A current approach to the empirical study of the relationship between affect and the performance of athletes before and during a competition is idiographic in nature. Affect-performance zones are estimated for each athlete based on a sufficient number of paired affect and performance observations. Though extremely important for practitioners, the idiographic approaches introduced in the literature until now do not readily support generalizations across different populations (e.g., for different genders, levels of experience, and levels of expertise). This article illustrates how hierarchical linear modeling (HLM) can be effectively used to retain this idiographic focus, while also adding a nomothetic perspective describing the variation of individual affect-performance relationships across athletes. The article illustrates the computational and graphical options that, when appropriately used, can expand our understanding of the affect-performance linkage for both individual cases and populations of interest.