We focus on the post-retrieval query performance prediction (QPP) task. Specifically, we make a new use of passage information for this task. Using such information we derive a new mean score calibration predictor that provides a more accurate prediction. Using an empirical evaluation over several common TREC benchmarks, we show that, QPP methods that only make use of document-level features are mostly suited for short query prediction tasks; while such methods perform significantly worse in verbose query prediction settings. We further demonstrate that, QPP methods that utilize passage-information are much better suited for verbose settings. Moreover, our proposed predictor, which utilizes both document-level and passage-level features provides a more accurate and consistent prediction for both types of queries. Finally, we show a connection between our predictor and a recently proposed supervised QPP method, which results in an enhanced prediction.