Abstract Paired comparison models are used for analysing data that involves pairwise comparisons among a set of objects. When the outcomes of the pairwise comparisons have no ties, the paired comparison models can be generalized as a class of binary response models. Receiver operating characteristic (ROC) curves and their corresponding areas under the curves are commonly used as performance metrics to evaluate the discriminating ability of binary response models. Despite their individual wide range of usage and their close connection to binary response models, ROC analysis to our knowledge has never been extended to paired comparison models since the problem of using different objects as the reference in paired comparison models prevents traditional ROC approach from generating unambiguous and interpretable curves. We focus on addressing this problem by proposing two novel methods to construct ROC curves for paired comparison data which provide interpretable statistics and maintain desired asymptotic properties. The methods are then applied and analysed on head-to-head professional sports competition data.