Data

Author

Affiliation

Landon Carpenter, Matthew Moriarty
Alex Pattarini, Michael Varnerin

Georgetown University
M.S. Data Science and Analytics

About the Data

For our analysis, we use a variety of data provided by the nflverse (Carl et al. 2022), nflreadr (Ho and Carl 2023), and nfl-data-py (Adams 2023) packages. Here, we briefly describe the notable contents of each one.

nflverse

The nflverse package offers several sub-packages, including nflfastR, nflplotR, nflreadr, and more.

This package can be installed and utilized with the following commands in R:

  install.packages("nflverse")
  library("nflverse")

nflreadr

The nflreadr package in R offers numerous datasets regarding NFL play-by-play data, team data, combine data, and more.

This package can be installed and utilized with the following commands in R:

  install.packages("nflreadr")
  library("nflreadr")

nfl-data-py

The nfl-data-py package in Python also offers numerous datasets regarding NFL play-by-play data, team data, combine data, and more.

This package can be installed and utilized with the following commands in Python:

  !pip install nfl-data-py
  import nfl-data-py as nfl

References

Adams, Cooper. 2023. Nfl-Data-Py 0.3.0. https://pypi.org/project/nfl-data-py/.

Carl, Sebastian, Ben Baldwin, Lee Sharpe, Tan Ho, and John Edwards. 2022. Nflverse: Easily Install and Load the ’Nflverse’. https://nflverse.nflverse.com/, https://github.com/nflverse/nflverse.

Ho, Tan, and Sebastian Carl. 2023. Nflreadr: Download ’Nflverse’ Data. https://nflreadr.nflverse.com, https://github.com/nflverse/nflreadr.