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Exploring CrossFit performance prediction and analysis via extensive data and machine learning.

Byunggul Lim1,2, Wook Song3,2,4

  • 1Health and Exercise Science Laboratory, Department of Physical Education, Seoul National University, Seoul, South Korea.

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This summary is machine-generated.

Machine learning models predict CrossFit performance. The MLR model accurately predicted clean and jerk, while RF excelled in deadlift, revealing key performance factors.

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Area of Science:

  • Sports Science
  • Data Analytics
  • Machine Learning

Background:

  • Athletic performance analysis is crucial for sports scientists.
  • A comprehensive CrossFit dataset was used to explore performance factors.
  • Machine learning models were developed to identify performance trends.

Purpose of the Study:

  • To build predictive models for CrossFit performance using machine learning.
  • To identify key factors influencing performance in major weightlifting exercises.
  • To uncover emerging trends in CrossFit performance data.

Main Methods:

  • Random Forest (RF) and Multiple Linear Regression (MLR) were used for prediction.
  • Performance was evaluated using R-squared (R²) and Mean Squared Error (MSE).
  • Feature importance was analyzed using RF, XGBoost, and AdaBoost.

Main Results:

  • RF model achieved R²=0.80 for deadlift prediction.
  • MLR model achieved R²=0.93 for clean and jerk prediction.
  • Clean and jerk was a key predictor across exercises; gender impacted deadlift performance.

Conclusions:

  • Machine learning advances performance prediction in CrossFit.
  • Actionable insights are provided for practitioners to optimize performance.
  • This study highlights the potential of data-driven sports analytics.