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Become a sponsor to Patrick McFarlane

@basketballrelativity

Patrick McFarlane

basketballrelativity
Philadelphia

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!

Featured work

  1. basketballrelativity/py_ball

    Python API for stats.nba.com with a focus on NBA and WNBA applications

    Python 107
  2. basketballrelativity/franchise_history

    WNBA and NBA franchise history visualization

    Jupyter Notebook 2
  3. basketballrelativity/draft_combine

    NBA Draft Combine Analysis

    Jupyter Notebook 5
  4. basketballrelativity/scoreboard

    Using Jupyter to build a dashboard displaying a basketball scoreboard

    Jupyter Notebook 3
  5. basketballrelativity/shot_probability

    Building a shot probability model from NBA shot chart data

    Jupyter Notebook 11
  6. basketballrelativity/location_data

    Generating shot, foul, and assist charts

    Jupyter Notebook 11

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This tier is simply for those that want to share a small token of appreciation!

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Tier benefits:

  • Direct support for any py_ball package questions or issues related to analysis

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Tier benefits:

  • Direct support for any py_ball package questions or issues related to analysis
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Tier benefits:

  • Direct support for any py_ball package questions or issues related to analysis
  • Priority for package requests for features or additional data sources
  • Priority for tutorial requests