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Central Penn

Also known as: Central Penn
Program History

Model Outputs

2025-2026
Catalog

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. HCA +62 elo More → 904 (#606) HCA +62 elo
Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. HCA +2.9 More → -70.5 (#728) HCA +2.9
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → 0.253 (#560) -
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet -40.4 More → 0.009 (#668) AdjNet -40.4
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet -42.8 More → 0.007 (#669) AdjNet -42.8
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 39.4 | AdjD 64.9 More → 0.090 (#667) AdjO 39.4 | AdjD 64.9
Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. AdjO 47.1 | AdjD 72.4 More → 0.091 (#706) AdjO 47.1 | AdjD 72.4
Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. Blend of Elo, BT, Margin, PythLog, PtsOD More → 0.020 (#715) 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. Blend of Elo, BT, Margin, PythLog, PtsOD More → 0.022 (#716) Blend of Elo, BT, Margin, PythLog, PtsOD
Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. RD 233 | GP 1 More → 745 (#511) RD 233 | GP 1

2026 Schedule & Results

Date Vs/At Opponent Result Score
2026-02-02 @ Valley Forge L 50 - 59

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%
Nyree Roberson-Waters - 1 38.1 18.0 15.0 4.0 2.0 0.0 3.0 21.0 15.0 - - - 42.9 0.0 0.0 - 40.3 42.9
Mikayla Kirby - 1 35.9 16.0 4.0 3.0 3.0 0.0 5.0 16.0 5.0 - - - 25.0 25.0 58.3 - 37.6 28.1
Jayla Pearson - 1 36.7 8.0 4.0 3.0 4.0 0.0 1.0 9.0 9.0 - - - 44.4 0 0 - 44.4 44.4
Diamond Whitaker - 1 32.4 7.0 5.0 0.0 0.0 0.0 1.0 5.0 6.0 - - - 60.0 50.0 0 - 70.0 70.0
Karen Kirby - 1 22.2 1.0 2.0 0.0 2.0 0.0 6.0 3.0 -4.0 - - - 0.0 0.0 50.0 - 12.9 0.0
Sidney Green - 1 34.7 0.0 8.0 0.0 1.0 0.0 4.0 5.0 0.0 97 6.9 75.7 0.0 0.0 0 1.57 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