This is about what I have found too. Maybe if you are fitting your bot for battles on 1000 X 1000 fields you should opt for 29 bins or something. I haven't found that assuming a bot width of 3 bins should be better than assuming 1 bin though. Not that I have tested it very much. -- PEZ
I have done some limited testing of no smoothing and I find that assuming a width of 3 bins seems to make me slightly more accurate in the short run but has no effect at all in the long run. I am not sure why this is. Maybe it helps to "smear" the data when you do not have many observations but does not hurt to have the data "smeared" when you have a lot. I find all of this interesting. My initial assumption was that the more bins I used the more finely chopped my data would be and there for the data would show more spikes. Now I am wondering if I have over segmented any of my other segments as well. -- jim
What is the difference between what you call bin smoothing and assuming the bot width is 3? Isn't the last actually a form of bin smoothing? My testing has showed that there is a relationship between the number of bins and accounting for botwidth. At one time, I had a low number of bins (I think it was 31). Then I tried a much higher number (75). My rating dropped. I reverted that, and introduced accounting for bot width. My rating dropped. Then I combined 75 bins with accounting for botwidth. Bingo! My rating improved. Note that I account for bot width dynamically, taking the distance into account. Another thing: I found that accounting for botwidth has two pitfalls which are easy to overlook. You can find these if you put your bot against sitting duck after implementing botwidth accounting. If you contantly hit it on its side and occasionally even miss completely then you have fallen for both of them :-)
--Vic