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Bigger data, better questions, and a return to fourth down behavior: an introduction to a special issue on tracking datain the National football League

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  • Lopez Michael J.

    (National Football League and Skidmore College, New York, NY, USA)

Abstract

Most historical National Football League (NFL) analysis, both mainstream and academic, has relied on public, play-level data to generate team and player comparisons. Given the number of oft omitted variables that impact on-field results, such as play call, game situation, and opponent strength, findings tend to be more anecdotal than actionable. With the release of player tracking data, however, analysts can better ask and answer questions to isolate skill and strategy. In this article, we highlight the limitations of traditional analyses, and use a decades-old punching bag for analysts, fourth-down strategy, as a microcosm for why tracking data is needed. Specifically, we assert that, in absence of using the precise yardage needed for a first down, past findings supporting an aggressive fourth down strategy may have been overstated. Next, we synthesize recent work that comprises this special Journal of Quantitative Analysis in Sports issue into player tracking data in football. Finally, we conclude with some best practices and limitations regarding usage of this data. The release of player tracking data marks a transition for the league and its’ analysts, and we hope this issue helps guide innovation in football analytics for years to come.

Suggested Citation

  • Lopez Michael J., 2020. "Bigger data, better questions, and a return to fourth down behavior: an introduction to a special issue on tracking datain the National football League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(2), pages 73-79, June.
    • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:2:p:73-79:n:8
    DOI: 10.1515/jqas-2020-0057
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    References listed on IDEAS

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    1. Kenneth Kovash & Steven D. Levitt, 2009. "Professionals Do Not Play Minimax: Evidence from Major League Baseball and the National Football League," NBER Working Papers 15347, National Bureau of Economic Research, Inc.
    2. David Romer, 2006. "Do Firms Maximize? Evidence from Professional Football," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 340-365, April.
    3. Schatz Aaron, 2005. "Football's Hilbert Problems," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 1(1), pages 1-8, October.
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    Cited by:

    1. Marius Ötting & Dimitris Karlis, 2023. "Football tracking data: a copula-based hidden Markov model for classification of tactics in football," Annals of Operations Research, Springer, vol. 325(1), pages 167-183, June.

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