  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.
