Regardless of the application domain, adversaries may conduct spoofing attacks in order to bypass an authentication system. The difficulty of fooling a biometric sensor, known as circumvention; can be paired with an additional property based on the easiness of identifying ongoing presentation attacks which could help selecting the most suitable characteristic(s) when designing a biometric system. To such extent, this paper proposes spoofing detectability, as a property of biometric characteristics, to indicate the likelihood of detecting ongoing presentation attacks aiming at overcoming authentication mechanisms. We define and then quantitatively estimate spoofing detectability through unsupervised anomaly detection on publicly available biometric datasets, collecting metric scores which are then converted into the Low, Medium, High categories for 8 different biometric characteristics. We built our results upon unsupervised algorithms as they represent the most suitable answer to the detection of zero-day attacks. Alongside with our experimental process, we show the intrinsic relevance of spoofing detectability to complement circumvention. As a final contribution of the paper, we show how to embed an anomaly-based spoofing detection module into an authentication system for runtime support.