Shorter constant work rate cycling tests as proxies for longer tests in highly trained cyclists.

Severe-intensity constant work rate (CWR) cycling tests simulate the high-intensity competition environment and are useful for monitoring training progression and adaptation, yet impose significant physiological and psychological strain, require substantial recovery, and may disrupt athlete training or competition preparation. A brief, minimally fatiguing test providing comparable information is desirable. Purpose To determine whether physiological variables measured during, and functional decline in maximal power output immediately after, a 2-min CWR test can act as a proxy for 4-min test outcomes. Methods Physiological stress ([Formula: see text] kinetics, heart rate, blood lactate concentrations ([La-]b)) was monitored and performance fatigability was estimated (as pre-to-post-CWR changes in 10-s sprint power) during 2- and 4-min CWR tests in 16 high-level cyclists ([Formula: see text] ml∙kg-1∙min-1). The relationship between the 2- and 4-min CWR tests and the physiological variables that best relate to the performance fatigability were investigated. Results The 2-min CWR test evoked a smaller decline in sprint mechanical power (32% vs. 47%, p<0.001). Both the physiological variables (r = 0.66-0.96) and sprint mechanical power (r = 0.67-0.92) were independently and strongly correlated between 2- and 4-min tests. Differences in [Formula: see text] and [La-]b in both CWR tests were strongly associated with the decline in sprint mechanical power. Conclusion Strong correlations between 2- and 4-min severe-intensity CWR test outcomes indicated that the shorter test can be used as a proxy for the longer test. A shorter test may be more practical within the elite performance environment due to lower physiological stress and performance fatigability and should have less impact on subsequent training and competition preparation.

Bayesian prediction of winning times for elite swimming events.

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.

Predicting performance in 4 x 200-m freestyle swimming relay events.

AIM: The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy. DATA AND METHODS: Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010-2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events. RESULTS: Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [-0.94, -0.30]) and American swimmers (-0.48 s [-0.89, -0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively. DISCUSSION: Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer's season's best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success.

Longitudinal Strength, Power, and Push-Start Performance Changes in a Skeleton Athlete: Case Study.

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.

On-block mechanistic determinants of start performance in high performance swimmers.

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.

Monitoring the Heart Rate Variability Responses to Training Loads in Competitive Swimmers Using a Smartphone Application and the Banister Impulse-Response Model.

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.

Maturity-related developmental inequalities in age-group swimming: The testing of 'Mat-CAPs' for their removal.

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' - χ2 (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.

The non-linear relationship between sum of 7 skinfolds and fat and lean mass in elite swimmers.

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 R2 = 0.91; standard error = 1.0 kg) and lean mass (Adjusted R2 = 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.

The impact of different training load quantification and modelling methodologies on performance predictions in elite swimmers.

The use of rolling averages to analyse training data has been debated recently. We evaluated two training load quantification methods (five-zone, seven-zone) fitted to performances over two race distances (50 and 100 m) using four separate longitudinal models (Banister, Busso. rolling averages and exponentially weighted rolling averages) for three swimmers ranked in the top 8 in the world. A total of 1610 daily load measures and 108 performances were collected. Banister (standard error of the estimate (SEE) 0.64 and 0.62 s; five-zone and seven-zone quantification methods), Busso (SEE 0.73 and 0.70 s) and exponentially weighted rolling averages (SEE 0.57 and 0.63 s) models fitted more accurately (p < 0.001) than the rolling averages approach (SEE 1.32 and 1.36 s). The seven-zone quantification method did not produce more accurate performance predictions than the five-zone method, despite being a more detailed form of training load quantification. Four neural network models were fitted and had a lower error (SEE 0.38, 0.41, 0.35 and 0.60 s) than all longitudinal models, but did not track as predictably over time. Exponentially weighted impulse-response models and exponentially weighted rolling averages appear more effective at predicting performance using training load data in elite swimmers than a rolling averages approach.

Key performance indicators in Australian sub-elite rugby union.

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.

Responsiveness and Seasonal Variation of a 12x25m Swimming Test.

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.

The relationship between talent identification testing parameters and performance in elite junior 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 (R2Adj=0.88, p<0.001), while loaded jump height, chest depth and sitting height predicted female 100m performances (R2Adj=0.74, p=0.002). Loaded and unloaded jump height, mass, CS and PHV (R2Adj=0.73, p=0.003) and CS and chest depth (R2Adj=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.

Reliability and validity of a modified 3-minute all-out swimming test in elite swimmers.

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 = a ebt + 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.