WNBA and Women's College Basketball Shot Charts in R

A shot chart puts every field goal attempt where it was taken, so you can see a player’s or a team’s shot diet at a glance. This article loads a full season of WNBA and women’s college shots from the wehoop data releases, draws them on a regulation court with sportyR, and builds the standard views: makes and misses, a hexbin density map, shot zones and field goal percentage (FG%) by zone.

If you are new to wehoop, start with Getting Started.

What you’ll learn

Which wehoop loaders return shot locations, and which coordinate frame each uses.
How to put every shot on one basket so a half-court chart shows them all.
How to draw makes and misses for one player, and a hexbin density map for a team.
How to classify shots into zones and compute FG% by zone.
How to convert WNBA Stats API shot locations with sdvplotR::sdv_court_coords(), and dress the court in team colors with sdvplotR::sdv_surface().

library(wehoop)
library(dplyr)
library(ggplot2)
library(sportyR)

Which loaders have shot locations?

Loader	Source	Location columns	Units and origin
`load_wnba_shots()`	ESPN play-by-play	`coordinate_x`, `coordinate_y`	Feet, center court at (0, 0), `x` along the court (baskets at about ±41.75), `y` across it (±25)
`load_wbb_shots()`	ESPN play-by-play	`coordinate_x`, `coordinate_y`	Same frame as the WNBA loader
`load_wnba_stats_shots()`	WNBA Stats API	`x_legacy`, `y_legacy`	Tenths of a foot, origin at the shooter’s basket
`load_ncaa_wbb_shots()`	stats.ncaa.org play-by-play	none (shot type and outcome only)

All three loaders with locations read pre-built season files from GitHub releases; none of them calls a live API. The ESPN coordinates are already in the center-court frame that sportyR::geom_basketball() draws, so no conversion is needed. The WNBA Stats API frame needs one, covered below.

Load a season of WNBA shots

wnba_shots <- load_wnba_shots(seasons = 2025)

wnba_shots |>
  select(athlete_name_1, team_abbrev, type_text, scoring_play, score_value,
         coordinate_x, coordinate_y) |>
  head()
#> # A tibble: 6 × 7
#>   athlete_name_1     team_abbrev type_text scoring_play score_value coordinate_x
#>   <chr>              <chr>       <chr>     <lgl>              <int>        <dbl>
#> 1 Kahleah Copper     PHX         Pullup J… FALSE                  0         19.8
#> 2 NaLyssa Smith      LV          Layup Sh… FALSE                  0        -40.8
#> 3 NaLyssa Smith      LV          Layup Sh… TRUE                   2        -38.8
#> 4 Monique Akoa Maka… PHX         Pullup J… TRUE                   2         27.8
#> 5 A'ja Wilson        LV          Free Thr… TRUE                   1        -28  
#> 6 A'ja Wilson        LV          Free Thr… TRUE                   1        -28  
#> # ℹ 1 more variable: coordinate_y <dbl>

The table includes free throws (type_text such as “Free Throw - 1 of 2” in the WNBA feed, “MadeFreeThrow” in the college feed). They are not field goal attempts and their location is the free throw line, so drop them.

wnba_fga <- wnba_shots |>
  filter(!grepl("free ?throw", type_text, ignore.case = TRUE))

nrow(wnba_fga)
#> [1] 42193

Put every shot on one basket

Teams switch baskets at halftime, so half of a team’s shots sit at each end of the court. Fold every shot onto the left basket. Reflecting x alone would flip the shooter’s left and right, so rotate the court half-turn instead: negate both coordinates for shots taken at the right-hand basket.

fold_to_left_basket <- function(shots) {
  shots |>
    mutate(
      x = -abs(coordinate_x),
      y = if_else(coordinate_x > 0, -coordinate_y, coordinate_y),
      # distance in feet from the center of the left basket (41.75 ft from center court)
      shot_distance = sqrt((x + 41.75)^2 + y^2),
      result = if_else(scoring_play, "Made", "Missed")
    )
}

wnba_fga <- fold_to_left_basket(wnba_fga)

Every shot now sits on the left half, which geom_basketball(display_range = "defense") draws.

Makes and misses for one player

The player with the most field goal attempts in the 2025 release:

top_shooter <- wnba_fga |>
  count(athlete_name_1, sort = TRUE) |>
  slice(1) |>
  pull(athlete_name_1)

top_shooter
#> [1] "A'ja Wilson"

player_shots <- wnba_fga |> filter(athlete_name_1 == top_shooter)

geom_basketball("wnba", display_range = "defense") +
  geom_point(
    aes(x, y, color = result),
    data = player_shots, alpha = 0.6, size = 1.8,
    position = position_jitter(width = 0.3, height = 0.3, seed = 1)
  ) +
  scale_color_manual(values = c(Made = "#221A4D", Missed = "grey55")) +
  labs(
    title = paste(top_shooter, "2025 field goal attempts"),
    subtitle = sprintf("%d attempts, %.1f%% made",
                       nrow(player_shots), 100 * mean(player_shots$scoring_play)),
    color = NULL,
    caption = "Data: wehoop::load_wnba_shots()"
  )

Half-court shot chart of one WNBA player's 2025 field goal attempts, makes in purple and misses in grey.

ESPN records locations on a one-foot grid, so many shots share a point. The small jitter keeps them from hiding each other.

Where does a team shoot from? Hexbin density

With thousands of shots, points overlap into a blob. A hexbin chart counts shots in hexagonal cells instead. This needs the hexbin package.

team_shots <- wnba_fga |> filter(team_abbrev == "LV")

geom_basketball("wnba", display_range = "defense") +
  geom_hex(aes(x, y), data = team_shots, binwidth = c(2, 2), alpha = 0.85) +
  scale_fill_gradient(low = "#e8e3f3", high = "#221A4D", trans = "sqrt") +
  labs(
    title = "Las Vegas Aces, 2025: shot density",
    fill = "Attempts",
    caption = "Data: wehoop::load_wnba_shots()"
  )

Hexbin shot density map of one WNBA team's 2025 field goal attempts, darker hexagons where more shots were taken.

Read a hexbin map by where the dark cells cluster: at the rim, along the three-point arc, or in the mid-range between them. The zone table below turns that impression into numbers.

Shot zones

A zone chart groups shots into areas with similar value. We use five zones, defined by distance from the basket:

Zone	Rule (feet from the basket center)
Restricted area	under 4 ft
Paint	4 ft to 8 ft
Mid-range	8 ft out to the three-point line
Corner three	beyond the line in the straight corner section (within about 14 ft of the baseline, more than 21 ft from the middle of the court)
Above-the-break three	beyond the arc, which we take as 22 ft

The WNBA three-point line is 22 ft 1.75 in at the top of the arc and 22 ft in the corners; NCAA women’s basketball has used the same distance since the 2021-22 season. The zone cut-offs are a simplification, so check them against the data: a made shot records its point value, so the share of made shots whose zone agrees with a two or a three tells us how well the rule works.

add_zone <- function(shots) {
  shots |>
    mutate(
      corner = abs(y) > 21 & x <= -33,
      zone = case_when(
        corner ~ "Corner three",
        shot_distance >= 22 ~ "Above-the-break three",
        shot_distance < 4 ~ "Restricted area",
        shot_distance < 8 ~ "Paint",
        TRUE ~ "Mid-range"
      ),
      zone_is_three = zone %in% c("Corner three", "Above-the-break three"),
      zone = factor(zone, levels = c("Restricted area", "Paint", "Mid-range",
                                     "Corner three", "Above-the-break three"))
    )
}

wnba_fga <- add_zone(wnba_fga)

# Agreement between the zone rule and the recorded value of made shots
wnba_fga |>
  filter(scoring_play) |>
  summarise(agreement = mean(zone_is_three == (score_value == 3)))
#> # A tibble: 1 × 1
#>   agreement
#>       <dbl>
#> 1     0.999

FG% by zone

league_zones <- wnba_fga |>
  group_by(zone) |>
  summarise(
    attempts = n(),
    makes = sum(scoring_play),
    fg_pct = makes / attempts,
    .groups = "drop"
  ) |>
  mutate(
    share_of_attempts = attempts / sum(attempts),
    points_per_shot = fg_pct * if_else(grepl("three", zone), 3, 2)
  )

league_zones
#> # A tibble: 5 × 6
#>   zone                  attempts makes fg_pct share_of_attempts points_per_shot
#>   <fct>                    <int> <int>  <dbl>             <dbl>           <dbl>
#> 1 Restricted area          10352  6634  0.641            0.245            1.28 
#> 2 Paint                     7434  3180  0.428            0.176            0.856
#> 3 Mid-range                 9244  3598  0.389            0.219            0.778
#> 4 Corner three              3182  1143  0.359            0.0754           1.08 
#> 5 Above-the-break three    11981  3971  0.331            0.284            0.994

Points per shot (FG% times the shot’s point value) is why the corner three and the restricted area are prized: they return the most per attempt.

Compare one player against the league in each zone:

# Fixed label positions, one per zone, so labels do not pile up at the rim
label_spots <- tibble(
  zone = levels(wnba_fga$zone),
  x = c(-44, -33, -24, -44, -14),
  y = c(0, -12, 10, -23, 0)
)

player_zones <- add_zone(player_shots) |>
  group_by(zone) |>
  summarise(attempts = n(), fg_pct = mean(scoring_play), .groups = "drop") |>
  mutate(zone = as.character(zone)) |>
  left_join(label_spots, by = "zone") |>
  left_join(mutate(select(league_zones, zone, league_fg = fg_pct),
                   zone = as.character(zone)), by = "zone") |>
  mutate(label = sprintf("%s\n%.0f%% (lg %.0f%%)", zone, 100 * fg_pct, 100 * league_fg))

geom_basketball("wnba", display_range = "defense") +
  geom_point(aes(x, y, color = zone), data = add_zone(player_shots),
             alpha = 0.5, size = 1.4) +
  geom_label(aes(x, y, label = label), data = player_zones, size = 2.6, alpha = 0.9) +
  labs(
    title = paste(top_shooter, "2025: FG% by zone"),
    subtitle = "League FG% in each zone in brackets",
    color = NULL,
    caption = "Data: wehoop::load_wnba_shots()"
  ) +
  theme(legend.position = "bottom")

Half-court chart with one WNBA player's shots colored by zone and labelled with her field goal percentage in each zone.

The corner-three label covers both corners.

Women’s college basketball

load_wbb_shots() returns the same columns for Division I, so every helper above works unchanged. One difference to watch: the college feed spells shot types without spaces ("LayUpShot", "JumpShot", "MadeFreeThrow"), which is why the free throw filter is case-insensitive and allows for a missing space. The 2026 season (2025-26) is large, so subset to one team.

wbb_shots <- load_wbb_shots(seasons = 2026)

nrow(wbb_shots)
#> [1] 907805

wbb_team <- wbb_shots |>
  filter(team_name == "South Carolina", !grepl("free ?throw", type_text, ignore.case = TRUE)) |>
  fold_to_left_basket() |>
  add_zone()

nrow(wbb_team)
#> [1] 2599

geom_basketball("ncaa", display_range = "defense") +
  geom_hex(aes(x, y), data = wbb_team, binwidth = c(2, 2), alpha = 0.85) +
  scale_fill_gradient(low = "#fbe3e8", high = "#73000a", trans = "sqrt") +
  labs(
    title = "South Carolina, 2025-26: shot density",
    fill = "Attempts",
    caption = "Data: wehoop::load_wbb_shots()"
  )

Hexbin shot density map of South Carolina's 2025-26 field goal attempts on an NCAA court.

# The same zone-rule check as for the WNBA
wbb_team |>
  filter(scoring_play) |>
  summarise(agreement = mean(zone_is_three == (score_value == 3)))
#> # A tibble: 1 × 1
#>   agreement
#>       <dbl>
#> 1     0.999

wbb_team |>
  group_by(zone) |>
  summarise(attempts = n(), fg_pct = mean(scoring_play), .groups = "drop") |>
  mutate(share_of_attempts = attempts / sum(attempts))
#> # A tibble: 5 × 4
#>   zone                  attempts fg_pct share_of_attempts
#>   <fct>                    <int>  <dbl>             <dbl>
#> 1 Restricted area            768  0.719            0.295 
#> 2 Paint                      568  0.484            0.219 
#> 3 Mid-range                  621  0.375            0.239 
#> 4 Corner three               208  0.375            0.0800
#> 5 Above-the-break three      434  0.371            0.167

WNBA Stats API shots and `sdv_court_coords()`

load_wnba_stats_shots() reads the WNBA Stats API’s shot chart data from a GitHub release. It records the exact shot value (shot_value), the result (shot_result) and the league’s own shot_distance, but its locations are in the legacy stats frame: tenths of a foot, with the origin at the shooter’s basket. sdvplotR converts that frame into the sportyR court frame with sdv_court_coords(), which adds court_x and court_y in feet with the basket at x = -41.75. The "nba" provider is the right one here: stats.wnba.com uses the same frame as stats.nba.com, and the WNBA court shares the NBA court’s 94-foot length and basket position.

stats_shots <- load_wnba_stats_shots(seasons = 2025) |>
  sdvplotR::sdv_court_coords(x_column = "x_legacy", y_column = "y_legacy")

stats_shots |>
  select(player_name, team_tricode, action_type, shot_value, shot_distance,
         x_legacy, y_legacy, court_x, court_y) |>
  head()
#> # A tibble: 6 × 9
#>   player_name team_tricode action_type shot_value shot_distance x_legacy
#>   <chr>       <chr>        <chr>            <int>         <int>    <int>
#> 1 Howard      ATL          Made Shot            3            24      215
#> 2 Sykes       WAS          Missed Shot          2            17     -151
#> 3 Jones       ATL          Missed Shot          3            24       -2
#> 4 Citron      WAS          Missed Shot          2            17      151
#> 5 Iriafen     WAS          Missed Shot          2             7      -72
#> 6 Sykes       WAS          Missed Shot          2            18       -1
#> # ℹ 3 more variables: y_legacy <int>, court_x <dbl>, court_y <dbl>

stats_team <- stats_shots |> filter(team_tricode == "LVA")

geom_basketball("wnba", display_range = "defense") +
  geom_point(aes(court_x, court_y, color = factor(shot_value)),
             data = stats_team, alpha = 0.35, size = 1) +
  scale_color_manual(values = c(`2` = "#c2bccc", `3` = "#221A4D")) +
  labs(
    title = "Las Vegas Aces, 2025: two- and three-point attempts",
    color = "Shot value",
    caption = "Data: wehoop::load_wnba_stats_shots(), converted with sdvplotR::sdv_court_coords()"
  )

Half-court chart of one team's 2025 WNBA Stats API shots, colored by two-point or three-point attempt.

Because this table carries the true shot value, you can check zone rules exactly or use shot_value directly. Do not pass ESPN coordinate_x/coordinate_y to sdv_court_coords(): they are already in feet on the center-court frame.

Team-colored courts with `sdv_surface()`

sdvplotR::sdv_surface() wraps sportyR::geom_basketball() and paints the lane and apron in a team’s colors, optionally with its logo at center court (center_logo = TRUE). The "wnba" key draws the WNBA court and "wbb" the NCAA court. It needs sportyR 2.4.5 or later.

library(sdvplotR)

sdv_surface("wnba", team = "LV", display_range = "defense") +
  geom_hex(aes(x, y), data = team_shots, binwidth = c(2, 2), alpha = 0.8)

WNBA half court in Las Vegas Aces colors with a hexbin of the team's shots, and a plain NCAA half court with one women's college team's makes and misses.


# Plain NCAA court for women's college basketball
sdv_surface("wbb", display_range = "defense") +
  geom_point(aes(x, y, color = result), data = wbb_team, alpha = 0.4)