Women's College Basketball Visualizations with wehoop and sdvplotR

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Division I women’s basketball has more than 360 teams, and each shares its ESPN id with the school’s men’s team. These ten examples chart the 2025-26 season: one id for two programs, the efficiency landscape of the best teams, a conference table, conference strength with conference logos, a headshot leaderboard, a shot chart on a college court, a ranked top 25, seed-line tiers, the champion’s tournament run and a conference race as a bump chart, then the efficiency landscape as an interactive chart. The data are wehoop’s ESPN release files on GitHub (load_wbb_*()): box scores, standings, schedules, player box scores and shots. Nothing here calls stats.ncaa.org.

Setup

library(sdvplotR)
library(ggplot2)
library(dplyr)
library(tidyr)
library(gt)

season <- 2026 # the 2025-26 season: wehoop names a season by the year it ends
label <- "2025-26"
source_note <- "Data: wehoop (ESPN) | Viz: sdvplotR"

theme_set(
  theme_minimal(base_size = 12) +
    theme(
      plot.title = element_text(face = "bold"),
      plot.title.position = "plot",
      plot.caption = element_text(color = "grey40", size = 8),
      plot.caption.position = "plot",
      panel.grid.minor = element_blank()
    )
)

1. One school id, two programs

ESPN gives a school one team id for its men’s and women’s teams. sdvplotR keeps them as separate leagues, so the league argument picks the program: 2633 is the Tennessee Volunteers in "mbb" and the Lady Volunteers in "wbb", each with its own name and mark.

wbb_teams <- sdv_teams("wbb") |>
  mutate(team_id = as.integer(team_id))
nrow(wbb_teams)
#> [1] 364

bind_rows(mutate(sdv_teams("mbb"), team_id = as.integer(team_id)), wbb_teams) |>
  filter(team_id == 2633) |>
  select(league, team_id, abbr, name)
#>   league team_id abbr                      name
#> 1    mbb    2633 TENN      Tennessee Volunteers
#> 2    wbb    2633 TENN Tennessee Lady Volunteers

tenn <- data.frame(x = c(1, 2), y = 1, team = "TENN", label = c('league = "mbb"', 'league = "wbb"'))

ggplot(tenn, aes(x, y)) +
  geom_sdv_logos(aes(team = team), data = tenn[1, ], league = "mbb", height = 0.6) +
  geom_sdv_logos(aes(team = team), data = tenn[2, ], league = "wbb", height = 0.6) +
  geom_text(aes(y = 0.55, label = label), family = "mono", size = 4) +
  scale_x_continuous(limits = c(0.4, 2.6)) +
  scale_y_continuous(limits = c(0.45, 1.4)) +
  labs(title = "One ESPN team id (2633), two marks") +
  theme_void() +
  theme(plot.title = element_text(face = "bold", hjust = 0.5))

The two Tennessee logos sdvplotR draws for ESPN team id 2633: the men's Volunteers mark for league mbb on the left and the women's Lady Volunteers mark for league wbb on the right.

Names and ESPN ids both resolve; a nickname alone does not:

sdv_resolve(
  c("Tennessee", "Tennessee Lady Volunteers", "2633"),
  league = "wbb"
)
#> [1] "2633" "2633" "2633"

2. The efficiency landscape of the top 36

Box scores are enough for a simple rating: points scored and allowed per 100 possessions, counting only regular-season games between two Division I teams, with possessions estimated as FGA - OREB + TOV + 0.475 x FTA (the usual college weight on free throws) and averaged with the opponent’s. It is a raw rating, not adjusted for schedule. Each team’s conference comes from the season’s standings; the full conference name is matched to the conference row’s own key ("American Conference", not "American", which is American University).

standings <- wehoop::load_wbb_standings(seasons = season)
conf_rows <- sdv_teams("wbb", include_conferences = TRUE) |>
  filter(program == "conference")
conferences <- standings |>
  distinct(team_id, conference = group_short_name, group_name) |>
  mutate(conference_key = conf_rows$team_id[match(group_name, conf_rows$name)]) |>
  select(-group_name)
stopifnot(!anyDuplicated(conferences$team_id))

d1 <- wbb_teams$team_id
box <- wehoop::load_wbb_team_box(seasons = season) |>
  filter(season_type == 2, team_id %in% d1, opponent_team_id %in% d1) |>
  mutate(
    poss = field_goals_attempted - offensive_rebounds +
      coalesce(total_turnovers, turnovers) + 0.475 * free_throws_attempted
  )
box <- box |>
  inner_join(
    box |> select(game_id, opponent_team_id = team_id, opp_poss = poss),
    by = c("game_id", "opponent_team_id")
  ) |>
  mutate(game_poss = (poss + opp_poss) / 2, margin = team_score - opponent_team_score)

ratings <- box |>
  group_by(team_id) |>
  summarise(
    games = n(),
    ortg = 100 * sum(team_score) / sum(game_poss),
    drtg = 100 * sum(opponent_team_score) / sum(game_poss),
    .groups = "drop"
  ) |>
  mutate(net = ortg - drtg) |>
  inner_join(select(wbb_teams, team_id, team = abbr, team_name = name), by = "team_id") |>
  left_join(conferences, by = "team_id") |>
  arrange(desc(net), team)
nrow(ratings)
#> [1] 363

One way to keep logos legible is to zoom to the teams you care about: here the axes hold only the 36 best teams by net rating, so every logo gets room. The subtitle gives the Division I averages, far below and left of every logo. Defense is points allowed, so its axis is reversed to put good defenses on top.

top36 <- head(ratings, 36)

ggplot(top36, aes(ortg, drtg)) +
  geom_sdv_logos(aes(team = team), league = "wbb", width = 0.05) +
  scale_y_reverse(expand = expansion(mult = 0.08)) +
  scale_x_continuous(expand = expansion(mult = 0.06)) +
  labs(
    title = paste("The efficiency landscape of the top 36,", label),
    subtitle = sprintf(
      "Division I averages: %.1f offense, %.1f defense; games between Division I teams only",
      mean(ratings$ortg), mean(ratings$drtg)
    ),
    x = "Offensive rating (points per 100 possessions)",
    y = "Defensive rating (allowed per 100)",
    caption = source_note
  )

The 36 best Division I women's basketball teams of 2025-26 by net rating drawn as logos, placed by offensive rating (horizontal) and defensive rating (vertical, reversed so better defenses are higher).

3. A conference table

The SEC’s 2025-26 standings, with home and road records and the record against AP-ranked teams. College standings repeat every stat for each split (home, road, conference, against ranked teams), so the unique key to pivot on is stat_type. gt_color_pills() with a fixed domain keeps the margin colors comparable from one table to the next.

standings |>
  filter(group_short_name == "SEC") |>
  select(team_id, stat_type, display_value) |>
  pivot_wider(names_from = stat_type, values_from = display_value) |>
  inner_join(select(ratings, team_id, team, team_name), by = "team_id") |>
  mutate(
    seed = as.integer(playoffseed),
    logo = team,
    margin = as.numeric(avgpointsfor) - as.numeric(avgpointsagainst)
  ) |>
  arrange(seed, team) |>
  select(seed, logo, team_name, total, vsconf, home, road, vsaprankedteams, margin) |>
  gt() |>
  tab_header(
    title = paste("SEC standings,", label),
    subtitle = "Margin is points per game over the whole season"
  ) |>
  cols_label(
    seed = "", logo = "", team_name = "Team", total = "Overall", vsconf = "Conf",
    home = "Home", road = "Road", vsaprankedteams = "vs AP 25", margin = "Margin"
  ) |>
  tab_source_note(source_note) |>
  gt_sdv_logos(columns = logo, league = "wbb", height = 26) |>
  gt_color_pills(margin, digits = 1, domain = c(-40, 40)) |>
  gt_theme_athletic() |>
  cols_align("left", columns = team_name)

	Team	Overall	Conf	Home	Road	vs AP 25	Margin
SEC standings, 2025-26
Margin is points per game over the whole season
1	South Carolina Gamecocks	36-4	15-1	18-0	11-1	14-4	27.8
2	Vanderbilt Commodores	29-5	13-3	18-0	7-2	7-4	19.1
3	Texas Longhorns	35-4	13-3	20-0	7-3	16-4	28.1
4	LSU Tigers	29-6	12-4	15-2	10-2	6-6	33.9
5	Oklahoma Sooners	26-8	11-5	16-2	6-3	6-8	17.3
6	Tennessee Lady Volunteers	16-14	8-8	9-4	7-6	2-10	6.8
7	Kentucky Wildcats	25-11	8-8	12-3	9-5	6-8	14.0
8	Ole Miss Rebels	24-12	8-8	12-2	4-6	5-8	13.1
9	Georgia Lady Bulldogs	22-10	8-8	12-4	6-4	3-8	11.5
10	Texas A&M Aggies	14-13	7-9	8-6	4-5	3-7	-2.9
11	Alabama Crimson Tide	24-11	7-9	16-4	3-6	3-9	8.4
12	Florida Gators	18-15	5-11	12-5	2-9	1-10	6.4
13	Mississippi State Bulldogs	18-13	5-11	14-5	3-7	2-8	7.7
14	Missouri Tigers	17-17	4-12	9-8	4-7	0-9	-4.1
15	Auburn Tigers	15-17	3-13	9-7	3-8	1-10	-3.6
16	Arkansas Razorbacks	12-20	1-15	9-9	1-10	0-9	-5.6
Data: wehoop (ESPN) \| Viz: sdvplotR

4. Conference strength, with conference logos

Conferences are rows in the team index too, with logos and colors, so a chart of conferences works the same way as a chart of teams: scale_y_sdv() draws the conference marks on the axis and each bar is filled in the conference’s color from sdv_teams(). Each bar is the average net rating of the conference’s teams.

ratings |>
  filter(!is.na(conference_key)) |>
  group_by(conference_key) |>
  summarise(teams = n(), net = mean(net)) |>
  arrange(desc(net), conference_key) |>
  mutate(conference_key = factor(conference_key, levels = rev(conference_key))) |>
  ggplot(aes(net, conference_key, fill = conference_key)) +
  geom_col(width = 0.75, color = "black", linewidth = 0.2) +
  geom_vline(xintercept = 0, linewidth = 0.4) +
  geom_text(
    aes(label = sprintf("%+.1f", net), hjust = if_else(net > 0, -0.15, 1.15)),
    size = 3.2
  ) +
  scale_fill_manual(values = setNames(conf_rows$color_primary, conf_rows$team_id)) +
  scale_y_sdv(league = "wbb", size = 18) +
  scale_x_continuous(expand = expansion(mult = 0.12)) +
  labs(
    title = paste("Conference strength,", label),
    subtitle = "Average net rating per 100 possessions of each conference's teams",
    x = "Average net rating",
    y = NULL,
    caption = source_note
  ) +
  theme_y_sdv() +
  theme(legend.position = "none", panel.grid.major.y = element_blank())

Horizontal bar chart of the 31 Division I women's basketball conferences of 2025-26 by the average net rating of their teams, strongest at the top, each bar in the conference's color with the conference logo on the vertical axis.

5. A scoring leaderboard with headshots

Player box scores carry ESPN athlete ids: gt_sdv_headshots() turns them into headshots and gt_sdv_logos() the team abbreviations into logos. A light theme keeps dark logos readable. Only Division I teams count, a player’s team is the one she played for last, and leaders need at least 20 games.

players <- wehoop::load_wbb_player_box(seasons = season) |>
  filter(season_type == 2, !did_not_play, team_id %in% d1)

leaders <- players |>
  arrange(game_date) |>
  group_by(athlete_id, athlete_display_name) |>
  summarise(
    games = n(),
    team_id = last(team_id),
    headshot_url = last(athlete_headshot_href),
    ppg = mean(points, na.rm = TRUE),
    fg_pct = sum(field_goals_made, na.rm = TRUE) / sum(field_goals_attempted, na.rm = TRUE),
    fg3_pct = sum(three_point_field_goals_made, na.rm = TRUE) /
      sum(three_point_field_goals_attempted, na.rm = TRUE),
    .groups = "drop"
  ) |>
  filter(games >= 20) |>
  arrange(desc(ppg), athlete_id) |>
  head(10) |>
  inner_join(select(ratings, team_id, team, team_name), by = "team_id")

leaders |>
  mutate(rank = row_number(), headshot = athlete_id, logo = team) |>
  select(rank, headshot, athlete_display_name, logo, team_name, games, ppg, fg_pct, fg3_pct) |>
  gt() |>
  tab_header(
    title = paste("Division I scoring leaders,", label),
    subtitle = "Regular season, 20 or more games"
  ) |>
  cols_label(
    rank = "", headshot = "", athlete_display_name = "Player", logo = "",
    team_name = "Team", games = "GP", ppg = "PPG", fg_pct = "FG%", fg3_pct = "3P%"
  ) |>
  fmt_number(ppg, decimals = 1) |>
  fmt_percent(c(fg_pct, fg3_pct), decimals = 1) |>
  tab_source_note(source_note) |>
  gt_sdv_headshots(columns = headshot, league = "wbb", height = 42) |>
  gt_sdv_logos(columns = logo, league = "wbb", height = 28) |>
  gt_theme_sofa()

	Player	Team	GP	PPG	FG%	3P%
Division I scoring leaders, 2025-26
Regular season, 20 or more games
1	Mikayla Blakes	Vanderbilt Commodores	31	27.0	45.8%	36.1%
2	Audi Crooks	Iowa State Cyclones	30	25.5	64.7%	9.1%
3	Hannah Hidalgo	Notre Dame Fighting Irish	32	25.2	48.1%	25.0%
4	Mia Nicastro	Western Illinois Leathernecks	31	24.2	52.2%	38.8%
5	Hannah Wickstrom	UC Riverside Highlanders	29	23.4	48.5%	34.0%
6	Shay Ciezki	Indiana Hoosiers	31	22.8	52.6%	44.6%
7	Jaloni Cambridge	Ohio State Buckeyes	33	22.8	49.0%	34.6%
8	Adrianna Smith	Maine Black Bears	32	22.8	45.1%	33.3%
9	Liv McGill	Florida Gators	31	22.5	43.1%	26.4%
10	Halli Poock	Murray State Racers	34	22.4	43.6%	34.8%
Data: wehoop (ESPN) \| Viz: sdvplotR

6. A shot chart on a college court, with a headshot in the title

load_wbb_shots() holds ESPN’s shot locations in feet from center court, which wehoop has already converted to the frame sdv_surface()’s sportyR court draws, so they need no conversion (sdv_court_coords() is only for the stats-API legacy frame). Two cleanups first: free throws are placed at the rim, and a shot with no location carries a placeholder of about ±215 million. Then fold both halves onto one basket. ggtitle_image() puts the scorer’s headshot beside the title; it takes an image URL as well as a team. The leader plays for Vanderbilt, whose black and gold match the painted area and the wood, so the shots are drawn in white and red rather than team colors.

star <- slice(leaders, 1)

shots <- wehoop::load_wbb_shots(seasons = season) |>
  filter(
    athlete_id_1 == star$athlete_id,
    !grepl("FreeThrow", type_text),
    abs(coordinate_x) <= 47, abs(coordinate_y) <= 25
  ) |>
  mutate(
    right = coordinate_x > 0,
    x = if_else(right, -coordinate_x, coordinate_x),
    y = if_else(right, -coordinate_y, coordinate_y)
  )

sdv_surface("wbb", star$team, display_range = "defense") +
  geom_point(
    aes(x, y),
    data = filter(shots, !scoring_play),
    shape = 4, color = "#D7191C", size = 1.7, stroke = 0.9
  ) +
  geom_point(
    aes(x, y),
    data = filter(shots, scoring_play),
    shape = 21, fill = "white", color = "black", stroke = 0.4, size = 2
  ) +
  ggtitle_image(
    title_image = star$headshot_url,
    title = paste0("**", star$athlete_display_name, "**, ", star$team_name, ", ", label),
    image_height = 48,
    subtitle = sprintf(
      "Every field goal attempt: %d made (white), %d missed (red crosses), %.1f%%",
      sum(shots$scoring_play), sum(!shots$scoring_play), 100 * mean(shots$scoring_play)
    ),
    league = "wbb"
  ) +
  labs(caption = source_note) +
  theme_title_image(size = 14)

Half-court shot chart of the 2025-26 Division I women's scoring leader on a college court painted in her team's colors, made shots as white circles and missed shots as red crosses, with her headshot beside the title.

7. A ranked top 25, logos on the axis

The top 25 by net rating, as columns. scale_x_sdv() swaps the team abbreviations on a discrete horizontal axis for logos; the factor order sets the bar order.

head(ratings, 25) |>
  mutate(team = factor(team, levels = team)) |>
  ggplot(aes(team, net, fill = team)) +
  geom_col(width = 0.75, color = "black", linewidth = 0.2) +
  geom_text(aes(label = sprintf("%.1f", net)), vjust = -0.4, size = 3) +
  scale_fill_sdv(league = "wbb") +
  scale_x_sdv(league = "wbb", size = 18) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.08))) +
  labs(
    title = paste("The top 25 by net rating,", label),
    subtitle = "Points per 100 possessions, regular-season games between Division I teams",
    x = NULL,
    y = "Net rating",
    caption = source_note
  ) +
  theme_x_sdv() +
  theme(legend.position = "none", panel.grid.major.x = element_blank())

Column chart of the 25 best Division I women's basketball teams of 2025-26 by net rating, best on the left, each column in the team's color with its logo under it on the horizontal axis.

8. Tiers as seed lines

The NCAA tournament seeds four teams to a line, so seed lines make natural tiers: the top four by net rating on the 1 line, the next four on the 2 line, and so on. This is a seed list from one rating, not the committee’s bracket. On the Tiermaker’s dark background each team draws its dark-background logo, so West Virginia’s navy mark comes out white.

tiers <- head(ratings, 16) |>
  mutate(tier_no = (row_number() - 1) %/% 4 + 1) |>
  select(tier_no, team)

sdv_team_tiers(
  tiers,
  league = "wbb",
  title = paste("Seed lines by net rating,", label),
  subtitle = "Four teams to a line, best first; not the selection committee's bracket",
  caption = source_note,
  tier_desc = c("1" = "1 seeds", "2" = "2 seeds", "3" = "3 seeds", "4" = "4 seeds"),
  alpha = 1,
  width = 0.1
)

The 16 best Division I women's basketball teams of 2025-26 by net rating as logos in four tiers of four, labeled as the 1 to 4 seed lines.

9. March: the champion’s run

The schedule’s notes_headline names each NCAA tournament game. Find the title game’s winner, then chart its six wins: the margin of each, the opponent’s logo on top, and the champion’s logo beside the title through ggtitle_image(), which takes a team abbreviation as well as a URL.

schedule <- wehoop::load_wbb_schedule(seasons = season)
ncaa <- schedule |>
  filter(grepl("^NCAA Women's Basketball Championship", notes_headline))
final <- filter(ncaa, grepl("National Championship", notes_headline))
champion_id <- if_else(final$home_winner, final$home_id, final$away_id)
champion <- wbb_teams$abbr[wbb_teams$team_id == champion_id]

run <- ncaa |>
  filter(home_id == champion_id | away_id == champion_id) |>
  arrange(game_date) |>
  transmute(
    round = sub("National Championship", "Title game", sub(".* - ", "", notes_headline)),
    round = factor(round, levels = unique(round)),
    opponent_id = if_else(home_id == champion_id, away_id, home_id),
    margin = if_else(home_id == champion_id, home_score - away_score, away_score - home_score)
  ) |>
  inner_join(select(wbb_teams, opponent_id = team_id, opponent = abbr), by = "opponent_id")

ggplot(run, aes(round, margin)) +
  geom_col(fill = sdv_team_colors(champion, league = "wbb"), width = 0.65) +
  geom_text(aes(y = margin / 2, label = paste0("+", margin)), color = "white", fontface = "bold", size = 5) +
  geom_sdv_logos(aes(y = margin + 6, team = opponent), league = "wbb", height = 0.13) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.05)), limits = c(0, max(run$margin) + 11)) +
  ggtitle_image(
    title_image = champion,
    title = paste0("**The national champion's run, ", season, "**"),
    image_height = 40,
    subtitle = "Winning margin in each NCAA tournament game, opponent's logo on top",
    league = "wbb"
  ) +
  labs(x = NULL, y = "Winning margin (points)", caption = source_note) +
  theme_title_image(size = 15) +
  theme(panel.grid.major.x = element_blank())

Column chart of the 2026 women's national champion's six NCAA tournament wins in order, each column the winning margin in the champion's color with the opponent's logo above it, and the champion's logo beside the title.

10. The SEC race, week by week

A conference race as a bump chart: rank the SEC’s teams by conference win percentage at the end of each week, then draw one line per team in its color with its logo at the finish. The schedule’s conference_competition flag marks conference games, and a game with a notes_headline is a conference-tournament game, so it is left out. Ties are broken by conference point differential, then by abbreviation, so the same data always draw the same chart.

conference_games <- schedule |>
  filter(season_type == 2, conference_competition, is.na(notes_headline) | notes_headline == "") |>
  distinct(game_id)
sec <- filter(ratings, conference == "SEC")

weekly <- box |>
  semi_join(conference_games, by = "game_id") |>
  filter(team_id %in% sec$team_id) |>
  mutate(week = as.Date(cut(game_date, "week", start.on.monday = TRUE))) |>
  group_by(team_id, week) |>
  summarise(wins = sum(team_winner), games = n(), margin = sum(margin), .groups = "drop") |>
  complete(team_id, week, fill = list(wins = 0, games = 0, margin = 0)) |>
  arrange(team_id, week) |>
  group_by(team_id) |>
  mutate(across(c(wins, games, margin), cumsum)) |>
  ungroup() |>
  inner_join(select(sec, team_id, team), by = "team_id") |>
  filter(week >= sort(unique(week))[3]) |> # the first two weeks hold a game or two
  arrange(week, desc(wins / pmax(games, 1)), desc(margin), team) |>
  group_by(week) |>
  mutate(rank = row_number()) |>
  ungroup()
finish <- filter(weekly, week == max(week))

ggplot(weekly, aes(week, rank)) +
  geom_line(aes(group = team), color = "black", linewidth = 1.6) +
  geom_line(aes(color = team), linewidth = 1) +
  geom_sdv_logos(aes(x = week + 4, team = team), data = finish, league = "wbb", width = 0.04) +
  scale_color_sdv(league = "wbb") +
  scale_y_reverse(breaks = 1:16) +
  scale_x_date(date_labels = "%b %d", expand = expansion(add = c(1, 7))) +
  labs(
    title = paste("The SEC race,", label),
    subtitle = "Rank by conference win percentage at the end of each week",
    x = NULL,
    y = "SEC rank",
    caption = source_note
  ) +
  theme(legend.position = "none", panel.grid.major.x = element_blank())

Bump chart of the 16 SEC teams' weekly rank by conference win percentage through the 2025-26 regular season, one line per team in its color with its logo at its final rank.

11. Make it interactive

Section 2’s efficiency landscape, built with native plotly. A transparent marker under each team carries the hover text, and add_sdv_logos() places the logos as layout images at the same coordinates. Hover a logo for the school, its conference, its games against Division I teams and its three ratings; that helps most where logos sit close together, and you can drag a box to zoom in on them.

top36_hover <- top36 |>
  mutate(tip = sprintf(
    "<b>%s</b> (%s)<br>%d games against Division I teams<br>Offense %.1f, defense %.1f<br>Net %+.1f per 100 possessions",
    team_name, coalesce(conference, "conference unknown"), games, ortg, drtg, net
  ))

pad <- 2

plotly::plot_ly(
  top36_hover, x = ~ortg, y = ~drtg, type = "scatter", mode = "markers",
  marker = list(size = 26, opacity = 0), text = ~tip, hoverinfo = "text"
) |>
  plotly::layout(
    title = list(text = paste("The efficiency landscape of the top 36,", label), x = 0.02),
    xaxis = list(title = "Offensive rating (points per 100 possessions)", range = range(top36$ortg) + c(-pad, pad)),
    yaxis = list(title = "Defensive rating (allowed per 100)", range = rev(range(top36$drtg) + c(-pad, pad)))
  ) |>
  add_sdv_logos(top36_hover$ortg, top36_hover$drtg, top36_hover$team, "wbb", size = 0.07)

A range given high to low reverses the axis, so good defenses sit at the top. plotly loads each logo from its URL, so the page stays light; the men’s college basketball article uses the same approach to make all 360 teams hoverable. Interactive charts covers plotly, ggiraph and leaflet.

Our Authors

Citation

To cite the sdvplotR R package in publications, use:

BibTeX Citation

@misc{gilani_carl_lee_kay_weatherman_sdvplotR,
  author = {Gilani, Saiem and Carl, Sebastian and Lee, Jared and Kay, Camden and Weatherman, Andrew},
  title = {sdvplotR: The SportsDataverse's R Package for Multi-League Sports Plotting.},
  url = {https://sdvplotR.sportsdataverse.org},
  year = {2026}
}

sdvplot - the Python plotting package
sdvplot-js - the JavaScript / TypeScript plotting packages
cfbfastR - college football
hoopR - men’s basketball
wehoop - women’s basketball
baseballr - baseball
fastRhockey - hockey
oddsapiR - betting odds
sportyR - playing surfaces
sportsdataverse-py - the Python package
sportsdataverse-R - the R meta-package

Saiem Gilani