SimulaMet, Oslo, Norway.
UiT The Arctic University of Norway, Tromsø, Norway.
Sci Data. 2024 May 30;11(1):553. doi: 10.1038/s41597-024-03386-x.
Data analysis for athletic performance optimization and injury prevention is of tremendous interest to sports teams and the scientific community. However, sports data are often sparse and hard to obtain due to legal restrictions, unwillingness to share, and lack of personnel resources to be assigned to the tedious process of data curation. These constraints make it difficult to develop automated systems for analysis, which require large datasets for learning. We therefore present SoccerMon, the largest soccer athlete dataset available today containing both subjective and objective metrics, collected from two different elite women's soccer teams over two years. Our dataset contains 33,849 subjective reports and 10,075 objective reports, the latter including over six billion GPS position measurements. SoccerMon can not only play a valuable role in developing better analysis and prediction systems for soccer, but also inspire similar data collection activities in other domains which can benefit from subjective athlete reports, GPS position information, and/or time-series data in general.
数据分析在运动表现优化和损伤预防方面对运动队和科学界都具有巨大的吸引力。然而，由于法律限制、不愿分享以及缺乏人员资源来处理繁琐的数据整理过程，运动数据通常是稀疏且难以获取的。这些限制使得难以开发用于分析的自动化系统，这些系统需要大型数据集进行学习。因此，我们提出了 SoccerMon，这是目前最大的足球运动员数据集，包含来自两个不同的精英女子足球队在两年内收集的主观和客观指标。我们的数据集包含 33849 份主观报告和 10075 份客观报告，后者包括超过 60 亿条 GPS 位置测量数据。SoccerMon 不仅可以在开发更好的足球分析和预测系统方面发挥重要作用，还可以启发其他领域进行类似的数据收集活动，这些领域可以从主观运动员报告、GPS 位置信息和/或一般时间序列数据中受益。
