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2025-2026 NCAAWD3 Model Performance Analysis

Scope
All Games Ranked Games

All scored games in the selected league and season. AP Poll is excluded here.

Season
2025-2026

Comparing prediction accuracy across 2304 games using multiple rating models.

Model Catalog

No usable 7-day holdout rows for this season right now. Leaderboard is using full-season metrics.

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.

Log Loss Brier AUC Accuracy

No rolling weekly validation rows are stored for this league/season yet.

Model Performance Leaderboard

Models ranked by strict holdout AUC when available (fallback: full-season AUC). Hover over column headers for explanations.

# Model 7d Split AUC Acc Brier LogLoss n AUC 7d Acc 7d Brier 7d n 7d
1 Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → FULL
no 7d
 0.954 88.4% 0.129 0.429 1454 - - - 0
2 Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → FULL
no 7d
 0.954 88.0% 0.101 0.336 1454 - - - 0
3 Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → FULL
no 7d
 0.943 86.2% 0.097 0.304 1454 - - - 0
4 Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → FULL
no 7d
 0.942 86.2% 0.098 0.307 1454 - - - 0
5 Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → FULL
no 7d
 0.909 81.7% 0.157 0.495 1454 - - - 0
6 Avg Margin Baseline Avg Margin Baseline Predict from simple average scoring margin in the training window. More → FULL
no 7d
 0.894 80.6% 0.134 0.416 1454 - - - 0
7 Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → FULL
no 7d
 0.881 80.3% 0.145 0.446 1454 - - - 0
8 Home Team Baseline Home Team Baseline Always favor the home team with a fixed prior. More → FULL
no 7d
 0.555 55.5% 0.249 0.691 1454 - - - 0
- Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → FULL
no 7d
 - - - - 0 - - - 0
- Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → FULL
no 7d
 - - - - 0 - - - 0
- Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → FULL
no 7d
 - - - - 0 - - - 0
- Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → FULL
no 7d
 - - - - 0 - - - 0
- Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → FULL
no 7d
 - - - - 0 - - - 0
- Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → FULL
no 7d
 - - - - 0 - - - 0
- Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → FULL
no 7d
 - - - - 0 - - - 0
- Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → FULL
no 7d
 - - - - 0 - - - 0
- Adjusted Context Blend Adjusted Context Blend Experimental context-heavy win model blending strong team components with rest and venue context. More → FULL
no 7d
 - - - - 0 - - - 0

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