Mechanical determinants of acceleration and maximal sprinting speed in highly trained young soccer players

Abstract

The aim of the present study was to examine, in highly trained young soccer players, the mechanical horizontal determinants of acceleration (Acc) and maximal sprinting speed (MSS). Eighty-six players (14.1 ± 2.4 year) performed a 40-m sprint to assess Acc and MSS. Speed was measured with a 100-Hz radar, and theoretical maximal velocity (V₀), horizontal force (F₀) and horizontal power (P_max) were calculated. Within each age group, players were classified as high Acc/fast MSS (>2% faster than group mean), medium (between ?2% and +2%), and low/slow (>2% slower). Acc and MSS were very largely correlated (?0.79; 90% confidence limit [?0.85; ?0.71]). The determinants (multiple regression r² = 0.84 [0.78; 0.89]) of Acc were V₀ (partial r: 0.80 [0.72; 0.86]) and F₀ (0.57 [0.44; 0.68]); those of MSS (r² = 0.96 [0.94; 0.97]) were V₀ (0.96 [0.94; 0.97]) and P_max (0.73 [0.63; ?0.80]). High/Med have likely greater F₀ (Cohen’s d: +0.8 [0.0; 1.5]), V₀ (+0.6 [?0.1; 1.3]) and P_max (+0.9 [0.2; 1.7]) than Low/Med. High/Fast have an almost certainly faster V₀ (+2.1 [1.5; 2.7]) and a likely greater P_max (+0.6 [?0.1; 1.3]) than High/Med, with no clear differences in F₀ (?0.0 [?0.7; 0.6]). Speed may be a generic quality, but the mechanical horizontal determinants of Acc and MSS differ. While maximal speed training may improve both Acc and MSS, improving horizontal force production capability may be efficient to enhance sprinting performance over short distances.     

Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University,Rangpur, Bangladesh

Education and Information Technologies - Digital transformation and emerging technologies open a horizon to a new method of teaching and learning and revolutionizes the e-learning industry....     

A Performance Approach to Job Analysis

When we think of conducting analyses with a performance view, we commonly lean toward tools like front end analysis, needs assessment, performance analysis, and several variations. Usually, this starts because of a performance problem or because of an anticipated new performance. What about existing training? We look to training evaluation in its various levels to determine whether people like it, learn from it, transfer it, and whether the organization is benefiting from it. This paper describes a scenario where existing training was occurring, people suspected it could be more efficient, and yet the individuals' performance was for the most part satisfactory. We wanted to determine where the training could be made more efficient, determine if there were other barriers to performance, and do this with valid and reliable data from a large workforce. The Coast Guard's Performance Technology Center was in its infancy and was given the permission to try out alternative methods of conducting its work. This article describes the lessons learned about the process, about the technologies employed, and even the logistics of carrying out a rather large‐scale effort in minimal time.