Ecosyste.ms sponsors
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Sports data scientist focused on basketball and baseball.
Funding Links: https://github.com/sponsors/basketballrelativity
My open source work is solely dedicated to democratizing public basketball data and lowering the barrier to entry for those interested in sports analytics. This is done in two main ways:
py_ball: My Python package, an API wrapper for stats.nba.com with a focus on NBA and WNBA applications. This allows users to more easily interact with public professional basketball data. You can learn more here.
Tutorials: By leveraging the above package, I write interactive tutorials on introductory analytics concepts using Jupyter notebooks. The tutorials all have a basketball application, but leverage the skills of data cleansing/transforming, data visualization, and predictive modeling. You can view the tutorials in their individual repositories under my GitHub profile.
Your support would aid in the development of both the above. Expanding the data sources that can be accessed via py_ball would create more possibilities for analysis and exploration. More tutorials could then be written to guide users through the additional data.
Thank you for your consideration!
Python API for stats.nba.com with a focus on NBA and WNBA applications
Language: Python - Stars: 104WNBA and NBA franchise history visualization
Language: Jupyter Notebook - Stars: 2NBA Draft Combine Analysis
Language: Jupyter Notebook - Stars: 5Using Jupyter to build a dashboard displaying a basketball scoreboard
Language: Jupyter Notebook - Stars: 3Building a shot probability model from NBA shot chart data
Language: Jupyter Notebook - Stars: 11Generating shot, foul, and assist charts
Language: Jupyter Notebook - Stars: 11