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Extracting NFL tracking data from images to evaluate quarterbacks and pass defenses

Author

Listed:
  • Mallepalle Sarah

    (Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA)

  • Yurko Ronald

    (Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA)

  • Pelechrinis Konstantinos

    (School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA)

  • Ventura Samuel L.

    (Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA)

Abstract

The NFL collects detailed tracking data capturing the location of all players and the ball during each play. Although the raw form of this data is not publicly available, the NFL releases a set of aggregated statistics via their Next Gen Stats (NGS) platform. They also provide charts showing the locations of pass attempts and outcomes for individual quarterbacks. Our work aims to partially close the gap between what data is available privately (to NFL teams) and publicly, and our contribution is two-fold. First, we introduce an image processing tool designed specifically for extracting the raw data from the NGS pass charts. We extract the pass outcome, coordinates, and other metadata. Second, we analyze the resulting dataset, examining the spatial tendencies and performances of individual quarterbacks and defenses. We use a generalized additive model for completion percentages by field location. We introduce a naive Bayes approach for estimating the 2-D completion percentage surfaces of individual teams and quarterbacks, and we provide a one-number summary, completion percentage above expectation (CPAE), for evaluating quarterbacks and team defenses. We find that our pass location data closely matches the NFL’s tracking data, and that our CPAE metric closely matches the NFL’s proprietary CPAE metric.

Suggested Citation

  • Mallepalle Sarah & Yurko Ronald & Pelechrinis Konstantinos & Ventura Samuel L., 2020. "Extracting NFL tracking data from images to evaluate quarterbacks and pass defenses," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(2), pages 95-120, June.
    • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:2:p:95-120:n:4
    DOI: 10.1515/jqas-2019-0052
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    References listed on IDEAS

    as
    1. Daniel Cervone & Alex D’Amour & Luke Bornn & Kirk Goldsberry, 2016. "A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 585-599, April.
    2. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    3. Baumer Benjamin S. & Jensen Shane T. & Matthews Gregory J., 2015. "openWAR: An open source system for evaluating overall player performance in major league baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(2), pages 69-84, June.
    4. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    5. Franks Alexander M. & D’Amour Alexander & Cervone Daniel & Bornn Luke, 2016. "Meta-analytics: tools for understanding the statistical properties of sports metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(4), pages 151-165, December.
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    Cited by:

    1. Sabin R. Paul, 2021. "Estimating player value in American football using plus–minus models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(4), pages 313-364, December.

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