Timsort optimizes for partially sorted data, but that's a long way from saying it's not intended for uniformly distributed data. It's intended for all data, that's why it's the default sort in Python. It's no less intended for all data than merge sort is.

Even on random data, 50% of the time you'll pick a bad pivot. Get a slightly unlucky run and you're into quadratic behavior for that subset; the more data you're sorting, the more likely introsort's switch to heapsort on deep recursion (and insertion sort on small ranges) will help.