General_Data_Proyect_RMR_Soccer

Resting Metabolic Rate (RMR) is a major component of Total Daily Energy Expenditure and varies substantially across individuals due to factors such as age, sex, body composition, and body surface area. Accurate RMR estimation is essential for optimizing energy balance, nutritional status, and athletic performance in professional soccer. While indirect calorimetry (IC) is the gold-standard method, predictive equations (EEQs) remain widely used due to their practicality in field settings. This study aimed to evaluate the agreement between twelve traditional RMR predictive equations and IC measurements in professional male soccer players and to test a pilot machine learning (ML) model.