Can Functional Movement Predict Athlete Performance?
Notas do episódio
In this episode of the Movement Podcast, Gray Cook and Lee Burton explore one of the most complex and debated topics in sports and rehabilitation: predicting athletic performance and ranking talent.
With AI and machine learning beginning to shape how we analyze athletes, the conversation unpacks:
• How performance is measured—and why it’s more than just speed and strength.
• The rise of durable performers: athletes who stay healthy, consistent, and coachable.
• What new research says about FMS scores and their ability to predict performance—not just prevent injury.
• The growing need to consider psychological readiness, engagement, and self-awareness as critical metrics.
• Why looking at movement quality, not just quantity, is essential in both elite and youth sports.
Whether you're a coach, trainer, healthcare provider, or performance nerd, this discussion will help you rethink how we define, assess, and develop talent.
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Referenced Research Articles:
Predictive Athlete Performance Modeling with Machine Learning and Biometric Data Integration
Prediction and Injury Risk Based on Movement Patterns and Flexibility (2023)
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