Real-time Prediction of Dota 2 Match Outcomes using In-game Chat Logs

Abstract

This paper investigates the application of supervised learning for the purpose of match outcome prediction from Dota 2 in game chat logs. We analyze a dataset of 50,000 ranked matches, evaluating the predictive power of communication data alone and in combination with game events. Using LSTM and DistilBERT architectures, alongside a logistic regression baseline, we demonstrate that chat logs alone enable accurate prediction (up to 81.4% accuracy), while incorporating game events substantially improves performance (up to 98.4% accuracy). Our temporal analysis reveals that prediction reliability increases significantly during the mid-game phase (15-30 minutes), with models exhibiting different strengths - LSTM achieves higher accuracy while DistilBERT demonstrates greater prediction confidence. This study contributes to esports analytics by establishing chat logs as a viable predictive data source.