DOI: 10.2478/bhk-2026-0027 ISSN: 2080-2234

Anthropometric characteristics of competitive Indian male judo athletes: A multivariate analysis across weight categories

Vineet Mehta, Ajit Kumar, Dhananjoy Shaw, Tarun Routhan, Deepika Malhotra

Abstract

Study aim

The aim of this study is to describe the anthropometric profile of competitive Indian male judo athletes, identify its underlying dimensional structure, and determine how anthropometric measurements differ across senior weight categories once stature is statistically controlled.

Material and methods

Two hundred and fourteen competitive male judoka were assessed across the seven senior International Judo Federation weight categories. Eleven primary anthropometric measurements were subjected to principal component analysis (Varimax rotation, correlation matrix). Category differences were examined using stature-adjusted multivariate and univariate analyses of covariance, using Pillai’s trace, Bonferroni-corrected comparisons, and partial eta squared ( η 2 ).

Results

The profile reduced to two interpretable, distinct but related dimensions (skeletal length and mass/girth) accounting for 76.6% of the variance. After adjusting for stature, the weight category had a significant multivariate effect (Pillai’s trace = 0.942, p < 0.001, η 2 = 0.157). Significant stature-adjusted differences were found for calf circumference ( η 2 = 0.632), palm circumference ( η 2 = 0.286), trunk height ( η 2 = 0.226), arm length ( η 2 = 0.112) and leg length ( η 2 = 0.092), but not for hand length or sitting height. Stature-adjusted limb dimensions increased from the lightest to the middle categories and showed no further significant increase across the heaviest categories.

Conclusions

Anthropometric variation in competitive male judoka is organised along two distinct but related dimensions, and category differences persist after adjustment for stature, with heavier athletes being broader rather than longer limbed. These profiles provide descriptive reference data for monitoring and contextualisation rather than performance prediction or talent identification.

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