ITHACA, N.Y. – A Cornell University research team has employed a variation of a theory first used to predict the collective actions of electrons in quantum mechanical systems to evaluate basketball players.
A group led by Tomás Arias , professor of physics, has adapted density-functional fluctuation theory (DFFT) to predict player positions and rank players based on their defensive contributions. They've also attempted to quantify "player gravity" – how strongly a player attracts defenders, indicating he's a scoring threat.
Analyzing NBA Player Positions and Interactions With Density-Functional Fluctuation Theory published in Scientific Reports.
A relatively new formulation, DFFT has already been applied to systems as diverse as insect group organization, racial segregation in urban areas and simulations of crowd dynamics.
The researchers' challenge with basketball: Given a moment in time on the court – the location of all 10 players and the ball – what is the probability of the offense scoring 0, 2 or 3 points?
For their study, the group used player-tracking data from the first half of the 2022-23 NBA season, and analyzed player and ball positions, during half-court possessions (excluding fast breaks), no more than three seconds before a shot was taken. By training the DFFT model on relevant subsets of the massive dataset, the researchers could predict where an individual player was likely to be and evaluate probabilities of various scoring outcomes.
The researchers demonstrated that it is possible to improve defensive player positioning and identify player-specific tendencies, such as the consistency with which a player positions himself to help his team collectively defend against 2-point or 3-point shots.
Future research in this area will explore the concept of "defensive IQ" – a player's instincts and the ability to "see" a play before it develops.
