2025-2026 NCAAMD3 Model Performance
Season
2026-2027 has no completed games yet. Showing 2025-2026, the newest season with results.
Every ranked model predicted the same 3977 games (2025-12-01 to 2026-04-05, 18 weekly windows) out of sample: each week's games were predicted by models fit only on the season's earlier games. 17 of 3994 games are left out because a model had no rating for a team yet.
Model Performance Leaderboard
Ranked by full-season AUC. The final week is the season's last 7 played days (1 games, 2026-03-30 to 2026-04-05), predicted from every earlier game: a small sample that shows the latest form.
| # | Model | AUC | Acc | Brier | LogLoss | AUC final wk | Acc final wk |
|---|---|---|---|---|---|---|---|
| 1 | Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | 0.829 | 74.6% | 0.172 | 0.517 | - | 0.0% |
| 2 | Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.828 | 74.4% | 0.174 | 0.524 | - | 0.0% |
| 3 | Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | 0.827 | 74.6% | 0.172 | 0.518 | - | 0.0% |
| 4 | Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.824 | 74.1% | 0.177 | 0.532 | - | 0.0% |
| 5 | Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.821 | 73.8% | 0.174 | 0.521 | - | 0.0% |
| 6 | Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.821 | 73.7% | 0.174 | 0.522 | - | 0.0% |
| 7 | Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.818 | 73.4% | 0.178 | 0.537 | - | 100.0% |
| 8 | Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.818 | 73.3% | 0.178 | 0.537 | - | 100.0% |
| 9 | Adjusted Context Blend Adjusted Context Blend Experimental context-heavy win model blending strong team components with rest and venue context. More → | 0.816 | 73.4% | 0.181 | 0.551 | - | 0.0% |
| 10 | Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 0.788 | 71.0% | 0.188 | 0.555 | - | 0.0% |
| 11 | Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 0.788 | 70.9% | 0.188 | 0.555 | - | 0.0% |
| 12 | Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 0.781 | 70.4% | 0.194 | 0.571 | - | 0.0% |
| 13 | Avg Margin Baseline Avg Margin Baseline Predict from simple average scoring margin in the training window. More → | 0.779 | 70.5% | 0.192 | 0.565 | - | 100.0% |
| 14 | Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.771 | 70.3% | 0.198 | 0.581 | - | 100.0% |
| 15 | Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → | 0.759 | 68.1% | 0.201 | 0.583 | - | 0.0% |
| 16 | Home Team Baseline Home Team Baseline Always favor the home team with a fixed prior. More → | 0.578 | 57.8% | 0.244 | 0.682 | - | 0.0% |
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Efficiency
Efficiency
Tempo-adjusted efficiency version of Pythagorean ratings.
More →
not compared: predicted 0 of 3994 games
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Methodology
Out-of-sample windows
- The season is split into 7-day windows ending on its last played day
- Each window is predicted by models refit on all earlier games of the season
- The first window starts after 21 days of games
- Every model is scored on the same games; a model that cannot predict most of them is listed as not compared
Elo / Bradley-Terry
- Elo: the published league Elo (preseason priors and carryover where configured), frozen at the window start
- Bradley-Terry: static logistic fit on the earlier games
- Recency variants weight games with a 30-day half-life
Pythagorean and margin models
- Raw, adjusted and log-adjusted Pythagorean from scores; efficiency from box-score possessions
- Margin: ridge regression on point margin with a learned home advantage
- Ensembles average the log-odds of five families
Baselines
- Home Team: the home team wins 60% (50% at neutral sites)
- Avg Margin: the higher average margin so far wins
- Models should beat these to add value