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. 2023 Jul 7;11(7):131.
doi: 10.3390/sports11070131.

Profiling the Physical Performance of Young Boxers with Unsupervised Machine Learning: A Cross-Sectional Study

Rodrigo Merlo  1   2 , Ángel Rodríguez-Chávez  1 , Pedro E Gómez-Castañeda  2   3 , Andrés Rojas-Jaramillo  4   5 , Jorge L Petro  4   6 , Richard B Kreider  7 , Diego A Bonilla  4   6   8
Affiliations

Profiling the Physical Performance of Young Boxers with Unsupervised Machine Learning: A Cross-Sectional Study

Rodrigo Merlo et al. Sports (Basel). .

Abstract

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.

Keywords: boxing; machine learning; physical assessment; profiling; strength.

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Conflict of interest statement

R.M. is president of the International Circle of Experts in Combat Sports and serves as academic director of the World Council of Mixed Martial Arts. P.E.G-C. is the current president of the ‘Colegio Profesional de Licenciados en Entrenamiento Deportivo’–CPLED in Mexico. R.B.K. has conducted industry-sponsored studies at the universities he has been affiliated with and occasionally serves as a scientific and legal consultant related to exercise and nutrition intervention studies. D.A.B. has conducted academic-sponsored research on resistance training and has received honoraria for selling linear position transducers and speaking about exercise sciences at international conferences and private courses. The other authors declare no conflict of interest. All authors are responsible for the content of this article.

Figures

Figure 1
Figure 1
Measure of the straight rear hand punch impact force. Reproduced with permission from Merlo and Rodríguez-Chávez [39]. Source: the authors.
Figure 2
Figure 2
Draftsman correlation plot. Positive correlations are displayed in blue and negative correlations in red color. The color intensity and the size of the circle are proportional to the correlation coefficients. BM: body mass; CMJ: countermovement jump height; HG: maximal isometric handgrip; PF: punch impact force. * p < 0.05; ** p < 0.01.
Figure 3
Figure 3
Cluster diagram of the k-Medoids analysis.
See this image and copyright information in PMC

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