学术文献库

A new range of statistical analysis has emerged in sports after the introduction of the high-resolution player tracking technology, specifically in basketball. However, this high dimensional data is often challenging for statistical inference and decision making. In this article, we employ Hidalgo, a state-of-the-art Bayesian mixture model that allows the estimation of heterogeneous intrinsic dimensions (ID) within a dataset and propose some theoretical enhancements. ID results can be interpreted as indicators of variability and complexity of basketball plays and games. This technique allows classification and clustering of NBA basketball player's movement and shot charts data. Analyzing movement data, Hidalgo identifies key stages of offensive actions such as creating space for passing, preparation/shooting and following through. We found that the ID value spikes reaching a peak between 4 and 8 seconds in the offensive part of the court after which it declines. In shot charts, we obtained groups of shots that produce substantially higher and lower successes. Overall, game-winners tend to have a larger intrinsic dimension which is an indication of more unpredictability and unique shot placements. Similarly, we found higher ID values in plays when the score margin is small compared to large margin ones. These outcomes could be exploited by coaches to obtain better offensive/defensive results.