My guess is Rednaxlea (and maybe others) have tried something like that. A quick search turns up oldwiki:Rednaxela/MultiplePlaneRegressionClustering and Talk:Targeting Matrix, but I'm not sure either of them are really the same.
The best guns currently are nothing super fancy in terms of algorithm complexity: k-nearest neighbors to find similar situations, and kernel density among the firing angles (usually GuessFactors) recorded in those situations to choose one. (This is sometimes called Dynamic Clustering in Robocode.) Multi-variate histograms (aka Visit Count Stats) are a close second, and were the dominant strategy for a long time. Darkcanuck has an excellent neural network gun in his bots, too, which he describes a bit at Gaff/Targeting. Of course, there's lots of room for innovation and variation within any of those techniques, too.
I've personally played with various clustering methods, but found nothing that can top simple KNN, and all of which were much slower than KNN. I still think there's room for improvement with clustering algorithms, and probably other totally different algorithms that can compete, as well. WaveSim can be pretty fun if you just want to hack away at targeting algorithms. =) RoboResearch for running massive battles for testing anything else.
Hmm, not sure about the RoboRumble terms being explained anywhere. APS is "Average Percent Score" - score against each bot is (your score / total score), and APS is average of all those scores. Survival is the same, but only counting survival scores. ELO and Glicko are just chess-like rating systems based on those scores. PL is pure win/loss.