An Empirical Analysis of the Validity of Competitive Pokémon Rule Sets

In competitive Pokémon battling, players have adopted a set of extra rules that are meant to encourage fair play. They are used to constrain team formation so that no one team has an overwhelming advantage over all others. These rule sets are often derived based on trial and error, intuition, or post-hoc evaluations of team performance, which means that the rules may not be ideal solutions to the problem they are supposed to address, or the problem may not have been worth addressing. In this paper, we explore how artificial intelligence and machine learning techniques can be used to potentially evaluate the quality of a rule set. This is meant to be a preliminary study that will ultimately lead to the automatic formulation of such rule sets. Our case study investigates how the inclusion or exclusion of one-hit-knock-out (OHKO) moves affects the outcomes and player behaviors in games between two teams battling under Generation 1 rules.