Position-Independent Lactate Kinetic Phenotypes in Professional Soccer Players: A Machine Learning Approach for...

This study aimed to identify distinct lactate kinetic phenotypes in professional soccer players using unsupervised machine learning and determine their relationship with maximal running velocity (V_max) through explainable artificial intelligence methods. A total of 361 professional male soccer players from the First Division participated in the study. Incremental treadmill tests measured lactate concentrations at five standardized velocities, alongside VO_2max, V_max, lactate threshold (LT), and anaerobic threshold (AT) parameters. Three distinct lactate kinetic phenotypes emerged: Economical Aerobic (n = 216), Balanced Metabolic (n = 19), and High Producer (n = 126). The Economical Aerobic phenotype demonstrated superior performance metrics compared to High Producer (V_max: 15.85 ± 0.85 km/h; VO_2max: 56.20 ± 4.26 mL/kg/min; p < 0.001). Initial multicollinearity assessment revealed notable collinearity among all 10 candidate predictors (VIF > 10; maximum VIF = 10.75 for V_AT), necessitating rigorous feature selection. Ridge regression with 4 selected features (V_AT, VO_2max, 9.5 km/h lactate, 14 km/h lactate) achieved moderate but statistically significant predictive performance: 10-fold cross-validation R²= 0.392 ± 0.147 (permutation test p = 0.001). Standardized coefficients identified V_AT (β = 0.399) as the dominant predictor, followed by VO_2max (β = 0.253), 9.5 km/h lactate (β = 0.107), and 14 km/h lactate (β = -0.066). Lactate kinetic phenotyping reveals position-independent metabolic profiles with potentially meaningful performance associations in professional soccer. The Economical Aerobic phenotype demonstrates performance advantages associated with superior anaerobic threshold capacity. These exploratory findings suggest that individualized training strategies based on metabolic phenotype rather than playing position alone warrant further investigation, with potential applications for talent identification, training periodization, and return-to-play protocols pending prospective validation.