Data-Driven Ghosting using Deep Imitation Learning

Current state-of-the-art sports statistics compare players and teams to league average performance. For example, metrics such as “Wins-above-Replacement” (WAR) in baseball [1], “Expected Point Value” (EPV) in basketball [2] and “Expected Goal Value” (EGV) in soccer [3] and hockey [4] are now commonplace in performance analysis. Such measures allow us to answer the question “how does this player or team compare to the league average?” Even “personalized metrics” which can answer how a “player’s or team’s current performance compares to its expected performance” have been used to better analyze and improve prediction of future outcomes [5].