AI-Driven Strategic Insights: Predicting Competitive RTS Game Outcomes

This study investigates the application of advanced machine learning techniques for analyzing and predicting match outcomes in real-time strategy (RTS) games, with a particular focus on StarCraft II as a representative competitive platform. By leveraging large-scale historical gameplay data, the research explores both supervised and unsupervised learning methods to identify crucial player behaviors, map dynamics, and strategic decision-making patterns that influence in-game outcomes. The methodology integrates predictive modeling with domain-specific feature engineering, aiming to extract and interpret critical factors such as unit composition, actions per minute (APM), resource management, race selection, and map characteristics. These factors are shown to have significant impacts on win probabilities, especially as matches progress through different phases. The core contribution includes the development of a machine learning-based Win Prediction Engine (WPE) capable of producing real-time win probability estimates during live gameplay, providing tactical and strategic insights for both human players and AI agents. This framework not only advances the precision of predictive analytics in eSports contexts but also has broader implications for autonomous agent training, military simulation environments, and real-time decision-support systems. The research highlights the interplay between macro- and micro-level strategies, showing how ensemble models like Gradient Boosted Trees outperform linear models in forecasting match outcomes with high accuracy, particularly beyond early-game phases. Overall, the findings pave the way for scalable, interpretable AI-driven game intelligence systems and contribute to the ongoing evolution of data-centric strategic analysis in complex, dynamic environments.