sportsdataverse 0.0.50

Retrieve Sports data in Python

Table of Contents generated with DocToc

• sportsdataverse-py
  • Installation
    • Standard install (pip)
    • Modern install (uv — recommended)
    • Conda install
    • Development install
    • Notes
  • Examples and tutorials
  • Companion packages
• Our Authors
  • Citations

sportsdataverse-py

See CHANGELOG.md for details.

The goal of sportsdataverse-py is to provide the community with a python package for working with sports data as a companion to the cfbfastR, hoopR, and wehoop R packages. Beyond data aggregation and tidying ease, one of the multitude of services that sportsdataverse-py provides is for benchmarking open-source expected points and win probability metrics for American Football.

Installation

The package metadata lives entirely in pyproject.toml (PEP 621 [project] table). There is no setup.py source-of-truth.

Standard install (pip)

pip install sportsdataverse

With optional extras (defined in [project.optional-dependencies] in pyproject.toml):

pip install "sportsdataverse[all]"      # everything below
pip install "sportsdataverse[models]"   # extra deps for the EPA / WP model code
pip install "sportsdataverse[tests]"    # adds pytest, mypy, ruff, etc.
pip install "sportsdataverse[docs]"     # adds sphinx + sphinx-markdown-builder for the doc build

Modern install (uv — recommended)

uv is the fast, drop-in package manager we use day to day.

# Add to a uv-managed project:
uv add sportsdataverse

# With extras:
uv add "sportsdataverse[all]"

# Or install the latest dev snapshot from GitHub:
uv add "sportsdataverse @ git+https://github.com/sportsdataverse/sportsdataverse-py"

Conda install

Once the conda-forge feedstock is published the package is also available via:

conda install -c conda-forge sportsdataverse
# or
mamba install -c conda-forge sportsdataverse

Until then, conda users can build a local package from this repo:

conda install conda-build conda-verify
conda build recipe/
conda install --use-local sportsdataverse

See recipe/README.md for the full conda workflow.

Development install

For contributing or running the test suite:

git clone https://github.com/sportsdataverse/sportsdataverse-py.git
cd sportsdataverse-py

# uv (recommended) — fully resolved editable install with every extra:
uv pip install -e ".[all]"

# Plain pip works too if uv isn't available:
pip install -e ".[all]"

Note: once we add a PEP 735 [dependency-groups] block (currently the repo only ships PEP 621 [project.optional-dependencies]), uv sync --all-extras --all-groups will become the one-shot dev incantation. Until then, uv pip install -e ".[all]" is the equivalent path.

Run the test suite:

uv run pytest                       # offline tests only
SDV_PY_LIVE_TESTS=1 uv run pytest   # include live API tests (slower; hits ESPN / nflverse)

For deeper dev-environment detail (lint, mypy, dep-bumping workflow), see CONTRIBUTING.md.

Notes

• Python target: 3.9–3.14.
• DataFrame engine: polars 1.x. Most loaders accept return_as_pandas=True if you prefer pandas.
• NFL caching: loaders cache to memory by default. Set SDV_PY_NFL_CACHE=filesystem for cross-session reuse, or SDV_PY_NFL_CACHE=off to disable. See sportsdataverse.nfl.config.update_config() for runtime control.

Examples and tutorials

Every public function ships a runnable Example: block in its docstring showing a quick-start call, common parameter combinations, and a one-line pipeline next-step. Render the API reference locally with bash create_docs.sh or browse the live docs at py.sportsdataverse.org.

For longer-form walkthroughs, see the intro/intermediate Jupyter notebooks under examples/notebooks/:

Notebook	Covers
01_quickstart.ipynb	Cross-sport intro — package layout, polars vs pandas, the download() retry layer
02_cfb_intro.ipynb	College football PBP, schedule, teams, espn_cfb_play_participants
03_nfl_intro.ipynb	NFL — nflreadpy parity surface, caching layer, current-season helpers
04_nba_intro.ipynb	NBA — PBP, schedule, teams, game rosters, shot distribution
05_wbb_wnba_intro.ipynb	Women's basketball — NCAA + WNBA parallels, multi-table stats
06_mbb_intro.ipynb	Men's college basketball — PBP, schedule, conference standings
07_nhl_intro.ipynb	NHL — PBP, schedule, teams, shot-event filter

Companion packages

sportsdataverse-py is one corner of the broader SportsDataverse ecosystem. The R sister packages cover the same data sources with deeper sport-specific coverage:

• wehoop — women's basketball (WNBA + NCAA)
• hoopR — men's basketball (NBA + NCAA)
• cfbfastR — college football
• baseballr — baseball (MLB + MiLB + NCAA)
• fastRhockey — hockey (NHL + WHL)

The NFL submodule is a near drop-in replacement for nflreadpy; the broader nflverse ecosystem is the upstream data source for many of those loaders.

Our Authors

• Saiem Gilani

Citations

To cite the sportsdataverse-py Python package in publications, use:

BibTex Citation

@misc{gilani_sdvpy_2021,
  author = {Gilani, Saiem},
  title = {sportsdataverse-py: The SportsDataverse's Python Package for Sports Data.},
  url = {https://py.sportsdataverse.org},
  season = {2021}
}