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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.
Supported leagues and data sources
| League | Module | Surfaces covered |
|---|---|---|
| NBA | sportsdataverse.nba |
ESPN (Site v2 + Web v3 + Core v2) + stats.nba.com (nba_stats_*, 128 wrappers; G-League league_id="20" / Summer League "15") + Fox Sports (Bifrost) |
| WNBA | sportsdataverse.wnba |
ESPN + stats.wnba.com (wnba_stats_*, 111 wrappers) |
| MBB (NCAA M) | sportsdataverse.mbb |
ESPN + NCAA-only (rankings, recruits) + stats.ncaa.org (ncaa_mbb_* bigballR-parity family + mbb_ncaa_* pbp/lineup/stint engine) + Fox Sports (Bifrost) |
| WBB (NCAA W) | sportsdataverse.wbb |
ESPN + NCAA-only + stats.ncaa.org (ncaa_wbb_* family) |
| CFB | sportsdataverse.cfb |
ESPN + NCAA + stats.ncaa.org (cfb_ncaa_pbp + box/drives/officials parsers) + football-only (QBR) + Fox Sports (Bifrost) + Yahoo Sports + ESPN dataset loaders (teams / rosters / unified schedules / team info) |
| NFL | sportsdataverse.nfl |
ESPN + NFL.com API (api.nfl.com "Shield") + nflverse loaders (nflreadpy parity) + football-only (QBR) |
| MLB | sportsdataverse.mlb |
ESPN + MLB Stats API (statsapi.mlb.com) + Baseball Savant / Statcast (43-endpoint mlb_statcast_* surface) + Fox Sports (Bifrost) |
| NHL | sportsdataverse.nhl |
api-web.nhle.com/v1/ (game-feed) + NHL EDGE (player tracking) + Stats REST + Records site + Fox Sports (Bifrost) |
| PWHL | sportsdataverse.pwhl |
HockeyTech/LeagueStat (schedule / pbp / shifts / strength-state / xG) |
| Minor & junior hockey | sportsdataverse.hockey.<lg> |
HockeyTech — 20 registry-driven league families (ahl, echl, ohl, whl, qmjhl, ushl, bchl, …), 13 callables each |
| College hockey (M/W) | sportsdataverse.hockey.mch / .wch |
ESPN |
| College baseball & softball | sportsdataverse.baseball |
ESPN + stats.ncaa.org pbp parsers + run-expectancy helpers |
| Soccer | sportsdataverse.soccer |
ESPN (league-parameterized wrappers) + 12 per-league families (espn_mls_*, espn_epl_*, espn_ucl_*, …), 112 wrappers each |
| Cricket | sportsdataverse.cricket |
ESPN (league-parameterized) + bundled win-probability models |
| UFL / XFL / CFL | sportsdataverse.football |
ESPN |
| Odds | sportsdataverse.odds |
Odds & betting-lines wrappers and loaders |
The big-league modules export roughly 240–680 public functions each (ESPN
wrappers + that league's native-API wrappers + dataset loaders + parsers) —
about 6,180 exported names package-wide. Fox Sports adds fox_<league>_*
Bifrost wrappers (pbp / boxscore / odds / roster / stats / standings / leaders)
for nba, mbb, cfb, mlb, nhl; Yahoo Sports adds yahoo_cfb_* season-stats /
scoreboard wrappers for college football. sportsdataverse.release ports the
sportsdataversedata R release utilities (GitHub-release asset publish /
download helpers, including a pure-Python RDS writer).
Polars / pandas parser layer
Parser-backed wrappers return a tidy polars DataFrame by default
(0.0.54+). Pass return_parsed=False for the raw Dict, or
return_as_pandas=True for pandas. Wrappers without a registered
parser return the raw Dict.
from sportsdataverse.nba import espn_nba_team_roster
df = espn_nba_team_roster(team_id=13) # → polars (default)
raw = espn_nba_team_roster(team_id=13, return_parsed=False) # → Dict
pdf = espn_nba_team_roster(team_id=13,
return_as_pandas=True) # → pandas
For the NHL and MLB sibling-API wrappers, compose the wrapper with its parser:
from sportsdataverse.nhl import nhl_web_pbp, parse_nhl_web_pbp
df = parse_nhl_web_pbp(nhl_web_pbp(2023030417)) # 331-row polars frame
See py.sportsdataverse.org/docs/architecture/espn-cross-league and py.sportsdataverse.org/docs/parsers/index for the full architecture + parser registry.
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.
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"
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-groupswill 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=Trueif you prefer pandas. - NFL caching: loaders cache to memory by default. Set
SDV_PY_NFL_CACHE=filesystemfor cross-session reuse, orSDV_PY_NFL_CACHE=offto disable. Seesportsdataverse.nfl.config.update_config()for runtime control. - stats.nba.com / stats.wnba.com surface (
nba_stats_*/wnba_stats_*): 112 NBA (+ G-League + Summer League vialeague_id) and 95 WNBA wrappers are available — the capture-confirmed live, non-deprecated endpoints (the full active/dying/barren/dead matrix lives insdv-internal-refs/nba/ENDPOINT_HEALTH.md). The generic parser also handles the family's non-uniform shapes — the shot-location endpoints' grouped (2-level) headers and thescoreboardv3game feed. Live calls tostats.nba.comrequire thecurl_cffipackage (TLS fingerprint protection); install it viapip install "sportsdataverse[all]"orpip install curl_cffiseparately.
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. Regenerate the API reference locally with
uv run python tools/codegen/generate.py --docs (then cd docs && yarn build
to preview the Docusaurus site) 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_intro.ipynb |
Women's college basketball — PBP, schedule, multi-table player stats |
06_mbb_intro.ipynb |
Men's college basketball — PBP, schedule, conference standings |
07_nhl_intro.ipynb |
NHL — PBP, schedule, teams, shot-event filter |
08_wnba_intro.ipynb |
WNBA — PBP, schedule, rosters, player stats |
09_mlb_intro.ipynb |
MLB — Stats API + Statcast search / leaderboards / gamefeed |
10_pwhl_intro.ipynb |
PWHL — HockeyTech schedule, PBP, shifts, xG |
11_junior_hockey_intro.ipynb |
HockeyTech minor/junior leagues — one family shape across 20 leagues |
12_odds_intro.ipynb |
Odds & betting lines |
13_soccer_intro.ipynb |
ESPN soccer — league-parameterized wrappers |
14_cricket_intro.ipynb |
ESPN cricket + win-probability models |
15_other_espn_leagues_intro.ipynb |
UFL/XFL/CFL, college hockey, college baseball/softball ESPN families |
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
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}
}
1 maintainer
Metadata
1 maintainer
Release files for sportsdataverse 0.1.3
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Total release size: 37.5 MB
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