Is it possible to predict the motivation of players just by observing their
gameplay data? Even if so, how should we measure motivation in the first place?
To address the above questions, on the one end, we collect a large dataset of
gameplay data from players of the popular game Tom Clancy's The Division. On
the other end, we ask them to report their levels of competence, autonomy,
relatedness and presence using the Ubisoft Perceived Experience Questionnaire.
After processing the survey responses in an ordinal fashion we employ
preference learning methods based on support vector machines to infer the
mapping between gameplay and the reported four motivation factors. Our key
findings suggest that gameplay features are strong predictors of player
motivation as the best obtained models reach accuracies of near certainty, from
92% up to 94% on unseen players.Comment: Version accepted for IEEE Conference on Games, 201