Boris Barron, Nathan S. Sitaraman, Tomas A. Arias
Player tracking data can enhance the quantification of player abilities and our understanding of team composition and broader team strategies. In this work, we demonstrate how density-functional fluctuation theory (DFFT), an extension of a Nobel Prize-winning physics approach, can process basketball tracking data by treating players as interacting densities. By training the interactions on different play outcomes, we can evaluate play-outcome likelihoods based on player positions, determine which players are in strong or weak positions, and understand which players consistently instigate strong responses from the opposing team (i.e., ‘player gravity’). We find that our approach not only identifies the overall strengths of a player, but also identifies subtleties such as those who are left-handed (e.g., D. Russell) or who instigate changes non-locally through frequent passes (e.g., N. Jokic).