IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v133y2020ics0960077920300242.html
   My bibliography  Save this article

Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons

Author

Listed:
  • Clemente, Filipe Manuel
  • Sarmento, Hugo
  • Aquino, Rodrigo

Abstract

Among elite national soccer team play of the 2018 FIFA World Cub, we analyzed (a) network centrality variations between playing positions during passing sequences, and (b) their relationship to match outcomes. We observed and coded the 64 matches played by 32 teams and collected passing distribution data between teammates. We then converted it into adjacency matrices to calculate passing network data. We found large decreases in degree prestige (inbound pass links) among players in winger positions compared to external defenders (−41.8%; ES (effect size): −1.79). Large decreases in degree prestige were also found in central forwards in comparison to external defenders (−38.7%; ES: −1.62), central defenders (−42.3%; ES: −1.60), defensive midfielders (−47.1%; ES: −1.87) and midfielders (−40.8%; ES: −1.59). Comparisons of passing network centrality levels between won and lost matches revealed small increases in degree prestige among midfielders (17.4%; ES: 0.31) and small increases among forwards (33.9%,; ES: 0.53) among matches won. Thus, match outcome (and possibly scoring status during the match) was somewhat related to the passing network centrality of various playing positions during passing sequences.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:133:y:2020:i:c:s0960077920300242
    DOI: 10.1016/j.chaos.2020.109625
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920300242
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.109625?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. Filipe Manuel Clemente & Fernando Manuel Lourenço Martins & P. Del Wong & Dimitris Kalamaras & Rui Sousa Mendes, 2015. "Midfielder as the prominent participant in the building attack: A network analysis of national teams in FIFA World Cup 2014," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 15(2), pages 704-722, August.
    2. Jordi Duch & Joshua S Waitzman & Luís A Nunes Amaral, 2010. "Quantifying the Performance of Individual Players in a Team Activity," PLOS ONE, Public Library of Science, vol. 5(6), pages 1-7, June.
    3. Paulo Paixão & Jaime Sampaio & Carlos H. Almeida & Ricardo Duarte, 2015. "How does match status affects the passing sequences of top-level European soccer teams?," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 15(1), pages 229-240, March.
    4. Yuji Yamamoto & Keiko Yokoyama, 2011. "Common and Unique Network Dynamics in Football Games," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-6, December.
    5. 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.
    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. 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).
    2. 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).
    3. 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.
    4. 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.
    5. Marcelino, Rui & Sampaio, Jaime & Amichay, Guy & Gonçalves, Bruno & Couzin, Iain D. & Nagy, Máté, 2020. "Collective movement analysis reveals coordination tactics of team players in football matches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. 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).
    7. 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.
    8. 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).

    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. 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).
    2. Sarmento, Hugo & Clemente, Filipe Manuel & Gonçalves, Eder & Harper, Liam D & Dias, Diogo & Figueiredo, António, 2020. "Analysis of the offensive process of AS Monaco professional soccer team: A mixed-method approach," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    3. Narizuka, Takuma & Yamamoto, Ken & Yamazaki, Yoshihiro, 2014. "Statistical properties of position-dependent ball-passing networks in football games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 412(C), pages 157-168.
    4. Carlota Torrents & Angel Ric & Robert Hristovski & Lorena Torres-Ronda & Emili Vicente & Jaime Sampaio, 2016. "Emergence of Exploratory, Technical and Tactical Behavior in Small-Sided Soccer Games when Manipulating the Number of Teammates and Opponents," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-15, December.
    5. Joseph D O’Brien & James P Gleeson & David J P O’Sullivan, 2021. "Identification of skill in an online game: The case of Fantasy Premier League," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
    6. Rubén Maneiro & Claudio A Casal & Antonio Ardá & José Luís Losada, 2019. "Application of multivariant decision tree technique in high performance football: The female and male corner kick," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    7. Albert Cohen & Jimmy Risk, 2023. "European Football Player Valuation: Integrating Financial Models and Network Theory," Papers 2312.16179, arXiv.org, revised Oct 2024.
    8. Filippo Radicchi, 2011. "Who Is the Best Player Ever? A Complex Network Analysis of the History of Professional Tennis," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
    9. Gur Yaari & Gil David, 2012. "“Hot Hand” on Strike: Bowling Data Indicates Correlation to Recent Past Results, Not Causality," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-9, January.
    10. Modric Toni & Versic Sime & Sekulic Damir, 2021. "Relationship Between Yo-Yo Intermittent Endurance Test-Level 1 and Match Running Performance in Soccer: Still on the Right Path?," Polish Journal of Sport and Tourism, Sciendo, vol. 28(4), pages 16-20, December.
    11. Lot Verburgh & Erik J A Scherder & Paul AM van Lange & Jaap Oosterlaan, 2014. "Executive Functioning in Highly Talented Soccer Players," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-7, March.
    12. 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.
    13. Rubén Maneiro & José Luís Losada & Claudio A. Casal & Antonio Ardá, 2021. "Identification of Explanatory Variables in Possession of the Ball in High-Performance Women’s Football," IJERPH, MDPI, vol. 18(11), pages 1-14, May.
    14. Rubén D. Aguado-Méndez & José Antonio González-Jurado & Juan Evaristo Callejas-Jerónimo & Fernando Manuel Otero-Saborido, 2021. "Analysis of the goal-scoring opportunities conceded in football: a study case in the Spanish La Liga," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1477-1496, August.
    15. Tomás Rodríguez & Jorge Tovar, 2023. "The hedgehog or the fox: Versatility and performance in professional soccer," Documentos CEDE 20757, Universidad de los Andes, Facultad de Economía, CEDE.
    16. 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).
    17. 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).
    18. António Sérgio Ribeiro & Francisco Lima & Sascha Kraus & Ferran Calabuig, 2022. "Tournaments within football teams: players’ performance and wages," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 4884-4901, December.
    19. Brian Skinner, 2012. "The Problem of Shot Selection in Basketball," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-8, January.
    20. 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).

    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:133:y:2020:i:c:s0960077920300242. 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.