Women's college basketball intro — sportsdataverse-py
A tour of the NCAA women's basketball (sdv.wbb) submodule: teams, rosters, schedules, play-by-play, team stats, standings, conferences, and the parquet data loaders. The wrappers wrap ESPN's women's-college-basketball endpoints and return tidy polars frames (pass return_as_pandas=True for pandas).
R companion: wehoop. Part of the SportsDataverse.
Setup
pip install sportsdataverse
import polars as pl
import sportsdataverse as sdv
import sportsdataverse.wbb as wbb
Teams
espn_wbb_teams() returns one wide row per D-I program. Note NCAA team frames carry no conference column — conference membership comes from espn_wbb_standings() or espn_wbb_conferences() below.
teams = wbb.espn_wbb_teams()
print(teams.shape)
teams.select(['team_id', 'team_location', 'team_name', 'team_abbreviation', 'team_display_name']).head()
(361, 14)
shape: (5, 5)
┌─────────┬───────────────────┬──────────────┬───────────────────┬────────────────────────────┐
│ team_id ┆ team_location ┆ team_name ┆ team_abbreviation ┆ team_display_name │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ str ┆ str │
╞═════════╪═══════════════════╪══════════════╪═══════════════════╪════════════════════════════╡
│ 2000 ┆ Abilene Christian ┆ Wildcats ┆ ACU ┆ Abilene Christian Wildcats │
│ 2005 ┆ Air Force ┆ Falcons ┆ AF ┆ Air Force Falcons │
│ 2006 ┆ Akron ┆ Zips ┆ AKR ┆ Akron Zips │
│ 2010 ┆ Alabama A&M ┆ Bulldogs ┆ AAMU ┆ Alabama A&M Bulldogs │
│ 333 ┆ Alabama ┆ Crimson Tide ┆ ALA ┆ Alabama Crimson Tide │
└─────────┴───────────────────┴──────────────┴───────────────────┴────────────────────────────┘
Team roster
espn_wbb_team_roster(team_id=..., season=...) returns one row per player with unprefixed athlete columns. Here is the 2024-25 UConn Huskies (team_id=2509), the eventual national champions.
uconn_roster = wbb.espn_wbb_team_roster(team_id=2509, season=2025)
print(uconn_roster.shape)
uconn_roster.select(
['athlete_id', 'full_name', 'jersey', 'position_abbreviation', 'display_height', 'display_weight']
).head(10)
(13, 24)
shape: (10, 6)
┌────────────┬────────────────────┬────────┬─────────────────────┬────────────────┬────────────────┐
│ athlete_id ┆ full_name ┆ jersey ┆ position_abbreviati ┆ display_height ┆ display_weight │
│ --- ┆ --- ┆ --- ┆ on ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ --- ┆ str ┆ str │
│ ┆ ┆ ┆ str ┆ ┆ │
╞════════════╪════════════════════╪════════╪═════════════════════╪════════════════╪════════════════╡
│ 5311737 ┆ Carley Barrett ┆ 24 ┆ G ┆ 5' 7" ┆ null │
│ 5106182 ┆ Tara Daye ┆ 44 ┆ G ┆ 5' 10" ┆ null │
│ 5107710 ┆ Taylor Feldman ┆ 5 ┆ G ┆ 5' 8" ┆ null │
│ 5311739 ┆ Avery Gordon ┆ 55 ┆ F ┆ 6' 7" ┆ null │
│ 5108895 ┆ Taylor Henderson ┆ 2 ┆ G ┆ 5' 11" ┆ null │
│ 5311736 ┆ Hila Karsh ┆ 8 ┆ G ┆ 5' 8" ┆ null │
│ 5175722 ┆ McKenna Layden ┆ 11 ┆ G ┆ 6' 2" ┆ null │
│ 4433438 ┆ Madison Layden-Zay ┆ 33 ┆ G ┆ 6' 1" ┆ null │
│ 5240041 ┆ Lana McCarthy ┆ 35 ┆ F ┆ 6' 4" ┆ null │
│ 5240040 ┆ Kendall Puryear ┆ 22 ┆ F ┆ 6' 3" ┆ null │
└────────────┴────────────────────┴────────┴─────────────────────┴────────────────┴────────────────┘
# South Carolina Gamecocks (team_id=2579), the runners-up
scar_roster = wbb.espn_wbb_team_roster(team_id=2579, season=2025)
scar_roster.select(['athlete_id', 'full_name', 'jersey', 'position_abbreviation']).head()
shape: (5, 4)
┌────────────┬ ────────────────┬────────┬───────────────────────┐
│ athlete_id ┆ full_name ┆ jersey ┆ position_abbreviation │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ str │
╞════════════╪════════════════╪════════╪═══════════════════════╡
│ 5239100 ┆ Joyce Edwards ┆ 8 ┆ F │
│ 5174284 ┆ Tessa Johnson ┆ 5 ┆ G │
│ 5121055 ┆ Chloe Kitts ┆ 21 ┆ F │
│ 5311577 ┆ Agot Makeer ┆ 44 ┆ G │
│ 5239099 ┆ Maddy McDaniel ┆ 1 ┆ G │
└────────────┴────────────────┴────────┴───────────────────────┘
Schedule — single date
espn_wbb_schedule(dates=YYYYMMDD) returns one row per game. Team-name columns are home_display_name / away_display_name; home_score / away_score are strings, so cast before arithmetic. April 4, 2025 was the women's Final Four.
final_four = wbb.espn_wbb_schedule(dates=20250404)
final_four.select(
['id', 'date', 'away_display_name', 'away_score', 'home_display_name', 'home_score', 'status_type_completed']
)
shape: (2, 7)
┌───────────┬───────────────┬──────────────┬────────────┬──────────────┬────────────┬──────────────┐
│ id ┆ date ┆ away_display ┆ away_score ┆ home_display ┆ home_score ┆ status_type_ │
│ --- ┆ --- ┆ _name ┆ --- ┆ _name ┆ --- ┆ completed │
│ str ┆ str ┆ --- ┆ str ┆ --- ┆ str ┆ --- │
│ ┆ ┆ str ┆ ┆ str ┆ ┆ bool │
╞═══════════╪═══════════════╪══════════════╪════════════╪══════════════╪════════════╪══════════════╡
│ 401746073 ┆ 2025-04-04T23 ┆ Texas ┆ 57 ┆ South ┆ 74 ┆ true │
│ ┆ :00Z ┆ Longhorns ┆ ┆ Carolina ┆ ┆ │
│ ┆ ┆ ┆ ┆ Gamecocks ┆ ┆ │
│ 401746074 ┆ 2025-04-05T01 ┆ UConn ┆ 85 ┆ UCLA Bruins ┆ 51 ┆ true │
│ ┆ :30Z ┆ Huskies ┆ ┆ ┆ ┆ │
└───────────┴───────────────┴──────────────┴────────────┴──────────────┴────────────┴──────────────┘
Schedule — date range
Pass a 'YYYYMMDD-YYYYMMDD' string to span multiple days. Here is the Final Four through the national championship (April 4–6, 2025).
title_weekend = wbb.espn_wbb_schedule(dates='20250404-20250406')
title_weekend.select(
['id', 'date', 'away_display_name', 'away_score', 'home_display_name', 'home_score']
).with_columns(
pl.col('home_score').cast(pl.Int64, strict=False),
pl.col('away_score').cast(pl.Int64, strict=False),
)
shape: (4, 6)
┌───────────┬───────────────────┬───────────────────┬────────────┬────────────────────┬────────────┐
│ id ┆ date ┆ away_display_name ┆ away_score ┆ home_display_name ┆ home_score │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ i64 ┆ str ┆ i64 │
╞═══════════╪═══════════════════╪═══════════════════╪════════════╪════════════════════╪════════════╡
│ 401746073 ┆ 2025-04-04T23:00Z ┆ Texas Longhorns ┆ 57 ┆ South Carolina ┆ 74 │
│ ┆ ┆ ┆ ┆ Gamecocks ┆ │
│ 401746074 ┆ 2025-04-05T01:30Z ┆ UConn Huskies ┆ 85 ┆ UCLA Bruins ┆ 51 │
│ 401746075 ┆ 2025-04-06T19:00Z ┆ UConn Huskies ┆ 82 ┆ South Carolina ┆ 59 │
│ ┆ ┆ ┆ ┆ Gamecocks ┆ │
│ 401762436 ┆ 2025-04-05T21:00Z ┆ Troy Trojans ┆ 84 ┆ Buffalo Bulls ┆ 88 │
└───────────┴───────────────────┴───────────────────┴────────────┴────────────────────┴────────────┘
Play-by-play
espn_wbb_pbp(game_id=...) returns a dict of game components (keys like plays, boxscore, header, winprobability, ...). The plays value is a list of dicts — build a frame with pl.DataFrame(pbp['plays'], infer_schema_length=None). Columns use ESPN dot-notation (period.number, clock.displayValue, type.text, scoringPlay).
Game 401746075 is the 2025 national championship: South Carolina vs. UConn.
pbp = wbb.espn_wbb_pbp(game_id=401746075)
list(pbp.keys())[:10]
['gameId',
'plays',
'winprobability',
'boxscore',
'header',
'format',
'broadcasts',
'videos',
'playByPlaySource',
'standings']
plays = pl.DataFrame(pbp['plays'], infer_schema_length=None)
print(plays.shape)
plays.select(['period.number', 'clock.displayValue', 'type.text', 'scoringPlay', 'text']).head()
(443, 58)
shape: (5, 5)
┌───────────────┬────────────────────┬───────────────────┬─────────────┬───────────────────────────┐
│ period.number ┆ clock.displayValue ┆ type.text ┆ scoringPlay ┆ text │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str ┆ bool ┆ str │
╞═══════════════╪════════════════════╪═══════════════════╪═════════════╪═══════════════════════════╡
│ 1 ┆ 10:00 ┆ Jumpball ┆ false ┆ Start game │
│ 1 ┆ 9:57 ┆ Jumpball ┆ false ┆ Jump Ball won by UConn │
│ 1 ┆ 9:57 ┆ Jumpball ┆ false ┆ Jump Ball lost by South │
│ ┆ ┆ ┆ ┆ Caroli… │
│ 1 ┆ 9:40 ┆ JumpShot ┆ false ┆ Kaitlyn Chen missed Three │
│ ┆ ┆ ┆ ┆ Poin… │
│ 1 ┆ 9:33 ┆ Offensive Rebound ┆ false ┆ Paige Bueckers Offensive │
│ ┆ ┆ ┆ ┆ Rebou… │
└───────────────┴────────────────────┴───────────────────┴─────────────┴───────────────────────────┘
# Scoring plays only, with the running score
plays.filter(pl.col('scoringPlay') == True).select(
['period.number', 'clock.displayValue', 'awayScore', 'homeScore', 'text']
).head(8)
shape: (8, 5)
┌───────────────┬────────────────────┬───────────┬───────────┬─────────────────────────────────┐
│ period.number ┆ clock.displayValue ┆ awayScore ┆ homeScore ┆ text │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ i64 ┆ i64 ┆ str │
╞═══════════════╪════════════════════╪═══════════╪═══════════╪═════════════════════════════════╡
│ 1 ┆ 9:18 ┆ 0 ┆ 3 ┆ Te-Hina Paopao made Three Poin… │
│ 1 ┆ 8:58 ┆ 2 ┆ 3 ┆ Sarah Strong made Jumper. │
│ 1 ┆ 8:36 ┆ 2 ┆ 5 ┆ Chloe Kitts made Jumper. │
│ 1 ┆ 8:13 ┆ 4 ┆ 5 ┆ Paige Bueckers made Jumper. │
│ 1 ┆ 7:24 ┆ 6 ┆ 5 ┆ Azzi Fudd made Jumper. Assiste… │
│ 1 ┆ 7:00 ┆ 6 ┆ 7 ┆ Raven Johnson made Layup. Assi… │
│ 1 ┆ 6:40 ┆ 8 ┆ 7 ┆ Kaitlyn Chen made Jumper. │
│ 1 ┆ 6:23 ┆ 8 ┆ 9 ┆ Bree Hall made Jumper. │
└───────────────┴────────────────────┴───────────┴───────────┴─────────────────────────────────┘
Team season stats
espn_wbb_team_stats(team_id=..., season=...) returns a dict keyed by category — {'Averages', 'Totals', 'Misc'} — each a tidy long frame of stat_name / value rows. (ESPN's per-player NCAA season stats are usually unavailable, so team stats are the reliable season-level source here.)
team_stats = wbb.espn_wbb_team_stats(team_id=2509, season=2025)
{k: v.shape for k, v in team_stats.items()}
{'Averages': (9, 8), 'Totals': (30, 8), 'Misc': (6, 8)}
# Per-game averages for UConn
team_stats['Averages'].select(['stat_name', 'abbreviation', 'display_value', 'value'])
shape: (9, 4)
┌──────────────────────────┬──────────────┬───────────────┬───────────┐
│ stat_name ┆ abbreviation ┆ display_value ┆ value │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ f64 │
╞══════════════════════════╪══════════════╪═══════════════╪═══════════╡
│ Rebounds Per Game ┆ REB ┆ 33.8 ┆ 33.827587 │
│ Assist To Turnover Ratio ┆ AST/TO ┆ 0.8 ┆ 0.8326693 │
│ Fouls Per Game ┆ PF ┆ 18.2 ┆ 18.241379 │
│ Games Played ┆ GP ┆ 29 ┆ 29.0 │
│ Games Started ┆ GS ┆ 0 ┆ 0.0 │
│ Minutes ┆ MIN ┆ 5800 ┆ 5800.0 │
│ Minutes Per Game ┆ MIN ┆ 200.0 ┆ 200.0 │
│ Rebounds ┆ REB ┆ 981 ┆ 981.0 │
│ Rebounds ┆ REB ┆ 981 ┆ 981.0 │
└──────────────────────────┴─── ───────────┴───────────────┴───────────┘
team_stats['Totals'].select(['stat_name', 'abbreviation', 'display_value']).head(10)
shape: (10, 3)
┌─────────────────────────────────┬──────────────┬───────────────┐
│ stat_name ┆ abbreviation ┆ display_value │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞═════════════════════════════════╪══════════════╪═══════════════╡
│ Free Throw Percentage ┆ FT% ┆ 70.2 │
│ Average Field Goals Made ┆ FGM ┆ 24.6 │
│ Average Field Goals Attempted ┆ FGA ┆ 58.0 │
│ Average 3-Point Field Goals Ma… ┆ 3PM ┆ 5.9 │
│ Average 3-Point Field Goals At… ┆ 3PA ┆ 18.3 │
│ Average Free Throws Made ┆ FTM ┆ 8.4 │
│ Average Free Throws Attempted ┆ FTA ┆ 12.0 │
│ Points Per Game ┆ PTS ┆ 63.4 │
│ Offensive Rebounds Per Game ┆ OR ┆ 8.9 │
│ Assists Per Game ┆ AST ┆ 14.4 │
└─────────────────────────────────┴──────────────┴───────────────┘
Standings
espn_wbb_standings(season=...) returns one wide row per team with win/loss records, conference membership, and points-for/against.
standings = wbb.espn_wbb_standings(season=2025)
print(standings.shape)
standings.select(
['team_display_name', 'conference_abbreviation', 'wins', 'losses', 'win_percent', 'points_for', 'points_against']
).sort('win_percent', descending=True).head(10)
(362, 29)
shape: (10, 7)
┌───────────────────┬──────────────────┬──────┬────────┬─────────────┬────────────┬────────────────┐
│ team_display_name ┆ conference_abbre ┆ wins ┆ losses ┆ win_percent ┆ points_for ┆ points_against │
│ --- ┆ viation ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ --- ┆ i64 ┆ i64 ┆ f64 ┆ f64 ┆ f64 │
│ ┆ str ┆ ┆ ┆ ┆ ┆ │
╞═══════════════════╪══════════════════╪══════╪════════╪═════════════╪════════════╪════════════════╡
│ Florida Gulf ┆ ASUN ┆ 18 ┆ 0 ┆ 1.0 ┆ 1367.0 ┆ 983.0 │
│ Coast Eagles ┆ ┆ ┆ ┆ ┆ ┆ │
│ UConn Huskies ┆ bige ┆ 18 ┆ 0 ┆ 1.0 ┆ 1480.0 ┆ 866.0 │
│ Norfolk State ┆ meac ┆ 14 ┆ 0 ┆ 1.0 ┆ 1145.0 ┆ 747.0 │
│ Spartans ┆ ┆ ┆ ┆ ┆ ┆ │
│ Fairleigh ┆ neast ┆ 16 ┆ 0 ┆ 1.0 ┆ 1086.0 ┆ 805.0 │
│ Dickinson Knights ┆ ┆ ┆ ┆ ┆ ┆ │
│ South Dakota ┆ summ ┆ 16 ┆ 0 ┆ 1.0 ┆ 1258.0 ┆ 933.0 │
│ State Jackrabbits ┆ ┆ ┆ ┆ ┆ ┆ │
│ James Madison ┆ belt ┆ 18 ┆ 0 ┆ 1.0 ┆ 1358.0 ┆ 1072.0 │
│ Dukes ┆ ┆ ┆ ┆ ┆ ┆ │
│ Grand Canyon ┆ wac ┆ 16 ┆ 0 ┆ 1.0 ┆ 1219.0 ┆ 894.0 │
│ Lopes ┆ ┆ ┆ ┆ ┆ ┆ │
│ Green Bay Phoenix ┆ hor ┆ 19 ┆ 1 ┆ 0.95 ┆ 1408.0 ┆ 1037.0 │
│ Fairfield Stags ┆ maac ┆ 19 ┆ 1 ┆ 0.95 ┆ 1498.0 ┆ 1034.0 │
│ SE Louisiana Lady ┆ land ┆ 19 ┆ 1 ┆ 0.95 ┆ 1330.0 ┆ 1013.0 │
│ Lions ┆ ┆ ┆ ┆ ┆ ┆ │
└───────────────────┴──────────────────┴──────┴────────┴─────────────┴────────────┴────────────────┘
Conferences
espn_wbb_conferences() lists the conference groups ESPN tracks, with their group ids — useful for filtering schedules and standings by league.
conferences = wbb.espn_wbb_conferences()
print(conferences.shape)
conferences.select(['group_id', 'name', 'abbreviation', 'short_name']).head(12)
(27, 8)
shape: (12, 4)
┌──────────┬───────────────────────────┬──────────────┬────────────┐
│ group_id ┆ name ┆ abbreviation ┆ short_name │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str ┆ str │
╞══════════╪═══════════════════════════╪══════════════╪════════════╡
│ null ┆ NCAA Division I ┆ NCAA ┆ null │
│ null ┆ America East Conference ┆ aeast ┆ null │
│ null ┆ American Conference ┆ American ┆ null │
│ null ┆ Atlantic 10 Conference ┆ atl10 ┆ null │
│ null ┆ Atlantic Coast Conference ┆ acc ┆ null │
│ … ┆ … ┆ … ┆ … │
│ null ┆ Big East Conference ┆ bige ┆ null │
│ null ┆ Big Sky Conference ┆ bsky ┆ null │
│ null ┆ Big South Conference ┆ bsou ┆ null │
│ null ┆ Big Ten Conference ┆ big10 ┆ null │
│ null ┆ Big West Conference ┆ bigw ┆ null │
└──────────┴───────────────────────────┴──────────────┴────────────┘
Data loaders (parquet releases)
load_wbb_*(seasons=[...]) read pre-built parquet releases from the wehoop-wbb-data repo and return polars frames — far faster than scraping season-long history through the ESPN endpoints. Loaders include load_wbb_schedule, load_wbb_team_boxscore, load_wbb_player_boxscore, load_wbb_pbp, load_wbb_rosters, load_wbb_standings, and more (dir(sdv.wbb) shows the full set).
schedule_2024 = wbb.load_wbb_schedule(seasons=[2024])
print(schedule_2024.shape)
schedule_2024.select(['id', 'date', 'home_display_name', 'away_display_name']).head()
(5923, 84)
shape: (5, 4)
┌───────────┬───────────────────┬──────────────────────────┬──────────────────────────┐
│ id ┆ date ┆ home_display_name ┆ away_display_name │
│ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ str ┆ str ┆ str │
╞═══════════╪═══════════════════╪══════════════════════════╪══════════════════════════╡
│ 401637613 ┆ 2024-04-07T19:00Z ┆ South Carolina Gamecocks ┆ Iowa Hawkeyes │
│ 401641587 ┆ 2024-04-06T19:00Z ┆ Saint Louis Billikens ┆ Minnesota Golden Gophers │
│ 401637612 ┆ 2024-04-06T01:32Z ┆ Iowa Hawkeyes ┆ UConn Huskies │
│ 401637611 ┆ 2024-04-05T23:00Z ┆ South Carolina Gamecocks ┆ NC State Wolfpack │
│ 401641629 ┆ 2024-04-03T23:00Z ┆ Villanova Wildcats ┆ Illinois Fighting Illini │
└───────────┴───────────────────┴──────────────────────────┴──────────────────────────┘
team_box_2024 = wbb.load_wbb_team_boxscore(seasons=[2024])
print(team_box_2024.shape)
team_box_2024.select(
['game_id', 'team_display_name', 'team_home_away', 'team_score', 'field_goal_pct', 'total_rebounds', 'assists']
).head()
(11796, 56)
shape: (5, 7)
┌───────────┬───────────────┬───────────────┬────────────┬───────────────┬───────────────┬─────────┐
│ game_id ┆ team_display_ ┆ team_home_awa ┆ team_score ┆ field_goal_pc ┆ total_rebound ┆ assists │
│ --- ┆ name ┆ y ┆ --- ┆ t ┆ s ┆ --- │
│ i32 ┆ --- ┆ --- ┆ i32 ┆ --- ┆ --- ┆ i32 │
│ ┆ str ┆ str ┆ ┆ f64 ┆ i32 ┆ │
╞═══════════╪═══════════════╪═══════════════╪════════════╪═══════════════╪═══════════════╪═════════╡
│ 401637613 ┆ Iowa Hawkeyes ┆ away ┆ 75 ┆ 39.7 ┆ 29 ┆ 13 │
│ 401637613 ┆ South ┆ home ┆ 87 ┆ 47.9 ┆ 51 ┆ 16 │
│ ┆ Carolina ┆ ┆ ┆ ┆ ┆ │
│ ┆ Gamecocks ┆ ┆ ┆ ┆ ┆ │
│ 401641587 ┆ Minnesota ┆ away ┆ 50 ┆ 33.9 ┆ 41 ┆ 12 │
│ ┆ Golden ┆ ┆ ┆ ┆ ┆ │
│ ┆ Gophers ┆ ┆ ┆ ┆ ┆ │
│ 401641587 ┆ Saint Louis ┆ home ┆ 69 ┆ 43.3 ┆ 38 ┆ 14 │
│ ┆ Billikens ┆ ┆ ┆ ┆ ┆ │
│ 401637612 ┆ UConn Huskies ┆ away ┆ 69 ┆ 46.0 ┆ 29 ┆ 21 │
└───────────┴───────────────┴───────────────┴────────────┴───────────────┴───────────────┴─────────┘
player_box_2024 = wbb.load_wbb_player_boxscore(seasons=[2024])
print(player_box_2024.shape)
player_box_2024.select(
['game_id', 'athlete_display_name', 'team_short_display_name', 'minutes', 'points', 'rebounds', 'assists']
).head()
(167412, 55)
shape: (5, 7)
┌───────────┬───────────────────── ─┬───────────────────────┬─────────┬────────┬──────────┬─────────┐
│ game_id ┆ athlete_display_name ┆ team_short_display_na ┆ minutes ┆ points ┆ rebounds ┆ assists │
│ --- ┆ --- ┆ me ┆ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ str ┆ --- ┆ f64 ┆ i32 ┆ i32 ┆ i32 │
│ ┆ ┆ str ┆ ┆ ┆ ┆ │
╞═══════════╪══════════════════════╪═══════════════════════╪═════════╪════════╪══════════╪═════════╡
│ 401637613 ┆ Hannah Stuelke ┆ Iowa ┆ 27.0 ┆ 11 ┆ 3 ┆ 2 │
│ 401637613 ┆ Sydney Affolter ┆ Iowa ┆ 35.0 ┆ 12 ┆ 3 ┆ 3 │
│ 401637613 ┆ Caitlin Clark ┆ Iowa ┆ 40.0 ┆ 30 ┆ 8 ┆ 5 │
│ 401637613 ┆ Gabbie Marshall ┆ Iowa ┆ 40.0 ┆ 6 ┆ 3 ┆ 1 │
│ 401637613 ┆ Kate Martin ┆ Iowa ┆ 40.0 ┆ 16 ┆ 5 ┆ 0 │
└───────────┴──────────────────────┴───────────────────────┴─────────┴────────┴──────────┴─────────┘
Pipeline example: top scorers of the 2023-24 season
Load the season-long player boxscore, then aggregate with polars to find the highest per-game scorers (minimum 20 games played).
top_scorers = (
player_box_2024
.group_by(['athlete_id', 'athlete_display_name', 'team_short_display_name'])
.agg(
games=pl.len(),
total_points=pl.col('points').sum(),
ppg=pl.col('points').mean().round(1),
)
.filter(pl.col('games') >= 20)
.sort('ppg', descending=True)
.head(10)
)
top_scorers
shape: (10, 6)
┌────────────┬──────────────────────┬─────────────────────────┬───────┬──────────────┬──────┐
│ athlete_id ┆ athlete_display_name ┆ team_short_display_name ┆ games ┆ total_points ┆ ppg │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ str ┆ str ┆ u32 ┆ i32 ┆ f64 │
╞════════════╪══════════════════════╪═════════════════════════╪═══════╪══════════════╪══════╡
│ 5177925 ┆ Ty'Mesha Reed ┆ Miss Valley St ┆ 31 ┆ 0 ┆ null │
│ 5178455 ┆ Paris Bass ┆ North Dakota ┆ 30 ┆ 0 ┆ null │
│ 5177967 ┆ Cameron Dill ┆ Lamar ┆ 31 ┆ 0 ┆ null │
│ 5178387 ┆ Adriana Arroyo ┆ E Illinois ┆ 33 ┆ 0 ┆ null │
│ 4400119 ┆ Taylor Caldwell ┆ Bakersfield ┆ 29 ┆ 0 ┆ null │
│ 4704165 ┆ Lindsey Syrek ┆ NJIT ┆ 30 ┆ 0 ┆ null │
│ 5175542 ┆ Nia Anderson ┆ CA Baptist ┆ 32 ┆ 0 ┆ null │
│ 4900575 ┆ Hannah Hartley ┆ Nevada ┆ 32 ┆ 0 ┆ null │
│ 5174965 ┆ Sahana Kanagasabay ┆ Providence ┆ 34 ┆ 0 ┆ null │
│ 4898948 ┆ Koi Sims ┆ Loyola MD ┆ 31 ┆ 0 ┆ null │
└────────────┴──────────────────────┴─────────────────────────┴───────┴──────────────┴──────┘
Pipeline example: best scoring offenses
Aggregate the team boxscore to rank programs by average points scored, then join back to the standings to attach each team's record.
team_offense = (
team_box_2024
.group_by(['team_id', 'team_display_name'])
.agg(
games=pl.len(),
ppg=pl.col('team_score').mean().round(1),
)
.filter(pl.col('games') >= 20)
.sort('ppg', descending=True)
.head(10)
)
team_offense
shape: (10, 4)
┌─────────┬────────────────────────────┬───────┬──────┐
│ team_id ┆ team_display_name ┆ games ┆ ppg │
│ --- ┆ --- ┆ --- ┆ --- │
│ i32 ┆ str ┆ u32 ┆ f64 │
╞═════════╪════════════════════════════╪═══════╪══════╡
│ 2294 ┆ Iowa Hawkeyes ┆ 39 ┆ 91.0 │
│ 99 ┆ LSU Tigers ┆ 37 ┆ 85.9 │
│ 2579 ┆ South Carolina Gamecocks ┆ 38 ┆ 85.4 │
│ 276 ┆ Marshall Thundering Herd ┆ 33 ┆ 85.3 │
│ 93 ┆ Murray State Racers ┆ 32 ┆ 84.5 │
│ 127 ┆ Michigan State Spartans ┆ 31 ┆ 82.8 │
│ 213 ┆ Penn State Lady Lions ┆ 35 ┆ 82.7 │
│ 198 ┆ Oral Roberts Golden Eagles ┆ 32 ┆ 82.1 │
│ 2181 ┆ Drake Bulldogs ┆ 35 ┆ 81.2 │
│ 2653 ┆ Troy Trojans ┆ 34 ┆ 80.9 │
└─────────┴────────────────────────────┴───────┴──────┘
Cross-references
- R companion: wehoop
- Data source: ESPN (women's college basketball)
- Data releases: wehoop-wbb-data
- Plotting: matplotlib, plotnine
Where to go next
- API docs:
docs/docs/wbb/index.md - Next notebook:
06_mbb_intro.ipynb— the parallel men's college basketball surface