Abstract

As a relatively new form of sport, esports offers unparalleled data availability. 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 compared to laboratory-based measurements. 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. We processed 55 “replaypacks” that contained 17930 files with game-state information. Our dataset is one of the few large publicly available sources of StarCraft II 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.

Details

Title
SC2EGSet: StarCraft II Esport Replay and Game-state Dataset
Author
Białecki, Andrzej 1   VIAFID ORCID Logo  ; Jakubowska, Natalia 2 ; Dobrowolski, Paweł 3 ; Białecki, Piotr; Krupiński, Leszek; Szczap, Andrzej 4 ; Białecki, Robert 5 ; Gajewski, Jan 5 

 Warsaw University of Technology, Electronics and Information Technology, Warsaw, Poland (GRID:grid.1035.7) (ISNI:0000000099214842) 
 SWPS University, Neurocognitive Research Center, Warsaw, Poland (GRID:grid.433893.6) (ISNI:0000 0001 2184 0541) 
 Polish Academy of Sciences, Institute of Psychology, Warsaw, Poland (GRID:grid.460447.5) (ISNI:0000 0001 2161 9572) 
 Adam Mickiewicz University in Poznań, Mathematics and Computer Science, Poznań, Poland (GRID:grid.5633.3) (ISNI:0000 0001 2097 3545) 
 Józef Piłsudski University of Physical Education in Warsaw, Physical Education, Warsaw, Poland (GRID:grid.449495.1) (ISNI:0000 0001 1088 7539) 
Pages
600
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2862610243
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.