Antipatterns are poor object-oriented solutions to recurring design problems. The identification of occurrences of antipatterns in systems has received recently some attention but current approaches have two main limitations: either (1) they classify classes strictly as being or not antipatterns, and thus cannot report accurate information for borderline classes, or (2) they return the probabilities of classes to be antipatterns but they require an expensive tuning by experts to have acceptable accuracy. To mitigate such limitations, we introduce a new identification approach, ABS (Antipattern identification using B-Splines), based on a numerical analysis technique. The results of a preliminary study show that ABS generally outperforms previous approaches in terms of accuracy when used to identify Blobs.