ff-statistical-methods

Regression method selection depends on your data structure and regularization needs. OLS works for simple relationships; Ridge and Lasso reduce overfitting through different penalty approaches; Elastic Net combines both; GAMs capture non-linear aging curves. This Skill provides guidance on matching each method to your rest-of-season or trade analysis scenario.

Monte Carlo simulations for championship probability require defining outcome distributions, sampling iterations, and uncertainty ranges. This Skill provides simulation templates and frameworks for rest-of-season projections, trade impact analysis, and lineup optimization that incorporate regression-to-mean and confidence intervals.

Uncertainty quantification combines regression models with prediction intervals and Monte Carlo resampling to estimate outcome ranges. This Skill covers confidence and prediction interval construction, hypothesis testing best practices, and uncertainty handling aligned with robust statistical standards.

Yes. Generalized Additive Models capture non-linear aging patterns better than linear regression. This Skill provides guidance on GAM implementation for aging curve modeling alongside regression method selection and cross-validation practices.

Regression-to-mean assumes reversion toward historical averages, which may not account for structural changes, injury recovery, or role shifts. This Skill addresses when and how to apply regression-to-mean analysis and provides uncertainty quantification methods to handle edge cases in trade impact and lineup scenarios.