San Francisco
2026 Team Stats (32 games)
75.0
PPG
73.5
Opp
+1.5
Margin
43.7%
FG%
35.1%
3P%
71.5%
FT%
35.8
RPG
14.0
APG
11.0
TO
77.8
Pace
76.3
AdjO
72.4
AdjD
#112
Rank
Model Outputs
2025-2026
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 → | 981 (#198) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 1029 (#150) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 1000 (#192) | HCA +109 elo |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 994 (#434) | HCA +109 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | +3.4 (#123) | HCA +2.2 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +14.6 (#161) | HCA +2.5 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -22.0 (#623) | HCA +2.5 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.506 (#172) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.498 (#174) | NetEff -0.1 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.647 (#112) | AdjNet +5.3 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.649 (#112) | AdjNet +5.3 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.589 (#112) | AdjO 76.3 | AdjD 72.4 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.518 (#215) | AdjO 74.4 | AdjD 73.6 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.431 (#549) | AdjO 71.8 | AdjD 74.8 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.788 (#147) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.183 (#591) | 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 → | 0.739 (#155) | 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 → | 0.225 (#591) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1101 (#139) | RD 145 | GP 32 |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 920 (#420) | RD 350 | GP 1 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ry. Beasley
|
- | 30 | 33.1 | 13.7 | 3.3 | 4.1 | 1.2 | 0.1 | 1.6 | 10.2 | 10.5 | 43 | 2.0 | 2.8 | 40.8 | 32.6 | 82.5 | 0.16 | 56.0 | 48.5 |
D. Fuchs
|
F | 29 | 24.0 | 12.7 | 7.8 | 1.4 | 0.5 | 0.3 | 1.9 | 8.0 | 12.9 | -6 | -0.5 | -10.7 | 51.7 | 30.3 | 64.5 | 0.12 | 58.9 | 53.9 |
T. Riley IV
|
- | 30 | 31.8 | 12.1 | 4.8 | 1.2 | 1.0 | 0.4 | 0.8 | 9.7 | 9.0 | 94 | 3.5 | 6.5 | 46.2 | 35.7 | 70.7 | 0.59 | 55.8 | 53.1 |
J. Wang
|
- | 32 | 20.2 | 8.5 | 3.0 | 0.8 | 0.5 | 0.3 | 1.2 | 6.2 | 5.6 | -13 | -0.5 | -1.6 | 42.5 | 35.5 | 75.9 | -0.58 | 57.3 | 52.0 |
Legend Smiley
|
- | 32 | 21.6 | 8.2 | 1.9 | 0.6 | 0.5 | 0.2 | 0.8 | 5.9 | 4.6 | -5 | -0.4 | -23.2 | 45.7 | 42.5 | 87.5 | -0.15 | 63.5 | 60.1 |
M. Cook
|
- | 24 | 20.5 | 7.6 | 3.9 | 1.3 | 0.7 | 0.8 | 1.8 | 5.6 | 6.9 | 39 | 2.4 | 5.2 | 45.5 | 41.8 | 71.2 | 0.23 | 58.0 | 54.1 |
V. Masic
|
G | 32 | 21.5 | 5.3 | 1.6 | 2.0 | 0.2 | 0.1 | 1.1 | 5.1 | 3.0 | 15 | 0.9 | 13.3 | 38.0 | 28.8 | 69.6 | 0.05 | 49.1 | 47.2 |
V. Abosi
|
- | 23 | 18.2 | 5.3 | 4.3 | 1.2 | 0.5 | 0.4 | 1.3 | 3.8 | 6.5 | 16 | 0.7 | 2.1 | 41.4 | 41.2 | 68.3 | -0.39 | 57.6 | 53.4 |
G. Diaz Graham
|
F | 32 | 17.0 | 5.2 | 3.1 | 1.3 | 0.5 | 0.5 | 0.8 | 3.8 | 6.1 | 41 | 2.4 | 84.5 | 41.3 | 39.1 | 71.7 | 0.09 | 57.2 | 52.5 |
S. Gigiberia
|
- | 26 | 5.9 | 2.2 | 2.5 | 0.6 | 0.2 | 0.3 | 0.4 | 2.0 | 3.2 | 29 | 1.4 | 11.4 | 45.3 | 0.0 | 34.8 | 0.02 | 44.4 | 45.3 |
Weilun Zhao
|
- | 15 | 5.1 | 1.2 | 0.2 | 1.0 | 0.1 | 0.0 | 0.5 | 1.3 | 0.7 | 2 | 0.2 | 2.0 | 26.3 | 0.0 | 72.7 | -0.01 | 37.8 | 26.3 |
A. Braccia
|
G | 16 | 5.6 | 0.8 | 0.6 | 0.2 | 0.2 | 0.0 | 0.1 | 1.1 | 0.6 | 14 | 1.3 | 13.0 | 17.6 | 13.3 | 66.7 | -0.14 | 30.5 | 23.5 |
Sean Blakely Drummond
|
G | 5 | 2.0 | 0.4 | 0.4 | 0.0 | 0.0 | 0.2 | 0.2 | 0.6 | 0.2 | -3 | -1.0 | -39.8 | 33.3 | 0.0 | 0.0 | 0.01 | 25.8 | 33.3 |
I. Silva
|
G | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
N. Newbury
|
F | 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