I think I see where you are coming from with a bubble sort comparison, but I don't think it's equivalent. Element rankings are based on win/loss percentage (more of a statistical approach.) In a bubble sort we can only swap adjacent elements. In a worst case bubble sort, if the highest ranking item starts at the bottom of the list it may take n*n passes to get everything properly placed -- but in our voting system a rough win/loss percentage can be arrived at very quickly with only a few comparisons -- and elements shift position directly without needing to "bubble" up. For example, if an element has won 4 of 6 contests (66.6%) and then wins the next contest (now 5 of 7 = 71.4%) it could jump up many places with a single comparison.

I see an analogy to a mipmapping system, where we start out with a really low level mipmap and as votes are casts we add detail and then we can add more and more detail as votes are cast over time. But factor in integer math, voting over a period of a couple weeks, voting by many people at different times with potentially wide ranging preferences and it becomes more interesting and harder to predict.

Another analogy would be the "schedule" for a sports league where each team plays every other team twice (home and away.) 2*(n-1) matches. Is this enough to perfectly rank the entries if we have a consistent scoring mechanism? The problem in computer science is that we'd then still have to sort by rank to actually spit out the list in correct order.

Edit: each entity would play 2*(n-1) rounds, but there would be n/2 matches in each round which does put us at bubble sort level comparisons. We have 165 entries -- bubble sort would require 165*165=27,225 comparisons. At the moment we have done about 28,000 comparisons with our voting system and each entry has played about 2*(n-1) rounds. So I still contend that this is not bubble sort (we get good results much earlier, and we can play much longer and factor in more "human element") but there are undeniable parallels.

Here is wikipedia's take on our approach:

http://en.wikipedia.org/wiki/Pairwise_comparison