Simulation-Based Decision Making in the NFL using NFLSimulatoR

TL;DR

This paper presents NFLSimulatoR, an R package that simulates NFL plays using historical data to aid strategic decision-making and evaluate potential rule changes.

Contribution

It introduces a novel simulation tool for NFL strategies based on play-by-play data, enabling data-driven decision analysis.

Findings

Simulations provide statistically rigorous insights into game strategies.

The package helps evaluate the effectiveness of going for it on fourth down.

Analysis suggests passing more could be beneficial for teams.

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 $\textit{going for it}$ on fourth down and investigating whether or not teams should pass more than the current standard.