Le Moyne
Also known as: Le Moyne
Program History
Model Outputs
2024-2025
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.998 (#97) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 1.000 (#18) | NetEff +74.0 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.968 (#118) | AdjNet +29.5 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.971 (#114) | AdjNet +29.2 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.893 (#105) | AdjO 90.6 | AdjD 67.3 |
2025 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2024-11-09 | vs | SUNY Poly | W | 107 - 73 |
| 2024-12-14 | vs | SUNY Delhi | W | 106 - 51 |
2025 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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jakob Blakley | - | 2 | 21.0 | 18.0 | 4.5 | 2.0 | 2.5 | 0.0 | 2.0 | 10.0 | 15.0 | - | - | - | 65.0 | 61.5 | 100.0 | - | 86.2 | 85.0 |
| Trent Mosquera | - | 1 | 13.0 | 14.0 | 2.0 | 1.0 | 2.0 | 0.0 | 2.0 | 8.0 | 9.0 | - | - | - | 75.0 | 33.3 | 50.0 | - | 78.8 | 81.2 |
| Robby Carmody | - | 1 | 9.0 | 14.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 7.0 | 8.0 | - | - | - | 71.4 | 66.7 | 0 | - | 100.0 | 100.0 |
| Dwayne Koroma | - | 2 | 18.0 | 12.5 | 5.0 | 2.5 | 1.0 | 0.0 | 1.0 | 6.5 | 13.5 | - | - | - | 84.6 | 0 | 50.0 | - | 79.9 | 84.6 |
| Isaac Nyakundi | - | 2 | 15.0 | 11.5 | 6.0 | 0.5 | 0.5 | 1.5 | 1.5 | 7.0 | 11.5 | - | - | - | 57.1 | 33.3 | 60.0 | - | 62.5 | 60.7 |
| Nate Fouts | - | 2 | 16.0 | 10.0 | 7.0 | 1.0 | 0.5 | 0.0 | 1.0 | 5.5 | 12.0 | - | - | - | 63.6 | 60.0 | 100.0 | - | 81.2 | 77.3 |
| Darrick Jones Jr. | - | 2 | 18.0 | 9.5 | 4.0 | 1.0 | 0.5 | 0.0 | 0.5 | 7.0 | 7.5 | 24 | 1.6 | 3.0 | 50.0 | 44.4 | 50.0 | 0.5 | 63.8 | 64.3 |
| AJ Dancler | - | 2 | 18.5 | 9.0 | 4.5 | 3.0 | 1.0 | 1.0 | 1.5 | 5.0 | 12.0 | - | - | - | 70.0 | 60.0 | 33.3 | - | 79.5 | 85.0 |
| Zek Tekin | - | 2 | 19.5 | 6.5 | 2.5 | 5.0 | 1.5 | 0.0 | 2.0 | 3.5 | 10.0 | - | - | - | 85.7 | 100.0 | 0 | - | 92.9 | 92.9 |
| Freds Pauls Bagatskis | - | 1 | 22.0 | 6.0 | 5.0 | 6.0 | 1.0 | 1.0 | 2.0 | 9.0 | 8.0 | - | - | - | 22.2 | 20.0 | 100.0 | - | 31.8 | 27.8 |
| Kaelin Thomas | - | 2 | 12.0 | 5.0 | 4.0 | 0.5 | 0.0 | 0.5 | 0.5 | 5.0 | 4.5 | 96 | 4.8 | 8.3 | 50.0 | 0.0 | 0.0 | 1.55 | 44.2 | 50.0 |
| Deng Garang | - | 2 | 17.0 | 4.5 | 2.0 | 3.0 | 1.5 | 0.0 | 1.0 | 6.0 | 4.0 | - | - | - | 33.3 | 25.0 | 0 | - | 37.5 | 37.5 |
| Sam Donnelly | - | 2 | 9.0 | 3.0 | 3.0 | 1.0 | 0.0 | 0.0 | 0.5 | 2.0 | 4.5 | - | - | - | 25.0 | 33.3 | 75.0 | - | 52.1 | 37.5 |
| Will Amica | - | 1 | 16.0 | 0.0 | 1.0 | 3.0 | 1.0 | 0.0 | 1.0 | 1.0 | 3.0 | - | - | - | 0.0 | 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