Winning duels in VALORANT, a visualization of optimal positioning

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Authors

DeMars DeRover

Abstract

This paper applies traditional sports analytics metrics with novel machine learning models in a brand new competitive Esport. By leveraging in-game positional data, we are able to evaluate the difficulty of a particular gun fight and assign a win probability to both sides. We use these predictions to identify players who are performing above or below expected, and identify strengths and weaknesses for NRG’s player development. We are hopeful for more analytics in Esports from current working professionals and the younger generation.  


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