The Explainability of Machine Learning Algorithms for Victory Prediction in the Video Game Dota 2

Video games, especially competitive ones such as Dota 2, have gained great relevance both as entertainment and in e-sports, where predicting the outcome of games can offer significant strategic advantages. In this context, machine learning (ML) is presented as a useful tool for analysing and predicting performance in these games based on data collected before the start of the games, such as character selection information. Thus, in this work, we have developed and tested ML models, including Random Forest and Gradient Boosting, to predict the outcome of Dota 2 matches. This study is innovative in that it incorporates explainability techniques using Shapley Additive Explanations (SHAP) graphs, allowing us to understand which specific factors influence model predictions. Data extracted from the OpenDota API were preprocessed and used to train the models, evaluating them using metrics such as accuracy, precision, recall, F1-score, and cross-validated accuracy. The results indicate that predictive models, particularly Random Forest, can accurately predict game outcomes based only on pregame information, also suggesting that the explainability of machine learning techniques can be effective for analysing strategic factors in competitive video games.