Volcano plots, stemming from the Sabatier principle, visualize descriptor-performance relationships, allowing rational catalyst design. Manually drawn volcanoes originating from experimental studies are potentially prone to human bias as no guidelines or metrics exist to quantify the goodness of fit. To address this limitation, we introduce a framework called SPOCK (systematic piecewise regression for volcanic kinetics) and validate it using experimental data from heterogeneous, homogeneous, and enzymatic catalysis to fit volcano-like relationships. We then generalize this approach to DFT-derived volcanoes and evaluate the tool’s robustness against noisy kinetic data and in identifying false-positive volcanoes, i.e., cases where studies claim a volcano-like relationship exists, but such correlations are not statistically significant. Once the SPOCK’s functional features are established, we demonstrate its potential to identify descriptor-performance relationships, exemplified via the ceria-promoted water-gas shift and single-atom-catalyzed electrocatalytic carbon dioxide reduction reactions. In both cases, the model uncovers descriptors previously unreported, revealing insights that are not easily recognized by human experts. Finally, we showcase SPOCK’s capabilities to formulate multivariable descriptors, an emerging topic in catalysis research. Our work pioneers an automated and standardized tool for volcano plot construction and validation, and we release the model as an open-source web application for greater accessibility and knowledge generation in catalysis.