Serving Up Success: Unveiling the Power of Machine Learning for Volleyball League Prediction

Year 2024, , 202 - 209, 31.07.2024

Emre Altundağ , Hasan Aka , Çağlar Soylu , Pervin Demir

https://doi.org/10.53434/gbesbd.1478533

Abstract

This study investigates the efficacy of Artificial Neural Networks (ANN) in predicting volleyball league standings, focusing on the Turkish Volleyball Federation's Sultanlar and Efeler leagues over five seasons (2018-19 to 2022-23). Given the complexity and volume of performance data in volleyball, traditional analysis methods often face challenges such as data overload and high operational costs. ANN models, known for their ability to learn from and generalize data, present a promising solution to these challenges. By analyzing 23 input variables related to match performance, including points scored, services, attacks, and blocks, this study aims to identify the most influential factors on final league standings and provide a more objective, rapid, and economical analysis method. The results indicate significant potential for ANN in sports analytics, demonstrating high accuracy rates in predictions, especially for the Sultanlar League. However, the study also acknowledges limitations such as data quality and model complexity, suggesting areas for future research to enhance predictive accuracy and applicability of ANN in volleyball and other sports analytics.

Keywords

Artificial Neural Networks, Volleyball League Standings, Machine Learning, Sports Analytics, Performance Prediction

Başarıya Hizmet Etmek: Voleybol Ligi Tahmini için Makine Öğreniminin Gücünün Ortaya Çıkarılması

Abstract

Bu çalışma, Türkiye Voleybol Federasyonu'nun Sultanlar ve Efeler liglerine odaklanarak, beş sezon boyunca (2018-19 - 2022-23) voleybol lig sıralamalarını tahmin etmede Yapay Sinir Ağlarının (YSA) etkinliğini araştırmaktadır. Voleybolda performans verilerinin karmaşıklığı ve büyüklüğü göz önüne alındığında, geleneksel analiz yöntemleri genellikle aşırı veri yükü ve yüksek operasyonel maliyetler gibi zorluklarla karşılaşmaktadır. Verilerden öğrenme ve genelleme yetenekleriyle bilinen YSA modelleri, bu zorluklara umut verici bir çözüm sunmaktadır. Bu çalışma, atılan sayılar, servisler, ataklar ve bloklar dahil olmak üzere maç performansıyla ilgili 23 girdi değişkenini analiz ederek, nihai lig sıralaması üzerinde en etkili faktörleri belirlemeyi ve daha objektif, hızlı ve ekonomik bir analiz yöntemi sağlamayı amaçlamaktadır. Sonuçlar, özellikle Sultanlar Ligi için tahminlerde yüksek doğruluk oranları göstererek spor analitiğinde YSA için önemli bir potansiyel olduğunu göstermektedir. Bununla birlikte, çalışma aynı zamanda veri kalitesi ve model karmaşıklığı gibi sınırlamaları da kabul etmekte ve YSA'nın voleybol ve diğer spor analitiklerinde tahmin doğruluğunu ve uygulanabilirliğini artırmak için gelecekteki araştırmalar için alanlar önermektedir.

Keywords

Yapay Sinir Ağları, Voleybol Lig Sıralamaları, Makine Öğrenimi, Spor Analitiği, Performans Tahmini

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