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

Ian G. McHale and Samuel D. Relton

European Journal of Operational Research, 2018, vol. 268, issue 1, 339-347

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.

Keywords: Sport; Big data; Football; Moneyball; Random effects (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:268:y:2018:i:1:p:339-347

DOI: 10.1016/j.ejor.2018.01.018

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