We address the problem of query performance prediction (QPP) using reference lists. To date, no previous QPP method has been fully successful in generating and utilizing several pseudo-effective and pseudo-ineffective reference lists. In this work, we try to fill the gaps. We first propose a novel unsupervised approach for generating and selecting both types of reference lists using query perturbation and statistical inference. We then propose an enhanced QPP approach that utilizes both types of selected reference lists.