IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n5743825.html

Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks

Author

Listed:
  • Zhongbo Bai
  • Xiaomei Bai

Abstract

With the rapid growth of information technology and sports, a large amount of sports social network data has emerged. Sports social network data contains rich entity information about athletes, coaches, sports teams, football, basketball, and other sports. Understanding the interaction among these entities is meaningful and challenging. To this end, we first introduce the background of sports social networks. Secondly, we review and categorize the recent research efforts in sports social networks and sports social network analysis based on passing networks, from the centrality and its variants to entropy, and several other metrics. Thirdly, we present and compare different sports social network models that have been used for sports social network analysis, modeling, and prediction. Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif‐based graph networks, finding the best collaborative partners in a sports team with attention‐aware graph networks, and finding the rising star for a sports team with attribute‐based convolutional neural networks. This paper aims to provide the researchers with a broader understanding of the sports social networks, especially valuable as a concise introduction for budding researchers interested in this field.

Suggested Citation

  • Zhongbo Bai & Xiaomei Bai, 2022. "Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:5743825
    DOI: 10.1155/2022/5743825
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/5743825
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/5743825?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
    ---><---

    References listed on IDEAS

    as
    1. Zhongbo Bai & Xiaomei Bai & chuan lin, 2021. "Sports Big Data: Management, Analysis, Applications, and Challenges," Complexity, Hindawi, vol. 2021, pages 1-11, January.
    2. Bruno Gonçalves & Diogo Coutinho & Sara Santos & Carlos Lago-Penas & Sergio Jiménez & Jaime Sampaio, 2017. "Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-13, January.
    3. Taige Zhao & Ningning Cui & Yunliang Chen & Man Li, 2020. "Efficient Strategy Mining for Football Social Network," Complexity, Hindawi, vol. 2020, pages 1-11, September.
    4. Ana Ramos & Patrícia Coutinho & Pedro Silva & Keith Davids & Eduardo Guimarães & Isabel Mesquita, 2017. "Entropy measures reveal collective tactical behaviours in volleyball teams: how variability and regularity in game actions influence competitive rankings and match status," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(6), pages 848-862, November.
    5. Fernando Martins & Ricardo Gomes & Vasco Lopes & Frutuoso Silva & Rui Mendes, 2020. "Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior," Mathematics, MDPI, vol. 8(9), pages 1-12, September.
    6. Bill Gerrard & Daniel Memmert & Dominik Raabe, 2019. "Data Analytics in Football: Positional Data Collection, Modelling and Analysis," Sport Management Review, Taylor & Francis Journals, vol. 22(4), pages 568-569, October.
    7. Rhoades, Harmony & Hsu, Hsun-ta & Rice, Eric & Harris, Taylor & La Motte-Kerr, Wichada & Winetrobe, Hailey & Henwood, Benjamin & Wenzel, Suzanne, 2021. "Social network change after moving into permanent supportive housing: Who stays and who goes?," Network Science, Cambridge University Press, vol. 9(1), pages 18-34, March.
    8. Fujimoto, Kayo & Snijders, Tom A. B. & Valente, Thomas W., 2018. "Multivariate dynamics of one-mode and two-mode networks: Explaining similarity in sports participation among friends," Network Science, Cambridge University Press, vol. 6(3), pages 370-395, September.
    9. Clemente, Filipe Manuel & Sarmento, Hugo & Aquino, Rodrigo, 2020. "Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    10. Meng, Dawen & Sun, Lei & Tian, Guoqiang, 2022. "Dynamic mechanism design on social networks," Games and Economic Behavior, Elsevier, vol. 131(C), pages 84-120.
    11. Takahiro Kawasaki & Kenichi Sakaue & Ryota Matsubara & Satoshi Ishizaki, 2019. "Football pass network based on the measurement of player position by using network theory and clustering," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(3), pages 381-392, May.
    12. Kelsey R. McDonald & William F. Broderick & Scott A. Huettel & John M. Pearson, 2019. "Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    13. Koh Sasaki & Takumi Yamamoto & Masahiko Miyao & Takashi Katsuta & Ichiro Kono, 2017. "Network centrality analysis to determine the tactical leader of a sports team," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(6), pages 822-831, November.
    14. Andrew R. Novak & Sam Palmer & Franco M. Impellizzeri & Cathal Garvey & Job Fransen, 2021. "Description of collective team behaviours and team performance analysis of elite rugby competition via cooperative network analysis," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 21(5), pages 804-819, September.
    15. Sung-Un Park & Hyunkyun Ahn & Dong-Kyu Kim & Wi-Young So, 2020. "Big Data Analysis of Sports and Physical Activities among Korean Adolescents," IJERPH, MDPI, vol. 17(15), pages 1-11, August.
    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. Sergio Caicedo-Parada & Carlos Lago-Peñas & Enrique Ortega-Toro, 2020. "Passing Networks and Tactical Action in Football: A Systematic Review," IJERPH, MDPI, vol. 17(18), pages 1-19, September.
    2. Riccardo Ievoli & Aldo Gardini & Lucio Palazzo, 2023. "The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 153-175, March.
    3. Antonio Cordón-Carmona & Abraham García-Aliaga & Moisés Marquina & Jorge Lorenzo Calvo & Daniel Mon-López & Ignacio Refoyo Roman, 2020. "What Is the Relevance in the Passing Action between the Passer and the Receiver in Soccer? Study of Elite Soccer in La Liga," IJERPH, MDPI, vol. 17(24), pages 1-15, December.
    4. Šuštaršič Ana & Videmšek Mateja & Karpljuk Damir & Miloloža Ivan & Meško Maja, 2022. "Big Data in Sports: A Bibliometric and Topic Study," Business Systems Research, Sciendo, vol. 13(1), pages 19-34, June.
    5. Qi, Yufei & Sajadi, S. Mohammad & Baghaei, S. & Rezaei, R. & Li, Wei, 2024. "Digital technologies in sports: Opportunities, challenges, and strategies for safeguarding athlete wellbeing and competitive integrity in the digital era," Technology in Society, Elsevier, vol. 77(C).
    6. Chen, Xi & Qiu, Yun & Shi, Wei & Yu, Pei, 2022. "Key links in network interactions: Assessing route-specific travel restrictions in China during the Covid-19 pandemic," China Economic Review, Elsevier, vol. 73(C).
    7. 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).
    8. Valerio Ficcadenti & Roy Cerqueti & Ciro Hosseini Varde’i, 2023. "A rank-size approach to analyse soccer competitions and teams: the case of the Italian football league “Serie A"," Annals of Operations Research, Springer, vol. 325(1), pages 85-113, June.
    9. Sung-Un Park, 2024. "Derivation of Key Attributes and Clusters of Korean Taekwondo Policies and Systems on Social Media: Comparative Big Data Analysis Based on Regime Change," SAGE Open, , vol. 14(2), pages 21582440241, April.
    10. Wang, Xianjia & Yang, Zhipeng & Liu, Yanli & Chen, Guici, 2023. "A reinforcement learning-based strategy updating model for the cooperative evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    11. Bruno Gonçalves & Diogo Coutinho & Juliana Exel & Bruno Travassos & Carlos Lago & Jaime Sampaio, 2019. "Extracting spatial-temporal features that describe a team match demands when considering the effects of the quality of opposition in elite football," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-20, August.
    12. Midhat Z. Jafry & Jayda Martinez & Tzuan A. Chen & Michael S. Businelle & Darla E. Kendzor & Lorraine R. Reitzel, 2021. "Perceived Social Support Attenuates the Association between Stress and Health-Related Quality of Life among Adults Experiencing Homelessness," IJERPH, MDPI, vol. 18(20), pages 1-14, October.
    13. Seiler, A. & Papanagnou, C. & Scarf, P., 2020. "On the relationship between financial performance and position of businesses in supply chain networks," International Journal of Production Economics, Elsevier, vol. 227(C).
    14. Meng, Dawen, 2025. "Targeting network intervention with social norm," Journal of Economic Theory, Elsevier, vol. 230(C).
    15. Li, Ming-Xia & Xu, Li-Gong & Zhou, Wei-Xing, 2025. "Motif analysis and passing behavior in football passing networks," Chaos, Solitons & Fractals, Elsevier, vol. 190(C).
    16. Wang, Xianjia & Wang, Linlin & Hu, Yaozhong, 2026. "Self-confirming Q-learning on unknown networks," Chaos, Solitons & Fractals, Elsevier, vol. 203(C).
    17. Lu, Jingfeng & Zhao, Wenbo, 2026. "Intertemporal bundling," Journal of Economic Theory, Elsevier, vol. 231(C).
    18. Longbing Cao & Chengzhang Zhu, 2022. "Personalized next-best action recommendation with multi-party interaction learning for automated decision-making," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-22, January.
    19. Widarti Widarti & Desfitrina Desfitrina & Zulfadhli Zulfadhli, 2020. "Business Process Life Cycle Affects Company Financial Performance: Micro, Small, and Medium Business Enterprises During The Covid-19 Period," International Journal of Economics and Financial Issues, Econjournals, vol. 10(5), pages 211-219.
    20. Lotito, Gianna & Migheli, Matteo & Ortona, Guido, 2025. "Instinctiveness and reflexivity in behavioural type variability," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 114(C).

    More about this item

    Statistics

    Access and download statistics

    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:wly:complx:v:2022:y:2022:i:1:n:5743825. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    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.