Tracking objects like a basketball from a monocular view is challenging due to its small size, potential to move at high velocities as well as the high frequency of occlusion. However, humans with a deep knowledge of a game like basketball can predict with high accuracy the location of the ball even without seeing it due to the location and motion of nearby objects, as well as information of where it was last seen. Learning from tracking data is problematic however, due to the high variance in player locations. In this paper, we show that by simply "permuting" the multi-agent data we obtain a compact role-ordered feature which accurately predict the ball owner. We also show that our formulation can incorporate other information sources such as a vision-based ball detector to improve prediction accuracy.
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