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Journals / Ordu Üniversitesi Bilim ve Teknoloji Dergisi / 2026 / Cilt: 16

Evaluation of Match Outcomes in Elite European Basketball: A Machine Learning Approach to Fenerbahçe Beko’s Euroleague Campaigns

Elit Avrupa Basketbolunda Maç Sonuçlarının Değerlendirilmesi: Fenerbahçe Beko'nun Euroleague Sezonlarına Bir Makine Öğrenmesi Yaklaşımı

Enes Filiz(BALIKESİR ÜNİVERSİTESİ)0000-0002-8006-9467
Pages
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Publication Date
2026-06-29
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AI Summary & Key Takeaways

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

Results show that the DL-ANN model achieves the highest success with an 89.40% accuracy rate. ‘Opponent defensive rebounds’, ‘halftime results’, and ‘3-Point %’ stand out as the effective variables for classification.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Basketball is a popular sport.
📊
Key Findings & Data:Coaches' strategies play a decisive role in team success.
💡
Conclusion & Impact:Results show that the DL-ANN model achieves the highest success with an 89.40% accuracy rate. ‘Opponent defensive rebounds’, ‘halftime results’, and ‘3-Point %’ stand out as the effective variables for classification.
🔬Design:Modeling & Simulation
📍Scope:2017-2018 Verileri
⚙️Variables:Euroleague · sınıflandırma · makine öğrenmesi

Abstract

Basketball is a popular sport. It features high competition and tactical depth. Coaches' strategies play a decisive role in team success. Field statistics also determine these results. This study aims to analyze the performance of Fenerbahçe Beko in the Euroleague. It uses machine learning algorithms for this analysis. The research examines an eight-year dataset between the 2017-2018 and 2024-2025 seasons. Artificial Neural Networks (ANN), deep learning (DL-ANN), and Dagging-ANN algorithms are used in the analysis. The Filtered Attribute algorithm identifies the 15 most critical variables affecting match results. Results show that the DL-ANN model achieves the highest success with an 89.40% accuracy rate. ‘Opponent defensive rebounds’, ‘halftime results’, and ‘3-Point %’ stand out as the effective variables for classification. Consequently, these findings demonstrate that data mining methods provide a specialized, data-driven decision support mechanism in professional sports management, offering elite basketball teams and stakeholders actionable insights to optimize tactical planning and game preparation in high-stakes organizations like the Euroleague.

Özet

Basketbol, dünya genelinde yüksek rekabetin ve taktiksel derinliğin ön planda olduğu popüler bir spor dalıdır. Takımların başarısında koçların stratejileri ve sahadaki istatistiksel veriler belirleyici rol oynamaktadır. Bu çalışma, Fenerbahçe Beko’nun Euroleague’deki performansını makine öğrenmesi algoritmalarıyla analiz etmeyi amaçlamaktadır. Araştırma kapsamında 2017-2018 ve 2024-2025 sezonları arasındaki sekiz yıllık veri seti incelenmektedir. Analizlerde Yapay Sinir Ağları (ANN), derin öğrenme (DL-ANN) ve Dagging-ANN algoritmaları kullanılmaktadır. Filtre tabanlı öznitelik seçim algoritması ile maç sonuçlarına etki eden en kritik 15 değişken belirlenmektedir. Elde edilen sonuçlar, DL-ANN modelinin %89,40 doğruluk oranıyla en yüksek başarıyı sergilediğini göstermektedir. Rakip savunma ribaundu, devre sonucu ve üç sayı yüzdesi maç sonucu sınıflandırmasının en güçlü belirleyicileri olarak öne çıkmaktadır. Sonuç olarak bu bulgular, veri madenciliği yöntemlerinin profesyonel spor yönetiminde uzmanlaşmış ve veri odaklı bir karar destek mekanizması sunduğunu göstermekte; elit basketbol takımlarına ve paydaşlarına, Euroleague gibi rekabetin yüksek olduğu organizasyonlarda taktiksel planlamayı ve maç hazırlıklarını optimize etmek için uygulanabilir öngörüler sağlamaktadır.

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Cite This Article

Filiz, E. (2026). Evaluation of Match Outcomes in Elite European Basketball: A Machine Learning Approach to Fenerbahçe Beko’s Euroleague Campaigns. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, Cilt: 16. https://doi.org/10.54370/ordubtd.1931137

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