Investigating trade-offs made by American football linebackers using tracking data

Eager Eric () and Seth Tej ()
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Eager Eric: SumerSports, Palm Beach, USA
Seth Tej: SumerSports, Palm Beach, USA

Journal of Quantitative Analysis in Sports, 2023, vol. 19, issue 3, 171-185

Abstract: In recent years, the game of football has made a shift towards being more quantitative. With the advent of charting and tracking data, player evaluation is able to be studied from several different angles. In this paper, we build and refine two novel metrics: Bite Distance Under Expected (BDUE) and Ground Covered Over Expected (GCOE) for the evaluation of linebackers in the National Football League (NFL). Here, we show that these metrics are heavily correlated with each other, which demonstrates the trade-off linebackers have to make between being aggressive against the run and being effective when the opposing offense is using play-action. We also show that these metrics are more stable than those in the public space. Finally, we show how these metrics measure deception by opposing offenses.

Keywords: American football; machine learning; tracking data; trade-offs (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/jqas-2022-0091

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