  Tracking data in the NFL is a sequence of spatial-temporal measurements that
vary in length depending on the duration of the play. In this paper, we
demonstrate how model-based curve clustering of observed player trajectories
can be used to identify the routes run by eligible receivers on offensive
passing plays. We use a Bernstein polynomial basis function to represent
cluster centers, and the Expectation Maximization algorithm to learn the route
labels for each of the 34,698 routes run on the 6,963 passing plays in the data
set. We go on to suggest ideas for new potential receiver metrics that account
for receiver deployment. The resulting route labels can also be paired with
film to enable streamlined queries of game film.
