Input—that is, meaningful samples of the target language to which learners are exposed—is generally considered the most essential condition for second language (L2) development. Theoretical and empirical research on this key construct, however, remains scarce. To date, the field’s understanding of input has mostly been fragmentary and shallow. Crucial questions, such as what input characteristics make it indispensable for mastering any linguistic construction, are partially addressed, at best. This study sought to advance a holistic conceptualization of input in empirical research via the theoretical lens of L2 input robustness from the Selective Fossilization Hypothesis (Han, 2009, 2013, 2014). Specifically, the distributional characteristics of the simple present and present progressive in a beginner-to-advanced English as a Second or Foreign Language textbook corpus were identified using form-function analysis. Input robustness was assessed as a function of the range and types of each target construction’s mappings of form, meaning, and function (FMF) in context (“input variability”) and the frequency distribution of those mappings (“input frequency”). A mathematical model was custom-built to simulate increasing magnitudes of input variability. Insights regarding each construction’s variability and frequency were then integrated for extrapolation through a novel application of the input robustness formula. The study found that specifying distributional characteristics with FMF descriptors (e.g., one form encoding multiple meanings in multiple contexts) provided objective benchmarks for cross-construction comparisons. Moreover, the most robust textbook datasets for both constructions consistently ranked as the top two lowest in variability and the top two highest in frequency. Furthermore, the less context-dependent the FMF mappings exemplified are, the less variable a target construction would become, and vice versa. Finally, both the simple present and present progressive were determined to be “quite robust” (i.e., somewhat invariable and somewhat infrequent) in the textbook corpus, despite differences in magnitude. Overall, these findings suggest that the input robustness approach leads to a more holistic understanding of input. Conceptually, this input robustness study addressed how a target construction’s FMF consistency and frequency distribution are intricately connected in the input. Methodologically, the study demonstrated how those intricacies can be integrated into falsifiable terms for further interpretation.