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