Explainable E-sports Win Prediction through Machine Learning Classification

The integration of real-time machine learning into competitive gaming addresses the critical need for predictive accuracy and transparency in high-stakes environments. By utilizing a stream-based framework for Counter-Strike: Global Offensive, this approach moves beyond static batch processing to accommodate the rapid, dynamic shifts inherent in professional e-sports. The primary objective is to bridge the gap between complex algorithmic decision-making and user trust, ensuring that predictive models are not only precise but also interpretable for players, analysts, and spectators.

The methodology centers on a sliding window data management system that synthesizes pre-game player statistics with live in-game performance metrics. To maintain computational efficiency without sacrificing performance, the system employs variance thresholding and prioritizes tree-based models, specifically the Adaptive Random Forest Classifier. This selection facilitates the extraction of clear decision paths, allowing the system to generate natural language descriptions and visual dashboard insights. These features enable stakeholders to understand the specific factors driving a predicted match outcome, effectively demystifying the AI’s logic.

Empirical results demonstrate that this framework achieves over 90% predictive accuracy, outperforming traditional models that rely on less flexible data structures. By situating these findings within the broader context of e-sports research—which spans various titles such as League of Legends and Dota 2—the study establishes a robust standard for explainable artificial intelligence in gaming. Ultimately, the synthesis of high-performance classification and accessible interpretability provides a scalable solution for real-time analytics, fostering greater accountability and engagement within the competitive gaming ecosystem.