This paper presents an empirical comparison of a metric based on Spearman's rank correlation coefficient with traditional and relatively new distance measures (Burrows's Delta, Cosine Delta, Labbé distance, Soergel distance, and Jensen-Shannon divergence, the latter receiving particular attention) used in stylometric text attribution. The relevance of the study lies in the need to search for and identify a stylometric tool (a distance measure and its “settings”) that is robust to within-author variability and feature space sparsity. The research problem stems from methodological uncertainty in choosing an optimal distance measure for stylometric attribution. Despite the development of distance-based approaches, the effectiveness of metrics critically depends on corpus structure, the length of the analyzed fragments, and feature space parameters. In the absence of a single reliable metric for attributing complex text corpora, this study for the first time substantiates the use of Spearman's rank distance as a valid tool for stylometric analysis. The empirical basis of the study comprises four text corpora heterogeneous in terms of time, genre, and language (49 authors in total, 899 literary texts). The study included unified text preprocessing (tokenization, lowercasing, wordform-level analysis) and systematic parameter tuning for the six metrics. For each combination (author, metric, fragment length), the optimal configuration of parameters (number of frequent words; absence/presence of standardization) was determined, providing the maximum true positive rate in attribution using the k-NN method, k=1. The results show that Spearman's rank distance demonstrates the greatest robustness and universality, maintaining high accuracy under genre heterogeneity, variability in text length, and the presence of stylistic anomalies. It is established that Spearman's rank distance systematically outperforms frequency-based and entropy-based metrics in robustness under conditions of genre heterogeneity and can serve as a reliable tool for stylometric attribution.