Maine-Fort Kent Bengals
2025 Team Stats (1 games)
47.0
PPG
98.0
Opp
-51.0
Margin
21.3%
FG%
24.1%
3P%
50.0%
FT%
39.0
RPG
10.0
APG
14.0
TO
96.0
Pace
Model Outputs
2025-2026 latest available
No materialized model snapshot for 2025 yet, so this section is showing the latest available team-model rows.
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 992 (#485) | HCA +109 elo |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -24.0 (#646) | HCA +2.5 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.418 (#580) | AdjO 72.8 | AdjD 76.5 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.183 (#592) | 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 → | 0.221 (#596) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 823 (#645) | RD 350 | GP 1 |
2025 Schedule & Results
| Date | Vs/At | Opponent | Result | Score |
|---|---|---|---|---|
| 2024-11-06 | @ | Maine Black Bears | L | 47 - 98 |
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% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T. Islamovic
|
- | 1 | 25.0 | 10.0 | 7.0 | 2.0 | 0.0 | 0.0 | 1.0 | 11.0 | 7.0 | - | - | - | 36.4 | 33.3 | 0 | - | 45.5 | 45.5 |
M. Vukcevic
|
- | 1 | 20.0 | 9.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 9.0 | 2.0 | - | - | - | 33.3 | 0.0 | 75.0 | - | 41.8 | 33.3 |
A. Grady
|
- | 1 | 21.0 | 6.0 | 6.0 | 2.0 | 1.0 | 1.0 | 2.0 | 8.0 | 6.0 | - | - | - | 25.0 | 0.0 | 50.0 | - | 30.7 | 25.0 |
J. Guerrero
|
- | 1 | 17.0 | 6.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 | 8.0 | 0.0 | - | - | - | 25.0 | 33.3 | 0 | - | 37.5 | 37.5 |
L. Novakovic
|
- | 1 | 13.0 | 4.0 | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 3.0 | - | - | - | 25.0 | 100.0 | 50.0 | - | 41.0 | 37.5 |
D. Shower
|
- | 1 | 11.0 | 3.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 3.0 | - | - | - | 100.0 | 100.0 | 0 | - | 150.0 | 150.0 |
D. Hodges
|
- | 1 | 14.0 | 3.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 6.0 | -3.0 | - | - | - | 16.7 | 33.3 | 0.0 | - | 21.8 | 25.0 |
M. Allen
|
- | 1 | 8.0 | 3.0 | 3.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 7 | 0.6 | 3.1 | 50.0 | 0 | 50.0 | -0.12 | 52.1 | 50.0 |
K. Sasaki
|
- | 1 | 12.0 | 2.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 | 2.0 | 2.0 | - | - | - | 50.0 | 0 | 0 | - | 50.0 | 50.0 |
S. Barro
|
- | 1 | 16.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 7.0 | -4.0 | - | - | - | 0.0 | 0.0 | 50.0 | - | 6.3 | 0.0 |
N. Gravely
|
- | 1 | 13.0 | 0.0 | 4.0 | 1.0 | 1.0 | 0.0 | 1.0 | 6.0 | -1.0 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
A. Creech
|
- | 1 | 15.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 5.0 | -3.0 | - | - | - | 0.0 | 0 | 0 | - | 0.0 | 0.0 |
K. Obiora
|
- | 1 | 11.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 6.0 | -6.0 | - | - | - | 0.0 | 0.0 | 0 | - | 0.0 | 0.0 |
S. Ramachandran
|
- | 1 | 4.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | - | - | - | 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