| Summary: | The entertainment industry includes companies engaged in telecommunications services, television, music streaming, video games, and live events. Gaming has gained momentum in revenue growth in the entertainment industry over the past decade. This momentum has made the gaming industry one of the most popular areas of the entertainment industry. Official leagues have been teamed up with professional players, and the concept of e-sports has become widespread. MOBA (Multiplayer Online Battle Arena), which is a derivative of MMO (massively multiplayer online) games, is the name given to the games played on the Internet in which players destroy the opponent's base by dominating specific objectives on a map, usually with two teams of five players each. LoL (League of Legends) is one of the most popular MOBA games. Predicting winners in online games has become an essential application for machine learning models. This research aims to predict classification with machine learning methods of match winner with LoL player metrics. Key performance metrics and their impact on each game model were analyzed. The results show that winner prediction is possible in League of Legends, also, LightGBM (0.97), Logistic Regression (0.96), SVM and GBC (Gradient Boosting Classifier) (0.95) are outperformed with a high accuracy ratio. This paper will contribute to the classification research on topic of gaming with machine learning.
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