The impact of precompetition state on athletic performance among track and field athletes using machine learning.

Objective: This study aims to compare the differences in the precompetition status (nutritional, physiological, biochemical, psychological, and sleep statuses) among college track and field athletes with different competition performances and to screen for key indicators of differences affecting athletic performance.

Methods: Multiple indicators, traditional methods, and machine learning methods are used to detect the exercise load, fatigue index, and precompetition state of athletes with different sports performances.

Results: (1) Two weeks before the competition, the fat mass in the left upper limb in the BP group was significantly higher than that in the BnP group ( < 0.