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 → | 927 (#430) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 960 (#552) | HCA +62 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +8.6 (#221) | HCA +2.9 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.306 (#535) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.256 (#429) | NetEff -7.9 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.397 (#410) | AdjNet -3.6 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.398 (#409) | AdjNet -3.5 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.462 (#391) | AdjO 69.3 | AdjD 70.9 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.577 (#263) | AdjO 65.5 | AdjD 62.1 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.557 (#332) | 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 → | 0.546 (#342) | Blend of Elo, BT, Margin, PythLog, PtsOD |
2026 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2025-12-02 | @ | UC Santa Cruz | L | 72 - 79 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sarina Nagra | - | 1 | 40.1 | 22.0 | 4.0 | 3.0 | 3.0 | 0.0 | 2.0 | 17.0 | 13.0 | - | - | - | 52.9 | 25.0 | 50.0 | - | 58.6 | 58.8 |
| Mariah Flores | - | 1 | 32.4 | 13.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 11.0 | 7.0 | - | - | - | 36.4 | 75.0 | 100.0 | - | 54.7 | 50.0 |
| Quinn Godfrey | - | 1 | 38.0 | 12.0 | 9.0 | 4.0 | 1.0 | 2.0 | 3.0 | 12.0 | 13.0 | - | - | - | 50.0 | 0 | 0.0 | - | 45.0 | 50.0 |
| Paloma Ramirez | - | 1 | 25.3 | 7.0 | 6.0 | 1.0 | 1.0 | 0.0 | 5.0 | 7.0 | 3.0 | - | - | - | 28.6 | 0.0 | 75.0 | - | 40.0 | 28.6 |
| Nicole Lukito | - | 1 | 4.4 | 5.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 1.0 | - | - | - | 50.0 | 100.0 | 0 | - | 62.5 | 62.5 |
| Jazzy Beltran | - | 1 | 37.1 | 4.0 | 2.0 | 6.0 | 2.0 | 0.0 | 1.0 | 8.0 | 5.0 | - | - | - | 12.5 | 0.0 | 66.7 | - | 21.5 | 12.5 |
| Ixchel Rojo Valdez | - | 1 | 9.4 | 4.0 | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 4.0 | - | - | - | 33.3 | 0.0 | 50.0 | - | 42.0 | 33.3 |
| Elise LeBeau | - | 1 | 18.9 | 3.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 2.0 | 2.0 | - | - | - | 50.0 | 50.0 | 0 | - | 75.0 | 75.0 |
| Campbell Mathews | - | 1 | 6.0 | 2.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 2.0 | -12 | -0.7 | -4.1 | 0.0 | 0 | 100.0 | -1.87 | 53.2 | 0.0 |
| Sydney Boyd | - | 1 | 7.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | -2.0 | - | - | - | 0.0 | 0 | 0 | - | 0.0 | 0.0 |
| Kylie Salzler | - | 1 | 5.6 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | -3.0 | - | - | - | 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