Abstract

Esports analytics has gained traction in recent years, leveraging machine learning (ML) to predict in-game events and enhance strategic decision making. This study develops structured datasets from Counter-Strike: Global Offensive (CS: GO) competitive matches to address two predictive tasks: round result prediction and player death prediction. Using data from professional Dust 2 matches in 2022, we extract key playerand team-level features such as health, spatial positioning, and economy-related metrics. Various ML models, including Logistic Regression, Decision Trees, and XGBoost, are evaluated and benchmarked against random guessing and majority-class baselines. Results show that XGBoost consistently outperforms other models, effectively capturing gameplay dynamics and providing accurate predictions. These findings offer valuable insights for esports strategy optimization, coaching, real-time …

Date

2025

Authors

Xinyao Xia, Abel Salinas, Fred Morstatter

Conference

2025 IEEE Conference on Games (CoG)

Pages

1-8

Publisher

IEEE