> 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.
I even wrote an idea about promoting the "time series racing contest" a few months ago: https://gist.github.com/jonatas/a84b734645d288051fb861d9522f...
Orchestrate the race will require a lot of collaboration from all players involved :)
Another small step would be encourage DB companies to bring the most optimized defaults to tsbs or extra scripts and templates to tune the DB server.