🐻⬇️🏀

1993-1994 NCAAM Model Performance

Scope
All Games Ranked Games

All scored games in the selected league and season. AP Poll is excluded here.

Season
2026-2027 2025-2026 2024-2025 2023-2024 2022-2023 2021-2022 2020-2021 2019-2020 2018-2019 2017-2018 2016-2017 2015-2016 2014-2015 2013-2014 2012-2013 2011-2012 2010-2011 2009-2010 2008-2009 2007-2008 2006-2007 2005-2006 2004-2005 2003-2004 2002-2003 2001-2002 2000-2001 1999-2000 1998-1999 1997-1998 1996-1997 1995-1996 1994-1995 1993-1994 1992-1993 1991-1992 1990-1991 1989-1990 1988-1989

Every ranked model predicted the same 3517 games (1993-12-14 to 1994-04-04, 16 weekly windows) out of sample: each week's games were predicted by models fit only on the season's earlier games. 2 of 3519 games are left out because a model had no rating for a team yet.

Does recruiting help team predictions?
Men: yes, mostly early. Adding freshman recruiting rankings to the preseason estimate makes Elo's game predictions better by 6.6 milli-nats per game in weeks 1-4 (95% CI 4.2 to 9.1) and by 3.1 over the full season (2.3 to 3.9), on six seasons it never saw; in 2025-26, also unseen, by 10.0 and 3.7. Picks right in weeks 1-4: 76.3% with recruiting, 76.0% without, 74.6% carrying last season over.
Milli-nats: 0.001 of log loss per game, lower is better. Recruiting model report · Preseason prior backtest

Model Catalog · Player models · Preseason prior backtest (team Elo with and without the player-based start)

Model Performance Leaderboard

Ranked by full-season AUC. The final week is the season's last 7 played days (5 games, 1994-03-29 to 1994-04-04), 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 Recency Margin Recency Margin regression with exponential recency weights on newer games. More → 0.821 73.1% 0.177 0.529 1.000 80.0%
2 Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → 0.820 73.1% 0.177 0.529 1.000 80.0%
3 Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → 0.817 73.2% 0.179 0.534 1.000 60.0%
4 Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → 0.816 73.1% 0.179 0.534 1.000 80.0%
5 Adjusted Context Blend Adjusted Context Blend Chronologically trained component blend with rest and signed venue context. More → 0.814 72.8% 0.182 0.550 0.833 60.0%
6 Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → 0.812 73.5% 0.177 0.526 0.500 40.0%
7 Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → 0.803 71.9% 0.182 0.539 1.000 80.0%
8 Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → 0.803 71.9% 0.182 0.539 1.000 80.0%
9 Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → 0.783 70.5% 0.196 0.579 0.500 40.0%
10 Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → 0.783 70.5% 0.196 0.580 0.500 40.0%
11 Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → 0.771 69.7% 0.195 0.573 0.833 80.0%
12 Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → 0.771 69.6% 0.195 0.573 0.833 60.0%
13 Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → 0.770 69.3% 0.195 0.571 1.000 100.0%
14 Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → 0.762 69.4% 0.201 0.586 0.667 80.0%
15 Avg Margin Baseline Avg Margin Baseline Predict from simple average scoring margin in the training window. More → 0.755 68.1% 0.200 0.582 0.833 80.0%
16 Home Team Baseline Home Team Baseline Always favor the home team with a fixed prior. More → 0.650 63.6% 0.232 0.656 0.500 60.0%

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 120-day half-life in NCAA leagues (keeping the non-conference games that link conferences) and a 30-day half-life in pro leagues

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