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With the rapid prevalence and explosive development of MOBA esports(Multiplayer Online Battle Arena electronic sports), much research effort hasbeen devoted to automatically predicting game results (win predictions). Whilethis task has great potential in various applications, such as esports livestreaming and game commentator AI systems, previous studies fail to investigatethe methods to interpret these win predictions. To mitigate this issue, wecollected a large-scale dataset that contains real-time game records with richinput features of the popular MOBA game Honor of Kings. For interpretablepredictions, we proposed a Two-Stage Spatial-Temporal Network (TSSTN) that cannot only provide accurate real-time win predictions but also attribute theultimate prediction results to the contributions of different features forinterpretability. Experiment results and applications in real-world livestreaming scenarios showed that the proposed TSSTN model is effective both inprediction accuracy and interpretability.

Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport