Version: 0.0.72

WNBA — additional Python functions

Hand-written wrappers, loaders, and helpers in sportsdataverse.wnba not covered by the generated API-endpoint reference above.

Play-by-play, schedule & rosters

`espn_wnba_game_officials(game_id: 'int', season: 'int | None' = None, *, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'pl.DataFrame | pd.DataFrame | dict[str, Any]'`

Pull the officials assigned to a WNBA game.

See sportsdataverse.wbb.espn_wbb_game_officials for full documentation of the column set, the empty-frame fallback when ESPN ships no officials, and the raw / return_as_pandas flag semantics.

Parameters

Parameter	Type	Default	Description
`game_id`	`int`		ESPN WNBA event identifier (e.g. `401620238` for Game 1 of the 2024 WNBA Finals).
`season`	`int \| None`	`None`	Season year (recorded as output column only).
`raw`	`bool`	`False`	If True, returns the parsed JSON dict before any flattening.
`return_as_pandas`	`bool`	`False`	If True, returns a pandas DataFrame; otherwise polars.

Returns

Polars (or pandas) DataFrame with the same columns documented in sportsdataverse.wbb.espn_wbb_game_officials. If raw=True, returns the raw response dict.

col_name	type	description
`game_id`	integer	Unique game identifier.
`season`	integer	Season identifier (4-digit year or 'YYYY-YY' string).
`official_id`	character	Unique official / referee identifier.
`first_name`	character	Player's first name.
`last_name`	character	Player's last name.
`full_name`	character	Player's full name.
`display_name`	character	Display name.
`position_id`	character	Unique position identifier.
`position_name`	character	Listed roster position ('Guard', 'Forward', 'Center').
`position_display_name`	character	Position display name.
`order`	integer	Display order within the result set.

Example

from sportsdataverse.wnba import espn_wnba_game_officials
refs = espn_wnba_game_officials(game_id=401620238, season=2024)
print(refs.shape)
refs.select(["full_name", "position_name", "order"]).head()

# Pandas round-trip

refs_pd = espn_wnba_game_officials(
    game_id=401620238, season=2024, return_as_pandas=True
)
refs_pd[["full_name", "position_name"]].head()

# Inspect the raw ESPN payload (e.g. for fields not flattened)

payload = espn_wnba_game_officials(game_id=401620238, season=2024, raw=True)
list(payload.keys())[:8]

`espn_wnba_player_stats(athlete_id: 'int', season: 'int', *, season_type: 'str' = 'regular', total: 'bool' = False, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'pl.DataFrame | pd.DataFrame | dict[str, Any]'`

Pull a WNBA athlete's ESPN season stat line.

See sportsdataverse.wbb.espn_wbb_player_stats for full documentation of the wide return shape, the {category}_{stat} stat columns, the athlete / team metadata blocks, and the season_type / total parameters.

Parameters

Parameter	Type	Default	Description
`athlete_id`	`int`		ESPN WNBA athlete identifier (e.g. `3149391` for A'ja Wilson).
`season`	`int`		Season year, used in the core-v2 path.
`season_type`	`str`	`'regular'`	`"regular"` (type 2) or `"postseason"` (type 3).
`total`	`bool`	`False`	Forward-compat totals passthrough.
`raw`	`bool`	`False`	If True, returns the raw core-v2 statistics JSON dict.
`return_as_pandas`	`bool`	`False`	If True, returns a pandas DataFrame; else polars.

Returns

A single-row wide DataFrame (polars by default). When raw=True returns the raw statistics JSON dict. See sportsdataverse.wbb.espn_wbb_player_stats for the column layout.

col_name	type	description
`season`	integer	Season identifier (4-digit year or 'YYYY-YY' string).
`season_type`	character	Season type (1=pre-season, 2=regular season, 3=postseason, 4=off-season for ESPN; or string label for WNBA Stats).
`total`	logical	Total.
`athlete_id`	integer	Unique athlete identifier (ESPN).
`athlete_uid`	character	ESPN athlete UID (universal identifier).
`athlete_guid`	character	ESPN athlete GUID.
`athlete_type`	character	Athlete type / class.
`first_name`	character	Player's first name.
`last_name`	character	Player's last name.
`full_name`	character	Player's full name.
`display_name`	character	Display name.
`short_name`	character	Short display name.
`weight`	double	Player weight in pounds.
`display_weight`	character	Player weight in display format (e.g. '180 lbs').
`height`	double	Player height (string e.g. '6-2' or inches).
`display_height`	character	Player height in display format (e.g. '6-2').
`age`	integer	Player age (in years).
`date_of_birth`	character	Date of birth (YYYY-MM-DD).
`jersey`	character	Jersey number worn by the player.
`slug`	character	URL-safe identifier.
`active`	logical	TRUE if the row represents an active record (player / team / season).
`position_id`	integer	Unique position identifier.
`position_name`	character	Listed roster position ('Guard', 'Forward', 'Center').
`position_display_name`	character	Position display name.
`position_abbreviation`	character	Position abbreviation ('G' / 'F' / 'C').
`college_name`	character	College name.
`status_id`	integer	Status identifier.
`status_name`	character	Status label.
`defensive_blocks`	double	Short for blocked shot, number of times when a defensive player legally deflects a field goal attempt from an offensive player.
`defensive_defensive_rebounds`	double	The number of times when the defense obtains the possession of the ball after a missed shot by the offense.
`defensive_steals`	double	The number of times a defensive player forced a turnover by intercepting or deflecting a pass or a dribble of an offensive player.
`defensive_avg_defensive_rebounds`	double	The average defensive rebounds per game.
`defensive_avg_blocks`	double	The average blocks per game.
`defensive_avg_steals`	double	The average steals per game.
`defensive_avg48_defensive_rebounds`	double	Player's defensive rebounds per 48 minutes of play, drawn from the defensive statistical category.
`defensive_avg48_blocks`	double	Player's blocked shots per 48 minutes of play, drawn from the defensive statistical category.
`defensive_avg48_steals`	double	Player's steals per 48 minutes of play, drawn from the defensive statistical category.
`general_disqualifications`	double	The number of times a player reached the foul limit.
`general_flagrant_fouls`	double	The number of fouls that the officials thought were unnecessary or excessive.
`general_fouls`	double	The number of times a player had illegal contact with the opponent.
`general_ejections`	double	The number of times a player or coach is removed from the game as a result of a serious offense.
`general_technical_fouls`	double	The number of times an player or coach was called for a technical foul (unsportsmanlike conduct or violations).
`general_rebounds`	double	The total number of rebounds (offensive and defensive).
`general_vorp`	double	Value Over Replacement Player.
`general_minutes`	double	The total number of minutes played.
`general_avg_minutes`	double	The average number of minutes per game.
`general_fantasy_rating`	double	The Fantasy Rating of a player.
`general_nba_rating`	double	General nba rating.
`general_plus_minus`	double	A player's estimated on-court impact on team performance measured in point differential per 100 possessions.
`general_avg_rebounds`	double	The average rebounds per game.
`general_avg_fouls`	double	The average fouls committed per game.
`general_avg_flagrant_fouls`	double	The average number of flagrant fouls per game.
`general_avg_technical_fouls`	double	The average number of technical fouls per game.
`general_avg_ejections`	double	The average ejections per game.
`general_avg_disqualifications`	double	The average number of disqualifications per game.
`general_assist_turnover_ratio`	double	The average number of assists a player or team records per turnover.
`general_steal_foul_ratio`	double	The average number of steals a player or team records per foul committed.
`general_block_foul_ratio`	double	The average number of blocks a player or record per foul committed.
`general_avg_team_rebounds`	double	The average number of rebounds for a team per game.
`general_total_rebounds`	double	The total number of rebounds for a team or player.
`general_total_technical_fouls`	double	The total number of technical fouls for a team or player.
`general_team_assist_turnover_ratio`	double	The number of assists per turnover for a team.
`general_steal_turnover_ratio`	double	The number of steals per turnover.
`general_avg48_rebounds`	double	Player's total rebounds (offensive plus defensive) per 48 minutes of play, drawn from the general statistical category.
`general_avg48_fouls`	double	Player's personal fouls per 48 minutes of play, drawn from the general statistical category.
`general_avg48_flagrant_fouls`	double	Player's flagrant fouls per 48 minutes of play, drawn from the general statistical category.
`general_avg48_technical_fouls`	double	Player's technical fouls per 48 minutes of play, drawn from the general statistical category.
`general_avg48_ejections`	double	Player's ejections per 48 minutes of play, drawn from the general statistical category.
`general_avg48_disqualifications`	double	Player's disqualifications (fouling out) per 48 minutes of play, drawn from the general statistical category.
`general_games_played`	double	Games Played.
`general_games_started`	double	The number of games started by an athlete.
`general_double_double`	double	The number of times double digit values were accumulated in 2 of the following categories: points, rebounds, assists, steals, and blocked shots.
`general_triple_double`	double	The number of times double digit values were accumulated in 3 of the following categories: points, rebounds, assists, steals, and blocked shots.
`offensive_assists`	double	The number of times a player who passes the ball to a teammate in a way that leads to a score by field goal, meaning that he or she was "assisting" in the basket. There is some judgment involved in deciding whether a passer should be credited with an assist.
`offensive_field_goals`	double	Field Goal makes and attempts.
`offensive_field_goals_attempted`	double	The number of times a 2pt field goal was attempted.
`offensive_field_goals_made`	double	The number of times a 2pt field goal was made.
`offensive_field_goal_pct`	double	The ratio of field goals made to field goals attempted: FGM / FGA.
`offensive_free_throws`	double	Free Throw makes and attempts.
`offensive_free_throw_pct`	double	The ratio of free throws made to free throws attempted: FTM / FTA.
`offensive_free_throws_attempted`	double	The number of times a free throw was attempted.
`offensive_free_throws_made`	double	The number of times a free throw was made.
`offensive_offensive_rebounds`	double	The number of times when the offense obtains the possession of the ball after a missed shot.
`offensive_points`	double	The number of points scored.
`offensive_turnovers`	double	The number of times a player loses possession to the other team.
`offensive_three_point_pct`	double	The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA.
`offensive_three_point_field_goals_attempted`	double	The number of times a 3pt field goal was attempted.
`offensive_three_point_field_goals_made`	double	The number of times a 3pt field goal was made.
`offensive_total_turnovers`	double	The number of turnovers plus team turnovers for the team.
`offensive_points_in_paint`	double	The amount of points scored in the area known as "the Paint"(the rectangle between the foul line and the baseline).
`offensive_brick_index`	double	How many points a player costs his team with his shooting compared with the league average on a per-40-minute basis. ((52.8 - TS%) x (FGA + (FTA x 0.44))) / (Min/40) .
`offensive_avg_field_goals_made`	double	The average field goals made per game.
`offensive_avg_field_goals_attempted`	double	The average field goals attempted per game.
`offensive_avg_three_point_field_goals_made`	double	The average three point field goals made per game.
`offensive_avg_three_point_field_goals_attempted`	double	The average three point field goals attempted per game.
`offensive_avg_free_throws_made`	double	The average free throw shots made per game.
`offensive_avg_free_throws_attempted`	double	The average free throw shots attempted per game.
`offensive_avg_points`	double	The average number of points scored per game.
`offensive_avg_offensive_rebounds`	double	The average offensive rebounds per game.
`offensive_avg_assists`	double	The average assists per game.
`offensive_avg_turnovers`	double	The average turnovers committed per game.
`offensive_offensive_rebound_pct`	double	The percentage of the number of times they obtain the possession of the ball after a missed shot.
`offensive_estimated_possessions`	double	An estimation of the number of possessions for a team or player.
`offensive_avg_estimated_possessions`	double	The average number of estimated possessions per game for a team or player.
`offensive_points_per_estimated_possessions`	double	The number of points per estimated possession for a team or player.
`offensive_avg_team_turnovers`	double	The average number of turnovers for a team per game.
`offensive_avg_total_turnovers`	double	The average number of total turnovers for a team per game.
`offensive_three_point_field_goal_pct`	double	The ratio of 3pt field goals made to 3pt field goals attempted: 3PM / 3PA.
`offensive_two_point_field_goals_made`	double	The number of 2-point field goals made for a team or player.
`offensive_two_point_field_goals_attempted`	double	The number of 2-point field goals attempted for a team or player.
`offensive_avg_two_point_field_goals_made`	double	The number of 2-point field goals made per game for a team or player.
`offensive_avg_two_point_field_goals_attempted`	double	The number of 2-point field goals attempted per game for a team or player.
`offensive_two_point_field_goal_pct`	double	The percentage of 2-points fields goals made by a team or player.
`offensive_shooting_efficiency`	double	The efficiency with which a team or player shoots the basketball.
`offensive_scoring_efficiency`	double	The efficiency with which a team or player scores the basketball.
`offensive_avg48_field_goals_made`	double	Player's field goals made per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_field_goals_attempted`	double	Player's field goal attempts per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_three_point_field_goals_made`	double	Player's three-point field goals made per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_three_point_field_goals_attempted`	double	Player's three-point field goal attempts per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_free_throws_made`	double	Player's free throws made per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_free_throws_attempted`	double	Player's free throw attempts per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_points`	double	Player's points scored per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_offensive_rebounds`	double	Player's offensive rebounds per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_assists`	double	Player's assists per 48 minutes of play, drawn from the offensive statistical category.
`offensive_avg48_turnovers`	double	Player's turnovers per 48 minutes of play, drawn from the offensive statistical category.
`team_id`	integer	Unique team identifier.
`team_uid`	character	ESPN universal team identifier (UID format 's:40~l:...~t:...').
`team_guid`	character	ESPN team GUID.
`team_slug`	character	URL-safe team identifier (e.g. 'lasvegas-aces' / 'aces').
`team_location`	character	Team city or location string.
`team_name`	character	Full team display name (e.g. 'Las Vegas Aces').
`team_abbreviation`	character	Short team abbreviation (e.g. 'LAS').
`team_display_name`	character	Full team display name.
`team_short_display_name`	character	Short team display name (e.g. 'Aces').
`team_color`	character	Team primary color (hex without leading '#').
`team_alternate_color`	character	Team alternate color (hex without leading '#').
`team_is_active`	logical	TRUE if the team is currently active.
`team_logo_href`	character	Default team logo URL; `team_detail = TRUE` only.

Example

from sportsdataverse.wnba import espn_wnba_player_stats
df = espn_wnba_player_stats(athlete_id=3149391, season=2024)
df.select(["full_name", "team_display_name", "offensive_points"])

`espn_wnba_schedule(dates=None, season_type=None, limit=500, return_as_pandas=False, **kwargs) -> 'pl.DataFrame'`

espn_wnba_schedule - look up the WNBA schedule for a given season

Parameters

Parameter	Type	Default	Description
`dates`	`int`	`None`	Used to define different seasons. 2002 is the earliest available season.
`season_type`	`int`	`None`	2 for regular season, 3 for post-season, 4 for off-season.
`limit`	`int`	`500`	number of records to return, default: 500.
`return_as_pandas`		`False`

Returns

Polars dataframe containing schedule dates for the requested season. Returns None if no games

col_name	type	description
`id`	character	Unique play identifcation number
`uid`	character	ESPN UID string.
`date`	character	Date in YYYY-MM-DD format.
`attendance`	integer	Reported attendance.
`time_valid`	logical	Whether the start time is confirmed.
`neutral_site`	logical	Neutral site.
`conference_competition`	logical	Conference competition.
`play_by_play_available`	logical	Whether play-by-play data is available.
`recent`	logical	Whether the game is recent.
`start_date`	character	Start date (YYYY-MM-DD).
`broadcast`	character	Broadcast information string.
`highlights`	integer	Game highlight urls.
`notes_type`	character	Notes type.
`notes_headline`	character	Notes headline.
`broadcast_market`	character	Broadcast market label (e.g. 'national', 'home').
`broadcast_name`	character	Broadcast name.
`type_id`	character	Type identifier (numeric).
`type_abbreviation`	character	Play type abbreviation
`venue_id`	character	Unique venue identifier.
`venue_full_name`	character	Venue full name.
`venue_address_city`	character	Venue address city.
`venue_address_state`	character	Venue address state / region.
`venue_indoor`	logical	Whether the home venue is indoors.
`status_clock`	double	Game clock in seconds.
`status_display_clock`	character	Status display clock.
`status_period`	integer	Current period.
`status_type_id`	character	Unique identifier for status type.
`status_type_name`	character	Status type name.
`status_type_state`	character	Status state (pre/in/post).
`status_type_completed`	logical	Whether the game is complete.
`status_type_description`	character	Status type description.
`status_type_detail`	character	Status type detail.
`status_type_short_detail`	character	Status type short detail.
`format_regulation_periods`	integer	Format regulation periods.
`home_id`	character	Unique identifier for home.
`home_uid`	character	Home team's uid.
`home_location`	character	Home team's location.
`home_name`	character	Home team display name.
`home_abbreviation`	character	Home team's abbreviation.
`home_display_name`	character	Home team display name.
`home_short_display_name`	character	Home short display name.
`home_color`	character	Home team primary color hex.
`home_alternate_color`	character	Color code (hex) for home alternate.
`home_is_active`	logical	Home team's is active.
`home_venue_id`	character	Unique identifier for home venue.
`home_logo`	character	Home team logo URL.
`home_score`	character	Home team score at the time of the play.
`home_winner`	logical	Whether the home team won.
`home_linescores`	list	Period-by-period point totals for the home team, stored as a list of integer scores.
`home_records`	character	Win-loss record strings for the home team across relevant splits (e.g., overall, home/away, conference).
`away_id`	character	Unique identifier for away.
`away_uid`	character	Away team's uid.
`away_location`	character	Away team's location.
`away_name`	character	Away team display name.
`away_abbreviation`	character	Away team's abbreviation.
`away_display_name`	character	Away team display name.
`away_short_display_name`	character	Away short display name.
`away_color`	character	Away team primary color hex.
`away_alternate_color`	character	Color code (hex) for away alternate.
`away_is_active`	logical	Away team's is active.
`away_venue_id`	character	Unique identifier for away venue.
`away_logo`	character	Away team logo URL.
`away_score`	character	Away team score at the time of the play.
`away_winner`	logical	Whether the away team won.
`away_linescores`	list	Period-by-period point totals for the away team, stored as a list of integer scores.
`away_records`	character	Win-loss record strings for the away team across relevant splits (e.g., overall, home/away, conference).
`game_id`	integer	Unique game identifier.
`season`	integer	Season identifier (4-digit year or 'YYYY-YY' string).
`season_type`	integer	Season type (1=pre-season, 2=regular season, 3=postseason, 4=off-season for ESPN; or string label for WNBA Stats).

Example

from sportsdataverse.wnba import espn_wnba_schedule
sched = espn_wnba_schedule(dates=20241011)  # 2024 WNBA Finals Game 1
print(sched.shape)
sched.select(["game_id", "home_name", "away_name", "status_type_description"]).head()

# Pull a full regular season's worth of games

reg = espn_wnba_schedule(dates=2024, season_type=2, limit=500)
reg.group_by("status_type_description").len().sort("len", descending=True)

# Pandas round-trip for a single date

espn_wnba_schedule(dates=20241011, return_as_pandas=True).head()

`espn_wnba_team_stats(team_id: 'int', season: 'int', *, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'dict[str, pl.DataFrame] | dict[str, pd.DataFrame] | dict[str, Any]'`

Pull ESPN team season stats for a WNBA team.

See sportsdataverse.wbb.espn_wbb_team_stats for full documentation of the return shape, the canonical three category keys ("Averages", "Totals", "Misc"), the per-category column set, and the "Other" fallback bucket.

Parameters

Parameter	Type	Default	Description
`team_id`	`int`		ESPN WNBA team identifier (e.g. `17` for the Las Vegas Aces).
`season`	`int`		Season year, forwarded to ESPN as `?season=YYYY`.
`raw`	`bool`	`False`	If True, returns the parsed JSON dict before any flattening.
`return_as_pandas`	`bool`	`False`	If True, returns a dict of pandas DataFrames; otherwise polars.

Returns

Dict with one DataFrame per stat category — see sportsdataverse.wbb.espn_wbb_team_stats for the full column / key documentation. If raw=True, returns the raw response dict.

Example

from sportsdataverse.wnba import espn_wnba_team_stats
frames = espn_wnba_team_stats(team_id=17, season=2024)
sorted(frames.keys())  # 'Averages', 'Totals', 'Misc' (plus optional 'Other')
frames["Averages"].head()

# Compare per-game and totals at a glance

avgs = frames["Averages"]
totals = frames["Totals"]
print(avgs.shape, totals.shape)
avgs.select(["games_played", "points_per_game", "rebounds_per_game"])

# Pandas round-trip

frames_pd = espn_wnba_team_stats(team_id=17, season=2024, return_as_pandas=True)
frames_pd["Misc"].head()

Dataset loaders

`load_wnba_stats_lineups(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'`

Load season-level WNBA 5-man lineup statistics (deprecated).

Parameters

Parameter	Type	Default	Description
`seasons`			an int or iterable of seasons.
`return_as_pandas`	`bool`	`False`	return a pandas DataFrame instead of polars.

Returns

A polars (or pandas) DataFrame, one row per lineup-season-measure_type, stacked from the wnba_stats_leaguedash cube's lineups_{base, advanced} assets filtered to group_quantity == 5 — matching the old wnba_stats_lineups tag's 5-man-only, Base+Advanced-only coverage. Call the cube's lineups_* assets directly (unfiltered) for 2/3/4-man lineups or the other 4 measure types.

Example

from sportsdataverse.wnba import load_wnba_stats_lineups
df = load_wnba_stats_lineups(seasons=2026)
print(df.shape)

`load_wnba_stats_player_season_stats(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'`

Load season-level WNBA player statistics (deprecated).

Parameters

Parameter	Type	Default	Description
`seasons`			an int or iterable of seasons.
`return_as_pandas`	`bool`	`False`	return a pandas DataFrame instead of polars.

Returns

A polars (or pandas) DataFrame, one row per player-season-measure_type, stacked from the wnba_stats_leaguedash cube's player_stats_* assets (Base/Advanced/Misc/Scoring/Usage/Defense — matches the old wnba_stats_player_season_stats tag's coverage; player-level Opponent/Four Factors are empty upstream and were never populated by either version).

Example

from sportsdataverse.wnba import load_wnba_stats_player_season_stats
df = load_wnba_stats_player_season_stats(seasons=2026)
print(df.shape)

# Pipeline next step (Advanced-only rows)

import polars as pl
adv = df.filter(pl.col("measure_type") == "Advanced")

`load_wnba_stats_standings(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'`

Load season-level WNBA standings (deprecated).

Parameters

Parameter	Type	Default	Description
`seasons`			an int or iterable of seasons.
`return_as_pandas`	`bool`	`False`	return a pandas DataFrame instead of polars.

Returns

A polars (or pandas) DataFrame, one row per team-season, read from the wnba_stats_leaguedash cube's standings asset -- the same underlying leaguestandingsv3 endpoint/params as the old wnba_stats_standings tag, so this is close to a pure passthrough.

Example

from sportsdataverse.wnba import load_wnba_stats_standings
df = load_wnba_stats_standings(seasons=2026)
print(df.shape)

`load_wnba_stats_team_season_stats(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'`

Load season-level WNBA team statistics (deprecated).

Parameters

Parameter	Type	Default	Description
`seasons`			an int or iterable of seasons.
`return_as_pandas`	`bool`	`False`	return a pandas DataFrame instead of polars.

Returns

A polars (or pandas) DataFrame, one row per team-season-measure_type, stacked from the wnba_stats_leaguedash cube's team_stats_* assets (Base/Advanced/Misc/Scoring/Defense/ Opponent — matches the old wnba_stats_team_season_stats tag's coverage; team-level Usage/Four Factors are empty upstream).

Example

from sportsdataverse.wnba import load_wnba_stats_team_season_stats
df = load_wnba_stats_team_season_stats(seasons=2026)
print(df.shape)

Utilities & helpers

`most_recent_wnba_season()`

most_recent_wnba_season - return the most recent (likely-completed) WNBA season year.

Returns the current calendar year if it's May or later (the WNBA regular season has tipped off), otherwise the previous calendar year.

Returns

Year (e.g. 2024) suitable for passing as a season argument to schedule / loader functions.

Example

from sportsdataverse.wnba import most_recent_wnba_season, espn_wnba_calendar
season = most_recent_wnba_season()
cal = espn_wnba_calendar(season=season)
print(season, cal.height)

Other

`build_athlete_identity_lookup(rosters: 'dict[int | str, dict]') -> 'dict[str, dict[str, Any]]'`

R build_athlete_identity_lookup: athlete_id -> identity from team rosters.

Parameters

Parameter	Type	Default	Description
`rosters`	`dict[int \| str, dict]`		Mapping of team_id -> that team's raw roster payload (`wbb/team_rosters/json/{season}/{team_id}.json`). NOTE: R walks `raw$athletes` directly here (no position-bucket unwrap, unlike the rosters dataset itself).

Returns

athlete_id (str) -> identity fields for helper_wbb_player_season_stats.

`espn_wnba_teams(return_as_pandas=False, **kwargs) -> 'pl.DataFrame'`

espn_wnba_teams - look up WNBA teams

Parameters

Parameter	Type	Default	Description
`return_as_pandas`	`bool`	`False`	If True, returns a pandas dataframe. If False, returns a polars dataframe.

Returns

Polars dataframe containing teams for the requested league. This function caches by default, so if you want to refresh the data, use the command sportsdataverse.wnba.espn_wnba_teams.clear_cache().

col_name	type	description
`team_abbreviation`	character	Short team abbreviation (e.g. 'LAS').
`team_alternate_color`	character	Team alternate color (hex without leading '#').
`team_color`	character	Team primary color (hex without leading '#').
`team_display_name`	character	Full team display name.
`team_id`	character	Unique team identifier.
`team_is_active`	logical	TRUE if the team is currently active.
`team_is_all_star`	logical	TRUE if the row represents an All-Star team.
`team_location`	character	Team city or location string.
`team_logos`	integer	Team logo metadata.
`team_name`	character	Full team display name (e.g. 'Las Vegas Aces').
`team_nickname`	character	Team nickname.
`team_short_display_name`	character	Short team display name (e.g. 'Aces').
`team_slug`	character	URL-safe team identifier (e.g. 'lasvegas-aces' / 'aces').
`team_uid`	character	ESPN universal team identifier (UID format 's:40~l:...~t:...').

Example

from sportsdataverse.wnba import espn_wnba_teams
teams = espn_wnba_teams()
print(teams.shape)
teams.select(["team_id", "team_abbreviation", "team_display_name"]).head()

# Find Las Vegas Aces (team_id 17)

teams.filter(__import__("polars").col("team_id") == "17").to_dicts()

# Refresh the cache (the call is ``lru_cache``'d)

espn_wnba_teams.cache_clear()  # cached at function-level
teams_pd = espn_wnba_teams(return_as_pandas=True)

`make_prob_by_context(ptshots: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'dict[str, Union[pl.DataFrame, pd.DataFrame]]'"`

Marginal FG% tables by defender distance and by shot clock.

The public API exposes defender-distance and shot-clock only as aggregate bucket tables (playerdashptshots), not per-shot fields, so this aggregates Σfgm/Σfga across players within each bucket.

Parameters

Parameter	Type	Default	Description
`ptshots`	`DataFrame`		The stacked `playerdashptshots` fixture — one frame with a `result_set` tag (`ClosestDefenderShooting` / `ShotClockShooting`) plus `bucket, fga, fgm`.
`return_as_pandas`	`bool`	`False`	Return pandas DataFrames instead of polars.

Returns

{"defender": frame, "shot_clock": frame} each with rows per bucket (bucket, fga, fgm, fg_pct). Missing result sets return the zero-row schema.

Example

from sportsdataverse.nba.nba_shot_value import make_prob_by_context
tables = make_prob_by_context(ptshots)
tables["defender"].sort("fg_pct")

`make_prob_joint(defender: 'pl.DataFrame', shot_clock: 'pl.DataFrame', overall_fg_pct: 'float', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

Independence-combined defender x shot-clock make probability.

Combines the two marginal FG% tables under a conditional-independence assumption via odds multipliers: odds(p) = p/(1-p); odds_joint = odds_overall * (odds_def/odds_overall) * (odds_clock/odds_overall); joint = odds_joint/(1+odds_joint). This assumes defender distance and shot-clock effects are independent given the league baseline — a simplification (a late clock correlates with tighter defense), documented here so callers weigh it.

Parameters

Parameter	Type	Default	Description
`defender`	`DataFrame`		The `"defender"` marginal table from `make_prob_by_context` (`bucket, fg_pct`).
`shot_clock`	`DataFrame`		The `"shot_clock"` marginal table (`bucket, fg_pct`).
`overall_fg_pct`	`float`		The league overall FG% baseline.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

One row per (close_def_dist_range, shot_clock_range): close_def_dist_range:Utf8, shot_clock_range:Utf8, joint_fg_pct:Float64. Empty inputs return the zero-row schema.

Example

from sportsdataverse.nba.nba_shot_value import make_prob_by_context, make_prob_joint
t = make_prob_by_context(ptshots)
joint = make_prob_joint(t["defender"], t["shot_clock"], 0.47)

`score_shot_xpoints(shots: 'pl.DataFrame', league_avgs: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

Score each shot with expected points from the league-average baseline.

Joins the per-shot frame to the zone baseline (falling back to the within-shot_zone_range mean when a zone triple is unmatched) and adds shot_value (3 for a 3PT shot else 2), xpoints = base_fg_pct * shot_value, and actual_points = shot_made_flag * shot_value.

Parameters

Parameter	Type	Default	Description
`shots`	`DataFrame`		Per-shot `Shot_Chart_Detail` frame (needs `shot_type` + the three zone keys + `shot_made_flag`).
`league_avgs`	`DataFrame`		The `LeagueAverages` frame (see `xpoints_baseline`).
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

The input shots plus shot_value:Int64, base_fg_pct:Float64, xpoints:Float64, actual_points:Float64. Empty input returns the augmented schema with zero rows.

Example

from sportsdataverse.nba.nba_shot_value import score_shot_xpoints
scored = score_shot_xpoints(shots, league_avgs)

# Pipeline next step (one line)

scored.group_by("player_id").agg(pl.col("xpoints").sum())

`scoreboard_event_parsing(event)`

No description available.

Parameters

Parameter	Type	Default	Description
`event`

`shooter_talent(scored_shots: 'pl.DataFrame', *, league_id: 'str' = '00', min_attempts: 'int' = 50, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

Regressed shooter true-talent: make%-above-expected, shrunk to the mean.

Aggregates score_shot_xpoints output per shooter and regresses the raw over-expected rate toward zero by n/(n+k) (k = get_shrinkage_k(league_id), fitted split-half). As-of leakage boundary: to score a shooter's talent for shots after date D, pass only that shooter's shots before D -- this function does not enforce the cut itself.

Parameters

Parameter	Type	Default	Description
`scored_shots`	`DataFrame`		`score_shot_xpoints` output (needs `player_id`, `shot_made_flag`, `base_fg_pct`, `xpoints`, `actual_points`).
`league_id`	`str`	`'00'`	`"00"` NBA, `"10"` WNBA, `"20"` G-League.
`min_attempts`	`int`	`50`	Drop shooters with fewer attempts (unstable estimate).
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

One row per player_id: player_id:Int64, n_att:Int64, actual_makes:Int64, exp_makes:Float64, points_above_expected:Float64, raw_above_pct:Float64, talent_pct:Float64. Empty input returns the zero-row schema.

Example

from sportsdataverse.nba.nba_shot_value import score_shot_xpoints, shooter_talent
talent = shooter_talent(score_shot_xpoints(shots, league_avgs))

# Pipeline next step (one line)

talent.sort("talent_pct", descending=True).head(15)

`shot_selection_quality(scored_shots: 'pl.DataFrame', *, min_attempts: 'int' = 50, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

Player shot-selection quality: mean expected value vs the league mean.

xev_per_shot is a player's mean xpoints (the value of the LOOKS they take, independent of makes); selection_quality is that minus the league-wide mean xpoints over the same frame -- a rim-and-three diet scores positive, a mid-range diet negative.

Parameters

Parameter	Type	Default	Description
`scored_shots`	`DataFrame`		`score_shot_xpoints` output (needs `player_id`, `xpoints`).
`min_attempts`	`int`	`50`	Drop players with fewer attempts.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

One row per player_id: player_id:Int64, n_att:Int64, xev_per_shot:Float64, league_xev_per_shot:Float64, selection_quality:Float64. Empty input returns the zero-row schema.

Example

from sportsdataverse.nba.nba_shot_value import score_shot_xpoints, shot_selection_quality
sel = shot_selection_quality(score_shot_xpoints(shots, league_avgs))

# Pipeline next step (one line)

sel.sort("selection_quality", descending=True).head(15)

`wnba_aging_curve(*, return_as_pandas: 'bool' = False) -> "'pl.DataFrame | pd.DataFrame'"`

WNBA aging curve -- the NBA core bound to league="wnba".

See sportsdataverse.nba.nba_aging_curve.nba_aging_curve for the full contract; this is a by-reference re-export, same algorithm, women's bundled artifact.

Parameters

Parameter	Type	Default	Description
`return_as_pandas`	`bool`	`False`

Returns

Frame age:Int64, rel_value:Float64, peak_age:Float64.

Example

from sportsdataverse.wnba import wnba_aging_curve
curve = wnba_aging_curve()

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

WNBA availability -- the NBA core bound to league="wnba".

See sportsdataverse.nba.nba_availability.nba_availability for the full contract; avail_pct is availability, not skill.

Parameters

Parameter	Type	Default	Description
`seasons`	`int \| list[int]`		A season (start year) or list of seasons.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

Frame player_id:Utf8, season:Int64, avail_pct:Float64.

Example

from sportsdataverse.wnba import wnba_availability
proj = wnba_availability(2023)

`wnba_career_trajectory(player_values: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'pl.DataFrame | pd.DataFrame'"`

WNBA career trajectory -- the NBA core bound to league="wnba".

See sportsdataverse.nba.nba_aging_curve.nba_career_trajectory for the full contract.

Parameters

Parameter	Type	Default	Description
`player_values`	`DataFrame`		Frame `player_id, age:Int64, value:Float64`.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

player_values plus age_adjusted_value and proj_next_value.

Example

import polars as pl
from sportsdataverse.wnba import wnba_career_trajectory
player_values = pl.DataFrame({"player_id": ["1"], "age": [26], "value": [10.0]})
wnba_career_trajectory(player_values)

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

Project WNBA prospect career value + draft probability from draft slot.

Parameters

Parameter	Type	Default	Description
`draft_year`	`int \| list[int]`		A draft year (e.g. `2023`) 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_career_value:Float64, draft_prob:Float64, projected_pick:Int64, pro_tier:Utf8. Empty input returns the zero-row schema, never raises.

Example

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

`wnba_enhanced_pbp(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

Return a normalised enhanced play-by-play frame for a WNBA game.

Fetches the raw playbyplayv3 payload from stats.wnba.com via ~sportsdataverse.wnba.wnba_stats.wnba_stats_playbyplayv3 then delegates all transformation to the league-agnostic ~sportsdataverse.nba.nba_enhanced_pbp.enhanced_pbp_from_payload core with league_id="10". Never raises on malformed or empty payloads — returns a zero-row frame instead.

Parameters

Parameter	Type	Default	Description
`game_id`	`str`		WNBA game identifier string (e.g. `"1022400001"`).
`return_as_pandas`	`bool`	`False`	If `True`, convert the result to a `pandas.DataFrame` before returning.

Returns

Polars (or pandas) DataFrame with schema sportsdataverse.nba.nba_enhanced_pbp.ENHANCED_PBP_SCHEMA. Key columns include game_id (Utf8), action_number (Int64), period (Int64), seconds_remaining (Float64), team_id (Int64), person_id (Int64), is_substitution (Boolean), and one Boolean flag per event type.

Example

from sportsdataverse.wnba.wnba_engine import wnba_enhanced_pbp
df = wnba_enhanced_pbp("1022400001")
print(df.shape)

# Pandas output

df_pd = wnba_enhanced_pbp("1022400001", return_as_pandas=True)
print(type(df_pd))

# Filter substitution events

subs = df.filter(df["is_substitution"] == True)  # noqa: E712
print(subs.select(["period", "seconds_remaining", "person_id"]))

`wnba_expected_turnovers(season: 'str', *, base: "'Optional[pl.DataFrame]'" = None, player_mix: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

WNBA expected turnovers / ball-security skill (league_id="10").

Thin wrapper binding sportsdataverse.nba.nba_expected_turnovers.nba_expected_turnovers to the women's league.

Parameters

Parameter	Type	Default	Description
`season`	`str`		Season string, e.g. `"2024"`.
`base`	`Optional[DataFrame]`	`None`	Injected `nba_stats_leaguedashplayerstats` (`Base`) frame.
`player_mix`	`Optional[DataFrame]`	`None`	Injected Synergy player-level offensive mix.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

Same schema as sportsdataverse.nba.nba_expected_turnovers.nba_expected_turnovers.

Example

from sportsdataverse.wnba import wnba_expected_turnovers
t = wnba_expected_turnovers("2024")
print(t.sort("ball_security_skill", descending=True).head())

`wnba_foul_drawing(season: 'str', *, base: "'Optional[pl.DataFrame]'" = None, advanced: "'Optional[pl.DataFrame]'" = None, player_mix: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

WNBA foul-drawing / FT-generation (league_id="10").

Thin wrapper binding sportsdataverse.nba.nba_foul_drawing.nba_foul_drawing to the women's league.

Parameters

Parameter	Type	Default	Description
`season`	`str`		Season string, e.g. `"2024"`.
`base`	`Optional[DataFrame]`	`None`	Injected `nba_stats_leaguedashplayerstats` (`Base`) frame.
`advanced`	`Optional[DataFrame]`	`None`	Injected `Advanced`-measure frame.
`player_mix`	`Optional[DataFrame]`	`None`	Injected Synergy player-level offensive mix.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

Same schema as sportsdataverse.nba.nba_foul_drawing.nba_foul_drawing.

Example

from sportsdataverse.wnba import wnba_foul_drawing
f = wnba_foul_drawing("2024")
print(f.sort("foul_draw_skill", descending=True).head())

`wnba_in_game_win_prob(pbp: 'pl.DataFrame', pregame_home_prob: 'float', *, league_id: 'str' = '00', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

WNBA in-game win probability (league_id='10'). See sportsdataverse.nba.nba_game_predict.nba_in_game_win_prob.

Parameters

Parameter	Type	Default
`pbp`	`DataFrame`
`pregame_home_prob`	`float`
`league_id`	`str`	`'00'`
`return_as_pandas`	`bool`	`False`

`wnba_matchup_drapm(season: 'str', *, matchups: "'Optional[pl.DataFrame]'" = None, config: "'Optional[PlaytypeConfig]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

WNBA matchup defensive RAPM (league_id="10").

Thin wrapper binding sportsdataverse.nba.nba_matchup_drapm.nba_matchup_drapm to the women's league.

Parameters

Parameter	Type	Default	Description
`season`	`str`		Season string, e.g. `"2024"`.
`matchups`	`Optional[DataFrame]`	`None`	Injected `nba_stats_leagueseasonmatchups`-shaped frame.
`config`	`Optional[PlaytypeConfig]`	`None`	`~sportsdataverse.nba.nba_playtype_constants.PlaytypeConfig` override.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

Same schema as sportsdataverse.nba.nba_matchup_drapm.nba_matchup_drapm.

Example

from sportsdataverse.wnba import wnba_matchup_drapm
d = wnba_matchup_drapm("2024")
print(d.sort("matchup_drapm", descending=True).head())

`wnba_on_court(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

Return the rotation-keyed on-court player frame for a WNBA game.

Makes three network calls (play-by-play v3, game rotation, box-score traditional v3), infers on-court rosters from the rotation stints via ~sportsdataverse.nba.nba_lineups.players_on_court_from_rotation, and returns one row per PBP action with ten Int64 player-ID columns (home_player_1..5 / away_player_1..5). All transformation is performed by the shared nba/ core with league_id="10" forwarded to the rotation endpoint. Never raises on malformed payloads.

Parameters

Parameter	Type	Default	Description
`game_id`	`str`		WNBA game identifier string (e.g. `"1022400001"`).
`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 PBP action and columns home_player_1 … home_player_5, away_player_1 … away_player_5 (all Int64), plus the action_number join key.

Example

from sportsdataverse.wnba.wnba_engine import wnba_on_court
oc = wnba_on_court("1022400001")
print(oc.select(["action_number", "home_player_1"]).head())

# Pandas output

oc_pd = wnba_on_court("1022400001", return_as_pandas=True)
print(type(oc_pd))

# Join on enhanced PBP

from sportsdataverse.wnba.wnba_engine import wnba_enhanced_pbp
enh = wnba_enhanced_pbp("1022400001")
joined = enh.join(oc, on="action_number", how="left")

`wnba_pbp_disk(game_id, path_to_json)`

No description available.

Parameters

Parameter	Type	Default	Description
`game_id`
`path_to_json`

`wnba_play_context(game_id: 'str', *, transition_seconds: 'float' = 6.0, transition_variant: 'str' = 'hoop_math', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

Return a WNBA game's possessions with the full CTG play-context surface.

WNBA sibling of ~sportsdataverse.nba.nba_play_context.nba_play_context — the Cleaning the Glass recreation (possession start-type taxonomy + the halfcourt / transition / putback contexts + CTG's garbage-time and heave filters). One network call (playbyplayv3 on stats.wnba.com); every transformation is done by the league-agnostic ~sportsdataverse.nba.nba_play_context.add_play_context core, so there is no WNBA-specific classification logic to drift.

Two caveats worth stating plainly:

CTG is NBA-only. There is no published WNBA play-context table to calibrate against, so transition_seconds inherits the NBA's fitted 6.0 s default. The WNBA fixtures land inside the NBA's transition-frequency gate at that value (tests/wnba/test_wnba_play_context_shim.py), which is a sanity check on the shared engine — not evidence that 6.0 s is the right WNBA cutoff. Re-fit it if a WNBA oracle ever appears.
Shot-zone boundaries are league-agnostic (feet from the rim), and the corner-three test uses the same legacy coordinates, which the WNBA feed also ships.

Parameters

Parameter	Type	Default	Description
`game_id`	`str`		WNBA game identifier (e.g. `"1022400001"`).
`transition_seconds`	`float`	`6.0`	Transition initial-play cutoff, in seconds.
`transition_variant`	`str`	`'hoop_math'`	See `~sportsdataverse.nba.nba_play_context.add_transition`.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

The possession frame (POSSESSIONS_SCHEMA) plus ~sportsdataverse.nba.nba_play_context.PLAY_CONTEXT_POSSESSIONS_SCHEMA. Empty or malformed payloads return a zero-row frame — never raises on payload content.

Example

from sportsdataverse.wnba.wnba_engine import wnba_play_context
poss = wnba_play_context("1022400001")
print(poss["possession_start_type_ctg"].value_counts())

# Transition rate (CTG's default filtered view)

import polars as pl
clean = poss.filter(
    (pl.col("is_garbage_time") == False)  # noqa: E712
    & (pl.col("is_heave_possession") == False)  # noqa: E712
)
print(clean["is_transition"].mean())

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

WNBA player props (league_id='10'). See sportsdataverse.nba.nba_player_props.nba_player_props.

Parameters

Parameter	Type	Default
`season`	`int`
`game_id`	`str`
`home_team_id`	`str`
`away_team_id`	`str`
`league_id`	`str`	`'00'`
`return_as_pandas`	`bool`	`False`

`wnba_playtype_ratings(season: 'str', *, off_team: "'Optional[pl.DataFrame]'" = None, def_team: "'Optional[pl.DataFrame]'" = None, schedule: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

WNBA Synergy play-type-adjusted offense/defense (league_id="10").

Thin wrapper binding sportsdataverse.nba.nba_playtype.nba_playtype_ratings to the women's league. Synergy coverage is sparse for the WNBA; an empty upstream fetch degrades to a zero-row frame (never raises).

Parameters

Parameter	Type	Default	Description
`season`	`str`		Season string, e.g. `"2024"`.
`off_team`	`Optional[DataFrame]`	`None`	Injected Synergy offensive team frame (bypasses the live fetch).
`def_team`	`Optional[DataFrame]`	`None`	Injected Synergy defensive team frame.
`schedule`	`Optional[DataFrame]`	`None`	Injected `team_id`/`opp_team_id` schedule frame.
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

Same schema as sportsdataverse.nba.nba_playtype.nba_playtype_ratings.

Example

from sportsdataverse.wnba import wnba_playtype_ratings
r = wnba_playtype_ratings("2024")
print(r.sort("adj_off", descending=True).head())

`wnba_possessions(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

Return the possession-level lineup stint matrix for a WNBA game.

Builds possessions from the enhanced PBP via ~sportsdataverse.nba.nba_possessions.build_possessions, resolves on-court rosters via ~sportsdataverse.nba.nba_lineups.players_on_court_from_rotation, then attaches the 5v5 lineups via ~sportsdataverse.nba.nba_possessions.attach_possession_lineups. All transformation is performed by the shared nba/ cores — no WNBA- specific logic. Never raises on malformed payloads.

Parameters

Parameter	Type	Default	Description
`game_id`	`str`		WNBA game identifier string (e.g. `"1022400001"`).
`return_as_pandas`	`bool`	`False`	If `True`, convert the result to a `pandas.DataFrame` before returning.

Returns

Polars (or pandas) DataFrame with schema combining POSSESSIONS_SCHEMA and ten lineup columns: off_player_1 … off_player_5, def_player_1 … def_player_5 (all Int64). One row per possession. Empty or malformed inputs return a zero-row frame.

Example

from sportsdataverse.wnba.wnba_engine import wnba_possessions
poss = wnba_possessions("1022400001")
print(poss.shape)

# Pandas output

poss_pd = wnba_possessions("1022400001", return_as_pandas=True)
print(type(poss_pd))

# Total points check

total = int(poss["points"].sum())
print(f"Total points scored: {total}")

`wnba_predict_games(games: 'pl.DataFrame', ratings: 'pl.DataFrame', *, league_id: 'str' = '00', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

WNBA vectorized pregame predictions (league_id='10'). See sportsdataverse.nba.nba_game_predict.nba_predict_games.

Parameters

Parameter	Type	Default
`games`	`DataFrame`
`ratings`	`DataFrame`
`league_id`	`str`	`'00'`
`return_as_pandas`	`bool`	`False`

`wnba_predict_margin(home_net: 'float', away_net: 'float', *, home_pace: 'float', away_pace: 'float', neutral: 'bool' = False, league_id: 'str' = '00') -> 'float'`

WNBA expected margin (league_id='10'). See sportsdataverse.nba.nba_game_predict.predict_margin.

Parameters

Parameter	Type	Default
`home_net`	`float`
`away_net`	`float`
`home_pace`	`float`
`away_pace`	`float`
`neutral`	`bool`	`False`
`league_id`	`str`	`'00'`

`wnba_predict_total(home_off: 'float', home_def: 'float', away_off: 'float', away_def: 'float', home_pace: 'float', away_pace: 'float', *, league_id: 'str' = '00') -> 'float'`

WNBA expected total (league_id='10'). See sportsdataverse.nba.nba_game_predict.predict_total.

Parameters

Parameter	Type	Default
`home_off`	`float`
`home_def`	`float`
`away_off`	`float`
`away_def`	`float`
`home_pace`	`float`
`away_pace`	`float`
`league_id`	`str`	`'00'`

`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_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_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(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
`season`	`int`
`league_id`	`str`	`'00'`
`return_as_pandas`	`bool`	`False`

`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
`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(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(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(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(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(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(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(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'`

`zone_value_map(scored_shots: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

Per-player per-zone value map: points and expected points per shot.

Collapses shot_zone_basic to a canonical zone via ZONE_COLLAPSE (the two corner-3 zones merge) and aggregates realized vs expected points per shot in each zone.

Parameters

Parameter	Type	Default	Description
`scored_shots`	`DataFrame`		`score_shot_xpoints` output (needs `player_id`, `shot_zone_basic`, `shot_made_flag`, `actual_points`, `xpoints`).
`return_as_pandas`	`bool`	`False`	Return a pandas DataFrame instead of polars.

Returns

One row per (player_id, zone): player_id:Int64, zone:Utf8, att:Int64, makes:Int64, pts:Float64, pps:Float64, xpps:Float64, pps_above_expected:Float64 (pps = points per shot, xpps = expected). Empty input returns the zero-row schema.

Example

from sportsdataverse.nba.nba_shot_value import score_shot_xpoints, zone_value_map
zmap = zone_value_map(score_shot_xpoints(shots, league_avgs))

# Pipeline next step (one line)

zmap.filter(pl.col("zone") == "corner_3").sort("pps_above_expected", descending=True)

Play-by-play, schedule & rosters
Dataset loaders
Utilities & helpers
- most_recent_wnba_season()
Other

Play-by-play, schedule & rosters​

espn_wnba_game_officials(game_id: 'int', season: 'int | None' = None, *, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'pl.DataFrame | pd.DataFrame | dict[str, Any]'​

espn_wnba_player_stats(athlete_id: 'int', season: 'int', *, season_type: 'str' = 'regular', total: 'bool' = False, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'pl.DataFrame | pd.DataFrame | dict[str, Any]'​

espn_wnba_schedule(dates=None, season_type=None, limit=500, return_as_pandas=False, **kwargs) -> 'pl.DataFrame'​

espn_wnba_team_stats(team_id: 'int', season: 'int', *, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'dict[str, pl.DataFrame] | dict[str, pd.DataFrame] | dict[str, Any]'​

Dataset loaders​

load_wnba_stats_lineups(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'​

load_wnba_stats_player_season_stats(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'​

load_wnba_stats_standings(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'​

load_wnba_stats_team_season_stats(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'​

Utilities & helpers​

most_recent_wnba_season()​

Other​

build_athlete_identity_lookup(rosters: 'dict[int | str, dict]') -> 'dict[str, dict[str, Any]]'​

espn_wnba_teams(return_as_pandas=False, **kwargs) -> 'pl.DataFrame'​

make_prob_by_context(ptshots: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'dict[str, Union[pl.DataFrame, pd.DataFrame]]'"​

make_prob_joint(defender: 'pl.DataFrame', shot_clock: 'pl.DataFrame', overall_fg_pct: 'float', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

score_shot_xpoints(shots: 'pl.DataFrame', league_avgs: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

scoreboard_event_parsing(event)​

shooter_talent(scored_shots: 'pl.DataFrame', *, league_id: 'str' = '00', min_attempts: 'int' = 50, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

shot_selection_quality(scored_shots: 'pl.DataFrame', *, min_attempts: 'int' = 50, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

wnba_aging_curve(*, return_as_pandas: 'bool' = False) -> "'pl.DataFrame | pd.DataFrame'"​

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

wnba_career_trajectory(player_values: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'pl.DataFrame | pd.DataFrame'"​

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

wnba_enhanced_pbp(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'​

wnba_expected_turnovers(season: 'str', *, base: "'Optional[pl.DataFrame]'" = None, player_mix: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

wnba_foul_drawing(season: 'str', *, base: "'Optional[pl.DataFrame]'" = None, advanced: "'Optional[pl.DataFrame]'" = None, player_mix: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

wnba_in_game_win_prob(pbp: 'pl.DataFrame', pregame_home_prob: 'float', *, league_id: 'str' = '00', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'​

wnba_matchup_drapm(season: 'str', *, matchups: "'Optional[pl.DataFrame]'" = None, config: "'Optional[PlaytypeConfig]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

wnba_on_court(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'​

wnba_pbp_disk(game_id, path_to_json)​

wnba_play_context(game_id: 'str', *, transition_seconds: 'float' = 6.0, transition_variant: 'str' = 'hoop_math', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'​

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

wnba_playtype_ratings(season: 'str', *, off_team: "'Optional[pl.DataFrame]'" = None, def_team: "'Optional[pl.DataFrame]'" = None, schedule: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

wnba_possessions(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'​

wnba_predict_games(games: 'pl.DataFrame', ratings: 'pl.DataFrame', *, league_id: 'str' = '00', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'​

wnba_predict_margin(home_net: 'float', away_net: 'float', *, home_pace: 'float', away_pace: 'float', neutral: 'bool' = False, league_id: 'str' = '00') -> 'float'​

wnba_predict_total(home_off: 'float', home_def: 'float', away_off: 'float', away_def: 'float', home_pace: 'float', away_pace: 'float', *, league_id: 'str' = '00') -> 'float'​

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

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

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

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

zone_value_map(scored_shots: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"​

Play-by-play, schedule & rosters

`espn_wnba_game_officials(game_id: 'int', season: 'int | None' = None, *, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'pl.DataFrame | pd.DataFrame | dict[str, Any]'`

`espn_wnba_player_stats(athlete_id: 'int', season: 'int', *, season_type: 'str' = 'regular', total: 'bool' = False, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'pl.DataFrame | pd.DataFrame | dict[str, Any]'`

`espn_wnba_schedule(dates=None, season_type=None, limit=500, return_as_pandas=False, **kwargs) -> 'pl.DataFrame'`

`espn_wnba_team_stats(team_id: 'int', season: 'int', *, raw: 'bool' = False, return_as_pandas: 'bool' = False, **kwargs: 'Any') -> 'dict[str, pl.DataFrame] | dict[str, pd.DataFrame] | dict[str, Any]'`

Dataset loaders

`load_wnba_stats_lineups(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'`

`load_wnba_stats_player_season_stats(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'`

`load_wnba_stats_standings(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'`

`load_wnba_stats_team_season_stats(seasons, return_as_pandas: 'bool' = False) -> 'pl.DataFrame'`

Utilities & helpers

`most_recent_wnba_season()`

Other

`build_athlete_identity_lookup(rosters: 'dict[int | str, dict]') -> 'dict[str, dict[str, Any]]'`

`espn_wnba_teams(return_as_pandas=False, **kwargs) -> 'pl.DataFrame'`

`make_prob_by_context(ptshots: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'dict[str, Union[pl.DataFrame, pd.DataFrame]]'"`

`make_prob_joint(defender: 'pl.DataFrame', shot_clock: 'pl.DataFrame', overall_fg_pct: 'float', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

`score_shot_xpoints(shots: 'pl.DataFrame', league_avgs: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

`scoreboard_event_parsing(event)`

`shooter_talent(scored_shots: 'pl.DataFrame', *, league_id: 'str' = '00', min_attempts: 'int' = 50, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

`shot_selection_quality(scored_shots: 'pl.DataFrame', *, min_attempts: 'int' = 50, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

`wnba_aging_curve(*, return_as_pandas: 'bool' = False) -> "'pl.DataFrame | pd.DataFrame'"`

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

`wnba_career_trajectory(player_values: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'pl.DataFrame | pd.DataFrame'"`

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

`wnba_enhanced_pbp(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

`wnba_expected_turnovers(season: 'str', *, base: "'Optional[pl.DataFrame]'" = None, player_mix: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

`wnba_foul_drawing(season: 'str', *, base: "'Optional[pl.DataFrame]'" = None, advanced: "'Optional[pl.DataFrame]'" = None, player_mix: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

`wnba_in_game_win_prob(pbp: 'pl.DataFrame', pregame_home_prob: 'float', *, league_id: 'str' = '00', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

`wnba_matchup_drapm(season: 'str', *, matchups: "'Optional[pl.DataFrame]'" = None, config: "'Optional[PlaytypeConfig]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

`wnba_on_court(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

`wnba_pbp_disk(game_id, path_to_json)`

`wnba_play_context(game_id: 'str', *, transition_seconds: 'float' = 6.0, transition_variant: 'str' = 'hoop_math', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

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

`wnba_playtype_ratings(season: 'str', *, off_team: "'Optional[pl.DataFrame]'" = None, def_team: "'Optional[pl.DataFrame]'" = None, schedule: "'Optional[pl.DataFrame]'" = None, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`

`wnba_possessions(game_id: 'str', *, return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

`wnba_predict_games(games: 'pl.DataFrame', ratings: 'pl.DataFrame', *, league_id: 'str' = '00', return_as_pandas: 'bool' = False) -> 'Union[pl.DataFrame, pd.DataFrame]'`

`wnba_predict_margin(home_net: 'float', away_net: 'float', *, home_pace: 'float', away_pace: 'float', neutral: 'bool' = False, league_id: 'str' = '00') -> 'float'`

`wnba_predict_total(home_off: 'float', home_def: 'float', away_off: 'float', away_def: 'float', home_pace: 'float', away_pace: 'float', *, league_id: 'str' = '00') -> 'float'`

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

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

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

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

`zone_value_map(scored_shots: 'pl.DataFrame', *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"`