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WNBA Visualizations with wehoop and sdvplotR

Saiem Gilani
@saiemgilani @saiemgilani

Source: vignettes/wnba-viz.Rmd
wnba-viz.Rmd
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Ten charts and tables from the 2026 WNBA regular season, the league’s first with 15 teams: an offense-vs-defense quadrant, the expansion teams’ win races, net-rating bars with logos on the axis, tiers, a scoring leaderboard with headshots as axis labels, a binned shot chart, a standings table, running point differential by conference, game-by-game margins and a season-long bump chart, then the scoring leaders as an interactive chart. Every number comes from wehoop’s ESPN release files on GitHub (load_wnba_*()), so nothing here calls stats.wnba.com.

Setup 

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

season <- 2026
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()
    )
)

The team box score has one row per team per game; season_type 2 is the regular season. 2025 is loaded too, for Golden State’s first season. ESPN files each All-Star Game as a regular-season game between two teams that aren’t WNBA clubs, so an inner join on the ESPN team id to sdv_teams("wnba") drops them, and adds each team’s sdvplotR abbreviation and conference.

wnba_teams <- sdv_teams("wnba") |>
  transmute(team_id = as.integer(team_id), team = abbr, conference = sub(" Conference$", "", conference))

box_all <- wehoop::load_wnba_team_box(seasons = c(season - 1, season)) |>
  filter(season_type == 2) |>
  inner_join(wnba_teams, by = "team_id")
box <- filter(box_all, season == !!season)

count(box, team, name = "games") |>
  count(games, name = "teams")
#> # A tibble: 2 × 2
#>   games teams
#>   <int> <int>
#> 1    44    13
#> 2    45     2

The Commissioner’s Cup final is filed as a regular-season game as well, which is why two teams show 45 games. It stays in the box scores (it was played) but does not count in the standings in example 7.

1. Offense vs defense, with logos

Points scored and allowed per 100 possessions, with possessions estimated from the box score (FGA - OREB + TOV + 0.44 x FTA) and averaged with the opponent’s estimate for the same game. Defensive rating is points allowed, so its axis is reversed to put good defenses on top.

games <- box |>
  mutate(
    poss = field_goals_attempted - offensive_rebounds +
      coalesce(total_turnovers, turnovers) + 0.44 * free_throws_attempted
  )
games <- games |>
  inner_join(
    games |> 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)

ratings <- games |>
  group_by(team, conference) |>
  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) |>
  arrange(desc(net), team)
stopifnot(nrow(ratings) == 15, !anyNA(ratings$net))
x_lim <- range(ratings$ortg) + c(-1.5, 1.5)
y_lim <- range(ratings$drtg) + c(-2, 1.5)

ggplot(ratings, aes(ortg, drtg)) +
  geom_mean_lines(
    aes(x0 = ortg, y0 = drtg),
    color = "grey60", linetype = "dashed", linewidth = 0.4
  ) +
  geom_sdv_logos(aes(team = team), league = "wnba", width = 0.075) +
  annotate(
    "text",
    x = x_lim[c(2, 1, 2, 1)], y = y_lim[c(1, 1, 2, 2)],
    hjust = c(1, 0, 1, 0), vjust = c(1.3, 1.3, -0.4, -0.4),
    label = c("Good offense, good defense", "Defense first", "Offense first", "Struggling"),
    color = "grey45", fontface = "italic", size = 3.5
  ) +
  scale_x_continuous(expand = expansion(0)) +
  scale_y_reverse(expand = expansion(0)) +
  expand_limits(x = x_lim, y = y_lim) +
  labs(
    title = paste("WNBA offense vs defense,", season, "regular season"),
    x = "Offensive rating (points per 100 possessions)",
    y = "Defensive rating (points allowed per 100)",
    caption = source_note
  )

WNBA teams of the 2026 regular season drawn as their logos, placed by offensive rating (horizontal) and defensive rating (vertical, reversed so better defenses are higher), with dashed league-average lines and a label in each corner.

2. The expansion teams, game by game

Golden State joined in 2025, Portland and Toronto in 2026. Cumulative wins by game number put all four seasons on one chart; the dashed line is a .500 pace. Portland’s primary color is a pale ice blue, so every line is drawn over a black one that outlines it on white.

runs <- box_all |>
  filter(team == "GS" | (team %in% c("POR", "TOR") & season == 2026)) |>
  arrange(season, team, game_date) |>
  group_by(season, team) |>
  mutate(game_no = row_number(), wins = cumsum(team_winner)) |>
  ungroup() |>
  mutate(run = paste(team, season))

ends <- runs |>
  group_by(run) |>
  slice_max(game_no, n = 1) |>
  ungroup()

ggplot(runs, aes(game_no, wins, group = run)) +
  annotate("segment", x = 0, y = 0, xend = 44, yend = 22, color = "grey55", linetype = "dashed") +
  geom_line(color = "black", linewidth = 2.4) +
  geom_line(aes(color = team), linewidth = 1.4) +
  geom_sdv_logos(aes(x = game_no + 2, team = team), data = ends, league = "wnba", width = 0.045) +
  geom_text(
    aes(x = game_no + 3.6, label = sprintf("%s: %d-%d", season, wins, game_no - wins)),
    data = ends, hjust = 0, size = 3.6
  ) +
  scale_color_sdv(league = "wnba") +
  scale_x_continuous(breaks = seq(0, 44, 11), expand = expansion(add = c(0.5, 10))) +
  labs(
    title = "The WNBA's expansion teams, game by game",
    subtitle = "Cumulative wins; the dashed line is a .500 pace",
    x = "Game of the season",
    y = "Wins",
    caption = source_note
  ) +
  theme(legend.position = "none")

Cumulative wins by game number for the WNBA's expansion teams: Golden State in 2025 and 2026, Portland and Toronto in 2026, each line in the team's color with a black outline, a logo and the final record at its end, and a dashed .500 pace line.

3. Net rating, with logos on the axis

scale_y_sdv() turns a discrete vertical axis of abbreviations into logos and theme_y_sdv() lets the axis draw them. The factor order sets the bar order: here the best net rating sits on top.

ratings |>
  mutate(team = factor(team, levels = rev(team))) |>
  ggplot(aes(net, team, fill = team)) +
  geom_col(width = 0.72, 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 = 3.4
  ) +
  scale_fill_sdv(league = "wnba") +
  scale_y_sdv(league = "wnba", size = 18) +
  scale_x_continuous(expand = expansion(mult = 0.12)) +
  labs(
    title = paste("WNBA net rating,", season, "regular season"),
    x = "Net rating (points per 100 possessions)",
    y = NULL,
    caption = source_note
  ) +
  theme_y_sdv() +
  theme(legend.position = "none", panel.grid.major.y = element_blank())

Horizontal bar chart of WNBA net rating per 100 possessions in 2026, best at the top, each bar in the team's color with a thin black outline and the team's logo on the vertical axis.

4. Team tiers from net rating

sdv_team_tiers() draws a tier list from tier_no and team. The tiers here are cut from the net ratings above, so they are the season’s numbers. Its default background is dark, where each team draws its dark-background logo (Toronto’s plum mark turns light blue), and alpha = 1 draws the logos at full strength.

tiers <- ratings |>
  mutate(tier_no = 5L - cut(net, c(-Inf, -5, 0, 5, Inf), labels = FALSE, right = FALSE)) |>
  arrange(tier_no, desc(net)) |>
  select(tier_no, team)

sdv_team_tiers(
  tiers,
  league = "wnba",
  title = paste("WNBA tiers by net rating,", season),
  subtitle = "Regular-season net rating per 100 possessions, cut at +5, 0 and -5",
  caption = source_note,
  tier_desc = c("1" = "Title contenders", "2" = "Playoff teams", "3" = "Fringe", "4" = "Rebuilding"),
  alpha = 1
)

WNBA team logos in four tiers cut from 2026 net rating per 100 possessions: Title contenders, Playoff teams, Fringe and Rebuilding.

5. Scoring leaders, with headshots as the axis labels

scale_y_sdv_headshots() draws a headshot for each ESPN athlete id on a discrete axis, the way scale_y_sdv() draws logos. A thin black edge keeps the pale team colors (Portland, New York) visible. Players need 30 games.

players <- wehoop::load_wnba_player_box(seasons = season) |>
  semi_join(distinct(box, game_id), by = "game_id") |>
  filter(!did_not_play)

scorers <- players |>
  arrange(game_date) |>
  group_by(athlete_id, athlete_display_name) |>
  summarise(games = n(), team = last(team_abbreviation), ppg = mean(points), .groups = "drop") |>
  filter(games >= 30) |>
  arrange(desc(ppg), athlete_id) |>
  head(10)
scorers |>
  mutate(athlete_id = factor(athlete_id, levels = rev(athlete_id))) |>
  ggplot(aes(ppg, athlete_id)) +
  geom_col(aes(fill = team), width = 0.7, color = "black", linewidth = 0.2) +
  geom_text(
    aes(x = ppg + 0.4, label = sprintf("%s  %.1f", athlete_display_name, ppg)),
    hjust = 0, size = 3.8
  ) +
  scale_fill_sdv(league = "wnba") +
  scale_y_sdv_headshots(league = "wnba", size = 34) +
  scale_x_continuous(limits = c(0, max(scorers$ppg) + 9), expand = expansion(0)) +
  labs(
    title = paste("WNBA scoring leaders,", season),
    subtitle = "Points per game, 30 or more games",
    x = "Points per game",
    y = NULL,
    caption = source_note
  ) +
  theme_y_sdv() +
  theme(legend.position = "none", panel.grid.major.y = element_blank())

Horizontal bar chart of the ten WNBA scoring leaders of 2026 (30 or more games), each bar in the player's team color, with her headshot as the axis label and her name and points per game at the end of the bar.

6. A binned shot chart on a team-colored court

load_wnba_shots() holds ESPN’s shot locations, which wehoop already converts to feet on a center-court frame, the frame sdv_surface()’s sportyR court uses. They need no conversion; sdv_court_coords() is only for the stats.wnba.com legacy frame (x_legacy/y_legacy, tenths of a foot around the hoop). Fold the right-basket shots onto the left one, then bin them into 3-foot squares: the size of each point is the attempts from there, its color the field goal percentage.

star <- slice(scorers, 1)

zones <- wehoop::load_wnba_shots(seasons = season) |>
  semi_join(distinct(box, game_id), by = "game_id") |>
  filter(athlete_id_1 == star$athlete_id, !grepl("Free Throw", type_text)) |>
  mutate(
    right = coordinate_x > 0,
    x = if_else(right, -coordinate_x, coordinate_x),
    y = if_else(right, -coordinate_y, coordinate_y)
  ) |>
  group_by(x = round(x / 3) * 3, y = round(y / 3) * 3) |>
  summarise(attempts = n(), fg_pct = mean(scoring_play), .groups = "drop")

sdv_surface("wnba", star$team, display_range = "defense") +
  geom_point(
    aes(x, y, size = attempts, fill = fg_pct),
    data = zones, shape = 21, color = "grey15", stroke = 0.3
  ) +
  scale_size_area(max_size = 11, name = "Attempts") +
  scale_fill_gradient2(
    low = "#2C7BB6", mid = "white", high = "#D7191C", midpoint = 0.45,
    limits = c(0, 1), breaks = c(0, 0.5, 1), labels = scales::percent, name = "FG%"
  ) +
  labs(
    title = paste0(star$athlete_display_name, ": where she shot from, ", season),
    subtitle = sprintf("%d field goal attempts in 3-foot squares", sum(zones$attempts)),
    caption = source_note
  ) +
  theme(legend.position = "bottom", plot.title = element_text(face = "bold"))

Half-court chart of the 2026 WNBA scoring leader's field goal attempts binned into 3-foot squares on a court painted in her team's colors: point size shows attempts and color shows field goal percentage, blue below 45 percent and red above.

7. A standings table with logos and a playoff line

ESPN’s standings come long (one row per team and stat), so pivot them wide. The clincher column marks the eight teams that clinched a playoff spot with an x, and they are the eight best records, so one league-wide table with a cut line after eighth tells the story.

standings <- wehoop::load_wnba_standings(seasons = season) |>
  select(group_name, team_abbreviation, team_short_display_name, stat_name, display_value) |>
  pivot_wider(names_from = stat_name, values_from = display_value)

standings |>
  mutate(
    conf = sub(" Conference", "", group_name),
    logo = team_abbreviation,
    pct = as.numeric(winPercent)
  ) |>
  arrange(desc(pct), desc(as.numeric(differential)), team_abbreviation) |>
  mutate(rank = row_number()) |>
  select(rank, logo, team_short_display_name, conf, wins, losses, winPercent, Home, Road, `Last Ten Games`, differential) |>
  gt() |>
  tab_header(
    title = paste("WNBA standings,", season),
    subtitle = "The eight best records made the playoffs"
  ) |>
  cols_label(
    rank = "", logo = "", team_short_display_name = "Team", conf = "Conf", wins = "W",
    losses = "L", winPercent = "Pct", `Last Ten Games` = "L10",
    differential = "Diff"
  ) |>
  tab_source_note(source_note) |>
  gt_sdv_logos(columns = logo, league = "wnba", height = 26) |>
  gt_theme_athletic() |>
  cols_align("left", columns = team_short_display_name) |>
  gt_cutline(after = 8, label = "Playoffs", label_position = "above")
WNBA standings, 2026
The eight best records made the playoffs
Team Conf W L Pct Home Road L10 Diff
1 Minnesota Lynx Lynx Western 33 11 .750 15-7 18-4 6-4 +7.1
2 Golden State Valkyries Valkyries Western 32 12 .727 17-5 15-7 7-3 +7.1
3 Las Vegas Aces Aces Western 31 13 .705 15-7 16-6 8-2 +5.7
4 Atlanta Dream Dream Eastern 30 14 .682 15-7 15-7 9-1 +6.8
5 Indiana Fever Fever Eastern 28 16 .636 15-7 13-9 6-4 +5.6
6 Washington Mystics Mystics Eastern 28 16 .636 15-7 13-9 8-2 +1.3
7 Dallas Wings Wings Western 27 17 .614 16-6 11-11 7-3 +4.1
8 New York Liberty Liberty Eastern 26 18 .591 14-8 12-10 6-4 +3.2
9 Portland Fire Fire Western 17 27 .386 9-13 8-14 3-7 -4.8
10 Phoenix Mercury Mercury Western 16 28 .364 7-15 9-13 4-6 -3.2
11 Chicago Sky Sky Eastern 16 28 .364 11-11 5-17 4-6 -3.9
12 Los Angeles Sparks Sparks Western 16 28 .364 8-14 8-14 4-6 -4.3
13 Toronto Tempo Tempo Eastern 11 33 .250 7-15 4-18 1-9 -8.7
14 Connecticut Sun Sun Eastern 11 33 .250 8-14 3-19 2-8 -9.2
15 Seattle Storm Storm Western 8 36 .182 6-16 2-20 2-8 -6.7
Data: wehoop (ESPN) | Viz: sdvplotR

8. The season as a running point differential, by conference

Running point differential through the season, one line per team in its color (scale_color_sdv()), one panel per conference, with geom_sdv_logos() marking where each team finished.

running <- box |>
  arrange(team, game_date) |>
  group_by(team) |>
  mutate(game_no = row_number(), diff = cumsum(team_score - opponent_team_score)) |>
  ungroup()
finish <- running |>
  group_by(team) |>
  slice_max(game_no, n = 1) |>
  ungroup()

ggplot(running, aes(game_no, diff)) +
  geom_hline(yintercept = 0, color = "grey50", linewidth = 0.4) +
  geom_line(aes(group = team), color = "black", linewidth = 1.6) +
  geom_line(aes(color = team), linewidth = 1) +
  geom_sdv_logos(aes(x = game_no + 3, team = team), data = finish, league = "wnba", width = 0.07) +
  facet_wrap(~conference, labeller = labeller(conference = function(x) paste(x, "Conference"))) +
  scale_color_sdv(league = "wnba") +
  scale_x_continuous(expand = expansion(add = c(1, 6))) +
  labs(
    title = paste("Running point differential,", season),
    x = "Game of the season",
    y = "Point differential",
    caption = source_note
  ) +
  theme(legend.position = "none", strip.text = element_text(face = "bold", size = 11))

Two panels, Eastern and Western Conference, with one line per WNBA team showing its running point differential by game number through the 2026 regular season, each line in the team's color with its logo at the end.

9. Every game’s margin, in team colors

A box plot shows the spread of a season; the points on top are the games, in each team’s color, and scale_x_sdv() labels the axis with logos. Teams are ordered by their median margin. position_jitter(seed = 1) keeps the jitter the same from one build to the next.

margins <- box |>
  mutate(margin = team_score - opponent_team_score)
order <- margins |>
  group_by(team) |>
  summarise(med = median(margin), avg = mean(margin)) |>
  arrange(desc(med), desc(avg), team) |>
  pull(team)

margins |>
  mutate(team = factor(team, levels = order)) |>
  ggplot(aes(team, margin)) +
  geom_hline(yintercept = 0, color = "grey50", linewidth = 0.4) +
  geom_boxplot(outlier.shape = NA, fill = NA, color = "grey40", width = 0.6) +
  geom_point(
    aes(fill = team),
    shape = 21, color = "black", stroke = 0.2, size = 1.9, alpha = 0.85,
    position = position_jitter(width = 0.18, height = 0, seed = 1)
  ) +
  scale_fill_sdv(league = "wnba") +
  scale_x_sdv(league = "wnba", size = 20) +
  labs(
    title = paste("Every game's final margin,", season),
    subtitle = "Teams ordered by median margin",
    x = NULL,
    y = "Final margin (points)",
    caption = source_note
  ) +
  theme_x_sdv() +
  theme(legend.position = "none", panel.grid.major.x = element_blank())

Box plots of every 2026 WNBA game's final margin by team, ordered by median margin, with each game as a point in the team's color and team logos along the horizontal axis.

10. The standings race, as a bump chart

Rank all 15 teams by win percentage at the end of every week, then draw one line per team in its color with its logo at the finish. Ties are broken by point differential, then by abbreviation, so the same data always draw the same chart.

weekly <- box |>
  mutate(
    week = as.Date(cut(game_date, "week", start.on.monday = TRUE)),
    margin = team_score - opponent_team_score
  ) |>
  group_by(team, week) |>
  summarise(wins = sum(team_winner), games = n(), margin = sum(margin), .groups = "drop") |>
  complete(team, week, fill = list(wins = 0, games = 0, margin = 0)) |>
  arrange(team, week) |>
  group_by(team) |>
  mutate(across(c(wins, games, margin), cumsum)) |>
  ungroup() |>
  mutate(week_no = dense_rank(week)) |>
  filter(week_no >= 3) |> # the first two weeks hold only a game or two
  arrange(week_no, desc(wins / games), desc(margin), team) |>
  group_by(week_no) |>
  mutate(rank = row_number()) |>
  ungroup()
last_week <- filter(weekly, week_no == max(week_no))

ggplot(weekly, aes(week_no, rank)) +
  geom_line(aes(group = team), color = "black", linewidth = 1.7) +
  geom_line(aes(color = team), linewidth = 1.1) +
  geom_sdv_logos(aes(x = week_no + 0.9, team = team), data = last_week, league = "wnba", width = 0.045) +
  scale_color_sdv(league = "wnba") +
  scale_y_reverse(breaks = 1:15) +
  scale_x_continuous(breaks = seq(3, 30, 3), expand = expansion(add = c(0.4, 1.4))) +
  labs(
    title = paste("The WNBA standings race,", season),
    subtitle = "League rank by win percentage at the end of each week",
    x = "Week of the season",
    y = "League rank",
    caption = source_note
  ) +
  theme(legend.position = "none", panel.grid.major.x = element_blank())

Bump chart of the 15 WNBA teams' weekly league rank by win percentage through the 2026 regular season, one line per team in its color, with each team's logo at its final rank on the right.

11. Make it interactive

Section 5’s scoring leaders as a ggiraph chart. geom_col_interactive() from ggiraph takes a tooltip and a data_id beside the usual bar aesthetics, and ggiraph::girafe() renders the ggplot as an interactive SVG. The tooltip is HTML, so it carries the headshot too: sdv_headshot_url() gives each player’s image URL, and the browser loads it only when you hover. Hover a bar for her headshot, team, games and points per game.

scorers_hover <- scorers |>
  mutate(
    player = factor(athlete_display_name, levels = rev(athlete_display_name)),
    tip = sprintf(
      "<img src='%s' height='80'><br><b>%s</b> (%s)<br>%.1f points per game in %d games",
      sdv_headshot_url(athlete_id, league = "wnba"), athlete_display_name, team, ppg, games
    )
  )

p_scorers <- ggplot(scorers_hover, aes(ppg, player)) +
  ggiraph::geom_col_interactive(
    aes(fill = team, tooltip = tip, data_id = athlete_id),
    width = 0.7, color = "black", linewidth = 0.2
  ) +
  scale_fill_sdv(league = "wnba") +
  scale_x_continuous(expand = expansion(mult = c(0, 0.05))) +
  labs(
    title = paste("WNBA scoring leaders,", season),
    subtitle = "Points per game, 30 or more games; hover a bar for the player",
    x = "Points per game", y = NULL,
    caption = source_note
  ) +
  theme(legend.position = "none", panel.grid.major.y = element_blank())

ggiraph::girafe(
  ggobj = p_scorers, width_svg = 8, height_svg = 5.5,
  options = list(
    ggiraph::opts_tooltip(css = "background:white;color:black;padding:6px;border-radius:6px;"),
    ggiraph::opts_hover(css = "opacity:0.8;")
  )
)

A headshot drawn on the chart itself, with geom_sdv_headshots_interactive(), is embedded in the SVG as a copy at most 160 px a side, about 25 KB each; the URL in the tooltip adds nothing to the page and shows the photo at full size. Interactive charts covers ggiraph, plotly 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}
}