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e‑ISSN 2148-905X Open Access · CC BY 4.0
Vol. 13 · Issue 3 · 2026 Sep 15, 2026 Research Article

Comparison of National League Performances of Top-Ranked FIFA Countries Using Principal Component Analysis

BK
Baris Karakoc Corresponding Haliç University, Faculty of Sport Sciences, Istanbul, Türkiye bariskarakoc@halic.edu.tr Turkey
Pages274-288 PublishedSep 15, 2026 LicenseOpen Access
PDF (Türkçe)
VOL 13 · ISS 3 · 2026 Sportive Perspective: Journal of Sports and Educational Sciences View issue

Abstract

The data-driven transformation of modern football necessitates a comparative examination of the national leagues of top-ranked nations in the FIFA world rankings. This study aims to analyze the technical performance profiles of eight prestigious European football leagues (Premier League, La Liga, Serie A, Bundesliga, Ligue 1, Eredivisie, Liga Portugal, and Pro League) using principal component analysis. The research group comprises technical performance data from 2604 matches played by 148 teams across these leagues during the 2024-2025 season. During the analysis, an initially extensive set of technical indicators was refined to 27-33 significant variables per league based on a .6 factor-loading threshold. Data analysis was conducted using the Kaiser-Meyer-Olkin measure of sampling adequacy, Bartlett’s test of sphericity, and PCA with varimax rotation. Findings indicated that KMO values for all leagues exceeded .773, with the total explained variance ranging from 76.1% to 79.7%. While attacking efficiency and finishing emerged as the most dominant component in the Premier League and La Liga, passing game and organization was identified as the primary component in Serie A and Liga Portugal. Furthermore, leagues such as the Bundesliga and Ligue 1 exhibited a six-component structure, whereas Serie A followed a more concentrated five-component technical architecture. In conclusion, the technical identities of elite-level leagues are characterized by multidimensional and unique structures shaped by their tactical traditions and playing philosophies, rather than a homogeneous global model

Keywords

#European leagues #football #performance analysis #principal component analysis #technical variables

References

  1. Bortnik, L., Bruce-Low, S., Burger, J. A., Alexander, J., Harper, D., Morgans, R., Carling, C., McDaid, K., & Rhodes, D. (2023). Physical match demands across different playing positions during transitional play and high-pressure activities in elite soccer. Biology of Sport, 41(2), 73-82. https://doi.org/10.5114/biolsport.2024.131815
  2. Casal, C. A., Losada, J. L., Barreira, D., & Maneiro, R. (2021). Multivariate exploratory comparative analysis of laliga teams: Principal component analysis. International Journal of Environmental Research and Public Health, 18(6), 3176-3176. https://doi.org/10.3390/ijerph18063176
  3. Eryılmaz, K., & Karakoç, B. (2025). Türkiye efeler ligi'nde set sonucunu ayrıştıran değişkenlerin incelenmesi: Tanımlayıcı araştırma. Turkiye Klinikleri Journal of Sports Sciences, 17(2). https://doi.org/10.5336/sportsci.2024-106603
  4. Eusebio, P., Prieto-González, P., & Marcelino, R. (2025). Decoding the complexities of transitions in football: A comprehensive narrative review. German Journal of Exercise and Sport Research, 55(3), 332-342. https://doi.org/10.1007/s12662-024-00951-9
  5. 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
  6. Gásquez, R., & Royuela, V. (2016). The determinants of international football success: A panel data analysis of the elo rating. Social Science Quarterly, 97(2), 125-141. https://doi.org/10.1111/ssqu.12262
  7. Kong, L., Zhang, T., Zhou, C., Gómez, M., Hu, Y., & Zhang, S. (2022). The evaluation of playing styles integrating with contextual variables in professional soccer. Frontiers in Psychology, 13:1002566, 1-10. https://doi.org/10.3389/fpsyg.2022.1002566
  8. Lathuilière, S., Mesejo, P., Alameda-Pineda, X., & Horaud, R. (2020). A comprehensive analysis of deep regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(9), 2065-2081. https://doi.org/10.1109/tpami.2019.2910523
  9. Li, C., & Zhao, Y. (2021). Comparison of goal scoring patterns in “the big five” european football leagues. Frontiers in Psychology, 11:619304. 1-7. https://doi.org/10.3389/fpsyg.2020.619304
  10. Liu, H., Hopkins, W. G., Gómez, A., & 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
  11. Lolli, L., Bauer, P., Irving, C., Bonanno, D., Höner, O., Gregson, W., & Salvo, V. D. (2024). Data analytics in the football industry: A survey investigating operational frameworks and practices in professional clubs and national federations from around the world. Science and Medicine in Football, 9(2), 189-198. https://doi.org/10.1080/24733938.2024.2341837
  12. Memmert, D., & Rein, R. (2018). Match analysis, big data and tactics: Current trends in elite soccer. German Journal of Sports Medicine, 69(3), 65-72. https://doi.org/10.5960/dzsm.2018.322
  13. Moto, S., Ratanaolarn, T., Tuntiwongwanich, S., & Pimdee, P. (2018). A thai junior high school students’ 21st century information literacy, media literacy, and ICT literacy skills factor analysis. International Journal of Emerging Technologies in Learning, 13(9), 87-87. https://doi.org/10.3991/ijet.v13i09.8355
  14. Nosek, P., Sormaz, M., Andrew, M., Drust, B., & Brownlee, T. E. (2023). The use of principal component analysis for reduction of training load data in professional soccer. Kinesiology, 55(2), 202-212. https://doi.org/10.26582/k.55.2.5
  15. Plakias, S., Kokkotis, C., Moustakidis, S., Tsatalas, T., Papalexi, M., Kasioura, C., Giakas, G., & Tsaopoulos, D. (2023a). Identifying playing styles of European soccer teams during the key moments of the game. Journal of Physical Education and Sport, 23(4), 878-890. https://doi.org/10.7752/jpes.2023.04111
  16. Plakias, S., Moustakidis, S., Mitrotasios, M., Kokkotis, C., Tsatalas, T., Papalexi, M., Giakas, G., & Tsaopoulos, D. (2023b). A Multivariate and cluster analysis of diverse playing styles across european football leagues. Journal of Physical Education and Sport, 23(7), 1631-1641. https://doi.org/10.7752/jpes.2023.07200
  17. Rechenchosky, L., Menegassi, V. M., Jaime, M. de O., Borges, P. H., Serra-Olivares, J., & Rinaldi, W. (2022). Adaptation and validation of a test for the evaluation of tactical knowledge in soccer: Test de conocimiento táctico ofensivo en fútbol for the brazilian context (TCTOF-BRA). Frontiers in Psychology, 13:849255, 1-20. https://doi.org/10.3389/fpsyg.2022.849255
  18. Rockerbie, D. W. (2016). The importance of domestic football leagues to international performance: Predicting FIFA points. Soccer and Society, 20(1), 21-38. https://doi.org/10.1080/14660970.2016.1267627
  19. Rojas-Valverde, D., Pino-Ortega, J., Gómez-Carmona, C. D., & Rico-González, M. (2020). A systematic review of methods and criteria standard proposal for the use of principal component analysis in team’s sports science. International Journal of Environmental Research and Public Health, 17(23), 8712-8712. https://doi.org/10.3390/ijerph17238712
  20. Sarmento, H., Marcelino, R., Anguera, M. T., CampaniÇo, J., Matos, N., & LeitÃo, J. C. (2014). Match analysis in football: A systematic review. Journal of Sports Sciences, 32(20), 1831-1843. https://doi.org/10.1080/02640414.2014.898852
  21. Silva, H., & Marcelino, R. (2022). Inter-operator reliability of InStat Scout in female football games. Science & Sports, 38(1), 42-46. https://doi.org/10.1016/j.scispo.2021.07.015
  22. Weaving, D., Beggs, C., Dalton-Barron, N., Jones, B., & Abt, G. (2019). Visualizing the complexity of the athlete-monitoring cycle through principal-component analysis. International Journal of Sports Physiology and Performance, 14(9), 1304-1310. https://doi.org/10.1123/ijspp.2019-0045
  23. Yi, Q., Groom, R., Chen, D., Liu, H., & Gómez, M. (2019). Differences in technical performance of players from ‘the big five’ european football leagues in the UEFA champions league. Frontiers in Psychology, 10:2738, 1-8. https://doi.org/10.3389/fpsyg.2019.02738
  24. Yunus, M., & Aditya, R. S. (2024). Talent scouting and standardizing fitness data in football club: Systematic review. Retos, 62, 1382-1389.

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