Predicting Events in MOBA Games: Prediction, Attribution, and Evaluation

Zelong Yang, Yan Wang, Piji Li, Shaobin Lin, Shuming Shi, Shao-Lun Huang, Wei Bi

Abstract: The multiplayer online battle arena (MOBA) games have become increasingly popular in recent years. Consequently, many efforts have been devoted to providing pregame or in-game predictions for them. These predictions can be used in many MOBA esports-related applications, such as artificial intelligence commentator systems, in-game data analysis, and game-assistant bots. However, these works are limited in the following two aspects: the lack of sufficient in-game features and the absence of interpretability in the prediction results. These two limitations greatly restrict the practical performance and industrial application of the current works. In this work, we collect a large-scale dataset containing rich in-game features for the popular MOBA game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Honor of Kings</i> . We then propose to predict four types of prediction tasks in an interpretable way by attributing the predictions to the input features using two gradient-based attribution methods: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Integrated Gradients</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmoothGrad</i> . To evaluate the explanatory power of different models and attribution methods, a fidelity-based evaluation metric is further proposed. Finally, we evaluate the accuracy and fidelity of several competitive methods to assess how well machines predict events in MOBA games.