WNBA — additional Python functions — Wnba: rapm–win

wnba_rapm_from_games

wnba_rapm_from_games(game_ids: 'Sequence[str]', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'

Compute per-player RAPM estimates over a sequence of WNBA games.

Iterates game_ids, builds the possession-level stint matrix for each via wnba_possessions, concatenates the results, and fits a ridge-regression RAPM model via ~sportsdataverse.nba.nba_rapm.nba_rapm. Games whose possession frame is empty (e.g. a malformed payload) are silently skipped. Returns a zero-row frame when no valid possessions are found.

Parameters

Parameter	Type	Default	Description
game_ids	Sequence[str]		Sequence of WNBA game identifier strings.
return_as_pandas	bool	False	If True, convert the result to a pandas.DataFrame before returning.

Returns

Polars (or pandas) DataFrame with one row per player and columns player_id (Int64), o_rapm (Float64), d_rapm (Float64), rapm (Float64), off_poss (Int64), def_poss (Int64).

Example

from sportsdataverse.wnba.wnba_engine import wnba_rapm_from_games
rapm = wnba_rapm_from_games(["1022400001", "1022400003"])
print(rapm.sort("rapm", descending=True).head())

# Pandas output

rapm_pd = wnba_rapm_from_games(["1022400001"], return_as_pandas=True)
print(type(rapm_pd))

# Multi-season aggregation

import polars as pl
game_ids = pl.read_parquet("wnba_schedule.parquet")["game_id"].to_list()
rapm = wnba_rapm_from_games(game_ids)
print(rapm.sort("rapm", descending=True).head(10))

wnba_referee_assignments

wnba_referee_assignments(date: 'str | _dt.date', *, raw: 'bool' = False, return_as_pandas: 'bool' = False, proxy: 'dict | None' = None) -> 'dict[str, Any]'

Fetch and parse WNBA referee assignments for a given date from official.nba.com.

Retrieves the referee crew assignments and replay center officials for all WNBA games on a given date. The crew_position column (1–4) represents the feed's slot order; slot 1 is inferred to be the crew chief. The season column is the WNBA single-year season (feed year converted as-is). This is a thin shim over sportsdataverse.nba.nba_officiating.nba_referee_assignments that sets league="wnba".

Parameters

Parameter	Type	Default	Description
date	str | date		The date to fetch assignments for (str in "YYYY-MM-DD" format or datetime.date).
raw	bool	False	If True, return the raw JSON payload (dict) with all three leagues instead of parsed DataFrames.
return_as_pandas	bool	False	If True, return pandas DataFrames instead of polars.
proxy	dict | None	None	Optional proxy dict passed through to the HTTP layer.

Returns

A dict with keys "officials" and "replay_center" mapping to DataFrames. If raw=True, returns the full three-league JSON payload instead.

col_name	type	description
officials.league	character	League the assignment belongs to: nba, gl (G League), or wnba.
officials.game_id	character	10-digit game id (zero-padded) for the assigned game.
officials.game_date	date	Game date parsed from the feed's MM/DD/YYYY format.
officials.season	integer	Season end year, converted from the feed's code: start year + 1 for NBA/G League's two-calendar-year seasons, start year unchanged for WNBA's single-year seasons.
officials.season_type	character	Season type decoded from the feed's season code first digit: preseason, regular, all-star, playoffs, play-in, or nba-cup-final.
officials.game_code	character	League game code in YYYYMMDD/AWYHOM format, matching the away and home team abbreviations.
officials.home_team_id	integer	10-digit team id of the home team.
officials.home_team_abbr	character	Three-letter abbreviation of the home team.
officials.away_team_id	integer	10-digit team id of the away team.
officials.away_team_abbr	character	Three-letter abbreviation of the away team.
officials.crew_position	integer	Feed's official slot order (1-4); slot 1 is inferred to be the crew chief since the API does not label roles.
officials.official_id	integer	Numeric official id from the feed (source field official{n}_code); expected to match stats.nba.com's OFFICIAL_ID.
officials.official_name	character	Official's display name for this crew slot.
officials.jersey_num	character	Official's jersey number as a string, from the feed's official{n}_JNum field.
replay_center.league	character	League the replay-center staffing belongs to: nba, gl, or wnba.
replay_center.game_date	date	Date the replay-center official worked; a date-level staffing record, not tied to one game.
replay_center.official_id	integer	Numeric replay-center official id from the feed.
replay_center.official_name	character	Replay-center official's display name for that date.

Example

from sportsdataverse.wnba.wnba_officiating import wnba_referee_assignments
result = wnba_referee_assignments("2026-06-13")
officials = result["officials"]
print(f"Found {officials.height} official slots")

wnba_rookie_projection

wnba_rookie_projection(draft_year: "'int | list[int]'", *, return_as_pandas: 'bool' = False) -> "'pl.DataFrame | pd.DataFrame'"

WNBA rookie/sophomore projection -- composes the WNBA draft/aging/availability pieces.

Parameters

Parameter	Type	Default	Description
draft_year	int | list[int]		A draft year or list of years.
return_as_pandas	bool	False	Return a pandas DataFrame instead of polars.

Returns

Frame player_id:Utf8, draft_year:Int64, proj_rookie_value:Float64, proj_soph_value:Float64, proj_rookie_min:Float64, proj_avail_pct:Float64, pro_tier:Utf8. Empty input -> zero-row schema.

Example

from sportsdataverse.wnba import wnba_rookie_projection
board = wnba_rookie_projection(2023)

wnba_schedule_crosswalk

wnba_schedule_crosswalk(season: 'Optional[int]' = None, *, stats_games: 'Optional[pl.DataFrame]' = None, return_as_pandas: 'bool' = False, strict: 'bool' = False, **kwargs: 'Any') -> "Union[pl.DataFrame, 'pd.DataFrame']"

Build the WNBA cross-source schedule crosswalk (ESPN / WNBA Stats).

One row per game, joined on (game_date, home_espn_team_id, away_espn_team_id) after both sides reduce to the Eastern-Time date. The Stats CDN serves the current season only, so the live builder is effectively current-season.

Parameters

Parameter	Type	Default	Description
season	Optional[int]	None	Season year (e.g. 2026). Defaults to the most recent WNBA season.
stats_games	Optional[DataFrame]	None	Pre-fetched Stats schedule frame; None fetches live.
return_as_pandas	bool	False	Return pandas instead of polars.
strict	bool	False	Raise on the first failed per-date ESPN scoreboard fetch (a 404 is still skipped) instead of skipping isolated failures. Default False matches the R producers; a provider whose every item failed raises either way. An item the host answered -- including a 404 -- counts as answered.

Returns

pl.DataFrame (or pandas) with SCHEDULE_COLUMNS.

Example

from sportsdataverse.wnba import wnba_schedule_crosswalk
df = wnba_schedule_crosswalk(season=2026)
print(df["match_method"].value_counts())

# Pipeline next step (one line)

df.filter(pl.col("match_method") == "both").select("espn_game_id", "wnba_game_id").head()

wnba_shot_value

wnba_shot_value(player_ids: "'list[int]'", season: 'str', *, include_context: 'bool' = False, return_as_pandas: 'bool' = False) -> "'dict[str, Union[pl.DataFrame, pd.DataFrame]]'"

WNBA one-call shot-value spine (league_id="10").

Thin wrapper binding sportsdataverse.nba.nba_shot_value.nba_shot_value to the women's league; fetches each player's shotchartdetail, scores per-shot expected points from the free LeagueAverages zone table, and returns the scored shots plus shooter talent, selection quality, and zone-value maps (and the defender/shot-clock context tables when include_context=True). Women's court geometry + shrinkage constant are keyed "10" in nba_shot_value_constants.

Parameters

Parameter	Type	Default	Description
player_ids	list[int]		Player ids to fetch.
season	str		Season string, e.g. "2024".
include_context	bool	False	Also fetch + return the playerdashptshots defender/shot-clock context tables.
return_as_pandas	bool	False	Return pandas frames instead of polars.

Returns

{"shots", "talent", "selection", "zones"} (plus "context" when requested). An empty fetch returns a dict of zero-row frames.

Example

from sportsdataverse.wnba import wnba_shot_value
out = wnba_shot_value([1628886], "2024")
out["talent"].head()

wnba_team_clutch

wnba_team_clutch(season: 'int', *, league_id: 'str' = '00', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'

WNBA clutch skill (league_id='10'). See sportsdataverse.nba.nba_clutch.nba_team_clutch.

Parameters

Parameter	Type	Default	Description
season	int
league_id	str	'00'
return_as_pandas	bool	False

wnba_team_crosswalk

wnba_team_crosswalk(season: 'Optional[int]' = None, *, stats: 'Optional[pl.DataFrame]' = None, fox: 'Optional[pl.DataFrame]' = None, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> "Union[pl.DataFrame, 'pd.DataFrame']"

Build the WNBA cross-source team crosswalk (ESPN / WNBA Stats / Fox).

One row per ESPN team, keyed on espn_team_id. The Stats side is derived from the season schedule's home/away team fields (as in wehoop) and joined on the normalized city + name.

Parameters

Parameter	Type	Default	Description
season	Optional[int]	None	Season year (e.g. 2026). Defaults to the most recent WNBA season.
stats	Optional[DataFrame]	None	Pre-fetched Stats team directory. None derives it from the Stats schedule.
fox	Optional[DataFrame]	None	Pre-fetched fox_wnba_teams() frame. None fetches live.
return_as_pandas	bool	False	Return pandas instead of polars.

Returns

pl.DataFrame (or pandas), one row per ESPN team, with TEAM_COLUMNS.

Example

from sportsdataverse.wnba import wnba_team_crosswalk
df = wnba_team_crosswalk(season=2026)
print(df.shape)

# Offline with pre-fetched provider frames

df = wnba_team_crosswalk(season=2026, stats=my_stats, fox=my_fox)

# Pipeline next step (one line)

df.select("espn_team_id", "wnba_team_id", "match_method").head()

wnba_team_ratings

wnba_team_ratings(seasons: 'Union[int, list[int]]', *, league_id: 'str' = '00', as_of_date: 'Union[dt.date, None]' = None, return_as_pandas: 'bool' = False) -> "Union[pl.DataFrame, 'pd.DataFrame']"

WNBA team ratings (league_id='10'). See sportsdataverse.nba.nba_team_ratings.nba_team_ratings.

Parameters

Parameter	Type	Default	Description
seasons	Union[int, list[int]]
league_id	str	'00'
as_of_date	Union[date, None]	None
return_as_pandas	bool	False

wnba_tracking_drive_value

wnba_tracking_drive_value(seasons: "'int | str | list'", *, league_id: 'str' = '10', per_mode: 'str' = 'Totals', by_position: 'bool' = True, positions: 'Optional[pl.DataFrame]' = None, return_as_pandas: 'bool' = False, _get_fn: 'Optional[Callable[..., dict]]' = None) -> "'Union[pl.DataFrame, pd.DataFrame]'"

WNBA drive value + rim-pressure (league_id="10" by-reference shim).

See sportsdataverse.nba.nba_tracking_value.nba_tracking_drive_value for the full recipe.

Parameters

Parameter	Type	Default	Description
seasons	int | str | list		A single season or list of seasons.
league_id	str	'10'	Defaults to "10" (WNBA); pass "20" here for G-League.
per_mode	str	'Totals'	per_mode_simple passed to the fetch (default "Totals").
by_position	bool	True	Compute the baseline within role buckets (default); False forces one league-wide bucket.
positions	Optional[DataFrame]	None	Optional pre-fetched positions frame.
return_as_pandas	bool	False	Return a pandas.DataFrame instead of polars.
_get_fn	Optional[Callable[..., dict]]	None	Injectable replacement for nba_stats_leaguedashptstats.

Returns

One row per player-season: season:Int64, player_id:Utf8, player_name:Utf8, team_id:Utf8, position_bucket:Utf8, gp:Int64, min:Float64, drives:Float64, drive_pts:Float64, drive_baseline_rate:Float64, drive_expected:Float64, drive_pts_oe:Float64, drive_pts_oe_per_36:Float64, drive_fta:Float64, rim_pressure:Float64, drive_ast:Float64, drive_tov:Float64, league_id:Utf8. Empty/malformed input returns a zero-row frame with this schema.

Example

from sportsdataverse.wnba import wnba_tracking_drive_value
df = wnba_tracking_drive_value(2024)
print(df.sort("drive_pts_oe", descending=True).head())

wnba_tracking_pass_value

wnba_tracking_pass_value(seasons: "'int | str | list'", *, league_id: 'str' = '10', per_mode: 'str' = 'Totals', by_position: 'bool' = True, positions: 'Optional[pl.DataFrame]' = None, fetch_potential_assists: 'bool' = False, max_players: 'int' = 0, return_as_pandas: 'bool' = False, _get_fn: 'Optional[Callable[..., dict]]' = None, _pass_get_fn: 'Optional[Callable[..., dict]]' = None) -> "'Union[pl.DataFrame, pd.DataFrame]'"

WNBA expected-assists / passer value (league_id="10" by-reference shim).

See sportsdataverse.nba.nba_tracking_value.nba_tracking_pass_value for the full recipe (Passing-measure proxy + optional playerdashptpass enrichment).

Parameters

Parameter	Type	Default	Description
seasons	int | str | list		A single season or list of seasons.
league_id	str	'10'	Defaults to "10" (WNBA); pass "20" here for G-League.
per_mode	str	'Totals'	per_mode_simple passed to the fetch (default "Totals").
by_position	bool	True	Compute the baseline within role buckets (default); False forces one league-wide bucket.
positions	Optional[DataFrame]	None	Optional pre-fetched positions frame.
fetch_potential_assists	bool	False	Enrich the top passers with playerdashptpass potential-assist counts.
max_players	int	0	Cap on per-player enrichment fetches; 0 disables enrichment regardless of fetch_potential_assists.
return_as_pandas	bool	False	Return a pandas.DataFrame instead of polars.
_get_fn	Optional[Callable[..., dict]]	None	Injectable replacement for nba_stats_leaguedashptstats.
_pass_get_fn	Optional[Callable[..., dict]]	None	Injectable replacement for nba_stats_playerdashptpass.

Returns

One row per player-season: season:Int64, player_id:Utf8, player_name:Utf8, team_id:Utf8, position_bucket:Utf8, gp:Int64, min:Float64, ast:Float64, passes:Float64, ast_baseline_rate:Float64, ast_expected:Float64, ast_oe:Float64, ast_oe_per_36:Float64, ast_pts_created:Float64, league_id:Utf8. Empty/malformed input returns a zero-row frame with this schema.

Example

from sportsdataverse.wnba import wnba_tracking_pass_value
df = wnba_tracking_pass_value(2024)
print(df.sort("ast_oe", descending=True).head())

wnba_tracking_reb_oe

wnba_tracking_reb_oe(seasons: "'int | str | list'", *, league_id: 'str' = '10', per_mode: 'str' = 'Totals', by_position: 'bool' = True, positions: 'Optional[pl.DataFrame]' = None, return_as_pandas: 'bool' = False, _get_fn: 'Optional[Callable[..., dict]]' = None) -> "'Union[pl.DataFrame, pd.DataFrame]'"

WNBA rebounding-over-expected (league_id="10" by-reference shim).

See sportsdataverse.nba.nba_tracking_value.nba_tracking_reb_oe for the full recipe (contest-difficulty-adjusted expected rebounds, role-bucket baseline).

Parameters

Parameter	Type	Default	Description
seasons	int | str | list		A single season or list of seasons.
league_id	str	'10'	Defaults to "10" (WNBA); pass "20" here for G-League.
per_mode	str	'Totals'	per_mode_simple passed to the fetch (default "Totals").
by_position	bool	True	Compute the baseline within role buckets (default); False forces one league-wide bucket.
positions	Optional[DataFrame]	None	Optional pre-fetched positions frame.
return_as_pandas	bool	False	Return a pandas.DataFrame instead of polars.
_get_fn	Optional[Callable[..., dict]]	None	Injectable replacement for nba_stats_leaguedashptstats.

Returns

One row per player-season: season:Int64, player_id:Utf8, player_name:Utf8, team_id:Utf8, position_bucket:Utf8, gp:Int64, min:Float64, reb:Float64, reb_chances:Float64, reb_baseline_rate:Float64, reb_expected:Float64, reb_oe:Float64, reb_oe_per_36:Float64, oreb_oe:Float64, dreb_oe:Float64, league_id:Utf8. Empty/malformed input returns a zero-row frame with this schema.

Example

from sportsdataverse.wnba import wnba_tracking_reb_oe
df = wnba_tracking_reb_oe(2024)
print(df.sort("reb_oe", descending=True).head())

wnba_tracking_rim_protect_value

wnba_tracking_rim_protect_value(seasons: "'int | str | list'", *, league_id: 'str' = '10', per_mode: 'str' = 'Totals', by_position: 'bool' = True, positions: 'Optional[pl.DataFrame]' = None, source: 'str' = 'leaguedash', max_players: 'int' = 0, return_as_pandas: 'bool' = False, _get_fn: 'Optional[Callable[..., dict]]' = None, _defend_get_fn: 'Optional[Callable[..., dict]]' = None) -> "'Union[pl.DataFrame, pd.DataFrame]'"

WNBA rim-protection / shot-defend points-saved (league_id="10"

by-reference shim).

See sportsdataverse.nba.nba_tracking_value.nba_tracking_rim_protect_value for the full recipe (bucket-mean defended-rate baseline; optional playerdashptshotdefend rim-band enrichment).

Parameters

Parameter	Type	Default	Description
seasons	int | str | list		A single season or list of seasons.
league_id	str	'10'	Defaults to "10" (WNBA); pass "20" here for G-League.
per_mode	str	'Totals'	per_mode_simple passed to the fetch (default "Totals").
by_position	bool	True	Compute the baseline within role buckets (default); False forces one league-wide bucket.
positions	Optional[DataFrame]	None	Optional pre-fetched positions frame.
source	str	'leaguedash'	"leaguedash" (default) or "shotdefend".
max_players	int	0	Cap on per-player shotdefend enrichment fetches; ignored unless source="shotdefend".
return_as_pandas	bool	False	Return a pandas.DataFrame instead of polars.
_get_fn	Optional[Callable[..., dict]]	None	Injectable replacement for nba_stats_leaguedashptstats.
_defend_get_fn	Optional[Callable[..., dict]]	None	Injectable replacement for nba_stats_playerdashptshotdefend.

Returns

One row per player-season: season:Int64, player_id:Utf8, player_name:Utf8, team_id:Utf8, position_bucket:Utf8, gp:Int64, min:Float64, d_fga:Float64, d_fgm:Float64, d_fg_pct:Float64, normal_fg_pct:Float64, rim_protect_pts_saved:Float64, rim_protect_pts_saved_per_36:Float64, source:Utf8, league_id:Utf8. Empty/malformed input returns a zero-row frame with this schema.

Example

from sportsdataverse.wnba import wnba_tracking_rim_protect_value
df = wnba_tracking_rim_protect_value(2024)
print(df.sort("rim_protect_pts_saved", descending=True).head())

wnba_tracking_shot_diet_value

wnba_tracking_shot_diet_value(seasons: "'int | str | list'", *, league_id: 'str' = '10', per_mode: 'str' = 'Totals', by_position: 'bool' = True, positions: 'Optional[pl.DataFrame]' = None, return_as_pandas: 'bool' = False, _get_fn: 'Optional[Callable[..., dict]]' = None) -> "'Union[pl.DataFrame, pd.DataFrame]'"

WNBA catch-&-shoot vs pull-up points-over-expected (league_id="10"

by-reference shim).

See sportsdataverse.nba.nba_tracking_value.nba_tracking_shot_diet_value for the full recipe.

Parameters

Parameter	Type	Default	Description
seasons	int | str | list		A single season or list of seasons.
league_id	str	'10'	Defaults to "10" (WNBA); pass "20" here for G-League.
per_mode	str	'Totals'	per_mode_simple passed to each fetch (default "Totals").
by_position	bool	True	Compute each measure's baseline within role buckets (default); False forces one league-wide bucket.
positions	Optional[DataFrame]	None	Optional pre-fetched positions frame.
return_as_pandas	bool	False	Return a pandas.DataFrame instead of polars.
_get_fn	Optional[Callable[..., dict]]	None	Injectable replacement for nba_stats_leaguedashptstats.

Returns

One row per player-season: season:Int64, player_id:Utf8, player_name:Utf8, team_id:Utf8, position_bucket:Utf8, cs_fga:Float64, cs_pts:Float64, cs_pts_oe:Float64, pu_fga:Float64, pu_pts:Float64, pu_pts_oe:Float64, shot_diet_delta:Float64, league_id:Utf8. Empty/malformed input returns a zero-row frame with this schema.

Example

from sportsdataverse.wnba import wnba_tracking_shot_diet_value
df = wnba_tracking_shot_diet_value(2024)
print(df.sort("cs_pts_oe", descending=True).head())

wnba_tracking_touch_value

wnba_tracking_touch_value(seasons: "'int | str | list'", *, league_id: 'str' = '10', per_mode: 'str' = 'Totals', by_position: 'bool' = True, positions: 'Optional[pl.DataFrame]' = None, return_as_pandas: 'bool' = False, _get_fn: 'Optional[Callable[..., dict]]' = None) -> "'Union[pl.DataFrame, pd.DataFrame]'"

WNBA touch / possession-time value (league_id="10" by-reference shim).

See sportsdataverse.nba.nba_tracking_value.nba_tracking_touch_value for the full recipe.

Parameters

Parameter	Type	Default	Description
seasons	int | str | list		A single season or list of seasons.
league_id	str	'10'	Defaults to "10" (WNBA); pass "20" here for G-League.
per_mode	str	'Totals'	per_mode_simple passed to the fetch (default "Totals").
by_position	bool	True	Compute the baseline within role buckets (default); False forces one league-wide bucket.
positions	Optional[DataFrame]	None	Optional pre-fetched positions frame.
return_as_pandas	bool	False	Return a pandas.DataFrame instead of polars.
_get_fn	Optional[Callable[..., dict]]	None	Injectable replacement for nba_stats_leaguedashptstats.

Returns

One row per player-season: season:Int64, player_id:Utf8, player_name:Utf8, team_id:Utf8, position_bucket:Utf8, gp:Int64, min:Float64, touches:Float64, pts:Float64, touch_baseline_rate:Float64, touch_expected:Float64, pts_per_touch_oe:Float64, time_of_poss:Float64, time_of_poss_eff:Float64, league_id:Utf8. Empty/malformed input returns a zero-row frame with this schema.

Example

from sportsdataverse.wnba import wnba_tracking_touch_value
df = wnba_tracking_touch_value(2024)
print(df.sort("pts_per_touch_oe", descending=True).head())

wnba_win_prob_from_margin

wnba_win_prob_from_margin(exp_margin: 'float', *, league_id: 'str' = '00') -> 'float'

WNBA home win probability (league_id='10'). See sportsdataverse.nba.nba_game_predict.win_prob_from_margin.

Parameters

Parameter	Type	Default	Description
exp_margin	float
league_id	str	'00'