Hack NFL data using Postgres (and maybe win your fantasy draft)(blog.timescale.com) |
Hack NFL data using Postgres (and maybe win your fantasy draft)(blog.timescale.com) |
https://docs.timescale.com/timescaledb/latest/tutorials/nfl-...
i still suspect that would be hard to determine given the subjective nature of what constitutes a penalty and what (dis)advantage the penalized team was carrying at any given moment.
> 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.
If the NFL made its data available weekly, you could probably join it with PFF data for some interesting insight. There’s a ton of power in joining time-series metrics with purely relational data.