In experimental results it was shown to be highly efficient, often outperforming traditional algorithms such as quicksort, particularly on distributions exhibiting structure and string sorting.
It uses distribution-based techniques to accomplish this, first locating the minimum and maximum value in the list, and then dividing the region between them into n/c equal-sized bins.
Like other distribution-based sorts, spreadsort has the weakness that the programmer is required to provide a means of converting each element into a numeric key, for the purpose of identifying which bin it falls in.
On lists of integers and floats spreadsort shows a roughly 2–7× runtime improvement for random data on various operating systems.
An interesting result for algorithms of this general type (splitting based on the radix, then comparison-based sorting) is that they are O(n) for any bounded and integrable probability density function.