Planning Training Workload in Football Using Small-Sided Games' Density. Issue 10 (October 2019)

• Record Type: Journal Article
• Authors: Sangnier, Sebastien; Cotte, Thierry; Brachet, Olivier; Coquart, Jeremy; Tourny, Claire
• Is Part Of: Journal of strength and conditioning research. Volume 33:Issue 10(2019)
• Publication Date: 2019-10
• Subjects: soccer match -- physical training integrated -- professional team -- physical qualities; Physical education and training -- Periodicals; Weight training -- Physiological aspects -- Periodicals; Physical fitness -- Periodicals; 613.7
• DOI: 10.1519/JSC.0000000000002598
• Languages: English
• ISSNs: 1064-8011

Abstract

Abstract : Abstract: Sangnier, S, Cotte, T, Brachet, O, Coquart, J, and Tourny, C. Planning training workload in football using small-sided games density. J Strength Cond Res 33(10): 2801–2811, 2019—To develop the physical qualities, the small-sided games' (SSGs) density may be essential in soccer. Small-sided games are games in which the pitch size, players' number, and rules are different to those for traditional soccer matches. The purpose was to assess the relation between training workload and SSGs' density. The 33 densities data (41 practice games and 3 full games) were analyzed through global positioning system (GPS) data collected from 25 professional soccer players (80.7 ± 7.0 kg; 1.83 ± 0.05 m; 26.4 ± 4.9 years). From total distance, distance metabolic power, sprint distance, and acceleration distance, the data GPS were divided into 4 categories: endurance, power, speed, and strength. Statistical analysis compared the relation between GPS values and SSGs' densities, and 3 methods were applied to assess models (R-squared, root-mean-square error, and Akaike information criterion). The results suggest that all the GPS data match the player's essential athletic skills. They were all correlated with the game's density. Acceleration distance, deceleration distance, metabolic power, and total distance followed a logarithmic regression model, whereas distance and number of sprints follow a linear regression model. The research reveals options to monitor the training workload. Coaches could anticipate the load resulting from the SSGs and adjust the field size to the players' number. Taking into account the field size during SSGs enables coaches to target the most favorable density for developing expected physical qualities. Calibrating intensity during SSGs would allow coaches to assess each athletic skill in the same conditions of intensity as in the competition.