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A fuzzy inference system with application to player selection and team formation in multi-player sports

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  • Tavana, Madjid
  • Azizi, Farshad
  • Azizi, Farzad
  • Behzadian, Majid

Abstract

The success or failure of any team lies in the skills and abilities of the players that comprise the team. The process of player selection and team formation in multi-player sports is a complex multi-criteria problem where the ultimate success is determined by how the collection of individual players forms an effective team. In general, the selection of soccer players and formation of a team are judgments made by the coaches on the basis of the best available information. Very few structured and analytical models have been developed to support coaches in this effort. We propose a two-phase framework for player selection and team formation in soccer. The first phase evaluates the alternative players with a fuzzy ranking method and selects the top performers for inclusion in the team. The second phase evaluates the alternative combinations of the selected players with a Fuzzy Inference System (FIS) and selects the best combinations for team formation. A case study is used to illustrate the performance of the proposed approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:spomar:v:16:y:2013:i:1:p:97-110
    DOI: 10.1016/j.smr.2012.06.002
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Tavana, Madjid & Khosrojerdi, Ghasem & Mina, Hassan & Rahman, Amirah, 2019. "A hybrid mathematical programming model for optimal project portfolio selection using fuzzy inference system and analytic hierarchy process," Evaluation and Program Planning, Elsevier, vol. 77(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

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