Tough to beat

Maybe I'm late on noticing this (took some days off from Robocode), but your bot is really tough to beat! Congrats on that, that's amazing since a reasonable portion of the bots on top-10 are pretty much beatable. I'm struggling against ScalarBot as hard as I struggle against Gilgalad. Keep up the good work. And 8% vote is just incredible :)

Thanks! I'm also surprised a lot by the vote ;) I think my success is largely contributed by the way I'm keeping track of enemy firing ;) I use only one best match from each tree (which can be considered a good source of randomness, from the view of the guns), making the movement really unpredictable for the strongest guns ;) What made this approach is that my Kd-tree, when searching for more than one point, has some bug when I was first developing my movement. All my attempts to use more than one match failed at that time, but it, however, works surprisingly well against the top bots, even without a flattener.

However, with this approach, some simple GuessFactor gun can hit it almost as good as the top guns ;/ Therefore its APS rank is generally much lower than it's PL rank.

And that is also why a lot of top bots are easy to beat — they are really good at dodging GuessFactor guns, making them pretty predictable by the top guns.

Then they use flatteners, but their nature of being easy to hit will never change. Flatteners only work against fast decay lightly segmented guns, not kNN "pattern matchers".

It seems that the ideal thing would be to figure out a way to learn which class of targeting a robot belongs to. We know that it is harder than predicting how an enemy moves because the enemy give us way less information about his gun, but you could try some conditions (not only hit rate) to estimate this instead of letting your learner figure out everything about enemy's targeting method. I usually hate to mix hard-coded conditions with learning methods, but it could work out here.