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FantasyBaseballPythonNotebooks

Python and JupyterLab notebooks for drafting and maintaining a fantasy baseball team.

This project is primarily for personal use, but it is organized to be clean, reproducible, and easy for others to run locally.

What this repo is for

  • Pulling MLB data for fantasy baseball workflows (draft prep and in-season management).
  • Exploring player performance with basic and intermediate statistics.
  • Building lightweight, reusable modeling workflows (including basic sabermetric-style analysis).
  • Caching pulled data in Parquet format for fast iteration in notebooks.

Environment setup (pyenv + virtual environment)

These steps assume pyenv is installed on your machine.

  1. Install and activate the Python version used by this repo:

    pyenv install -s 3.13.0
pyenv local 3.13.0

  2. Create and activate a virtual environment:

    python -m venv .venv
source .venv/bin/activate

  3. Install dependencies:

    pip install --upgrade pip
pip install -r requirements.txt

  4. Start JupyterLab:

    jupyter lab

Core dependencies

  • jupyterlab for notebook development
  • ipywidgets for notebook UI controls
  • ipydatagrid for interactive spreadsheet-like table browsing in notebooks
  • pybaseball for MLB data collection
  • pandas for data wrangling
  • pyarrow for Parquet-based caching/storage

Repository layout

  • notebooks/ contains all Jupyter notebooks for this project.
  • requirements.txt contains Python dependencies.
  • README.md contains setup guidance and project notes.

notebooks/draft_helper.ipynb interactive requirements

The notebooks/draft_helper.ipynb notebook uses ipywidgets and ipydatagrid for the tabbed hitter/pitcher UI.

If the final display(tab) cell runs but no grid appears:

  1. Confirm the environment has the widget packages installed:

    pip install -r requirements.txt

  2. Verify Jupyter can see the widget extensions:

    jupyter labextension list

  3. Restart JupyterLab and restart the notebook kernel, then run all cells from the top.

  4. If output is still blank, trust the notebook and reload it:

    • In JupyterLab: File -> Trust Notebook
    • Refresh the browser tab and rerun the notebook

pybaseball automatically uses its own cache once cache.enable() is called in the notebook.

Next planned notebooks

  • Initial draft board and player pool pull
  • Position scarcity exploration
  • Recent performance window analysis
  • Player status and transaction monitoring notebook
  • Simple model-based ranking experiments