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On the Development of a Soccer Player Performance Rating System for the English Premier League

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  • Ian G. McHale

    (Centre for Operations Management, Management Science and Statistics, and Centre for Gambling Studies, Salford Business School, University of Salford, Salford M5 4WT, United Kingdom)

  • Philip A. Scarf

    (Centre for Operations Management, Management Science and Statistics, Salford Business School, University of Salford, Salford M5 4WT, United Kingdom)

  • David E. Folker

    (Football DataCo Ltd., London W1U 8PL, United Kingdom)

Abstract

The EA Sports Player Performance Index is a rating system for soccer players used in the top two tiers of soccer in England—the Premier League and the Championship. Its development was a collaboration among professional soccer leagues, a news media association, and academia. In this paper, we describe the index and its construction. The novelty of the index lies in its attempts to rate all players using a single score, regardless of their playing specialty, based on player contributions to winning performances. As one might expect, players from leading teams lead the index, although surprises happen.

Suggested Citation

  • Ian G. McHale & Philip A. Scarf & David E. Folker, 2012. "On the Development of a Soccer Player Performance Rating System for the English Premier League," Interfaces, INFORMS, vol. 42(4), pages 339-351, August.
    • Handle: RePEc:inm:orinte:v:42:y:2012:i:4:p:339-351
    DOI: 10.1287/inte.1110.0589
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    References listed on IDEAS

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    1. R. Kenett & P. Thyregod, 2006. "Aspects of statistical consulting not taught by academia," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(3), pages 396-411, August.
    2. McHale, Ian & Morton, Alex, 2011. "A Bradley-Terry type model for forecasting tennis match results," International Journal of Forecasting, Elsevier, vol. 27(2), pages 619-630, April.
    3. A J Lewis, 2005. "Towards fairer measures of player performance in one-day cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(7), pages 804-815, July.
    4. McHale, Ian & Morton, Alex, 2011. "A Bradley-Terry type model for forecasting tennis match results," International Journal of Forecasting, Elsevier, vol. 27(2), pages 619-630.
    5. R J Ormerod, 2002. "On the nature of OR: taking stock," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(5), pages 475-491, May.
    6. Paul M. Swamidass & William T. Newell, 1987. "Manufacturing Strategy, Environmental Uncertainty and Performance: A Path Analytic Model," Management Science, INFORMS, vol. 33(4), pages 509-524, April.
    7. Ian McHale & Phil Scarf, 2007. "Modelling soccer matches using bivariate discrete distributions with general dependence structure," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 432-445, November.
    8. Rick Audas & Stephen Dobson & John Goddard, 1999. "Organizational performance and managerial turnover," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(6), pages 305-318.
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    Cited by:

    1. Sam McIntosh & Stephanie Kovalchik & Sam Robertson, 2019. "Comparing subjective and objective evaluations of player performance in Australian Rules football," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-16, August.
    2. Buraimo, Babatunde & Forrest, David & McHale, Ian G. & Tena, J.D., 2022. "Armchair fans: Modelling audience size for televised football matches," European Journal of Operational Research, Elsevier, vol. 298(2), pages 644-655.
    3. 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.
    4. Gavin A. Whitaker & Ricardo Silva & Daniel Edwards & Ioannis Kosmidis, 2021. "A Bayesian approach for determining player abilities in football," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 174-201, January.
    5. 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.
    6. Babatunde Buraimo & David Forrest & Ian G. McHale & J.D. Tena, 2020. "Armchair Fans: New Insights Into The Demand For Televised Soccer," Working Papers 202020, University of Liverpool, Department of Economics.
    7. 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.
    8. Kosuke Toda & Masakiyo Teranishi & Keisuke Kushiro & Keisuke Fujii, 2022. "Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-14, January.
    9. Maurizio Carpita & Paola Pasca & Serena Arima & Enrico Ciavolino, 2023. "Clustering of variables methods and measurement models for soccer players’ performances," Annals of Operations Research, Springer, vol. 325(1), pages 37-56, June.

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