Bandit Modeling of Map Selection in Counter-Strike: Global Offensive

TL;DR

This paper models the map selection process in CSGO using a contextual bandit framework, revealing suboptimal team strategies and proposing a method to improve match outcomes through better decision modeling.

Contribution

Introduces a novel bandit-based approach to analyze and improve map selection strategies in CSGO, including a new reward metric for bans and demonstrating potential performance gains.

Findings

Teams have suboptimal map pick and ban policies.

Incorporating ban rewards improves model accuracy.

Model can increase predicted and overall match win probabilities.

Abstract

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.