Classification of Soccer and Basketball Players' Jumping Performance Characteristics: A Logistic Regression Approach

Abstract

This study aimed to examine countermovement jump (CMJ) kinetic data using logistic regression, in order to distinguish sports-related mechanical profiles. Eighty-one professional basketball and soccer athletes participated, each performing three CMJs on a force platform. Inferential parametric and nonparametric statistics were performed to explore group differences. Binary logistic regression was used to model the response variable (soccer or not soccer). Statistical significance (p < 0.05) was reached for differences between groups in maximum braking rate of force development (RFD_Dmax, U₇₉ = 1035), mean braking rate of force development (RFD_Davg, U₇₉ = 1038), propulsive impulse (IMP_U, t₇₉ = 2.375), minimum value of vertical displacement for center of mass (S_BCMmin, t₇₉ = 3.135), and time difference (% of impulse time; Δ_Τ) between the peak value of maximum force value (F_Umax) and S_BCMmin (U₇₉ = 1188). Logistic regression showed that RFD_Davg, impulse during the downward phase (IMP_D), IMP_U, and Δ_Τ were all significant predictors. The model showed that soccer group membership could be strongly related to IMP_U, with the odds ratio being 6.48 times higher from the basketball group, whereas RFD_Davg, IMP_D, and Δ_Τ were related to basketball group. The results imply that soccer players execute CMJ differently compared to basketball players, exhibiting increased countermovement depth and impulse generation during the propulsive phase.

Keywords: biomechanics; impulse; rate of force development; sport specificity; vertical ground reaction force.

Figure 1

Example of characteristic difference in Δ_Τ: (a) typical force–time curve of a basketball player; (b) typical force–time curve of a soccer player. Vertical lines represent the maximum value of countermovement depth (solid blue line) and maximum value of force (green dashed line). The grey horizontal line indicates subjects’ body weight, and the dashed grey horizontal line indicates the zero value for S_BCM.

Figure 2

Diagnostic plots of the logistic regression model predicting group membership: (a) Model’s AUC performance; (b) Scatterplot of the respective classes probability; (c) Histogram—basketball probability (cutoff threshold, dashed line); (d) Histogram—soccer probability (cutoff threshold, dashed line).