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Multiplayer Online Battle Arena (MOBA) games currently dominate the esports landscape, offering a concrete and vivid embodiment for team comparisons, where accurately predicting the winning team is both important and challenging. Recent studies often rely on the static features of team members to evaluate a team’s capabilities and predict their likelihood of winning. However, such approaches fail to consider the critical game-contextual influences of interactions with teammates and opponents on individuals. During a match, an individual’s performance is consistently influenced by teammates and opponents, leading to variations in individual features across different games. Moreover, the measurement of team capabilities also needs improvement, as the single scalar rating employed is too simplistic for sophisticated MOBA games. To this end, we propose a novel Context-Enhanced Match Outcome Prediction model (CEMOP), which incorporates the effect of both teammates and opponents for a better measurement of team capabilities. Specifically, we designed a match context encoder for MOBA games to evaluates the impact of teammates and opponents on individuals separately and embeds the information into the individual representations. After that the single team member’s representations which contain the influence of teammates and opponents are aggregated based on their importance to create a comprehensive representation of the team capabilities, enables CEMOP to adaptively adjust team evaluations. Experimental results from three real-world MOBA games clearly verify the effectiveness of CEMOP compared with several state-of-the-art methods.
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