Typological datasets for quantitative historicallinguistic inquiry are growing in breadth, but a challenge is also to increase their depth, since advanced methods often ideally require many hundreds of traits per language. Using biphone transition probabilities from phonemicized vocabulary data, we extract several hundred high-definition phonotactic traits per language, for 17 languages in the Ngumpin-Yapa and Yolngu subgroups of the Pama-Nyungan family, Australia. We detect phylogenetic signal at a significant level (p < 0.001 for both subgroups), measured against a reference phylogeny inferred from basic vocabulary cognacy data. This contrasts with simpler, binary coding of biphones’ occurrence, which provides insufficient detail for the detection of phylogenetic signal. Thus, we demonstrate the viability of a new method in quantitative historical linguistics, and emphasize the inferential power to be harnessed from high-definition, trait-rich datasets for comparative research.