2025-2026 NCAAW Model Performance Analysis
All scored games in the selected league and season. AP Poll is excluded here.
Source season: 2698 games. Comparative evidence is unavailable until a verified common holdout cohort is recorded.
Comparative evaluation is held for this season. Legacy/full-fit rows are not ranked because they do not prove one shared out-of-sample game cohort. Reason: pending_cohort_contract.
Rolling Holdout Curves
Each point is a strict weekly holdout: train on all games before that week, test on that week. This first version uses a 21-day warmup, then 7-day holdouts stepped forward weekly.
No cohort-receipted rolling validation is available for this league/season yet. Legacy rolling rows are intentionally hidden until their common-cohort promotion contract is installed.
Model Performance Leaderboard
Models are ranked only by AUC on one verified common 7-day holdout cohort. Unsupported or pending families are never treated as zero.
| # | Model | Cohort | AUC 7d | Acc 7d | Brier 7d | LogLoss 7d | n 7d |
|---|---|---|---|---|---|---|---|
| - | Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Home Team Baseline Home Team Baseline Always favor the home team with a fixed prior. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Avg Margin Baseline Avg Margin Baseline Predict from simple average scoring margin in the training window. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → |
PENDING
receipt required
|
- | - | - | - | - |
| - | Adjusted Context Blend Adjusted Context Blend Experimental context-heavy win model blending strong team components with rest and venue context. More → |
PENDING
receipt required
|
- | - | - | - | - |
Methodology
ELO / Bradley-Terry
- ELO: Iterative updates, K=64, HCA=100
- BT: Static logistic regression on all games
- Both model win probability, not margin
- ELO updates after each game; BT fits once
Pythagorean Models
- Raw: Classic points scored/allowed formula
- Efficiency: Pace-adjusted (pts per possession)
- Adjusted: Opponent-adjusted efficiency
- Log: Log-linear multiplicative scale
Margin Regression
- Team-level ridge regression on point margin
- Linear Bradley-Terry (margin target)
- Alpha=0.05 (CV-tuned)
- Learns home advantage from data (~6 pts)
Baselines
- Home Team: Always predict home wins (60%)
- Avg Margin: Higher average margin wins
- Models should beat these to add value