When you think of sports analysis, you probably think of raw stats like time in the opposing half or shots on goal. However, that doesn't really tell teams how they should have played beyond vague suggestions. Researchers at Disney, Caltech and STATS believe they can do better: they've developed a system that uses deep learning to analyze athletes' decision-making processes. After enough training based on players' past actions, the system's neural networks can predict future moves and create a "ghost" of a player's typical performance. If a team flubbed a play, it could compare the real action against the predictive ghosts of more effective teams to see how players should have acted.
The Toronto Raptors already have a manual ghosting system where coaches mark out where they think players should have been. This technology, however, lifts that burden. It can create ghosts in real time, even in soccer (aka football) and other sports where the continuous play can lead to predictions that gradually veer from realistic outcomes. The scientists rely on imitation learning, where AI bases its actions on demonstrations, to keep that long-term prediction in check.
The early results are promising. In an example soccer match between Fulham and Swansea, a league-average ghost team replacing Swansea performed about as well in a defensive situation... not well at all, unfortunately. However, swapping in the ghost of a strong defensive team dramatically improved Swansea's chances of preventing a goal. Provided this approach can be adapted to both offense and a wider range of sports, you could see coaches offering more specific advice and shoring up weaknesses that might otherwise go unaddressed.