Perspectives on sports analytics to inform constraint manipulation, representative learning and functional...

Area of Science:

• Sports Science
• Skill Acquisition
• Data Analytics

Background:

• Increasing technology and data use in sports presents opportunities for practice design.
• Analytical techniques can enhance skill acquisition frameworks like the constraints-led approach.
• Machine learning (ML) is a viable method for detailed insights due to its ability to handle complex data relationships.

Purpose of the Study:

• To explore how machine learning algorithms can operationalize principles of skill acquisition.
• To demonstrate the application of ML techniques for informing sports practice design.
• To support coaches and applied sport scientists in leveraging technology and data.

Main Methods:

• Application of various machine learning algorithms, including decision trees, random forests, rule association, and clustering.
• Utilizing ML to analyze multivariate and non-linear relationships in sports data.
• Operationalizing principles of constraint manipulation, representative learning, and functional variability through ML.

Main Results:

• Decision trees/random forests can predict optimal constraint manipulations for player behavior.
• Rule association identifies interacting constraints for replication in practice.
• Clustering techniques group similar player movements, aiding training variability prescription.

Conclusions:

• Machine learning provides valuable tools for enhancing sports practice design.
• These techniques support coaches and applied sport scientists in data-driven decision-making.
• ML can lead to more effective and individualized athlete training programs.