New mathematical models for team formation of sports clubs before the match

  • Gerçek BudakEmail author
  • İmdat Kara
  • Yusuf Tansel İç
  • Refail Kasımbeyli
Original Paper


Coaches of sports clubs aim to form the team that optimally determines the roles of positions before the match. These types of decisions are referred to as the team formation problem, and they are critical for the sports industry in the financial sense. Finding the optimal solution to the team formation problem is more difficult without the use of systematical approaches, as the number of players and their past performance records have increased substantially in recent years. In this paper, we discuss previous studies on the team formation problems of sports clubs and outline the deficiencies of their results in real-life decision processes. Then, we propose two new formulations that address coaches’ preferences in the decision-making process. A real-life application of the proposed models is displayed for a volleyball team that participates in the first division of the Turkish Volleyball League.


Team formation Sports Mathematical modelling Decision making Goal programming 


  1. Ahmed F, Deb K, Jindal A (2013) Multi-objective optimization and decision making approaches to cricket team selection. Appl Soft Comput 13(1):402–414CrossRefGoogle Scholar
  2. Atkinson MP, Kress M, Szechtman R (2012) Carrots, sticks and fog during insurgencies. Math Soc Sci 64(3):203–213CrossRefGoogle Scholar
  3. Boon BH, Sierksma G (2003) Team formation: matching quality supply and quality demand. Eur J Oper Res 148(2):277–292CrossRefGoogle Scholar
  4. Budak G, Kara İ, İç YT (2017) Weighting the positions and skills of volleyball sport by using AHP: a real life application. IOSR J Sports Phys Educ 4(1):23–29CrossRefGoogle Scholar
  5. Caro CA (2012) College football success: the relationship between recruiting and winning. Int J Sports Sci Coach 7(1):139–152CrossRefGoogle Scholar
  6. Cattrysse DG, Van Wassenhove LN (1992) A survey of algorithms for the generalized assignment problem. Eur J Oper Res 60(3):260–272CrossRefGoogle Scholar
  7. Chen CC, Lee YT, Tsai CM (2014) Professional baseball team starting pitcher selection using AHP and TOPSIS methods. Int J Perform Anal Sport 14(2):545–563CrossRefGoogle Scholar
  8. Dadelo S, Turskis Z, Zavadskas EK, Dadeliene R (2014) Multi-criteria assessment and ranking system of sport team formation based on objective-measured values of criteria set. Expert Syst Appl 41(14):6106–6113CrossRefGoogle Scholar
  9. Downward P, Dawson A (2000) The economics of professional team sports. Psychology Press, Palo AltoGoogle Scholar
  10. Gigerenzer G, Gaissmaier W (2011) Heuristic decision making. Annu Rev Psychol 62:451–482CrossRefGoogle Scholar
  11. Hillier FS (2012) Introduction to operations research. Tata McGraw-Hill Education, New DelhiGoogle Scholar
  12. Liebermann DG, Katz L, Hughes MD, Bartlett RM, McClements J, Franks IM (2002) Advances in the application of information technology to sport performance. J Sports Sci 20(10):755–769CrossRefGoogle Scholar
  13. Locke EA, Latham GP (1985) The application of goal setting to sports. J Sport Psychol 7(3):205–222CrossRefGoogle Scholar
  14. Lorains M, Ball K, MacMahon C (2012) Performance analysis of decision making in team sports. In: Proceedings of the world congress of performance analysis in sport IX, Worcester, EnglandGoogle Scholar
  15. Makridakis SG, Wheelwright SC (1978) Forecasting: methods and applications. Wiley/Hamilton series in management and administration, WisconsinGoogle Scholar
  16. Özceylan E (2016) A mathematical model using AHP priorities for soccer player selection: a case study. S Afr J Ind Eng 27(2):190–205Google Scholar
  17. Romero C (2014) Handbook of critical issues in goal programming. Elsevier, New YorkGoogle Scholar
  18. Rosner S, Shropshire KL (2004) The business of sports. Jones & Bartlett Learning, BurlingtonGoogle Scholar
  19. Saaty TL (1990) Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS Publications, PittsburghGoogle Scholar
  20. Tavana M, Azizi F, Azizi F, Behzadian M (2013) A fuzzy inference system with application to player selection and team formation in multi-player sports. Sport Manag Rev 16(1):97–110CrossRefGoogle Scholar
  21. Toyoda H (2011) Fédération Internationale de Volleyball: coaches manual I: chapter V-Volleyball for beginners, [EBOOK]Google Scholar
  22. Villa G, Lozano S (2016) Assessing the scoring efficiency of a football match. Eur J Oper Res 255(2):559–569CrossRefGoogle Scholar
  23. Wang J, Zhang J (2015) A win-win team formation problem based on the negotiation. Eng Appl Artif Intell 44:137–152CrossRefGoogle Scholar
  24. Winter EM, Jones AM, Davison RR, Bromley PD, Mercer TH (eds) (2006) Sport and exercise physiology testing guidelines: volume I-sport testing: the British association of sport and exercise sciences guide. Routledge, New YorkGoogle Scholar
  25. Zardari NH, Ahmed K, Shirazi SM, Yusop ZB (2015) Literature Review. In: Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management, SpringerBriefs in Water Science and Technology. Springer, New York, pp 7–67Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Industrial EngineeringAdana Science and Technology UniversityAdanaTurkey
  2. 2.Department of Industrial EngineeringBaşkent UniversityAnkaraTurkey
  3. 3.Department of Industrial EngineeringAnadolu UniversityEskisehirTurkey

Personalised recommendations