Phoenix, September 11, 2024. A’ja Wilson, late third quarter, takes a feed from Chelsea Gray fifteen feet from the basket, faces up against the Mercury’s center, takes one dribble right, and rises into a fadeaway that splashes through the net. The Aces are up 16. Wilson is on her way to a 31-point night on 64% true shooting. The basket is her 1,000th point of the season — the first WNBA player to break four digits in a single regular season, set against a backdrop of compressed schedules and physical opposing defenses that, ten years ago, would have considered the math impossible. Two months later, she is the unanimous MVP of a league that has, in the same calendar year, drawn the largest average attendance in its 28-season history and a national television audience that has, in pieces, finally matched the quality of play it has been broadcasting all along.
The WNBA in 2026 is a different league than the one the public was largely ignoring in 2022. The growth is real — viewership is up triple-digit percentages year over year, attendance is up 48% across recent seasons, and the analytics conversation has accelerated alongside the audience. What has not kept up, in my opinion, is the quality of the coverage. Mainstream WNBA writing is still, too often, structured as a profile of the league’s emergence rather than analysis of the basketball being played within it. The vocabulary that has long applied to the NBA — true shooting percentage, usage rate, on/off splits, lineup net ratings — applies to the WNBA without modification. It just has not been applied at scale. That gap is what this article is about.
I have been writing about basketball analytics since 2014, mostly NBA, but a growing share of the conversation in the last three years has been about the WNBA, and the writing I want to read about this league mostly does not exist yet. The WNBA, read with the same analytical vocabulary we apply to the men’s league, what the data shows about its best players, and where the public conversation still falls short, is the subject of this article.
The origin: where WNBA analytics caught up
The WNBA was founded in 1996, played its first season in 1997, and for most of the league’s first two decades, the public analytical conversation around it consisted of points-per-game leaderboards and the occasional eye-test debate. The advanced metrics that had reshaped NBA writing in the 2000s — true shooting, usage rate, BPM — were calculable for WNBA players, but the audience for those calculations was small, the publishing infrastructure smaller, and the broadcast partners largely uninterested.
The pivot point was not a single event but a cluster. Basketball-Reference launched its WNBA section in the late 2010s, mirroring the structure and depth of its NBA data. The site now publishes per-100-possessions stats, advanced stats, win shares, and Plus/Minus splits for every WNBA player and team going back to 1997. The data, in clean public form, has been available for years.
Around the same time, the league’s tracking-data initiative — modeled on the NBA’s 2013 deployment — produced shot location data, possession counts, and lineup splits that put WNBA analytics on roughly the same technical footing as the NBA. The challenge has been less data availability and more analytical adoption. The writers who built their reputations on NBA work were slow to import the same toolkit to the women’s game.
By 2023, that began changing meaningfully. Her Hoop Stats emerged as the leading public analytics outlet for women’s basketball, providing the same depth of statistical coverage for WNBA and college women’s basketball that established sites had long provided for the men’s leagues. Writers like Chantel Jennings, Kim Klement, Lyndsey D’Arcangelo, and Howard Megdal began producing analytics-informed long-form work in The Athletic, Just Women’s Sports, and Power Plays. By the time A’ja Wilson’s 1,000-point season happened, the analytical vocabulary was finally catching up to the basketball being played.
How WNBA analytics works: in plain language
The mechanics are identical to NBA analytics. A possession ends when a team takes a shot that ends in a basket, a defensive rebound, or a turnover. Pace is measured per 40 minutes rather than per 48 (the WNBA plays four 10-minute quarters), but the per-possession math is the same. Offensive rating measures points scored per 100 possessions; defensive rating, points allowed per 100; net rating, the difference. True shooting percentage uses the same formula. Usage rate, on/off splits, lineup net ratings — same vocabulary, same calculations, same interpretive load.
What differs is the statistical environment. The WNBA has, on average, played at a slower pace than the modern NBA — about 78-82 possessions per 40 minutes in recent seasons, compared to the NBA’s 99-101 per 48. The three-point revolution has reached the WNBA but on a smaller scale; the league’s three-point attempt rate in 2024 was about 33% of total attempts, comparable to the NBA in the early 2010s. Defensive intensity is high; offensive rebounding is more important in the WNBA’s possession structure; the league’s elite scorers operate at true-shooting percentages broadly comparable to the NBA’s mid-tier stars.
The league’s most efficient scorers — A’ja Wilson, Breanna Stewart, Alyssa Thomas, Napheesa Collier in 2024 — post true shooting numbers in the .620 range, which is elite by any league standard. League average TS% in the WNBA in 2024 sat around .545. The gap between elite and average mirrors the NBA’s structural gap. The math behaves the same way; the absolute numbers shift slightly with the pace and shot profile of the league.
The critical component: per-possession reads, not per-game
The single most important analytical correction for WNBA writing is the same one that matters for NBA writing: read per-possession, not per-game. WNBA games are 40 minutes, not 48. Pace is variable across teams. A player averaging 20 points per game on a fast Las Vegas team is producing differently than a player averaging the same on a methodical Connecticut team. The per-game number flattens the comparison; the per-possession number reveals it.
The 2024 MVP race, by per-game scoring, looked close at the top — Wilson, Caitlin Clark, Arike Ogunbowale all averaging within a few points of each other. Per-100 possessions and adjusted for usage rate, Wilson’s dominance was structurally more clear: she was scoring at elite efficiency on a usage rate that approached 30%, while sustaining a true shooting percentage in the top tier of the league. The MVP voting got the answer right. The per-game data alone would have made the argument harder.
WNBA metrics vs the comparable men’s basketball reads: a comparison
The cleanest way to develop a feel for WNBA analytics is to understand the absolute numbers shift relative to the NBA. A short comparison of typical 2024 league environments:
| Metric | WNBA 2024 league average | NBA 2024 league average | What this tells you |
|---|---|---|---|
| Pace (poss/40 or 48) | ~80 per 40 min | ~99 per 48 min | Per-minute possession rate roughly comparable; total game possessions lower |
| True shooting %% | 0.545 | 0.580 | WNBA average lower; elite TS%% still ~0.620, similar gap to mean |
| 3-point rate (3PA/FGA) | ~33%% | ~40%% | WNBA still behind on three-point revolution; mid-range more central |
| Offensive rebound rate | ~22%% | ~27%% | WNBA possession structure favors defensive rebound conversion |
| Usage by elite scorers | 26-30%% | 30-36%% | WNBA stars carry somewhat lower max usage; team-context dependent |
The honest WNBA piece reads these numbers in context. A 0.620 true shooting WNBA forward is not 0.620 in NBA terms; she is, relative to her league environment, one of the most efficient scorers in the world. The same player in NBA terms would be Nikola Jokic, Stephen Curry, Kevin Durant. The math says so. The coverage often does not.
What the data needs: inputs
The same inputs that power NBA analytics power WNBA analytics. Play-by-play data is published by the league and aggregated by Basketball-Reference. Shot location data is available through the WNBA’s player-tracking system. Per-100 possession stats, on/off splits, and lineup net ratings are all calculable from the same public sources.
For the writers and fans who want to do this themselves: Her Hoop Stats is the cleanest current public source, with WNBA leaderboards, advanced metrics, and shot location data. Basketball-Reference’s WNBA section provides the historical depth — full career advanced stats for every player since 1997, sorted, filtered, and freely searchable. WNBA.com/stats publishes the league’s official advanced metrics, including player-tracking-derived measurements that emerged in the late 2010s.
The infrastructure exists. The challenge has been adoption — getting writers and editors at mainstream outlets to use the per-100 reads consistently, rather than defaulting to the per-game numbers that flatter or distort player profiles depending on team pace.
Building the analysis: a working framework
The practical workflow for WNBA writing using the analytical toolkit:
- Start with offensive and defensive rating. The 2024 New York Liberty led the league at 110.4 offensive and 95.8 defensive. The Las Vegas Aces, the team they ultimately lost to in the Finals, were 109.0/94.0 — a slightly better defense, slightly less explosive offense. The Finals MVP outcome reflected the matchup numbers.
- Check true shooting and usage for the team’s primary creator. A team built around a 0.620 TS%, 28% usage forward is structured differently than one built around a 0.560 TS%, 33% usage guard. Both can win; they win differently.
- Look at the lineup data. The closing five-player lineup matters in tight games. The WNBA’s smaller league size (12 teams, 12-player rosters) means lineup combinations stabilize more quickly than in the NBA. Two hundred minutes of WNBA lineup data is roughly equivalent, in informational value, to five hundred NBA minutes.
- Cross-check defensive impact via on/off. Defensive measurement is harder in any league, but the WNBA’s compressed roster makes on/off splits particularly informative — fewer minutes of garbage time, less noise from bench-only stretches.
- Watch the film. The numbers tell you which players to focus on. The film tells you what’s actually happening in the matchups.
Where this gets weird: common mistakes
WNBA analytics has its own version of every common pitfall.
Small samples are smaller. A WNBA regular season is 40 games, compared to the NBA’s 82. Every per-100 number stabilizes more slowly, in absolute terms. A 15-game stretch in the WNBA covers more than a third of the season — the equivalent of about 30 NBA games, in terms of informational weight. Writers extrapolating from small WNBA samples are working with even less foundation than the analogous NBA writers.
Schedule strength matters more in compressed format. A WNBA team that plays five back-to-backs in three weeks is not playing the same league as a team with rest. The compressed schedule, dictated partly by arena availability and partly by the league’s broadcast-driven structure, creates more rest-disparity than the NBA. Schedule-adjusted records, when they exist, are particularly informative.
Injuries hit harder. A 12-player roster, with a top-heavy talent distribution at the very top, means a single injury can change a team’s per-100 numbers more than the NBA equivalent. Filter on-floor minutes for the rotation that has actually been on the court.
Cross-league comparison invites bad faith. The most common bad WNBA writing compares the league to the NBA in absolute terms — pace, average athleticism, shooting percentages — without context. The comparison is, at best, uninteresting. The leagues operate in different environments. Comparing A’ja Wilson’s TS% directly to LeBron James’s is a category mistake. Comparing each to her league’s elite tier is, instead, the actual analytical question.
When the analytical toolkit shines: use cases
The strongest applications:
MVP and award races. The WNBA’s MVP voting has, in the last three years, converged with what the per-possession data was already showing. A’ja Wilson’s 2022 MVP, her 2024 unanimous MVP, Breanna Stewart’s 2023 MVP — the analytics had each of these as the correct answer well before the voting concluded. Writers using the toolkit can call these races early and accurately.
Cross-team comparison in season. A 16-9 team with a 110/96 efficiency profile is structurally different from a 16-9 team with a 105/100 profile. The records look identical. The trajectory toward the playoffs is opposite. WNBA writers using per-100 reads can identify the playoff teams that are quietly under-rated and the regular-season darlings whose efficiency profile predicts an early exit.
Roster construction and trade evaluation. The WNBA’s compressed offseason and trade market makes roster construction a higher-stakes exercise than the NBA. A team that adds a 0.610 TS%, 24% usage forward to a roster anchored by a primary creator is solving a different problem than one adding a 0.555 TS%, 30% usage guard. The math, applied carefully, predicts which transactions will work.
College-to-pro projection. The expanded NCAA Division I women’s basketball analytics, particularly the work at Her Hoop Stats and Coordinator Stats, now produces per-possession profiles for college players that translate, with appropriate scaling, to WNBA expectations. The 2024 draft class — Caitlin Clark, Angel Reese, Cameron Brink — was projected with reasonable accuracy by the public analytics community before they played a single professional minute.
The limits: what the analytical toolkit cannot tell you
The honest version of this writing names the limits.
The analytics cannot tell you who will win the Finals. They can tell you which teams have been the better units in the regular season, and which matchups favor which teams in a playoff series. The translation from regular-season profile to seven-game series outcome is non-trivial, especially in the WNBA’s compressed playoff structure (single-elimination first round, then best-of-five or best-of-seven).
The analytics cannot evaluate intangibles like leadership, locker-room culture, or coaching adjustments in real time. A team that has integrated a new midseason addition smoothly will look better on tape than the cumulative per-100 numbers initially capture. The lag is small but real.
The analytics cannot, on their own, support the broader case for women’s basketball as a product worth covering. The growth in viewership, attendance, and cultural relevance is a sociological and commercial story; the analytics are a separate, parallel conversation about how to read the basketball being played. Both stories matter. They are not the same story.
The analytics cannot replace the watching. The Connecticut Sun’s defensive identity. Las Vegas’s offensive sequencing in late-game situations. Minnesota’s young-roster volatility. These are best understood by watching the games, and the writers who do this well combine the data with the eye in roughly the same way the best NBA writers do.
A working example: A’ja Wilson’s 2024 MVP campaign
A’ja Wilson’s 2024 season is the clearest illustration of how the analytical toolkit applies cleanly to the WNBA. Her per-game numbers — 26.9 points, 11.9 rebounds, 2.6 blocks — were historic on their face. Her per-100 possessions numbers were more revealing. Wilson posted an offensive rating of 122 with a usage rate of 30.4%, sustaining a true shooting percentage of 0.624 — elite by any league’s standards. Her on-court net rating for the Aces was +9.7; her off-court was -2.1, a swing of nearly 12 points per 100 possessions. By every advanced metric available, she was the best player in the league by a clear margin.
What the analytics added to the conversation, beyond the eye test, was the specifics. Wilson’s defensive impact, measured by opponent field-goal percentage at the rim with her on the floor, was historically suppressive — opponents shot 49% at the rim against the Aces with Wilson defending the paint, against a league average closer to 62%. That single number is the kind of thing that does not surface in points-per-game coverage but is, structurally, why Las Vegas’s defensive rating was elite in 2024 even as the team’s perimeter defense was uneven. The MVP voting got the answer right, unanimously. The analytics had been telling the same story since June, in the kind of detail that the polls alone never reach.
One final limit, less methodological and more sociological. The WNBA’s analytics conversation, despite the genuine progress of the last three years, still operates against a backdrop where mainstream coverage frequently confuses analytical literacy with criticism of the league. Writing that says “A’ja Wilson posted a 0.624 TS%% in 2024” gets read by some audiences as undermining the women’s game, even when the same writing about an NBA player would be unambiguously positive. The corrective is to write the analytics in absolute terms, with appropriate league context, and to refuse the false framing. The numbers are not commentary on the league’s worth. They are how the league becomes intelligible.
The other emerging limit is roster volatility. The WNBA’s offseason free-agency cycle, expanded by the new CBA, has created a level of player movement that the league had not previously experienced. Analytical reads that worked when teams stayed largely intact season to season have to adapt to a context where a team’s identity can flip meaningfully in March. The data is still the data. The interpretive load on the writer is higher when the underlying personnel is in flux. Expect the analytical writing to evolve over the next few seasons to keep up.
The final practical limit worth naming: WNBA-specific advanced analysis still depends on writers who actually watch the games. The data infrastructure exists. The cultural infrastructure — the network of writers, podcasters, and analysts who specialize in the league with the depth that NBA Twitter takes for granted — is growing but unevenly. A reader who wants weekly analytical depth on the WNBA still has to seek out a smaller set of voices than they would for the NBA. Her Hoop Stats is the canonical resource, and the work at The Athletic and Just Women’s Sports is improving year over year. The metric vocabulary is the same; the writing layer is younger. Both will mature. The decade ahead is, in my opinion, going to make this article look like an early draft of a much fuller conversation, and that is the most encouraging analytical fact about the WNBA in 2026.
Frequently asked questions
How do WNBA advanced stats compare to the NBA’s?
The vocabulary is identical — true shooting, usage, BPM, net rating, on/off — and the calculations are the same. The absolute numbers differ because the leagues have different paces, different shot profiles, and different overall offensive environments. WNBA league average TS% (~0.545) is lower than NBA league average (~0.580). The gap between elite and average is similar in both leagues, which means an elite WNBA scorer (0.620 TS%) is, relative to her environment, comparable to an elite NBA scorer in his.
Why don’t broadcasts use advanced WNBA stats more?
A mix of legacy and infrastructure. NBA broadcasts have spent fifteen years integrating advanced metrics into their on-air vocabulary, mostly because their parent network analytics departments have invested in producing them. WNBA broadcasts are catching up, with ESPN, Ion, and CBS now occasionally surfacing per-100 and TS% reads, but the integration is uneven. Expect this to continue improving as viewership and ad spend grow.
How does Caitlin Clark’s first WNBA season grade out, analytically?
Clark’s rookie year was structurally similar to elite college guards adjusting to pro defenses — high usage (around 28%), efficient by rookie standards but well below her college numbers, with a strong assist rate offsetting a turnover rate that reflected the steeper defensive environment. The per-100 reads on her were closer to “promising starter on a developing team” than “MVP candidate,” which the voting reflected. Her trajectory toward elite status will depend on whether her efficiency stabilizes as the Indiana roster matures around her.
Where can I find this data?
Her Hoop Stats is the cleanest current source, with searchable WNBA advanced metrics. Basketball-Reference’s WNBA section provides historical depth back to 1997. WNBA.com/stats publishes the league’s official advanced data, including shot location.
Sources and further reading
- Her Hoop Stats — the leading public analytics outlet for women’s basketball, indispensable for current WNBA writing.
- Basketball-Reference WNBA — historical depth, full advanced stats archives back to 1997.
- Just Women’s Sports — long-form writing on women’s basketball with growing analytics integration.
- The Athletic WNBA coverage — particularly Chantel Jennings, Sabreena Merchant, Kim Klement.
- WNBA.com/stats — the league’s official statistics portal, including player-tracking data.
The Wilson 1,000-point season that opened this article — 31 points on 64% true shooting in Phoenix, the league’s first four-digit regular season — was, for analytics readers, the kind of result the per-possession numbers had been predicting all year. The Aces’ offensive rating with her on the floor was historic. Her on/off net rating was the highest in the league. The MVP vote came in unanimous because the math had been telling the same story since June. The WNBA in 2026 is a league that rewards the writers who bring the right toolkit. For the NBA-side foundations of the same vocabulary, our field guide to NBA advanced stats is the natural starting point — most of it translates.
