Cited By

Rahman MKhaliq LPiper TGeyer HEquey TBaume NAikin RMaass WLarson K(2024)SACNNProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/665(6017-6025)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/665
Wang KWang WHsieh PPeng W(2024)Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian MotionMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_22(348-364)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70381-2_22
Feng YSun H(2023)Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer MethodInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.33436518:1(1-17)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.4018/IJITWE.334365
Zhao Y(2023)Automatic Shuttlecock Motion Recognition Using Deep LearningIEEE Access10.1109/ACCESS.2023.332245511(111281-111291)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3322455

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