Caltech
Model Outputs
2025-2026
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → | 882 (#427) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 805 (#533) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 800 (#689) | HCA +62 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | -12.1 (#304) | HCA +2.3 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -13.1 (#533) | HCA +2.9 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.254 (#558) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.051 (#522) | NetEff -21.2 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.358 (#429) | AdjNet -5.1 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.350 (#429) | AdjNet -5.2 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.404 (#433) | AdjO 58.6 | AdjD 62.9 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.246 (#638) | AdjO 53.7 | AdjD 66.1 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.186 (#606) | 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 → | 0.188 (#615) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 703 (#539) | RD 119 | GP 18 |
2026 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2025-11-08 | vs | MIT | L | 50 - 64 |
| 2025-11-14 | vs | Texas Lutheran | L | 37 - 68 |
| 2025-11-19 | @ | LIFE Pacific | L | 58 - 78 |
| 2025-11-22 | vs | Stanton | W | 75 - 30 |
| 2025-12-03 | vs | Pomona-Pitzer | L | 65 - 78 |
| 2025-12-13 | vs | UC Santa Cruz | L | 57 - 67 |
| 2025-12-16 | vs | Illinois Tech | W | 53 - 31 |
| 2025-12-31 | vs | Claremont-M-S | L | 49 - 81 |
| 2026-01-10 | vs | Occidental | L | 59 - 77 |
| 2026-01-17 | @ | Chapman | L | 58 - 85 |
| 2026-01-21 | @ | La Verne | L | 61 - 67 |
| 2026-01-24 | vs | Whittier | L | 57 - 70 |
| 2026-01-28 | @ | Pomona-Pitzer | L | 41 - 63 |
| 2026-01-31 | @ | Occidental | L | 44 - 69 |
| 2026-02-07 | vs | Cal Lutheran | L | 51 - 64 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Kyra Phaychanpheng | - | 13 | 33.3 | 16.4 | 7.2 | 1.1 | 1.9 | 0.7 | 2.6 | 14.9 | 9.8 | -90 | -6.0 | -31.9 | 37.1 | 24.2 | 70.9 | -1.9 | 45.9 | 39.2 |
| Zeynep Goktepe | - | 14 | 35.1 | 13.4 | 4.4 | 2.1 | 1.3 | 0.2 | 3.4 | 11.1 | 6.7 | -120 | -7.5 | -43.1 | 47.4 | 40.7 | 58.3 | -3.26 | 52.8 | 51.0 |
| Isabella Lo | - | 14 | 36.7 | 10.8 | 4.2 | 2.5 | 2.1 | 0.0 | 3.3 | 10.1 | 6.2 | -131 | -8.2 | -45.3 | 39.7 | 29.5 | 72.2 | -0.62 | 48.1 | 44.3 |
| Louise Scriven | - | 13 | 31.1 | 5.9 | 6.4 | 0.5 | 1.2 | 2.2 | 1.0 | 8.0 | 7.2 | -100 | -7.1 | -31.6 | 26.9 | 25.5 | 43.8 | -1.74 | 34.7 | 33.7 |
| Carolyn Ruan | - | 14 | 31.8 | 5.5 | 2.1 | 1.4 | 0.5 | 0.0 | 2.5 | 6.4 | 0.6 | -122 | -7.6 | -50.0 | 28.9 | 26.7 | 75.0 | -1.81 | 40.4 | 37.8 |
| Isabella Gray-Musolff | - | 1 | 4.0 | 4.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 3.0 | - | - | - | 66.7 | 0.0 | 0 | - | 66.7 | 66.7 |
| Anamaria Robertson | - | 14 | 28.6 | 3.5 | 3.5 | 1.2 | 1.4 | 0.1 | 1.4 | 5.9 | 2.3 | -80 | -5.0 | -35.8 | 21.7 | 13.3 | 50.0 | -0.44 | 26.9 | 24.1 |
| Alyssa Smith | - | 1 | 5.0 | 2.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 2.0 | 3.0 | 16 | 0.9 | 6.1 | 50.0 | 0.0 | 0 | 3.61 | 50.0 | 50.0 |
| Samantha Miller | - | 1 | 4.0 | 2.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.0 | - | - | - | 100.0 | 0 | 0 | - | 100.0 | 100.0 |
| Kaylee Shimoda | - | 1 | 5.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | - | - | - | 100.0 | 0 | 0 | - | 100.0 | 100.0 |
| Jasmine Prajitno | - | 1 | 5.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | - | - | - | 0 | 0 | 50.0 | - | 56.8 | 0 |
| Ellie Yamada | - | 8 | 7.7 | 0.0 | 0.4 | 0.2 | 0.1 | 0.0 | 0.4 | 0.6 | -0.2 | -6 | -0.8 | -17.1 | 0.0 | 0.0 | 0 | -1.13 | 0.0 | 0.0 |
| Shyna Kashi | - | 10 | 5.0 | 0.0 | 0.4 | 0.0 | 0.2 | 0.0 | 0.0 | 0.4 | 0.2 | -45 | -4.1 | -524.3 | 0.0 | 0.0 | 0 | -0.26 | 0.0 | 0.0 |
| Maya Sanborn | - | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
| Jayda Felix | - | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
| Zaria Gomez | - | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
| Mcguire Lennon | - | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
| Niani Shields | - | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | -1.0 | - | - | - | 0.0 | 0 | 0 | - | 0.0 | 0.0 |
Numbers/Game vs RAPM
X-axis = Numbers/Game (PTS+REB+AST+STL+BLK-TO-FGA), Y-axis = RAPM.
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