Tulsa Golden Hurricane
2026 Team Stats (28 games)
68.2
PPG
65.8
Opp
+2.4
Margin
41.2%
FG%
30.7%
3P%
70.8%
FT%
37.3
RPG
12.9
APG
19.1
TO
85.3
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 → | 990 (#397) | HCA +113 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +12.7 (#165) | HCA +2.8 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.499 (#215) | AdjO 64.0 | AdjD 64.1 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.741 (#177) | 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 → | 0.701 (#179) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1051 (#173) | RD 132 | GP 28 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
H. Riddick
|
F | 26 | 26.9 | 15.9 | 7.2 | 1.8 | 1.2 | 0.8 | 2.8 | 11.9 | 12.1 | 58 | 2.8 | 4.4 | 50.5 | 30.0 | 64.6 | 3.94 | 55.3 | 51.5 |
M. Cartwright
|
G | 28 | 33.1 | 15.1 | 3.6 | 2.4 | 1.6 | 0.3 | 3.2 | 12.4 | 7.5 | 56 | 2.4 | 3.3 | 34.7 | 29.8 | 82.4 | 0.74 | 51.2 | 42.9 |
J. Clack
|
G | 26 | 28.7 | 10.3 | 5.0 | 1.7 | 1.5 | 0.8 | 3.3 | 8.7 | 7.1 | -13 | -0.6 | -1.0 | 44.1 | 29.9 | 66.7 | -0.81 | 52.1 | 49.1 |
D. Toman
|
G | 23 | 28.7 | 8.8 | 5.2 | 2.8 | 1.8 | 0.4 | 2.5 | 6.8 | 9.7 | 61 | 3.0 | 3.9 | 47.1 | 32.3 | 67.6 | 1.52 | 58.7 | 57.0 |
A. Jegede
|
G | 22 | 28.0 | 7.5 | 4.2 | 0.7 | 0.9 | 0.1 | 2.0 | 6.6 | 4.8 | 40 | 1.7 | 2.5 | 41.1 | 33.3 | 70.0 | -0.58 | 50.4 | 46.9 |
B. Eshoo
|
F | 2 | 7.5 | 6.5 | 2.5 | 0.0 | 0.0 | 0.0 | 1.0 | 5.0 | 3.0 | -11 | -5.5 | -12.4 | 50.0 | 66.7 | 50.0 | -2.8 | 59.7 | 60.0 |
H. Fisher
|
G | 28 | 12.9 | 3.7 | 1.2 | 0.7 | 0.5 | 0.0 | 0.9 | 3.9 | 1.3 | 0 | 0.0 | 0.0 | 35.8 | 36.4 | 52.0 | 0.93 | 42.9 | 41.3 |
L. Cameron
|
G | 25 | 18.7 | 3.6 | 2.6 | 1.2 | 0.6 | 0.2 | 1.6 | 3.0 | 3.6 | -64 | -3.2 | -8.5 | 36.8 | 23.5 | 81.2 | -2.2 | 50.0 | 42.1 |
S. Duncan
|
G | 13 | 12.5 | 3.1 | 1.5 | 0.8 | 0.5 | 0.2 | 0.8 | 3.1 | 2.2 | -2 | -0.2 | -1.1 | 32.5 | 30.4 | 77.8 | -0.43 | 45.5 | 41.2 |
A. Peavy
|
G | 28 | 19.9 | 2.9 | 3.4 | 1.4 | 0.7 | 0.4 | 1.5 | 3.4 | 3.8 | 42 | 1.8 | 3.6 | 33.0 | 24.2 | 58.8 | 1.87 | 39.4 | 37.2 |
L. Di Stefano
|
F | 20 | 10.3 | 2.8 | 1.4 | 0.7 | 0.2 | 0.3 | 0.7 | 2.5 | 2.1 | -11 | -0.7 | -2.3 | 37.3 | 32.4 | 50.0 | -3.0 | 48.9 | 48.0 |
R. Grays
|
F | 27 | 9.5 | 2.2 | 2.4 | 0.4 | 0.4 | 0.3 | 1.1 | 1.4 | 3.2 | -27 | -1.2 | -4.6 | 42.1 | 40.0 | 74.2 | -1.27 | 57.1 | 47.4 |
T. Seppala
|
G | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
Gina Nikola Pirjak
|
G | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
Josie Megehee
|
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