Bandit Modeling of Map Selection in Counter-Strike: Global Offensive
This work addresses suboptimal decision-making in esports map selection, offering a domain-specific improvement for CSGO teams.
The paper tackled the problem of map selection in CSGO by introducing a contextual bandit framework to analyze and optimize teams' pick and ban decisions, finding that using their model could improve predicted map win probability by up to 11% and overall match win probability by 19.8% for evenly-matched teams.
Many esports use a pick and ban process to define the parameters of a match before it starts. In Counter-Strike: Global Offensive (CSGO) matches, two teams first pick and ban maps, or virtual worlds, to play. Teams typically ban and pick maps based on a variety of factors, such as banning maps which they do not practice, or choosing maps based on the team's recent performance. We introduce a contextual bandit framework to tackle the problem of map selection in CSGO and to investigate teams' pick and ban decision-making. Using a data set of over 3,500 CSGO matches and over 25,000 map selection decisions, we consider different framings for the problem, different contexts, and different reward metrics. We find that teams have suboptimal map choice policies with respect to both picking and banning. We also define an approach for rewarding bans, which has not been explored in the bandit setting, and find that incorporating ban rewards improves model performance. Finally, we determine that usage of our model could improve teams' predicted map win probability by up to 11% and raise overall match win probabilities by 19.8% for evenly-matched teams.