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Predicting Competitive Pokémon VGC Leads Using Latent Semantic Analysis
Bruno Luvizotto Carli Universidade Federal do Paraná, Curitiba, PR, Brazil. brunolcarli (at) gmail (dot) com Download PDF Competitive Pokémon battles often hinge on the initial selection of Pokémon…
In this paper, I explore the use of Latent Semantic Analysis (LSA), a natural language processing algorithm, applied to over 5,000 Pokémon Showdown battle logs, to predict likely lead pairs based on team compositions. The predictive paradigm is reenforced by Traylor ( apud Zheng, 2020) as an important part of the game, thus supporting the idea of proposing a narrative-based framework, in which players simulate possible match leads during team preview. While the model does not capture all the nuance of VGC gameplay (e.g., movesets, synergy, in-game momentum), it offers a surprising amount of strategic value simply by analyzing team compositions from previous matches played in simulation games and confirming the existence of a pattern that describes the more likely lead players choose to pick based on the matchup.
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