Babson
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 → | 1094 (#83) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 1097 (#152) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 1135 (#68) | HCA +62 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | +4.5 (#143) | HCA +2.3 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +6.6 (#255) | HCA +2.9 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.747 (#331) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.702 (#278) | NetEff +6.3 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.860 (#234) | AdjNet +15.8 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.865 (#234) | AdjNet +15.7 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.695 (#243) | AdjO 67.6 | AdjD 58.6 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.686 (#137) | AdjO 65.2 | AdjD 56.6 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.717 (#200) | 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 → | 0.725 (#187) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1159 (#102) | RD 99 | GP 21 |
2026 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2025-11-07 | @ | Roger Williams | W | 71 - 53 |
| 2025-11-10 | @ | Curry | W | 78 - 46 |
| 2025-11-14 | @ | Endicott | L | 53 - 64 |
| 2025-11-18 | vs | Tufts | L | 63 - 67 |
| 2025-11-22 | vs | Albertus Magnus | W | 74 - 50 |
| 2025-11-24 | vs | Brandeis | L | 52 - 64 |
| 2025-11-28 | @ | Pomona-Pitzer | W | 65 - 59 |
| 2025-11-29 | @ | Claremont-M-S | L | 66 - 82 |
| 2025-12-29 | vs | Hartford | W | 67 - 65 |
| 2025-12-30 | vs | UC Santa Cruz | W | 72 - 70 |
| 2026-01-13 | vs | Trinity (CT) | L | 55 - 61 |
| 2026-01-17 | vs | Springfield | W | 64 - 63 |
| 2026-01-21 | @ | Coast Guard | L | 50 - 68 |
| 2026-01-24 | @ | MIT | W | 63 - 48 |
| 2026-01-28 | vs | WPI | W | 52 - 30 |
| 2026-01-31 | vs | Mount Holyoke | W | 79 - 50 |
| 2026-02-04 | @ | Emerson | W | 72 - 60 |
| 2026-02-07 | vs | Wellesley | W | 70 - 46 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Emily Flynn | - | 16 | 26.0 | 13.6 | 8.2 | 3.4 | 0.5 | 0.7 | 3.6 | 12.5 | 10.3 | 61 | 4.4 | 19.9 | 41.0 | 29.7 | 70.6 | 0.39 | 50.7 | 48.5 |
| Samantha Reale | - | 7 | 27.2 | 10.7 | 8.3 | 1.7 | 2.1 | 0.4 | 1.9 | 9.1 | 12.3 | 20 | 5.0 | 9.8 | 42.2 | 25.0 | 79.2 | 2.38 | 50.3 | 43.8 |
| Rylie Rosenberg | - | 17 | 25.1 | 9.5 | 2.8 | 2.0 | 1.5 | 0.0 | 1.8 | 9.1 | 4.9 | 85 | 5.7 | 22.7 | 34.2 | 29.5 | 85.2 | 4.48 | 48.5 | 44.8 |
| Chloe Perreault | - | 17 | 25.3 | 7.9 | 6.4 | 1.3 | 0.6 | 0.1 | 1.1 | 8.2 | 7.1 | 45 | 3.0 | 16.1 | 37.4 | 31.0 | 57.1 | 1.64 | 47.2 | 46.8 |
| Julia St. Laurent | - | 17 | 23.0 | 7.5 | 3.7 | 1.3 | 1.0 | 0.4 | 2.0 | 6.5 | 5.4 | 55 | 5.0 | 43.7 | 45.9 | 0.0 | 61.9 | -0.35 | 49.4 | 45.9 |
| Alessa Mendoza | - | 17 | 15.1 | 5.6 | 1.9 | 1.5 | 0.8 | 0.0 | 1.1 | 5.2 | 3.5 | 45 | 3.0 | 20.4 | 50.0 | 0.0 | 61.5 | 1.08 | 51.2 | 50.0 |
| Allessia Carlo | - | 17 | 29.1 | 5.0 | 1.6 | 2.3 | 1.3 | 0.1 | 1.8 | 6.3 | 2.2 | 78 | 5.2 | 25.5 | 27.1 | 24.6 | 86.7 | 0.53 | 37.4 | 33.6 |
| Alyssa Hopps | - | 15 | 13.7 | 4.5 | 4.4 | 0.5 | 0.4 | 0.6 | 2.1 | 4.1 | 4.3 | 61 | 5.1 | 26.3 | 47.5 | 0.0 | 52.6 | -0.26 | 49.0 | 47.5 |
| Molly Donovan | - | 17 | 12.7 | 3.6 | 2.2 | 0.6 | 0.6 | 0.2 | 0.8 | 4.0 | 2.4 | 12 | 0.8 | 6.7 | 29.4 | 27.1 | 66.7 | 0.31 | 41.6 | 39.0 |
| Mary Kate Flynn | - | 16 | 11.8 | 2.8 | 4.1 | 0.7 | 0.7 | 0.2 | 1.2 | 2.8 | 4.5 | 38 | 2.5 | 33.4 | 48.9 | 0 | 0.0 | 3.32 | 48.0 | 48.9 |
| St. Laurent,Julia | - | 1 | 17.0 | 2.0 | 3.0 | 1.0 | 1.0 | 0.0 | 3.0 | 4.0 | 0.0 | - | - | - | 25.0 | 0 | 0 | - | 25.0 | 25.0 |
| Brooke Canty | - | 14 | 9.5 | 1.6 | 0.9 | 0.4 | 0.1 | 0.1 | 0.7 | 1.5 | 0.9 | 12 | 0.9 | 14.5 | 38.1 | 0.0 | 77.8 | 1.43 | 46.1 | 38.1 |
| Devon Burke | - | 4 | 7.0 | 1.0 | 0.8 | 0.5 | 0.5 | 0.0 | 0.2 | 1.0 | 1.5 | - | - | - | 50.0 | 0 | 0 | - | 50.0 | 50.0 |
| Gianna Langone | - | 3 | 3.1 | 0.7 | 0.7 | 0.3 | 0.0 | 0.0 | 0.7 | 0.3 | 0.7 | 0 | 0.0 | 0.0 | 100.0 | 0 | 0 | -0.31 | 100.0 | 100.0 |
| Sophie Mahar | - | 2 | 3.5 | 0.0 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
| Flynn,Mary Kate | - | 1 | 16.0 | 0.0 | 6.0 | 3.0 | 0.0 | 0.0 | 2.0 | 2.0 | 5.0 | - | - | - | 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