SavedAdd your journal

Journals / Pamukkale Journal of Sport Sciences / 2026 / Volume: 17 Issue: 1

Determining the Contribution of Performance Variables to Game Outcomes in Elite Male Soccer

Barış Karakoç(Halic University)0000-0002-2128-8483
Erdem Şanli(FATIH SULTAN MEHMET VAKIF UNIVERSITY, VOCATIONAL SCHOOL)0000-0003-3828-0215
Alper Aşçı(HALIC UNIVERSITY)0000-0003-3958-8908
Pages
—
Publication Date
2026-03-07
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Future research could develop models for different tournaments, especially using time-related variables, if applicable.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The increased amount of soccer data challenges performance evaluation using classical methods.
📊
Key Findings & Data:The present study aims to determine which performance variables most strongly contribute to game outcomes in elite male soccer by using machine learning models trained under different venue conditions.
💡
Conclusion & Impact:Future research could develop models for different tournaments, especially using time-related variables, if applicable.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Game Analysis · Extreme Gradient Boost · Machine Learning

Abstract

The increased amount of soccer data challenges performance evaluation using classical methods. Therefore, recent machine learning approaches can help address complex datasets in sports. The present study aims to determine which performance variables most strongly contribute to game outcomes in elite male soccer by using machine learning models trained under different venue conditions. Technical, tactical, and physical variables obtained from 542 matches played over two consecutive seasons were used to predict results for three venue conditions (all, home, and away). Variance Inflation Factor analysis and BorutaPy were applied before extreme gradient boost (XGBoost) modeling. Feature importance rankings and SHAP analysis were used to identify variables affecting model performance and outputs across different conditions. The models showed high accuracy on game outcome predictions, especially in the win and loss conditions (between 93.38-95.93%), while lower results in the draw (between 68.99-88.46%). The variables that most impacted the model's predictions were the Conversion rate, the Opponent's xG per goal, the Opponent's xG, and xG Conversion. The teams’ performance predictions for game outcomes differ, and draws are difficult to predict in this study's competition. The technical variables contributed the most to the models and outputs. Coaches should consider the structure and needs of their competitions while evaluating the data they possess. Future research could develop models for different tournaments, especially using time-related variables, if applicable.

🔖

Cite This Article

Karakoç, B., Şanli, E., Aşçı, A. (2026). Determining the Contribution of Performance Variables to Game Outcomes in Elite Male Soccer. Pamukkale Journal of Sport Sciences, Volume: 17 Issue: 1. https://doi.org/10.54141/psbd.1681109

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (31)

  1. Badiella, L., Puig, P., Lago-Peñas, C., & Casals, M. (2023). Influence of Red and Yellow cards on team performance in elite soccer. Annals of Operations Research, 325(1), 149-165. https://doi.org/10.1007/s10479-022-04733-0
  2. Bilek, G., & Ulas, E. (2019). Predicting match outcome according to the quality of opponent in the English premier league using situational variables and team performance indicators. International Journal of Performance Analysis in Sport, 19(6), 930–941. https://doi.org/10.1080/24748668.2019.1684773
  3. Bunker, R., Yeung, C., & Fujii, K. (2024). Machine learning for soccer match result prediction. In Artificial Intelligence, Optimization, and Data Sciences in Sports (pp. 7-49). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-76047-1_2
  4. Carling, C., Williams, A. M., & Reilly, T. (2007). Handbook of Soccer Match Analysis: A systematic approach to improving performance. Routledge.
  5. Castellano, J., Casamichana, D., & Lago, C. (2012). The use of match statistics that discriminate between successful and unsuccessful soccer teams. Journal of Human Kinetics, 31(1), 139–147. https://doi.org/10.2478/v10078-012-0015-7
  6. Cho, Y., Yoon, J., & Lee, S. (2018). Using social network analysis and gradient boosting to develop a soccer win–lose prediction model. Engineering Applications of Artificial Intelligence, 72, 228–240. https://doi.org/10.1016/j.engappai.2018.04.010
  7. Dellal, A., Chamari, K., Wong, D. P., Ahmaidi, S., Keller, D., Barros, R., Bisciotti, G. N., & Carling, C. (2011). Comparison of physical and technical performance in European soccer match-play: Fa Premier League and La Liga. European Journal of Sport Science, 11(1), 51–59. https://doi.org/10.1080/17461391.2010.481334
  8. Forcher, L., Forcher, L., Woll, A., & Altmann, S. (2025). AI in Bundesliga match analysis—expected possession value (EPV) vs. expected goals (xG) to predict match outcomes in soccer. Frontiers in Sports and Active Living, 7, 1713852. https://doi.org/10.3389/fspor.2025.1713852
  9. Geurkink, Y., Boone, J., Verstockt, S., & Bourgois, J. G. (2021). Machine learning-based identification of the strongest predictive variables of winning and losing in Belgian professional soccer. Applied Sciences (Switzerland), 11(5), 1–11. https://doi.org/10.3390/app11052378
  10. Gomez, M. A., Lago-Peñas, C., & Owen, L. A. (2016). The influence of substitutions on elite soccer teams’ performance. International Journal of Performance Analysis in Sport, 16(2), 553-568. https://doi.org/10.1080/24748668.2016.11868908
  11. Hassan, A., Akl, A. R., Hassan, I., & Sunderland, C. (2020). Predicting wins, losses and attributes’ sensitivities in the soccer world cup 2018 using neural network analysis. Sensors (Switzerland), 20(11). https://doi.org/10.3390/s20113213
  12. Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis. In Journal of Sports Sciences (Vol. 20, Issue 10, pp. 739–754). https://doi.org/10.1080/026404102320675602
  13. Joseph, A., Fenton, N. E., & Neil, M. (2006). Predicting football results using Bayesian nets and other machine learning techniques. Knowledge-Based Systems, 19(7), 544–553. https://doi.org/10.1016/j.knosys.2006.04.011
  14. Kubayi, A. (2020). Analysis of Goal Scoring Patterns in the 2018 FIFA World Cup. Journal of Human Kinetics, 71(1), 205–210. https://doi.org/10.2478/hukin-2019-0084
  15. Kubayi, A., & Larkin, P. (2020). Technical performance of soccer teams according to match outcome at the 2019 FIFA Women’s World Cup. International Journal of Performance Analysis in Sport, 20(5), 908–916. https://doi.org/10.1080/24748668.2020.1809320
  16. Kvasnytsya, O., Tyshchenko, V., Latyshev, M., Kvasnytsya, I., Kirsanov, M., Plakhotniuk, O., & Buhaiov, M. (2024). Team Performance Indicators That Predict Match Outcome in Rugby Union. Pamukkale Journal of Sport Sciences, 15(1), 203–216. https://doi.org/10.54141/psbd.1342340
  17. Lago-Peñas, C., Lago-Ballesteros, J., Dellal, A., & Gómez, M. (2010). Game-related statistics that discriminated winning, drawing and losing teams from the Spanish soccer league. Journal of Sports Science and Medicine, 9, 288–293. http://www.jssm.org
  18. Lago-Peñas, C., Lago-Ballesteros, J., & Rey, E. (2011). Differences in performance indicators between winning and losing teams in the UEFA Champions League. Journal of Human Kinetics, 27(1), 135–146. https://doi.org/10.2478/v10078-011-0011-3
  19. Mohr, M., Krustrup, P., & Bangsbo, J. (2003). Match performance of high-standard soccer players with special reference to development of fatigue. Journal of Sports Sciences, 21(7), 519–528. https://doi.org/10.1080/0264041031000071182
  20. Moreira Praça, G., Braga Jacinto, A. L., de Sousa Pinheiro, G., de Oliveira Abreu, C., & Teoldo da Costa, V. (2023). What are the key performance indicators related to winning matches in the German Bundesliga? International Journal of Performance Analysis in Sport, 23(4), 284–295. https://doi.org/10.1080/24748668.2023.2227923
  21. Pappalardo, L., & Cintia, P. (2018). Quantifying the relation between performance and success in soccer. Advances in Complex Systems, 21(3–4). https://doi.org/10.1142/S021952591750014X
  22. Perl, J., Grunz, A., & Memmert, D. (2013). Tactics Analysis in Soccer-An Advanced Approach. In International Journal of Computer Science in Sport (Vol. 12, Issue 1).
  23. Rahimian, P., Mihalyi, B. M., & Toka, L. (2024). In-game soccer outcome prediction with offline reinforcement learning. Machine Learning, 113(10), 7393-7419. https://doi.org/10.1007/s10994-024-06611-1
  24. Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., & Wisløff, U. (2009). Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. Journal of Science and Medicine in Sport, 12(1), 227–233. https://doi.org/10.1016/j.jsams.2007.10.002
  25. Rathke, A. (2017). An examination of expected goals and shot efficiency in soccer. Journal of Human Sport and Exercise, 12(Proc2). https://doi.org/10.14198/jhse.2017.12.proc2.05
  26. Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-3108-2
  27. Sampaio, J. (2013). Routledge Handbook of Sports Performance Analysis. In T. McGarry, P. O’Donoghue, & J. Sampaio (Eds.), Routledge Handbook of Sports Performance Analysis. Taylor&Francis Group. https://doi.org/10.4324/9780203806913
  28. Settembre, M., Buchheit, M., Hader, K., Hamill, R., Tarascon, A., Verheijen, R., & McHugh, D. (2024). Factors associated with match outcomes in elite European football–insights from machine learning models. Journal of Sports Analytics, 10(1), 1-16. https://doi.org/10.3233/JSA-240745
  29. Silva, H., & Marcelino, R. (2023). Inter-operator reliability of InStat Scout in female football games. Science and Sports, 38(1), 42–46. https://doi.org/10.1016/j.scispo.2021.07.015
  30. Taylor, B. J., Mellalieu, D. S., & James, N. (2005). A Comparison of Individual and Unit Tactical Behaviour and Team Strategy in Professional Soccer. International Journal of Performance Analysis in Sport, 5(2), 87–101. https://doi.org/10.1080/24748668.2005.11868329
  31. Yang, G., Leicht, A. S., 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. https://doi.org/10.1080/15438627.2018.1431539