New deep learning techniques analyze athletes' decision-making

Sports analytics is routinely used to assign values to such things as shots taken or to compare player performance, but a new automated method based on deep learning techniques - developed by researchers at Disney Research, California Institute of Technology and STATS, a supplier of sports data - will provide coaches and teams with a quicker tool to help assess defensive athletic performance in any game situation. The innovative method analyzes detailed game data on player and ball positions to create models, or "ghosts," of how a typical player in a league or on another team would behave when an opponent is on the attack. It is then possible to visually compare what a team's players actually did during a defensive play versus what the ghost players would have done. "With the innovation of data-driven ghosting, we can now, for the first time, scalably quantify, analyze and compare detailed defensive behavior," said Peter Carr, research scientist at Disney Research. "Despite what skeptics might say, you can indeed measure defense."