Northern Colorado Bears
2026 Team Stats (31 games)
69.0
PPG
59.9
Opp
+9.1
Margin
42.2%
FG%
30.4%
3P%
74.5%
FT%
37.7
RPG
12.9
APG
17.6
TO
84.1
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 → | 1080 (#89) | HCA +113 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +13.4 (#157) | HCA +2.8 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.597 (#128) | AdjO 63.7 | AdjD 59.3 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.831 (#115) | 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 → | 0.797 (#119) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1135 (#114) | RD 112 | 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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
N. George
|
G | 31 | 29.5 | 12.9 | 3.0 | 3.3 | 1.3 | 0.1 | 2.7 | 11.3 | 6.7 | 131 | 5.2 | 7.7 | 37.5 | 29.8 | 76.0 | 2.38 | 48.5 | 41.5 |
H. Baymon
|
G | 31 | 25.9 | 12.2 | 2.8 | 1.4 | 1.7 | 0.0 | 1.4 | 11.4 | 5.3 | 135 | 5.4 | 10.5 | 36.9 | 34.3 | 85.0 | 1.44 | 50.1 | 46.6 |
T. West
|
F | 31 | 26.3 | 11.2 | 9.1 | 1.4 | 2.0 | 0.5 | 2.5 | 7.4 | 14.4 | 114 | 4.6 | 7.0 | 60.1 | 0.0 | 75.5 | 2.23 | 64.2 | 60.1 |
A. Hall
|
F | 31 | 19.4 | 9.6 | 5.0 | 0.7 | 1.0 | 1.0 | 2.0 | 6.4 | 8.9 | 86 | 3.4 | 9.5 | 51.3 | 0 | 70.8 | 2.34 | 58.1 | 51.3 |
G. Fields
|
G | 31 | 31.4 | 7.4 | 3.0 | 3.7 | 2.3 | 0.2 | 2.5 | 6.3 | 7.8 | 53 | 2.1 | 2.7 | 43.1 | 25.0 | 71.7 | -4.03 | 51.5 | 47.4 |
L. Gamble
|
G | 2 | 12.0 | 6.5 | 2.5 | 1.0 | 0.5 | 0.5 | 0.5 | 5.5 | 5.0 | - | - | - | 45.5 | 75.0 | 0 | - | 59.1 | 59.1 |
J. Riley
|
G | 30 | 17.4 | 4.8 | 2.5 | 0.4 | 0.5 | 0.1 | 0.7 | 4.6 | 3.0 | 96 | 4.4 | 11.2 | 36.5 | 34.5 | 75.0 | 0.03 | 51.5 | 51.1 |
E. Powell
|
F | 31 | 18.6 | 3.8 | 2.7 | 0.6 | 1.0 | 0.0 | 1.0 | 4.6 | 2.5 | 72 | 2.9 | 5.7 | 33.6 | 20.6 | 88.9 | -1.39 | 40.1 | 38.5 |
R. Loomis-Goltl
|
F | 28 | 10.0 | 3.0 | 2.1 | 0.2 | 0.2 | 0.6 | 0.9 | 2.4 | 2.9 | 45 | 2.0 | 7.3 | 49.3 | 0 | 77.3 | -0.6 | 54.1 | 49.3 |
L. Dykstra
|
F | 3 | 3.7 | 2.7 | 0.3 | 0.0 | 0.3 | 0.3 | 0.3 | 1.3 | 2.0 | 5 | 2.5 | 85.1 | 100.0 | 0 | 0.0 | -0.22 | 82.0 | 100.0 |
L. Tanuvasa
|
G | 13 | 9.2 | 2.5 | 1.1 | 0.6 | 0.5 | 0.2 | 1.5 | 2.5 | 0.7 | 15 | 1.0 | 3.2 | 39.4 | 0.0 | 66.7 | -2.12 | 43.3 | 39.4 |
L. Moore
|
F | 20 | 13.9 | 2.0 | 1.2 | 0.2 | 0.5 | 0.3 | 0.9 | 1.9 | 1.6 | 17 | 1.4 | 4.4 | 37.8 | 36.7 | 50.0 | 0.91 | 52.9 | 52.7 |
C. Fraser
|
F | 23 | 12.8 | 1.8 | 1.3 | 0.9 | 0.5 | 0.1 | 1.1 | 2.3 | 1.2 | 66 | 2.9 | 10.8 | 29.6 | 25.7 | 25.0 | -0.73 | 37.7 | 38.0 |
O. Loomis-Goltl
|
G | 5 | 7.0 | 1.0 | 1.6 | 0.4 | 0.2 | 0.0 | 0.4 | 1.8 | 1.0 | 17 | 4.2 | 289.4 | 11.1 | 20.0 | 100.0 | -0.22 | 25.3 | 16.7 |
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