# Valuing Player Actions in Counter-Strike: Global Offensive

arXiv: https://arxiv.org/abs/2011.01324 · HTML: https://mlsanity.com/paper/2011.01324/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Graph Neural Networks to Predict Sports Outcomes](https://mlsanity.com/paper/2207.14124/) (2022)
- [ESTA: An Esports Trajectory and Action Dataset](https://mlsanity.com/paper/2209.09861/) (2022)
- [Bandit Modeling of Map Selection in Counter-Strike: Global Offensive](https://mlsanity.com/paper/2106.08888/) (2021)
- [Optimal Team Economic Decisions in Counter-Strike](https://mlsanity.com/paper/2109.12990/) (2021)
- [Reliable validation of Reinforcement Learning Benchmarks](https://mlsanity.com/paper/2203.01075/) (2022)
- [PandaSkill - Player Performance and Skill Rating in Esports: Application to League of Legends](https://mlsanity.com/paper/2501.10049/) (2025)
- [Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis](https://mlsanity.com/paper/2510.17199/) (2025)

## Abstract (arXiv)

Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks. Even Counter-Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation of CSGO players, a task important to teams, media, bettors and fans, is difficult. To address this, we introduce (1) a data model for CSGO with an open-source implementation; (2) a graph distance measure for defining distances in CSGO; and (3) a context-aware framework to value players' actions based on changes in their team's chances of winning. Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence compared to existing valuation frameworks. We also provide use cases demonstrating high-impact play identification and uncertainty estimation.