Wargame has emerged as a preferred instrument for simulating combat decision-making. However, the protracted duration of wargame matches and the extensive volume of intricate data records have led to a scarcity of comprehensive datasets containing explicit information. Consequently, a substantial reservoir of detailed match data remains underutilized, hindering research endeavors in data mining and the analysis of player behavior within the realm of wargaming. To address these formidable challenges, this article employs machine learning methodologies to predict the outcome of wargame matches. Initially, we conducted data preprocessing on 335 wargame match replays, extracting and generating features from both macro- and microperspectives, thereby capturing player strategies and operational nuances. This meticulous process culminated in the formation of a comprehensive player behavioral feature dataset. Subsequently, we harnessed six distinct machine learning models to prognosticate match results in the domain of wargaming using this dataset, achieving a peak prediction accuracy of 96.11%. The primary emphasis lies in the identification of prevalent determinants contributing to player triumphs in wargaming. To this end, we conducted an attribution analysis to ascertain the significance of diverse macro- and microfeatures. Guided by the importance of these features, we propose a method for evaluating player performance. This methodology can be instrumental in scrutinizing disparate player wargaming styles, dissecting customary strategic behaviors that lead to player victories, and assisting wargame designers in crafting AI agents capable of adapting to a spectrum of human player behaviors. Consequently, this study offers substantial insights for the advancement of research in the realm of human–AI hybrid gameplay.