用模拟足球数据推动连续运动分析研究
Simulating Tracking Data to Advance Sports Analytics Research
- 基于Google Research Football生成仿真追踪数据
- 数据结构仿真实迹，支持高阶特征提取
- 助力AI与体育分析交叉研究，破解数据稀缺难题
先进分析已深刻改变棒球等回合制运动的运作方式，但对足球、冰球等连续对抗性运动的影响受限于比赛复杂度高及高分辨率追踪数据获取困难。本文演示一种从Google Research Football环境获取并利用仿真足球追踪数据的方法，支持连续追踪数据模型的研发。数据采用与真实追踪数据一致的存储结构，并提供提取高层特征和事件的流程。通过展示已有追踪数据模型的应用效果，验证了仿真数据的有效性。针对公开可得追踪数据稀缺的问题，为人工智能与体育分析交叉研究提供有力支持。
原文摘要 · Abstract (English)
Advanced analytics have transformed how sports teams operate, particularly in episodic sports like baseball. Their impact on continuous invasion sports, such as soccer and ice hockey, has been limited due to increased game complexity and restricted access to high-resolution game tracking data. In this demo, we present a method to collect and utilize simulated soccer tracking data from the Google Research Football environment to support the development of models designed for continuous tracking data. The data is stored in a schema that is representative of real tracking data and we provide processes that extract high-level features and events. We include examples of established tracking data models to showcase the efficacy of the simulated data. We address the scarcity of publicly available tracking data, providing support for research at the intersection of artificial intelligence and sports analytics.
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