IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v13y2017i3p113-129n2.html
   My bibliography  Save this article

The Football Team Composition Problem: a Stochastic Programming approach

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2017-0030
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jqas-2017-0030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. S. Rajagopalan & Andreas C. Soteriou, 1994. "Capacity Acquisition and Disposal with Discrete Facility Sizes," Management Science, INFORMS, vol. 40(7), pages 903-917, July.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Kjetil Høyland & Stein W. Wallace, 2001. "Generating Scenario Trees for Multistage Decision Problems," Management Science, INFORMS, vol. 47(2), pages 295-307, February.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gerçek Budak & İmdat Kara & Yusuf Tansel İç & Refail Kasımbeyli, 2019. "New mathematical models for team formation of sports clubs before the match," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(1), pages 93-109, March.
    2. 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.
    3. Miquel Carreras-Simó & Jaume García, 2022. "Offensive/Defensive Talent and Sporting Success in Football: Evidence From the Big Five European Leagues," Journal of Sports Economics, , vol. 23(3), pages 251-276, April.
    4. Herm, Steffen & Callsen-Bracker, Hans-Markus & Kreis, Henning, 2014. "When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community," Sport Management Review, Elsevier, vol. 17(4), pages 484-492.
    5. Vojtěch Kotrba, 2020. "Heuristics in fantasy sports: is it profitable to strategize based on favourite of the match?," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 19(1), pages 195-206, June.
    6. Jeffrey D'Silva & Adriana Ortega & Abdul Sulaiman, 2016. "Influence of Personal and Task Interdependence on Task Conflict and Team Effectiveness," Modern Applied Science, Canadian Center of Science and Education, vol. 10(4), pages 1-95, April.
    7. Martínez-Costa, Carme & Mas-Machuca, Marta & Benedito, Ernest & Corominas, Albert, 2014. "A review of mathematical programming models for strategic capacity planning in manufacturing," International Journal of Production Economics, Elsevier, vol. 153(C), pages 66-85.
    8. Ansaripoor, Amir H. & Oliveira, Fernando S., 2018. "Flexible lease contracts in the fleet replacement problem with alternative fuel vehicles: A real-options approach," European Journal of Operational Research, Elsevier, vol. 266(1), pages 316-327.
    9. Giovanni Pantuso & Trine K. Boomsma, 2020. "On the number of stages in multistage stochastic programs," Annals of Operations Research, Springer, vol. 292(2), pages 581-603, September.
    10. Pieter Smet & Annelies Lejon & Greet Vanden Berghe, 2021. "Demand smoothing in shift design," Flexible Services and Manufacturing Journal, Springer, vol. 33(2), pages 457-484, June.
    11. Ezgi Erturk & Ebru Akcapinar Sezer, 2016. "Software fault prediction using Mamdani type fuzzy inference system," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 8(1), pages 14-28.
    12. Faisal Al-Madi & Khalaf Ibrahim Al-Tarawneh & Marwan Ahmad Alshammari, 2016. "HR Practices in the Soccer Industry: Promising Research Arena," International Review of Management and Marketing, Econjournals, vol. 6(4), pages 641-653.
    13. Mørch, Ove & Fagerholt, Kjetil & Pantuso, Giovanni & Rakke, Jørgen, 2017. "Maximizing the rate of return on the capital employed in shipping capacity renewal," Omega, Elsevier, vol. 67(C), pages 42-53.
    14. Vernon Ning Hsu, 2002. "Dynamic Capacity Expansion Problem with Deferred Expansion and Age-Dependent Shortage Cost," Manufacturing & Service Operations Management, INFORMS, vol. 4(1), pages 44-54, June.
    15. Giovanni Pantuso & Kjetil Fagerholt & Stein W. Wallace, 2016. "Uncertainty in Fleet Renewal: A Case from Maritime Transportation," Transportation Science, INFORMS, vol. 50(2), pages 390-407, May.
    16. Owadally, Iqbal & Jang, Chul & Clare, Andrew, 2021. "Optimal investment for a retirement plan with deferred annuities," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 51-62.
    17. Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
    18. Gulpinar, Nalan & Rustem, Berc & Settergren, Reuben, 2004. "Simulation and optimization approaches to scenario tree generation," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1291-1315, April.
    19. Peiling Wu & Joseph C. Hartman & George R. Wilson, 2005. "An Integrated Model and Solution Approach for Fleet Sizing with Heterogeneous Assets," Transportation Science, INFORMS, vol. 39(1), pages 87-103, February.
    20. Tanaka, Ken'ichiro & Toda, Alexis Akira, 2015. "Discretizing Distributions with Exact Moments: Error Estimate and Convergence Analysis," University of California at San Diego, Economics Working Paper Series qt7g23r5kh, Department of Economics, UC San Diego.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:13:y:2017:i:3:p:113-129:n:2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.