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It also depends on how you process each segment. From your words I assume you use multiple separate arrays (segments) and accumulate them with a weight. I use one multidimensional array, which means that I can't give different weight to segments. This works a bit like a tree, going deeper into dimensions when enough data is present. Bringing one more dimension (segment) into such a structure will scatter the data, so I really only add it when it has proven that it helps. As my bot tries to keep its preferred distance, against the majority the distance segment gives no additional info about the movement of the opponent.

Well, my old VCS gun uses anti-aliasing, meaning data is logged to neighbor segment weighted based on data point distance. This way it really works like knn (actually, more like range search), and by weight of attributes, I mean how many slices, since the more slice it has, the more it is sensitive to difference of that attribute. So my VCS weight is just analog to knn weight.

I also have an old multi-buffered VCS gun (no anti-alias), like what you guessed. This way attributes are weighted on both how many slices, and how often it appears.

Note that I always weight buffers equally. And buffers are normalized (so it feels like probability) before accumulating.