OpenSTARLab is an open-source research platform designed to democratize spatio-temporal agent data analysis across sports and other dynamic multi-agent systems. Initially applied in soccer, OpenSTARLab provides tools for event annotation, data standardization, and predictive modeling using deep learning and reinforcement learning frameworks.

We believe in making cutting-edge analytics accessible to everyone, from researchers to analysts, by providing open-source tools that overcome traditional data limitations in sports analytics. Our platform enables data collection from video, structured data, and simulated environments, advancing both academic research and real-world applications.

• Accessibility: Open tools for data preparation, model training, and analysis.
• Transparency: Reproducible research through well-documented tools.
• Interdisciplinary Collaboration: Bridging computer science, sports analytics, and data science.
• Scalability: Designed for both academic studies and real-world applications in various domains.

• Data Accessibility: Limited access to high-quality sports data.
• Data Standardization: Inconsistent data formats across providers.
• Advanced Modeling: Need for complex modeling pipelines, such as deep learning and reinforcement learning.

1. STE Label Tool: Intuitive event annotation from videos.
2. Preprocessing Package: Standardizes data into a unified format (UIED).
3. Event Modeling Package: Supports state-of-the-art prediction models.
4. RLearn Package: Multi-agent deep reinforcement learning tools.