Santa Clara
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 → | 1060 (#150) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 997 (#301) | HCA +95 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +15.3 (#99) | HCA +3.3 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.994 (#50) | - |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.984 (#32) | AdjNet +35.5 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.984 (#34) | AdjNet +34.5 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.919 (#38) | AdjO 89.6 | AdjD 62.9 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.569 (#183) | AdjO 75.8 | AdjD 72.8 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.771 (#86) | 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 → | 0.736 (#100) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1080 (#148) | RD 350 | GP 1 |
2026 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2025-11-04 | vs | Cal Poly Humboldt | W | 83 - 53 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Elijah Mahi | - | 1 | 29.2 | 14.0 | 3.0 | 3.0 | 1.0 | 2.0 | 1.0 | 9.0 | 13.0 | - | - | - | 55.6 | 25.0 | 60.0 | - | 62.5 | 61.1 |
| Thierry Darlan | - | 1 | 27.8 | 13.0 | 13.0 | 2.0 | 0.0 | 1.0 | 2.0 | 10.0 | 17.0 | - | - | - | 50.0 | 42.9 | 0 | - | 65.0 | 65.0 |
| Christian Hammond | - | 1 | 29.9 | 13.0 | 9.0 | 2.0 | 5.0 | 0.0 | 3.0 | 11.0 | 15.0 | - | - | - | 54.5 | 25.0 | 0 | - | 59.1 | 59.1 |
| Jake Ensminger | - | 1 | 25.0 | 12.0 | 5.0 | 4.0 | 4.0 | 1.0 | 0.0 | 9.0 | 17.0 | - | - | - | 66.7 | 0.0 | 0.0 | - | 60.7 | 66.7 |
| Aleksandar Gavalyugov | - | 1 | 23.2 | 8.0 | 2.0 | 3.0 | 0.0 | 0.0 | 0.0 | 10.0 | 3.0 | - | - | - | 30.0 | 28.6 | 0 | - | 40.0 | 40.0 |
| Francis Chukwudebelu | - | 1 | 24.6 | 7.0 | 5.0 | 4.0 | 0.0 | 2.0 | 0.0 | 5.0 | 13.0 | - | - | - | 60.0 | 50.0 | 0 | - | 70.0 | 70.0 |
| Allen Graves | - | 1 | 13.3 | 7.0 | 4.0 | 2.0 | 1.0 | 0.0 | 0.0 | 7.0 | 7.0 | - | - | - | 42.9 | 100.0 | 0 | - | 50.0 | 50.0 |
| Brenton Knapper | - | 1 | 23.8 | 7.0 | 3.0 | 2.0 | 3.0 | 0.0 | 2.0 | 12.0 | 1.0 | - | - | - | 25.0 | 12.5 | 0 | - | 29.2 | 29.2 |
| Bukky Oboye | - | 1 | 0.9 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | - | - | - | 100.0 | 0 | 0 | - | 100.0 | 100.0 |
| Juan Reyna III | - | 1 | 2.2 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | -62 | -4.8 | -33.4 | 0 | 0 | 0 | 0.37 | 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