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Putting the Fun in Functional Data: A tidy pipeline to identify routes in NFL tracking data - Dani Chu
Currently in football many hours are spent watching game film to manually label the routes run on passing plays. Using tracking data, each route can be described as a sequence of spatial-temporal measurements that varies in length depending on the duration of the play. This data can be conveniently analyzed using nested columns in tidyr and purrr. We demonstrate how model-based curve clustering using Bernstein polynomial basis functions (i.e. Bézier curves) fit using the Expectation Maximization algorithm can cluster route trajectories. Each cluster can then be labelled to obtain route names for each route and create route trees for all receivers. The clusters and routes can be visualized nicely using ggplot and seen developing over time using gganimate.