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Characteristics and optimization of core local network: Big data analysis of football matches

Author

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  • Wu, Yao
  • Xia, Zeyu
  • Wu, Tian
  • Yi, Qing
  • Yu, Runyu
  • Wang, Jun

Abstract

The current study constructed social network using player positions and passing process based on available literature. Multiple indicators were used to measure the importance of positions comprehensively. The results showed that in the football passing process, the attacking midfielder was the most important position, followed by the central defending midfielder. Based on two-sample difference tests, the results showed that the winning teams usually had better performance on positions of forward on left, central forward, defender on left and defender on right. To analyze the effects of playing positions on the whole network and test the sensitivity of passing networks, we deleted n (n = 1, 2, 3…, 10) positions of a team, and then tested the efficiency of the networks based on positions left.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920305324
    DOI: 10.1016/j.chaos.2020.110136
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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).
    4. 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.
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    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. 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).

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