2021
DOI: 10.1080/24733938.2021.1899274
|View full text |Cite
|
Sign up to set email alerts
|

Age- and size-corrected kicking speed and accuracy in elite junior soccer players

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
8
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
5

Relationship

2
3

Authors

Journals

citations
Cited by 6 publications
(8 citation statements)
references
References 46 publications
0
8
0
Order By: Relevance
“…For the continuous measure of age, each player's date of birth (day and month) was converted to a decimal proportion of the calendar year, with 0 denoting the first day of January through to 1 denoting the 31st day of December of the same year. We also used two different methods of quantifying each measured trait, one using standardised raw scores, and the other using raw scores corrected for age and size 40 (see below) . Thus, these data were analysed four different ways: traits (standardised) ∼ age (categorical) (ANOVAs); traits (standardised) ∼ age (continuous) (linear regression models); traits (corrected) ∼ age (categorical) (ANOVAs); and traits (corrected) ∼ age (continuous) (linear regression models).…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…For the continuous measure of age, each player's date of birth (day and month) was converted to a decimal proportion of the calendar year, with 0 denoting the first day of January through to 1 denoting the 31st day of December of the same year. We also used two different methods of quantifying each measured trait, one using standardised raw scores, and the other using raw scores corrected for age and size 40 (see below) . Thus, these data were analysed four different ways: traits (standardised) ∼ age (categorical) (ANOVAs); traits (standardised) ∼ age (continuous) (linear regression models); traits (corrected) ∼ age (categorical) (ANOVAs); and traits (corrected) ∼ age (continuous) (linear regression models).…”
Section: Discussionmentioning
confidence: 99%
“…The sprinting and dribbling performance of each player was measured using four 30 m long paths that differed in curvature, as in Refs. [38][39][40] (Supplementary Figure 1). On the ground, two lengths of 6 mm plastic chain (Kateli, Brazil) defined the outer boundaries of each path, creating a 1-m wide channel between them.…”
Section: Sprinting and Dribbling Along With Curved Pathsmentioning
confidence: 99%
See 1 more Smart Citation
“…An outstanding player may have a high relative performance score when competing against average players but lower when competing against other high‐level players. By discovering which isolated athletic, technical, or psychological traits underlie high performance in small‐sided competitions, one can thus provide metrics of performance that are independent of other players, and comparable to groups for other teams, competitions, or countries 40,41 . Because our design of small‐sided games provides a single metric of overall performance, it is possible to design experiments that identify the traits most predictive of overall performance.…”
Section: Discussionmentioning
confidence: 99%
“…Soccer techniques can be divided into techniques with and without the ball (Anam et al, 2019). Techniques with the ball include: 1) kicking techniques (Rabello et al, 2021;Hunter et al, 2021), 2) holding and controlling the ball, 3) dribbling techniques, 4) heading the ball, 5) throwing the ball, and 6) goalkeeping techniques (Anam et al, 2021). While the techniques without the ball include: 1) stealing the ball, 2) body cart technique, 3) deception without the ball, 4) running technique, 5) jumping technique, and 6) goalkeeping technique (Anam et al, 2018).…”
Section: Introductionmentioning
confidence: 99%