Predicting Round and Game Winners in CSGO

Allen Rubin

No u9j5g, OSF Preprints from Center for Open Science

Abstract: Win probabilities have become a staple on scoreboards in physical sports such as baseball and basketball. Esports, or competitive video games with sponsored teams and major audiences, typically lack this detailed statistical analysis, beyond bare-bones metrics and commentator intuition. However, the advantage of esports in their tendency to have a central record of every game event makes them ripe for statistical analysis through machine learning. Previous research has covered popular video game genres such as MOBAs, and has found success in predicting game winners most of the time. Counterstrike: Global Offensive (CSGO) is an esport that is unique in its round and game-based nature, allowing researchers to examine how short and long-term decisions can interplay in competitive environments. We introduce a dataset of CSGO games To assess factors such as player purchasing decisions and individual scores, we introduce 3 round and game win probability models. Finally, we evaluate the performances of the models. We successfully predict winners in the majority of cases, better than the map average baseline win statistics.

Date: 2022-01-12
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:u9j5g

DOI: 10.31219/osf.io/u9j5g

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