Precision Under Fire: Analysis and Predictive Modeling in Counter-Strike: Global Offensive

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 decision support, and applications in live-game analysis and betting. The dataset and methodology also establish a foundation for future esports research and predictive modeling across other game environments.