Mexico City is the location with the largest number of boxers in Mexico; in fact, it is the first city in the country to open a Technological Baccalaureate in Education and Sports Promotion with a pugilism orientation. This cross-sectional study aimed to determine the physical–functional profile of applicants for admission to the baccalaureate in sports. A total of 227 young athletes (44F; 183M; 15.65 (1.79) years; 63.66 (14.98) kg; >3 years of boxing experience) participated in this study. Body mass (BM), maximal isometric handgrip (HG) strength, the height of the countermovement jump (CMJ), the velocity of straight boxing punches (PV), and the rear hand punch impact force (PIF) were measured. The young boxers were profiled using unsupervised machine learning algorithms, and the probability of superiority (ρ) was calculated as the effect size of the differences. K-Medoids clustering resulted in two sex-independent significantly different groups: Profile 1 (n = 118) and Profile 2 (n = 109). Except for BM, Profile 2 was statistically higher (p < 0.001) with a clear distinction in terms of superiority on PIF (ρ = 0.118), the PIF-to-BM ratio (ρ = 0.017), the PIF-to-HG ratio (ρ = 0.079) and the PIF-to-BM+HG ratio (ρ = 0.008). In general, strength levels explained most of the data variation; therefore, it is reasonable to recommend the implementation of tests aimed at assessing the levels of isometric and applied strength in boxing gestures. The identification of these physical–functional profiles might help to differentiate training programs during sports specialization of young boxing athletes.

This feasibility study aimed to explore the relationship between mean propulsive velocity (MPV) and a scale of perceived velocity (SPV) in back-squat and deadlift exercises performed with heavy loads during a cluster-sets resistance training (CS-RT). Twelve resistance trained males (24.1[2.94] years; 80.7[9.05] kg; 172[4.7] cm; 19.1[6.17] %BF; 4.71[2.72] years of training experience) participated. Participants visited the laboratory three times, spaced 72 to 96 hours. Load-velocity profiles for each exercise were measured in first visit. During the second and third visits, participants engaged in CS-RT sessions with different intra-set rest period (20 vs 40 seconds, randomly), and consisted of three sets of squat and deadlift exercises at 80%1-RM. Each set concluded upon reaching a 10% velocity loss on two occasions. Bayesian Pearson correlation coefficients (r), 95% credible intervals (95%CrI) and Bayes factors (BF10) were computed to assess the relationship between variables. A low positive correlation was observed between MPV and SPV in deadlift (r=0.368, 95%CrI [0.144, 0.544]), with strong evidence supporting the alternative hypothesis (BF10=20.7). Interestingly, moderate correlation values were observed in the 40-second CS-RT configuration (r=0.47, 95%CrI [0.144, 0.544]) and in the first set of the deadlift (r=0.44, 95%CrI [0.118, 0.654]). Conversely, a negligible Bayesian correlation was identified for squat (r=0.101, 95%CrI [-0.132, 0.319]), with substantial evidence favoring the null hypothesis (BF10=0.208). In conclusion, a positive correlation between MPV and SPV in deadlift during a CS-RT configuration, indicating potential utility for perceived velocity. However, velocity feedback prior SPV use and validity for squatting warrants further investigation. Keywords: Strength training, Perception, Powerlifting, Physical performance, Cluster training.