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2025-2026 NCAAM Model Performance Analysis

Comparing prediction accuracy across 6300 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 0.861 76.6% 0.160 0.486 5950 0.750 53.8% 0.225 13
2 Dynamic Bradley-Terry 0.857 77.1% 0.160 0.490 5950 0.625 61.5% 0.248 13
3 Core Ensemble 0.843 76.4% 0.205 0.597 5950 0.750 69.2% 0.215 13
4 Recency Ensemble 0.842 76.2% 0.204 0.598 5950 0.650 61.5% 0.223 13
5 Points O/D Recency 0.836 75.4% 0.179 0.539 5950 0.675 61.5% 0.227 13
6 Bradley-Terry 0.823 73.8% 0.175 0.523 5361 0.625 61.5% 0.228 13
7 Margin Regression 0.822 73.4% 0.178 0.531 5361 0.800 69.2% 0.195 13
8 Pythagorean Adjusted 0.815 73.1% 0.179 0.529 5361 0.825 76.9% 0.179 13
9 Pythagorean Log 0.814 73.2% 0.179 0.530 5361 0.825 76.9% 0.178 13
10 Adjusted Context Blend 0.813 72.4% 0.210 0.601 5950 0.925 76.9% 0.180 13
11 Points O/D 0.812 73.3% 0.182 0.541 5361 0.825 69.2% 0.196 13
12 Pythagorean Efficiency 0.796 71.6% 0.191 0.571 5361 - - - 0
13 Avg Margin (Baseline) 0.784 70.7% 0.195 0.573 5361 0.550 69.2% 0.239 13
14 Pure ELO 0.783 70.5% 0.191 0.564 5361 0.500 53.8% 0.263 13
15 Bradley-Terry Recency 0.769 68.6% 0.206 0.600 5950 0.400 38.5% 0.266 13
16 Pythagorean Raw 0.752 69.8% 0.208 0.603 5361 0.600 61.5% 0.235 13
17 Home Team (Baseline) 0.638 64.0% 0.232 0.657 5361 0.650 61.5% 0.237 13

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