This paper presents a quantitative study of Object‐Verb (OV) order in Archaic Chinese based on a Universal Dependencies (UD) treebanks. Treating word order as a binary choice (OV vs VO), we train a sparse logistic‐regression classifier that selects the most salient syntactic features needed for an accurate prediction to investigate the specific syntactic contexts allowing OV word order and to identify to what extent do these factors favour this order. The ranked features are understood as interpretable rules, and their coverage and precision as quantitative properties of each rule. The approach confirms earlier qualitative findings (e.g. pronoun object fronting and negation favour OV) and uncovers new contrasts in word order between different reflexive pronouns. It also identifies annotation errors that we corrected in the final analysis, illustrating how the quantitative models, combined with fine-grained corpus analysis, can improve treebank quality. Our study demonstrates that lightweight machine‐learning techniques applied to an existing syntactic resource can reveal fine‐grained patterns in historical word order and this can be reapplied to other languages.