  Is it possible to predict moment-to-moment gameplay engagement based solely
on game telemetry? Can we reveal engaging moments of gameplay by observing the
way the viewers of the game behave? To address these questions in this paper,
we reframe the way gameplay engagement is defined and we view it, instead,
through the eyes of a game's live audience. We build prediction models for
viewers' engagement based on data collected from the popular battle royale game
PlayerUnknown's Battlegrounds as obtained from the Twitch streaming service. In
particular, we collect viewers' chat logs and in-game telemetry data from
several hundred matches of five popular streamers (containing over 100,000 game
events) and machine learn the mapping between gameplay and viewer chat
frequency during play, using small neural network architectures. Our key
findings showcase that engagement models trained solely on 40 gameplay features
can reach accuracies of up to 80% on average and 84% at best. Our models are
scalable and generalisable as they perform equally well within- and
across-streamers, as well as across streamer play styles.
