Long Beach State Beach
2023 Team Stats (5 games)
56.6
PPG
69.8
Opp
-13.2
Margin
39.0%
FG%
32.7%
3P%
68.8%
FT%
25.4
RPG
11.2
APG
14.0
TO
73.6
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 → | 843 (#355) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 827 (#380) | - |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | -3.2 (#366) | HCA +2.6 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.014 (#398) | - |
2023 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2022-11-20 | @ | Arizona Wildcats | L | 64 - 86 |
| 2022-11-26 | @ | San Francisco Dons | L | 50 - 74 |
| 2022-12-21 | @ | Baylor Bears | L | 52 - 73 |
| 2023-03-08 | vs | UC Riverside Highlanders | W | 55 - 49 |
| 2023-03-10 | vs | Hawai'i Rainbow Wahine | L | 62 - 67 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
K. Hamilton-Fisher
|
G | 5 | 31.4 | 11.6 | 3.4 | 1.2 | 2.2 | 0.2 | 1.8 | 12.2 | 4.6 | - | - | - | 36.1 | 33.3 | 75.0 | - | 44.9 | 42.6 |
C. Murphy
|
G | 5 | 17.8 | 8.8 | 1.8 | 0.8 | 0.6 | 0.0 | 0.6 | 6.2 | 5.2 | - | - | - | 51.6 | 54.5 | 0.0 | - | 69.0 | 71.0 |
M. Bambrick
|
G | 5 | 31.0 | 8.4 | 2.2 | 2.0 | 1.0 | 0.0 | 0.6 | 8.6 | 4.4 | - | - | - | 34.9 | 36.8 | 83.3 | - | 46.0 | 43.0 |
M. Berry
|
G | 5 | 34.2 | 7.6 | 4.6 | 4.4 | 2.4 | 0.8 | 3.4 | 7.8 | 8.6 | - | - | - | 43.6 | 0.0 | 80.0 | - | 46.1 | 43.6 |
K. Jeskeova
|
G | 3 | 23.0 | 3.0 | 4.7 | 1.0 | 1.3 | 1.0 | 3.0 | 5.0 | 3.0 | - | - | - | 26.7 | 20.0 | 0 | - | 30.0 | 30.0 |
P. Chung
|
G | 5 | 7.8 | 2.6 | 0.8 | 0.2 | 0.2 | 0.0 | 0.6 | 2.4 | 0.8 | - | - | - | 25.0 | 0.0 | 58.3 | - | 37.6 | 25.0 |
A. Shoff
|
F | 3 | 4.3 | 1.7 | 1.7 | 0.3 | 0.3 | 0.0 | 1.0 | 2.0 | 1.0 | - | - | - | 33.3 | 33.3 | 0.0 | - | 36.3 | 41.7 |
S. Tucker
|
G | 4 | 9.2 | 1.5 | 0.8 | 0.0 | 0.0 | 0.0 | 0.0 | 1.2 | 1.0 | - | - | - | 60.0 | 0.0 | 0 | - | 60.0 | 60.0 |
L. Green
|
F | 5 | 9.4 | 1.2 | 1.0 | 0.0 | 0.2 | 0.2 | 0.8 | 1.2 | 0.6 | - | - | - | 50.0 | 0 | 0 | - | 50.0 | 50.0 |
K. Ka
|
F | 5 | 9.6 | 0.6 | 0.4 | 0.2 | 0.2 | 0.0 | 0.4 | 1.6 | -0.6 | - | - | - | 12.5 | 0.0 | 50.0 | - | 16.9 | 12.5 |
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