nba_api VS NBA_Tutorials

Thanks to the people putting together the open source nba_api, as well as the people at Basketball Reference and the NBA stats page.

NBA Stats API

Yea ref stuff is hard to acquire. NBA api could b helpful tho and this is prob a good place to start

I think you'll find this useful: https://github.com/swar/nba_api.

All data were collected using the nba_api. The dataset consists of per-game stat averages for all teams starting from the 1983-84 season through the 2021-22 season. In total: 1103 regular season teams, 624 of which made the playoffs in their respective years. Traditional stats include: PTS, FGM, FGA, FG_PCT, FG3M, FG3A, FG3_PCT, FTM, FTA, FT_PCT, OREB, DREB, REB, AST, STL, BLK, TOV, PF, PLUS_MINUS. Advanced stats include: NET_RATING, OFF_RATING, DEF_RATING, EFG_PCT, TS_PCT, PACE, OREB_PCT, DREB_PCT, REB_PCT, AST_PCT, AST_TO, AST_RATIO, TM_TOV_PCT. Note: for plus/minus and all of the advanced stats, I could only get data beginning from the 1997-98 season onwards, which resulted in 743 total regular season teams, 400 of which made the playoffs.

Is it not on the nba api? https://github.com/swar/nba_api/tree/master/docs/nba_api/stats/endpoints

The below (text between the horizontal rules) are excerpts from [this](https://www.theringer.com/2023/4/13/23681148/2023-nba-playoff-preview-how-to-use-and-not-use-lineup-data) really great explainer, which was published in April 2023, about the pitfalls of NBA lineup data in small sample sizes. It also serves as an explanation for why data scientist [Andrew Patton](https://www.andrewpatton.org) created the tool ["Should I use this Rating?" ](https://apanalytics.shinyapps.io/should_I_use_this_rating/) (which includes the Warriors starting 5's ORTG, DRTG, and possessions played plugged into it) from which the screenshot in this post was taken. All of the code for the tool as well as an extremely detailed and in depth breakdown of how the tool works and why can be found [here](https://github.com/rd11490/NBA_Tutorials/tree/master/five_man_net_rating).

If you use python you can look up nba_scraper and pbpstats packages. Their source code pulls from the NBA API endpoints. Also here is a GitHub repo showing how to find the specific NBA API endpoint (and for other websites)

His RAPM calculations are also open sourced and available on Github here: https://github.com/rd11490/NBA_Tutorials.

The NBA tracks data, so somewhere in a database they have information on every shot like (x,y) position on the court, closest defender distance, number of dribbles before the shot, etc. As for how they get these labels, they either have a person watch film and track or they use something complicated like computer vision to generate them. But you can do analytics on them yourself if you can get your hands on this data, which you can do by going to the NBA stats website or NBA Shot Charts.com. An NBA team would have direct access to a db, but you can get the data yourself by going to one of those sites, opening your network tab in your browser, and grabbing the data from there and saving it on your machine. Or you can follow a tutorial like this. Once you have the data, you can play around with it in either R or Python (or even Excel). I’m not sure which of R or Python is more in use by teams but I’ve heard you’re supposed to just pick one and learn it well.