Title: Round-level outcome prediction in CS:GO using feature-optimised random forest models

Authors: Ermira Daka; Dren Haziri

Addresses: The University for Business and Technology – UBT, Lagjja Kalabria, 10000 Prishtine, Kosovo ' The University for Business and Technology – UBT, Lagjja Kalabria, 10000 Prishtine, Kosovo

Abstract: Predictive analytics in eSports remains underexplored at the round level, particularly in fast-paced titles like Counter-Strike: Global Offensive (CS:GO). This study introduces a novel application of a feature-optimised Random Forest model to predict round winners in CS:GO, focusing on both accuracy and robustness across varying gameplay contexts. Unlike previous work, our approach incorporates detailed feature importance analysis to reduce noise and enhance interpretability. The model achieved a cross-validated mean accuracy of 0.87 (SD = 0.00) and demonstrated strong generalisation, reaching 91% accuracy on rounds with high-impact features and 75% in diverse scenarios. Performance was evaluated using a confusion matrix, ROC curve, and precision-recall curve. The study contributes a scalable framework for round-level prediction and offers insights into key variables influencing outcomes. Our results suggest promising avenues for real-time integration of machine learning in strategic support tools for professional and semi-professional eSports teams.

Keywords: computer video games; machine-learning; Counter Strike Global Offensive; electronic sports; eSport.

DOI: 10.1504/IJHFMS.2026.153481

International Journal of Human Factors Modelling and Simulation, 2026 Vol.8 No.2, pp.130 - 144

Received: 05 Jun 2025
Accepted: 02 Aug 2025
Published online: 11 May 2026 *

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