hoopR VS nba-sql

Thankfully, I've made use of the hoopR package which lets me get data that is up to date, primarily by pulling info from ESPN's APIs. While that does most of the work, there's tweaking that I need to do (most particularly for streamed game broadcasts that aren't ESPN3/ESPN+) but it usually goes fairly swimmingly.

hoopR (college and pro basketball)

https://github.com/saiemgilani/hoopR Digging around through here will probably help you find a csv which is easy to convert to excel

Next, we’ll look at a few measures of performance on the court to see whether certain signs are better or worse suited to particular aspects of the game. For each of these, I took player box score statistics (sourced from https://github.com/saiemgilani/hoopR), grouped them by the player’s astrological sign, and calculated their average numbers through the 2020-21 season. First, we’ll look at scoring -- the chart below shows each sign’s average points per game.

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.