LIM, B.; SONG, W. Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning. The Journal of Sports Medicine and Physical Fitness 2024, 64, doi:10.23736/s0022-4707.24.15786-6.
LIM, B.; SONG, W. Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning. The Journal of Sports Medicine and Physical Fitness 2024, 64, doi:10.23736/s0022-4707.24.15786-6.
LIM, B.; SONG, W. Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning. The Journal of Sports Medicine and Physical Fitness 2024, 64, doi:10.23736/s0022-4707.24.15786-6.
LIM, B.; SONG, W. Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning. The Journal of Sports Medicine and Physical Fitness 2024, 64, doi:10.23736/s0022-4707.24.15786-6.
Abstract
(1) Background: The analysis of athletic performance has always aroused great interest from sport scientist. This study utilized machine learning methods to build predictive models using a comprehensive CrossFit (CF) dataset, aiming to reveal valuable insights into the factors influencing performance and emerging trends.; (2) Methods: The study used Random Forest (RF) and Multiple Linear Regression (MLR) models to predict performance in four key weightlifting exercises within CF: clean & jerk, snatch, back squat, and deadlift. Performance was evaluated using R-squared (R2) values and Mean Squared Error (MSE). Feature importance analysis was conducted using RF, XGBoost, and AdaBoost models.; (3) Results: The RF model excelled in deadlift performance prediction (R2 = 0.80), while the MLR model demonstrated remarkable accuracy in clean & jerk (R2 = 0.93). Across exercises, clean & jerk consistently emerged as a crucial predictor. The feature importance analysis revealed intricate relationships among exercises, with gender significantly impacting deadlift performance.; (4) Conclusions: This research advances our understanding of performance prediction in CF through machine learning techniques. It provides actionable insights for practitioners, optimize performance, and demonstrates the potential for future advancements in data-driven sports analytics.
Keywords
machine learning; crossfit; sport analytics; weightlifting; performance prediction
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
