WNBA — additional Python functions — Analytics: wnba_player–wnba_team

wnba_player_war

wnba_player_war(seasons: 'int | list[int]', *, league_id: 'str' = '10', return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA WAR and VORP per player-season from our BPM, RAPM and BOBAN (league_id='10'). See sportsdataverse.nba.nba_player_war.nba_player_war.

Parameters

Parameter	Type	Default	Description
seasons	int | list[int]
league_id	str	'10'
return_as_pandas	bool	False

Returns

One row per (season, player_id) (stats.nba.com ids, Int64): player_name, team_id (the team he played most possessions for), teams (how many he played for), min, poss (on-court possessions, offensive plus defensive over 2), rating and rating_bpm / rating_rapm / rating_boban (anchored, per 100), vorp, war with war_lo / war_hi (a nominal 90% interval from a bootstrap over each team-season's games: the sampling uncertainty of the team side and the playing time) and war_bpm / war_rapm / war_boban (the source spread), with _rank (1 = most), _pct and _n (possessions) for vorp and war among every player of the season. Sorted by season and war. Empty input returns that schema with zero rows.

col_name	type	description
season	Int64	Season, named by the year it ends (2026 is 2025-26; the WNBA season is its calendar year).
player_id	Int64	stats.nba.com player id.
player_name	String	Player name, from the player impact release.
team_id	Int64	stats.nba.com team id of the team he played the most possessions for.
teams	Int64	How many teams he played for in the regular season.
min	Float64	Regular-season minutes (the player impact release's).
poss	Float64	On-court possessions, offensive plus defensive over 2, counted from the stats.nba.com possessions (every possession row, BB-D3's convention). The minutes basis of vorp and war.
rating	Float64	Points per 100 possessions above a league-average player: the mean of rating_bpm, rating_rapm and rating_boban.
rating_bpm	Float64	Our BPM per 100, team-anchored and centred: the release's bpm with its team constant repaired (minute-weighted team mean = net rating / 5, BPM 2.0's), minus the player's team mean, plus the team's net rating / 5, centred so the league's possession-weighted mean is 0.
rating_rapm	Float64	Our prior-informed RAPM (adj_rapm) per 100, team-anchored and centred like rating_bpm. Only the net adj_rapm is read: the o_adj_rapm / d_adj_rapm split carries a +11 / -11 offset.
rating_boban	Float64	Our BOBAN (boban_filtered_skill) per 100, team-anchored and centred like rating_bpm.
vorp	Float64	Value over replacement player: (rating - R) * poss / the season's possessions per team, R the league's replacement level (REPLACEMENT_LEVEL; NBA -1.98, WNBA -3.18 per 100). Basketball-Reference's VORP scale: a season of possessions at R + 1 is 1.
war	Float64	Wins above replacement: (rating - R) * poss / 100 / points per win (PTS_PER_WIN; NBA 31.56, WNBA 31.31). 0 at replacement level; a season's war adds up to the league's wins minus a team of replacement players' (.189 NBA 2024-25, .104 WNBA 2025).
war_lo	Float64	Low end of the nominal 90% interval for war (5th percentile of 1000 bootstrap replicates, each team-season seeded on its own): each team-season's games resampled with replacement (a game's player possessions, team possessions and margin move together), then his possessions, the team net rating, the anchoring and the centring recomputed. The source ratings and the replacement level are held fixed, so this is the sampling uncertainty of the team side and the playing time; the war_bpm / war_rapm / war_boban spread is the source uncertainty.
war_hi	Float64	High end of the nominal 90% interval for war (95th percentile of the same 1000 game-resampled replicates as war_lo).
war_bpm	Float64	war from rating_bpm alone. Every source shares the league's replacement level, so war_bpm, war_rapm and war_boban add up to the same league total.
war_rapm	Float64	war from rating_rapm alone.
war_boban	Float64	war from rating_boban alone. BOBAN shrinks low-minute players toward average, so the replacement pool carries more war_boban than war_bpm (the replacement level is a box-score level).
vorp_rank	Float64	Rank of vorp within the season among every player (1 = most; ties average); null under 10 players.
war_rank	Float64	Rank of war within the season among every player (1 = most; ties average); null under 10 players.
vorp_pct	Float64	Percentile of vorp within the season: 100 * (n + 1 - rank) / (n + 1), n the players ranked; null when the rank is null.
war_pct	Float64	Percentile of war within the season: 100 * (n + 1 - rank) / (n + 1), n the players ranked; null when the rank is null.
vorp_n	Int64	Sample size of vorp: on-court possessions, rounded.
war_n	Int64	Sample size of war: on-court possessions, rounded.

wnba_player_wowy

wnba_player_wowy(seasons: 'int | list[int]', *, league_id: 'str' = '10', season_type: 'str' = 'regular-season', return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA WOWY pairs (league_id='10'). See sportsdataverse.nba.nba_player_on_off.nba_player_wowy.

Parameters

Parameter	Type	Default	Description
seasons	int | list[int]
league_id	str	'10'
season_type	str	'regular-season'
return_as_pandas	bool	False

Returns

One row per (season, team_id, player_id_a, player_id_b) (player_id_a the smaller id) for every pair of the team's players: for _both (together), _a_only, _b_only and _neither the minutes, poss / opp_poss, pts / opp_pts, plus_minus and off_rtg / def_rtg / net_rtg. The four splits' totals add up to the team's season; the ratings do not add. Empty input returns that schema with zero rows.

col_name	type	description
season	Int64	Season, named by the year it ends (the WNBA season is its calendar year).
team_id	Int64	stats.nba.com team id.
player_id_a	Int64	The first player of the pair, the smaller id.
player_id_b	Int64	The second player of the pair, the larger id.
minutes_both	Float64	Game-clock minutes of the team's season with both A and B on the floor.
poss_both	Int64	The team's offensive possessions with both A and B on the floor (a possession belongs to the five on the floor when it starts).
opp_poss_both	Int64	The team's defensive possessions with both A and B on the floor.
pts_both	Int64	The team's points with both A and B on the floor, each scoring action credited to the lineup at that action (a free throw to the lineup at the last foul).
opp_pts_both	Int64	The opponent's points with both A and B on the floor, credited as pts_both is.
plus_minus_both	Int64	pts_both - opp_pts_both.
off_rtg_both	Float64	Offensive rating with both A and B on the floor: pts_both per 100 poss_both; null with no possessions.
def_rtg_both	Float64	Defensive rating with both A and B on the floor: opp_pts_both per 100 opp_poss_both; null with no possessions.
net_rtg_both	Float64	Net rating with both A and B on the floor: off_rtg_both - def_rtg_both; null when either is null.
minutes_a_only	Float64	Game-clock minutes of the team's season with A on the floor and B off.
poss_a_only	Int64	The team's offensive possessions with A on the floor and B off (a possession belongs to the five on the floor when it starts).
opp_poss_a_only	Int64	The team's defensive possessions with A on the floor and B off.
pts_a_only	Int64	The team's points with A on the floor and B off, each scoring action credited to the lineup at that action (a free throw to the lineup at the last foul).
opp_pts_a_only	Int64	The opponent's points with A on the floor and B off, credited as pts_a_only is.
plus_minus_a_only	Int64	pts_a_only - opp_pts_a_only.
off_rtg_a_only	Float64	Offensive rating with A on the floor and B off: pts_a_only per 100 poss_a_only; null with no possessions.
def_rtg_a_only	Float64	Defensive rating with A on the floor and B off: opp_pts_a_only per 100 opp_poss_a_only; null with no possessions.
net_rtg_a_only	Float64	Net rating with A on the floor and B off: off_rtg_a_only - def_rtg_a_only; null when either is null.
minutes_b_only	Float64	Game-clock minutes of the team's season with B on the floor and A off.
poss_b_only	Int64	The team's offensive possessions with B on the floor and A off (a possession belongs to the five on the floor when it starts).
opp_poss_b_only	Int64	The team's defensive possessions with B on the floor and A off.
pts_b_only	Int64	The team's points with B on the floor and A off, each scoring action credited to the lineup at that action (a free throw to the lineup at the last foul).
opp_pts_b_only	Int64	The opponent's points with B on the floor and A off, credited as pts_b_only is.
plus_minus_b_only	Int64	pts_b_only - opp_pts_b_only.
off_rtg_b_only	Float64	Offensive rating with B on the floor and A off: pts_b_only per 100 poss_b_only; null with no possessions.
def_rtg_b_only	Float64	Defensive rating with B on the floor and A off: opp_pts_b_only per 100 opp_poss_b_only; null with no possessions.
net_rtg_b_only	Float64	Net rating with B on the floor and A off: off_rtg_b_only - def_rtg_b_only; null when either is null.
minutes_neither	Float64	Game-clock minutes of the team's season with neither A nor B on the floor.
poss_neither	Int64	The team's offensive possessions with neither A nor B on the floor (a possession belongs to the five on the floor when it starts).
opp_poss_neither	Int64	The team's defensive possessions with neither A nor B on the floor.
pts_neither	Int64	The team's points with neither A nor B on the floor, each scoring action credited to the lineup at that action (a free throw to the lineup at the last foul).
opp_pts_neither	Int64	The opponent's points with neither A nor B on the floor, credited as pts_neither is.
plus_minus_neither	Int64	pts_neither - opp_pts_neither.
off_rtg_neither	Float64	Offensive rating with neither A nor B on the floor: pts_neither per 100 poss_neither; null with no possessions.
def_rtg_neither	Float64	Defensive rating with neither A nor B on the floor: opp_pts_neither per 100 opp_poss_neither; null with no possessions.
net_rtg_neither	Float64	Net rating with neither A nor B on the floor: off_rtg_neither - def_rtg_neither; null when either is null.

wnba_playtype_ratings

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.

No returns table is published for this function: no capture: it reads stats.nba.com (WNBA league id 10), 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_playtype_ratings
r = wnba_playtype_ratings("2024")
print(r.sort("adj_off", descending=True).head())

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	String	League the assignment belongs to: nba, gl (G League), or wnba.
officials.game_id	String	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	Int64	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	String	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	String	League game code in YYYYMMDD/AWYHOM format, matching the away and home team abbreviations.
officials.home_team_id	Int64	10-digit team id of the home team.
officials.home_team_abbr	String	Three-letter abbreviation of the home team.
officials.away_team_id	Int64	10-digit team id of the away team.
officials.away_team_abbr	String	Three-letter abbreviation of the away team.
officials.crew_position	Int64	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	Int64	Numeric official id from the feed (source field official{n}_code); expected to match stats.nba.com's OFFICIAL_ID.
officials.official_name	String	Official's display name for this crew slot.
officials.jersey_num	String	Official's jersey number as a string, from the feed's official{n}_JNum field.
replay_center.league	String	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	Int64	Numeric replay-center official id from the feed.
replay_center.official_name	String	Replay-center official's display name for that date.

Raises

Exception	Description
ValueError	date is not a valid "YYYY-MM-DD" date (checked before any request).
NoDataError	Official.nba.com answered 404, or 403 with S3's AccessDenied body. A date without WNBA games is not this (see the Note).
AssetFetchError	The fetch failed (network error, rate limit, or Akamai WAF block), the 200 body is not a JSON object, or the wnba block's Table/Table1 rows are missing or malformed (a row without game_id included). With raw=True all three leagues (nba, gl and wnba) are checked, since the whole payload is returned.

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_season_sim

wnba_season_sim(season: 'int', *, as_of_date: 'dt.date | None' = None, league_id: 'str' = '10', n_sims: 'int' = 10000, seed: 'int' = 20261103, chances: 'list[float] | None' = None, cup_schedule: 'pl.DataFrame | None' = None, return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA season sim: projected W-L, seed, playoff and lottery odds (league_id='10'). See sportsdataverse.nba.nba_season_sim.nba_season_sim.

Parameters

Parameter	Type	Default	Description
season	int
as_of_date	date | None	None
league_id	str	'10'
n_sims	int	10000
seed	int	20261103
chances	list[float] | None	None
cup_schedule	DataFrame | None	None
return_as_pandas	bool	False

Returns

One row per team (stats.nba.com team_id, Int64), sorted by conference and mean seed: * wins / losses so far, games_remaining, and the as-of adj_net_rtg with its posterior sd adj_net_rtg_sd (capped so two teams' shift carries at most 3/4 of a game's variance; it binds only near the prior, before many games are played). * proj_wins / proj_losses (means), proj_wins_sd, the 10th and 90th percentiles, and proj_wins_se (the Monte Carlo error of the mean). * p_division (NBA), p_seed_1 .. p_seed_15 (the regular-season finish in the conference, NBA, or the league, WNBA) and mean_seed. * p_top4 (first-round home court), p_top6 (NBA, straight to the playoffs), p_play_in (NBA, seeds 7-10), p_playoffs (NBA: top 6 or through the play-in; WNBA: top 8) and its Monte Carlo p_playoffs_se. * p_lottery, p_pick_1, p_drawn_pick (a pick from the drawing: the NBA's top four, the WNBA's top two) and mean_pick (given a lottery finish). The pick columns are null when the league has no published chances for that many lottery teams. * n_sims.

col_name	type	description
season	Int64	Season, named by the year it ends (2026 is 2025-26); the WNBA season is its calendar year.
as_of_date	Date	The as-of date: games on or after it are simulated, the ones before it are played. Null when every game with a score counts as played.
team_id	Int64	stats.nba.com team id (Int64).
conference	String	NBA conference (East / West) from the 2004-05 alignment; "WNBA" for every WNBA team (one table).
division	String	NBA division (Atlantic, Central, Southeast, Northwest, Pacific, Southwest); "WNBA" for every WNBA team.
wins	Int64	Regular-season wins in the games already played.
losses	Int64	Regular-season losses in the games already played.
games_remaining	Int64	Regular-season games on the schedule still to play (simulated).
adj_net_rtg	Float64	As-of adjusted net rating from the possession ridge at the sim's penalty: points per 100 over an average opponent on a neutral court.
adj_net_rtg_sd	Float64	Posterior sd of adj_net_rtg (points per 100) from the ridge, capped so two teams' shift carries at most 3/4 of a game's variance; the sd of the per-season rating shift each simulated season draws.
proj_wins	Float64	Mean final regular-season wins over the simulated seasons (played wins plus simulated ones).
proj_losses	Float64	Mean final regular-season losses over the simulated seasons.
proj_wins_sd	Float64	Standard deviation of the final wins over the simulated seasons.
proj_wins_p10	Float64	10th percentile of the final wins over the simulated seasons.
proj_wins_p90	Float64	90th percentile of the final wins over the simulated seasons.
proj_wins_se	Float64	Monte Carlo standard error of proj_wins: proj_wins_sd / sqrt(n_sims).
p_division	Float64	Share of simulated seasons in which the team wins its division under the NBA tiebreakers; null for the WNBA.
p_seed_1	Float64	Share of simulated seasons in which the team finishes the regular season seeded 1: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_2	Float64	Share of simulated seasons in which the team finishes the regular season seeded 2: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_3	Float64	Share of simulated seasons in which the team finishes the regular season seeded 3: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_4	Float64	Share of simulated seasons in which the team finishes the regular season seeded 4: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_5	Float64	Share of simulated seasons in which the team finishes the regular season seeded 5: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_6	Float64	Share of simulated seasons in which the team finishes the regular season seeded 6: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_7	Float64	Share of simulated seasons in which the team finishes the regular season seeded 7: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_8	Float64	Share of simulated seasons in which the team finishes the regular season seeded 8: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_9	Float64	Share of simulated seasons in which the team finishes the regular season seeded 9: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_10	Float64	Share of simulated seasons in which the team finishes the regular season seeded 10: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_11	Float64	Share of simulated seasons in which the team finishes the regular season seeded 11: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_12	Float64	Share of simulated seasons in which the team finishes the regular season seeded 12: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_13	Float64	Share of simulated seasons in which the team finishes the regular season seeded 13: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_14	Float64	Share of simulated seasons in which the team finishes the regular season seeded 14: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
p_seed_15	Float64	Share of simulated seasons in which the team finishes the regular season seeded 15: in its conference for the NBA, in the league for the WNBA (before the NBA play-in). 0 for a seed past the number of teams.
mean_seed	Float64	Mean regular-season seed over the simulated seasons (1 = best).
p_top4	Float64	Share of simulated seasons with a top-4 seed: first-round home court.
p_top6	Float64	NBA: share of simulated seasons with a top-6 conference seed, straight to the playoffs; null for the WNBA.
p_play_in	Float64	NBA: share of simulated seasons with a conference seed of 7 to 10, the play-in; null for the WNBA.
p_playoffs	Float64	Share of simulated seasons in which the team makes the playoffs: NBA top 6 or through the simulated play-in; WNBA top 8.
p_playoffs_se	Float64	Monte Carlo standard error of p_playoffs: sqrt(p (1 - p) / n_sims).
p_lottery	Float64	Share of simulated seasons in which the team misses the playoffs and enters the draft lottery.
p_pick_1	Float64	Share of simulated seasons in which the team's own lottery slot draws the first pick; null when the league has no published chances for that many lottery teams.
p_drawn_pick	Float64	Share of simulated seasons in which the team's slot gets a drawn pick (NBA top 4, WNBA top 2); null as p_pick_1.
mean_pick	Float64	Mean draft pick over the simulated seasons in which the team is in the lottery; null when it never is, or as p_pick_1.
n_sims	Int64	Number of simulated seasons behind every share on the row.

wnba_season_standings

wnba_season_standings(games: 'pl.DataFrame', *, league_id: 'str' = '10', seed: 'int' = 0, return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA standings with the WNBA tiebreakers (league_id='10'). See sportsdataverse.nba.nba_season_rules.nba_season_standings.

Parameters

Parameter	Type	Default	Description
games	DataFrame
league_id	str	'10'
seed	int	0
return_as_pandas	bool	False

Returns

One row per (season, team_id) that played: conference / division (NBA; the WNBA rows say "WNBA"), games, wins, losses, win_pct, point_diff, division_rank (1 = the division winner), division_leader, seed (rank in the conference for the NBA, in the league for the WNBA, 1 = best) and games_back (behind the seed-1 team). Sorted by season, conference and seed.

col_name	type	description
season	Int64	Season, named by the year it ends (2026 is 2025-26); the WNBA season is its calendar year.
team_id	Int64	stats.nba.com team id (Int64).
conference	String	NBA conference (East / West); "WNBA" for every WNBA team.
division	String	NBA division; "WNBA" for every WNBA team.
games	Int64	Regular-season games played.
wins	Int64	Regular-season wins.
losses	Int64	Regular-season losses.
win_pct	Float64	Regular-season winning percentage, wins divided by games played; the first key of the seeding.
point_diff	Int64	Points scored minus points allowed over the regular-season games.
division_rank	Int64	Rank within the division under the NBA tiebreakers, 1 = the division winner (the WNBA rows rank the league).
division_leader	Boolean	True for the NBA division winner after the division tiebreak; False for every WNBA row.
seed	Int64	Seed under the league's tiebreakers: rank in the conference (NBA) or the league (WNBA), 1 = best.
games_back	Float64	Games behind the seed-1 team of the conference (NBA) or league (WNBA): ((leader wins - wins) + (losses - leader losses)) / 2.

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_situational_lineup

wnba_situational_lineup(seasons: 'int | list[int]', *, league_id: 'str' = '10', season_type: 'str' = 'regular-season', top_n: 'int | None' = None, return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA five-man unit splits by situation (league_id='10'). See sportsdataverse.nba.nba_situational.nba_situational_lineup.

Parameters

Parameter	Type	Default	Description
seasons	int | list[int]
league_id	str	'10'
season_type	str	'regular-season'
top_n	int | None	None
return_as_pandas	bool	False

Returns

One row per (season, team_id, lineup_id, family, split): lineup_id is the five player ids sorted and joined with - (BB-D4's id), player_1..5 the same ids, then the columns of nba_situational_team. Empty input returns that schema with zero rows.

col_name	type	description
season	Int64	Season, named by the year it ends (the WNBA season is its calendar year).
team_id	Int64	stats.nba.com team id.
lineup_id	String	The five player ids of the unit on the floor as the possession starts, sorted and joined with '-' (BB-D4's id).
player_1	Int64	Player 1 of the unit, in lineup_id order.
player_2	Int64	Player 2 of the unit, in lineup_id order.
player_3	Int64	Player 3 of the unit, in lineup_id order.
player_4	Int64	Player 4 of the unit, in lineup_id order.
player_5	Int64	Player 5 of the unit, in lineup_id order.
family	String	The split family: all, clutch, period, half, garbage, score, venue, opponent, play_type or chance; within a family the splits add up to the all row.
split	String	The split: all; clutch / not_clutch; p1..p4 / ot; h1 / h2 / ot; garbage_time / not_garbage_time; leading / tied / trailing; home / away / neutral; vs_top / vs_rest; transition / half_court; second_chance / first_chance.
games	Int64	Games with at least one possession in the split (does not add up across a family).
minutes	Float64	Game-clock minutes of the split's possessions (seconds / 60).
seconds	Float64	Game-clock seconds of the split's possessions: each possession's start clock minus its end clock.
poss	Int64	Offensive possessions in the split.
opp_poss	Int64	Defensive possessions in the split.
pts	Int64	Points scored on the offensive possessions.
opp_pts	Int64	Points allowed on the defensive possessions.
plus_minus	Int64	Plus-minus over the split: pts - opp_pts.
off_rtg	Float64	Offensive rating: pts per 100 poss; null with no offensive possessions.
def_rtg	Float64	Defensive rating: opp_pts per 100 opp_poss; null with no defensive possessions.
net_rtg	Float64	Net rating: off_rtg - def_rtg; null when either is null.
off_rtg_n	Int64	Sample size of off_rtg: poss.
def_rtg_n	Int64	Sample size of def_rtg: opp_poss.
net_rtg_n	Int64	Sample size of net_rtg: poss + opp_poss.

wnba_situational_player

wnba_situational_player(seasons: 'int | list[int]', *, league_id: 'str' = '10', season_type: 'str' = 'regular-season', top_n: 'int | None' = None, return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA player on-floor splits by situation (league_id='10'). See sportsdataverse.nba.nba_situational.nba_situational_player.

Parameters

Parameter	Type	Default	Description
seasons	int | list[int]
league_id	str	'10'
season_type	str	'regular-season'
top_n	int | None	None
return_as_pandas	bool	False

Returns

One row per (season, team_id, player_id, family, split), the columns of nba_situational_team: the team's possessions, points and ratings with the player on the floor as the possession starts. player_id is the stats.nba.com person id (Int64). Empty input returns that schema with zero rows.

col_name	type	description
season	Int64	Season, named by the year it ends (the WNBA season is its calendar year).
team_id	Int64	stats.nba.com team id.
player_id	Int64	stats.nba.com person id. A possession counts for the five on the floor as it starts, with all its points.
family	String	The split family: all, clutch, period, half, garbage, score, venue, opponent, play_type or chance; within a family the splits add up to the all row.
split	String	The split: all; clutch / not_clutch; p1..p4 / ot; h1 / h2 / ot; garbage_time / not_garbage_time; leading / tied / trailing; home / away / neutral; vs_top / vs_rest; transition / half_court; second_chance / first_chance.
games	Int64	Games with at least one possession in the split (does not add up across a family).
minutes	Float64	Game-clock minutes of the split's possessions (seconds / 60).
seconds	Float64	Game-clock seconds of the split's possessions: each possession's start clock minus its end clock.
poss	Int64	Offensive possessions in the split.
opp_poss	Int64	Defensive possessions in the split.
pts	Int64	Points scored on the offensive possessions.
opp_pts	Int64	Points allowed on the defensive possessions.
plus_minus	Int64	Plus-minus over the split: pts - opp_pts.
off_rtg	Float64	Offensive rating: pts per 100 poss; null with no offensive possessions.
def_rtg	Float64	Defensive rating: opp_pts per 100 opp_poss; null with no defensive possessions.
net_rtg	Float64	Net rating: off_rtg - def_rtg; null when either is null.
off_rtg_n	Int64	Sample size of off_rtg: poss.
def_rtg_n	Int64	Sample size of def_rtg: opp_poss.
net_rtg_n	Int64	Sample size of net_rtg: poss + opp_poss.

wnba_situational_team

wnba_situational_team(seasons: 'int | list[int]', *, league_id: 'str' = '10', season_type: 'str' = 'regular-season', top_n: 'int | None' = None, return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA team splits by situation (league_id='10'). See sportsdataverse.nba.nba_situational.nba_situational_team.

Parameters

Parameter	Type	Default	Description
seasons	int | list[int]
league_id	str	'10'
season_type	str	'regular-season'
top_n	int | None	None
return_as_pandas	bool	False

Returns

One row per (season, team_id, family, split): games, minutes / seconds, poss / opp_poss, pts / opp_pts, plus_minus, off_rtg / def_rtg / net_rtg (per 100 possessions) and their _n. Ids are the stats.nba.com ids (Int64). Empty input returns that schema with zero rows.

col_name	type	description
season	Int64	Season, named by the year it ends (the WNBA season is its calendar year).
team_id	Int64	stats.nba.com team id.
family	String	The split family: all, clutch, period, half, garbage, score, venue, opponent, play_type or chance; within a family the splits add up to the all row.
split	String	The split: all; clutch / not_clutch; p1..p4 / ot; h1 / h2 / ot; garbage_time / not_garbage_time; leading / tied / trailing; home / away / neutral; vs_top / vs_rest; transition / half_court; second_chance / first_chance.
games	Int64	Games with at least one possession in the split (does not add up across a family).
minutes	Float64	Game-clock minutes of the split's possessions (seconds / 60).
seconds	Float64	Game-clock seconds of the split's possessions: each possession's start clock minus its end clock.
poss	Int64	Offensive possessions in the split.
opp_poss	Int64	Defensive possessions in the split.
pts	Int64	Points scored on the offensive possessions.
opp_pts	Int64	Points allowed on the defensive possessions.
plus_minus	Int64	Plus-minus over the split: pts - opp_pts.
off_rtg	Float64	Offensive rating: pts per 100 poss; null with no offensive possessions.
def_rtg	Float64	Defensive rating: opp_pts per 100 opp_poss; null with no defensive possessions.
net_rtg	Float64	Net rating: off_rtg - def_rtg; null when either is null.
off_rtg_n	Int64	Sample size of off_rtg: poss.
def_rtg_n	Int64	Sample size of def_rtg: opp_poss.
net_rtg_n	Int64	Sample size of net_rtg: poss + opp_poss.

wnba_team_clutch

wnba_team_clutch(season: 'int', *, league_id: 'str' = '10', 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	'10'
return_as_pandas	bool	False

Returns

One row per team: season, team_id, clutch_net_rating, adj_net_rtg, clutch_delta, clutch_skill_shrunk, clutch_poss. Empty input returns that schema with zero rows.

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.

wnba_team_luck

wnba_team_luck(seasons: 'int | list[int]', *, league_id: 'str' = '10', season_type: 'str' = 'regular-season', return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA luck and close-game record: Pythagorean expected wins vs actual, close-game W-L (league_id='10'). See sportsdataverse.nba.nba_team_luck.nba_team_luck.

Parameters

Parameter	Type	Default	Description
seasons	int | list[int]
league_id	str	'10'
season_type	str	'regular-season'
return_as_pandas	bool	False

Returns

One row per (season, team_id); team_id is the stats.nba.com team id (Int64). games, wins, losses, pts and opp_pts are from the final scores; win_pct, pythag_win_pct, exp_wins and luck_wins (wins - exp_wins) follow. pbp_games counts the games with published possessions, and close_games, close_wins, close_losses and close_win_pct (null with no close game) are the close games among them. pythag_win_pct, luck_wins and close_win_pct have <m>_rank (1 = highest), <m>_pct (the Weibull percentile) and <m>_n (games, or close games); ranks are within the season, among teams with at least half the most games (close_win_pct also needs possessions for half of them and five close games), and null under five teams. Empty input returns that schema with zero rows.

col_name	type	description
season	Int64	Season, the calendar year.
team_id	Int64	stats.nba.com team id (the 10-digit franchise id).
games	Int64	Games in the season type's completed games (the stats schedule's final scores).
wins	Int64	Wins in the season type's completed games (the stats schedule's final scores).
losses	Int64	Losses in the season type's completed games (the stats schedule's final scores).
pts	Int64	Points scored in those games (final scores).
opp_pts	Int64	Points allowed in those games (final scores).
win_pct	Float64	Win fraction: wins divided by games, from the final scores.
pythag_win_pct	Float64	Pythagorean win fraction: pts^x / (pts^x + opp_pts^x), x = PYTHAG_EXPONENT['wnba'], fitted on past seasons by tools/fit_team_luck_pythag.py.
exp_wins	Float64	Expected wins: pythag_win_pct * games.
luck_wins	Float64	Luck in wins: wins minus exp_wins. Positive means the team won more games than its scoring says it should have.
pbp_games	Int64	Games with published possessions (wnba_stats_possessions); close_games is counted among these. A game whose late possessions are missing from the release counts as not close.
close_games	Int64	Close games: at least one possession in play with 5:00 or less left (starting there, or starting before 5:00 and ending after it) in the 4th quarter (the 2nd half through 2005, when the WNBA played two 20-minute halves) or an overtime, the score within 5 as that possession starts (stats.nba.com's clutch window; BB-D3's close split). The margin is the running sum of the possessions' points, so it can drift from the scoreboard: NBA 2025-26 agrees with an independent ESPN play-by-play recount on 1215 of 1229 games.
close_wins	Int64	Wins in close games.
close_losses	Int64	Losses in close games.
close_win_pct	Float64	close_wins / close_games; null with no close game.
pythag_win_pct_rank	Float64	Rank of pythag_win_pct within the season (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the season; null for a non-qualifier, a null value, or fewer than five qualifiers, or when every qualifier has the same value.
luck_wins_rank	Float64	Rank of luck_wins within the season (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the season; null for a non-qualifier, a null value, or fewer than five qualifiers, or when every qualifier has the same value.
close_win_pct_rank	Float64	Rank of close_win_pct within the season (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the season, have possessions for at least half their games and played at least five close games (MIN_CLOSE_GAMES); null otherwise, for a null value, or with fewer than five qualifiers, or when every qualifier has the same value.
pythag_win_pct_pct	Float64	Percentile of pythag_win_pct within the season among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
luck_wins_pct	Float64	Percentile of luck_wins within the season among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
close_win_pct_pct	Float64	Percentile of close_win_pct within the season among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
pythag_win_pct_n	Int64	Sample size of pythag_win_pct: games.
luck_wins_n	Int64	Sample size of luck_wins: games.
close_win_pct_n	Int64	Sample size of close_win_pct: close games.

wnba_team_season_flow

wnba_team_season_flow(seasons: 'int | list[int]', *, league_id: 'str' = '10', season_type: 'str' = 'regular-season', return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA game flow per team and season, with ranks (league_id='10'). See sportsdataverse.nba.nba_game_flow.nba_team_season_flow.

Parameters

Parameter	Type	Default	Description
seasons	int | list[int]
league_id	str	'10'
season_type	str	'regular-season'
return_as_pandas	bool	False

Returns

One row per (season, team_id): games, wins; lead_pct, trail_pct, tied_pct, avg_margin and avg_wp (weighted by game clock); lead_changes_pg and times_tied_pg; largest_lead, largest_comeback and max_run (season bests); runs_10 / opp_runs_10 and their per-game rates; avg_longest_drought_sec; def_poss, stops, stop_pct; kills, double_kills, kills_pg, double_kills_pg and kills_per_100. The ten rated metrics carry <m>_rank (1 = best; opp_runs_10_pg and avg_longest_drought_sec rank low first), <m>_pct and <m>_n (games; the games with possessions for kills_pg / double_kills_pg and with a win probability for avg_wp; defensive possessions for stop_pct and kills_per_100), among teams with at least half the season's most games, null under five teams. Empty input returns that schema with zero rows.

col_name	type	description
season	Int64	Season, named by the year it ends (the WNBA season is its calendar year).
team_id	Int64	stats.wnba.com team id.
games	Int64	Games played (completed games of the season type).
wins	Int64	Games the team won among its completed games of the season type (pts > opp_pts).
trail_pct	Float64	Share of the team's game clock spent behind (sum trail_sec / sum game_seconds).
tied_pct	Float64	Share of the team's game clock spent tied.
lead_changes_pg	Float64	Lead changes per game.
times_tied_pg	Float64	Times tied per game.
largest_lead	Int64	The team's biggest lead of the season.
largest_comeback	Int64	The biggest deficit the team overcame in a win this season (0 if none).
max_run	Int64	The team's biggest n-0 run of the season.
runs_10	Int64	The team's runs of 10 or more unanswered points, summed over the season.
opp_runs_10	Int64	The opponents' runs of 10 or more unanswered points against the team, summed.
def_poss	Int64	Defensive possessions, summed.
stops	Int64	Stops (scoreless defensive possessions), summed.
kills	Int64	Kills (three straight stops), summed.
double_kills	Int64	Double kills (six straight stops), summed.
avg_margin	Float64	Game control: the score margin averaged over the season's game clock.
avg_wp	Float64	Game control: the in-game win probability averaged over the season's game clock.
lead_pct	Float64	Share of the team's game clock spent ahead (sum lead_sec / sum game_seconds).
runs_10_pg	Float64	The team's 10-0 (or better) runs per game.
opp_runs_10_pg	Float64	The opponents' 10-0 (or better) runs against the team per game.
avg_longest_drought_sec	Float64	The team's longest scoring drought, in seconds, averaged over its games.
stop_pct	Float64	stops / def_poss; null with no possessions.
kills_pg	Float64	Kills per game.
double_kills_pg	Float64	Double kills per game.
kills_per_100	Float64	Kills per 100 defensive possessions; null with no possessions.
avg_margin_rank	Float64	Rank of avg_margin within the season (the highest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
avg_wp_rank	Float64	Rank of avg_wp within the season (the highest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
lead_pct_rank	Float64	Rank of lead_pct within the season (the highest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
runs_10_pg_rank	Float64	Rank of runs_10_pg within the season (the highest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
opp_runs_10_pg_rank	Float64	Rank of opp_runs_10_pg within the season (the lowest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
avg_longest_drought_sec_rank	Float64	Rank of avg_longest_drought_sec within the season (the lowest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
stop_pct_rank	Float64	Rank of stop_pct within the season (the highest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
kills_pg_rank	Float64	Rank of kills_pg within the season (the highest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
double_kills_pg_rank	Float64	Rank of double_kills_pg within the season (the highest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
kills_per_100_rank	Float64	Rank of kills_per_100 within the season (the highest value ranks 1; ties average), among teams with at least half the season's most games; null for a non-qualifier, a null value, or fewer than five qualifiers.
avg_margin_pct	Float64	Percentile of avg_margin among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
avg_wp_pct	Float64	Percentile of avg_wp among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
lead_pct_pct	Float64	Percentile of lead_pct among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
runs_10_pg_pct	Float64	Percentile of runs_10_pg among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
opp_runs_10_pg_pct	Float64	Percentile of opp_runs_10_pg among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
avg_longest_drought_sec_pct	Float64	Percentile of avg_longest_drought_sec among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
stop_pct_pct	Float64	Percentile of stop_pct among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
kills_pg_pct	Float64	Percentile of kills_pg among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
double_kills_pg_pct	Float64	Percentile of double_kills_pg among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
kills_per_100_pct	Float64	Percentile of kills_per_100 among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked; null when the rank is null.
avg_margin_n	Int64	Sample size of avg_margin: games.
avg_wp_n	Int64	Sample size of avg_wp: the games with a win probability.
lead_pct_n	Int64	Sample size of lead_pct: games.
runs_10_pg_n	Int64	Sample size of runs_10_pg: games.
opp_runs_10_pg_n	Int64	Sample size of opp_runs_10_pg: games.
avg_longest_drought_sec_n	Int64	Sample size of avg_longest_drought_sec: games.
stop_pct_n	Int64	Sample size of stop_pct: the teams defensive possessions.
kills_pg_n	Int64	Sample size of kills_pg: the games with published possessions.
double_kills_pg_n	Int64	Sample size of double_kills_pg: the games with published possessions.
kills_per_100_n	Int64	Sample size of kills_per_100: the teams defensive possessions.

wnba_team_season_rates

wnba_team_season_rates(seasons: 'int | list[int]', *, league_id: 'str' = '10', season_type: 'str' = 'regular-season', return_as_pandas: 'bool' = False) -> 'pl.DataFrame | pd.DataFrame'

WNBA season team rates from possessions (league_id='10'). See sportsdataverse.nba.nba_team_season_rates.nba_team_season_rates.

Parameters

Parameter	Type	Default	Description
seasons	int | list[int]
league_id	str	'10'
season_type	str	'regular-season'
return_as_pandas	bool	False

Returns

One row per (season, team_id, split). team_id is the stats.nba.com team id (Int64). games, poss / opp_poss (offensive / defensive possessions) and pts / opp_pts are the split's totals. The twelve rates are off_rtg, def_rtg, net_rtg (per 100 possessions), pace (possessions per 48 minutes, 40 in the WNBA), the four factors efg_pct, tov_pct, orb_pct, ft_rate and the same four against (opp_*), all fractions. Each rate has <m>_rank (1 = best; def_rtg, tov_pct, opp_efg_pct, opp_orb_pct and opp_ft_rate rank low first; pace ranks high first), <m>_pct (the Weibull percentile) and <m>_n (the sample: possessions for the ratings, the clock in regulation games for pace, field-goal attempts for eFG% and FT rate, possessions for turnover rate, rebound chances for ORB%). Ranks are within the season and split, among teams with at least half the most games whose <m>_n reaches the league's ~sportsdataverse._common.rate_gates.TEAM_MIN_N gate (a clutch split reaches none), and null under five teams. Empty input returns that schema with zero rows.

col_name	type	description
season	Int64	WNBA season (its calendar year).
team_id	Int64	stats.nba.com team id (the 10-digit franchise id).
split	String	Which possessions the row covers: all; home or away (the team's home or road games; neutral-site games are in neither: the curated nba_neutral_site_games list, plus every game dated inside the 2020 bubble (from the schedule's game_date)); close (every possession of a game that had a clutch possession); clutch (a possession in play with 5:00 or less left (starting there, or starting before 5:00 and ending after it) in the 4th quarter (the 2nd half through 2005, when the WNBA played two 20-minute halves) or an overtime, the score within 5 as it starts).
games	Int64	Games with at least one possession in the split.
poss	Int64	Offensive possessions in the split, counted from possession rows.
opp_poss	Int64	Defensive possessions in the split (the opponents' offensive possessions).
pts	Int64	Points scored on the team's offensive possessions in the split. Counted from each possession's made shots and free throws.
opp_pts	Int64	Points allowed on the team's defensive possessions in the split.
off_rtg	Float64	Offensive rating: points scored per 100 offensive possessions.
def_rtg	Float64	Defensive rating: points allowed per 100 defensive possessions.
net_rtg	Float64	Net rating: off_rtg minus def_rtg.
pace	Float64	Pace: possessions per 40 minutes, the mean of poss and opp_poss over the game clock those possessions used.
efg_pct	Float64	Effective field-goal percentage, (FGM + 0.5 * 3PM) / FGA, as a fraction.
tov_pct	Float64	Turnovers per offensive possession, as a fraction.
orb_pct	Float64	Offensive rebound rate: offensive rebounds over offensive-rebound chances (own OREB plus the defense's DREB), as a fraction.
ft_rate	Float64	Free-throw rate, FTA / FGA.
opp_efg_pct	Float64	Opponents' effective field-goal percentage, (FGM + 0.5 * 3PM) / FGA, as a fraction.
opp_tov_pct	Float64	Opponents' turnovers per possession (turnovers forced), as a fraction.
opp_orb_pct	Float64	Opponents' offensive rebound rate, as a fraction (one minus the team's defensive rebound rate).
opp_ft_rate	Float64	Opponents' free-throw rate, FTA / FGA.
off_rtg_rank	Float64	Rank of off_rtg within the season and split (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose off_rtg_n is at least 660 offensive possessions (the sample at which the rate is half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
def_rtg_rank	Float64	Rank of def_rtg within the season and split (the lowest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose def_rtg_n is at least 810 defensive possessions (the sample at which the rate is half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
net_rtg_rank	Float64	Rank of net_rtg within the season and split (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose net_rtg_n is at least 840 possessions (offense plus defense) (the sample at which the rate was, on the fit seasons, half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
pace_rank	Float64	Rank of pace within the season and split (the highest value ranks 1; pace has no better direction; ties average), among teams that played at least half the most games any team played in the split, whose pace_n is at least 7 games (the sample at which the rate was, on the fit seasons, half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers. An owner-accepted exception (2026-11-09), as on the unseen 2023-2025 seasons the 7-game gate is 0.33 signal, under a third, as measured by the gate's proxy (ESPN box possessions per game over whole regular seasons, not per split); this table's own pace scores 0.44 there. Pace stays ranked, as no data or source defect was found (rate_gates).
efg_pct_rank	Float64	Rank of efg_pct within the season and split (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose efg_pct_n is at least 750 field-goal attempts (the sample at which the rate is half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
tov_pct_rank	Float64	Rank of tov_pct within the season and split (the lowest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose tov_pct_n is at least 1,100 offensive possessions (the sample at which the rate is half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
orb_pct_rank	Float64	Rank of orb_pct within the season and split (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose orb_pct_n is at least 220 offensive-rebound chances (the sample at which the rate is half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
ft_rate_rank	Float64	Rank of ft_rate within the season and split (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose ft_rate_n is at least 720 field-goal attempts (the sample at which the rate is half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
opp_efg_pct_rank	Float64	Rank of opp_efg_pct within the season and split (the lowest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose opp_efg_pct_n is at least 1,300 opponent field-goal attempts (the sample at which the rate was, on the fit seasons, half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
opp_tov_pct_rank	Float64	Rank of opp_tov_pct within the season and split (the highest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose opp_tov_pct_n is at least 350 defensive possessions (the sample at which the rate was, on the fit seasons, half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
opp_orb_pct_rank	Float64	Rank of opp_orb_pct within the season and split (the lowest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose opp_orb_pct_n is at least 860 opponent offensive-rebound chances (the sample at which the rate was, on the fit seasons, half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
opp_ft_rate_rank	Float64	Rank of opp_ft_rate within the season and split (the lowest value ranks 1; ties average), among teams that played at least half the most games any team played in the split, whose opp_ft_rate_n is at least 880 opponent field-goal attempts (the sample at which the rate was, on the fit seasons, half signal, rate_gates); null for a non-qualifier, a sample under that gate, a null rate, or fewer than five qualifiers.
off_rtg_pct	Float64	Percentile of off_rtg within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
def_rtg_pct	Float64	Percentile of def_rtg within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
net_rtg_pct	Float64	Percentile of net_rtg within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
pace_pct	Float64	Percentile of pace within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
efg_pct_pct	Float64	Percentile of efg_pct within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
tov_pct_pct	Float64	Percentile of tov_pct within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
orb_pct_pct	Float64	Percentile of orb_pct within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
ft_rate_pct	Float64	Percentile of ft_rate within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
opp_efg_pct_pct	Float64	Percentile of opp_efg_pct within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
opp_tov_pct_pct	Float64	Percentile of opp_tov_pct within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
opp_orb_pct_pct	Float64	Percentile of opp_orb_pct within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
opp_ft_rate_pct	Float64	Percentile of opp_ft_rate within the season and split among the same qualified teams: 100 * (n + 1 - rank) / (n + 1), n the number ranked, so the best team is under 100 and nobody is 0; null when the rank is null.
off_rtg_n	Int64	Sample size of off_rtg: the team's offensive possessions in the split.
def_rtg_n	Int64	Sample size of def_rtg: the team's defensive possessions in the split.
net_rtg_n	Int64	Sample size of net_rtg: the team's offensive plus defensive possessions in the split.
pace_n	Int64	Sample size of pace: the game clock the split's possessions used, in regulation games (48 minutes, 40 in the WNBA and college), rounded: a full game counts 1, a clutch split's five-minute windows about 0.1 each.
efg_pct_n	Int64	Sample size of efg_pct: the team's field-goal attempts in the split.
tov_pct_n	Int64	Sample size of tov_pct: the team's offensive possessions in the split.
orb_pct_n	Int64	Sample size of orb_pct: the team's offensive-rebound chances in the split.
ft_rate_n	Int64	Sample size of ft_rate: the team's field-goal attempts in the split.
opp_efg_pct_n	Int64	Sample size of opp_efg_pct: the opponents' field-goal attempts in the split.
opp_tov_pct_n	Int64	Sample size of opp_tov_pct: the team's defensive possessions in the split.
opp_orb_pct_n	Int64	Sample size of opp_orb_pct: the opponents' offensive-rebound chances in the split.
opp_ft_rate_n	Int64	Sample size of opp_ft_rate: the opponents' field-goal attempts in the split.