Title: Self-Supervised Off-Policy Ranking via Crowd Layer. Abstract: Off-policy evaluation (OPE) aims to estimate the online performance of target policies given dataset collected by some behavioral policies. OPE is crucial in many applications where online policy evaluation is expensive. However, existing OPE methods are far from reliable. Fortunately, in many real-world scenarios, we care only about the ranking of the evaluating policies, rather than their exact online performance. Existing works on off-policy ranking (OPR) adopt a supervised training paradigm, which assumes that there are plenty of deployed policies and the labels of their performance are available. However, this assumption does not apply to most OPE scenarios because collecting such training data might be highly expensive. In this paper, we propose a novel OPR framework called SOCCER, where the existing OPE methods are modeled as workers in a crowdsourcing system. SOCCER can be trained in a self-supervised way as it does not require any ground-truth labels of policies. Moreover, in order to capture the relative discrepancies between policies, we propose a novel transformer-based architecture to learn effective pairwise policy representations. Experimental results show that SOCCER achieves significantly high accuracy in a variety of OPR tasks. Surprisingly, SOCCER even performs better than baselines trained in a supervised way using additional labeled data, which further demonstrates the superiority of SOCCER in OPR tasks.