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General network analysis of national soccer teams in FIFA World Cup 2014

Author

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  • Filipe Manuel Clemente
  • Fernando Manuel Lourenço Martins
  • Dimitris Kalamaras
  • P. Del Wong
  • Rui Sousa Mendes

Abstract

This study analyzed the network characteristics of successful and unsuccessful national teams that participated in FIFA World Cup 2014. The relationship between the variables of overall team performance and the network characteristics measured on the basis of the passes between teammates was also investigated. A dataset of 37,864 passes between teammates in 64 soccer matches enabled the study on network structure and team performance of 32 national soccer teams. Our results showed significant differences in the dependent variables of network density (F4,123 = 2.72; p = 0.03; η2p = 0.04; small effect size) and total links (F4,123 = 2.73; p = 0.03; η2p = 0.04; small effect size) between the teams that reached the later stages of the tournament. Goals scored presented a small positive correlation with total links (r = 0.24; p = 0.001), network density (r = 0.24; p = 0.001), and clustering coefficient (r = 0.17; p > 0.050). High levels of goals scored were associated with high levels of total links, network density, and clustering coefficient. This study showed that successful teams have a high level of network density, total links, and clustering coefficient. Thus, large values of connectivity between teammates are associated with better overall team performance.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:rpanxx:v:15:y:2015:i:1:p:80-96
    DOI: 10.1080/24748668.2015.11868778
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    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. 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).
    3. 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).
    4. Calissano, Anna & Feragen, Aasa & Vantini, Simone, 2022. "Graph-valued regression: Prediction of unlabelled networks in a non-Euclidean graph space," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    5. 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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