Multivariate internal load quantification in university basketball: principal components, mixed models and Mahalanobis-Taguchi
DOI:
https://doi.org/10.47197/retos.v82.119410Keywords:
Multivariate analysis , Basketball, internal load, RPE, TRIMPAbstract
Introduction: Internal load quantification constituted a fundamental element for training planning and injury prevention in team sports. Most basketball studies employed univariate approaches that failed to capture the multidimensional nature of load.
Objective: Internal load in Mexican university basketball was analyzed using an integrated multivariate approach combining principal component analysis, linear mixed models, the Mahalanobis-Taguchi system and Bland-Altman concordance, evaluating differences by playing position, game period, and match outcome.
Methodology: Seventeen male players from a university team participated, yielding 236 observations across 22 official games. Physiological load was quantified using the Training Impulse (Edwards' TRIMP) per game quarter and perceptual load with the Rating of Perceived Exertion scale. Principal component analysis, linear mixed models, the Mahalanobis-Taguchi system, and Bland-Altman concordance analysis were applied.
Results: Principal component analysis revealed a single component explaining 55.28% of variance, evidencing unidimensionality of internal load. Linear mixed models identified game period as the dominant factor, with greater demand in the second and fourth quarters. In lost games, load did not decrease in the third quarter. Perceived exertion differed between positions while Training Impulse showed no differences, evidencing perceptual-physiological dissociation. Discussion: Results were consistent with prior studies reporting unidimensionality of internal load in team sports. The sawtooth temporal pattern replicated findings from elite basketball and the positional perceptual-physiological dissociation suggested selecting monitoring indicators according to playing position.
Conclusions: The integrated multivariate approach reveals internal load patterns undetectable through conventional univariate analysis, with direct implications for individualized training load management.
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