Comment by thiago_fm
7 hours ago
Meanwhile I like the optimization analysis, the initial assumptions are very wrong. I know the author mentioned that they are optimizing for stats + being resistant to other Pokémon types, but that analysis will lead to very bad results.
There are Pokémon with certain abilities or tricks that makes it much better than legendary ones, with certain move sequences that could wipe the entire other team.
There are also Pokémon with certain types that are actually good against what the 'data' would say otherwise.
Maybe the analysis could be better done if you instead analyzed matches data.
BTW, the way people pay Pokémon since many years is to also divide the Pokémon into tiers and in a competitive setting, you are only allowed to pick Pokémon from the same tier or lower. This adds another level of complexity.
I generally agree with you on the point that a "good Pokémon team" can be better encapsulated by other attributes, including those you mentioned. I would disagree on assumptions being very wrong, because I am not assuming that the objective and constraints chosen are ideal or even good enough, I am choosing them simple for illustration purposes.
I actually found it interesting that in spite of what is a clearly overly simple model, the non-legendary non-multi-starter you eventually get is quite a good one, in my opinion better than what the naive constraints would lead me to think.
Also, keep in mind that I'm not talking about competitive matches here, just mainline gaming. For that end, types are usually all you need, and in that area the main thing I would do is generalize type constraints to not be just defensive but also ensure each resistant Pokémon has a good enough attack against that type.
In my opinion, abilities, nature, objects are: 1. Too complex for such models (MIPs are still exponential-time) 2. Overkill strategy when all you wanna do is beat the league
But that last part is just my opinion.