  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.
