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Using network science to unveil badminton performance patterns

Author

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  • Gómez, Miguel–Ángel
  • Rivas, Fernando
  • Leicht, Anthony S.
  • Buldú, Javier M.

Abstract

Bipartite networks are related to non-linear and ecological approaches where, at least, two different kinds of entities are considered. In sports, we can consider players as entities that base their decisions (actions and reactions) on opponents and their own actions. Incorporation of bipartite networks into modelling of racket sport performances may bridge the gap between the performance analysis sub-discipline and coaches for greater preparation of training sessions and competitions for enhanced success. Thus, the main aim of this study was to create badminton stroke networks (BSN), from the match activities of a player and their opponents, to describe and quantify the performance of elite Olympic badminton players. The use of a Network Science approach required the development of a series of methodologies that accounted for strokes played by all medallists within an Olympic tournament and included: (i) the construction of BSN; (ii) the one-mode projections of bipartite networks (self- and opponent- networks); (iii) the centrality of one-mode projections; and (iv) the identifiability of badminton players. The BSN identified different playing patterns for medallists with the Silver medallist categorised with the less predictable and defined style of play, the Bronze medallist exhibiting the most defined style; and the Gold medallist exhibiting the greatest predictability, but only when losing points (self-networks). The use of Network Science enabled the identification of distinctive styles of play (self- and opponent–related), based on stroke performance, during successful and unsuccessful points within an Olympic tournament. Specifically, the identifiability of each player's network and its associations with point outcome, provided a better understanding of stroke performances and individual features of world-class badminton players. The use of non-linear approaches (such as bipartite networks) to measure and visualize player's performances, accounting for the specific nature of badminton and opponents, may support coaches and players with the contextualized demands of playing patterns and their performances (i.e., winning and losing points) for future success.

Suggested Citation

  • Gómez, Miguel–Ángel & Rivas, Fernando & Leicht, Anthony S. & Buldú, Javier M., 2020. "Using network science to unveil badminton performance patterns," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:chsofr:v:135:y:2020:i:c:s0960077920302344
    DOI: 10.1016/j.chaos.2020.109834
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    References listed on IDEAS

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    Cited by:

    1. Song, Honglin & Li, Yutao & Fu, Chenyi & Xue, Feng & Zhao, Qiyue & Zheng, Xingyu & Jiang, Kunkun & Liu, Tianbiao, 2024. "Using complex networks and multiple artificial intelligence algorithms for table tennis match action recognition and technical-tactical analysis," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    2. Galeano, Javier & Gómez, Miguel-Ángel & Rivas, Fernando & Buldú, Javier M., 2022. "Using Markov chains to identify player’s performance in badminton," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    3. Zhou, Yunjing & Zong, Shouxin & Cao, Run & Gómez, Miguel-Ángel & Chen, Chuqi & Cui, Yixiong, 2023. "Using network science to analyze tennis stroke patterns," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

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