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Determining the best strategy for changing the configuration of a football team

Author

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  • N Hirotsu

    (Lancaster University)

  • M Wright

    (Lancaster University)

Abstract

This paper proposes a dynamic programming (DP) approach to find the optimal substitution strategy for a football match, which maximises the probability of winning or the expected number of league points, supported by real data of the English Premier League. We use a Markov process model to evaluate the offensive and defensive strengths of teams by means of maximum likelihood estimators. We develop a DP formulation to derive quantitatively the optimal substitution strategy of a team, in relation to the number required of each type of outfield player. We demonstrate how this approach may help to determine how many of each type of player should start a match and be substituted during a match. We also show how the expected league points would increase if the optimal strategy were followed.

Suggested Citation

  • N Hirotsu & M Wright, 2003. "Determining the best strategy for changing the configuration of a football team," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 878-887, August.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:8:d:10.1057_palgrave.jors.2601591
    DOI: 10.1057/palgrave.jors.2601591
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    References listed on IDEAS

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    1. Alan Washburn, 1991. "Still More on Pulling the Goalie," Interfaces, INFORMS, vol. 21(2), pages 59-64, April.
    2. S R Clarke & J M Norman, 1999. "To run or not?: Some dynamic programming models in cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(5), pages 536-545, May.
    3. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
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    Cited by:

    1. Yori Zwols & Gerard Sierksma, 2009. "OR Practice---Training Optimization for the Decathlon," Operations Research, INFORMS, vol. 57(4), pages 812-822, August.
    2. Jarvandi Ali & Sarkani Shahram & Mazzuchi Thomas, 2013. "Modeling team compatibility factors using a semi-Markov decision process: a data-driven approach to player selection in soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(4), pages 347-366, December.
    3. Poojan Thakkar & Manan Shah, 2021. "An Assessment of Football Through the Lens of Data Science," Annals of Data Science, Springer, vol. 8(4), pages 823-836, December.
    4. Hirotsu Nobuyoshi & Wright Mike B, 2006. "Modeling Tactical Changes of Formation in Association Football as a Zero-Sum Game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(2), pages 1-22, April.

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