WNBA — additional Python functions — IDs and crosswalks

wnba_player_crosswalk

wnba_player_crosswalk(season: 'Optional[int]' = None, min_confidence: 'float' = 0.92, *, return_as_pandas: 'bool' = False, strict: 'bool' = False, **kwargs: 'Any') -> "Union[pl.DataFrame, 'pd.DataFrame']"

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

One row per ESPN athlete per team. match_method / match_confidence describe the Stats API match (normalized exact name, then Jaro-Winkler with jersey and DOB tiebreaks); Fox contributes fox_athlete_id only.

Parameters

Parameter	Type	Default	Description
season	Optional[int]	None	Season year (e.g. 2026). Defaults to the most recent WNBA season.
min_confidence	float	0.92	Jaro-Winkler floor for fuzzy matches (R default 0.92).
return_as_pandas	bool	False	Return pandas instead of polars.
strict	bool	False	Raise on the first failed per-team ESPN or Fox roster 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), one row per ESPN athlete, 21 columns.

No returns table is published for this function: no capture: it reads stats.wnba.com, which answers HTTP 403 to the datacenter IP the docs are built on; the function works from a residential IP.

Example

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

# Tighten the fuzzy floor

strict = wnba_player_crosswalk(season=2026, min_confidence=0.97)

# Pipeline next step (one line)

df.filter(pl.col("match_method") == "fuzzy_jw").head()

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.

No returns table is published for this function: no capture: it reads stats.wnba.com, which answers HTTP 403 to the datacenter IP the docs are built on; the function works from a residential IP.

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_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.

No returns table is published for this function: no capture: it reads stats.wnba.com, which answers HTTP 403 to the datacenter IP the docs are built on; the function works from a residential IP.

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()