1. Bush, M., Barnes, C., Archer, D.A., Hogg, B., & Bradley P.S. (2015). Evolution of match performance parameters for various playing positions in the English Premier League. Human Movement Science, 39, 1-11. DOI:
10.1016/j.humov.2014.10.003.
2. Fernandez-Navarro, J., Fradua, L. Z., & McRobert, A. P. (2018). Influence of contextual variables on styles of play in soccer. International Journal of Performance Analysis in Sport, 18(3), 423-436. DOI:
10.1080/24748668.2018.1479925.
3. Gai, Y., Leicht, A.S., Lago, C., & Gomez, M. (2019). Physical and technical differences between domestic and foreign soccer players according to playing positions in the China Super League. Research in Sports Medicine, 27(3), 314-325. DOI:
10.1080/15438627.2018.1540005.
4. Gómez, M. A., Gómez-Lopez, M., Lago, C., & Sampaio, J. (2012). Effects of game location and final outcome on game-related statistics in each zone of the pitch in professional football. European Journal of Sport Science, 12(5), 393-398. DOI:
10.1080/17461391.2011.566373.
5. Hong, S. J. (2010). The development of performance determinants criteria for evaluating positional soccer player. Korean Journal of Sport Science, 21(2), 1172-1182. DOI:
10.24985/kjss.2010.21.2.1172.
6. Hong, S.J. (2017). The relative importance of football skill factors based on the position. The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science, 19(4), 89-98. DOI:
10.21797/ksme.2017.19.4.008.
7. Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417-441. DOI:
10.1037/h0071325.
8. James, N., Mellaiue, S., Jones, N. (2005). The development of position-specific performance indicators in professional rugby union. Journal of Sports Sciences, 23(1), 63-72. DOI:
10.1080/02640410410001730106.
9. Jolliffe, I. T. (2002). Principal component analysis, NY: Springer.
10. Jung, T. Y. (2018). Sustainable Development Goals in the Republic of Korea, London: Routledge. DOI:
10.4324/9781351067478.
11. Lago-Peñas, C., Gomez-Ruano, M., & Yang, G. (2017). Styles of play in professional soccer: an approach of the Chinese Soccer Super League. International Journal of Performance Analysis in Sport, 17(6), 1073-1084. DOI:
10.1080/24748668.2018.1431857.
12. Liu, H., Hopkins, W., Gómez, A, M. & Molinuevo, S, J. (2013). Inter-operator reliability of live football match statistics from OPTA Sportsdata. International Journal of Performance Analysis in Sport, 13(3), 803-821. DOI:
10.1080/24748668.2013.11868690.
13. McHale, I.G., Scarf, P.A., & Folker, D.E. (2012). On the development of a soccer player performance rating system for the English Premier League. Interfaces, 42(4), 339-351. DOI:
10.1287/inte.1110.0589.
14. Memmert, D., & Raabe, D. (2018). Data Analytics in Football: Positional Data Collection, Modelling and Analysis, NY:Routledge. DOI:
10.4324/9781351210164.
15. Moura, F, A., Martins, E, B., & Cunha, S, A. (2014). Analysis of football game-related statistics using multivariate techniques. Journal of Sports Sciences, 32(20), 1881-1887. DOI:
10.1080/02640414.2013.853130.
16. Nardo, M., Saisana, M., Saltelli, A., & Tarantola, S. (2005). Tools for Composite Indicators Building. European Comission, Ispra, 15(1), 19-20.
17. Nicoletti, G., Scarpette, S. & Boylaud, O. (2000). Summary Indicators of Product Market Regulation with an Extension to Employment Protection Legislation, OECD Economics Department Working Papers. 226. DOI:
10.2139/ssrn.201668.
18. Pearson, K. (1901). On lines and planes of closest fit to a system of points in space. Philosophy Magazin,. 2, 557-572. DOI:
10.1080/14786440109462720.
19. Perl, J., & Memmert, D. (2018). Key performance indicators: Modelling and Simulation in Sport and Exercise, NY : Routledge.
20. Poli, R., Besson, R., & Ravenel, L. (2018). Football analytics: The CIES Football Observatory 2017/18 season. Football-obeservatory.com. CIES Football Observatory. Retrieved April 18, 2019, from http://www.football-observatory.com/IMG/pdf/cies_football_analytics_2018.pdf.
21. Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringPlus, 5(1). DOI:
10.1186/s40064-016-3108-2.
22. Rösch, D., Hodgson, R., Peterson, L., Graf-Baumann, T., Junge, Astrid., Chomiak, J., & Dvorak, J. (2000). Assessment and evaluation of football performance. The American Journal of Sports Medicine, 28(5), 29-30. DOI:
10.1177/28.suppl_5.s-29.
23. Schuth, G., Carr, G., Barness, C., Carling & Bradley, P.S. (2016). Positional interchanges influence the physical and technical match performance variables of elite soccer players. Journal of Sports Sciences, 34(6), 501-508. DOI:
10.1080/02640414.2015.1127402.
24. Taylor, J., Mellalieu, S., & James, N. (2004). Behavioural comparisons of positional demands in professional soccer. International Journal of Performance Analysis in Sport, 4(1), 81-97. DOI:
10.1080/24748668.2004.11868294.
25. UEFA. (2018). Association club coefficients. Uefa.com. Retrieved April 16, 2019, from https://www.uefa.com/memberassociations/uefarankings/country/#/yr/2018.
26. WhoScored. (2019). Explanations. Whoscored.com. Retrieved April 14, 2019, from https://www.whoscored.com/Explanation.
27. Yang G, Leicht A, Lago C, Gómez M Á. (2018). Key team physical and technical performance indicators indicative of team quality in the soccer Chinese super league. Research in Sports Medicine, 26(2), 158-167. DOI:
10.1080/15438627.2018.1431539.
28. You, K.W. (2013). Degree of significance in evaluation of competency in soccer games using fuzzy AHP. Korean Society for the Study of Physical Education, 18(1), 223-235.