San Francisco
2024 Team Stats (34 games)
77.7
PPG
66.5
Opp
+11.2
Margin
48.7%
FG%
35.5%
3P%
75.5%
FT%
34.1
RPG
16.3
APG
11.5
TO
77.8
Pace
Model Outputs
2023-2024
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 → | 1154 (#128) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 1279 (#76) | - |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | +32.2 (#75) | HCA +2.7 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.763 (#10) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.857 (#14) | NetEff +16.3 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.594 (#57) | AdjNet +3.3 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.596 (#57) | AdjNet +3.3 |
2024 Schedule & Results
2024 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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
J. Mogbo
|
- | 34 | 28.9 | 14.2 | 10.1 | 3.6 | 1.6 | 0.8 | 1.8 | 9.5 | 19.1 | 2 | 0.3 | 0.4 | 63.6 | 0.0 | 69.2 | 0.01 | 65.4 | 63.6 |
Marcus Williams
|
- | 34 | 30.8 | 14.0 | 3.3 | 3.9 | 1.4 | 0.3 | 2.2 | 12.0 | 8.7 | 28 | 4.0 | 5.0 | 45.6 | 34.4 | 67.2 | 0.44 | 54.8 | 53.4 |
M. Thomas
|
- | 33 | 23.1 | 12.4 | 2.7 | 0.5 | 0.8 | 0.0 | 0.9 | 8.8 | 6.8 | 17 | 2.4 | 3.7 | 47.4 | 40.0 | 86.1 | 0.24 | 61.3 | 55.7 |
N. Newbury
|
- | 34 | 25.7 | 9.4 | 3.7 | 1.7 | 0.8 | 1.1 | 1.7 | 6.8 | 8.2 | 37 | 5.3 | 7.4 | 52.6 | 40.0 | 76.6 | 0.78 | 63.8 | 61.7 |
Ry. Beasley
|
- | 30 | 22.3 | 7.8 | 2.4 | 1.6 | 1.2 | 0.2 | 0.8 | 5.9 | 6.6 | 25 | 4.2 | 6.6 | 39.5 | 35.4 | 85.9 | 0.49 | 56.4 | 49.2 |
M. Sharavjamts
|
- | 34 | 25.0 | 7.7 | 3.0 | 2.7 | 0.9 | 0.4 | 1.4 | 6.7 | 6.6 | 27 | 3.9 | 6.8 | 42.4 | 36.0 | 82.4 | 0.25 | 53.9 | 51.3 |
I. Hawthorne
|
- | 29 | 11.4 | 3.9 | 1.4 | 0.4 | 0.6 | 0.1 | 0.6 | 3.6 | 2.1 | -9 | -3.0 | -89.6 | 43.3 | 30.8 | 40.0 | -0.18 | 52.7 | 52.9 |
S. Todorovic
|
- | 22 | 9.7 | 3.8 | 1.5 | 0.4 | 0.3 | 0.0 | 0.4 | 3.2 | 2.5 | -1 | -0.5 | -5.1 | 40.0 | 28.9 | 88.2 | -0.01 | 54.2 | 49.3 |
Ro. Beasley
|
- | 17 | 10.1 | 3.2 | 1.2 | 0.3 | 0.2 | 0.0 | 0.3 | 2.5 | 2.1 | 25 | 4.2 | 6.6 | 48.8 | 40.9 | 75.0 | 0.49 | 60.3 | 59.3 |
J. Wang
|
- | 20 | 7.5 | 2.8 | 1.1 | 0.3 | 0.1 | 0.1 | 0.2 | 2.3 | 2.0 | -51 | -2.7 | -57.2 | 43.5 | 32.0 | 70.0 | -0.09 | 54.6 | 52.2 |
J. Kunen
|
- | 32 | 12.3 | 2.1 | 1.8 | 0.6 | 0.3 | 0.2 | 0.6 | 1.9 | 2.6 | 9 | 1.5 | 6.4 | 45.0 | 36.7 | 50.0 | 0.06 | 54.2 | 54.2 |
V. Markovetskyy
|
- | 28 | 6.9 | 1.8 | 1.7 | 0.3 | 0.1 | 0.3 | 0.3 | 1.1 | 2.8 | -4 | -0.6 | -3.0 | 75.0 | 0 | 15.4 | -0.05 | 66.3 | 75.0 |
J. Bieker
|
G | 28 | 6.5 | 1.2 | 0.8 | 0.7 | 0.3 | 0.0 | 0.3 | 1.0 | 1.8 | 3 | 0.4 | 6.1 | 48.1 | 25.0 | 55.6 | 0.02 | 54.9 | 53.7 |
J. Cioe
|
- | 8 | 3.4 | 0.2 | 0.4 | 0.2 | 0.2 | 0.0 | 0.1 | 0.6 | 0.4 | -1 | -0.5 | -12.4 | 0.0 | 0.0 | 100.0 | -0.01 | 17.0 | 0.0 |
S. Gigiberia
|
C | 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