Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport
This work addresses the need for interpretable win predictions in esports, such as for live streaming and commentator AI systems, but it is incremental as it builds on existing prediction methods by adding interpretability.
The paper tackled the problem of predicting game outcomes in the mobile MOBA esport Honor of Kings by developing an interpretable model, achieving effective results in both accuracy and interpretability as demonstrated in real-world live streaming applications.
With the rapid prevalence and explosive development of MOBA esports (Multiplayer Online Battle Arena electronic sports), much research effort has been devoted to automatically predicting 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 fail to investigate the methods to interpret these win predictions. To mitigate this issue, we collected 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 proposed 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 showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.