Scalable Psychological Momentum Forecasting in Esports
This work addresses the challenge of enhancing player success and enjoyment in competitive esports, though it appears incremental as it builds on existing temporal data and modeling approaches.
The paper tackles the problem of predicting wins in esports by developing an intelligent agent recommendation engine that uses learned representations of player psychological momentum and tilt, combined with player expertise, to achieve state-of-the-art performance in pre- and post-draft win prediction.
The world of competitive Esports and video gaming has seen and continues to experience steady growth in popularity and complexity. Correspondingly, more research on the topic is being published, ranging from social network analyses to the benchmarking of advanced artificial intelligence systems in playing against humans. In this paper, we present ongoing work on an intelligent agent recommendation engine that suggests actions to players in order to maximise success and enjoyment, both in the space of in-game choices, as well as decisions made around play session timing in the broader context. By leveraging temporal data and appropriate models, we show that a learned representation of player psychological momentum, and of tilt, can be used, in combination with player expertise, to achieve state-of-the-art performance in pre- and post-draft win prediction. Our progress toward fulfilling the potential for deriving optimal recommendations is documented.