Herein we implement a data-driven workflow to understand the basic principles governing this reaction class. Leveraging the structural modularity of the species involved in the model reaction, we expanded the initially reported experimental data set and built parameter libraries for the carboxylic acids, substrates, and catalysts. The new dataset was designed based on common chemical modifications involved in conventional empiricism-driven optimization routes in asymmetric C-H oxidation reactions. Using fundamental principles of physical organic chemistry in combination with modern data science tools, we identified the parameters controlling the product yield and enantioselectivity with demonstrated predictive ability. The statistical models provide useful mechanistic information and significantly contribute to reaction comprehension. We envision that guidelines derived from this work can be generalized and successively employed to develop new asymmetric C-H oxidation reactions.