> The lack of publicly available National Football League (NFL) data sources has been a major obstacle in the creation of modern, reproducible research in football analytics. While clean play-by-play data is available via open-source software packages in other sports (e.g. nhlscrapr for hockey; PitchF/x data in baseball; the Basketball Reference for basketball), the equivalent datasets are not freely available for researchers interested in the statistical analysis of the NFL. To solve this issue, a group of Carnegie Mellon University statistical researchers including Maksim Horowitz, Ron Yurko, and Sam Ventura, built and released nflscrapR an R package which uses an API maintained by the NFL to scrape, clean, parse, and output clean datasets at the individual play, player, game, and season levels.
Re:Hack NFL data using Postgres (and maybe win your fantasy ...
by LoriP on Wednesday July 28, 2021 @05:05AM
(#27981750)
You can get 2020 data from the same source that the NFL tutorial uses but that's only two years. It must exist I guess?
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Re:Hack NFL data using Postgres (and maybe win your fantasy ...
by jonatasdp on Wednesday July 28, 2021 @09:21AM
(#27983369)
This tool looks promising to keep it up to date.