WNBA: a live experiment, year one
The honest prior, stated up front: in six-season backtesting this model is market-competitive on accuracy but has no established cash edge. Its known weak spots (September, large favorites) are named below and under active work. The live season — starting July 19, 2026 — is the test.
Spreads — live record vs the close
Champion model only. Graded against the closing line (the hardest benchmark); CLV = how far the market moved from our published-at opener toward the close, the experiment’s primary metric.
| Slice | Record | Win % | Avg CLV (pts) | n |
|---|---|---|---|---|
| All graded picks | 21-23 | 47.7% | -0.21 | 44 |
| Divergence ≥ 2 pts (cumulative) | 11-11 | 50.0% | -0.26 | 22 |
| Divergence ≥ 4 pts (cumulative) | 6-2 | 75.0% | -0.19 | 8 |
| Divergence ≥ 6 pts (cumulative) | — | — | — | 0 |
Divergence bands are cumulative (≥) and measured against the opener the pick was published into. Small samples early — percentages stabilize as the season accumulates.
Month by month
| Month | Record | Win % | Avg CLV (pts) | n |
|---|---|---|---|---|
| Jul 2026 | 12-14 | 46.2% | -0.33 | 26 |
| Aug 2026 | 9-9 | 50.0% | -0.04 | 18 |
Published, awaiting grade
| Date | Matchup | Model line | Opener | Edge |
|---|---|---|---|---|
| 2026-08-08 | Las Vegas Aces @ Minnesota Lynx | -2.4 | -4.5 | 2.1 |
| 2026-08-08 | Indiana Fever @ Chicago Sky | +3.5 | +6.5 | 3.0 |
| 2026-08-08 | Seattle Storm @ Portland Fire | -1.2 | -1.5 | 0.3 |
| 2026-08-09 | Phoenix Mercury @ Washington Mystics | -3.6 | -1.5 | 2.1 |
| 2026-08-09 | Golden State Valkyries @ Los Angeles Sparks | +7.5 | +6.0 | 1.5 |
Totals EXPERIMENTAL
Backtesting showed no edge on totals — they’re tracked here for the record, not as a claim. That’s what an experiment publishes: the nulls too.
Graded: 22-18 (55.0%) · avg CLV -0.23 · n=40
Methodology & the honest priors
The model: opponent-adjusted efficiency ratings computed from box scores (a from-scratch WNBA equivalent of the ratings that drive our college model), the same line-assembly anatomy as our NCAA champion, with every parameter — home-court, scale, variance, seasonal weights — re-fit on four seasons of WNBA data under walk-forward rules (the model only ever sees data available before each game it predicts).
Backtest, labeled as backtest: across 2023-25 (835 games vs true closing lines) the model missed final margins by 9.96 points on average vs the market’s 9.47 — market-competitive, not market-beating. Its disagreements showed an ordered curve (52.7% at ≥2 divergence, 54.1% at ≥4, 56.3% at ≥6 on n=167) — but tournament-thin samples and a known September weakness mean we claim no cash edge. Those backtest figures never appear in the live tables above; the live season stands on its own.
Known open problems, named before the season instead of after: September performance (late-season roster states the model can’t yet see) and very large favorites. Both are under active, pre-registered improvement work; any model change during the season will be disclosed here with a date.
Every row’s pre-tip hash is re-verified when this page’s feed is generated — a mutated snapshot would be caught mechanically. Questions about the methodology are welcome; the answers are longer and nerdier than you want.