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The Football Team Composition Problem: a Stochastic Programming approach

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  • Pantuso Giovanni

    (University of Copenhagen, Department of Mathematical Sciences, Universitetsparken 5, Copenhagen 2100, Denmark)

Abstract

Most professional European football clubs are well-structured businesses. Therefore, the financial performance of investments in players becomes crucial. In this paper, after the problem is discussed and formalized, an optimization model with the objective of maximizing the expected value of the team is presented. The model ensures that the team has the required mix of skills, that competition regulations are met, and that budget limits are respected. The model explicitly takes into account the uncertainty in the career development of football players. A case study based on the English Premier League is presented. Our results show that the model has significant potential to improve current decisions ensuring a steady growth of the value of the team. The team value growth reported is particularly driven by investments in young prospects.

Suggested Citation

  • Pantuso Giovanni, 2017. "The Football Team Composition Problem: a Stochastic Programming approach," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(3), pages 113-129, September.
    • Handle: RePEc:bpj:jqsprt:v:13:y:2017:i:3:p:113-129:n:2
    DOI: 10.1515/jqas-2017-0030
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    References listed on IDEAS

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    1. Timothy C. Y. Chan & Justin A. Cho & David C. Novati, 2012. "Quantifying the Contribution of NHL Player Types to Team Performance," Interfaces, INFORMS, vol. 42(2), pages 131-145, April.
    2. Boon, Bart H. & Sierksma, Gerard, 2003. "Team formation: Matching quality supply and quality demand," European Journal of Operational Research, Elsevier, vol. 148(2), pages 277-292, July.
    3. Komarudin, & Guerry, Marie-Anne & De Feyter, Tim & Vanden Berghe, Greet, 2013. "The roster quality staffing problem – A methodology for improving the roster quality by modifying the personnel structure," European Journal of Operational Research, Elsevier, vol. 230(3), pages 551-562.
    4. Becker Adrian & Sun Xu Andy, 2016. "An analytical approach for fantasy football draft and lineup management," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 17-30, March.
    5. Madjid Tavana & Farshad Azizi & Farzad Azizi & Majid Behzadian, 2013. "A fuzzy inference system with application to player selection and team formation in multi-player sports," Sport Management Review, Taylor & Francis Journals, vol. 16(1), pages 97-110, January.
    6. Tavana, Madjid & Azizi, Farshad & Azizi, Farzad & Behzadian, Majid, 2013. "A fuzzy inference system with application to player selection and team formation in multi-player sports," Sport Management Review, Elsevier, vol. 16(1), pages 97-110.
    7. De Bruecker, Philippe & Van den Bergh, Jorne & Beliën, Jeroen & Demeulemeester, Erik, 2015. "Workforce planning incorporating skills: State of the art," European Journal of Operational Research, Elsevier, vol. 243(1), pages 1-16.
    8. Fry Michael J & Lundberg Andrew W & Ohlmann Jeffrey W, 2007. "A Player Selection Heuristic for a Sports League Draft," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(2), pages 1-35, April.
    9. Kjetil Høyland & Stein W. Wallace, 2001. "Generating Scenario Trees for Multistage Decision Problems," Management Science, INFORMS, vol. 47(2), pages 295-307, February.
    10. Chand, Suresh & McClurg, Tim & Ward, Jim, 2000. "A model for parallel machine replacement with capacity expansion," European Journal of Operational Research, Elsevier, vol. 121(3), pages 519-531, March.
    11. Giovanni Pantuso & Kjetil Fagerholt & Stein W. Wallace, 2015. "Solving Hierarchical Stochastic Programs: Application to the Maritime Fleet Renewal Problem," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 89-102, February.
    12. S. Rajagopalan & Andreas C. Soteriou, 1994. "Capacity Acquisition and Disposal with Discrete Facility Sizes," Management Science, INFORMS, vol. 40(7), pages 903-917, July.
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    1. Smith Zachary J. & Bickel J. Eric, 2023. "A roster construction decision tool for MLS expansion teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(1), pages 1-14, March.
    2. Alberto Calahorro-López & Melinda Ratkai & Julio Vena-Oya, 2022. "The Financial/Accounting Impact of FFP on Participating in European Competitions: An Analysis of the Spanish League," IJFS, MDPI, vol. 10(3), pages 1-19, September.

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