Carnegie Mellon
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 → | 979 (#268) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 993 (#327) | HCA +95 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -3.1 (#374) | HCA +3.3 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.030 (#579) | - |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.259 (#513) | AdjNet -9.1 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.260 (#518) | AdjNet -8.9 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.357 (#509) | AdjO 73.2 | AdjD 79.7 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.463 (#373) | AdjO 76.3 | AdjD 77.9 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.446 (#360) | 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 → | 0.445 (#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 → | 958 (#266) | 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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Buckley DeJardin | - | 1 | 24.8 | 20.0 | 4.0 | 1.0 | 1.0 | 1.0 | 0.0 | 8.0 | 19.0 | - | - | - | 87.5 | 83.3 | 50.0 | - | 112.6 | 118.8 |
| Ian Brown | - | 1 | 25.4 | 15.0 | 3.0 | 0.0 | 0.0 | 0.0 | 1.0 | 9.0 | 8.0 | - | - | - | 55.6 | 62.5 | 0 | - | 83.3 | 83.3 |
| Nikola Dimitrijevic | - | 1 | 33.5 | 14.0 | 10.0 | 6.0 | 0.0 | 0.0 | 6.0 | 13.0 | 11.0 | - | - | - | 53.8 | 0.0 | 0 | - | 53.8 | 53.8 |
| Sam Adusei | - | 1 | 33.4 | 9.0 | 3.0 | 8.0 | 0.0 | 0.0 | 3.0 | 6.0 | 11.0 | - | - | - | 16.7 | 0.0 | 87.5 | - | 47.3 | 16.7 |
| Isaac Higgs | - | 1 | 34.5 | 9.0 | 1.0 | 3.0 | 0.0 | 0.0 | 4.0 | 7.0 | 2.0 | - | - | - | 28.6 | 25.0 | 100.0 | - | 51.4 | 35.7 |
| Chase Collignon | - | 1 | 14.1 | 3.0 | 2.0 | 0.0 | 0.0 | 1.0 | 2.0 | 2.0 | 2.0 | - | - | - | 50.0 | 0 | 50.0 | - | 52.1 | 50.0 |
| Robert Uhl | - | 1 | 8.0 | 2.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 | - | - | - | 50.0 | 0 | 0 | - | 50.0 | 50.0 |
| Dylan Bronner | - | 1 | 4.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | -1.0 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
| Nolan Casey | - | 1 | 20.4 | 0.0 | 7.0 | 2.0 | 0.0 | 0.0 | 1.0 | 2.0 | 6.0 | - | - | - | 0.0 | 0 | 0 | - | 0.0 | 0.0 |
| Carter Klaus | - | 1 | 1.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | -2.0 | - | - | - | 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