Analysis of player tracking data for American football is in its infancy, since the National Football League (NFL) released its Next Gen Stats tracking data publicly for the first time in December 2018. While tracking datasets in other sports often contain detailed annotations of on-field events, annotations in the NFL's tracking data are limited. Methods for creating these annotations typically require extensive human labeling, which is difficult and expensive. We begin tackling this class of problems by creating annotations for pass coverage types by defensive backs using unsupervised learning techniques, which require no manual labeling or human oversight. We define a set of features from the NFL's tracking data that help distinguish between "zone" and "man" coverage. We use Gaussian mixture modeling and hierarchical clustering to create clusters corresponding to each group, and we assign the appropriate type of coverage to each cluster through qualitative analysis of the plays in each cluster. We find that the mixture model's "soft" cluster assignments allow for more flexibility when identifying coverage types. Our work makes possible several potential avenues of future NFL research, and we provide a basic exploration of these in this paper.
Since the NFL tracking data was released early 2019, there is not much work done involving providing annotations for tracking data in football. As mentioned above, the work of @cite_2 is a notable exception here. However, similar work has been done in other sports. Most specifically, work with tracking data has been done in other invasion'' style sports, such as soccer and basketball.
@cite_2
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8 ASUBTITLE: THE SALE OF 20 REGIONAL JETS TO COMAIR HAS BOOSTED CONFIDENCE AT CANADAIR: NOW THE PRESSURE IS STARTING TO MOUNT FOR THE BOMBARDIER SUBSIDIARY TO LAUNCH A STRETCHED VERSION OF THE 50-SEATER.