Dataset Information

Predicting dive start performance from kinematic variables at water entry in (sub-)elite swimmers.

ABSTRACT: The dive start is an important component of competitive swimming, especially at shorter race distances. Previous research has suggested that start performance depends on kinematic variables pertaining to the swimmer at water entry, notably the distance from the block, the horizontal velocity of the centre of mass and the angle between body and water surface. However, the combined and relative contributions of these variables to start performance remain to be determined. The aim of the present study was therefore to develop a model to predict start performance (time from take-off to reaching the 15-m line) from a set of kinematic variables that collectively define the swimmer's entry state. To obtain an appropriate database for this purpose, fifteen well-trained, (sub-)elite swimmers performed dive starts under different instructions intended to induce substantial variation in entry state. Kinematic data were extracted from video recordings of these starts, optimised and analysed statistically. A mixed effects analysis of the relation between entry state and start performance was conducted, which revealed a significant and robust dependence of start performance on entry state (?2(3) = 88, p < .001), explaining 86.1% of the variance. Start time was reduced by 0.6 s (p < .001) when the horizontal displacement at water entry was 1 m further, by 0.3 s (p < .001) when the horizontal velocity of the centre of mass was 1 m/s higher, and by 0.5 s (p < .01) when the entry angle was 1 radian flatter. The robustness of the analysis was confirmed by a similar mixed effects analysis of the relation between entry state and time to the 5-m line. In conclusion, dive start performance can be predicted to a considerable extent from the swimmer's state at water entry. The implications of those findings for studying and improving block phase kinetics are discussed.

SUBMITTER: van Dijk MP

PROVIDER: S-EPMC7598512 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Predicting dive start performance from kinematic variables at water entry in (sub-)elite swimmers.

van Dijk Marit P MP Beek Peter J PJ van Soest A J Knoek AJK

PloS one 20201030 10

The dive start is an important component of competitive swimming, especially at shorter race distances. Previous research has suggested that start performance depends on kinematic variables pertaining to the swimmer at water entry, notably the distance from the block, the horizontal velocity of the centre of mass and the angle between body and water surface. However, the combined and relative contributions of these variables to start performance remain to be determined. The aim of the present st ...[more]

PMID: 33125412

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Project description:BackgroundThe human ACTN3 gene encodes α-actinin-3, an actin-binding protein with a pivotal role in muscle structure and metabolism. A common genetic single nucleotide polymorphism (SNP) at codon 577 of the ACTN3 results in the replacement of an arginine (R) with a stop codon (X). The R allele is a normal functional version of the gene, whereas the X allele contains a sequence change that completely stops production of functional α-actinin-3 protein. The ACTN3 R577X polymorphism was found to be associated with power athletic performance especially among track and field athletes. The aim of the current study was to compare allelic and genotype frequencies of the ACTN3 R577X polymorphism among runners and swimmers specializing in different distances, and >non-athletic controls.MethodsOne hundred and thirty-seven runners, 91 swimmers and 217 controls, participated in the study. Runners were assigned to two subgroups according to their event specialty-long-distance runners (LDR) and short-distance runners (SDR). Swimmers were also assigned to two subgroups according to their main swimming event-long-distance swimmers (LDS) and short-distance swimmers (SDS). Genomic DNA was extracted from peripheral EDTA-treated anti-coagulated blood using a standard protocol. Genotypes were determined using the Taqman allelic discrimination assay.ResultsRunners' genotype and allele differed significantly between LDR, SDR, and controls, with the lowest prevalence of RR genotype and R allele among LDR. XX genotype and X allele prevalence was significantly higher among LDR compared to the other groups (p < 0.01 for all). On the other hand, swimmers' genotype and allele frequencies did not differ significantly between subgroups (LDS and SDS). Yet, LDS had significantly higher RR genotype and R allele frequencies compared to LDR.ConclusionsThe findings suggest that while ACTN3 R577X polymorphism is a genetic polymorphism that may distinguish between SDR and LDR, it cannot differentiate significantly between SDS and LDS.Trial registrationClinicalTrials.gov: NCT01319032.Key pointsACTN3 R577X polymorphism is largely associated with running events specialization, with high prevalence of RR genotype and R allele frequency among short-distance runners compare to long-distance runners.Unlike in running, ACTN3 R577X polymorphism is not associated with swimming specialization.The inability of the ACTN3 R577X polymorphism to distinguish between swimmers specializing in different events, presumably since other factors such as body physique, technique, tactics, etc., are more likely to determine such a distinction.

Project description:The purposes of this study were to (i) develop a field-goal shooting performance analysis template and (ii) explore the impact of each identified variable upon the likely outcome of a field-goal attempt using binary logistic regression modelling in elite men's wheelchair basketball. First, a field-goal shooting performance analysis template was developed that included 71 Action Variables (AV) grouped within 22 Categorical Predictor Variables (CPV) representing offensive, defensive and game context variables. Second, footage of all 5,105 field-goal attempts from 12 teams during the men's 2016 Rio De Janeiro Paralympic Games wheelchair basketball competition were analysed using the template. Pearson's chi-square analyses found that 18 of the CPV were significantly associated with field-goal attempt outcome (p < 0.05), with seven of them reaching moderate association (Cramer's V: 0.1-0.3). Third, using 70% of the dataset (3,574 field-goal attempts), binary logistic regression analyses identified that five offensive variables (classification category of the player, the action leading up to the field-goal attempt, the time left on the clock, the location of the shot, and the movement of the player), two defensive variables (the pressure being exerted by the defence, and the number of defenders within a 1-meter radius) and 1 context variable (the finishing position of the team in the competition) affected the probability of a successful field-goal attempt. The quality of the developed model was determined acceptable (greater than 65%), producing an area under the curve value of 68.5% when the model was run against the remaining 30% of the dataset (1,531 field-goal attempts). The development of the model from such a large sample of objective data is unique. As such it offers robust empirical evidence to enable coaches, performance analysts and players to move beyond anecdote, in order to appreciate the potential effect of various and varying offensive, defensive and contextual variables on field-goal success.

Dataset Information

Predicting dive start performance from kinematic variables at water entry in (sub-)elite swimmers.

Publications

Predicting dive start performance from kinematic variables at water entry in (sub-)elite swimmers.

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