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2025-2026 NCAAW Model Performance Analysis

Comparing prediction accuracy across 6031 games using multiple rating models.

Model Performance Leaderboard

Models ranked by AUC on 2025-2026 games. Hover over column headers for explanations.

# Model AUC Acc Brier LogLoss n AUC 7d Acc 7d Brier 7d n 7d
1 Margin Recency 0.893 79.9% 0.140 0.434 5395 0.800 71.4% 0.180 14
2 Dynamic Bradley-Terry 0.887 79.2% 0.143 0.445 5395 0.733 78.6% 0.204 14
3 Margin Regression 0.878 79.4% 0.148 0.455 5048 0.711 71.4% 0.202 14
4 Core Ensemble 0.877 80.7% 0.189 0.562 5395 0.756 71.4% 0.194 14
5 Bradley-Terry 0.876 78.8% 0.149 0.462 5048 0.800 57.1% 0.212 14
6 Recency Ensemble 0.876 80.4% 0.187 0.560 5395 0.756 71.4% 0.191 14
7 Pythagorean Adjusted 0.871 78.0% 0.151 0.466 5048 0.711 71.4% 0.208 14
8 Pythagorean Log 0.870 78.0% 0.151 0.468 5048 0.711 71.4% 0.204 14
9 Points O/D 0.870 78.1% 0.153 0.469 5048 0.711 78.6% 0.197 14
10 Points O/D Recency 0.867 77.7% 0.158 0.486 5395 0.756 78.6% 0.180 14
11 Adjusted Context Blend 0.857 76.9% 0.194 0.567 5395 0.667 64.3% 0.216 14
12 Avg Margin (Baseline) 0.843 75.9% 0.166 0.502 5048 0.644 50.0% 0.226 14
13 Pure ELO 0.841 75.4% 0.168 0.511 5048 0.689 64.3% 0.215 14
14 Pythagorean Efficiency 0.840 75.4% 0.170 0.528 5048 - - - 0
15 Bradley-Terry Recency 0.836 74.3% 0.183 0.548 5395 0.689 57.1% 0.224 14
16 Pythagorean Raw 0.794 73.6% 0.189 0.563 5048 0.600 50.0% 0.226 14
17 Home Team (Baseline) 0.595 59.6% 0.241 0.675 5048 0.633 64.3% 0.231 14

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: Bill James formula (pts scored/allowed)
  • Efficiency: Pace-adjusted (pts per possession)
  • Adjusted: KenPom-style opponent adjustment
  • 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