Baylor
2023 Team Stats (29 games)
77.7
PPG
70.0
Opp
+7.3
Margin
44.7%
FG%
36.5%
3P%
73.3%
FT%
35.0
RPG
14.3
APG
11.9
TO
79.8
Pace
Model Outputs
2022-2023
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → | 1402 (#32) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 1433 (#21) | - |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | +40.6 (#18) | HCA +2.8 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.585 (#37) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.802 (#22) | NetEff +12.7 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.842 (#12) | AdjNet +14.5 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.843 (#12) | AdjNet +14.6 |
2023 Schedule & Results
2023 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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Adam Flagler
|
- | 29 | 33.7 | 15.5 | 2.3 | 4.7 | 1.1 | 0.0 | 1.7 | 12.0 | 10.0 | - | - | - | 41.8 | 38.3 | 78.1 | - | 57.5 | 51.4 |
K. George
|
- | 30 | 28.7 | 15.4 | 4.4 | 2.8 | 1.1 | 0.2 | 2.8 | 12.0 | 9.0 | - | - | - | 37.7 | 35.0 | 79.0 | - | 54.9 | 47.4 |
Lj Cryer
|
- | 28 | 32.4 | 14.7 | 2.1 | 2.0 | 0.5 | 0.0 | 1.6 | 11.0 | 6.7 | - | - | - | 44.0 | 40.2 | 88.3 | - | 61.4 | 55.0 |
Jalen Bridges
|
- | 31 | 28.0 | 11.1 | 5.8 | 1.0 | 0.9 | 1.1 | 1.1 | 7.3 | 11.5 | - | - | - | 53.1 | 35.6 | 76.6 | - | 66.0 | 61.1 |
L. Love
|
- | 27 | 17.8 | 6.9 | 2.4 | 0.9 | 0.4 | 0.1 | 0.7 | 5.1 | 4.9 | -47 | -4.7 | -12.4 | 43.9 | 38.3 | 69.4 | -0.01 | 58.2 | 52.2 |
J. Tchamwa Tchatchoua
|
- | 12 | 18.6 | 5.1 | 4.8 | 0.5 | 0.3 | 0.3 | 1.0 | 3.8 | 6.2 | - | - | - | 47.8 | 42.9 | 84.6 | - | 59.0 | 54.3 |
F. Thamba
|
- | 31 | 20.8 | 5.0 | 4.5 | 0.6 | 0.5 | 0.4 | 1.5 | 2.5 | 7.1 | - | - | - | 56.6 | 0 | 64.2 | - | 63.2 | 56.6 |
Dale Bonner
|
- | 26 | 18.9 | 4.7 | 1.1 | 2.8 | 1.5 | 0.1 | 1.1 | 3.2 | 5.9 | - | - | - | 46.3 | 41.7 | 71.4 | - | 60.2 | 55.5 |
J. Ojianwuna
|
- | 26 | 13.4 | 4.0 | 3.6 | 0.2 | 0.4 | 0.4 | 0.7 | 2.7 | 5.2 | - | - | - | 54.9 | 0 | 64.1 | 0.18 | 59.6 | 54.9 |
C. Lohner
|
- | 31 | 11.6 | 2.7 | 3.4 | 0.3 | 0.5 | 0.0 | 0.9 | 2.0 | 4.1 | - | - | - | 50.8 | 14.3 | 63.3 | - | 57.3 | 52.5 |
Zach Loveday
|
- | 12 | 3.2 | 1.0 | 0.6 | 0.0 | 0.1 | 0.1 | 0.2 | 0.7 | 0.9 | - | - | - | 62.5 | 0.0 | 0.0 | - | 64.4 | 62.5 |
J. Turner
|
- | 7 | 2.1 | 0.1 | 0.7 | 0.0 | 0.0 | 0.0 | 0.1 | 0.3 | 0.4 | 61 | 5.5 | 7.5 | 0.0 | 0 | 25.0 | 1.28 | 13.3 | 0.0 |
A. Sacks
|
- | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | -1.0 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
J. Younkin
|
- | 8 | 1.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8 | -0.8 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
C. Kozinski
|
G | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
G. Ivory Iii
|
- | 1 | 6.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 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