587
Views
1
CrossRef citations to date
0
Altmetric
Sports Performance

Characterizing player’s playing styles based on player vectors for each playing position in the Chinese Football Super League

ORCID Icon, , , , & ORCID Icon
Pages 1629-1640 | Received 28 Oct 2021, Accepted 21 Jun 2022, Published online: 06 Jul 2022

References

  • Aalbers, B., & Van Haaren, J. (2019). Distinguishing between roles of football players in play-by-play match event data. In U. Brefeld, J. Davis, J. V. Haaren, & A. Zimmermann (Eds.), Machine learning and data mining for sports analytics MLSA 2018. Lecture notes in computer science (Vol. 11330, pp. 31–41). Springer. https://doi.org/10.1007/978-3-030-17274-9_3
  • Ayyagari, S. (2018). Why are traditional centre-backs diminishing in world football Retrieved 20 December 2021. https://blog.playo.co/why-are-traditional-centre-backs-diminishing/
  • Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., & Matthews, I. (2014). Large-scale analysis of soccer matches using spatiotemporal tracking data. Paper presented at the 2014 IEEE International Conference on Data Mining, Shenzhen, China.
  • Bush, M., Barnes, C., Archer, D. T., 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), 1–11. https://doi.org/10.1016/j.humov.2014.10.003
  • Butterworth, A., O’Donoghue, P., & Cropley, B. (2013). Performance profiling in sports coaching: A review. International Journal of Performance Analysis in Sport, 13(3), 572–593. https://doi.org/10.1080/24748668.2013.11868672
  • Castellano, J., & Pic, M. (2019). Identification and preference of game styles in LaLiga associated with match outcomes. International Journal of Environmental Research and Public Health, 16(24), 5090. https://doi.org/10.3390/ijerph16245090
  • Clemente, F. M., Martins, F. M. L., Wong, P. D., Kalamaras, D., & Mendes, R. S. (2015). Midfielder as the prominent participant in the building attack: A network analysis of national teams in FIFA world cup 2014. International Journal of Performance Analysis in Sport, 15(2), 704–722. https://doi.org/10.1080/24748668.2015.11868825
  • Clemente, F. M., José, F., Oliveira, N., Martins, F. M. L., Mendes, R. S., Figueiredo, A. J., … Kalamaras, D. (2016a). Network structure and centralization tendencies in professional football teams from Spanish La Liga and English premier leagues. Journal of Human Sport and Exercise, 11(3), 376–389. https://doi.org/10.14198/jhse.2016.113.06
  • Clemente, F. M., Silva, F., Martins, F. M. L., Kalamaras, D., & Mendes, R. S. (2016b). Performance analysis tool for network analysis on team sports: A case study of FIFA soccer world cup 2014. Journal of Human Sport and Exercise. 230(3), 158–170. https://doi.org/10.1177/1754337115597335
  • Crossbarhub. (2020a). Midfielder Types in Football Retrieved 21 December 2021. https://crossbarhub.com/tactical-analysis/midfielder-types-in-football/
  • Crossbarhub. (2020b). Types of fullbacks Retrieved 21 December 2021. https://crossbarhub.com/tactical-analysis/types-of-fullbacks/
  • Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019). Actions speak louder than goals: Valuing player actions in soccer. Paper presented at the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage, AK, USA. https://doi.org/10.1145/3292500.3330758
  • Decroos, T., & Davis, J. (2020). Player vectors: Characterizing soccer players’ playing style from match event streams. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine learning and knowledge discovery in databases. ECML PKDD 2019. Lecture notes in computer science (Vol. 11908, pp. 569–584). Springer. https://doi.org/10.1007/978-3-030-46133-1_34
  • Duarte, R., Araújo, D., Correia, V., & Davids, K. (2012). Sports teams as superorganisms. Sports Medicine, 42(8), 633–642. https://doi.org/10.1007/BF03262285
  • Fernandez-Navarro, J., Fradua, L., Zubillaga, A., Ford, P. R., & McRobert, A. P. (2016). Attacking and defensive styles of play in soccer: Analysis of Spanish and English elite teams. Journal of Sports Sciences, 34(24), 2195–2204. https://doi.org/10.1080/02640414.2016.1169309
  • Gai, Y., Volossovitch, A., Lago, C., & Gómez, M.-Á. (2019). Technical and tactical performance differences according to player’s nationality and playing position in the Chinese football super league. International Journal of Performance Analysis in Sport, 19(4), 632–645. https://doi.org/10.1080/24748668.2019.1644804
  • Garcia-Aliaga, A., Marquina, M., Coteron, J., Rodriguez-Gonzalez, A., & Luengo-Sanchez, S. (2020). In-game behaviour analysis of football players using machine learning techniques based on player statistics. International Journal of Sports Science & Coaching, 16(1), 148–157. https://doi.org/10.1177/1747954120959762
  • Garrido, D., Burriel, B., Resta, R., Del Campo, R. L., & Buldu, J. (2021). Heatmaps in soccer: Event vs tracking datasets. arXiv preprint. https://arxiv.org/abs/2106.04558
  • Goes, F., Meerhoff, L. A., Bueno, M. J. O., Rodrigues, D. M., Moura, F. A., Brink, M. S., … Lemmink, K. (2020). Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review. European Journal of Sport Science, 21(4), 481–496. https://doi.org/10.1080/17461391.2020.1747552
  • Goes, F., Kempe, M., van Norel, J., & Lemmink, K. A. P. M. (2021). Modelling team performance in soccer using tactical features derived from position tracking data. IMA Journal of Management Mathematics, 32(4), 519–533. https://doi.org/10.1093/imaman/dpab006
  • Gómez, M.-Á., Mitrotasios, M., Armatas, V., & Lago-Penas, C. (2018). Analysis of playing styles according to team quality and match location in Greek professional soccer. International Journal of Performance Analysis in Sport, 18(6), 986–997. https://doi.org/10.1080/24748668.2018.1539382
  • Gonçalves, B. V., Figueira, B. E., Maçãs, V., & Sampaio, J. (2014). Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. Journal of Sports Sciences, 32(2), 191–199. https://doi.org/10.1080/02640414.2013.816761
  • Gudmundsson, J., & Horton, M. (2017). Spatio-temporal analysis of team sports. ACM Computing Surveys, 50(2), 1–34. https://doi.org/10.1145/3054132
  • Guide to FM. (2020). Central attack roles Retrieved 26 December 2021. https://www.guidetofm.com/tactics/central-attack-roles/
  • Hewitt, A., Greenham, G., & Norton, K. (2016). Game style in soccer: What is it and can we quantify it? International Journal of Performance Analysis in Sport, 16(1), 355–372. https://doi.org/10.1080/24748668.2016.11868892
  • Kalén, A., Rey, E., de Rellán-Guerra, A. S., & Lago-Peñas, C. (2019). Are soccer players older now than before? Aging trends and market value in the last three decades of the UEFA champions league. Frontiers in Psychology, 10(1):548. https://doi.org/10.3389/fpsyg.2019.00076
  • Konefał, M., Chmura, P., Andrzejewski, M., Pukszta, D., & Chmura, J. (2015). Analysis of match performance of full-backs from selected European soccer leagues. Central European Journal of Sport Sciences Medicine, 11(3), 45–53. https://doi.org/10.18276/cej.2015.3-05
  • Korte, F., Link, D., Groll, J., & Lames, M. (2019). Play-by-play network analysis in football. Frontiers in Psychology, 10:1738 https://doi.org/10.3389/fpsyg.2019.01738
  • Lago-Peñas, C., Gómez-Ruano, M., & Yang, G. (2018). 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. https://doi.org/10.1080/24748668.2018.1431857
  • Lasek, J., Szlávik, Z., & Bhulai, S. (2013). The predictive power of ranking systems in association football. International Journal of Applied Pattern Recognition, 1(1), 27–46. https://doi.org/10.1504/IJAPR.2013.052339
  • Lee, D. D., & Seung, H. S. (2000). Algorithms for non-negative matrix factorization. Paper presented at the Proceedings of the 13th International Conference on Neural Information Processing Systems, Denver, CO, USA.
  • Li, Y., Ma, R., Gonçalves, B., Gong, B., Cui, Y., & Shen, Y. (2020). Data-driven team ranking and match performance analysis in Chinese football super league. Chaos, Solitons & Fractals, 141(1), 110330. https://doi.org/10.1016/j.chaos.2020.110330
  • Liu, H., Hopkins, W., Gómez, M.-Á., & 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. https://doi.org/10.1080/24748668.2013.11868690
  • Liu, H., Gómez, M.-Á., Gonçalves, B., & Sampaio, J. (2016). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34(6), 509–518. https://doi.org/10.1080/02640414.2015.1117121
  • Lord, F., Pyne, D. B., Welvaert, M., & Mara, J. K. (2020). Methods of performance analysis in team invasion sports: A systematic review. Journal of Sports Sciences, 38(20), 2338–2349. https://doi.org/10.1080/02640414.2020.1785185
  • Mazurek, J. (2018). Which football player bears most resemblance to Messi? A statistical analysis. arXiv preprint. https://arxiv.org/abs/1802.00967
  • 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. https://doi.org/10.1287/inte.1110.0589
  • McLean, S., Salmon, P. M., Gorman, A. D., Naughton, M., & Solomon, C. (2017). Do inter-continental playing styles exist? Using social network analysis to compare goals from the 2016 EURO and COPA football tournaments knock-out stages. Theoretical Issues in Ergonomics Science, 18(4), 370–383. https://doi.org/10.1080/1463922X.2017.1290158
  • Pappalardo, L., Cintia, P., Ferragina, P., Massucco, E., Pedreschi, D., & Giannotti, F. (2019). PlayeRank: Data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology, 10(5), 1–27. https://doi.org/10.1145/3343172
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Dubourg, V. (2011). Scikit-learn: Machine learning in python. the Journal of Machine Learning Research, 12(85), 2825–2830.
  • Peña, J. L., & Navarro, R. S. (2015). Who can replace Xavi? A passing motif analysis of football players. arXiv preprint. https://arxiv.org/abs/1506.07768
  • Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
  • Whoscored. (2018). Super league player statistics 2018 Retrieved 26 December 2021. https://www.whoscored.com/Regions/45/Tournaments/162/Seasons/7242/Stages/15995/PlayerStatistics/China-Super-league-2018
  • Wu, Y., Xia, Z., Wu, T., Yi, Q., Yu, R., & Wang, J. (2020). Characteristics and optimization of core local network: Big data analysis of football matches. Chaos, Solitons & Fractals, 138, 110136. https://doi.org/10.1016/j.chaos.2020.110136
  • Yu, Q., Gai, Y., Gong, B., Gómez, M.-Á., & Cui, Y. (2020). Using passing network measures to determine the performance difference between foreign and domestic outfielder players in Chinese football super league. International Journal of Sports Science & Coaching, 15(3), 398–404. https://doi.org/10.1177/1747954120905726
  • Zhou, C., Calvo, A. L., Robertson, S., & Gómez, M.-Á. (2020). Long-term influence of technical, physical performance indicators and situational variables on match outcome in male professional Chinese soccer. Journal of Sports Sciences, 39(6), 598–608. https://doi.org/10.1080/02640414.2020.1836793
  • Zhou, C., Lago-Peñas, C., Lorenzo, A., & Gómez, M.-Á. (2021). Long-Term trend analysis of playing styles in the Chinese soccer super league. Journal of Human Kinetics, 79(1), 237–247. https://doi.org/10.2478/hukin-2021-0077

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.