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

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Abstract

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.

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References

  • Ahmed F, Deb K, Jindal A (2013) Multi-objective optimization and decision making approaches to cricket team selection. Appl Soft Comput 13(1):402–414

    Article  Google Scholar 

  • Atkinson MP, Kress M, Szechtman R (2012) Carrots, sticks and fog during insurgencies. Math Soc Sci 64(3):203–213

    Article  Google Scholar 

  • Boon BH, Sierksma G (2003) Team formation: matching quality supply and quality demand. Eur J Oper Res 148(2):277–292

    Article  Google Scholar 

  • 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):23–29

    Article  Google Scholar 

  • Caro CA (2012) College football success: the relationship between recruiting and winning. Int J Sports Sci Coach 7(1):139–152

    Article  Google Scholar 

  • Cattrysse DG, Van Wassenhove LN (1992) A survey of algorithms for the generalized assignment problem. Eur J Oper Res 60(3):260–272

    Article  Google Scholar 

  • 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):545–563

    Article  Google Scholar 

  • 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):6106–6113

    Article  Google Scholar 

  • Downward P, Dawson A (2000) The economics of professional team sports. Psychology Press, Palo Alto

    Google Scholar 

  • Gigerenzer G, Gaissmaier W (2011) Heuristic decision making. Annu Rev Psychol 62:451–482

    Article  Google Scholar 

  • Hillier FS (2012) Introduction to operations research. Tata McGraw-Hill Education, New Delhi

    Google Scholar 

  • 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):755–769

    Article  Google Scholar 

  • Locke EA, Latham GP (1985) The application of goal setting to sports. J Sport Psychol 7(3):205–222

    Article  Google Scholar 

  • 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, England

  • Makridakis SG, Wheelwright SC (1978) Forecasting: methods and applications. Wiley/Hamilton series in management and administration, Wisconsin

    Google Scholar 

  • Özceylan E (2016) A mathematical model using AHP priorities for soccer player selection: a case study. S Afr J Ind Eng 27(2):190–205

    Google Scholar 

  • Romero C (2014) Handbook of critical issues in goal programming. Elsevier, New York

    Google Scholar 

  • Rosner S, Shropshire KL (2004) The business of sports. Jones & Bartlett Learning, Burlington

    Google Scholar 

  • Saaty TL (1990) Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS Publications, Pittsburgh

    Google Scholar 

  • 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):97–110

    Article  Google Scholar 

  • Toyoda H (2011) Fédération Internationale de Volleyball: coaches manual I: chapter V-Volleyball for beginners, [EBOOK]

  • Villa G, Lozano S (2016) Assessing the scoring efficiency of a football match. Eur J Oper Res 255(2):559–569

    Article  Google Scholar 

  • Wang J, Zhang J (2015) A win-win team formation problem based on the negotiation. Eng Appl Artif Intell 44:137–152

    Article  Google Scholar 

  • 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 York

    Google Scholar 

  • 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 7–67

Download references

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Authors

Corresponding author

Correspondence to Gerçek Budak.

Appendices

Appendix 1: Weights of positions \((MS_j)\) (Budak et al. 2017)

Positions

S

L

MB 1

MB 2

SP 1

SP 2

OP

Weights

0.26

0.07

0.12

0.12

0.13

0.13

0.18

Weights of skills for each position \((SM_{yj})\) (Budak et al. 2017)

Skills/positions

S

L

MB 1

MB 2

SP 1

SP 2

OP

SE

0.21

0.00

0.27

0.27

0.21

0.21

0.27

R

0.00

1.00

0.00

0.00

0.44

0.44

0.00

B

0.16

0.00

0.54

0.54

0.12

0.12

0.22

A

0.00

0.00

0.18

0.18

0.22

0.22

0.51

P

0.63

0.00

0.00

0.00

0.00

0.00

0.00

Appendix 2: Forecasts of player skill performances \((SS_{iy})\)

Player/skills

SE

R

B

A

P

Player #1

50.0

0.0

33.3

25.0

0.0

Player #2

50.0

16.7

8.3

58.3

42.8

Player #3

0.0

66.8

0.0

0.0

0.0

Player #4

52.4

33.3

11.0

90.3

0.0

Player #5

31.3

33.3

33.3

65.2

0.0

Player #6

23.6

30.3

24.3

74.2

0.0

Player #7

42.6

33.3

37.7

72.2

0.0

Player #8

41.2

0.0

33.0

72.7

0.0

Player #9

40.4

60.3

35.3

69.5

0.0

Player #10

46.5

0.0

35.7

79.0

0.0

Player #11

50.3

0.0

55.7

87.3

0.0

Player #12

16.7

0.0

11.0

26.7

0.0

Player #13

0.0

16.7

0.0

4.2

0.0

Player #14

45.0

0.0

20.0

0.0

48.0

Appendix 3: Possible position that players are able to play \((PM_{ij})\)

Player/positions

S

L

MB 1

MB 2

SP 1

SP 2

OP

Player #1

0

0

1

1

0

0

0

Player #2

1

0

0

0

0

0

0

Player #3

0

1

0

0

0

0

0

Player #4

0

0

1

1

0

0

0

Player #5

0

0

0

0

1

1

1

Player #6

0

0

0

0

1

1

0

Player #7

0

0

1

1

0

0

0

Player #8

0

0

0

0

0

0

1

Player #9

0

0

0

0

1

1

0

Player #10

0

0

1

1

0

0

1

Player #11

0

0

1

1

0

0

0

Player #12

0

0

0

0

1

1

1

Player #13

0

1

0

0

0

0

0

Player #14

1

0

0

0

0

0

0

Thresholds for each position’s skill \((TH_{jy})\):

\(\hbox {Position}\backslash \hbox {skill}\)

S

L

MB 1

MB 2

SP 1

SP 2

OP

SE

35

0

20

20

20

20

20

R

0

10

0

0

20

20

0

B

10

0

20

20

20

20

20

A

0

0

25

25

20

20

40

P

40

0

0

0

0

0

0

Appendix 4: Higher thresholds for each position’s skill \((TH_{jy})\)

\(\hbox {Position}\backslash \hbox {skill}\)

S

L

MB 1

MB 2

SP 1

SP 2

OP

SE

40

0

30

30

30

30

30

R

0

30

0

0

30

30

0

B

25

0

30

30

30

30

30

A

0

0

35

35

30

30

50

P

50

0

0

0

0

0

0

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Budak, G., Kara, İ., İç, Y.T. et al. New mathematical models for team formation of sports clubs before the match. Cent Eur J Oper Res 27, 93–109 (2019). https://doi.org/10.1007/s10100-017-0491-x

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  • DOI: https://doi.org/10.1007/s10100-017-0491-x

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