Functional data analysis demonstrates how sprint-endurance bias affects mean-maximal power variations in cyclists.
Key Points
The F3 model accurately predicts exercise performance using 97% of the variance from mean-maximal power data, and reveals valuable insights into training.
Key findings indicate that the identified functions, interpretable as gain and sprint-endurance bias, significantly enhance understanding of cyclist performance.
The methodology involved functional principal component analysis to extract effective data patterns from a comprehensive data set of cyclists.
Highlighting potential performance advantages, the out-of-sample validation of the F3 model signifies an improvement in predictive analytics for athletic training.