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Beyond completion rate: evaluating the passing ability of footballers

Author

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  • Łukasz Szczepański
  • Ian McHale

Abstract

type="main" xml:id="rssa12115-abs-0001"> Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the attempted pass nor the various levels of uncertainty involved in empirical observations based on different numbers of passes per player. We address both these deficiencies by building a statistical model in which the success of a pass depends on the skill of the executing player as well as other factors including the origin and destination of the pass, the skill of his teammates and the opponents, and proxies for the defensive pressure put on the executing player as well as random chance. We fit the model by using data from the 2006–2007 season of the English Premier League provided by Opta, estimate each player's passing skill and make predictions for the next season. The model predictions considerably outperform a naive method of simply using the previous season's completion rate as a predictor of the following season's completion rate. In particular, we show how a change in the difficulty of passes attempted in both seasons explains a significant proportion of the shift in the observed performance of some players—a fact that is ignored if the raw completion rate is used to evaluate player skill.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jorssa:v:179:y:2016:i:2:p:513-533
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    File URL: http://hdl.handle.net/10.1111/rssa.2016.179.issue-2
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    References listed on IDEAS

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    Cited by:

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    2. Jorge Tovar & Andrés Clavijo & Julián Cárdenas, 2017. "A strategy to predict association football players’ passing skills," Documentos CEDE 15821, Universidad de los Andes, Facultad de Economía, CEDE.
    3. 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.
    4. Ali Cakmak & Ali Uzun & Emrullah Delibas, 2018. "Computational Modeling Of Pass Effectiveness In Soccer," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(03n04), pages 1-28, May.
    5. Avila-Cano, Antonio & Owen, P. Dorian & Triguero-Ruiz, Francisco, 2023. "Measuring competitive balance in sports leagues that award bonus points, with an application to rugby union," European Journal of Operational Research, Elsevier, vol. 309(2), pages 939-952.
    6. Wu Lucas Y. & Danielson Aaron J. & Hu X. Joan & Swartz Tim B., 2021. "A contextual analysis of crossing the ball in soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(1), pages 57-66, March.
    7. Kharrat, Tarak & McHale, Ian G. & Peña, Javier López, 2020. "Plus–minus player ratings for soccer," European Journal of Operational Research, Elsevier, vol. 283(2), pages 726-736.

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