Related Experiment Video
Updated: Sep 10, 2026

Effects of a Novel Neuromuscular Training Intervention on Jump, Sprint, and Change of Direction in Adult Female Soccer Players
Published on: June 10, 2025
The Impact of Anthropometric Characteristics and Motor Abilities on Match Running Performance (MRP) in Elite Soccer
Radivoje Radaković1,2, Borko Katanić3, Mima Stanković4
1Institute of Information Technologies, University of Kragujevac, Kragujevac, Serbia.
Abstract:
The aim of the study was to examine the impact of anthropometric characteristics and motor abilities on match running performance (MRP) in elite soccer players. For this purpose, a sample of 41 professional soccer players was evaluated considering five anthropometric characteristics, 11 motor abilities (including sprint and acceleration performance, agility, power, and endurance) as regressors, and six variables describing MRP (time spent in running at < 8 km/h, 8-15 km/h, 15.1-19 km/h, 19.1-23 km/h, > 23 km/h and total time spent in running) as criteria. Regression analysis identified significant regression models (p < 0.05) predicting 8.1-15 km/h, 19.1-23 km/h, and total time spent in running variables for both sets of regressors, with morphology explaining 29.6%, 29.8%, and 37.4%, and motor abilities explaining 59.6%, 65.9% and 68.7% of criterion variables, respectively. It can be concluded that body composition variables such as muscle and fat percentages are associated with jogging, high-speed running, and total distance covered, whilst sprinting variables at 10, 20, and 30 m, the 10/20 index, and the CMJ are associated with jogging and total distance covered. Also, the squat jump is associated with jogging, while shuttle run test performance is associated with high-speed running only. Results provide deep insight into the structure of relationships between MRP and both morphological and motor variables, which are essential to structure advanced training systems focused on optimization of MRP in elite soccer players.
Related Concept Videos
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Regression Toward the Mean

