Vocal Dynamics as Predictors of Competitive Success in E-Sports: Evidence from Professional Counter-Strike Matches

Objective To identify acoustic and contextual predictors of competitive success in professional Counter-Strike: Global Offensive (CS:GO) players by analyzing voice-based biomarkers of emotional and cognitive dynamics. Methods Naturalistic voice recordings from official matches were processed to extract 68 temporal, spectral, and cepstral features using the PyAudioAnalysis library. Logistic regression models tested associations between these acoustic parameters and match outcomes (win/loss), first considering voice-only predictors and then combining them with contextual performance indicators (team ranking, opponent ranking, and ranking difference). Results Two vocal features—Chroma₁ and ΔMFCC₁₃—emerged as significant predictors of victory, indicating that greater tonal organization and spectral variability were associated with winning outcomes. The inclusion of contextual ranking variables improved model fit (AUC = 0.787), yet both acoustic predictors remained significant, demonstrating that vocal expression contributes unique information beyond historical performance. Conclusion The findings suggest that voice dynamics during competitive play reflect real-time affective and cognitive processes linked to arousal regulation, coordination, and engagement. By integrating behavioral acoustics with contextual performance data, this study advances a multimodal framework for understanding and predicting human performance in high-pressure environments such as e-sports.