Yes, for a lot of my music this data is as far off as this example. From my limited understanding as a user without any deeper audio analysis knowledge, the algorithms used to get the data where trained on a limited data set. That's especially clear with the genres, were there are different models applied to deduce the genre, see also the blog entry at https://blog.musicbrainz.org/2014/11/21/what-do-650000-files-look-like-anyway/ and the comments there.
As I understood it, part of the goals with AcousticBrainz is to apply the algorithms and models to a larger data set to allow researchers and other interested parties to analyse the results and improve upon it.
The documentation of the Essentia toolkit provides also some background: http://essentia.upf.edu/documentation/
I hope this is about right, but some people involved in AcousticBrainz can for sure give a more in depth answer and correct me