# Optimal Team Economic Decisions in Counter-Strike

arXiv: https://arxiv.org/abs/2109.12990 · HTML: https://mlsanity.com/paper/2109.12990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation](https://mlsanity.com/paper/1805.11088/) (2018)
- [Real-time eSports Match Result Prediction](https://mlsanity.com/paper/1701.03162/) (2016)
- [Valuing Player Actions in Counter-Strike: Global Offensive](https://mlsanity.com/paper/2011.01324/) (2020)
- [Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games](https://mlsanity.com/paper/1711.06498/) (2017)
- [A Confidence-Calibrated MOBA Game Winner Predictor](https://mlsanity.com/paper/2006.15521/) (2020)

## Abstract (arXiv)

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.