TÜRKİYE SÜPER LİGİ MAÇ SONUÇ TAHMİNLERİNDE MAKİNE ÖĞRENME YÖNTEMLERİNİN PERFORMANS KARŞILAŞTIRILMASI

Year 2024, Volume: 22 Issue: 2, 59 - 72, 30.07.2024

Duygu Topcu , Özgül Vupa Çilengiroğlu

https://doi.org/10.33689/spormetre.1381602

Abstract

Bu çalışmanın amacı, Türkiye Süper Ligi'nde oynanan maçların sonuçlarının tahmin edilmesinde en etkili değişkenlerin oluşturduğu modellerin performanslarını makine öğrenmesi yöntemlerini kullanarak belirlemek, incelemek, yorumlamak ve karşılaştırmaktır. Bu amaçla Türkiye Futbol Süper Liginde 2018-2021 sezonlarındaki veriler kullanılarak 23 takımın 743 maçı incelenmiştir. Takımların kazanma ve kaybetme durumları, lojistik regresyon, karar ağaçları ve rassal orman gibi makine öğrenme yöntemleri kullanılarak modellenmiştir. Modellerin performansları duyarlılık, seçicilik, doğruluk ve F-puanı kriterlerine göre karşılaştırılmıştır. Makine öğrenme yöntemleri ve modelleri karşılaştırıldığında “rakip takımın olumlu pas yüzdesi”, “ev sahibi takımın hücum gücü” ve “rakip takımın savunma gücü” değişkenleri ile sınıflandırma ve regresyon ağaçları (CART) %67.4 doğrulukla en iyi model olarak belirlenmiştir.

Keywords

Lojistik regresyon, karar ağaçları, rassal orman, savunma gücü, hücum gücü

PERFORMANCE COMPARISON OF MACHINE LEARNING METHODS IN TURKISH SUPER LEAGUE MATCH RESULT PREDICTIONS

Abstract

The aim of this study is to determine, examine, interpret and compare the performances of the models formed by the most effective variables in predicting the results of the matches played in the Turkish Super League, using machine learning methods. For this purpose, 743 matches of 23 teams in the Turkish Football Super League were examined using data from the 2018-2021 seasons. The winning and losing situations of the teams were modeled using machine learning methods such as logistic regression, decision trees and random forest. The performances of the models were compared according to sensitivity, specificity, accuracy and F-score criteria. When the machine learning methods and models were compared, it was determined that the best model with 67.4% accuracy was the classification and regression trees (CART) with the variables "pozitive passing percentage of the opponent team", "offensive power of the home team" and "defensive power of the opponent team".

Keywords

Logistic regression, decision trees, random forest, offensive power, defensive power

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