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

Year 2023, Volume: 6 Issue: 2, 119 - 129, 23.09.2023

Vahid Sinap

https://doi.org/10.38016/jista.1235031

Cited By: 3

Abstract

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.

Keywords

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

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

Abstract

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

References

• Ali, P. J. M., Faraj, R. H., Koya, E., Ali, P. J. M., & Faraj, R. H. (2014). Data normalization and standardization: a technical report. Mach Learn Tech Rep, 1(1), 1-6.
• Bengio, Y., Courville, A. C., & Vincent, P. (2012). Unsupervised feature learning and deep learning: A review and new perspectives. CoRR, 1(2665), 2012.
• Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197-227.
• Boyd, K., Eng, K. H., & Page, C. D. (2013, September). Area under the precision-recall curve: point estimates and confidence intervals. In Joint European conference on machine learning and knowledge discovery in databases (pp. 451-466). Springer.
• Böhning, D. (1992). Multinomial logistic regression algorithm. Annals of the institute of Statistical Mathematics, 44(1), 197-200.
• Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., & Chen, K. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4), 1-4.
• Cunningham, P., & Delany, S. J. (2021). K-nearest neighbour classifiers-a tutorial. ACM Computing Surveys (CSUR), 54(6), 1-25.
• Davis, W. (2021). As esports grows, so too do its sponsorships. URL https://win.gg/news/as-esports-grows-so-too-do-its-sponsorships (Erişim tarihi: 28.12.2022)
• Gök, M., 2017. Makine öğrenmesi yöntemleri ile akademik başarının tahmin edilmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(3), 139-148.
• Hamari, J. & Sjöblom, M. (2017). What is eSports and why do people watch it? Internet Research, 27(2), 211-232. https://doi.org/10.1108/IntR-04-2016-0085
• Hodge, V. J., Devlin, S., Sephton, N., Block, F., Cowling, P. I., & Drachen, A. (2019). Win prediction in multiplayer esports: Live professional match prediction. IEEE Transactions on Games, 13(4), 368-379.
• Huang, W. X., Wang, J., & Xu, Y. (2022, April). Predicting round result in Counter-Strike: Global Offensive using machine learning. In 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 1685-1691). IEEE.
• Lillelund, C. (2020). CS:GO round winner classification. URL https://www.kaggle.com/datasets/christianlillelund/csgo-round-winner-classification (Erişim tarihi: 08.12.2022)
• Makarov, I., Savostyanov, D., Litvyakov, B., & Ignatov, D. I. (2018). Predicting winning team and probabilistic ratings in “Dota 2” and “Counter-Strike: Global Offensive” video games. In International Conference on Analysis of Images, Social Networks and Texts (pp. 183-196). Springer, Cham.
• Minka, T.P., Cleven, R., & Zaykov, Y. (2018). TrueSkill 2: An improved Bayesian skill rating system. Technical Report. https://www.microsoft.com/en-us/research/uploads/prod/2018/03/trueskill2.pdf
• Noble, W. S. (2006). What is a support vector machine?. Nature Biotechnology, 24(12), 1565-1567.
• Priyam, A., Abhijeeta, G. R., Rathee, A., & Srivastava, S. (2013). Comparative analysis of decision tree classification algorithms. International Journal of current engineering and technology, 3(2), 334-337.
• Ray, S. (2019, February). A quick review of machine learning algorithms. In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 35-39). IEEE.
• Sevli, O. (2022). Farklı sınıflandırıcılar ve yeniden örnekleme teknikleri kullanılarak kalp hastalığı teşhisine yönelik karşılaştırmalı bir çalışma. Journal of Intelligent Systems: Theory and Applications, 5(2), 92-105.
• Shen, Q. (2022, February). A machine learning approach to predict the result of League of Legends. In 2022 International Conference on Machine Learning and Knowledge Engineering (MLKE) (pp. 38-45). IEEE.
• Statista. (2023). eSports market size worldwide in 2021, with a forecast for 2022 and 2029. URL https://www.statista.com/statistics/1256162/global-esports-market-size/ (Erişim tarihi: 04.01.2023)
• UOK. (2023). IOC confirms Singapore as host of first Olympic Esports Week in June 2023. URL https://olympics.com/en/news/ioc-confirms-singapore-host-first-olympic-esports-week-june-2023 (Erişim tarihi: 08.01.2023)
• Xenopoulos, P., Coelho, B., & Silva, C. (2021). Optimal Team Economic Decisions in Counter-Strike. arXiv preprint arXiv, abs/2109.12990.
• Xenopoulos, P., Doraiswamy, H., & Silva, C. (2020, December). Valuing player actions in counter-strike: Global offensive. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 1283-1292). IEEE.
• Yang, B. (2018). Predicting e-sports winners with machine learning. URL https://blog.insightdatascience.com/hero2vec-d42d6838c941 (Erişim tarihi: 22.12.2022)
• Zhang, H., & Li, D. (2007, November). Naïve Bayes text classifier. In 2007 IEEE international conference on granular computing (GRC 2007) (pp. 708-708). IEEE.
• Zhang, Z. (2016). Introduction to machine learning: k-nearest neighbors. Annals of Translational Medicine, 4(11), 218.
• Zhu, X., & Goldberg, A. B. (2009). Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning, 3(1), 1-130.