Sport Analytics Tools is a project dedicated to publishing information (code, tutorials, media) to help data scientists and sports enthusiasts work with sports data effectively.
- I believe that open-source solutions are superior to proprietary technologies.
- I believe that if you've solved a problem or possess valuable knowledge, you should share it.
The goal of this project is to assist people interested in sports data science in tackling data analysis tasks.
- I've developed nba_data, a repository of NBA data that can be accessed in seconds instead of hours via the NBA API.
- I am the author of the nba-on-court library, which simplifies working with NBA data.
- I am a contributor to well-known sports libraries such as nba_api, hoopR, and worldfootballR.
Through this project, I aim to create a comprehensive knowledge base of tools and resources to enhance the workflow with sports data.
Tool for mapping player IDs between NBA Stats API and Basketball Reference.
- Automated ID mapping between different basketball data sources
- Multiple matching algorithms for high accuracy
- Handles special cases and non-English names
- Easy-to-use Python interface
- Python 3.8+
- Core dependencies: beautifulsoup4, numpy, pandas, requests, nba_api, python-Levenshtein
Learn more about NBA Player ID Mapping Tool →
| Name | Description |
|---|---|
| NBA Player ID Mapping Tool | Code automating the process of mapping ID from the NBA website and basketball-reference |
# Clone the repository
git clone https://github.com/shufinskiy/sport_analytics_tools.git
cd sport_analytics_tools
# Install dependencies
pip install -r requirements.txtContributions are welcome! Please feel free to submit pull requests, particularly for:
- Adding new tools for sports analytics
- Improving existing functionality
- Adding documentation and tutorials
- Bug fixes and optimizations
Apache License 2.0
