  From two coupled Multiclass Classification (MCC) and Response Manifold
Analytics (RMA) perspectives, we develop Categorical Exploratory Data Analysis
(CEDA) on PITCHf/x database for the information content of Major League
Baseball's (MLB) pitching dynamics. MCC and RMA information contents are
represented by one collection of multi-scales pattern categories from mixing
geometries and one collection of global-to-local geometric localities from
response-covariate manifolds, respectively. These collectives shed light on the
pitching dynamics and maps out uncertainty of popular machine learning
approaches. On MCC setting, an indirect-distance-measure based label embedding
tree leads to discover asymmetry of mixing geometries among labels'
point-clouds. A selected chain of complementary covariate feature groups
collectively brings out multi-order mixing geometric pattern categories. Such
categories then reveal the true nature of MCC predictive inferences. On RMA
setting, multiple response features couple with multiple major covariate
features to demonstrate physical principles bearing manifolds with a lattice of
natural localities. With minor features' heterogeneous effects being locally
identified, such localities jointly weave their focal characteristics into
system understanding and provide a platform for RMA predictive inferences. Our
CEDA works for universal data types, adopts non-linear associations and
facilitates efficient feature-selections and inferences.
