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WNBA Leaderboard

Saiem Gilani
@saiemgilani @saiemgilani

Source: vignettes/leaderboard-wnba.Rmd
leaderboard-wnba.Rmd
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Updated 2026-10-12: the final 2026 regular season; the playoffs are under way.

This page is rebuilt every week with the site. It builds the standings with offensive, defensive and net ratings, plots offense against defense and lists the scoring leaders with their headshots, for the latest WNBA season with games: the season to date from May to September, the final regular season once it is over. Data: wehoop’s ESPN box scores, read from release files (no stats.wnba.com calls).

Picking the season

The WNBA season tips off in May, so until then the calendar points at last season. wehoop::load_wnba_team_box() errors for a season past wehoop’s most recent one, and a new season’s file can hold only preseason games at first, so the helper treats “no regular-season games” as “not published” and the page steps back one season. The status line says what the numbers cover.

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

today <- Sys.Date()
day_label <- function(day, month = "%b") sub(" 0", " ", format(as.Date(day), paste(month, "%d"))) # "Oct 5"
# The season tips off in May, so until then the calendar points at last season
current <- as.integer(format(today, "%Y")) - (format(today, "%m") < "05")

# A season's team box scores, or NULL when it has no regular-season games yet:
# load_wnba_team_box() errors for a season past wehoop's most recent one, and a
# new season's file can hold only preseason games at first
team_games <- function(season) {
  box <- tryCatch(suppressWarnings(wehoop::load_wnba_team_box(seasons = season)), error = function(e) NULL)
  if (NROW(box) == 0 || !any(box$season_type == 2)) NULL else box
}

season <- current
box <- team_games(season)
if (is.null(box)) {
  season <- current - 1
  box <- team_games(season)
}
stopifnot(!is.null(box))

last_game <- max(box$game_date)
playoffs <- filter(box, season_type == 3)
if (season < current) {
  status <- sprintf(
    "the offseason, so this is the final %d regular season; the %d season has no games yet.",
    season, current
  )
  through <- "final regular season"
} else if (nrow(playoffs) == 0) {
  status <- sprintf("the %d season through %s.", season, day_label(last_game, "%B"))
  through <- paste("through", day_label(last_game))
} else if (as.numeric(today - max(playoffs$game_date)) <= 10) {
  status <- sprintf("the final %d regular season; the playoffs are under way.", season)
  through <- "final regular season"
} else {
  status <- sprintf("the offseason, so this is the final %d regular season.", season)
  through <- "final regular season"
}

ESPN files the All-Star Game and the Commissioner’s Cup final as regular-season games, though neither counts in the standings; the schedule marks them in type_abbreviation (ALLSTAR, CC), so keep the standard games (STD) only. That also drops the All-Star teams, whose abbreviations are not franchises.

standard <- wehoop::load_wnba_schedule(seasons = season) |>
  filter(season_type == 2, type_abbreviation == "STD")
stopifnot(identical(class(box$game_id), class(standard$game_id)))

games <- box |>
  filter(season_type == 2, game_id %in% standard$game_id) |>
  mutate(poss = field_goals_attempted - offensive_rebounds + total_turnovers +
    0.44 * free_throws_attempted) |>
  group_by(game_id) |>
  mutate(poss = mean(poss, na.rm = TRUE)) |> # one side's box can miss a count; the other covers it
  ungroup()

teams <- sdv_teams("wnba") |>
  transmute(team = abbr, name = short_name, conference = sub(" Conference$", "", conference))
ratings <- games |>
  arrange(game_date, game_id) |>
  group_by(team = team_abbreviation) |>
  summarise(
    w = sum(team_winner), l = sum(!team_winner),
    l10 = sum(tail(team_winner, 10)),
    ortg = 100 * sum(team_score) / sum(poss),
    drtg = 100 * sum(opponent_team_score) / sum(poss),
    .groups = "drop"
  ) |>
  mutate(pct = w / (w + l), net = ortg - drtg) |>
  inner_join(teams, by = "team") |>
  arrange(desc(pct), desc(net), team) # the team breaks a tie, so an unchanged week renders the same
nrow(ratings)
#> [1] 15

1. Standings with net rating

One table for the league, ordered by winning percentage (the WNBA seeds its playoffs league-wide), with the conference beside each team and its points scored and allowed per 100 possessions. gt_color_pills() draws net rating on a scale centred at zero.

standings <- ratings |>
  mutate(
    seed = row_number(),
    record = paste(w, l, sep = "-"),
    last10 = paste(l10, pmin(10, w + l) - l10, sep = "-"),
    logo = team
  ) |>
  select(seed, logo, name, conference, record, pct, last10, ortg, drtg, net)

standings |>
  gt(id = "wnba-standings") |>
  tab_header(
    title = paste("WNBA standings and ratings,", season),
    subtitle = paste0(toupper(substr(through, 1, 1)), substring(through, 2))
  ) |>
  fmt_number(pct, decimals = 3) |>
  fmt_number(c(ortg, drtg), decimals = 1) |>
  tab_spanner("Per 100 possessions", c(ortg, drtg, net)) |>
  cols_label(
    seed = "", logo = "", name = "Team", conference = "Conf", record = "W-L",
    pct = "Pct", last10 = "Last 10", ortg = "Off", drtg = "Def", net = "Net"
  ) |>
  tab_source_note(paste(
    "Data: wehoop (ESPN box scores). Possessions estimated from the box score;",
    "ties ordered by net rating. Viz: sdvplotR"
  )) |>
  gt_color_pills(
    net,
    palette = c("#c84630", "#f7f7f7", "#2e8b57"),
    domain = c(-1, 1) * max(abs(standings$net)), digits = 1, pill_height = 22
  ) |>
  gt_sdv_logos(columns = logo, league = "wnba", height = 26) |>
  gt_theme_pl()
WNBA standings and ratings, 2026
Final regular season
Team Conf W-L Pct Last 10
Per 100 possessions
Off Def Net
1 Minnesota Lynx Lynx Western 33-11 0.750 6-4 109.9 101.2 8.7
2 Golden State Valkyries Valkyries Western 32-12 0.727 7-3 104.8 95.8 9.0
3 Las Vegas Aces Aces Western 31-13 0.705 8-2 110.6 103.8 6.8
4 Atlanta Dream Dream Eastern 30-14 0.682 9-1 108.5 100.4 8.2
5 Indiana Fever Fever Eastern 28-16 0.636 6-4 112.2 105.7 6.5
6 Washington Mystics Mystics Eastern 28-16 0.636 8-2 102.3 100.8 1.6
7 Dallas Wings Wings Western 27-17 0.614 7-3 108.6 103.6 5.0
8 New York Liberty Liberty Eastern 26-18 0.591 6-4 108.1 104.3 3.8
9 Portland Fire Fire Western 17-27 0.386 3-7 103.5 109.3 -5.8
10 Phoenix Mercury Mercury Western 16-28 0.364 4-6 102.5 106.5 -3.9
11 Chicago Sky Sky Eastern 16-28 0.364 4-6 101.4 106.0 -4.5
12 Los Angeles Sparks Sparks Western 16-28 0.364 4-6 103.0 108.0 -5.1
13 Toronto Tempo Tempo Eastern 11-33 0.250 1-9 103.0 113.5 -10.5
14 Connecticut Sun Sun Eastern 11-33 0.250 2-8 95.3 106.4 -11.1
15 Seattle Storm Storm Western 8-36 0.182 2-8 97.6 105.6 -8.0
Data: wehoop (ESPN box scores). Possessions estimated from the box score; ties ordered by net rating. Viz: sdvplotR

2. Offense against defense

Points scored per 100 possessions against points allowed, one logo per team. The y axis is reversed so the better defenses sit higher, and the faint diagonals are lines of equal net rating, five points apart.

ggplot(ratings, aes(x = ortg, y = drtg)) +
  geom_abline(slope = 1, intercept = seq(-30, 30, by = 5), colour = "grey88", linewidth = 0.3) +
  geom_hline(yintercept = mean(ratings$drtg), linetype = "dashed", colour = "grey55") +
  geom_vline(xintercept = mean(ratings$ortg), linetype = "dashed", colour = "grey55") +
  geom_sdv_logos(aes(team = team), league = "wnba", width = 0.065) +
  # logos do not widen the limits, so leave room for the outermost ones
  scale_x_continuous(expand = expansion(mult = 0.08)) +
  scale_y_reverse(expand = expansion(mult = 0.12)) +
  labs(
    title = paste("WNBA offense and defense,", season),
    subtitle = paste("Points per 100 possessions,", through),
    x = "Offensive rating (points scored per 100 possessions)",
    y = "Defensive rating (points allowed, reversed)",
    caption = "Data: wehoop (ESPN box scores) | Viz: sdvplotR"
  ) +
  theme_minimal(base_size = 13) +
  theme(plot.title = element_text(face = "bold"), panel.grid.minor = element_blank())

Every WNBA team's logo placed by offensive rating (horizontal) and defensive rating (vertical, reversed so better defenses sit higher), with dashed lines at the league averages and faint diagonals of equal net rating.

3. Scoring leaders with headshots

Points per game from the player box scores (standard games only), for players who appeared in at least half of the most games any team has played. ESPN athlete ids are sdvplotR’s default id for WNBA headshots, and the table is dressed in the scoring leader’s team colors by gt_theme_sdv_team().

players <- wehoop::load_wnba_player_box(seasons = season) |>
  filter(season_type == 2, !did_not_play, !is.na(minutes), game_id %in% standard$game_id)
most_games <- max(count(distinct(players, team_abbreviation, game_id), team_abbreviation)$n)

leaders <- players |>
  arrange(game_date, game_id) |>
  group_by(athlete_id) |>
  summarise(
    name = last(athlete_display_name), team = last(team_abbreviation), gp = n(),
    ppg = mean(points), rpg = mean(rebounds), apg = mean(assists),
    fg_pct = sum(field_goals_made) / sum(field_goals_attempted),
    .groups = "drop"
  ) |>
  filter(gp >= most_games / 2) |>
  arrange(desc(ppg), athlete_id) |>
  slice_head(n = 10) |>
  mutate(rank = row_number(), headshot = athlete_id, logo = team)
head(leaders)
#> # A tibble: 6 × 11
#>   athlete_id name      team     gp   ppg   rpg   apg fg_pct  rank headshot logo 
#>        <int> <chr>     <chr> <int> <dbl> <dbl> <dbl>  <dbl> <int>    <int> <chr>
#> 1    3149391 A'ja Wil… LV       41  26.2  9.37  3.22  0.527     1  3149391 LV   
#> 2    3142191 Kelsey M… IND      44  24.7  1.70  2.75  0.509     2  3142191 IND  
#> 3    4433403 Caitlin … IND      40  22.3  3.95  8.32  0.445     3  4433403 IND  
#> 4    2998938 Kahleah … PHX      40  21.5  3.68  1.88  0.449     4  2998938 PHX  
#> 5    4433730 Paige Bu… DAL      42  20.9  4.14  5.88  0.512     5  4433730 DAL  
#> 6    3904576 Marina M… TOR      32  20.8  3.28  3.69  0.441     6  3904576 TOR
leaders |>
  select(rank, headshot, name, logo, gp, ppg, rpg, apg, fg_pct) |>
  gt(id = "wnba-leaders") |>
  tab_header(
    title = paste("WNBA scoring leaders,", season),
    subtitle = paste0(
      "Points per game, minimum ", ceiling(most_games / 2), " games, ", through
    )
  ) |>
  fmt_number(c(ppg, rpg, apg), decimals = 1) |>
  fmt_percent(fg_pct, decimals = 1) |>
  cols_label(
    rank = "", headshot = "", name = "Player", logo = "", gp = "GP",
    ppg = "PTS", rpg = "REB", apg = "AST", fg_pct = "FG%"
  ) |>
  tab_source_note("Data: wehoop (ESPN box scores) | Viz: sdvplotR") |>
  gt_sdv_headshots(columns = headshot, league = "wnba", height = 40) |>
  gt_sdv_logos(columns = logo, league = "wnba", height = 26) |>
  gt_theme_sdv_team(team = leaders$team[1], league = "wnba")
WNBA scoring leaders, 2026
Points per game, minimum 22 games, final regular season
Player GP PTS REB AST FG%
1 Player 3149391 headshot A'ja Wilson Las Vegas Aces 41 26.2 9.4 3.2 52.7%
2 Player 3142191 headshot Kelsey Mitchell Indiana Fever 44 24.7 1.7 2.8 50.9%
3 Player 4433403 headshot Caitlin Clark Indiana Fever 40 22.3 4.0 8.3 44.5%
4 Player 2998938 headshot Kahleah Copper Phoenix Mercury 40 21.5 3.7 1.9 44.9%
5 Player 4433730 headshot Paige Bueckers Dallas Wings 42 20.9 4.1 5.9 51.2%
6 Player 3904576 headshot Marina Mabrey Toronto Tempo 32 20.8 3.3 3.7 44.1%
7 Player 2998928 headshot Breanna Stewart New York Liberty 42 20.8 8.3 3.3 47.4%
8 Player 4433791 headshot Olivia Miles Minnesota Lynx 40 19.8 4.7 6.0 49.2%
9 Player 3058901 headshot Allisha Gray Atlanta Dream 44 19.0 3.5 2.6 46.2%
10 Player 4065870 headshot Jackie Young Las Vegas Aces 43 18.9 4.4 6.8 48.8%
Data: wehoop (ESPN box scores) | Viz: sdvplotR

4. Interactive version

The offense-against-defense chart from section 2 with ggiraph: hover a logo for the team’s record and ratings. geom_sdv_logos_interactive() is geom_sdv_logos() with a hover target under each logo, so tooltip and data_id go in the same aes() as team.

p_wnba <- ggplot(ratings, aes(x = ortg, y = drtg)) +
  geom_abline(slope = 1, intercept = seq(-30, 30, by = 5), colour = "grey88", linewidth = 0.3) +
  geom_hline(yintercept = mean(ratings$drtg), linetype = "dashed", colour = "grey55") +
  geom_vline(xintercept = mean(ratings$ortg), linetype = "dashed", colour = "grey55") +
  geom_sdv_logos_interactive(
    aes(team = team, data_id = team, tooltip = sprintf(
      "%s, %d-%d<br>Offense %.1f, defense %.1f<br>Net %+.1f per 100 possessions", name, w, l, ortg, drtg, net
    )),
    league = "wnba", width = 0.065
  ) +
  scale_x_continuous(expand = expansion(mult = 0.08)) +
  scale_y_reverse(expand = expansion(mult = 0.12)) +
  labs(
    title = paste("WNBA offense and defense,", season),
    x = "Offensive rating", y = "Defensive rating (reversed)"
  ) +
  theme_minimal(base_size = 13)

ggiraph::girafe(ggobj = p_wnba, width_svg = 7, height_svg = 5)

More recipes like this one, in plotly, ggiraph, leaflet and reactable, are in Interactive charts.

  • WNBA walks through wehoop and sdvplotR more broadly.
  • NBA builds the same standings from hoopR.
  • Social graphics, automated posts leaderboards like these on a schedule.

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}
}