Kaggle competitions determine final rankings based on a held-out test set.
A held-out test set is a sample; it may not be representative of the population being modeled. Since each submission is like a hypothesis, the algorithm that won the competition may just, by total chance, have ended up matching the test set better than the others. In other words, if a different test set were selected and the competition repeated, would the rankings remain the same?
For the sponsoring corporation, this doesn't really matter (probably the top 20 submissions would improve their baseline). Although, ironically, they might end up using a first-ranked model that is worse than the other top five. But, for the competition participants, it seems that Kaggle is ultimately a game of chance--luck isn't needed to stumble on the right solution, it's needed to stumble on the one that that matches the test set!
Is it possible to change the competition so that all the top teams who can't be statistically distinguished win? Or, in this group, could the most parsimonious or computationally cheap model win?