Pairwise comparison data are widely used to recover latent rankings, yet the models in dominant use assume stochastic transitivity. When preferences are in fact intransitive, a single scalar strength conflates genuine hierarchy with cycle-induced structure, biasing both the recovered ranking and any covariate effects attributed to it. To address this limitation, we propose the Covariate-Assisted Bayesian Intransitive Bradley-Terry (CA-BIBT) model, which uses a combinatorial Hodge decomposition to resolve the latent match-up into identifiable and mutually orthogonal flows, attributing the component lying in the covariate-induced subspace to observed covariates and assigning the remaining components to the residuals. A global-local shrinkage prior on the residual cycle-induced flow adapts the model from transitive to intransitive regimes without prespecifying the regime, and a Gibbs sampler yields, as posterior byproducts, calibrated uncertainty for each flow, the posterior probability of the level at which the entities are rankable, and a ranking summary for decision making that remains well-defined even under intransitivity. In simulations, the CA-BIBT model recovers all flow components accurately with near nominal coverage, and in an application to animal dominance data it reveals cyclic dominance that the observed covariates cannot account for, with strong posterior evidence for stochastic intransitivity.

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