University of Texas at Dallas

Using Machine Learning to Predict Game Outcomes Based on Player-Champion Experience in League of Legends

This research investigates the predictability of competitive League of Legends match outcomes by analyzing player-champion experience prior to the start of gameplay.

• Machine learning models can predict League of Legends match outcomes with approximately 75% accuracy based solely on pre-game player-champion experience.
• Gradient Boosting and Deep Neural Networks were the most effective models, achieving 75.4% and 75.1% predictive accuracy respectively using 2020 North American ranked match data.
• Champion mastery points and specific player-champion win rates are the primary statistical drivers of match success, while total games played and recent activity levels are insignificant.