A methodology for football players selection problem based on multi-measurements criteria analysis

Qader, M.A.

Abstract

Football is one of the most popular sports in the world. Professional football has become a significant contributor to global economics and business. The game attracts considerable funds, which motivate participants of the sporting process (players, coaches, club owners, administration, etc.) to strive for better athletic results. However, such a motivation simultaneously promotes internal and external rivalry. The increasing number of players, the teams’ desire to attract better team members, and the improved athletes’ performance boost the use of assessment and rating processes. The most popular and widely used player rating systems are based on performance statistics, which reflect situational factors of the game. Most specialists believe that such systems lack objectivity. Thus, this paper presents a new methodology to assess and rank football players based on multi-criteria decision making (MCDM). A hands-on study is conducted for the assessment. A sample of 24 players is grouped into four separate groups consisting of six players for each group. The age of U17 is examined by 12 tests distributed as follows: three anthropometrics, five fitness, and four skills tests. Players are ranked on the basis of a set of measurement metric outcomes using the technique for order performance by similarity to ideal solution (TOPSIS) method to select the appropriate player using a one-shot experiment. Then, this study utilizes the mean and standard deviation to ensure that the four groups of players undergo systematic ranking, respectively. Findings are as follows: (1) systematic: TOPSIS is an effective tool used to solve player selection problems, and (2) statistics: group number one is the best group among the four groups, identical to the results of the system.

References

[1] G. Di Toro, R. Han, T. Hirose, N. De Paola, S. Nielsen, K. Mizoguchi, T. Shimamoto, Fault lubrication during earthquakes, Nature 471 (7339) (2011) 494–498. [2] M.A. Jones, G. Stratton, T. Reilly, V.B. Unnithan, Biological risk indicators for recurrent non-specific low back pain in adolescents, Br. J. Sports Med. 39 (3) (2005) 137–140. [3] J.R. Katzenbach, D.K. Smith, The Wisdom of Teams: Creating the Highperformance Organization, Harvard Business Press, 1993. [4] B.H. Boon, G. Sierksma, Team formation: matching quality supply and quality demand, Eur. J. Oper. Res. 148 (2) (2003) 277–292. [5] B. Dezˇman, S. Trninic´, D. Dizdar, Expert model of decision-making system for efficient orientation of basketball players to positions and roles in the game– Empirical verification, Collegium Antropologicum 25 (1) (2001) 141–152. [6] S. Trninic´, V. Papic´, V. Trninic´, D. Vukicˇevic´, Player selection procedures in team sports games, Acta Kinesiologica 2 (1) (2008) 24–28. [7] M. Tavana, K. Khalili-Damghani, S. Sadi-Nezhad, A fuzzy group data envelopment analysis model for high-technology project selection: a case study at NASA, Comput. Ind. Eng. 66 (1) (2013) 10–23. [8] A. Arnason, S.B. Sigurdsson, A. Gudmundsson, I. Holme, L. Engebretsen, R. Bahr, Physical fitness, injuries, and team performance in soccer, Med. Sci. Sports Exerc. (2004). [9] M. Kagawa, Anthropometric skills in sports science and its significance [in Japanese: スポーツ科学における形態測定技術の活用法とその意義], Jpn. J. Sports Nutr. 1 (1) (2008) 15–21. [10] A. Hargreaves, R. Bate, Skills & Strategies for Coaching Soccer, second ed., Human Kinetics, 1990, p. 392. [11] S. Kasap, N. Kasap, Development of a database and decision support system for performance evaluation of soccer players, 2005. [12] J.G. Johnson, Cognitive modeling of decision making in sports, Psychol. Sport Exerc. 7 (6) (2006) 631–652. [13] F. Khatib, S. Cooper, M.D. Tyka, K. Xu, I. Makedon, Z. Popovic´, et al., Algorithm discovery by protein folding game players, Proc. Natl. Acad. Sci. 108 (47) (2011) 18949–18953. [14] S.S. Sathya, M.S. Jamal, Applying genetic algorithm to select an optimal cricket team, in: Proceedings of the International Conference on Advances in Computing, Communication and Control, ACM, 2009, pp. 43–47. [15] Merigó and Gil-Lafuente (2011) analyzed the use of the ordered weighted averaging (OWA) operator in the selection of human resources in sport management. [16] Z. Ahmed, D.S. Akerib, S. Arrenberg, C.N. Bailey, D. Balakishiyeva, L. Baudis, et al., Results from a low-energy analysis of the CDMS II germanium data, Phys. Rev. Lett. 106 (13) (2011) 131302. [17] R.D. Raut, H.V. Bhasin, S.S. Kamble, Supplier selection using integrated multicriteria decision-making methodology, Int. J. Operational Res. 13 (4) (2012) 359–394. [18] L.J. Miralles, M.I. Piñar López, D. Cárdenas Vélez, G. Sánchez-Delgado, J.C. Perales, Basketball training influences shot selection assessment: a multiattribute decision-making approach1, Revista de psicología del deporte 22 (1) (2013) 0223–0226. [19] S. Dadelo, Z. Turskis, E.K. Zavadskas, R. Dadeliene, Multi-criteria assessment and ranking system of sport team formation based on objective-mea values of criteria set, Expert Syst. Appl. 41 (14) (2014) 6106–6113. [20] C.A. Combs, M. Gravett, T.J. Garite, D.E. Hickok, J. Lapidus, R. Porreco, H. Miller, Amniotic fluid infection, inflammation, and colonization in preterm labor with intact membranes, Am. J. Obstet. Gynecol. 210 (2) (2014), 125-e1. [21] P. Krustrup, M. Mohr, A. Steensberg, J. Bencke, M. Kjær, J. Bangsbo, Muscle and blood metabolites during a soccer game: implications for sprint performance, Med. Sci. Sports Exercise 38 (6) (2006) 1165–1174. [22] D. Estampe, S. Lamouri, J.L. Paris, S. Brahim-Djelloul, A framework for analysing supply chain performance evaluation models, Int. J. Prod. Econ. 142 (2) (2013) 247–258. [23] E.K. Zavadskas, A. Kaklauskas, Z. Turskis, J. Tamošaitiene˙ , Multi-attribute decision-making model by applying grey numbers, Informatica 20 (2) (2009) 305–320. [24] H.S. Shih, H.J. Shyur, E.S. Lee, An extension of TOPSIS for group decision making, Math. Comput. Modell. 45 (7) (2007) 801–813. [25] S.-Y. Chou, Y.-H. Chang, C.-Y. Shen, A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes, Eur. J. Oper. Res. 189 (1) (2008) 132–145. [26] S. Opricovic, G.-H. Tzeng, Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS, Eur. J. Oper. Res. 156 (2) (2004) 445–455. [27] C. Kahraman, S. Çebı, A new multi-attribute decision making method: Hierarchical fuzzy axiomatic design, Expert Syst. Appl. 36 (3) (2009) 4848–4861. [28] A.R. Ravindran (Ed.), Operations Research Methodologies, CRC Press, 2008. [29] F.T. Bozbura, A. Beskese, C. Kahraman, Prioritization of human capital measurement indicators using fuzzy AHP, Expert Syst. Appl. 32 (4) (2007) 1100–1112. [30] F. Ahmed, A. Jindal, K. Deb, Multi-objective optimization and decision making approaches to cricket team selection, Appl. Soft Comput. 13 (1) (2013) 402– 414. [31] X. Zhongyou, Study on the application of TOPSIS method to the introduction of foreign players in CBA games, Phys. Proc. 33 (2012) 2034–2039. [32] T. Reilly, A.M. Williams, A. Nevill, A. Franks, A multidisciplinary approach to talent identification in soccer, J. Sports Sci. 18 (9) (2000) 695–702. [33] Anders Svensson, Katrine K. Andersen, Matthias Bigler, Henrik B. Clausen, Dorthe Dahl-Jensen, Siwan M. Davies, Sigfus J. Johnsen, The Greenland ice core chronology 2005, 15–42 ka. Part 2: comparison to other records, Quatern. Sci. Rev. 25 (23) (2006) 3258–3267. [34] P. Krustrup, J. Bangsbo, Physiological demands of top-class soccer refereeing in relation to physical capacity: effect of intense intermittent exercise training, J. Sport Sci. 19 (2001) 881–891. [35] P. Krustrup, M. Mohr, T. Amstrup, T. Rysgaard, J. Johansen, A. Steensberg, P.K. Pedersen, J. Bangsbo, The Yo-Yo intermittent recovery test: physiological response, reliability, and validity, Med. Sci. Sport Exerc. 35 (2003) 697–705. [36] P. Krustrup, M. Mohr, H. Ellingsgaard, J. Bangsbo, Physical demands during an elite female soccer game: Importance of training status, Med. Sci. Sports Exerc. 37 (2005) 1242–1248. [37] A. Shamilkameel, W. John, The International Congress on Science and Football Tehran 1–3 November 2009, 433 AFC U13 Football Skills Test Project, 2009. [38] K. Krieger, Soccer tryouts: what coaches look for [Online] Available: , 2013 (accessed 30-12 2013). [39] T. Stølen, K. Chamari, C. Castagna, U. Wisløff, Physiology of soccer, Sports Med. 35 (6) (2005) 501–536.