  The outputs of win probability models are often used to evaluate player
actions. However, in some sports, such as the popular esport Counter-Strike,
there exist important team-level decisions. For example, at the beginning of
each round in a Counter-Strike game, teams decide how much of their in-game
dollars to spend on equipment. Because the dollars are a scarce resource,
different strategies have emerged concerning how teams should spend in
particular situations. To assess team purchasing decisions in-game, we
introduce a game-level win probability model to predict a team's chance of
winning a game at the beginning of a given round. We consider features such as
team scores, equipment, money, and spending decisions. Using our win
probability model, we investigate optimal team spending decisions for important
game scenarios. We identify a pattern of sub-optimal decision-making for CSGO
teams. Finally, we introduce a metric, Optimal Spending Error (OSE), to rank
teams by how closely their spending decisions follow our predicted optimal
spending decisions.

Computer Science and Game Theory;Machine Learning