CFB/LABA BETTING INSTRUMENT
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MODEL TRAINING PACK

What the models see

The licensed CFBD Model Training Pack: 4,520 games, 86 columns, 72 usable features, 2016–2024 (Week 5 onward). Feature importances below are read from the pack's own fitted artifacts — not re-trained here.

Training dataset

DATASET

Games

4,520

Columns

86

Usable features

72

Seasons

2016–2024 (Week 5 onward)

Opponent-adjusted, no future leakage — each row's stats use games prior to that matchup only.

Ridge margin model

RIDGE — MARGINintercept 4.88
away_adjusted_epa_allowed
-25.80
away_adjusted_epa
24.40
home_adjusted_epa
-23.81
home_adjusted_epa_allowed
21.81
home_elo
-0.03
away_elo
0.03
away_talent
0.01
home_talent
-0.01

Coefficients predict training Margin (away points minus home points). Negative Margin favors the home team, opposite the dashboard’s Home Margin. Each coefficient is the change per feature unit; raw magnitudes are not directly comparable across differently scaled features. Faded bars are negative coefficients.

XGBoost win probability model

XGBOOST — HOME WIN PROBABILITY
spread
31.4%
away_elo
6.3%
home_talent
6.1%
home_adjusted_epa_allowed
5.9%
home_adjusted_success_allowed
5.8%
home_elo
5.8%
home_adjusted_success
5.8%
away_adjusted_epa_allowed
5.7%
away_adjusted_success_allowed
5.7%
away_adjusted_epa
5.5%
away_adjusted_success
5.5%
away_talent
5.5%
home_adjusted_epa
5.0%

Gain-based importance. Spread alone drives a third of the model.

FastAI model

FASTAI — HOME WIN PROBABILITY

Fitted artifact present (fastai_home_win_model.pkl) but not loadable in this environment — fastai not installed. Importances not available.

Training notebooks

7 TRAINING NOTEBOOKS
  • 01_linear_regression_margin.ipynbLinear Regression — MarginLinearRegressionFast interpretable baseline for score margin. MAE, RMSE, R², actual-vs-predicted scatter.
  • 02_random_forest_team_points.ipynbRandom Forest — Team PointsRandomForestRegressorTwo forests predict each team's score; implied margin from the pair.
  • 03_xgboost_win_probability.ipynbXGBoost — Win ProbabilityXGBClassifierAccuracy, AUC, log loss, calibration curve. The fitted xgb_home_win_model.pkl.
  • 04_fastai_win_probability.ipynbFastAI — Win Probabilityfastai.tabular_learnerTabular neural net. AUC, accuracy, F1. The fitted fastai_home_win_model.pkl.
  • 05_logistic_regression_win_probability.ipynbLogistic Regression — Win ProbabilityLogisticRegressionInterpretable classifier; coefficient plot shows key drivers.
  • 06_shap_interpretability.ipynbSHAP — Margin Explanationsshap.ExplainerShapley values on an XGBoost margin model: beeswarm + force plots.
  • 07_stacked_ensemble.ipynbStacked EnsembleLogistic + RF + XGB → Logistic stackerStacking meta-model combining three base learners.