I applied a Bayesian hierarchical binomial model to ~800k MLB pitches (2020-2025) to assess whether Statcast's breaking ball taxonomy has discriminant validity. The short version: it doesn't, at least not between sliders and sweepers. The setup: five outcome models (whiff rate, chase rate, strike rate, called strike rate, zone rate) with pitcher-level random intercepts, all six PCA-derived movement features as fixed covariates, and pitch type label as the variable of interest. ST (sweeper) is the reference. If the slider coefficient is indistinguishable from zero after conditioning on movement, the label carries no incremental predictive information. Result: beta_sl straddles zero on all five outcomes. The curveball/knuckle-curve vs. slider/sweeper contrast excludes zero cleanly on all five. The meaningful discriminant boundary in the data is one level up from where Statcast draws it.…