Baylor Bears
2026 Team Stats (34 games)
70.6
PPG
60.3
Opp
+10.3
Margin
42.0%
FG%
31.4%
3P%
74.6%
FT%
40.2
RPG
14.8
APG
16.7
TO
83.9
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 → | 1071 (#100) | HCA +113 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +30.3 (#42) | HCA +2.8 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.708 (#62) | AdjO 67.8 | AdjD 58.0 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.954 (#33) | 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 → | 0.933 (#34) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1278 (#30) | RD 114 | GP 34 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T. Scott
|
G | 33 | 31.6 | 19.7 | 2.6 | 2.7 | 1.2 | 0.1 | 3.3 | 14.6 | 8.3 | 85 | 3.5 | 5.1 | 37.7 | 32.1 | 89.5 | -0.09 | 55.6 | 46.0 |
D. Littlepage-Buggs
|
F | 34 | 29.2 | 10.6 | 10.1 | 1.9 | 1.1 | 0.7 | 2.5 | 8.7 | 13.2 | 89 | 3.3 | 4.9 | 51.9 | 30.0 | 61.0 | 4.72 | 55.0 | 53.4 |
B. Fontleroy
|
F | 34 | 26.1 | 9.0 | 5.6 | 0.9 | 1.5 | 1.3 | 1.5 | 9.1 | 7.7 | 63 | 2.3 | 3.7 | 33.8 | 25.9 | 80.0 | 0.47 | 45.0 | 40.4 |
J. Van Gytenbeek
|
G | 34 | 32.0 | 7.4 | 2.4 | 5.9 | 1.0 | 0.1 | 2.8 | 6.8 | 7.2 | 74 | 2.7 | 3.7 | 35.9 | 35.3 | 73.1 | 2.18 | 49.6 | 46.3 |
Y. Deng
|
G | 33 | 16.8 | 6.6 | 1.9 | 0.9 | 0.4 | 0.3 | 1.1 | 5.5 | 3.4 | 40 | 1.6 | 4.0 | 42.3 | 37.0 | 74.3 | -1.59 | 55.0 | 52.5 |
K. Johnson
|
F | 34 | 19.2 | 4.9 | 4.3 | 0.6 | 0.6 | 1.7 | 1.3 | 4.4 | 6.4 | 79 | 3.6 | 3.5 | 47.7 | 40.0 | 64.3 | 1.43 | 53.2 | 52.3 |
M. Johnson
|
G | 32 | 17.7 | 4.4 | 2.2 | 0.7 | 0.4 | 0.2 | 1.0 | 4.7 | 2.2 | 19 | 0.8 | 1.7 | 35.6 | 15.8 | 54.7 | -3.27 | 40.9 | 37.6 |
K. Abraham
|
F | 34 | 16.1 | 4.1 | 3.8 | 0.4 | 0.5 | 1.6 | 1.3 | 2.8 | 6.2 | 11 | 0.4 | 1.2 | 58.9 | 0 | 51.9 | 0.94 | 59.0 | 58.9 |
K. Nelms
|
F | 29 | 9.4 | 4.1 | 2.9 | 0.4 | 0.3 | 0.3 | 0.8 | 2.7 | 4.4 | 67 | 3.4 | 15.9 | 57.0 | 26.7 | 71.4 | 3.44 | 63.0 | 59.5 |
K. Pemberton
|
F | 18 | 5.7 | 1.9 | 0.9 | 0.2 | 0.3 | 0.2 | 0.4 | 1.1 | 2.0 | 5 | 0.4 | 2.4 | 57.9 | 33.3 | 75.0 | -0.21 | 67.2 | 60.5 |
I. Goryanova
|
G | 10 | 4.4 | 0.9 | 0.2 | 0.8 | 0.2 | 0.0 | 0.9 | 0.8 | 0.4 | 23 | 5.8 | 84.3 | 50.0 | 50.0 | 0.0 | 1.32 | 50.7 | 56.2 |
E. Brow
|
G | 12 | 6.3 | 0.5 | 0.4 | 0.9 | 0.3 | 0.0 | 0.4 | 0.5 | 1.2 | 14 | 3.5 | 550.8 | 33.3 | 20.0 | 50.0 | -0.08 | 43.6 | 41.7 |
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