Optimal Team Economic Decisions in Counter-Strike
This work addresses strategic decision-making for esports teams, though it is incremental as it applies existing win probability modeling to a new context.
The authors tackled the problem of evaluating team-level economic decisions in Counter-Strike by developing a game-level win probability model, identifying sub-optimal spending patterns and introducing a metric (Optimal Spending Error) to rank teams based on their adherence to predicted optimal spending.
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.