# SC2EGSet: StarCraft II Esport Replay and Game-state Dataset

arXiv: https://arxiv.org/abs/2207.03428 · HTML: https://mlsanity.com/paper/2207.03428/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 87 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 20 of 20)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II](https://mlsanity.com/paper/2012.13169/) (2020)
- [STARDATA: A StarCraft AI Research Dataset](https://mlsanity.com/paper/1708.02139/) (2017)
- [MSC: A Dataset for Macro-Management in StarCraft II](https://mlsanity.com/paper/1710.03131/) (2017)
- [Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset](https://mlsanity.com/paper/2011.00958/) (2020)
- [DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games](https://mlsanity.com/paper/2204.12750/) (2022)
- [Robust Vision-Based Cheat Detection in Competitive Gaming](https://mlsanity.com/paper/2103.10031/) (2021)
- [Combat Models for RTS Games](https://mlsanity.com/paper/1605.05305/) (2016)
- [Esports Athletes and Players: a Comparative Study](https://mlsanity.com/paper/1812.03200/) (2018)
- [Towards Understanding of eSports Athletes' Potentialities: The Sensing System for Data Collection and Analysis](https://mlsanity.com/paper/1908.06403/) (2019)
- [Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games](https://mlsanity.com/paper/1711.06498/) (2017)
- [Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning](https://mlsanity.com/paper/2012.02299/) (2020)
- [Approximation Models of Combat in StarCraft 2](https://mlsanity.com/paper/1403.1521/) (2014)
- [eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair](https://mlsanity.com/paper/1908.06402/) (2019)
- [Sensors and Game Synchronization for Data Analysis in eSports](https://mlsanity.com/paper/1908.06404/) (2019)

## Built on by (most cited first, 3 of 3)

- [Carefully Structured Compression: Efficiently Managing StarCraft II Data](https://mlsanity.com/paper/2410.08659/) (2024)
- [BEACON: A Multimodal Dataset for Learning Behavioral Fingerprints from Gameplay Data](https://mlsanity.com/paper/2605.10867/) (2026)
- [Play Like Champions: Counterfactual Feedback Generation in Latent Space](https://mlsanity.com/paper/2607.00190/) (2026)

## Abstract (arXiv)

As a relatively new form of sport, esports offers unparalleled data availability. Despite the vast amounts of data that are generated by game engines, it can be challenging to extract them and verify their integrity for the purposes of practical and scientific use. Our work aims to open esports to a broader scientific community by supplying raw and pre-processed files from StarCraft II esports tournaments. These files can be used in statistical and machine learning modeling tasks and related to various laboratory-based measurements (e.g., behavioral tests, brain imaging). We have gathered publicly available game-engine generated "replays" of tournament matches and performed data extraction and cleanup using a low-level application programming interface (API) parser library. Additionally, we open-sourced and published all the custom tools that were developed in the process of creating our dataset. These tools include PyTorch and PyTorch Lightning API abstractions to load and model the data. Our dataset contains replays from major and premiere StarCraft II tournaments since 2016. To prepare the dataset, we processed 55 tournament "replaypacks" that contained 17930 files with game-state information. Based on initial investigation of available StarCraft II datasets, we observed that our dataset is the largest publicly available source of StarCraft II esports data upon its publication. Analysis of the extracted data holds promise for further Artificial Intelligence (AI), Machine Learning (ML), psychological, Human-Computer Interaction (HCI), and sports-related studies in a variety of supervised and self-supervised tasks.