George Mason
Also known as: George Mason
Program History
Model Outputs
2025-2026
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 1088 (#118) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 999 (#290) | HCA +95 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +23.7 (#36) | HCA +3.3 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.995 (#40) | - |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.994 (#12) | AdjNet +43.1 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.994 (#12) | AdjNet +43.4 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.954 (#14) | AdjO 93.0 | AdjD 59.5 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.609 (#136) | AdjO 77.6 | AdjD 72.8 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.843 (#35) | 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 → | 0.804 (#56) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1081 (#144) | 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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jahari Long | - | 1 | 32.6 | 25.0 | 3.0 | 2.0 | 1.0 | 1.0 | 2.0 | 7.0 | 23.0 | - | - | - | 57.1 | 100.0 | 94.1 | - | 86.3 | 64.3 |
| Kory Mincy | - | 1 | 34.6 | 19.0 | 5.0 | 3.0 | 1.0 | 0.0 | 6.0 | 8.0 | 14.0 | - | - | - | 75.0 | 100.0 | 100.0 | - | 93.1 | 87.5 |
| Stas Sivka | - | 1 | 19.3 | 11.0 | 3.0 | 3.0 | 1.0 | 0.0 | 2.0 | 3.0 | 13.0 | - | - | - | 66.7 | 66.7 | 100.0 | - | 105.8 | 100.0 |
| Riley Allenspach | - | 1 | 20.5 | 10.0 | 5.0 | 0.0 | 0.0 | 1.0 | 1.0 | 6.0 | 9.0 | - | - | - | 66.7 | 0 | 100.0 | - | 72.7 | 66.7 |
| Malik Presley | - | 1 | 30.1 | 10.0 | 1.0 | 0.0 | 3.0 | 0.0 | 1.0 | 7.0 | 6.0 | - | - | - | 57.1 | 100.0 | 0.0 | - | 63.5 | 71.4 |
| Devin Booker | - | 1 | 17.5 | 7.0 | 4.0 | 1.0 | 0.0 | 0.0 | 3.0 | 4.0 | 5.0 | - | - | - | 75.0 | 0 | 50.0 | - | 71.7 | 75.0 |
| Nick Ellington | - | 1 | 11.3 | 2.0 | 3.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 3.0 | - | - | - | 100.0 | 0 | 0 | - | 100.0 | 100.0 |
| Emmanuel Kanga | - | 1 | 19.5 | 2.0 | 5.0 | 0.0 | 0.0 | 2.0 | 2.0 | 3.0 | 4.0 | - | - | - | 33.3 | 0 | 0 | - | 33.3 | 33.3 |
| Dola Adebayo | - | 1 | 9.9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 | -3.0 | - | - | - | 0.0 | 0 | 0 | - | 0.0 | 0.0 |
| T.J. Prosise | - | 1 | 2.4 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.0 | - | - | - | 0 | 0 | 0 | - | 0 | 0 |
| Ben Woodward | - | 1 | 2.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | -1.0 | - | - | - | 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