- (optional) for better read/write performance, copy the entire directory of raw data off the USB 2.0 stick to its long-term home. Probably want it on a USB 3.0 stick or directly on your a hard drive.
- clone this repo to your local machine (recommend working off of a branch, obviously).
- edit the directories and filepaths hard-coded in
.nba_pbp_analysis/data/cf.pyto match the locations on your local machine. - compile and store a relatively clean, uniform dataset from the various raw data sources by running
.nba_pbp_analysis/data/pbp/cleaning/clean.py. - confirm the data cleaning was successful and view some samples of the data by running
.nba_pbp_analysis/data/load.py.
Note: This repo is currently NOT a library that you can install to your venv. I recommend working out of PyCharm.
- was free
- has a small amount of documentation online
- the guy who made the website says DM him on twitter if interested
To-Do List
- figure out what numerical event codes mean (no definition online) and write some code to map event codes to text (e.g. EVTMSGTYPE==1 -> made FG).
- find 2019-2020 data.
- contains a lot of redundant data (e.g. columns for both team name and team abbreviation), so select the data points you want and add logic to the cleaner to drop the others.
- was not free
- zero documentation online
- has a handful of additional datapoints
- much more tedious to work with b/c not parsed out as cleanly
To-Do List
- rows generally only contain identifiers for one of the two teams playing (smdh). You'll need to create a totally separate data preparation step where you do something like group by game ID and deduce the home/away teams by mapping events to teams to players to home/away.