Valuing Player Actions in Counter-Strike: Global Offensive Peter Xenopoulos Harish Doraiswamy Claudio Silva New York University New York University New York University New York, NY New York, NY New York, NY
[email protected] [email protected] [email protected] Abstract—Esports, despite its expanding interest, lacks fun- harder or easier than others. Furthermore, existing frameworks damental sports analytics resources such as accessible data or can be hard to reproduce and lack measures of uncertainty. proven and reproducible analytical frameworks. Even Counter- The key factor affecting the development of involved analyt- Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation ics in esports is the lack of easily accessible and clean esports of CSGO players, a task important to teams, media, bettors data. Improved data capture, along with publicly accessible and fans, is difficult. To address this, we introduce (1) a data data, has traditionally enabled analytics to grow in a sport [2]. model for CSGO with an open-source implementation; (2) a Since esports are played entirely virtually, one would expect graph distance measure for defining distances in CSGO; and data capture to be straightforward. However, the tools for (3) a context-aware framework to value players’ actions based on changes in their team’s chances of winning. Using over 70 data acquisition are surprisingly limited and undocumented. million in-game CSGO events, we demonstrate our framework’s Furthermore, esports data is often stored in esoteric formats consistency and independence compared to existing valuation that are riddled with inconsistencies and lacks a commonly frameworks.