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

The role of intrinsic dimension in high-resolution player tracking data -- Insights in basketball