LUMA WNBA Stint and Lineup Data

LUMA is an open-source project measuring basketball player impact. This repository holds WNBA lineup-stint data and the ratings derived from it.

Site: https://court-share.com/luma

Contents

• Overview
• Files
• Record layout
• Usage
• Identifiers
• Ratings
• Limitations
• Licence and citation

Overview

Version 1.0.0, current through 2026-08-15.

Files

data/stints/rs_{2003..2026}.json      regular season
data/stints/po_{2003..2025}.json      postseason
data/crosswalk.csv                    player identifiers
data/quality.json                     per-season diagnostics
data/manifest.json                    SHA-256 per file
data/metrics/arc/                     ratings, per season and career
data/metrics/arc/windows/             multi-year boards, 2 to 5-season spans
data/metrics/rapm/                    pure RAPM, no box prior, all spans
data/metrics/arc/decay/               time-decayed boards, 45-day half-life
data/metrics/od/                      offence and defence components, incl. windows
data/metrics/channels/                on/off decomposition, shot profile, Box+ inputs
src/luma_wnba/           pip-installable loader
llms.txt                 briefing for AI assistants
AGENTS.md                conventions for coding agents
notebooks/quickstart.ipynb
sample/

Regular-season and postseason files share no games. Fields are defined in SCHEMA.md and METRICS.md.

Record layout

Each file is an object keyed by game identifier. Each stint is a seven-element array.

[home_five, away_five, seconds, home_points, away_points, home_tally, away_tally]

{"1022600001": {"date": "2026-05-08", "stints": [
  [["LUMA-W-0000030","LUMA-W-0000033","LUMA-W-0000035","LUMA-W-0000075","LUMA-W-0000120"],
   ["LUMA-W-0000010","LUMA-W-0000078","LUMA-W-0000124","LUMA-W-0000167","LUMA-W-0000172"],
   268.0, 16, 6,
   [3,4,2,2,5,9,1,1,1,2,0,0,6,1,0,0],
   [1,2,4,4,2,0,0,0,4,1,0,0,2,2,1,1]]
]}}

The tally counts that team's offensive events during the stint:

fga_rim  pts_rim  fga_mid  pts_mid  fga_three  pts_three  fta  pts_ft
tov  oreb  fb_att  fb_pts  ast_pts  tov_pass  tov_handle  tov_sys

Positions denote venue, not franchise.

Usage

No install — run it in the browser

Click the badge. Nothing to download, no GitHub account, no clone.

Python

pip install luma-wnba          # once released on PyPI
pip install https://github.com/lumahoops/WNBA/archive/refs/heads/main.tar.gz   # works today

import luma_wnba as luma

games   = luma.load_stints(2026)          # regular season, fetched and cached
games   = luma.load_stints(2024, kind="po")
seconds = luma.player_seconds(games)      # {luma_id: seconds on court}
names   = luma.load_crosswalk()           # {luma_id: identity row}
arc     = luma.load_arc(2026)             # rating board

Data is fetched over the network on first use and cached under ~/.cache/luma-wnba, so no clone is required. Standard library only — no pandas, no requests. If you have cloned the repository, local files are used automatically.

Pin a tag for reproducibility:

games = luma.load_stints(2026, ref="v1.0.0")

Full API

load_stints(season, kind='rs', source='auto', data_dir=None, ref='main')
load_crosswalk(source='auto', data_dir=None, ref='main')
load_metric(name, source='auto', data_dir=None, ref='main')
seasons(kind='rs')
iter_stints(games)
player_seconds(games)
tally_dict(tally)
seconds_to_minutes(secs)
load_arc(season=None, source='auto', data_dir=None, ref='main')
load_rapm(season=None, source='auto', data_dir=None, ref='main')
load_od(season, source='auto', data_dir=None, ref='main')
load_quality(source='auto', data_dir=None, ref='main')
cache_dir(ref='main')
clear_cache()

TALLY = ('fga_rim', 'pts_rim', 'fga_mid', 'pts_mid', 'fga_3', 'pts_3', 'fta', 'pts_ft',
         'tov', 'oreb', 'fb_att', 'fb_pts', 'ast_pts', 'tov_pass', 'tov_handle', 'tov_sys')
RS_SEASONS = 2003..2026
PO_SEASONS = 2003..2025
__version__ = '1.0.1'

Terminal

luma-wnba top 2026            # minutes leaders
luma-wnba fetch 2025 --kind po
luma-wnba seasons
luma-wnba cache --clear

With ChatGPT or any AI agent

llms.txt is a briefing you can paste into an assistant so it writes correct code against this dataset instead of guessing. It lists the complete API, the record shapes and the rules that matter.

Either paste the file, or give the assistant the URL:

Read https://raw.githubusercontent.com/lumahoops/WNBA/main/llms.txt and use it to answer
questions about WNBA lineup data.

R

No R package; the files are plain JSON.

library(jsonlite)
games <- fromJSON("https://raw.githubusercontent.com/lumahoops/WNBA/main/data/stints/rs_2026.json")

sample/ holds five games and a twenty-row rating board.

Possessions, following Oliver:

poss = t[0] + t[2] + t[4] + 0.44 * t[6] + t[8] - t[9]

Identifiers

LUMA-W-#######, assigned once and never reused. crosswalk.csv maps each to WNBA Stats and ESPN identifiers, with season span and source scheme.

Ratings

data/metrics/ holds ARC, a rating combining a ridge-regularised on/off estimate with a Box+ component, in points per 100 possessions relative to league average, where arc = imp_arc + box_arc. Channel files decompose the on/off component by shot and possession category.

Limitations

• Records carry no team identifier.
• Slots 10 and 11 are zero; the source feeds carry no fastbreak qualifier.
• home_points equals the sum of tally scoring slots in 98.1% of records. Where a scoring event spans a stint boundary, fields 3 and 4 are authoritative.
• 111 games carry a null date.
• 0.6% of records carry negative seconds or points from period-boundary deltas. Game totals reconcile; median game duration is 2,400 seconds.
• 2026 derives from a feed with 3.3% less recorded clock time, affecting per-second rates.

Licence and citation

Data under data/ is CC BY 4.0; code is MIT. Sources are documented in SOURCES.md. Not affiliated with the WNBA or ESPN.

Awoyemi, A. (2026). LUMA WNBA Stint and Lineup Data (Version 1.0.0) [Data set].
Zenodo. https://doi.org/10.5281/zenodo.21972004

CITATION.cff · aayoawoyemi@gmail.com