The world of competitive Esports and video gaming has seen andcontinues to experience steady growth in popularity and complex-ity. 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 orderto maximise success and enjoyment, both in the space of in-gamechoices, as well as decisions made around play session timing inthe 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.CCS CONCEPTS•Information systems?Personalization;Massively multi-player online games;Data mining;•Applied computing?Psychology;•Computing methodologies?Neural networks
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