Data modelling reveals inter-individual variability of front crawl swimming

Bideault, Gautier; Hérault, Romain; Seifert, Ludovic

doi:10.1016/j.jsams.2012.08.001

Cited by 34 publications

(30 citation statements)

References 21 publications

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“…For a long time sports research was based on the assumption that intra-and inter-subject variability should be minimized. Nowadays, dynamic systems theory and non-linear approaches suggest that variability should not be considered as a random error (Bideault, Herault, & Seifert, 2013). Evidence has been gathered lately about this topic in adult/elite swimmers (Costa et al, 2013;Komar, Sanders, Chollet, & Seifert, 2014) even though definitive answers are needed.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal modeling in sports: Young swimmers’ performance and biomechanics profile

Morais

Marques

Marinho

et al. 2014

Human Movement Science

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New theories about dynamical systems highlight the multi-factorial interplay between determinant factors to achieve higher sports performances, including in swimming. Longitudinal research does provide useful information on the sportsmen's changes and how training help him to excel. These questions may be addressed in one single procedure such as latent growth modeling. The aim of the study was to model a latent growth curve of young swimmers' performance and biomechanics over a season. Fourteen boys (12.33 ± 0.65 years-old) and 16 girls (11.15 ± 0.55 years-old) were evaluated. Performance, stroke frequency, speed fluctuation, arm's propelling efficiency, active drag, active drag coefficient and power to overcome drag were collected in four different moments of the season. Latent growth curve modeling was computed to understand the longitudinal variation of performance (endogenous variables) over the season according to the biomechanics (exogenous variables). Latent growth curve modeling showed a high inter- and intra-subject variability in the performance growth. Gender had a significant effect at the baseline and during the performance growth. In each evaluation moment, different variables had a meaningful effect on performance (M1: Da, β = -0.62; M2: Da, β = -0.53; M3: η(p), β = 0.59; M4: SF, β = -0.57; all P < .001). The models' goodness-of-fit was 1.40 ⩽ χ(2)/df ⩽ 3.74 (good-reasonable). Latent modeling is a comprehensive way to gather insight about young swimmers' performance over time. Different variables were the main responsible for the performance improvement. A gender gap, intra- and inter-subject variability was verified.

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Section: Introductionmentioning

confidence: 99%

Longitudinal modeling in sports: Young swimmers’ performance and biomechanics profile

Morais

Marques

Marinho

et al. 2014

Human Movement Science

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“…14 This procedure has been mainly applied in scientific fields such as genetics, 15 motor control, 14,16 and psychology. 17 A few studies have been conducted on adult/elite swimmers to classify coordination patterns, 18 start patterns, 19 and race analysis. 20 Cluster analysis can also be a feasible approach to identify and classify young athletes' determinant performance factors at a given moment.…”

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confidence: 99%

Cluster Stability as a New Method to Assess Changes in Performance and its Determinant Factors Over a Season in Young Swimmers

Morais¹,

Silva²,

Marinho³

et al. 2015

International Journal of Sports Physiology and Performance

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Purpose: To apply a new method to identify, classify, and follow up young swimmers based on their performance and its determinant factors over a season and analyze the swimmers' stability over a competitive season with that method. Methods: Fifteen boys and 18 girls (11.8 ± 0.7 y) part of a national talent-identification scheme were evaluated at 3 different moments of a competitive season. Performance (ie, official 100-m freestyle race time), arm span, chest perimeter, stroke length, swimming velocity, speed fluctuation, coefficient of active drag, propelling efficiency, and stroke index were selected as variables. Hierarchical and k-means cluster analysis were computed. Results: Data suggested a 3-cluster solution, splitting the swimmers according to their performance in all 3 moments. Cluster 1 was related to better performances (talented swimmers), cluster 2 to poor performances (nonproficient swimmers), and cluster 3 to average performance (proficient swimmers) in all moments.Stepwise discriminant analysis revealed that 100%, 94%, and 85% of original groups were correctly classified for the 1st, 2nd, and 3rd evaluation moments, respectively (0.11 ≤ Λ ≤ 0.80; 5.64 ≤ χ 2 ≤ 63.40; 0.001 < P ≤ .68). Membership of clusters was moderately stable over the season (stability range 46.1-75% for the 2 clusters with most subjects). Conclusion: Cluster stability is a feasible, comprehensive, and informative method to gain insight into changes in performance and its determinant factors in young swimmers. Talented swimmers were characterized by anthropometrics and kinematic features. Keywords: prepubescent swimmers, seasonal adaptations, longitudinal assessment, classificationTwo of the most interesting research topics in the field of sports performance, and specifically in competitive swimming, are the identification of performance-determinant factors and performance modeling. Several research groups have focused on identifying the main performance determinants and how they interplay to improve performance. Performance of young swimmers is influenced by growth and maturation. 1 Biological maturation may promote changes in their biomechanics, motor control, and energetics, which may affect their expertise achievement. 2 Young swimmers experience different rates of development that progress according to their own time scale. 3 For example, 2 structural-equation models reported that anthropometrics influence swimmers' kinematics and hence their performance. 4,5 The second topic of research interest is to model performance over time. The model enables a researcher or sports analyst to predict a subject's performance at a given moment, for example, at a given age or competition (ie, mean stability, withinsubject analysis). 6 Longitudinal assessments can also be carried out to understand the relative changes of performance among the main athletes (ie, normative stability, between-subjects analysis). 1,7 New trends in sports performance and expertise should adopt a multidisciplinary approach to enhance our understanding of the a...

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“…The assessment of individual trends and behaviors is a fair novelty in swimming. 9 Accordingly, to the best of our knowledge, a classification of young swimmers based on their individual biomechanical characteristics has never been attempted. The aim of this technical note has been to develop a classification system for young talented swimmers based on kinematical, hydrodynamic, and anthropometrical characteristics.…”

mentioning

confidence: 99%

“…, with classification functions as follows:Cluster 1, kinematics = 44.198⋅S -2.852⋅C Da + 4.604⋅dv/v -41.280(9) …”

mentioning

confidence: 99%

Young Swimmers’ Classification Based on Kinematics, Hydrodynamics, and Anthropometrics

Barbosa

Morais

Costa

et al. 2014

Journal of Applied Biomechanics

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The aim of this article has been to classify swimmers based on kinematics, hydrodynamics, and anthropometrics. Sixty-seven young swimmers made a maximal 25 m front-crawl to measure with a speedometer the swimming velocity (v), speed-fluctuation (dv) and dv normalized to v (dv/v). Another two 25 m bouts with and without carrying a perturbation device were made to estimate active drag coefficient (CDa). Trunk transverse surface area (S) was measured with photogrammetric technique on land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (ie, dv and dv/v), cluster 2 with anthropometrics (ie, S) and cluster 3 with a high hydrodynamic profile (ie, CDa). The variable that seems to discriminate better the clusters was the dv/v (F=53.680; P<.001), followed by the dv (F=28.506; P<.001), CDa (F=21.025; P<.001), S (F=6.297; P<.01) and v (F=5.375; P=.01). Stepwise discriminant analysis extracted 2 functions: Function 1 was mainly defined by dv/v and S (74.3% of variance), whereas function 2 was mainly defined by CDa (25.7% of variance). It can be concluded that kinematics, hydrodynamics and anthropometrics are determinant domains in which to classify and characterize young swimmers' profiles.

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Data modelling reveals inter-individual variability of front crawl swimming

Cited by 34 publications

References 21 publications

Longitudinal modeling in sports: Young swimmers’ performance and biomechanics profile

Longitudinal modeling in sports: Young swimmers’ performance and biomechanics profile

Cluster Stability as a New Method to Assess Changes in Performance and its Determinant Factors Over a Season in Young Swimmers

Young Swimmers’ Classification Based on Kinematics, Hydrodynamics, and Anthropometrics

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