The MAP (Mean Average Precision) metric is one of the most popular performance metrics in the field of Information Retrieval Fault Localization (IRFL). However, there are problematic implementations of this MAP metric used in IRFL research. These implementations deviate from the text book definitions of MAP, rendering the metric sensitive to the truncation of retrieval results and inaccuracies and impurities of the used datasets. The application of such a deviating metric can lead to performance overestimation. This can pose a problem for comparability, transferability, and validity of IRFL performance results. In this paper, we discuss the definition and mathematical properties of MAP and common deviations and pitfalls in its implementation. We investigate and discuss the conditions enabling such overestimation: the truncation of retrieval results in combination with ground truths spanning multiple files and improper handling of undefined AP results. We demonstrate the overestimation effects using the Bench4BL benchmark and five well known IRFL techniques. Our results indicate that a flawed implementation of the MAP metric can lead to an overestimation of the IRFL performance, in extreme cases by up to 70 %. We argue for a strict adherence to the text book version of MAP with the extension of undefined AP values to be set to 0 for all IRFL experiments. We hope that this work will help to improve comparability and transferability in IRFL research.