  To which degree can abstract gameplay metrics capture the player experience
in a general fashion within a game genre? In this comprehensive study we
address this question across three different videogame genres: racing, shooter,
and platformer games. Using high-level gameplay features that feed preference
learning models we are able to predict arousal accurately across different
games of the same genre in a large-scale dataset of over 1,000
arousal-annotated play sessions. Our genre models predict changes in arousal
with up to 74% accuracy on average across all genres and 86% in the best cases.
We also examine the feature importance during the modelling process and find
that time-related features largely contribute to the performance of both game
and genre models. The prominence of these game-agnostic features show the
importance of the temporal dynamics of the play experience in modelling, but
also highlight some of the challenges for the future of general affect
modelling in games and beyond.
