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LMU (CA)

Season: 2026
Also known as: LMU (CA)
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 Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → 1077 (#189) -
Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. HCA +62 elo More → 1017 (#315) HCA +62 elo
Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. HCA +2.9 More → +49.8 (#27) HCA +2.9
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → 1.000 (#22) -
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet +72.0 More → 1.000 (#12) AdjNet +72.0
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet +71.1 More → 1.000 (#11) AdjNet +71.1
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 74.8 | AdjD 35.7 More → 0.972 (#43) AdjO 74.8 | AdjD 35.7
Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. AdjO 70.6 | AdjD 53.5 More → 0.825 (#44) AdjO 70.6 | AdjD 53.5
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 → 0.973 (#14) 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 → 0.962 (#13) Blend of Elo, BT, Margin, PythLog, PtsOD
Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. RD 301 | GP 1 More → 1108 (#151) RD 301 | GP 1

2026 Schedule & Results

Date Vs/At Opponent Result Score
2025-12-16 vs Chapman W 81 - 27

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%
Andjela Matic - 1 22.8 17.0 2.0 2.0 3.0 0.0 2.0 9.0 13.0 - - - 77.8 60.0 0 - 94.4 94.4
Kayla Jones - 1 16.9 13.0 6.0 1.0 0.0 0.0 1.0 7.0 12.0 210 10.5 86.2 85.7 0 50.0 3.69 82.5 85.7
Jess Lawson - 1 18.3 11.0 8.0 2.0 1.0 0.0 2.0 5.0 15.0 - - - 60.0 0 83.3 - 72.0 60.0
Zawadi Ogot - 1 22.8 10.0 5.0 0.0 2.0 2.0 2.0 13.0 4.0 - - - 38.5 0 0.0 - 37.2 38.5
Ivana Krajina - 1 17.9 7.0 2.0 4.0 1.0 0.0 1.0 7.0 6.0 - - - 42.9 20.0 0 - 50.0 50.0
Maya Hernandez - 1 17.4 7.0 7.0 4.0 1.0 1.0 1.0 5.0 14.0 24 1.8 20.9 60.0 0 100.0 -0.36 64.3 60.0
Ana Milanovic - 1 13.6 7.0 1.0 2.0 1.0 0.0 2.0 3.0 6.0 - - - 100.0 0 100.0 - 101.7 100.0
Carly Heidger - 1 16.5 6.0 3.0 0.0 1.0 0.0 0.0 7.0 3.0 - - - 42.9 0.0 0 - 42.9 42.9
Paula Reus Piza - 1 13.7 3.0 5.0 2.0 0.0 0.0 1.0 3.0 6.0 - - - 33.3 0 50.0 - 38.7 33.3
Lova Lagerlid - 1 2.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 - - - 0 0 0 - 0 0
Allison Clarke - 1 25.4 0.0 1.0 3.0 0.0 0.0 3.0 6.0 -5.0 - - - 0.0 0.0 0 - 0.0 0.0
Mari Somvichian - 1 12.8 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 - - - 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