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Simulation-based decision making in the NFL using NFLSimulatoR

Author

Listed:
  • Benjamin Williams

    (University of Denver)

  • Will Palmquist

    (University of Denver)

  • Ryan Elmore

    (University of Denver)

Abstract

In this paper, we introduce an R software package for simulating plays and drives using play-by-play data from the National Football League. The simulations are generated by sampling play-by-play data from previous football seasons. The sampling procedure adds statistical rigor to any decisions or inferences arising from examining the simulations. We highlight that the package is particularly useful as a data-driven tool for evaluating potential in-game strategies or rule changes within the league. We demonstrate its utility by evaluating the oft-debated strategy of “going for it” on fourth down and investigating whether or not teams should pass more than the current standard.

Suggested Citation

  • Benjamin Williams & Will Palmquist & Ryan Elmore, 2023. "Simulation-based decision making in the NFL using NFLSimulatoR," Annals of Operations Research, Springer, vol. 325(1), pages 731-742, June.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-022-04524-7
    DOI: 10.1007/s10479-022-04524-7
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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.
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