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Explore practical solutions for overcoming data scarcity challenges in sports analytics through synthetic data generation techniques in this 40-minute conference talk. Learn how to bridge the gap when insufficient real-world data hampers analysis, particularly in sports where data collection requires physically attaching equipment to athletes during performance. Discover various approaches for creating and validating synthetic sports data, ranging from basic augmentation methods to advanced generative models, through real-world examples from StatSports. Master the identification of scenarios where synthetic data proves beneficial, understand different generation techniques specifically suited for sports data, and gain practical insights into validating synthetic datasets while maintaining the physical realism of athlete performance. Examine concrete examples of implementing these techniques in production environments, understand common pitfalls to avoid, and learn best practices for ensuring reliability of synthetic data in sports analytics applications. Designed for those with introductory to intermediate technical knowledge, this session provides actionable strategies for data scientists working in sports analytics who need to augment limited datasets with realistic synthetic alternatives.
