Futbolda başarılı penaltı atışı için en güçlü belirleyici özniteliklerin makine öğrenimi tabanlı tespiti

Year 2024, Volume: 13 Issue: 4, 1327 - 1335, 15.10.2024

Ural Akincioğlu , Önder Aydemir , Ahmet Çil

https://doi.org/10.28948/ngumuh.1485962

Abstract

Penaltı, futbolda gol atma şansının en yüksek olduğu durumlardan bir tanesidir. Bir penaltı vuruşunun başarısı penaltıyı kullananların yetenekleri, taraftar baskısının seviyesi, maçın dakikası ve mevcut skor dahil olmak üzere birçok etkene bağlı değişkenlik gösterir. Bu makalede, penaltı pozisyonlarından, penaltıyı kullananların bilgilerinden ve maç günü tercihlerinden 16 öznitelik çıkarılmıştır. Çıkarılan öznitelikler, makine öğrenimi aracılığıyla penaltı vuruşu sonucunu tahmin etmek için kullanılmıştır. Ayrıca bir penaltı vuruşunun başarısını büyük ölçüde etkileyen en önemli öznitelik kombinasyonu elde edilmiştir. Önerilen yöntem, Türkiye Süper Ligi'nden 120 penaltı vuruşu ile eğitilirken 50 penaltı vuruşu ile sınıflandırma doğruluğu ve poligon alanı metriği açısından test edilmiştir. Penaltı vuruşu sonucunun, k-en yakın komşu sınıflandırıcısı kullanılarak %79.80 ortalama sınıflandırma doğruluğu ve 0.60 ortalama poligon alanı metriği oranlarıyla tahmin edilebileceği sonucuna varılmıştır.

Keywords

Futbol, makine öğrenmesi, penaltı atışı, poligon alan metriği, sınıflandırma

Machine learning-based identification of the strongest predictive features of scoring penalty kick in football

Abstract

In football, the penalty is the situation that has one of the highest chances of scoring a goal. However, the success of a penalty kick highly depends on many kinds of attributes, including the penalty-takers’ abilities, the amount of fan pressure, the minute of the match, and the current score. In this paper, 16 features were extracted from penalty kick positions, penalty-takers’ information, and match-day preferences, and machine learning was used to predict penalty kick outcomes. Moreover, we revealed the most important feature combination that significantly affected the success of a penalty kick. The proposed method was trained with 120 and tested with 50 penalty kicks from the Turkish Super League in terms of classification accuracy and polygon area metric. We concluded that the result of a penalty kick can be predicted with an average classification accuracy and average polygon area metric rates of 79.80% and 0.60 using the k-nearest neighbor classifier.

Keywords

Football, machine learning, penalty kick, polygon area metric, classification

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