A theoretical framework for evaluating literary quality through analyzing narrative structures using simplified narrative representations in the form of story arcs is presented. This framework proposes two complementary models: the first employs Approximate Entropy to measure local unpredictability, while the second utilizes fractal analysis to assess global coherence. When applied to a substantial corpus of 9,089 novels, the findings indicate that narratives characterized by high literary quality, as indicated by reader ratings, exhibit a balance of local unpredictability and global coherence. This dual approach provides a formal and empirical basis for assessing literary quality and emphasizes the importance of considering intrinsic properties and reader perception in literary studies.