Clarkson
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 → | 919 (#380) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 917 (#443) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 946 (#571) | HCA +62 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | -16.5 (#336) | HCA +2.3 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -13.8 (#541) | HCA +2.9 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.227 (#567) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.244 (#433) | NetEff -7.9 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.081 (#556) | AdjNet -21.0 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.075 (#555) | AdjNet -21.2 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.235 (#553) | AdjO 53.1 | AdjD 66.1 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.294 (#607) | AdjO 56.0 | AdjD 65.7 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.248 (#566) | 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 → | 0.252 (#570) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 922 (#398) | RD 100 | GP 21 |
2026 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2025-11-11 | vs | SUNY Canton | L | 63 - 74 |
| 2025-11-14 | vs | Cedar Crest | W | 67 - 49 |
| 2025-11-15 | @ | Keystone | W | 70 - 33 |
| 2025-11-19 | vs | VTSU Castleton | L | 46 - 62 |
| 2025-11-22 | vs | SUNY Poly | W | 61 - 55 |
| 2025-11-25 | vs | SUNY Potsdam | W | 63 - 46 |
| 2026-01-10 | @ | Bard | W | 54 - 45 |
| 2026-01-16 | vs | William Smith | W | 66 - 57 |
| 2026-01-17 | vs | Skidmore | L | 48 - 77 |
| 2026-01-23 | vs | Ithaca | W | 79 - 69 |
| 2026-01-24 | vs | RIT | L | 53 - 68 |
| 2026-01-27 | vs | St. Lawrence | L | 58 - 71 |
| 2026-01-30 | vs | Bard | W | 53 - 42 |
| 2026-01-31 | vs | Vassar | L | 53 - 65 |
| 2026-02-06 | @ | RIT | L | 54 - 87 |
| 2026-02-07 | @ | Ithaca | W | 68 - 59 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fallon Griffin | - | 16 | 32.0 | 15.2 | 5.5 | 1.5 | 1.6 | 0.4 | 2.1 | 12.9 | 9.2 | -70 | -3.3 | -12.7 | 43.0 | 38.7 | 71.0 | -1.15 | 55.1 | 53.4 |
| Bella Doyle | - | 14 | 29.4 | 11.9 | 6.2 | 3.1 | 0.9 | 0.7 | 2.6 | 9.4 | 10.9 | -16 | -1.0 | -4.7 | 50.0 | 35.3 | 67.6 | 2.61 | 56.8 | 54.5 |
| Addie Kea | - | 16 | 29.5 | 9.7 | 4.4 | 4.1 | 1.4 | 0.1 | 4.4 | 7.9 | 7.5 | -13 | -0.6 | -2.8 | 44.4 | 33.3 | 70.3 | -0.63 | 54.5 | 51.2 |
| Maddy Polky | - | 16 | 23.5 | 6.2 | 2.3 | 1.3 | 0.6 | 0.3 | 2.0 | 6.2 | 2.5 | -19 | -0.9 | -3.9 | 35.0 | 26.5 | 66.7 | -1.48 | 43.7 | 39.5 |
| Alexis Kress | - | 12 | 21.5 | 6.1 | 2.2 | 0.7 | 0.7 | 0.2 | 1.5 | 5.0 | 3.3 | -73 | -4.3 | -28.4 | 33.3 | 30.0 | 82.8 | -3.08 | 50.2 | 40.8 |
| Emma Chambers | - | 16 | 27.8 | 4.1 | 4.3 | 1.7 | 1.3 | 0.4 | 2.1 | 4.9 | 4.8 | 24 | 1.1 | 6.0 | 30.8 | 32.4 | 50.0 | -1.1 | 39.0 | 37.8 |
| Kate Campbell | - | 16 | 13.9 | 3.9 | 3.1 | 0.4 | 0.3 | 0.2 | 0.8 | 4.3 | 2.9 | -12 | -0.6 | -7.4 | 37.7 | 27.3 | 50.0 | -1.0 | 42.7 | 42.0 |
| Elizabeth Aiossa | - | 16 | 22.6 | 3.0 | 5.8 | 2.0 | 0.9 | 0.2 | 1.4 | 3.6 | 6.9 | -25 | -1.2 | -6.6 | 27.6 | 0 | 53.3 | -1.38 | 33.7 | 27.6 |
| Eliana York | - | 3 | 6.9 | 2.7 | 1.3 | 0.7 | 0.0 | 0.3 | 0.0 | 3.7 | 1.3 | - | - | - | 36.4 | 0.0 | 0 | - | 36.4 | 36.4 |
| Riley Cullen | - | 13 | 6.7 | 2.5 | 0.5 | 0.3 | 0.3 | 0.1 | 0.5 | 2.4 | 0.8 | 21 | 1.3 | 24.6 | 51.6 | 0.0 | 0.0 | 1.01 | 48.8 | 51.6 |
| Aliza Flore | - | 4 | 2.9 | 0.5 | 0.5 | 0.0 | 0.0 | 0.2 | 0.0 | 0.5 | 0.8 | 2 | 0.3 | 88.9 | 50.0 | 0.0 | 0 | 0.46 | 50.0 | 50.0 |
| Olivia Powers | - | 4 | 4.2 | 0.5 | 2.5 | 0.5 | 0.2 | 0.0 | 0.2 | 0.8 | 2.8 | 5 | 1.0 | 90.9 | 33.3 | 0 | 0 | 0.61 | 33.3 | 33.3 |
| Sarah Abbott | - | 2 | 2.8 | 0.0 | 0.0 | 0.0 | 0.5 | 0.0 | 0.5 | 0.5 | -0.5 | 14 | 3.5 | 622.2 | 0.0 | 0 | 0 | 0.46 | 0.0 | 0.0 |
| Natalie Ellrodt | - | 1 | 1.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3 | 1.5 | 133.3 | 0 | 0 | 0 | 0.46 | 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