Machine learning-based personalized training models for optimizing marathon performance through pyramidal and polarized training intensity distributions

Training intensity distribution significantly influences marathon performance, yet individual variability in training responses remains poorly understood. This study compared pyramidal and polarized training methodologies using machine learning to identify optimal personalization strategies. A total of 120 recreational marathon runners were randomly assigned to 16-week pyramidal (n = 60) or polarized (n = 60) training interventions. Machine learning models analyzed individual responses using consumer-grade monitoring technology to predict optimal training methodology based on athlete characteristics. Polarized training produced superior marathon performance improvements (11.3 ± 3.2 vs. 8.7 ± 2.8 min, p 

This study compares pyramidal and polarized training methodologies using machine learning for marathon performance optimization.