Evaluation of Sex-Specific Movement Patterns in Judo Using Probabilistic Neural Networks.

The purpose of the present study was to create a probabilistic neural network to clarify the understanding of movement patterns in international judo competitions by gender. Analysis of 773 male and 638 female bouts was utilized to identify movements during the approach, gripping, attack (including biomechanical designations), groundwork, defense, and pause phases. Probabilistic neural network and chi-square (χ2) tests modeled and compared frequencies (p ≤ .05). Women (mean [interquartile range]: 9.9 [4; 14]) attacked more than men (7.0 [3; 10]) while attempting a greater number of arm/leg lever (women: 2.7 [1; 6]; men: 4.0 [0; 4]) and trunk/leg lever (women: 0.8 [0; 1]; men: 2.4 [0; 4]) techniques but fewer maximal length-moment arm techniques (women: 0.7 [0; 1]; men: 1.0 [0; 2]). Male athletes displayed one-handed gripping of the back and sleeve, whereas female athletes executed a greater number of groundwork techniques. An optimized probabilistic neural network model, using patterns from the gripping, attack, groundwork, and pause phases, produced an overall prediction accuracy of 76% for discrimination between men and women.