Change of direction asymmetry across different age categories in youth soccer

Trecroci, Athos; Rossi, Alessio; Dos’Santos, Thomas; Formenti, Damiano; Cavaggioni, Luca; Longo, Stefano; Iaia, F. Marcello; Alberti, Giampietro

doi:10.7717/peerj.9486

Cited by 20 publications

(14 citation statements)

References 28 publications

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“…This led to an increase in asymmetry as age rises ( Figure 1 ), which makes sense given the age-related performance changes, improving “good” but not “weak” CS. Unlike the current results, some authors concluded that asymmetry appears to emerge around periods associated with rapid growth (i.e., U16), possibly due to the demands of accumulated soccer-specific training and competitions, and the “motor awkwardness” phase that adolescents experience [ 22 ]. However, this study reported higher asymmetry values in older ages.…”

Section: Discussioncontrasting

confidence: 83%

How does curve sprint evolve across different age-categories in soccer players?

Fílter-Ruger¹,

Gantois²,

Henrique³

et al. 2022

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Research has shown that soccer players regularly execute curved sprints during matches. The purpose of this study was to determine the age-related effects on curve sprint (CS) performance to both sides, asymmetry, and association with linear sprint (LS). Eighty-four soccer players (aged 16.1 ± 1.6 categorized in U15, U17, and U20) were recruited, who performed CS and LS tests. One-way analysis of variance (ANOVA) and effect size (ES) were used to compare CS performance between age categories, and relationships between physical performance measures were calculated using Pearson’s correlation coefficient. The main findings of this study were that: 1) there were significant differences in the “good” side CS among age groups (p < 0.001; ES from moderate to large), but not in the “weak” side CS, 2) curve asymmetry was significantly higher in U20 than U15 (p < 0.05; ES large) and U17 players (p < 0.05; ES moderate), and 3) relationships between CS and LS times decreased with age (from significant and very large [p < 0.001] to non-significant and smallmoderate [p > 0.05]). This study highlights the importance of assessing and training CS in different age categories, an action that becomes less correlated with LS as age increases, with the aim of mitigating the increase in asymmetries as a result of the specialization process, focusing interventions mainly on improving the CS “weak” side.

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Section: Discussioncontrasting

confidence: 83%

How does curve sprint evolve across different age-categories in soccer players?

Fílter-Ruger¹,

Gantois²,

Henrique³

et al. 2022

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“…Therefore, inter-leg differences regarding COD ability must be known to optimize soccer players’ preparation. In this sense, Trecroci et al 22 observed worse values in COD ability and CODD for the non-preferred leg in youth soccer players. These results were supported by Raya-González et al 8 in under-19 soccer players.…”

Section: Introductionmentioning

confidence: 93%

Difference between preferred and non-preferred leg in peak speed, acceleration, and deceleration variables and their relationships with the change-of-direction deficit

Silva

Oliveira

Raya-González

et al. 2022

Sci Rep

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The aim of this study was two-fold: (i) analyze the variation of kinematic measures between using preferred and non-preferred legs while braking during the 5–0–5 change of direction test; and (ii) test the relationships between kinematic measures, and change-of-direction deficit (CODD). A cross-sectional study using twenty adult male soccer players (21.6 ± 2.0 years; 73.2 ± 6.1 kg; 174.8 ± 4.5 cm) was employed. Players performed three repetitions of the 5–0–5 test using each leg during the braking phase. Players have used the Polar Team Pro to obtain the kinematic measures of peak speed, peak acceleration, and peak deceleration. Additionally, the CODD was also obtained using single-beamed photocells. Comparisons revealed a significantly greater peak acceleration (+ 0.22 m/s2; p < 0.001) and deceleration (+ 0.17 m/s2; p = 0.004) for the non-preferred leg. There were no significant correlations were found between CODD and peak accelerations (r = − 0.014, [− 0.193; 0.166]), peak decelerations (r = − 0.052, [− 0.229; 0.128]) or peak speed (r = 0.118, [− 0.063; 0.291]). This study revealed that preferred and non-preferred leg must be analyzed differently since they are different in the kinematic variables. However, CODD seems independent of leg preference and the kinematic measures of a 5–0–5 change of direction test.

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“…COD ability was tested over a 5 m + 5 m course with a turning point (90 • ) represented by a cone. For a better description of the test please refer to the work of Trecroci et al [22]. The distance between the starting line to the cone and between the cone to the finish line was 5 m each.…”

Section: Physical Performance Assessmentmentioning

confidence: 99%

Influence of Upper and Lower Body Anthropometric Measures on An Aggregate Physical Performance Score in Young Elite Male Soccer Players: A Case Study

Rossi¹,

Bongiovanni²,

Martera³

et al. 2022

J. Mens. Health

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Background:The present study aimed to determine the association of anthropometry-based characteristics with an aggregate score (AS) of physical performance in young elite soccer players. Methods: Sixteen under 15 elite players were enrolled. Among numerous anthropometrics variables, upper arm contracted (UACC) and relaxed circumference (UARC), corrected arm muscle area (AMAcorr), arm muscle circumference (AMC), thigh muscle circumference (TMC) and suprapatellar girths were also employed in this study. Players' physical performance was assessed by the change of direction (COD), 10 m and 20 m sprint, countermovement jump (CMJ) test, sprint with 90 • turns (with ball), and yo-yo intermittent recovery test level 1 (Yo-Yo IRT1). The AS was computed by Principal Components Analysis technique with one component on normalized performance results. A stepwise regression analysis was conducted to assess potential association between anthropometry-based variables and AS. Results: Large negative correlations (r < -0.68) of AS with UACC, UARC, AMAcorr, and AMC were detected. UACC and TMC permits to accurately estimate AS explaining 60% of the total variance (p < 0.001). Conclusions: These findings demonstrated the importance of including anthropometry-based measures of both upper and lower body to the physical performance potential expressed by AS in elite youth soccer.

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Change of direction asymmetry across different age categories in youth soccer

Cited by 20 publications

References 28 publications

How does curve sprint evolve across different age-categories in soccer players?

How does curve sprint evolve across different age-categories in soccer players?

Difference between preferred and non-preferred leg in peak speed, acceleration, and deceleration variables and their relationships with the change-of-direction deficit

Influence of Upper and Lower Body Anthropometric Measures on An Aggregate Physical Performance Score in Young Elite Male Soccer Players: A Case Study

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