To make ML systems deployment ready, one of the prominent challenges is to debug the performance issues of the trained ML models. This can be done by associating the issues with a set of Data Slices - aggregates of validation data records - to help the developers investigate these technical issues at a deeper level of granularity. Since the possible number of data slices are exponential in number, there is a need to prioritize the order (i.e. ranking) of slices before presenting to the users. However, there does not exist any work that deals with ranking these automatically generated slices and we refer to this problem as the data slice ranking problem (DSRP). This problem is challenging to address as the top ranked slices should contain significant error concentration (i.e. number of mis-classified data points), be statistically significant (i.e. having large size), and be non-redundant (i.e. contain unique mis-classified data points).In this paper, we tackle this challenging problem by proposing a novel game theoretic framework building upon Shapley value concept to derive a rank order for a given collection of data slices. In particular, we formally present a scheme that explicitly accounts only for the error concentration and we refer to this as Shapley Slice Ranking with Error concentration (SSR-E). We then prove a few useful properties of this scheme. Using thorough experimentation on 7 open source data sets, we demonstrate the superior performance of SSR mechanism vis-à-vis two baseline methods.