While 360° videos watched in a VR headset are gaining in popularity, it is necessary to lower the required bandwidth to stream these immersive videos and obtain a satisfying quality of experience. Doing so requires predicting the user's head motion in advance, which has been tackled by a number of recent prediction methods considering the video content and the user's past motion. However, human motion is a complex process that can depend on many more parameters, including the type of attentional phase the user is currently in, and their emotions, which can be difficult to capture. This is the first article to investigate the effects of user emotions on the predictability of head motion, in connection with video-centric parameters. We formulate and verify hypotheses, and construct a structural equation model of emotion, motion and predictability. We show that the prediction error is higher for higher valence ratings, and that this relationship is mediated by head speed. We also show that the prediction error is lower for higher arousal, but that spatial information moderates the effect of arousal on predictability. This work opens the path to better capture important factors in human motion, to help improve the training process of head motion predictors.