In a traditional analysis of ordinal comparison data, the goal is to infer an overall ranking of objects from best to worst with each object having a unique rank. However, the ranks of some objects may not be statistically distinguishable. This could happen due to insufficient data or to the true underlying abilities or qualities being equal for some objects. In such cases, practitioners may prefer an overall ranking where groups of objects are allowed to have equal ranks or to be $\textit{rank-clustered}$. Existing models related to rank-clustering are limited by their inability to handle a variety of ordinal data types, to quantify uncertainty, or by the need to pre-specify the number and size of potential rank-clusters. We solve these limitations through the proposed Bayesian $\textit{Rank-Clustered Bradley-Terry-Luce}$ model. We allow for rank-clustering via parameter fusion by imposing a novel spike-and-slab prior on object-specific worth parameters in Bradley-Terry-Luce family of distributions for ordinal comparisons. We demonstrate the model on simulated and real datasets in survey analysis, elections, and sports.

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