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Applied Sciences
Volume 15
Issue 23
10.3390/app152312401
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Article

From Data to Decisions: Using Explainable Machine Learning to Predict EuroLeague Basketball Outcomes

by
Panagiotis F. Foteinakis
1,
Christos Kokkotis
1,
Georgios Karamousalidis
2,
Alexandra Avloniti
1,
Stefania Pavlidou
1,
Nikolaos Zaras
1,
Theodoros Stampoulis
1,
Dimitrios Pantazis
1,
Panagiotis Aggelakis
1,
Dimitrios Balampanos
1,
Junshi Liu
3,
Konstantinos Laparidis
1 and
Athanasios Chatzinikolaou
1,*
1
Department of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece
2
Laboratory of Evaluation of Human Biological Performance, Department of Physical Education and Sports Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Department of Physical Education, Guangdong University of Science and Technology, 99 Xihu Road, Nancheng District, Dongguan 523083, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(23), 12401; https://doi.org/10.3390/app152312401
Submission received: 19 October 2025 / Revised: 14 November 2025 / Accepted: 20 November 2025 / Published: 21 November 2025
Versions Notes

Featured Application

The study’s approach provides analysts with a repeatable and understandable framework that strikes a balance between statistical power and tactical utility. Coaches can focus on offensive strategies that produce high-quality shots using structured spacing and ball movement and develop decision-making protocols that reduce possession losses. Defensively, strategies must emphasize rebound control, forcing turnovers, and contesting shots. Teams are advised to de-prioritize offensive sets that result in inefficient mid-range attempts. Finally, this blueprint is essential for scouts to identify players who excel in high-percentage shooting, low-turnover decision-making, and disciplined rebounding. The methodology itself offers an interpretable model that successfully bridges data-driven insights with practical game strategy.

Abstract

Predicting basketball game outcomes in elite competitions is a complex task influenced by multiple interacting performance factors. This study applied a supervised machine learning (ML) framework to predict EuroLeague game outcomes using team-level game-related statistics. Four algorithms—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB)—were trained and compared following recursive feature elimination (RFE) to identify the most informative predictors. The dataset comprised comprehensive in-game statistics describing shooting efficiency, rebounding, ball security, and spatial shot distribution. Model performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score, ensuring both discrimination and calibration assessment. Among the four classifiers, SVM (AUC = 0.922, Accuracy = 0.841) and LR (AUC = 0.933, Accuracy = 0.818) achieved the highest predictive performance, outperforming RF and NB. Feature importance analysis using Shapley Additive Explanations (SHAP) on the best-performing SVM classifier revealed that true shooting percentage (TS%), defensive rebounds (DR), steals (ST), and turnovers (TO) were the most influential predictors of game outcomes. Teams that demonstrated higher shooting efficiency, greater rebounding control, and fewer turnovers showed a significantly higher probability of winning. These results confirm that well-validated and interpretable ML models can accurately predict game outcomes in professional basketball using readily available box-score statistics. The integration of RFE-based feature selection and SHAP interpretability provides transparent, evidence-based insights that can inform tactical decisions, enhance scouting accuracy, and support coaches in developing data-driven performance strategies within elite basketball environments.
Keywords: basketball; sports analytics; outcome prediction; performance analysis; machine learning; feature selection; explainability basketball; sports analytics; outcome prediction; performance analysis; machine learning; feature selection; explainability

Share and Cite

MDPI and ACS Style

Foteinakis, P.F.; Kokkotis, C.; Karamousalidis, G.; Avloniti, A.; Pavlidou, S.; Zaras, N.; Stampoulis, T.; Pantazis, D.; Aggelakis, P.; Balampanos, D.; et al. From Data to Decisions: Using Explainable Machine Learning to Predict EuroLeague Basketball Outcomes. Appl. Sci. 2025, 15, 12401. https://doi.org/10.3390/app152312401

AMA Style

Foteinakis PF, Kokkotis C, Karamousalidis G, Avloniti A, Pavlidou S, Zaras N, Stampoulis T, Pantazis D, Aggelakis P, Balampanos D, et al. From Data to Decisions: Using Explainable Machine Learning to Predict EuroLeague Basketball Outcomes. Applied Sciences. 2025; 15(23):12401. https://doi.org/10.3390/app152312401

Chicago/Turabian Style

Foteinakis, Panagiotis F., Christos Kokkotis, Georgios Karamousalidis, Alexandra Avloniti, Stefania Pavlidou, Nikolaos Zaras, Theodoros Stampoulis, Dimitrios Pantazis, Panagiotis Aggelakis, Dimitrios Balampanos, and et al. 2025. "From Data to Decisions: Using Explainable Machine Learning to Predict EuroLeague Basketball Outcomes" Applied Sciences 15, no. 23: 12401. https://doi.org/10.3390/app152312401

APA Style

Foteinakis, P. F., Kokkotis, C., Karamousalidis, G., Avloniti, A., Pavlidou, S., Zaras, N., Stampoulis, T., Pantazis, D., Aggelakis, P., Balampanos, D., Liu, J., Laparidis, K., & Chatzinikolaou, A. (2025). From Data to Decisions: Using Explainable Machine Learning to Predict EuroLeague Basketball Outcomes. Applied Sciences, 15(23), 12401. https://doi.org/10.3390/app152312401

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