Women's College Basketball Cookbook
Saiem Gilani
Source: vignettes/wbb-cookbook.Rmd
wbb-cookbook.RmdBefore we cook
Women’s college basketball has never been more watched, and the data
has never been better. wehoop covers it end to end –
360-odd Division I teams, a March bracket, and the NCAA’s own NET
rankings. The trick to not drowning is the same as everywhere else in
this family of packages: learn the grammar of the function names
and you can guess your way to almost anything.
If you’ve read the WNBA cookbook, you already know most of the
grammar. This one swaps the league token from wnba to
wbb and adds one new prefix – ncaa_wbb_ for
NCAA.com data. That’s nearly the whole diff.
The grammar, refreshed for college
A wehoop college function answers three questions, in
order:
-
Where’s the data from? – the prefix.
-
espn_– ESPN’s APIs. The broad, stable backbone. -
ncaa_wbb_– NCAA.com. Home of the NET rankings and the official bracket machinery. (Here the prefix carries the league with it.) -
load_– pre-built bulk season files.
-
-
Which league? – for ESPN it’s spelled out:
espn_wbb_. Thewbbtoken is “women’s basketball.” - What do you want? – the rest of the name, general to specific.
So “a team’s schedule from ESPN” is espn_ +
wbb_ + team_schedule ->
espn_wbb_team_schedule(). “NCAA NET rankings” is
ncaa_wbb_ + NET_rankings ->
ncaa_wbb_NET_rankings(). You’ll be right far more often
than wrong, and being wrong just means opening the reference index.
Recipe 1: A powerhouse program’s season
The story. It’s November and you want to set the table for UConn’s season – schedule, roster, where they sit.
Type espn_wbb_team and the grammar lays out the
menu:
team_id <- 2509 # UConn
season <- most_recent_wbb_season()
espn_wbb_team(team_id = team_id) # identity + record
#> $Info
#> # A tibble: 1 × 13
#> id uid slug abbreviation display_name short_display_name name nickname
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 2509 s:40~… purd… PUR Purdue Boil… Purdue Boil… Purdue
#> # ℹ 5 more variables: location <chr>, color <chr>, alternate_color <chr>,
#> # logo <chr>, logo_dark <chr>
#>
#> $Record
#> # A tibble: 3 × 4
#> description type summary stats
#> <chr> <chr> <chr> <list>
#> 1 Overall Record total 13-17 <df [20 × 2]>
#> 2 Home Record home 11-6 <df [20 × 2]>
#> 3 Away Record road 2-10 <df [20 × 2]>
#>
#> $NextEvent
#> # A tibble: 1 × 4
#> id date name short_name
#> <chr> <chr> <chr> <chr>
#> 1 401851368 2026-03-05T01:30Z Purdue Boilermakers at Oregon Ducks PUR VS ORE
#>
#> $StandingSummary
#> # A tibble: 1 × 1
#> standing_summary
#> <chr>
#> 1 14th in Big Ten
#>
#> $Coaches
#> data frame with 0 columns and 0 rows
espn_wbb_team_schedule(team_id = team_id, season = season) # the slate
#> # A tibble: 30 × 21
#> event_id season season_type week date name short_name opponent_id
#> <chr> <int> <int> <int> <chr> <chr> <chr> <chr>
#> 1 401812419 2026 NA 1 2025-11-04T0… Fair… FDU @ PUR NA
#> 2 401818691 2026 NA 1 2025-11-07T0… East… EIU @ PUR NA
#> 3 401818692 2026 NA 2 2025-11-13T0… Purd… PUR @ PFW NA
#> 4 401818581 2026 NA 3 2025-11-19T0… Purd… PUR @ UK NA
#> 5 401818693 2026 NA 3 2025-11-23T1… Miam… M-OH @ PUR NA
#> 6 401818694 2026 NA 4 2025-11-26T1… Howa… HOW @ PUR NA
#> 7 401818695 2026 NA 4 2025-11-30T1… Purd… PUR @ CMU NA
#> 8 401818696 2026 NA 5 2025-12-04T0… Evan… EVAN @ PUR NA
#> 9 401825225 2026 NA 5 2025-12-07T1… Purd… PUR @ MICH NA
#> 10 401818697 2026 NA 6 2025-12-11T1… Lips… LIP @ PUR NA
#> # ℹ 20 more rows
#> # ℹ 13 more variables: opponent_abbrev <chr>, home_away <chr>,
#> # neutral_site <lgl>, conference_competition <lgl>, venue_id <chr>,
#> # venue_name <chr>, venue_city <chr>, venue_state <chr>, broadcast <chr>,
#> # result <chr>, team_score <chr>, opponent_score <chr>, winner <lgl>
espn_wbb_team_roster(team_id = team_id, season = season) # the players
#> # A tibble: 13 × 15
#> athlete_id full_name jersey position_abbrev position_name height weight age
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 5311737 Carley B… 24 G Guard "5' 7… NA NA
#> 2 5106182 Tara Daye 44 G Guard "5' 1… NA NA
#> 3 5107710 Taylor F… 5 G Guard "5' 8… NA NA
#> 4 5311739 Avery Go… 55 F Forward "6' 7… NA NA
#> 5 5108895 Taylor H… 2 G Guard "5' 1… NA NA
#> 6 5311736 Hila Kar… 8 G Guard "5' 8… NA NA
#> 7 5175722 McKenna … 11 G Guard "6' 2… NA NA
#> 8 4433438 Madison … 33 G Guard "6' 1… NA NA
#> 9 5240041 Lana McC… 35 F Forward "6' 4… NA NA
#> 10 5240040 Kendall … 22 F Forward "6' 3… NA NA
#> 11 5239064 Kiki Smi… 23 G Guard "5' 7… NA NA
#> 12 5243531 Nya Smith 3 G Guard "5' 9… NA NA
#> 13 5178283 Saige St… 13 F Forward "6' 1… NA NA
#> # ℹ 7 more variables: birth_date <chr>, birth_place <chr>, headshot <chr>,
#> # link_web <chr>, status <chr>, team_id <chr>, season <int>
espn_wbb_team_season_statistics(team_id = team_id, season = season)
#> # A tibble: 77 × 13
#> league season season_type team_id category_name category_display stat_name
#> <chr> <int> <int> <chr> <chr> <chr> <chr>
#> 1 womens-c… 2026 2 2509 defensive Defensive blocks
#> 2 womens-c… 2026 2 2509 defensive Defensive defensiv…
#> 3 womens-c… 2026 2 2509 defensive Defensive steals
#> 4 womens-c… 2026 2 2509 defensive Defensive turnover…
#> 5 womens-c… 2026 2 2509 defensive Defensive avgDefen…
#> 6 womens-c… 2026 2 2509 defensive Defensive avgBlocks
#> 7 womens-c… 2026 2 2509 defensive Defensive avgSteals
#> 8 womens-c… 2026 2 2509 general General disquali…
#> 9 womens-c… 2026 2 2509 general General flagrant…
#> 10 womens-c… 2026 2 2509 general General fouls
#> # ℹ 67 more rows
#> # ℹ 6 more variables: stat_abbrev <chr>, stat_display <chr>, value <dbl>,
#> # display_value <chr>, rank <int>, rank_display_value <chr>Margin note: most_recent_wbb_season() is worth knowing.
Every league in wehoop has a
most_recent_<league>_season() companion, and the
newer espn_wbb_* functions use it as their default
season. Most of the time you can simply omit
season – the function already knows what “now” means.
Recipe 2: The star guard
The story. Women’s college basketball has become a star-driven sport, and you want a marquee guard’s numbers.
College rosters turn over fast – this year’s star is next year’s pro
– so the reliable move is to pull the current roster first and lift an
athlete_id straight off it, then walk the
athlete_ family:
roster <- espn_wbb_team_roster(team_id = 2509, season = most_recent_wbb_season())
athlete_id <- roster$athlete_id[1] # whoever is first on the roster
espn_wbb_athlete_info(athlete_id = athlete_id) # bio
#> $Bio
#> # A tibble: 1 × 16
#> id uid guid first_name last_name full_name display_name short_name
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 5311737 s:40~l:5… e082… Carley Barrett Carley B… Carley Barr… C. Barrett
#> # ℹ 8 more variables: height <dbl>, display_height <chr>, jersey <chr>,
#> # active <lgl>, headshot_href <chr>, birth_city <chr>, birth_state <chr>,
#> # birth_country <chr>
#>
#> $Team
#> # A tibble: 1 × 1
#> x_ref
#> <chr>
#> 1 http://sports.core.api.espn.com/v2/sports/basketball/leagues/womens-college-b…
#>
#> $Position
#> # A tibble: 1 × 5
#> id name display_name abbreviation leaf
#> <chr> <chr> <chr> <chr> <lgl>
#> 1 3 Guard Guard G FALSE
#>
#> $Status
#> # A tibble: 1 × 4
#> id name type abbreviation
#> <chr> <chr> <chr> <chr>
#> 1 1 Active active Active
#>
#> $College
#> # A tibble: 0 × 0
#>
#> $Draft
#> # A tibble: 0 × 0
espn_wbb_athlete_gamelog(athlete_id = athlete_id, season = most_recent_wbb_season())
#> # A tibble: 5 × 24
#> id week at_vs game_date score home_team_id away_team_id home_team_score
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 4018186… 7 vs 2025-12-… 93-62 2509 2296 93
#> 2 4018186… 6 vs 2025-12-… 80-43 2509 2168 80
#> 3 4018186… 6 vs 2025-12-… 88-45 2509 288 88
#> 4 4018186… 5 vs 2025-12-… 91-49 2509 339 91
#> 5 4018186… 4 vs 2025-11-… 92-62 2509 47 92
#> # ℹ 16 more variables: away_team_score <chr>, game_result <chr>,
#> # league_name <chr>, league_abbreviation <chr>, league_short_name <chr>,
#> # team_id <chr>, team_uid <chr>, team_abbreviation <chr>, team_logo <chr>,
#> # team_is_all_star <chr>, opponent_id <chr>, opponent_uid <chr>,
#> # opponent_display_name <chr>, opponent_abbreviation <chr>,
#> # opponent_logo <chr>, event_id <chr>
espn_wbb_athlete_career_stats(athlete_id = athlete_id) # career rollup, long format
#> # A tibble: 87 × 17
#> league athlete_id stat_type_id split_id split_name split_type category_name
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 womens-… 5311737 0 0 Season season defensive
#> 2 womens-… 5311737 0 0 Season season defensive
#> 3 womens-… 5311737 0 0 Season season defensive
#> 4 womens-… 5311737 0 0 Season season defensive
#> 5 womens-… 5311737 0 0 Season season defensive
#> 6 womens-… 5311737 0 0 Season season defensive
#> 7 womens-… 5311737 0 0 Season season defensive
#> 8 womens-… 5311737 0 0 Season season defensive
#> 9 womens-… 5311737 0 0 Season season general
#> 10 womens-… 5311737 0 0 Season season general
#> # ℹ 77 more rows
#> # ℹ 10 more variables: category_display <chr>, category_short <chr>,
#> # category_abbrev <chr>, stat_name <chr>, stat_abbrev <chr>,
#> # stat_display <chr>, stat_short <chr>, description <chr>, value <dbl>,
#> # display_value <chr>That “pull the index, lift an ID, drill down” move is the single most
useful habit in the whole package. athlete_gamelog in
particular only has data for players currently on a roster –
last year’s star has moved on – so feeding it an ID you just lifted off
team_roster is how you guarantee it returns something.
Margin note: athlete_info, athlete_gamelog,
athlete_career_stats all share the athlete_
stem. A repeated stem is a namespace – a promise that
everything under it is about the same subject. Browsing by stem
(espn_wbb_athlete + Tab) is often faster than
searching.
Recipe 3: The NET rankings
The story. Box scores and the eye test only get you so far in seeding arguments. The NCAA’s NET ranking is the number that actually drives the bracket, and you want it.
This is where the new prefix earns its keep. NCAA.com data is
ncaa_wbb_.
# ncaa_wbb / NET / rankings. The name is the thing.
net <- ncaa_wbb_NET_rankings()
net |>
select(rank, team, conference, net_wins, net_losses) |>
head(25)
#> Error in `select()`:
#> ! Can't select columns that don't exist.
#> ✖ Column `team` doesn't exist.The grammar lesson: a prefix is a compressed paragraph. The moment
you see ncaa_wbb_, you know the data is
women’s-college-only and comes from the NCAA’s own systems rather than
ESPN’s. Learn what each prefix promises and a lot of the documentation
becomes redundant.
Recipe 4: Game flow
The story. A December non-conference game went down to the wire. You want the play-by-play and a sense of the swing.
Same three zoom levels as every other cookbook – the grammar is league-agnostic:
game_id <- 401276115
espn_wbb_game_all(game_id = game_id) # everything, as a named list
#> $Plays
#> # A tibble: 386 × 53
#> id sequence_number text away_score home_score scoring_play score_value
#> <chr> <chr> <chr> <int> <int> <lgl> <int>
#> 1 4012761… 101899901 Jump… 0 0 FALSE 0
#> 2 4012761… 101904901 Morg… 0 2 TRUE 2
#> 3 4012761… 101907701 Kian… 3 2 TRUE 3
#> 4 4012761… 101914901 Vale… 3 2 FALSE 2
#> 5 4012761… 101914902 Morg… 3 2 FALSE 0
#> 6 4012761… 101915701 Kour… 3 5 TRUE 3
#> 7 4012761… 101919001 Dana… 3 5 FALSE 3
#> 8 4012761… 101919002 Kian… 3 5 FALSE 0
#> 9 4012761… 101919501 Myka… 5 5 TRUE 2
#> 10 4012761… 101924601 Morg… 5 5 FALSE 2
#> # ℹ 376 more rows
#> # ℹ 46 more variables: wallclock <chr>, shooting_play <lgl>,
#> # points_attempted <int>, short_description <chr>, type_id <int>,
#> # type_text <chr>, period_number <int>, period_display_value <chr>,
#> # clock_display_value <chr>, team_id <int>, coordinate_x_raw <dbl>,
#> # coordinate_y_raw <dbl>, coordinate_x <dbl>, coordinate_y <dbl>,
#> # play_id <chr>, athlete_id_1 <int>, athlete_id_2 <int>, …
#>
#> $Team
#> # A tibble: 2 × 56
#> game_id season season_type game_date game_date_time team_id team_uid
#> <int> <int> <int> <date> <dttm> <int> <chr>
#> 1 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 97 s:40~l:54…
#> 2 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 52 s:40~l:54…
#> # ℹ 49 more variables: team_slug <chr>, team_location <chr>, team_name <chr>,
#> # team_abbreviation <chr>, team_display_name <chr>,
#> # team_short_display_name <chr>, team_color <chr>,
#> # team_alternate_color <chr>, team_logo <chr>, team_home_away <chr>,
#> # team_score <int>, team_winner <lgl>, assists <int>, blocks <int>,
#> # defensive_rebounds <int>, fast_break_points <chr>, field_goal_pct <dbl>,
#> # field_goals_made <int>, field_goals_attempted <int>, fouls <int>, …
#>
#> $Player
#> # A tibble: 25 × 54
#> game_id season season_type game_date game_date_time athlete_id
#> <int> <int> <int> <date> <dttm> <int>
#> 1 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4433426
#> 2 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4433412
#> 3 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4398604
#> 4 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4281919
#> 5 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4281190
#> 6 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4703211
#> 7 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4398591
#> 8 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4398589
#> 9 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4433094
#> 10 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4398835
#> # ℹ 15 more rows
#> # ℹ 48 more variables: athlete_display_name <chr>, team_id <int>,
#> # team_name <chr>, team_location <chr>, team_short_display_name <chr>,
#> # minutes <dbl>, field_goals_made <int>, field_goals_attempted <int>,
#> # three_point_field_goals_made <int>,
#> # three_point_field_goals_attempted <int>, free_throws_made <int>,
#> # free_throws_attempted <int>, offensive_rebounds <int>, …
espn_wbb_team_box(game_id = game_id) # team box
#> # A tibble: 2 × 56
#> game_id season season_type game_date game_date_time team_id team_uid
#> <int> <int> <int> <date> <dttm> <int> <chr>
#> 1 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 97 s:40~l:54…
#> 2 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 52 s:40~l:54…
#> # ℹ 49 more variables: team_slug <chr>, team_location <chr>, team_name <chr>,
#> # team_abbreviation <chr>, team_display_name <chr>,
#> # team_short_display_name <chr>, team_color <chr>,
#> # team_alternate_color <chr>, team_logo <chr>, team_home_away <chr>,
#> # team_score <int>, team_winner <lgl>, assists <int>, blocks <int>,
#> # defensive_rebounds <int>, fast_break_points <chr>, field_goal_pct <dbl>,
#> # field_goals_made <int>, field_goals_attempted <int>, fouls <int>, …
espn_wbb_player_box(game_id = game_id) # player box
#> # A tibble: 25 × 54
#> game_id season season_type game_date game_date_time athlete_id
#> <int> <int> <int> <date> <dttm> <int>
#> 1 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4433426
#> 2 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4433412
#> 3 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4398604
#> 4 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4281919
#> 5 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4281190
#> 6 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4703211
#> 7 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4398591
#> 8 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4398589
#> 9 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4433094
#> 10 401276115 2021 2 2021-02-21 2021-02-21 14:00:00 4398835
#> # ℹ 15 more rows
#> # ℹ 48 more variables: athlete_display_name <chr>, team_id <int>,
#> # team_name <chr>, team_location <chr>, team_short_display_name <chr>,
#> # minutes <dbl>, field_goals_made <int>, field_goals_attempted <int>,
#> # three_point_field_goals_made <int>,
#> # three_point_field_goals_attempted <int>, free_throws_made <int>,
#> # free_throws_attempted <int>, offensive_rebounds <int>, …
espn_wbb_pbp(game_id = game_id) # play-by-play
#> # A tibble: 386 × 53
#> id sequence_number text away_score home_score scoring_play score_value
#> <chr> <chr> <chr> <int> <int> <lgl> <int>
#> 1 4012761… 101899901 Jump… 0 0 FALSE 0
#> 2 4012761… 101904901 Morg… 0 2 TRUE 2
#> 3 4012761… 101907701 Kian… 3 2 TRUE 3
#> 4 4012761… 101914901 Vale… 3 2 FALSE 2
#> 5 4012761… 101914902 Morg… 3 2 FALSE 0
#> 6 4012761… 101915701 Kour… 3 5 TRUE 3
#> 7 4012761… 101919001 Dana… 3 5 FALSE 3
#> 8 4012761… 101919002 Kian… 3 5 FALSE 0
#> 9 4012761… 101919501 Myka… 5 5 TRUE 2
#> 10 4012761… 101924601 Morg… 5 5 FALSE 2
#> # ℹ 376 more rows
#> # ℹ 46 more variables: wallclock <chr>, shooting_play <lgl>,
#> # points_attempted <int>, short_description <chr>, type_id <int>,
#> # type_text <chr>, period_number <int>, period_display_value <chr>,
#> # clock_display_value <chr>, team_id <int>, coordinate_x_raw <dbl>,
#> # coordinate_y_raw <dbl>, coordinate_x <dbl>, coordinate_y <dbl>,
#> # play_id <chr>, athlete_id_1 <int>, athlete_id_2 <int>, …
# ESPN's win-probability-per-play for the same game.
espn_wbb_event_probabilities(event_id = game_id)
#> # A tibble: 200 × 10
#> event_id sequence_number play_id period clock home_win_percentage
#> <chr> <chr> <chr> <int> <chr> <dbl>
#> 1 401276115 101899901 NA NA NA 0.26
#> 2 401276115 101904901 NA NA NA 0.283
#> 3 401276115 101907701 NA NA NA 0.241
#> 4 401276115 101914901 NA NA NA 0.233
#> 5 401276115 101914902 NA NA NA 0.243
#> 6 401276115 101915701 NA NA NA 0.29
#> 7 401276115 101919001 NA NA NA 0.307
#> 8 401276115 101919002 NA NA NA 0.294
#> 9 401276115 101919501 NA NA NA 0.27
#> 10 401276115 101924601 NA NA NA 0.255
#> # ℹ 190 more rows
#> # ℹ 4 more variables: away_win_percentage <dbl>, tie_percentage <dbl>,
#> # secs_to_end_of_period <dbl>, secs_to_end_of_game <dbl>If you read the WNBA cookbook, you’ve now seen
espn_*_game_all, espn_*_team_box,
espn_*_player_box, espn_*_pbp in two leagues
with only the league token changing. That’s the payoff of a
regular grammar: a recipe learned once transfers by editing a single
word.
Recipe 5: Per-player, per-game box scores
The story. You want one player’s line from one game,
tidy and long – not the whole player_box.
# event + team + athlete -- three IDs, because you're naming one cell
# of one game. More identifiers = finer grain. Always.
espn_wbb_event_player_box(
event_id = 401276115,
team_id = 52,
athlete_id = 4398583
)
#> # A tibble: 77 × 12
#> league event_id team_id athlete_id stat_type category_name category_display
#> <chr> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 womens-… 4012761… 52 4398583 0 defensive Defensive
#> 2 womens-… 4012761… 52 4398583 0 defensive Defensive
#> 3 womens-… 4012761… 52 4398583 0 defensive Defensive
#> 4 womens-… 4012761… 52 4398583 0 defensive Defensive
#> 5 womens-… 4012761… 52 4398583 0 defensive Defensive
#> 6 womens-… 4012761… 52 4398583 0 defensive Defensive
#> 7 womens-… 4012761… 52 4398583 0 defensive Defensive
#> 8 womens-… 4012761… 52 4398583 0 general General
#> 9 womens-… 4012761… 52 4398583 0 general General
#> 10 womens-… 4012761… 52 4398583 0 general General
#> # ℹ 67 more rows
#> # ℹ 5 more variables: stat_name <chr>, stat_abbrev <chr>, stat_display <chr>,
#> # value <dbl>, display_value <chr>Recipe 6: The conference race
The story. It’s February and your conference is a dogfight. You want the standings and the structure beneath them.
College conferences are a hierarchy – divisions,
conferences, the whole D-I grouping – and ESPN models that with the
season_group family.
season <- most_recent_wbb_season()
# A grammar gotcha worth flagging: espn_wbb_standings() takes `year`,
# not `season`. Most functions say `season`, a few older ones say
# `year` -- when an argument name surprises you, ?function is the
# two-second fix.
espn_wbb_standings(year = season)
#> # A tibble: 357 × 86
#> team_id team avgpointsagainst avgpointsfor gamesbehind leaguewinpercent
#> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 41 UConn Hus… 50.4 86.8 0 1
#> 2 26 UCLA Brui… 56.4 83.9 0 1
#> 3 2579 South Car… 57.8 85.6 0 0.938
#> 4 251 Texas Lon… 55.8 83.9 0.5 0.812
#> 5 93 Murray St… 73.9 85.6 0 0.95
#> 6 163 Princeton… 60.7 72.4 0 0.857
#> 7 161 Fairleigh… 51.5 68.5 0 1
#> 8 2449 North Dak… 59.8 78.4 0 0.938
#> 9 238 Vanderbil… 65.2 84.3 4 0.812
#> 10 2217 Fairfield… 56.5 75.0 0 0.95
#> # ℹ 347 more rows
#> # ℹ 80 more variables: losses <dbl>, playoffseed <dbl>,
#> # pointdifferential <chr>, pointsagainst <dbl>, pointsfor <dbl>,
#> # streak <dbl>, winpercent <dbl>, wins <dbl>, gamesahead <chr>, total <chr>,
#> # home_avgpointsagainst <dbl>, home_avgpointsfor <dbl>,
#> # home_gamesbehind <dbl>, home_leaguewinpercent <dbl>, home_losses <dbl>,
#> # home_playoffseed <dbl>, home_pointdifferential <chr>, …
# The group tree: groups -> one group -> its teams.
espn_wbb_season_groups(season = season, season_type = 2)
#> # A tibble: 2 × 5
#> league season season_type group_id ref
#> <chr> <int> <int> <chr> <chr>
#> 1 womens-college-basketball 2026 2 50 http://sports.core.api.…
#> 2 womens-college-basketball 2026 2 51 http://sports.core.api.…
espn_wbb_season_group(group_id = 50, season = season, season_type = 2)
#> # A tibble: 1 × 15
#> league season season_type group_id uid name abbreviation short_name
#> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 womens-colleg… 2026 2 50 s:40… NCAA… NCAA Division I
#> # ℹ 7 more variables: midsize_name <chr>, is_conference <lgl>, slug <chr>,
#> # parent_ref <chr>, children_ref <chr>, teams_ref <chr>, standings_ref <chr>
espn_wbb_season_group_teams(group_id = 50, season = season, season_type = 2)
#> # A tibble: 200 × 6
#> league season season_type group_id team_id ref
#> <chr> <int> <int> <chr> <chr> <chr>
#> 1 womens-college-basketball 2026 2 50 2 http://sports.…
#> 2 womens-college-basketball 2026 2 50 5 http://sports.…
#> 3 womens-college-basketball 2026 2 50 6 http://sports.…
#> 4 womens-college-basketball 2026 2 50 8 http://sports.…
#> 5 womens-college-basketball 2026 2 50 9 http://sports.…
#> 6 womens-college-basketball 2026 2 50 12 http://sports.…
#> 7 womens-college-basketball 2026 2 50 13 http://sports.…
#> 8 womens-college-basketball 2026 2 50 16 http://sports.…
#> 9 womens-college-basketball 2026 2 50 21 http://sports.…
#> 10 womens-college-basketball 2026 2 50 23 http://sports.…
#> # ℹ 190 more rowsMargin note: season_groups (plural) is the index;
season_group (singular) is one item. That singular/plural
pair runs through the whole package –
franchises/franchise,
tournaments/tournament,
positions/position. Plural = “give me the
list,” singular = “give me this one.” It’s the most reliable two-word
rule in the grammar.
Recipe 7: March
The story. The bracket. You want the tournament.
season <- most_recent_wbb_season()
espn_wbb_tournaments() # which tournaments exist
#> # A tibble: 38 × 3
#> tournament_id ref league
#> <chr> <chr> <chr>
#> 1 3 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 2 1 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 3 39 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 4 2 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 5 4 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 6 5 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 7 6 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 8 7 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 9 8 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> 10 9 http://sports.core.api.espn.com/v2/sports/basketball/le… women…
#> # ℹ 28 more rows
espn_wbb_tournament(tournament_id = 3) # one tournament
#> # A tibble: 1 × 4
#> tournament_id display_name seasons_ref league
#> <chr> <chr> <chr> <chr>
#> 1 3 Atlantic Coast Conf Tournament http://sports.core.api.es… women…
espn_wbb_tournament_seasons(tournament_id = 3) # its yearly editions
#> # A tibble: 11 × 4
#> league tournament_id season ref
#> <chr> <chr> <int> <chr>
#> 1 womens-college-basketball 3 2010 http://sports.core.api.espn.c…
#> 2 womens-college-basketball 3 2011 http://sports.core.api.espn.c…
#> 3 womens-college-basketball 3 2014 http://sports.core.api.espn.c…
#> 4 womens-college-basketball 3 2015 http://sports.core.api.espn.c…
#> 5 womens-college-basketball 3 2016 http://sports.core.api.espn.c…
#> 6 womens-college-basketball 3 2017 http://sports.core.api.espn.c…
#> 7 womens-college-basketball 3 2018 http://sports.core.api.espn.c…
#> 8 womens-college-basketball 3 2019 http://sports.core.api.espn.c…
#> 9 womens-college-basketball 3 2020 http://sports.core.api.espn.c…
#> 10 womens-college-basketball 3 2022 http://sports.core.api.espn.c…
#> 11 womens-college-basketball 3 2023 http://sports.core.api.espn.c…
espn_wbb_tournament_season(tournament_id = 3, season = season)
#> # A tibble: 1 × 8
#> league tournament_id season display_name short_display_name number_of_rounds
#> <chr> <chr> <int> <chr> <chr> <int>
#> 1 womens-… 3 2026 ACC Tournam… ACC 0
#> # ℹ 2 more variables: season_ref <chr>, bracketology_ref <chr>Same singular/plural rule as Recipe 6, same general-to-specific march
from tournaments down to one
tournament_season. By now you should be able to
predict this family before reading it – which is the whole
point of the cookbook.
Recipe 8: A whole season, in bulk
The story. You’re done with single games. You want every women’s college game of a season to model on.
Looping espn_wbb_pbp() over thousands of games works but
is slow and rude to ESPN. The load_ prefix exists for
exactly this:
# load / wbb / pbp -- one call, one season.
pbp <- load_wbb_pbp(seasons = 2024)
load_wbb_player_box(seasons = 2024)
load_wbb_team_box(seasons = 2024)
load_wbb_schedule(seasons = 2024)
load_wbb_shots(seasons = 2024) # shot locations, ready to chartAnd straight into a database, with the
(seasons, dbConnection, tablename) plumbing every
load_ function shares:
library(DBI)
con <- dbConnect(RSQLite::SQLite(), "wbb.sqlite")
load_wbb_pbp(seasons = 2020:2024, dbConnection = con, tablename = "wbb_pbp")
dbDisconnect(con)Margin note: consistency is a feature. Every load_
function shares the same signature, so learning one teaches you all of
them – guess the arguments the same way you guess the names.
Working through a proxy
Campus networks love a proxy. wehoop handles them in
three layers – reach for the least invasive one that works.
Layer 1 – set it once for the session. Covers everything: ESPN, NCAA, loaders.
options(wehoop.proxy = "http://proxy.university.edu:8080")
# Authenticated proxy? Pass a list instead of a string.
options(wehoop.proxy = list(
url = "http://proxy.university.edu",
port = 8080,
username = "netid",
password = Sys.getenv("PROXY_PASS")
))
# Then just work -- every call inherits it.
espn_wbb_team_roster(team_id = 2509, season = most_recent_wbb_season())
ncaa_wbb_NET_rankings()Layer 2 – environment variables. For shared scripts and CI, where the proxy belongs to the machine and not the analysis, set the standard variables and let libcurl pick them up:
Sys.setenv(
http_proxy = "http://proxy.university.edu:8080",
https_proxy = "http://proxy.university.edu:8080",
no_proxy = "localhost,127.0.0.1"
)A grammar-flavored note on what doesn’t take a per-call
proxy = argument: the espn_*,
ncaa_wbb_*, and load_* functions call the HTTP
layer directly, so they read the proxy from the session option or the
environment – not from an argument. (In the WNBA cookbook, the
wnba_* Stats API functions do accept per-call
proxy =, because they thread ... through.
Women’s college basketball has no equivalent Stats-API surface, so for
wbb you’ll use Layer 1 or Layer 2 every time.) Once again,
the prefix tells you the capability before you go looking.
Where to go next
The women’s college game has more teams, a deeper conference
hierarchy, and the ncaa_wbb_ prefix – but the grammar is
identical to every other league in this family. You decided where the
data lived, you named the league, you named the thing from general to
specific, and you let the singular/plural rule and the shared stems
carry you the rest of the way.
wehoop is the women’s-game sibling of
hoopR, and the two share this grammar bone for bone.
espn_wbb_team_roster has a mirror image in
hoopR’s espn_mbb_team_roster; a recipe you
learn in one package crosses straight into the other. Four leagues, one
grammar – and now it’s yours.