A Machine Learning Based Predictive Analysis Use Case for eSports Games

Abstract

League of Legends (LoL) is a popular multiplayer online battle arena (MOBA) game that is highly recognized in the professional esports scene due to its competitive environment, strategic gameplay, and large prize pools. This study aims to predict the outcome of LoL matches and observe the impact of feature selection on model performance using machine learning classification algorithms on historical game data obtained through the official API provided by Riot Games. Detailed examinations were conducted at both team and player levels, and missing data in the dataset were addressed. A total of 1045 data were used for training team-based models, and 5232 data were used for training player-based models. Seven different machine learning models were trained and their performances were compared. Models trained on team data achieved the highest accuracy of over 98% with the AdaBoost algorithm. The top 10 features that had the most impact on the prediction outcome were identified among the 47 features in the dataset, and a new dataset was created from team data to retrain the models. After feature selection, the results showed that the accuracy of Logistic Regression increased from 89% to 98% and the accuracy of Gradient Boosting algorithm increased from 96% to 98%.

Keywords

• league of legends
• riot game
• machine learning
• random forest
• gradient boosting

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