Model Outputs
2025-2026
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 904 (#464) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 983 (#506) | HCA +62 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -27.5 (#658) | HCA +2.9 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.039 (#677) | - |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.064 (#578) | AdjNet -23.3 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.059 (#578) | AdjNet -22.8 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.209 (#572) | AdjO 52.7 | AdjD 67.3 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.390 (#549) | AdjO 58.7 | AdjD 63.6 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.148 (#635) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.179 (#622) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 866 (#445) | RD 350 | GP 1 |
2026 Schedule & Results
2026 Roster
Minutes by Position
The surface stays filled across the five on-court roles. Use the labels or legend to isolate how each player absorbs guard-to-big minutes.
| Player | Pos | GP | MIN | PTS | REB | AST | STL | BLK | TO | FGA | Numbers | PM | PM/G | PM/40 | FG% | 3P% | FT% | RAPM | TS% | eFG% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gabrielle Haskie | - | 1 | 21.8 | 9.0 | 2.0 | 2.0 | 0.0 | 0.0 | 2.0 | 4.0 | 7.0 | - | - | - | 75.0 | 0 | 37.5 | - | 59.8 | 75.0 |
| Sophia Moron | - | 1 | 34.1 | 8.0 | 7.0 | 3.0 | 1.0 | 0.0 | 6.0 | 8.0 | 5.0 | - | - | - | 37.5 | 50.0 | 0 | - | 50.0 | 50.0 |
| Chloe Warren | - | 1 | 26.1 | 7.0 | 3.0 | 0.0 | 1.0 | 0.0 | 2.0 | 7.0 | 2.0 | - | - | - | 28.6 | 20.0 | 100.0 | - | 44.4 | 35.7 |
| Tatiana Ayala | - | 1 | 21.1 | 6.0 | 5.0 | 3.0 | 1.0 | 0.0 | 3.0 | 6.0 | 6.0 | - | - | - | 50.0 | 0 | 0 | - | 50.0 | 50.0 |
| Tatum Howlingwater | - | 1 | 15.0 | 5.0 | 3.0 | 0.0 | 0.0 | 0.0 | 5.0 | 5.0 | -2.0 | - | - | - | 40.0 | 0.0 | 33.3 | - | 39.6 | 40.0 |
| Emma Koker | - | 1 | 17.7 | 5.0 | 5.0 | 0.0 | 0.0 | 0.0 | 3.0 | 5.0 | 2.0 | - | - | - | 20.0 | 0 | 50.0 | - | 32.7 | 20.0 |
| Aerianz Brown | - | 1 | 9.9 | 2.0 | 2.0 | 1.0 | 1.0 | 0.0 | 1.0 | 2.0 | 3.0 | 14 | 1.4 | 37.3 | 50.0 | 0.0 | 0 | -0.51 | 50.0 | 50.0 |
| Jordan Hankston | - | 1 | 17.1 | 2.0 | 2.0 | 0.0 | 0.0 | 0.0 | 3.0 | 1.0 | 0.0 | - | - | - | 100.0 | 0 | 0 | - | 100.0 | 100.0 |
| Juliana Kirker | - | 1 | 22.9 | 0.0 | 5.0 | 2.0 | 1.0 | 0.0 | 3.0 | 6.0 | -1.0 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
| Takara Rocha | - | 1 | 14.4 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 5.0 | 1.0 | -5.0 | - | - | - | 0.0 | 0 | 0 | - | 0.0 | 0.0 |
Numbers/Game vs RAPM
Not enough players with both Numbers/Game and RAPM to plot.
Advanced: Numbers = PTS+REB+AST+STL+BLK-TO-FGA, PM = total +/-, PM/G = per game, PM/40 = per 40 minutes, RAPM = Regularized Adj Plus-Minus, TS% = True Shooting, eFG% = Effective FG