Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport
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With the rapid prevalence and explosive development of MOBA esports (Multiplayer Online Battle Arena electronic sports), many research efforts have been devoted to automatically predicting the game results (win predictions). While this task has great potential in various applications such as esports live streaming and game commentator AI systems, previous studies suffer from two major limitations: 1) insufficient real-time input features and high-quality training data; 2) non-interpretable inference processes of the black-box prediction models. To mitigate these issues, we collect and release a large-scale dataset that contains real-time game records with rich input features of the popular MOBA game Honor of Kings. For interpretable predictions, we propose a Two-Stage Spatial-Temporal Network (TSSTN) that can not only provide accurate real-time win predictions but also attribute the ultimate prediction results to the contributions of different features for interpretability. Experiment results and applications in real-world live streaming scenarios show that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
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