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

ABSTRACT: 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.
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.
SC2EGSet: StarCraft II Esport Replay and Game-state Dataset
http://arxiv.org/abs/2207.03428v2