Yale Bulldogs
2026 Team Stats (24 games)
59.1
PPG
68.0
Opp
-8.9
Margin
40.1%
FG%
30.9%
3P%
75.3%
FT%
36.5
RPG
13.1
APG
18.4
TO
81.6
Pace
Model Outputs
2025-2026
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 897 (#533) | HCA +113 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +11.9 (#174) | HCA +2.8 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.451 (#343) | AdjO 60.9 | AdjD 63.0 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.578 (#254) | 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 → | 0.547 (#257) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 877 (#451) | RD 159 | GP 24 |
2026 Schedule & Results
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C. Moore
|
G | 24 | 35.2 | 18.2 | 2.4 | 1.5 | 1.1 | 0.4 | 3.8 | 15.7 | 4.0 | -40 | -1.9 | -2.2 | 37.2 | 36.4 | 88.6 | 3.77 | 50.2 | 42.4 |
L. Vydrova
|
F | 24 | 32.2 | 10.9 | 4.7 | 2.0 | 1.0 | 0.8 | 2.6 | 10.3 | 6.4 | -32 | -1.5 | -1.9 | 47.0 | 47.1 | 81.2 | 2.22 | 51.4 | 50.2 |
K. Capstraw
|
F | 23 | 36.0 | 9.1 | 6.7 | 4.0 | 1.7 | 0.3 | 3.5 | 9.5 | 8.8 | -78 | -4.1 | -4.5 | 38.8 | 0.0 | 81.2 | -1.93 | 43.5 | 38.8 |
M. Chapman
|
G | 24 | 30.0 | 7.8 | 5.5 | 2.0 | 1.2 | 0.3 | 2.8 | 8.4 | 5.7 | -70 | -3.3 | -4.3 | 40.1 | 9.1 | 59.0 | -0.46 | 42.7 | 40.6 |
M. Meng
|
F | 16 | 31.1 | 6.8 | 8.8 | 1.3 | 1.6 | 2.3 | 1.7 | 5.2 | 13.9 | -24 | -2.4 | -3.3 | 50.6 | 0 | 60.0 | 0.21 | 53.7 | 50.6 |
K. Odume
|
G | 24 | 20.1 | 5.3 | 2.2 | 0.5 | 0.3 | 0.1 | 1.6 | 6.1 | 0.8 | -133 | -6.3 | -10.8 | 35.6 | 27.6 | 46.7 | -4.36 | 41.6 | 41.1 |
D. Kastl
|
F | 13 | 24.4 | 2.5 | 4.8 | 2.5 | 1.1 | 2.3 | 1.5 | 3.2 | 8.4 | -4 | -0.2 | -0.4 | 31.7 | 15.4 | 50.0 | 1.57 | 35.9 | 34.1 |
A. Guillen
|
F | 18 | 11.2 | 1.7 | 1.5 | 0.9 | 0.6 | 0.3 | 1.0 | 1.7 | 2.2 | -39 | -3.5 | -13.5 | 45.2 | 0.0 | 100.0 | -1.33 | 47.1 | 45.2 |
S. Gibson
|
F | 7 | 8.0 | 1.6 | 2.1 | 0.1 | 0.1 | 0.4 | 1.0 | 1.4 | 2.0 | -35 | -3.5 | -17.2 | 40.0 | 0 | 75.0 | -1.88 | 46.8 | 40.0 |
O. Kim
|
G | 11 | 7.6 | 1.0 | 1.8 | 0.3 | 0.6 | 0.0 | 0.7 | 1.3 | 1.7 | -68 | -6.8 | -21.6 | 21.4 | 33.3 | 33.3 | -1.85 | 33.1 | 32.1 |
M. Schmidt
|
G | 8 | 8.6 | 0.8 | 0.5 | 0.6 | 0.2 | 0.1 | 0.2 | 1.1 | 0.9 | -38 | -4.8 | -26.5 | 22.2 | 25.0 | 0 | -1.68 | 33.3 | 33.3 |
Hannah Wasserman
|
G | 2 | 2.5 | 0.5 | 0.0 | 0.0 | 0.5 | 0.0 | 0.0 | 0.0 | 1.0 | - | - | - | 0 | 0 | 50.0 | - | 56.8 | 0 |
L. Lynn
|
G | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
Radhika Garapaty
|
G | 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