IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v191y2025ics0960077924014450.html
   My bibliography  Save this article

Multilayer network framework and metrics for table tennis analysis: Integrating network science, entropy, and machine learning

Author

Listed:
  • Song, Honglin
  • Li, Yutao
  • Pan, Pengyu
  • Yuan, Bo
  • Liu, Tianbiao

Abstract

This study introduces two novel metrics within table tennis technical-tactical networks: technical decision-making style (TDS) and connecting technical style (CTS), inspired by the entropy concept, to quantify players' technical-tactical styles. This study proposes a multilayer technical-tactical network framework to capture interactive information and technique-to-technique confrontations between players. Additionally, we develop four new metrics—absorbing rate, releasing rate, stalemate rate, and usage rate—based on three states within table tennis matches: scoring, losing, and stalemate, to analyze inter-links within these networks. The champion table tennis player, who won gold medals in both the 2016 Rio Olympics and the 2020 Tokyo Olympics, and his opponents were analyzed in 5054 technical actions during these events. Metrics such as TDS, CTS, out-degree centrality (ODC), and in-degree centrality (IDC) within the networks were calculated. We also created datasets for TDS, CTS, ODC, and IDC and employed six machine learning algorithms for modeling. The results indicate that nodes utilizing TDS and CTS demonstrate superior predictive accuracy for game outcomes compared to those using ODC and IDC. SHAP analysis revealed the feature importance in the best-performing models for TDS and CTS, revealing non-linear relationships between the TDS and CTS values of each key node and game outcomes. The analysis of the multilayer network offers insights into the dynamic interactions between the champion player and his opponents, enhancing our understanding of the key factors influencing match victories. By integrating network science, entropy, and machine learning, this study presents a comprehensive framework and practical metrics for match analysis, with potential implications in performance analyses of other racket sports.

Suggested Citation

  • Song, Honglin & Li, Yutao & Pan, Pengyu & Yuan, Bo & Liu, Tianbiao, 2025. "Multilayer network framework and metrics for table tennis analysis: Integrating network science, entropy, and machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:chsofr:v:191:y:2025:i:c:s0960077924014450
    DOI: 10.1016/j.chaos.2024.115893
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924014450
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.115893?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pereira, Luis Ramada & Lopes, Rui J. & Louçã, Jorge & Araújo, Duarte & Ramos, João, 2021. "The soccer game, bit by bit: An information-theoretic analysis," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    2. Novillo, Álvaro & Gong, Bingnan & Martínez, Johann H. & Resta, Ricardo & del Campo, Roberto López & Buldú, Javier M., 2024. "A multilayer network framework for soccer analysis," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    3. Tal Neiman & Yonatan Loewenstein, 2011. "Reinforcement learning in professional basketball players," Nature Communications, Nature, vol. 2(1), pages 1-8, September.
    4. Ichinose, Genki & Tsuchiya, Tomohiro & Watanabe, Shunsuke, 2021. "Robustness of football passing networks against continuous node and link removals," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    5. 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).
    6. Wu, Yao & Xia, Zeyu & Wu, Tian & Yi, Qing & Yu, Runyu & Wang, Jun, 2020. "Characteristics and optimization of core local network: Big data analysis of football matches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    7. Zhe Wang & Petar Veličković & Daniel Hennes & Nenad Tomašev & Laurel Prince & Michael Kaisers & Yoram Bachrach & Romuald Elie & Li Kevin Wenliang & Federico Piccinini & William Spearman & Ian Graham &, 2024. "TacticAI: an AI assistant for football tactics," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    8. Beheshtian-Ardakani, Arash & Salehi, Mostafa & Sharma, Rajesh, 2023. "CMPN: Modeling and analysis of soccer teams using Complex Multiplex Passing Network," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    9. Tal Neiman & Yonatan Loewenstein, 2011. "Reinforcement learning in professional basketball players," Discussion Paper Series dp593, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    10. Jie Wang, 2019. "Comparison of table tennis serve and return characteristics in the London and the Rio Olympics," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(5), pages 683-697, September.
    11. Gong, Bingnan & Zhou, Changjing & Gómez, Miguel-Ángel & Buldú, J.M., 2023. "Identifiability of Chinese football teams: A complex networks approach," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    12. 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).
    13. Marta Hurst & Manuel Loureiro & Beatriz Valongo & Lorenzo Laporta & T. Pantelis Nikolaidis & José Afonso, 2016. "Systemic Mapping of High-Level Women’s Volleyball using Social Network Analysis: The Case of Serve (K0), Side-out (KI), Side-out Transition (KII) and Transition (KIII)," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(2), pages 695-710, August.
    14. Alessandro Chessa & Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale & Alfonso Gebbia, 2023. "Complex networks for community detection of basketball players," Annals of Operations Research, Springer, vol. 325(1), pages 363-389, June.
    15. 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).
    16. 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).
    17. Joaquín González-Rodenas & Jordi Ferrandis & Víctor Moreno-Pérez & Roberto López-Del Campo & Ricardo Resta & Juan Del Coso, 2023. "Differences in playing style and technical performance according to the team ranking in the Spanish football LaLiga. A thirteen seasons study," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-15, October.
    18. L. Laporta & J. Afonso & I. Mesquita, 2018. "The need for weighting indirect connections between game variables: Social Network Analysis and eigenvector centrality applied to high-level men’s volleyball," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(6), pages 1067-1077, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Chacoma, Andrés & Billoni, Orlando V., 2023. "Probabilistic model for Padel games dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Beheshtian-Ardakani, Arash & Salehi, Mostafa & Sharma, Rajesh, 2023. "CMPN: Modeling and analysis of soccer teams using Complex Multiplex Passing Network," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    4. Joshua B. Miller & Adam Sanjurjo, 2015. "Is it a Fallacy to Believe in the Hot Hand in the NBA Three-Point Contest?," Working Papers 548, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    5. Hanan Shteingart & Tal Neiman & Yonatan Loewenstein, 2012. "The Role of First Impression in Operant Learning," Discussion Paper Series dp626, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    6. Ofri Raviv & Merav Ahissar & Yonatan Loewenstein, 2012. "How Recent History Affects Perception: The Normative Approach and Its Heuristic Approximation," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-10, October.
    7. Joshua B. Miller & Adam Sanjurjo, 2014. "A Cold Shower for the Hot Hand Fallacy," Working Papers 518, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    8. Tal Neiman & Yonatan Loewenstein, 2014. "Spatial Generalization in Operant Learning: Lessons from Professional Basketball," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-8, May.
    9. Gerard Derosiere & Solaiman Shokur & Pierre Vassiliadis, 2025. "Reward signals in the motor cortex: from biology to neurotechnology," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    10. Aloys Prinz, 2019. "Learning (Not) to Evade Taxes," Games, MDPI, vol. 10(4), pages 1-18, September.
    11. Brian Skinner, 2012. "The Problem of Shot Selection in Basketball," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-8, January.
    12. Miller, Joshua B. & Sanjurjo, Adam, 2021. "Is it a fallacy to believe in the hot hand in the NBA three-point contest?," European Economic Review, Elsevier, vol. 138(C).
    13. Ofri Raviv & Merav Ahissar & Yonatan Loewenstein, 2012. "How recent history affects perception: the normative approach and its heuristic approximation," Discussion Paper Series dp628, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    14. Miller, Joshua Benjamin & Sanjurjo, Adam, 2018. "A Visible (Hot) Hand? Expert Players Bet on the Hot Hand and Win," OSF Preprints sd32u, Center for Open Science.
    15. Miller, Joshua Benjamin & Sanjurjo, Adam, 2018. "Is it a Fallacy to Believe in the Hot Hand in the NBA Three-Point Contest?," OSF Preprints dmksp, Center for Open Science.
    16. Tal Neiman & Yonatan Loewenstein, 2014. "Spatial Generalization in Operant Learning: Lessons from Professional Basketball," Discussion Paper Series dp665, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    17. repec:osf:osfxxx:dmksp_v1 is not listed on IDEAS
    18. Gianluigi Mongillo & Hanan Shteingart & Yonatan Loewenstein, 2014. "The Misbehavior of Reinforcement Learning," Discussion Paper Series dp661, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    19. Hanan Shteingart & Yonatan Loewenstein, 2014. "Reinforcement Learning and Human Behavior," Discussion Paper Series dp656, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    20. Ballı, Serkan & Özdemir, Engin, 2021. "A novel method for prediction of EuroLeague game results using hybrid feature extraction and machine learning techniques," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    21. PURCAREA, Theodor Valentin, 2024. "Perceiving with Great Lucidity the Changing Times, Staying Informed and Responsible Managing AI’s Risk While Enabling Innovation," Romanian Distribution Committee Magazine, Romanian Distribution Committee, vol. 15(1), pages 10-15, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:191:y:2025:i:c:s0960077924014450. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.