NBA Analysis Project (DuckDB + Quary w/ Interactive Python Notebook)

This project analyzes NBA data using DuckDB and Quary to transform raw data from .csv files into a structured database ready for analysis. The project also includes an interactive Python Notebook (analysis.ipynb) that provides visual insights into team and player performance metrics.

📂 What's in this repo?

This repo contains .csv data, including raw data on NBA players, teams, salaries, and draft information. The raw data is transformed using DuckDB and DBT into a structured database with the following views:

• stg_players: Player information and statistics
• team_performance: Team performance metrics
• player_performance: Player performance metrics
• player_salary_info: Player salary information
• team_salary_info: Team salary information
• player_draft_info: Player draft information
• player_combine_stats: Player measurements from the draft combine

🚀 Opening this project

To explore this project, follow these steps:

1. Clone the repository

git clone https://github.com/quarylabs/quary_basketball_analysis_duckdb.git

2. Open the project in Visual Studio Code and install Quary from the extension marketplace

3. Install the Quary CLI

brew install quarylabs/quary/quary

(Optional) 4. Explore the SQL file documentation (CMD/CTRL+D)

(Optional) 5. Build the database (deploy the models to DuckDB) using the Quary: RUN VSCode command or quary build in the CLI

(Optional) 6. Run the tests against the DuckDB database using Quary: Test VSCode command or quary test in the CLI.

Feel free to fork this project and make your own analysis!