🐻⬇️🏀

2026 WNBA Model Performance Analysis

Comparing prediction accuracy across 0 games using multiple rating models.

Model Performance Leaderboard

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

# Model AUC Acc Brier LogLoss n AUC 7d Acc 7d Brier 7d n 7d
1 Pure ELO - - - - 0 0.745 66.7% 0.192 21
2 Bradley-Terry - - - - 0 0.816 66.7% 0.183 21
3 Bradley-Terry Recency - - - - 0 0.765 66.7% 0.189 21
4 Pythagorean Raw - - - - 0 0.827 71.4% 0.185 21
5 Pythagorean Efficiency - - - - 0 - - - 0
6 Pythagorean Adjusted - - - - 0 0.837 76.2% 0.167 21
7 Pythagorean Log - - - - 0 0.837 76.2% 0.167 21
8 Margin Regression - - - - 0 0.806 71.4% 0.183 21
9 Margin Recency - - - - 0 0.827 71.4% 0.186 21
10 Points O/D - - - - 0 0.806 71.4% 0.183 21
11 Points O/D Recency - - - - 0 0.837 71.4% 0.188 21
12 Core Ensemble - - - - 0 0.806 71.4% 0.179 21
13 Recency Ensemble - - - - 0 0.816 71.4% 0.179 21
14 Adjusted Context Blend - - - - 0 0.816 71.4% 0.178 21
15 Dynamic Bradley-Terry - - - - 0 0.796 66.7% 0.182 21
16 Home Team (Baseline) - - - - 0 0.500 52.4% 0.255 21
17 Avg Margin (Baseline) - - - - 0 0.847 76.2% 0.176 21

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