sports-analytics VS nba-sql

The data is from stats.nba.com via the nba_api on GitHub. I compiled the data through an extraction script, and keep it updated daily via a fully automated Kaggle data pipeline. The pipeline is described here, and the project repository is here

I've searched for a good database to analyze NBA players and teams, and couldn't find one. So, I've been working on an app that builds a SQLite / Postgres / MySQL database for the NBA. You can get the Windows alpha here: https://github.com/mpope9/nba-sql/releases/tag/v0.0.6 For OSX / Linux, you need to run it from the commandline. This is very much in the alpha phase, but development is happening at a steady pace in my free time. I've been able to learn some interesting things already, there are examples in the wiki here. I have more advanced example queries that I'm still working on, and have yet to add them to the wiki if any are interested. I'd love some feedback, if anyone has experience with SQL or databases. nba-sql will build a SQLite database by default, but it also supports Postgres and MySQL. You run the application to build the database, then use something like DBeaver to query it.

This is what I've found - https://github.com/mpope9/nba-sql

I used the nba-sql database for this. That database has a table called shot_chart_detail. The table has alot of interesting data, but there are a few columns that are especially helpful: minutes_remaining, seconds_remaining, and shot_distance. Using those columns, the following graph can be made:

Thanks! I used Postgres and Apache Superset. I wrote a small wiki on how to use it https://github.com/mpope9/nba-sql/wiki/Data-Visualization-with-Superset. Superset doesn't work with SQLite sadly.

The latest Postgres dump can be found here: https://github.com/mpope9/nba-sql/releases/tag/v0.0.2. It is a compressed file, using xz. After decompressing it, you can run it using psql -U -P nba < nba.sql.