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While existing off-policy evaluation (OPE) methods typically estimate the value of a policy, in real-world applications, OPE is often used to compare and rank policies before deploying them in the real world. This is also known as the offline policy ranking problem. While one can rank the policies based on point estimates from OPE, it is beneficial to estimate the full distribution of outcomes for policy ranking and selection. This paper introduces Probabilistic Offline Policy Ranking that works with expert trajectories. It introduces rigorous statistical inference capabilities to offline evaluation, which facilitates probabilistic comparisons of candidate policies before they are deployed. We empirically demonstrate that POPR is effective for evaluating RL policies across various environments.