  Although orientation has proven to be a key skill of soccer players in order
to succeed in a broad spectrum of plays, body orientation is a
yet-little-explored area in sports analytics' research. Despite being an
inherently ambiguous concept, player orientation can be defined as the
projection (2D) of the normal vector placed in the center of the upper-torso of
players (3D). This research presents a novel technique to obtain player
orientation from monocular video recordings by mapping pose parts (shoulders
and hips) in a 2D field by combining OpenPose with a super-resolution network,
and merging the obtained estimation with contextual information (ball
position). Results have been validated with players-held EPTS devices,
obtaining a median error of 27 degrees/player. Moreover, three novel types of
orientation maps are proposed in order to make raw orientation data easy to
visualize and understand, thus allowing further analysis at team- or
player-level.
