Toward Explaining Competitive Success in League of Legends: A Machine Learning Analysis

Machine learning techniques have recently transformed the way we analyze competitive games. However, accurately detecting the impact of different insights on match outcomes remains a challenge. This study focuses on League of Legends, a popular multiplayer online battle arena game known for its strategic depth and teamwork requirements. We aim to understand how various actions and strategies influence match results, using a dataset from professional tournaments. Factors like “building damage”, “total gold”, and “assists” are analyzed as predictors. We employ tree-based and linear models to predict outcomes, supplemented by SHapley Additive exPlanations for explaining both local and global model outcomes. Our article offers a generalizable match analysis approach, compares explainable methods, and delves into key determinants of victory. The results, showcasing a remarkable 98.8% accuracy with the top-performing model, provide strong support for our conclusions, underlining their reliability.