The subgroup discovery task has been considered for more than two decades. It is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. 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. 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. To prepare the dataset, we processed 55 tournament 'replaypacks' that contained 17930 files with game-state information. Our dataset contains replays from major and premiere StarCraft II tournaments since 2016.

These tools include PyTorch and PyTorch Lightning API abstractions to load and model the data. Additionally, we open-sourced and published all the custom tools that were developed in the process of creating our dataset. 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. 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).

Our work aims to open esports to a broader scientific community by supplying raw and pre-processed files from StarCraft II esports tournaments. 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. As a relatively new form of sport, esports offers unparalleled data availability.

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