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Maximizing performance with an eye on the finances: a chance-constrained model for football transfer market decisions

Author

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  • G. Pantuso

    (University of Copenhagen)

  • L. M. Hvattum

    (Molde University College)

Abstract

Composing a team of professional players is among the most crucial decisions in association football. Nevertheless, transfer market decisions are often based on myopic objectives and are questionable from a financial point of view. This paper introduces a chance-constrained model to provide analytic support to club managers during transfer windows. The model seeks a top-performing team while adapting to different budgets and financial-risk profiles. In addition, it provides a new rating system that is able to numerically reflect the on-field performance of football players and thus contribute to an objective assessment of football players. The model and rating system are tested on a case study based on real market data. The data from the case study are available online for the benefit of future research.

Suggested Citation

  • G. Pantuso & L. M. Hvattum, 2021. "Maximizing performance with an eye on the finances: a chance-constrained model for football transfer market decisions," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 583-611, July.
    • Handle: RePEc:spr:topjnl:v:29:y:2021:i:2:d:10.1007_s11750-020-00584-9
    DOI: 10.1007/s11750-020-00584-9
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    References listed on IDEAS

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    1. Wladimir Andreff & Stefan Szymanski (ed.), 2006. "Handbook on the Economics of Sport," Books, Edward Elgar Publishing, number 3274.
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    5. Vineet M. Payyappalli & Jun Zhuang, 2019. "A data-driven integer programming model for soccer clubs’ decision making on player transfers," Environment Systems and Decisions, Springer, vol. 39(4), pages 466-481, December.
    6. Sloane, Peter J, 1971. "The Economics of Professional Football: The Football Club as a Utility Maximiser," Scottish Journal of Political Economy, Scottish Economic Society, vol. 18(2), pages 121-146, June.
    7. B. K. Pagnoncelli & S. Ahmed & A. Shapiro, 2009. "Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications," Journal of Optimization Theory and Applications, Springer, vol. 142(2), pages 399-416, August.
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    Cited by:

    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.

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