Optimal Team Economic Decisions in Counter-Strike

TL;DR

This paper develops a win probability model for Counter-Strike to analyze and identify optimal team spending strategies, revealing sub-optimal decision patterns and providing a metric to evaluate team decision quality.

Contribution

It introduces a novel game-level win probability model for Counter-Strike and proposes the Optimal Spending Error metric to assess team decision-making quality.

Findings

Teams often make sub-optimal spending decisions.

The model accurately predicts win probabilities based on in-game features.

OSE metric effectively ranks teams by decision quality.

Abstract

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.