I’ve much less experience doing data analysis than @gjm appears to. This was a really nice way to get started, I may go back and try some of the other D&D exercises. My analysis was less mathematically rigorous, but the roster I got for being helpful seems close-ish to me. One thing I did note is that
there does seem to be a strong territorial advantage:
- Pyros: win most in pyro & vita territory - Vita: wins most in pyro and vita - Geo: wins most in geo and vita - Cryo: cryo, pyro - Necro: pyro, necro, cryo
My roster was more or less eyeballed by looking at stats of who seemed to loss most against whom and then selecting from the top two candidates. I got VA, VA, GA, GB, C; the first four all defending, C attacking.
I was toying with the idea of trying to figure out more complex analysis of this, but then got too curious and read gjm’s spoilers above, which made the point that there isn’t enough data to support serious modelling.
I’ve much less experience doing data analysis than @gjm appears to. This was a really nice way to get started, I may go back and try some of the other D&D exercises. My analysis was less mathematically rigorous, but the roster I got for being helpful seems close-ish to me. One thing I did note is that
there does seem to be a strong territorial advantage:
- Pyros: win most in pyro & vita territory
- Vita: wins most in pyro and vita
- Geo: wins most in geo and vita
- Cryo: cryo, pyro
- Necro: pyro, necro, cryo
My roster was more or less eyeballed by looking at stats of who seemed to loss most against whom and then selecting from the top two candidates. I got VA, VA, GA, GB, C; the first four all defending, C attacking.
I was toying with the idea of trying to figure out more complex analysis of this, but then got too curious and read gjm’s spoilers above, which made the point that there isn’t enough data to support serious modelling.