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 → | 1017 (#335) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 1028 (#330) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 988 (#532) | HCA +56 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -1.2 (#365) | HCA +3.0 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.994 (#153) | - |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.880 (#189) | AdjNet +17.2 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.882 (#191) | AdjNet +17.0 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.802 (#176) | AdjO 87.5 | AdjD 72.2 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.547 (#301) | AdjO 74.4 | AdjD 72.4 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.554 (#351) | 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 → | 0.553 (#358) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1073 (#243) | RD 291 | 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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Justin Hawkins | - | 1 | 23.8 | 16.0 | 3.0 | 1.0 | 0.0 | 0.0 | 3.0 | 9.0 | 8.0 | - | - | - | 55.6 | 50.0 | 100.0 | - | 71.4 | 61.1 |
| Brian Griffith | - | 1 | 24.0 | 14.0 | 4.0 | 5.0 | 1.0 | 0.0 | 0.0 | 9.0 | 15.0 | - | - | - | 66.7 | 66.7 | 0 | - | 77.8 | 77.8 |
| Justin Page | - | 1 | 28.6 | 10.0 | 2.0 | 4.0 | 3.0 | 0.0 | 1.0 | 12.0 | 6.0 | - | - | - | 41.7 | 0.0 | 0 | - | 41.7 | 41.7 |
| Kabeya Tshibangu | - | 1 | 18.7 | 10.0 | 5.0 | 3.0 | 1.0 | 0.0 | 0.0 | 6.0 | 13.0 | - | - | - | 83.3 | 0 | 0 | - | 83.3 | 83.3 |
| Landon Williams | - | 1 | 30.1 | 10.0 | 7.0 | 4.0 | 0.0 | 4.0 | 1.0 | 11.0 | 13.0 | - | - | - | 36.4 | 40.0 | 0 | - | 45.5 | 45.5 |
| Drew Larson | - | 1 | 10.1 | 8.0 | 3.0 | 0.0 | 0.0 | 0.0 | 1.0 | 4.0 | 6.0 | - | - | - | 75.0 | 0 | 100.0 | - | 82.0 | 75.0 |
| Vice Zanki | - | 1 | 15.4 | 6.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 4.0 | 5.0 | - | - | - | 75.0 | 0.0 | 0 | - | 75.0 | 75.0 |
| Will Shortt | - | 1 | 15.0 | 3.0 | 5.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 6.0 | - | - | - | 100.0 | 0 | 50.0 | - | 79.8 | 100.0 |
| AJ McBride | - | 1 | 9.4 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 2.0 | 4.0 | - | - | - | 50.0 | 0.0 | 0 | - | 50.0 | 50.0 |
| Reggie Prudhomme | - | 1 | 19.4 | 2.0 | 3.0 | 0.0 | 1.0 | 0.0 | 3.0 | 2.0 | 1.0 | - | - | - | 50.0 | 0.0 | 0 | - | 50.0 | 50.0 |
| Gael Dalmau Torresola | - | 1 | 5.4 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | -1.0 | - | - | - | 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