ff-ml-modeling

Build projection models by guiding feature engineering (age curves, opportunity metrics), selecting algorithms (linear, regularized, tree-based, ensemble), and validating with time-series splits and nested cross-validation tailored for sports data to ensure robust NFL player forecasts.

Feature engineering patterns include age-curve features capturing career trajectories, opportunity and efficiency metrics reflecting playing time and production, rolling features for trend detection, game-context interactions, and position-specific engineered features designed for NFL player performance prediction.

Apply sports-specific validation strategies including time-series splits respecting temporal structure, nested cross-validation for hyperparameter tuning, and position-specific holdout sets to prevent information leakage and ensure projection models generalize to future NFL seasons.

Evaluate model selection frameworks spanning linear regression, regularized approaches (Ridge, Lasso), tree-based methods, and ensemble techniques; select based on feature interpretability requirements, prediction accuracy on time-series splits, and domain alignment with NFL player performance dynamics.

Apply interpretability techniques to understand feature importance, model predictions, and decision boundaries; use practical templates to communicate how age curves, opportunity metrics, and game interactions drive player projections for stakeholder validation and trust.