Makine Öğrenmesi Teknikleri ile Counter-Strike: Global Offensive Raunt Sonuçlarının Tahminlenmesi

Kamuya açık şekilde sunulan yapılandırılmış ve yapılandırılmamış büyük miktarlardaki verilerle birlikte Espor tahminlemeleri üzerine yapılan çalışmalar her geçen gün artmaktadır. Espor etkinliklerine yönelik tahminleme çalışmaları insan faktöründen büyük ölçüde etkilense de doğru çıktılara ulaşmada önemli birçok parametre sunan yapısıyla tahminlemelerin başarısını artırmaktadır. Bu bağlamda modellerin nasıl oluşturulacağı ve hangi makine öğrenmesi algoritmalarının seçileceği önem taşımaktadır. Bu çalışmada, Counter- Strike: Global Offensive adlı çevrimiçi oyundaki rauntların sonuçlarının tahminlemeye yönelik çeşitli makine öğrenmesi algoritmaları kullanılarak sınıflandırmalar gerçekleştirilmiştir. Araştırmada, Lojistik Regresyon, Karar Ağaçları, Rastgele Orman, XGBoost, Naive Bayes, K-En Yakın Komşu ve Destek Vektör Makinesi olmak üzere toplam yedi adet denetimli sınıflandırma algoritması kullanılmıştır. Bu algoritmaların performans ölçümünde Doğruluk, Kesinlik, Duyarlılık, F-Skor ve AUC değerleri hesaplanmıştır. Ayrıca, ROC eğrileri ve karışıklık matrisleri değerlendirilerek algoritmalar karşılaştırılmıştır. Bu ölçümler ve değerlendirmeler sonucunda Rastgele Orman algoritması %88 doğruluk oranı ile en başarılı algoritma olmuştur. Bunlara ek olarak, rauntların kazanılma durumları bağlamında Keşifsel Veri Analizleri yürütülerek Espor organizasyonlarına yönelik bazı önerilerde bulunulmuştur.

Anahtar Kelimeler:

Espor, Makine Öğrenmesi, Sınıflandırma Algoritmaları, Kazanan Tahminleme, CSGO

Prediction of Counter-Strike: Global Offensive Round Results with Machine Learning Techniques

With the large amounts of structured and unstructured data available to the public, studies on Esports forecasting are increasing day by day. Although prediction studies for esports events are greatly affected by the human factor, it increases the success of predictions with its structure that offers many important parameters in achieving accurate outputs. In this context, it is important how to create models and which machine learning algorithms to choose. In this study, classifications were carried out using various machine learning algorithms to predict the results of the rounds in the online game Counter-Strike: Global Offensive. In the research, a total of seven supervised classification algorithms, namely Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, K-Nearest Neighbor and Support Vector Machine were used. Accuracy, Precision, Sensitivity, F-Score and AUC values were calculated in the performance measurement of these algorithms. In addition, algorithms are compared by evaluating ROC curves and confusion matrix. As a result of these measurements and evaluations, the Random Forest algorithm was the most successful algorithm with an accuracy rate of 88%. In addition to these, some suggestions were made for Esports organizations by conducting Exploratory Data Analysis in the context of the winning status of the rounds.

Keywords:

Esports, CSGO, Machine Learning, Classification Algorithms, Winning Prediction,

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