Eastern Conn. St.
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 → | 1040 (#147) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 979 (#360) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 946 (#572) | HCA +62 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | -4.6 (#242) | HCA +2.3 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -4.3 (#426) | HCA +2.9 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.266 (#552) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.486 (#361) | NetEff -0.4 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.272 (#461) | AdjNet -8.6 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.219 (#476) | AdjNet -10.8 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.356 (#472) | AdjO 57.9 | AdjD 64.5 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.498 (#391) | AdjO 62.8 | AdjD 62.9 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.420 (#441) | 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 → | 0.428 (#437) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 953 (#372) | RD 101 | GP 21 |
2026 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2025-11-12 | vs | Lasell | L | 65 - 69 |
| 2025-11-14 | @ | Western New Eng. | L | 52 - 69 |
| 2025-11-18 | @ | Salve Regina | L | 50 - 60 |
| 2025-11-21 | @ | SUNY Oneonta | L | 69 - 74 |
| 2025-11-22 | @ | Hartwick | W | 55 - 54 |
| 2025-11-25 | vs | Connecticut Col. | L | 46 - 65 |
| 2025-12-03 | @ | WestConn | W | 65 - 59 |
| 2025-12-12 | @ | Bridgewater St. | W | 72 - 64 |
| 2026-01-10 | @ | Plymouth St. | W | 73 - 55 |
| 2026-01-14 | vs | VTSU Castleton | W | 82 - 52 |
| 2026-01-17 | @ | Southern Me. | L | 62 - 76 |
| 2026-01-21 | vs | Rhode Island Col. | L | 58 - 70 |
| 2026-01-28 | vs | WestConn | L | 54 - 57 |
| 2026-01-31 | @ | UMass Boston | W | 60 - 54 |
| 2026-02-04 | @ | UMass Dartmouth | L | 59 - 67 |
| 2026-02-07 | vs | Southern Me. | L | 72 - 98 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Marissa Nudd | - | 16 | 36.6 | 18.6 | 6.9 | 1.3 | 0.9 | 0.1 | 3.5 | 16.8 | 7.5 | - | - | - | 45.4 | 26.2 | 80.4 | - | 51.3 | 48.3 |
| Liv Cassesse | - | 16 | 35.9 | 12.4 | 4.4 | 4.4 | 1.6 | 0.1 | 3.2 | 10.8 | 9.0 | - | - | - | 39.5 | 29.3 | 79.6 | - | 51.4 | 46.5 |
| Maddie Topa | - | 16 | 26.3 | 10.7 | 3.5 | 1.4 | 1.6 | 0.7 | 1.8 | 9.4 | 6.8 | - | - | - | 40.4 | 27.0 | 72.7 | - | 50.2 | 46.0 |
| Alyssa Paquette | - | 16 | 25.7 | 6.8 | 6.1 | 0.8 | 0.6 | 1.9 | 2.1 | 7.7 | 6.4 | - | - | - | 35.8 | 27.3 | 64.3 | - | 40.3 | 37.0 |
| Katelyn Novak | - | 16 | 32.0 | 5.8 | 3.6 | 1.8 | 1.4 | 0.6 | 1.9 | 6.3 | 4.9 | - | - | - | 32.7 | 27.8 | 58.3 | - | 43.8 | 42.6 |
| Isabella Romero | - | 16 | 13.8 | 2.9 | 3.0 | 0.1 | 0.2 | 0.5 | 0.9 | 3.3 | 2.5 | - | - | - | 41.5 | 0.0 | 50.0 | - | 42.0 | 41.5 |
| Trinity Angel | - | 10 | 5.0 | 1.8 | 1.3 | 0.2 | 0.2 | 0.1 | 0.2 | 1.8 | 1.6 | - | - | - | 44.4 | 0 | 28.6 | - | 42.7 | 44.4 |
| Nevaeh King | - | 15 | 10.7 | 1.4 | 1.3 | 1.0 | 0.7 | 0.4 | 0.9 | 2.4 | 1.5 | - | - | - | 25.0 | 27.3 | 0.0 | - | 28.5 | 29.2 |
| Gabriella Kufuor | - | 7 | 4.2 | 1.3 | 0.6 | 0.4 | 0.1 | 0.0 | 0.3 | 1.0 | 1.1 | - | - | - | 57.1 | 33.3 | 0 | - | 64.3 | 64.3 |
| Morgan Yonush | - | 9 | 2.4 | 1.2 | 0.2 | 0.0 | 0.1 | 0.0 | 0.4 | 1.1 | 0.0 | - | - | - | 50.0 | 0 | 100.0 | - | 52.7 | 50.0 |
| Juliana Conte | - | 13 | 7.2 | 0.6 | 0.6 | 0.3 | 0.1 | 0.0 | 0.4 | 1.4 | -0.2 | -7 | -1.2 | -39.3 | 16.7 | 12.5 | 0 | -0.01 | 22.2 | 22.2 |
| Emilia Maria-Babcock | - | 15 | 2.8 | 0.5 | 0.7 | 0.1 | 0.1 | 0.0 | 0.1 | 0.5 | 0.9 | - | - | - | 42.9 | 50.0 | 0 | - | 57.1 | 57.1 |
| Grace Smith | - | 4 | 1.9 | 0.5 | 0.5 | 0.0 | 0.0 | 0.0 | 0.5 | 0.8 | -0.2 | -9 | -1.8 | -11.0 | 33.3 | 0 | 0 | -1.09 | 33.3 | 33.3 |
| Julia Knowles | - | 6 | 13.4 | 0.3 | 2.0 | 0.8 | 0.3 | 0.3 | 0.8 | 2.0 | 1.0 | - | - | - | 0.0 | 0.0 | 100.0 | - | 7.8 | 0.0 |
| Juliana Iovino | - | 1 | 3.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
| Aniya Jenkins | - | 3 | 3.3 | 0.0 | 0.3 | 0.3 | 0.0 | 0.0 | 0.7 | 0.3 | -0.3 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
| Gabriella Kufour | - | 1 | 4.0 | 0.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | - | - | - | 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