Guard Me If You Can: Revealing the Hidden Dynamics of Sports Interactions Using the Intrinsic Dimension

• Activity Number: 141 - Bayesian Meets Basketball
• Type: Invited
• Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistics in Sports
• Author(s): Edgar Santos-Fernandez* and Francesco Denti and Kerrie Mengersen and Antonietta Mira
• Companies: Queensland University of Technology and University of California, Irvine and Queensland University of Technology and Università della Svizzera italiana and University of Insubria
• Keywords: basketball ; Bayesian methods; clustering; sports analytics; sensor data

Abstract

A new range of statistical analysis has emerged in sports after the introduction of the high-resolution player tracking technology, specifically in basketball. However, these high dimensional datasets are often challenging for statistical inference and decision making. We employ a state-of-the-art Bayesian mixture model that allows the estimation of heterogeneous intrinsic dimensions (ID) within a dataset. The ID reveals the players’ hidden dynamics in space and time, helping to translate complex patterns into more coherent statistics. ID results can be interpreted as indicators of variability and complexity in sports. The application of this technique is illustrated using NBA basketball player's tracking data, allowing effective classification and clustering. Analyzing movement data, the model identifies key stages of offensive actions such as creating space for passing, preparation, shooting and following through, which are relevant for invasion sports. We found that game-winners tend to have a larger intrinsic dimension, which is indicative of greater unpredictability and unique shot placements. Exploiting these results can bring clear strategic advantages for teams.