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Identifying key players in soccer teams using network analysis and pass difficulty

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  • McHale, Ian G.
  • Relton, Samuel D.

Abstract

We use a unique dataset to identify the key members of a football team. The methodology uses a statistical model to determine the difficulty of a pass from one player to another, and combines this information with results from network analysis, to identify which players are pivotal to each team in the English Premier League during the 2012–13 season. We demonstrate the methodology by looking closely at one game, whilst also summarising player performance for each team over the entire season. The analysis is hoped to be of use to managers and coaches in identifying the best team lineup, and in the analysis of opposition teams to identify their key players.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:268:y:2018:i:1:p:339-347
    DOI: 10.1016/j.ejor.2018.01.018
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    References listed on IDEAS

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    1. Boshnakov, Georgi & Kharrat, Tarak & McHale, Ian G., 2017. "A bivariate Weibull count model for forecasting association football scores," International Journal of Forecasting, Elsevier, vol. 33(2), pages 458-466.
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    Cited by:

    1. 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.
    2. Håland Else Marie & Wiig Astrid Salte & Hvattum Lars Magnus & Stålhane 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.
    3. Ausloos, Marcel, 2024. "Hierarchy selection: New team ranking indicators for cyclist multi-stage races," European Journal of Operational Research, Elsevier, vol. 314(2), pages 807-816.
    4. Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A robust method for clustering football players with mixed attributes," Annals of Operations Research, Springer, vol. 325(1), pages 9-36, June.
    5. 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.
    6. 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).
    7. Ardekani, Aref Mahdavi & Distinguin, Isabelle & Tarazi, Amine, 2020. "Do banks change their liquidity ratios based on network characteristics?," European Journal of Operational Research, Elsevier, vol. 285(2), pages 789-803.

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