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.

翻译：随着MOBA ESBA ESBA ESBA (Multipple 在线作战电子体育竞技场)的迅速流行和爆炸性发展,许多研究努力都致力于自动预测游戏结果(双赢预测 ) 。 尽管这项任务在诸如 ESBO 实时流和游戏评论员 AI 系统等各种应用中具有巨大潜力,但先前的研究未能调查解释这些赢预测的方法。为了缓解这一问题,我们收集了一个大型数据集,其中包含实时游戏记录,具有流行的MOBA 游戏国王荣誉的丰富投入特征。对于可解释的预测,我们提出了一个双层空间-时空网络(TSSTN ), 它不仅能够提供准确的实时赢预测,而且能够将最终预测结果归因于不同特性对可解释性的贡献。 实验结果和现实世界活流情景的应用表明,拟议的 TSSTN 模型在预测准确性和可解释性两方面都是有效的。