2026 WNBA Basketball Teams
Click column headers to sort. Hover model headers for a brief description. Model columns show league rank within each model.
Sorted by Bradley-Terry: tied for the best held-out log loss, 2023 to 2025 (0.612 on 662 games, each week predicted from the games before it); within noise of Bradley-Terry Recency, Margin, Margin Recency, Adjusted Efficiency, Log Adjusted, Points Off/Def, Points Off/Def Recency, Core Ensemble, Recency Ensemble, Dynamic Bradley-Terry, Adjusted Context Blend (2 paired standard errors), and ties go to the simpler model.
| Team | Conf | ELO ELO Streaming paired-comparison rating with recency baked into sequential updates. More → ↓ | BT BT Static logistic paired-comparison model with one team strength parameter. More → ↓ | BTr BTr Static Bradley-Terry with exponential recency weights on newer games. More → ↓ | Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → ↓ | MargR MargR Margin regression with exponential recency weights on newer games. More → ↓ | PythRaw PythRaw Pythagorean win expectation from raw points scored and allowed. More → ↓ | PythAdj PythAdj Opponent-adjusted efficiency model with separate offensive and defensive components. More → ↓ | PythLog PythLog Log-scale adjusted efficiency model that downweights blowout leverage. More → ↓ | PtsOD PtsOD Raw points regression with separate offensive and defensive team parameters. More → ↓ | PtsR PtsR Off/def points regression with exponential recency weights. More → ↓ | Ens Ens Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → ↓ | EnsR EnsR Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → ↓ | DynBT DynBT Time-evolving paired-comparison model with latent team strength drift. More → ↓ | Adj+ Adj+ Chronologically trained component blend with rest and signed venue context. More → ↓ | W-L | Last/Next |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Western | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 2 | 1 | 1 | 41-16 | 10/31 | |
| Western | 4 | 2 | 7 | 3 | 8 | 3 | 3 | 3 | 3 | 8 | 4 | 5 | 5 | 4 | 33-13 | 09/29 | |
| Eastern | 2 | 3 | 2 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 38-18 | 10/31 | |
| Western | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 3 | 33-17 | 10/09 | |
| Eastern | 6 | 5 | 6 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 6 | 6 | 5 | 29-18 | 10/01 | |
| Western | 8 | 6 | 5 | 7 | 6 | 6 | 6 | 6 | 7 | 6 | 7 | 7 | 8 | 7 | 28-19 | 10/02 | |
| Eastern | 5 | 7 | 3 | 8 | 7 | 8 | 8 | 8 | 8 | 7 | 8 | 8 | 4 | 8 | 28-18 | 09/30 | |
| Eastern | 7 | 8 | 8 | 6 | 3 | 7 | 7 | 7 | 6 | 4 | 6 | 4 | 7 | 6 | 28-21 | 10/09 | |
| Western | 9 | 9 | 10 | 12 | 11 | 12 | 12 | 12 | 12 | 11 | 10 | 10 | 10 | 10 | 17-27 | 09/22 | |
| Western | 12 | 10 | 12 | 11 | 9 | 11 | 11 | 11 | 11 | 9 | 12 | 11 | 12 | 11 | 16-28 | 09/24 | |
| Western | 10 | 11 | 9 | 9 | 10 | 9 | 9 | 9 | 9 | 10 | 9 | 9 | 9 | 9 | 16-28 | 09/24 | |
| Eastern | 11 | 12 | 11 | 10 | 12 | 10 | 10 | 10 | 10 | 12 | 11 | 12 | 11 | 12 | 16-28 | 09/24 | |
| Eastern | 14 | 13 | 15 | 14 | 15 | 14 | 14 | 14 | 14 | 15 | 15 | 15 | 14 | 15 | 11-33 | 09/24 | |
| Eastern | 13 | 14 | 13 | 15 | 14 | 15 | 15 | 15 | 15 | 14 | 13 | 13 | 13 | 13 | 11-33 | 09/24 | |
| Western | 15 | 15 | 14 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 14 | 14 | 15 | 14 | 8-36 | 09/23 |
