Prediction of esports competition outcomes using EEG data from expert players

Abstract

Considerable efforts have focused on predicting sports event outcomes to enhance spectating, coaching, and betting. However, accurately predicting match results across various competitive scenes is challenging. Many researchers have achieved reasonable prediction accuracy using machine learning (ML) models and static data, such as player statistics up to the last match, to perform binary win/loss classification. However, the inherent uncertainty of sports often leads to unexpected outcomes. For example, narrowing the skill-level gap between players may negatively impact prediction accuracy. Moreover, accurately predicting “upsets,” where lower-skilled players defeat higher-skilled opponents, remains challenging. Conventional win/loss prediction techniques rely on static attributes derived from past match statistics, which fail to capture the player's condition immediately before the match. To address this limitation, this study focused on dynamic information reflecting the pre-match player's condition, particularly electroencephalography (EEG) data, recognized as a potent mental conditioning biomarker. We collected pre-match EEG data from esports experts and trained ML models on these data, aiming to assess the ability of ML techniques trained on EEG data to predict match outcomes, particularly in cases subject to high unpredictability. The findings revealed that the light gradient boosting machine (LightGBM) algorithm trained on EEG data achieved the highest prediction accuracy (80%), with parietal beta activity being the most relevant feature. Furthermore, predictive performance remained consistent even in match scenarios involving similar-level players and upset situations. Hence, this approach extends the applicability of win/loss prediction to traditionally unpredictable sports scenes, enhancing the quality of spectator experiences.