Cal Poly Mustangs
2023 Team Stats (5 games)
48.8
PPG
64.4
Opp
-15.6
Margin
34.7%
FG%
38.3%
3P%
72.3%
FT%
26.8
RPG
7.0
APG
17.6
TO
71.1
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 → | 764 (#388) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 846 (#372) | - |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | +3.6 (#326) | HCA +2.6 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.017 (#389) | - |
2023 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2022-11-16 | @ | Stanford Cardinal | L | 43 - 80 |
| 2022-11-22 | vs | South Carolina Gamecocks | L | 36 - 79 |
| 2022-11-26 | vs | San José State Spartans | W | 62 - 53 |
| 2023-01-16 | vs | UC Riverside Highlanders | W | 49 - 47 |
| 2023-03-07 | vs | UC Riverside Highlanders | L | 54 - 63 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A. Shah
|
G | 5 | 26.6 | 11.0 | 1.0 | 1.6 | 0.2 | 0.2 | 1.6 | 9.6 | 2.8 | - | - | - | 39.6 | 45.5 | 87.5 | - | 53.4 | 50.0 |
M. Willett
|
G | 5 | 21.6 | 7.6 | 2.4 | 0.8 | 0.6 | 0.0 | 1.2 | 5.0 | 5.2 | - | - | - | 48.0 | 58.3 | 87.5 | - | 66.6 | 62.0 |
N. Kovacikova
|
G | 3 | 21.0 | 6.7 | 3.3 | 0.3 | 0.3 | 0.0 | 2.0 | 7.0 | 1.7 | - | - | - | 38.1 | 42.9 | 50.0 | - | 45.7 | 45.2 |
J. Anousinh
|
G | 5 | 26.2 | 6.2 | 1.4 | 1.4 | 0.8 | 0.2 | 2.0 | 4.4 | 3.6 | - | - | - | 40.9 | 42.9 | 90.9 | - | 57.7 | 47.7 |
J. Nielacna
|
F | 3 | 16.7 | 6.0 | 1.3 | 0.3 | 0.0 | 0.0 | 2.3 | 4.0 | 1.3 | - | - | - | 41.7 | 100.0 | 70.0 | - | 54.9 | 45.8 |
O. Toure
|
G | 4 | 19.5 | 5.5 | 2.2 | 0.5 | 1.0 | 0.2 | 1.5 | 5.0 | 3.0 | - | - | - | 40.0 | 33.3 | 62.5 | - | 46.8 | 42.5 |
S. Lichtie
|
F | 3 | 11.3 | 3.7 | 0.7 | 0.7 | 0.7 | 0.0 | 1.0 | 2.7 | 2.0 | - | - | - | 50.0 | 40.0 | 100.0 | - | 65.2 | 62.5 |
J. Dickson
|
G | 3 | 14.7 | 2.7 | 2.3 | 0.3 | 0.0 | 0.0 | 0.7 | 3.7 | 1.0 | - | - | - | 27.3 | 40.0 | 0 | - | 36.4 | 36.4 |
N. Ackerman
|
F | 5 | 17.8 | 2.4 | 4.8 | 0.4 | 1.4 | 0.2 | 1.2 | 3.2 | 4.8 | - | - | - | 31.2 | 0 | 50.0 | - | 33.8 | 31.2 |
S. Bourland
|
G | 5 | 24.8 | 2.4 | 3.4 | 0.4 | 0.4 | 0.2 | 2.4 | 5.0 | -0.6 | - | - | - | 12.0 | 0.0 | 66.7 | - | 20.7 | 12.0 |
T. Wu
|
G | 5 | 18.6 | 2.2 | 1.4 | 1.0 | 0.6 | 0.0 | 1.8 | 3.8 | -0.4 | - | - | - | 21.1 | 18.2 | 25.0 | - | 26.5 | 26.3 |
M. Garcia
|
F | 3 | 9.0 | 1.3 | 1.7 | 0.0 | 0.0 | 0.7 | 0.3 | 2.3 | 1.0 | - | - | - | 28.6 | 0 | 0 | - | 28.6 | 28.6 |
A. Stajic
|
G | 3 | 5.0 | 0.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3 | 0.7 | -0.3 | - | - | - | 50.0 | 0.0 | 0 | - | 50.0 | 50.0 |
S. Karlin
|
G | 3 | 3.7 | 0.0 | 0.3 | 0.0 | 0.3 | 0.0 | 0.0 | 1.0 | -0.3 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
Numbers/Game vs RAPM
Not enough players with both Numbers/Game and RAPM to plot.
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