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WNBA Scoring Leaders Card

Saiem Gilani
@saiemgilani @saiemgilani

Source: vignettes/recipe-wnba-scoring-leaders-card.Rmd
recipe-wnba-scoring-leaders-card.Rmd
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The brief: the regular season just ended, and the social team wants a scoring-leaders card: the top ten in points per game with each player’s face and team, as a 1080 x 1080 image for Instagram, plus a top-five cut at 1200 x 675 for X and Bluesky. The box scores are ESPN’s through wehoop; the headshots come from ESPN’s CDN through geom_sdv_headshots().

library(sdvplotR)
library(ggplot2)
library(dplyr, warn.conflicts = FALSE)

season <- 2026
out_dir <- tempfile("sdvplotR-recipe-") # where the exports go; use your own folder
dir.create(out_dir)

1. Get the data

One row per player per game. ESPN files the All-Star Game and the Commissioner’s Cup final as regular-season games, but neither counts in the official stats; the schedule marks the standard games with type_abbreviation "STD", so a semi join keeps only those. Players who did not play are dropped, the qualifier is 30 games (about 70% of the 44-game schedule), and a player traded mid-season is listed with her last team.

regular <- wehoop::load_wnba_schedule(season) |> filter(season_type == 2)
count(regular, type_abbreviation) # STD, plus the All-Star Game and the Cup final
#> # A tibble: 3 × 2
#>   type_abbreviation     n
#>   <chr>             <int>
#> 1 ALLSTAR               1
#> 2 CC                    1
#> 3 STD                 331
standard <- regular |>
  filter(type_abbreviation == "STD") |>
  select(game_id)

box <- wehoop::load_wnba_player_box(season)
stopifnot(identical(class(box$game_id), class(standard$game_id))) # one type on both sides of the key

leaders <- box |>
  semi_join(standard, by = "game_id") |>
  filter(!did_not_play) |>
  group_by(athlete_id, athlete_display_name) |>
  summarise(
    games = n(),
    ppg = mean(points),
    team = last(team_abbreviation, order_by = game_date),
    .groups = "drop"
  ) |>
  filter(games >= 30) |>
  arrange(desc(ppg), athlete_display_name) |> # the name breaks a tie the same way every run
  slice_head(n = 10) |>
  mutate(rank = row_number())
leaders
#> # A tibble: 10 × 6
#>    athlete_id athlete_display_name games   ppg team   rank
#>         <int> <chr>                <int> <dbl> <chr> <int>
#>  1    3149391 A'ja Wilson             41  26.2 LV        1
#>  2    3142191 Kelsey Mitchell         44  24.7 IND       2
#>  3    4433403 Caitlin Clark           40  22.3 IND       3
#>  4    2998938 Kahleah Copper          40  21.5 PHX       4
#>  5    4433730 Paige Bueckers          42  20.9 DAL       5
#>  6    3904576 Marina Mabrey           32  20.8 TOR       6
#>  7    2998928 Breanna Stewart         42  20.8 NY        7
#>  8    4433791 Olivia Miles            40  19.8 MIN       8
#>  9    3058901 Allisha Gray            44  19.0 ATL       9
#> 10    4065870 Jackie Young            43  18.9 LV       10

2. The first draft

A horizontal bar chart is the right shape for a ranked list of names.

ggplot(leaders, aes(ppg, athlete_display_name)) +
  geom_col()

First draft: grey horizontal bars of points per game for the top ten WNBA scorers, in alphabetical order with no values.

The leader is buried (ggplot2 sorts a text axis alphabetically, bottom up), every bar is the same grey, the exact values are missing, and nothing says what or when.

3. Order, team colors and values

reorder() sorts the names by scoring, so No. 1 is on top. Each bar takes its team’s color from sdv_team_colors(), with one catch: a few primaries (the Aces’ silver, the Liberty’s seafoam) are too light to carry white text or to stand out on a light card, so a small WCAG luminance check swaps those to the team’s secondary color. The values go at the end of each bar, which makes the x axis unnecessary.

luminance <- function(hex) {
  rgb <- grDevices::col2rgb(hex) / 255
  rgb <- ifelse(rgb <= 0.03928, rgb / 12.92, ((rgb + 0.055) / 1.055)^2.4)
  drop(c(0.2126, 0.7152, 0.0722) %*% rgb) # WCAG relative luminance, 0 (black) to 1 (white)
}

primary <- sdv_team_colors("wnba", leaders$team)
secondary <- sdv_team_colors("wnba", leaders$team, which = "secondary")
leaders <- leaders |>
  mutate(color = unname(if_else(luminance(primary) < 0.3, primary, secondary)))

ggplot(leaders, aes(ppg, reorder(athlete_display_name, ppg))) +
  geom_col(aes(fill = color), width = 0.7) +
  geom_text(aes(label = sprintf("%.1f", ppg)), hjust = -0.2, fontface = "bold", size = 3.4) +
  scale_fill_identity() +
  scale_x_continuous(expand = expansion(mult = c(0, 0.08))) +
  labs(x = NULL, y = NULL) +
  theme_minimal() +
  theme(axis.text.x = element_blank(), panel.grid = element_blank())

The top ten WNBA scorers as horizontal bars sorted by points per game, each in a team color, with the value printed at the end of each bar.

4. Faces and logos

A card like this sells on faces. Moving to a blank canvas (an x axis fixed at 0 to 100, one unit per row on a reversed y axis) makes room for a column of headshots and a small team logo under each name. geom_sdv_headshots() takes ESPN athlete ids as they come, and both image geoms size their images with height, a share of the panel’s height, so with ten rows a headshot of 0.085 fills most of a row.

rows <- function(data, bar_from = 47, bar_to = 95) {
  n <- nrow(data)
  scale <- (bar_to - bar_from) / max(data$ppg)
  ggplot(data, aes(y = rank)) +
    geom_text(aes(x = 3, label = rank), size = 5.5, fontface = "bold", color = "grey60") +
    geom_sdv_headshots(aes(x = 12, player_id = athlete_id), league = "wnba", height = 0.85 / n) +
    geom_text(aes(x = 20, y = rank - 0.17, label = athlete_display_name), hjust = 0, size = 4, fontface = "bold") +
    geom_sdv_logos(aes(x = 21.6, y = rank + 0.2, team = team), league = "wnba", height = 0.36 / n) +
    geom_text(aes(x = 24, y = rank + 0.2, label = team), hjust = 0, size = 3, color = "grey40") +
    geom_rect(
      aes(xmin = bar_from, xmax = bar_from + ppg * scale, ymin = rank - 0.28, ymax = rank + 0.28, fill = color)
    ) +
    geom_text(
      aes(x = bar_from + ppg * scale - 1, label = sprintf("%.1f", ppg)),
      hjust = 1, size = 4, fontface = "bold", color = "white"
    ) +
    scale_fill_identity() +
    scale_y_reverse() +
    coord_cartesian(xlim = c(0, 100), ylim = c(0.5, n + 0.5), expand = FALSE) +
    theme_void()
}

rows(leaders)

The top ten scorers on a blank canvas: rank, headshot, name, a small team logo with the team abbreviation, and a team-color bar with the value inside it.

5. Make it a card

The finishing pass is the frame: a warm off-white background, the headline as the title (the stat goes in the subtitle), the qualifier stated, and a footer with the source. The frame is a theme on top of the rows, so the same function draws the square post and a wider one.

bg <- "#f6f4ef"
leader <- leaders[1, ]

card <- function(data) {
  rows(data) +
    labs(
      title = paste(leader$athlete_display_name, "led the WNBA in scoring"),
      subtitle = paste0("Points per game, ", season, " WNBA regular season (minimum 30 games)"),
      caption = "Data: ESPN via wehoop  |  #WNBA  |  made with sdvplotR"
    ) +
    theme(
      plot.background = element_rect(fill = bg, color = NA),
      plot.title = element_text(face = "bold", size = 19, color = "#1d1d1d", margin = margin(b = 4)),
      plot.subtitle = element_text(size = 10.5, color = "grey40", margin = margin(b = 10)),
      plot.caption = element_text(size = 8, color = "grey40", hjust = 0, margin = margin(t = 8)),
      plot.margin = margin(20, 20, 14, 20)
    )
}

card(leaders)

The finished scoring-leaders card: an off-white square with the headline, the top ten scorers with headshots, logos and team-color bars, and a source footer.

6. Export for Instagram and X

The square holds all ten. The 16:9 post for X and Bluesky is too short for ten readable rows, so it gets the top five from the same function: changing the content to fit the format beats shrinking the type. Inches times dpi gives the exact pixels, and bg matches the card so no white edge shows.

square <- file.path(out_dir, "wnba_scoring_1080x1080.png")
wide <- file.path(out_dir, "wnba_scoring_top5_1200x675.png")
ggsave(square, card(leaders), width = 7.2, height = 7.2, dpi = 150, bg = bg)
ggsave(wide, card(head(leaders, 5)), width = 8, height = 4.5, dpi = 150, bg = bg)

magick::image_info(magick::image_read(c(square, wide)))[c("width", "height")]
#> # A tibble: 2 × 2
#>   width height
#>   <int>  <int>
#> 1  1080   1080
#> 2  1200    675
magick::image_read(square)

The scoring-leaders card exported at 1080 by 1080 pixels for Instagram.

The top-five cut for X and Bluesky:

magick::image_read(wide)

The top-five version of the card exported at 1200 by 675 pixels for X and Bluesky.

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}
}