The fact that tree entropy yields worse values than the baseline is somehow predictable: Given a sentence with n possible trees (note that n may be in the order of dozens or even hundreds), if a small number m of those analyses (1 < m << n) concentrate a large portion of probability mass but exhibit small differences between them, the sentence will be rejected by the baseline (because there is not enough distance between the first and second analyses) but will be accepted by tree entropy (because entropy will be relatively low, given the large value of n).
tree entropy is a good measure for Baldridge and Osborne (2003), whose purpose is to select sentences where the model is less confident, but our simple baseline seems to be better when the goal is to select sentences where the first parse is the correct one.