WNBA

This page is regenerated every week by sdvplot's docs workflow. 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 through sportsdataverse-py (no stats.wnba.com calls).

The WNBA season tips off in May, so until then the calendar points at last season. For a season that is not published yet load_wnba_team_boxscore warns and returns an empty frame rather than raising, so the helper below turns "no regular-season games" into a NoDataError and the page steps back one season.

import datetime as dt
import warnings

import polars as pl
import sportsdataverse.wnba as wnba
from IPython.display import Markdown, display
from sportsdataverse.errors import NoDataError

import sdvplot

today = dt.date.today()
current = today.year if today.month >= 5 else today.year - 1
SOURCE = "Data: wehoop (ESPN) via sportsdataverse-py"


def label(season):
    return str(season)


def team_games(season):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")  # "no data for season(s)": handled just below
        box = wnba.load_wnba_team_boxscore(seasons=[season])
    if box.is_empty() or box.filter(pl.col("season_type") == 2).is_empty():
        raise NoDataError(f"no {label(season)} regular-season games yet")
    return box


try:
    season, box = current, team_games(current)
except NoDataError as err:
    print(f"{err}; showing {label(current - 1)} instead")
    season, box = current - 1, team_games(current - 1)

The status line is written when the page runs: the season to date, a finished regular season with the playoffs under way, or the offseason.

last_game = box["game_date"].max()
if season < current:
    status = f"**Offseason:** the final {label(season)} regular season; the {label(current)} season has no games yet."
    through = "final regular season"
elif box.filter(pl.col("season_type") == 3).is_empty():
    status = f"**Updated {today}:** the {label(season)} season through {last_game:%B} {last_game.day}."
    through = f"through {last_game:%b} {last_game.day}"
elif (today - last_game).days <= 10:
    status = f"**Updated {today}:** the final {label(season)} regular season; the playoffs are under way."
    through = "final regular season"
else:
    status = f"**Offseason:** the final {label(season)} regular season."
    through = "final regular season"
subtitle = through[:1].upper() + through[1:]
display(Markdown(status))

Updated 2026-10-05: the final 2026 regular season; the playoffs are under way.

ESPN files the All-Star Game and the in-season 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. resolve then maps the ESPN team ids to sdvplot's, for the team table's names and conferences.

standard = wnba.load_wnba_schedule(seasons=[season]).filter(
    (pl.col("season_type") == 2) & (pl.col("type_abbreviation") == "STD")
)
assert box.schema["game_id"] == standard.schema["game_id"]
regular = box.filter(pl.col("season_type") == 2).join(standard.select("game_id"), on="game_id", how="semi")
regular = regular.with_columns(
    team=pl.Series(sdvplot.resolve(regular["team_id"].cast(pl.String).to_list(), "wnba"), dtype=pl.String)
)
possessions = (  # the box-score estimate, averaged with the opponent's
    pl.col("field_goals_attempted")
    - pl.col("offensive_rebounds")
    + pl.col("total_turnovers")
    + 0.44 * pl.col("free_throws_attempted")
)
games = regular.with_columns(poss=possessions).with_columns(poss=pl.col("poss").mean().over("game_id"))
teams = sdvplot.teams("wnba").select(team="team_id", abbr="abbr", name="short_name", conference="conference")
ratings = (
    games.sort("game_date")
    .group_by("team")
    .agg(
        w=pl.col("team_winner").sum(),
        l=(~pl.col("team_winner")).sum(),
        l10=pl.col("team_winner").tail(10).sum(),
        ortg=100 * pl.col("team_score").sum() / pl.col("poss").sum(),
        drtg=100 * pl.col("opponent_team_score").sum() / pl.col("poss").sum(),
    )
    .with_columns(pct=pl.col("w") / (pl.col("w") + pl.col("l")), net=pl.col("ortg") - pl.col("drtg"))
    .join(teams, on="team")
    .sort(["pct", "net", "team"], descending=[True, True, False])  # a tiebreaker keeps re-renders stable
)
ratings.head()

team	w	l	l10	ortg	drtg	pct	net	abbr	name	conference
8	33	11	6	109.869267	101.183081	0.75	8.686187	MIN	Lynx	Western Conference
129689	32	12	7	104.772891	95.788217	0.727273	8.984674	GS	Valkyries	Western Conference
17	31	13	8	110.63893	103.794434	0.704545	6.844495	LV	Aces	Western Conference
20	30	14	9	108.548278	100.391627	0.681818	8.156651	ATL	Dream	Eastern Conference
5	28	16	6	112.18877	105.731672	0.636364	6.457099	IND	Fever	Eastern Conference

1. Standings with net rating

Each conference ordered by winning percentage, with every team's points scored and allowed per 100 possessions.

from great_tables import GT

from sdvplot.great_tables import gt_save_crop, gt_sdv_logos, gt_theme_athletic

table = ratings.with_columns(
    record=pl.format("{}-{}", "w", "l"),
    last10=pl.format("{}-{}", "l10", pl.min_horizontal(10, pl.col("w") + pl.col("l")) - pl.col("l10")),
    seed=pl.col("pct").rank("ordinal", descending=True).over("conference"),
).sort("conference", "seed")
table = table.select("conference", "seed", "abbr", "name", "record", "pct", "last10", "ortg", "drtg", "net")
gt = (
    GT(table, groupname_col="conference", id="wnba-standings")  # fixed id: no random one each run
    .tab_header(f"WNBA standings and ratings, {label(season)}", subtitle)
    .fmt_number("pct", decimals=3)
    .cols_align("left", "name")
    .fmt_number(["ortg", "drtg"], decimals=1)
    .fmt_number("net", decimals=1, force_sign=True)
    .data_color("net", palette=["#c84630", "#f7f7f7", "#2e8b57"], domain=[-15, 15])
    .tab_spanner("Per 100 possessions", ["ortg", "drtg", "net"])
    .cols_label(
        seed="", abbr="", name="Team", record="W-L", pct="Pct", last10="Last 10", ortg="Off", drtg="Def", net="Net"
    )
    .tab_source_note(SOURCE + ". Possessions estimated from the box score; ties ordered by net rating.")
)
gt = gt_theme_athletic(gt_sdv_logos(gt, "abbr", league="wnba", height=28))
gt

gt_save_crop renders the same table to a trimmed PNG, ready to post.

gt_save_crop(gt, width=900)

png

2. Offense against defense

plotnine with geom_sdv_logos: offensive rating against defensive rating, the defense axis reversed so the better defenses sit higher, and geom_mean_lines at the league averages.

from plotnine import aes, element_text, ggplot, labs, scale_x_continuous, scale_y_reverse, theme, theme_minimal

from sdvplot.plotnine import geom_mean_lines, geom_sdv_logos

(
    ggplot(ratings.to_pandas(), aes("ortg", "drtg", x0="ortg", y0="drtg", team="team"))
    + geom_mean_lines(color="grey")
    + geom_sdv_logos(league="wnba", season=season, height=0.09)
    + scale_x_continuous(expand=(0.06, 0))  # logos do not widen the limits: leave room for the outermost ones
    + scale_y_reverse(expand=(0.08, 0))
    + labs(
        x="Offensive rating (points per 100 possessions)",
        y="Defensive rating (reversed)",
        title=f"WNBA offense vs defense, {label(season)} {through}",
        caption=SOURCE,
    )
    + theme_minimal()
    + theme(figure_size=(8, 6), plot_title=element_text(weight="bold"))
)

png

3. Scoring leaders with headshots

Points, rebounds and assists 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. gt_sdv_headshots turns the ESPN athlete ids into headshots and gt_color_pills draws the scoring column.

from sdvplot.great_tables import gt_color_pills, gt_sdv_headshots, gt_theme_almanac

players = (
    wnba.load_wnba_player_boxscore(seasons=[season])
    .filter((pl.col("season_type") == 2) & ~pl.col("did_not_play") & pl.col("minutes").is_not_null())
    .join(standard.select("game_id"), on="game_id", how="semi")
)  # no All-Star or cup final
games_played = players.group_by("team_id").agg(pl.col("game_id").n_unique())["game_id"].max()
leaders = (
    players.sort("game_date")
    .group_by("athlete_id")
    .agg(
        name=pl.col("athlete_display_name").last(),
        team=pl.col("team_abbreviation").last(),
        gp=pl.len(),
        ppg=pl.col("points").mean(),
        rpg=pl.col("rebounds").mean(),
        apg=pl.col("assists").mean(),
    )
    .filter(pl.col("gp") >= games_played / 2)
    .sort(["ppg", "athlete_id"], descending=[True, False])
    .head(10)
    .with_row_index("rank", offset=1)
    .select("rank", "athlete_id", "name", "team", "gp", "ppg", "rpg", "apg")
)
leaders_gt = (
    GT(leaders, id="wnba-leaders")
    .tab_header(f"WNBA scoring leaders, {label(season)}", f"{subtitle}; minimum {games_played / 2:.0f} games")
    .fmt_number(["rpg", "apg"], decimals=1)
    .cols_label(rank="", athlete_id="", name="Player", team="", gp="GP", ppg="PPG", rpg="RPG", apg="APG")
    .tab_source_note(SOURCE)
)
leaders_gt = gt_color_pills(
    leaders_gt, "ppg", palette=["#f3e4c8", "#c8102e"], digits=1, domain=[leaders["ppg"].min(), leaders["ppg"].max()]
)
leaders_gt = gt_sdv_logos(
    gt_sdv_headshots(leaders_gt, "athlete_id", league="wnba", height=40), "team", league="wnba", height=24
)
leaders_gt = gt_theme_almanac(leaders_gt)
leaders_gt

gt_save_crop(leaders_gt, width=800)

png

Run it yourself

Download the notebook (outputs cleared) or open it on GitHub.

1. Standings with net rating​

2. Offense against defense​

3. Scoring leaders with headshots​

Run it yourself​

1. Standings with net rating

2. Offense against defense

3. Scoring leaders with headshots

Run it yourself