Tutorials
Hands-on guides for pulling, cleaning, and visualizing basketball data yourself — from your first nba_api call to a finished shot chart.
Everything in this lane is meant to be run, not just read. The natural order is to start with the survey of free data sources, so you know what each one will and will not give you, then install nba_api and pull your first table, then plot a season of shot locations on a half-court you draw yourself in matplotlib. The two spreadsheet builds — a Four Factors dashboard and a net-rating power ranking — need no Python at all; they are here because a spreadsheet is often the fastest way to find out whether an idea is worth coding. Each tutorial names the exact endpoint, column, or formula it uses and shows the output it produces, so you can check your result against the one on the page before moving on.
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Build a Team Four Factors Dashboard in a Spreadsheet
No code required. Pull four numbers per team and build a dashboard that explains why teams win — Dean Oliver's Four Factors, in a spreadsheet.
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Getting Started with nba_api in Python: Pull NBA Data in 10 Minutes
Install one package, run a few lines of Python, and you've got real NBA stats in a DataFrame. A complete, copy-pasteable starter guide.
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Every Free Public Basketball Data Source, Ranked
nba_api, Basketball-Reference, pbpstats, and more — ranked by coverage, reliability, and how much pain they'll cause you. A field guide.
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Build a Simple Net-Rating Power Ranking in a Spreadsheet
No code required. Pull one table of team ratings, add a couple of columns, and build a defensible power ranking in a spreadsheet.
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How to Build Your First Shot Chart from NBA Tracking Data (Python + matplotlib)
From a raw shot-location pull to a publishable hex shot chart on a drawn court. Real, runnable code, explained line by line.