Barron Boris, Sitaraman Nathan, Arias Tomás
Department of Physics, Cornell University, Ithaca, 14850, United States.
Max Planck Institute for Demographic Research, Rostock, 18057, Germany.
Sci Rep. 2025 Jun 5;15(1):19830. doi: 10.1038/s41598-025-04953-x.
The increasing availability of high-precision player-tracking data in sports-centimeter-precision positional information of athletes captured dozens of times per second-has the potential to improve the quantification of player abilities and overall team strategies. Working toward achieving this quantification, we adapt density-functional fluctuation theory (DFFT) to infer spatial preferences and player-to-player interactions in National Basketball Association (NBA) basketball. We first demonstrate several foundational results, including the ability of DFFT to predict the location of a player to within 3% of the half-court area roughly half the time, and to provide a team-position-based metric that correlates strongly with play outcomes. Building on these results, we demonstrate that it is possible to improve 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. Finally, we quantify how particular players attract the opposing team, with and without the ball, constituting the first advanced quantification of 'player gravity' that explicitly deconfounds the influence of teammate positioning.
体育领域中高精度运动员追踪数据的可用性日益提高——运动员的厘米级精确位置信息每秒可捕捉数十次——这有可能改进对运动员能力和整体团队策略的量化。为了实现这种量化，我们采用密度泛函涨落理论（DFFT）来推断美国职业篮球联赛（NBA）篮球比赛中的空间偏好和球员之间的互动。我们首先展示了几个基础性成果，包括DFFT有能力在大约一半的时间内将球员的位置预测在半场区域的3%以内，并提供一个基于球队位置的指标，该指标与比赛结果密切相关。基于这些结果，我们证明了可以改善球员的位置安排并识别球员特定的倾向，比如一名球员为帮助球队集体防守两分球或三分球而进行自我定位的一致性。最后，我们量化了特定球员在有球和无球情况下对对方球队的吸引程度，这构成了对“球员引力”的首次高级量化，明确区分了队友位置的影响。
