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2025-2026 GLEAGUE Model Performance Analysis

Comparing prediction accuracy across 543 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 Avg Margin (Baseline) 0.686 64.3% 0.223 0.637 756 0.667 75.0% 0.231 4
2 Pure ELO 0.544 52.4% 0.252 0.698 756 - - 0.375 4
3 Pythagorean Log 0.541 53.0% 0.301 0.861 756 0.667 75.0% 0.227 4
4 Pythagorean Adjusted 0.541 53.0% 0.301 0.861 756 0.667 75.0% 0.227 4
5 Home Team (Baseline) 0.539 54.0% 0.252 0.697 756 0.167 25.0% 0.310 4
6 Pythagorean Raw 0.532 53.4% 0.265 0.731 756 0.667 75.0% 0.243 4
7 Points O/D 0.521 52.2% 0.291 0.802 756 0.333 50.0% 0.249 4
8 Pythagorean Efficiency 0.518 53.6% 0.350 1.121 661 - - - 0
9 Bradley-Terry 0.486 48.4% 0.273 0.745 756 - 25.0% 0.272 4
10 Margin Regression 0.474 47.9% 0.286 0.775 756 0.333 50.0% 0.249 4
11 Bradley-Terry Recency - - - - 0 - - 0.367 4
12 Margin Recency - - - - 0 - 25.0% 0.296 4
13 Points O/D Recency - - - - 0 - 25.0% 0.293 4
14 Core Ensemble - - - - 0 - 25.0% 0.299 4
15 Recency Ensemble - - - - 0 - 25.0% 0.309 4
16 Adjusted Context Blend - - - - 0 - 25.0% 0.299 4
17 Dynamic Bradley-Terry - - - - 0 - - 0.332 4

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