Washington State Cougars
2026 Team Stats (31 games)
63.0
PPG
72.9
Opp
-9.9
Margin
41.6%
FG%
29.3%
3P%
75.4%
FT%
33.9
RPG
15.0
APG
16.2
TO
79.3
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 → | 937 (#486) | HCA +113 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +7.7 (#229) | HCA +2.8 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.424 (#415) | AdjO 63.6 | AdjD 67.0 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.621 (#234) | 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 → | 0.576 (#240) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 932 (#282) | RD 147 | GP 31 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
E. Villa
|
G | 31 | 34.6 | 16.0 | 2.4 | 3.6 | 0.9 | 0.2 | 2.9 | 13.6 | 6.6 | -106 | -4.2 | -4.7 | 46.1 | 32.2 | 70.3 | 2.46 | 53.3 | 50.5 |
A. Covill
|
C | 19 | 24.6 | 11.3 | 4.5 | 0.3 | 0.4 | 1.9 | 2.9 | 8.3 | 7.2 | -64 | -5.8 | -10.1 | 52.2 | 0 | 83.3 | -3.86 | 58.3 | 52.2 |
C. Abraham
|
G | 31 | 26.8 | 10.1 | 6.2 | 1.5 | 1.0 | 0.2 | 2.2 | 8.1 | 8.7 | -114 | -4.6 | -6.4 | 40.2 | 37.9 | 86.2 | -1.27 | 56.6 | 52.4 |
M. Ruud
|
F | 31 | 23.5 | 8.4 | 3.8 | 1.1 | 0.5 | 0.3 | 1.4 | 7.5 | 5.3 | -108 | -4.3 | -6.6 | 47.0 | 33.3 | 55.0 | 0.7 | 52.1 | 51.3 |
K. Koorits
|
G | 31 | 17.2 | 6.3 | 2.5 | 0.8 | 0.5 | 0.1 | 0.9 | 6.8 | 2.4 | -83 | -3.5 | -7.0 | 37.7 | 22.5 | 78.9 | -1.8 | 44.2 | 42.5 |
M. Chatfield
|
G | 31 | 25.6 | 4.8 | 3.5 | 3.4 | 1.1 | 0.0 | 1.9 | 4.6 | 6.3 | -106 | -4.2 | -6.6 | 39.2 | 0 | 94.7 | -0.11 | 46.3 | 39.2 |
T. Valancic
|
F | 31 | 19.7 | 3.4 | 3.4 | 1.7 | 0.7 | 0.1 | 1.7 | 3.3 | 4.2 | -22 | -0.9 | -1.7 | 37.9 | 23.1 | 58.3 | 2.9 | 45.8 | 43.7 |
L. Glazier
|
F | 24 | 8.8 | 3.2 | 1.8 | 0.6 | 0.2 | 0.4 | 0.6 | 3.9 | 1.8 | -23 | -1.4 | -5.0 | 34.4 | 20.0 | 90.0 | -0.18 | 39.0 | 36.0 |
M. Haziri
|
G | 28 | 9.5 | 2.3 | 0.8 | 0.6 | 0.2 | 0.0 | 0.6 | 2.7 | 0.6 | -41 | -1.9 | -6.3 | 29.3 | 28.3 | 100.0 | 0.24 | 42.3 | 40.7 |
M. Alsina
|
G | 29 | 16.5 | 2.0 | 2.2 | 1.5 | 0.7 | 0.1 | 1.4 | 2.7 | 2.3 | -97 | -4.4 | -9.3 | 28.6 | 10.0 | 56.2 | -2.45 | 33.9 | 31.2 |
J. Chiu
|
G | 29 | 8.1 | 0.9 | 0.7 | 0.4 | 0.3 | 0.0 | 0.6 | 1.0 | 0.8 | -62 | -2.7 | -13.6 | 33.3 | 23.1 | 50.0 | -1.53 | 39.4 | 38.3 |
Tahara Magassa
|
F | 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