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Quantifying the Performance of Individual Players in a Team Activity

Author

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  • Jordi Duch
  • Joshua S Waitzman
  • Luís A Nunes Amaral

Abstract

Background: Teamwork is a fundamental aspect of many human activities, from business to art and from sports to science. Recent research suggest that team work is of crucial importance to cutting-edge scientific research, but little is known about how teamwork leads to greater creativity. Indeed, for many team activities, it is not even clear how to assign credit to individual team members. Remarkably, at least in the context of sports, there is usually a broad consensus on who are the top performers and on what qualifies as an outstanding performance. Methodology/Principal Findings: In order to determine how individual features can be quantified, and as a test bed for other team-based human activities, we analyze the performance of players in the European Cup 2008 soccer tournament. We develop a network approach that provides a powerful quantification of the contributions of individual players and of overall team performance. Conclusions/Significance: We hypothesize that generalizations of our approach could be useful in other contexts where quantification of the contributions of individual team members is important.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0010937
    DOI: 10.1371/journal.pone.0010937
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    References listed on IDEAS

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    1. John Whitfield, 2008. "Collaboration: Group theory," Nature, Nature, vol. 455(7214), pages 720-723, October.
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    1. 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.
    2. 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.
    3. Medina, Pablo & Carrasco, Sebastián & Rogan, José & Montes, Felipe & Meisel, Jose D. & Lemoine, Pablo & Lago Peñas, Carlos & Valdivia, Juan Alejandro, 2021. "Is a social network approach relevant to football results?," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. 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).
    5. Łukasz Szczepański & Ian McHale, 2016. "Beyond completion rate: evaluating the passing ability of footballers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 513-533, February.
    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. 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.
    8. Albert Cohen & Jimmy Risk, 2023. "European Football Player Valuation: Integrating Financial Models and Network Theory," Papers 2312.16179, arXiv.org, revised Oct 2024.
    9. 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.
    10. 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.
    11. Gur Yaari & Shmuel Eisenmann, 2011. "The Hot (Invisible?) Hand: Can Time Sequence Patterns of Success/Failure in Sports Be Modeled as Repeated Random Independent Trials?," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-10, October.
    12. Brian Skinner, 2012. "The Problem of Shot Selection in Basketball," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-8, January.
    13. 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).
    14. 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.
    15. Mukherjee, Satyam, 2012. "Identifying the greatest team and captain—A complex network approach to cricket matches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6066-6076.
    16. 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).
    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. 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.
    19. 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).

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