Route identification in the National Football League: An application of model-based curve clustering using the EM algorithm

Chu Dani (), Reyers Matthew (), Thomson James () and Wu Lucas Yifan ()
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Chu Dani: Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
Reyers Matthew: Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
Thomson James: Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
Wu Lucas Yifan: Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada

Journal of Quantitative Analysis in Sports, 2020, vol. 16, issue 2, 121-132

Abstract: Tracking data in the National Football League (NFL) is a sequence of spatial-temporal measurements that varies 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 33,967 routes run on the 6963 passing plays in the data set. With few assumptions and no pre-existing labels, we are able to closely recreate the standard route tree from our algorithm. We go on to suggest ideas for new potential receiver metrics that account for receiver deployment and movement common throughout the league. The resulting route labels can also be paired with film to enable streamlined queries of game film.

Keywords: expectation maximization algorithm; functional data; model-based curve clustering; route identification (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/jqas-2019-0047

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