[eru]

> For sorting these mostly sorted lists, insertion sort tends towards O(n) while Quicksort tends towards O(n^2)!

Only if you pick your pivot really, really badly. You can randomise your pivot to get O(n log n), or in the case of already almost sorted lists, you can pick a pivot by just picking the element in the middle of the list.

But you are right, that even with optimal pivots, QuickSort is still O(n log n) even in the best case. There are simple variants of merge sort that give you O(n log k) behaviour where k is the number of runs (both ascending and descending) in the data. The 'sort' you get by default in eg Haskell's standard library uses such an algorithm. I think Python does, too.