Athlete Performance Prediction Using Random Forest

Abstract

Abstract: Fitness and health applications are increasingly being integrated into athletes' training routines, providing new opportunities for personalized performance optimization. This research paper investigates the utilization of smartwatch metrics and machine learning algorithms for predicting athlete performance, focusing on metrics such as power. Through a comprehensive analysis of collected data and employing advanced machine learning techniques, the study aims to provide insights into the predictive capabilities of smartwatch data in the realm of sports science. The introduction of a user-friendly athlete interface further enhances the accessibility and usability of this innovative technology. Smartwatch metrics combined with machine learning algorithms offer a potent toolset for predicting athlete performance. Smartwatches collect vast amounts of data on an athlete's biometrics, such as heart rate, activity levels, and more. Machine learning algorithms analyse this data to uncover patterns, correlations, and trends that are often imperceptible to human observation. By training models on historical data from athletes and their performances, these algorithms can make predictions about future performance, injury risk, optimal training schedules, and even suggest personalized strategies for improvement. This fusion of technology enables coaches and athletes to make data-driven decisions, optimize training regimens, and enhance overall performance while minimizing the risk of injury.