Modeling the load-velocity relationship from training-derived velocity data in resistance training: A proof-of-concept study

This proof-of-concept study investigated whether training-derived velocity data could be used to model the individual L-V relationship, providing a novel approach in which profiling emerges from training observations. This study retrospectively analyzed a freely available dataset from 51 resistance-trained participants performing the barbell back squat. The traditional L-V relationship was obtained from a standardized incremental loading protocol, whereas the training-based L-V relationship was modeled using velocity data collected during two simulated training sessions. Regression parameters (y-intercept, slope, and R 2 ) and 1RM estimates derived from both approaches were compared. No significant differences and significant equivalence were observed between traditional and training-based L-V relationship parameters for the y-intercept (169.42 ± 46.81 kg vs 169.82 ± 47.65 kg, p = 0.77), slope (−116.50 ± 31.08 vs −115.50 ± 31.60, p = 0.57), and coefficient of determination (0.98 ± 0.02 vs 0.98 ± 0.02, p = 0.19). The 1RM estimated from training-based data showed a very large correlation with direct 1RM assessment ( r = 0.97, p < 0.001), with a bias of 1.7 kg and a precision of 9.7 kg. Modeling the L-V relationship from training-derived velocity data may represent a feasible approach, although its applicability in ecological training environments warrants further investigation.