Search

This study aimed to 1) identify what starting block outcome kinetics have the greatest relationship to 15 m start time; 2) investigate key mechanistic determinants of the block phase and how these forces are sequenced. One hundred and fifty-two high-level competitive swimmers were included in the study. Linear mixed modelling identified four on-block outcome kinetic variables (work, average power, horizontal take-off velocity (HTOV), and average acceleration) as having a very large relationship (R2 = 0.79–0.83) to 15 m start time, with average power having the most substantial impact. On-block force sequencing started with the rear leg, followed by upper limb grab forces and the front leg. Further exploration of underlying determinants was performed for average power and HTOV of the centre of mass. Multiple linear regression identified grab resultant peak force, rear resultant average force, front horizontal peak force, and resultant peak force as significant predictors of average power (R2 = 0.88). HTOV was predicted using the same variables, apart from the inclusion of rear horizontal peak force instead of rear resultant average force (R2 = 0.73). These findings may influence how strength and conditioning and skill acquisition interventions are designed to improve swim start performance. [ABSTRACT FROM AUTHOR]

Purpose: There is uncertainty as to which knee angle during a squat jump (SJ) produces maximal jump performance. Importantly, understanding this information will aid in determining appropriate ratios for assessment and monitoring of the explosive characteristics of athletes. Method: This study compared SJ performance across different knee angles--90 degrees, 100 degrees, 110 degrees, 120 degrees, 130 degrees, and a self-selected depth--for jump height and other kinetic characteristics. For comparison between SJ and an unconstrained dynamic movement, participants also performed a countermovement jump from a self-selected depth. Thirteen participants (M[subscript age] = 25.4 ± 3.5 years, M[subscript height] = 1.8 ± 0.06 m, M[subscript weight] = 79.8 ± 9.5 kg) were recruited and tested for their SJ performance. Results: In the SJ, maximal jump height (35.4 ± 4.6 cm) was produced using a self-selected knee angle (98.7 ± 11.2 degrees). Differences between 90 degrees,100 degrees, and self-selected knee angles for jump height were trivial (ES ± 90% CL = 90 degrees-100 degrees 0.23 ± 0.12, 90 degrees-SS -0.04 ± 0.12, 100 degrees-SS -0.27 ± 0.20; 0.5-2.4 cm) and not statistically different. Differences between all other knee angles for jump height ranged from 3.8 ± 2.0 cm (mean ± 90% CL) to 16.6 ± 2.2 cm. A similar outcome to jump height was observed for velocity, force relative to body weight, and impulse for the assessed knee angles. Conclusions: For young physically active adult men, the use of a self-selected depth in the SJ results in optimal performance and has only a trivial difference to a constrained knee angle of either 90° or 100°.

Purpose: Critical speed (CS) and supra-CS distance capacity (D') are useful metrics for monitoring changes in swimmers' physiological and performance capacities. However, the utility of these metrics across a season has not been systematically evaluated in high-level swimmers. Methods: A total of 27 swimmers (mean [SD]: 18 females, age= 19.1 [2.9] y, and 9 males, age = 19.5 [1.9] y) completed the 12 x 25-m swimming test multiple times (4 [3] tests/swimmer) across a 2-y period. Season-best times in all distances for the test stroke were sourced from publicly available databases. Swimmers' distance speciality was determined as the event with the time closest to world record. Four metrics were calculated from the 12 x 25-m lest: CS, D', peak speed, and drop-off %. Results: Guyatt responsiveness index values were calculated to ascertain the practically relevant sensitivity of each 12 x 25-m metric: CS = 1.5, peak speed = 2.3, D' = 2.1, and drop-off % = 2.6. These values are modified effect sizes; all are large effects. Bayesian mixed modeling showed substantial between-subjects differences between genders and strokes for each variable but minimal within-subject changes across the season. Drop-off % was lower in 200-m swimmers (14.0% [3.3%]) than in 100-m swimmers (18.1% [4.1%], P - .003, effect size = 1.10). Conclusions: The 12x25-m test is best suited to differentiating between swimmers ol different strokes and events. Further development is needed to improve its utility in quantifying meaningful changes over a season for individual swimmers. [ABSTRACT FROM AUTHOR]

Initial short-track speed-skating 14-m start performance has substantial influence on 500-m race outcome at the international level, yet the relationship has not been systematically quantified. The purpose of this investigation was to examine the relationship between rank position entering first corner (RPEFC) and race outcome and to understand how this relationship changes with competition round and absolute race intensity. Data were compiled from 2011-2014 World Cup seasons and 2010 and 2014 Olympic Winter Games. Association between RPEFC and race outcome was determined through Kendall tau-rank correlations. A visual comparison was made of how the relationship changes with relative competition level (race tau correlations were sorted by competition round) and with race intensity (race tau correlations were sorted by within-event winning time). A very large relationship between RPEFC and race outcome was observed (correlations for cohort, τ = .60; men, τ = .53; women, τ = .67). When examined by competition round (quarter- to A-finals), no substantial change in relationship was observed (men, τ = .57-.46; women, τ = .73-.53). However, when the start-performance relationship was considered by within-event winning time, the relationship strength increased with decreasing time (men, τ = .61 to .46; women, τ = .76 to .57; fastest to 7th- and 8th-fastest combined, respectively). These results establish and quantify RPEFC as an important aspect of elite short-track 500-m race outcome. RPEFC as an indicator of race outcome becomes increasingly important with absolute race intensity, suggesting that RPEFC capability is a discriminating factor for competitors of similar top speed and speed endurance. [ABSTRACT FROM AUTHOR]

Critical speed (CS) testing is useful in monitoring training in swimmers, however, completing a series of time trials (TTs) regularly is time-consuming. The 3-minute test may be a solution with positive initial findings. This investigation examined whether a modified 3-minute test (12 × 25 m) could assess CS and supra-CS distance capacity (D') in swimmers. A series of 12 × 25 m intervals were completed unpaced at maximal effort, interspersed with 5 s rest periods. The model speed = aebt + c was fitted to the data and integrated to calculate D'. The slowest two of the last four efforts were averaged to calculate CS. To assess reliability, 15 highly trained swimmers (9 females, 6 males) completed the 12 × 25 m twice within 72 h. Four measures were deemed reliable: peak velocity (0.01 m s-1; 0.5%, typical error and % coefficient of variation), CS (0.02 m s-1; 1.2%), D' (1.22 m; 5.7%) and drop off % (0.70% points; 4.5%). To assess criterion validity, 21 swimmers (9 from reliability, 12 other) completed two competition races within 2 weeks of a 12 × 25 m in the same stroke. Traditional CS and D' measures were calculated from competition performances (TT method). TT CS and 12 × 25 m CS were highly correlated (adj. R2 = 0.92, p < .001). D' values were moderately correlated (adj. R2 = 0.60, p < .01). Two TTs may have been too few to estimate D' accurately. The 12 × 25 m all-out swimming test is a reliable method for assessing CS and D' in swimmers, however, the validity of D' requires further investigation. [ABSTRACT FROM AUTHOR]

To develop a statistical model of winning times for international swimming events with the aim of predicting winning time distributions and the probability of winning for the 2020 and 2024 Olympic Games. The data set included first and third place times from all individual swimming events from the Olympics and World Championships from 1990 to 2019. We compared different model formulations fitted with Bayesian inference to obtain predictive distributions; comparisons were based on mean percentage error in out-of-sample predictions of Olympics and World Championships winning swim times from 2011 to 2019. The Bayesian time series regression model, comprising auto-regressive and moving average terms and other predictors, had the smallest mean prediction error of 0.57% (CI 0.46-0.74%). For context, using the respective previous Olympics or World Championships winning time resulted in a mean prediction error of 0.70% (CI 0.59-0.82%). The Olympics were on average 0.5% (CI 0.3-0.7%) faster than World Championships over the study period. The model computes the posterior predictive distribution, which allows coaches and athletes to evaluate the probability of winning given an individual's swim time, and the probability of being faster or slower than the previous winning time or even the world record.

Objectives: The purpose of this study was to document the longitudinal strength and power characteristic changes and race performance changes of a skeleton athlete., Method: Longitudinal strength and power changes were assessed with strength and power diagnostic testing over a 9-year period. Trends over 9 years for relative strength were analyzed using a linear model. Push-start time was recorded across multiple tracks. Trends over 9 years for start performance at each track were assessed using a mixed-effects linear model to account for the impact of different tracks. Lower-body strength and power changes were assessed via a 1-repetition-maximum squat and a body-weight countermovement jump. The relationship between strength and power changes was assessed over time. The relationship between strength changes and start performance was determined by assessing the fixed effect of relative strength changes on push-start time., Results: Relative lower-body strength ranged from 1.6 kg per body weight to 1.9 kg per body weight and showed a significant mean improvement of 0.05 kg per body weight per year (R2 = .71, P < .01). A negative correlation (R2 = .79) between relative strength changes and push-start performance across multiple tracks was found. The mixed-effects model indicated that push-start time improved significantly year to year (0.02 s; P < .001; R2 = .74) when controlling for the effect of track., Conclusions: The longitudinal analysis of push-start time and the associations with changes in strength suggest that training this quality can have a positive effect on push-start performance.

Purpose: First, to examine whether heart rate variability (HRV) responses can be modeled effectively via the Banister impulse-response model when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilized. Second, to describe seasonal HRV responses and their associations with changes in critical speed (CS) in competitive swimmers., Methods: A total of 10 highly trained swimmers collected daily 1-minute HRV recordings, sRPE training load, and subjective well-being scores via a novel smartphone application for 15 weeks. The impulse-response model was used to describe chronic root mean square of the successive differences (rMSSD) responses to training, with sRPE and subjective well-being measures used as systems inputs. Changes in CS were obtained from a 3-minute all-out test completed in weeks 1 and 14., Results: The level of agreement between predicted and actual HRV data was R2 = .66 (.25) when sRPE alone was used. Model fits improved in the range of 4% to 21% when different subjective well-being measures were combined with sRPE, representing trivial-to-moderate improvements. There were no significant differences in weekly group averages of log-transformed (Ln) rMSSD (P = .34) or HRV coefficient of variation of Ln rMSSD (P = .12); however, small-to-large changes (d = 0.21-1.46) were observed in these parameters throughout the season. Large correlations were observed between seasonal changes in HRV measures and CS (changes in averages of Ln rMSSD: r = .51, P = .13; changes in coefficient of variation of Ln rMSSD: r = -.68, P = .03)., Conclusion: The impulse-response model and data collected via a novel smartphone application can be used to model HRV responses to swimming training and nontraining-related stressors. Large relationships between seasonal changes in measured HRV parameters and CS provide further evidence for incorporating a HRV-guided training approach.

Objectives: To (1) examine the association between maturity timing and performance-based selection levels in (N=708) Australian male 100-m Freestyle swimmers (12-17 years); (2) identify the relationship between maturation status and 100-m Freestyle performance; and (3) determine whether Maturation-based Corrective Adjustment Procedures (Mat-CAPs) could remove maturation-related differences in swimming performance., Methods: In Part 1, maturity timing category distributions ('Early', 'Early Normative', 'Late Normative' and 'Late') for 'All', 'Top 50%' and '25%' of raw swimming times were examined within and across age-groups. In Part 2, multiple regression analyses quantified the relationship between maturity offset (YPHV) and swimming performance. In Part 3, sample-based maturity timing category distributions were examined based on raw and correctively adjusted swim times for 12-17 year old age-groups., Results: Based on raw swim times, a high prevalence of 'Early-maturing' swimmers, with large effect sizes was identified (e.g., 14 years 'All' - χ 2 (3, 151=111.98, p<0.001; 'Early' v 'Late' OR=82.0 95%CI=4.77, 1409.9); while a complete absence of 'Late-maturers' was apparent in the sample (N=708). When maturity categories were re-defined based on sample mean±standard deviation, and when using the expected curvilinear trendline identified in Part 2, Mat-CAPs mitigated maturity timing biases across all age-groups and selection levels, and removed the Freestyle performance advantage afforded by advanced maturity timing and status., Conclusions: Removing the influence of maturation-related developmental differences could help improve youth swimmer participation experiences and improve the accuracy of identifying genuinely skilled age-group swimmers., (Copyright © 2020 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Body composition can substantially impact elite swimming performance. In practice, changes in fat and lean mass of elite swimmers are estimated using body mass, sum of seven skinfolds (∑7) and lean mass index (LMI). However, LMI may be insufficiently accurate to detect small changes in body composition which could meaningfully impact swimming performance. This study developed equations which estimate dual-energy x-ray absorptiometry (DXA)-derived lean and fat mass using body mass and ∑7 data. Elite Australian swimmers (n = 44; 18 male, 26 female) completed a DXA scan and standardised body mass and ∑7 measurements. Equations to estimate DXA-derived lean and fat mass based on body mass, ∑7 and sex were developed. The relationships between ∑7, body mass and DXA-derived lean and fat mass were non-linear. Fat mass (Adjusted R 2  = 0.91; standard error = 1.0 kg) and lean mass (Adjusted R 2  = 0.99; standard error = 1.0 kg) equations were considered sufficiently accurate. Lean mass estimates outperformed the LMI in identifying the correct direction of change in lean mass (82% correct; LMI 71%). Using the accurate estimations produced by these equations will enhance the prescription and evaluation of programmes to optimise the body composition and subsequent performance in swimmers.

Objectives: The primary aim of this study was to determine which key performance indicators (PIs) were most important to success in sub-elite rugby union, and whether the analysis of absolute or relative data sets as a method for determining match outcome was stronger than the other., Methods: Data was taken from 17 PIs from 76 matches across the 2018 Queensland Premier Rugby Union season. A random forest classification model was created using these data sets based on win/loss outcomes., Results: The randomForest model classified 53 from 73 losses (72.6%) and 53 from 73 wins for an overall percentage accuracy of 72.6%. The randomForest model based on the relative data set classified 57 from 73 losses (78.1%) and 57 from 73 wins for an overall percentage accuracy of 78.1%. McNemar's value of p=0.84 confirmed that the relative data model did not outperform the absolute data set. There were positive associations between match outcome and relative number of kicks in play, meters carried, turnovers conceded and initial clean breaks., Conclusions: Outcomes in Queensland Premier Rugby can be predicted using relative and absolute data sets, though the difference between absolute and relative set usage was not as substantial as in professional rugby. Absolute and relative data sets can be used to create match strategies and assess match performance. A game plan based around an out of hand kicking game and accumulating more metres than the opposition, whilst minimising turnovers when in possession were key to success., (Copyright © 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Purpose: Critical speed (CS) and supra-CS distance capacity (D') are useful metrics for monitoring changes in swimmers' physiological and performance capacities. However, the utility of these metrics across a season has not been systematically evaluated in high level swimmers., Methods: Twenty-seven swimmers (18 female; age 19.1 ± 2.9 y, 9 male; 19.5 ± 1.9 y, mean ± SD) completed the 12x25m swimming test multiple times (4 ± 3 tests/swimmer) across a two-year period. Season-best times in all distances for the test stroke were sourced from publicly available databases. Swimmers' distance speciality was determined as the event with the time closest to world record. Four metrics were calculated from the 12x25m test: CS, D', peak speed and drop off %., Results: Guyatt's Responsiveness Index values were calculated to ascertain the practically relevant sensitivity of each 12x25m metric: CS = 1.5, peak speed = 2.3, D' = 2.1 and drop off % = 2.6. These values are modified effect sizes (ES); all are large effects. Bayesian mixed-modelling showed substantial between-subject differences between genders and strokes for each variable, but minimal within-subject changes across the season. Drop off % was lower in 200 m swimmers (14.0 ± 3.3%) compared to 100 m swimmers (18.1 ± 4.1%, p = 0.003, ES = 1.10)., Conclusion: The 12x25m test is best suited to differentiating between swimmers of different strokes and events. Further development is needed to improve its utility in quantifying meaningful changes over a season for individual swimmers.

Objectives: In elite age-group swimming it is unclear to what degree common assessments of anthropometric, jump performance and front-crawl critical speed (CS) correlate with competition performance., Design: Cross-sectional field study., Methods: Forty eight elite national-level junior swimmers (22 males, age 16.5±1.2 y, 26 females, age 15.5±1.1 y; mean±SD) completed anthropometry tests, loaded and unloaded countermovement jumps and a series of front-crawl time-trials to determine CS and supra-CS distance capacity (D'). Years from peak height velocity (PHV) predicted from anthropometric data was used as a maturity indicator. Race performances within 3 months of testing were standardised to compare across distances and strokes. Multiple linear regression models were formulated using these data., Results: Loaded jump height, mass, D', PHV and humerus breadth best predicted 100m performance in males (R 2 Adj =0.88, p<0.001), while loaded jump height, chest depth and sitting height predicted female 100m performances (R 2 Adj =0.74, p=0.002). Loaded and unloaded jump height, mass, CS and PHV (R 2 Adj =0.73, p=0.003) and CS and chest depth (R 2 Adj =0.33, p=0.03) predicted 200m performance in males and females respectively., Conclusions: Common assessments of power and aerobic capacity in elite junior swimmers explain more variance in competition performance for male than female swimmers, as well as for 100m rather than 200m events. These findings highlight the need to empirically assess testing regimens and suggest new tests in this population may be required., (Copyright © 2018 Sports Medicine Australia. All rights reserved.)