  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.
