IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v166y2023ics0960077922011018.html
   My bibliography  Save this article

Identifiability of Chinese football teams: A complex networks approach

Author

Listed:
  • Gong, Bingnan
  • Zhou, Changjing
  • Gómez, Miguel-Ángel
  • Buldú, J.M.

Abstract

The current study proposes the use of Network Science as a complementary tool to analyse how specific and unique the playing style of Chinese football teams is. Departing from all passes made by a team during a whole season, we construct the pitch passing network of each match, where nodes are the different areas of the pitch, and the links account for the number of passes between any two areas. In this way, we obtain a network containing information about how a team moves the ball during the offensive phase of a match. For each match, we construct the pitch passing networks at different scales by using partitions of the pitch of different sizes. Next, we compare how consistent are the pitch-passing networks during a whole season and how the spatial scale affects the quantification of this consistency. Importantly, we also compare the networks of each team with the rest of the teams in the league, which allow us to obtain an identifiability parameter, which accounts for how particular the networks of a team are. Finally, we repeat the analysis during 5 consecutive seasons and detect what teams maintain their particular playing style during the years.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:chsofr:v:166:y:2023:i:c:s0960077922011018
    DOI: 10.1016/j.chaos.2022.112922
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922011018
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2022.112922?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. McHale, Ian G. & Relton, Samuel D., 2018. "Identifying key players in soccer teams using network analysis and pass difficulty," European Journal of Operational Research, Elsevier, vol. 268(1), pages 339-347.
    2. Richard Pollard & Jaime Prieto & Miguel-Ángel Gómez, 2017. "Global differences in home advantage by country, sport and sex," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(4), pages 586-599, July.
    3. Julio Del Corral & Carlos Pestana Barros & Juan Prieto-Rodríguez, 2008. "The Determinants of Soccer Player Substitutions," Journal of Sports Economics, , vol. 9(2), pages 160-172, April.
    4. Bingnan Gong & Yixiong Cui & Shaoliang Zhang & Changjing Zhou & Qing Yi & Miguel-Ángel Gómez-Ruano, 2021. "Impact of technical and physical key performance indicators on ball possession in the Chinese Super League," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 21(6), pages 909-921, November.
    5. Javier Fernandez-Navarro & Luis Fradua & Asier Zubillaga & Allistair P. McRobert, 2018. "Influence of contextual variables on styles of play in soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(3), pages 423-436, May.
    6. N Hirotsu & M Wright, 2002. "Using a Markov process model of an association football match to determine the optimal timing of substitution and tactical decisions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(1), pages 88-96, January.
    7. Alan L. Morse & Stephen L. Shapiro & Chad D. McEvoy & Daniel A. Rascher, 2008. "The Effects of Roster Turnover on Demand in the National Basketball Association," International Journal of Sport Finance, Fitness Information Technology, vol. 3(1), pages 8-18, February.
    8. Herrera-Diestra, J.L. & Echegoyen, I. & Martínez, J.H. & Garrido, D. & Busquets, J. & Io, F.Seirul. & Buldú, J.M., 2020. "Pitch networks reveal organizational and spatial patterns of Guardiola’s F.C. Barcelona," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    9. Richard Pollard & Vasilis Armatas, 2017. "Factors affecting home advantage in football World Cup qualification," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(1-2), pages 121-135, March.
    10. Filipe Manuel Clemente & Fernando Manuel Lourenço Martins & Dimitris Kalamaras & P. Del Wong & Rui Sousa Mendes, 2015. "General network analysis of national soccer teams in FIFA World Cup 2014," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 15(1), pages 80-96, March.
    11. N Hirotsu & M Wright, 2002. "Erratum: Hirotsu N and Wright M (2002). Using a Markov process model of an association football match to determine the optimal timing of substitution and tactical decisions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(10), pages 1174-1174, October.
    12. Adam Hewitt & Grace Greenham & Kevin Norton, 2016. "Game style in soccer: what is it and can we quantify it?," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(1), pages 355-372, April.
    13. Miguel-Ángel Gómez & Michalis Mitrotasios & Vasilis Armatas & Carlos Lago-Peñas, 2018. "Analysis of playing styles according to team quality and match location in Greek professional soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(6), pages 986-997, November.
    14. Carlos Lago-Peñas & Miguel Gómez-Ruano & Gai Yang, 2017. "Styles of play in professional soccer: an approach of the Chinese Soccer Super League," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(6), pages 1073-1084, November.
    15. Hugo Sarmento & Antonino Pereira & Nuno Matos & Jorge Campaniço & T. Maria Anguera & José Leitão, 2013. "English Premier League, Spaińs La Liga and Italýs Seriés A – What’s Different?," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 13(3), pages 773-789, December.
    16. Tianbiao Liu & Antonio García-De-Alcaraz & Liang Zhang & Yao Zhang, 2019. "Exploring home advantage and quality of opposition interactions in the Chinese Football Super League," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(3), pages 289-301, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    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).

    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. Julen Castellano & Miguel Pic, 2019. "Identification and Preference of Game Styles in LaLiga Associated with Match Outcomes," IJERPH, MDPI, vol. 16(24), pages 1-13, December.
    2. Li, Yuesen & Ma, Runqing & Gonçalves, Bruno & Gong, Bingnan & Cui, Yixiong & Shen, Yanfei, 2020. "Data-driven team ranking and match performance analysis in Chinese Football Super League," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    3. van Damme, Nils & Baert, Stijn, 2019. "Home advantage in European international soccer: Which dimension of distance matters?," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-17.
    4. 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).
    5. Liam Kneafsey & Stefan Müller, 2018. "Assessing the influence of neutral grounds on match outcomes," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(6), pages 892-905, November.
    6. Tunaru Radu S & Viney Howard P, 2010. "Valuations of Soccer Players from Statistical Performance Data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-23, April.
    7. Hirotsu Nobuyoshi & Wright Mike B, 2006. "Modeling Tactical Changes of Formation in Association Football as a Zero-Sum Game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(2), pages 1-22, April.
    8. Jordi Arboix-Alió & Guillem Trabal & Bernat Buscà & Javier Peña & Adrià Arboix & Raúl Hileno, 2021. "The Behaviour of Home Advantage during the COVID-19 Pandemic in European Rink Hockey Leagues," IJERPH, MDPI, vol. 19(1), pages 1-12, December.
    9. Serafeim Moustakidis & Spyridon Plakias & Christos Kokkotis & Themistoklis Tsatalas & Dimitrios Tsaopoulos, 2023. "Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics," Future Internet, MDPI, vol. 15(5), pages 1-18, May.
    10. Andrés Picazo-Tadeo & Francisco Gónzalez-Gómez & Jorge Guardiola Wanden-Berghe, 2011. "Referee home bias due to social pressure. Evidence from Spanish football," Working Papers 1119, Department of Applied Economics II, Universidad de Valencia.
    11. Myers Bret R., 2012. "A Proposed Decision Rule for the Timing of Soccer Substitutions," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-24, March.
    12. Bär, Sören & Benkel, Cathrin & Bezold, Thomas & Biebl, Petra & Breuer, Markus & Budzinski, Oliver & Chatrath, Stefan & Daumann, Frank & Faulstich, Sebastian & Feddersen, Arne & Frick, Bernd & Gassmann, 2023. "Wettbewerb und Wettbewerbspolitik im Sport: Sammelband zur 25. Jahrestagung des Arbeitskreises Sportökonomie," KCV Schriftenreihe, FOM Hochschule für Oekonomie & Management, KCV KompetenzCentrum für angewandte Volkswirtschaftslehre, volume 7, number 282198 edited by FOM Hochschule für Oekonomie & Management, KompetenzCentrum für angewandte Volkswirtschaftslehre (KC.
    13. Håland Else Marie & Wiig Astrid Salte & Stålhane Magnus & Hvattum Lars Magnus, 2020. "Evaluating the effectiveness of different network flow motifs in association football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(4), pages 311-323, December.
    14. van Ours, Jan C., 2017. "Artificial Pitches and Unfair Home Advantage in Professional Football," CEPR Discussion Papers 12341, C.E.P.R. Discussion Papers.
    15. Thomas Peeters & Jan C. van Ours, 2020. "Seasonal Home Advantage in English Professional Football; 1973-2018," Tinbergen Institute Discussion Papers 20-025/V, Tinbergen Institute.
    16. Jasper Beernaerts & Bernard De Baets & Matthieu Lenoir & Nico Van de Weghe, 2020. "Spatial movement pattern recognition in soccer based on relative player movements," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-16, January.
    17. Silva Rajitha M. & Swartz Tim B., 2016. "Analysis of substitution times in soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(3), pages 113-122, September.
    18. 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).
    19. Yiannis Nikolaidis, 2015. "Building a basketball game strategy through statistical analysis of data," Annals of Operations Research, Springer, vol. 227(1), pages 137-159, April.
    20. 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).

    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:166:y:2023:i:c:s0960077922011018. 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.