ECSS Paris 2023: OP-AP39
INTRODUCTION: Due to the increasing importance of start and turn performance in swim races throughout the recent years, the aim of the study was to compare the effect of start performance, turn performance, and swimming velocity on the race result between top-elite female junior and adult swimmers across all distances of freestyle pool races (50 m to 1500 m) and to provide a software tool to predict race outcomes based on the various key performance indicators. METHODS: In the present study, a total of 289 individual freestyle races from female participants at the European long-course championship, as well as 391 freestyle races at the Junior European long-course championship were analysed. All heats, semi-finals, and finals across all freestyle distances (50 m to 1500 m) were included. Linear regression analyses were used to assess the relationship between overall race time and three independent variables: start performance, turn performance, and swimming velocity. Regression equations were used to develop the predictor software tool. RESULTS: Start times showed the greatest effect on sprint distances, i.e. 50 m and 100 m. However, the effect diminished towards longer race distances. While swimming velocity had the largest effect on the race results across all distances for both junior and adult swimmers, the effect of turn times increased from 100 m to 400 m for both, junior and adult swimmers. Interestingly, the effect of turn times decreased again for 800 m and 1500 m races. Generally, there was a trend towards a greater effect of turn times in adult swimmers. The regression model explained 98-99% of the variance in race performance (r2 = 0.98-0.99). Based on the regression equations a software tool (potential predictor) was created to allow coaches and federation officials to identify individual swimmers’ potentials and create benchmarks for future development. CONCLUSION: For both junior and adult swimmers, swimming velocity had the greatest effect on race results. While the effect of start performance decreased from short to long race distances, turn performances showed a U-shaped, hence largest effect on middle-distance races, i.e. 200 m and 400 m. The prediction tool that was developed from the regression model allows the prediction of individual performance progressions and rankings at international swimming competitions by combining isolated performance analysis with race analysis data. REFERENCES: Born, D.-P., Romann, M., & Stöggl, T. (2022). Start fast, swim faster, turn fastest: section analyses and normative data for individual medley. Journal of sports science & medicine, 21(2), 233. Polach, M., Thiel, D., Kreník, J., & Born, D.-P. (2021). Swimming turn performance: the distinguishing factor in 1500 m world championship freestyle races? BMC Research Notes, 14, 1-7.
Read CV Chantal WidmerECSS Paris 2023: OP-AP39
INTRODUCTION: The ability to predict performance potential in racing sport events lasting from ~1 to 3 min is complex due to a heavy reliance on both aerobic and anaerobic energy systems. In swimming, this is made more difficult by the practical inaccessibility presented by the aquatic nature of the sport, which interferes with the collection of physiological measures that are commonplace in sports more suited to laboratory testing. Anaerobic speed reserve (ASR) and critical speed (CS) are mathematical models which can be calculated based solely on performance data in both predominantly aerobic (providing an estimate of maximal aerobic speed: MAS) and predominantly anaerobic events (providing an estimate of maximal sprint speed: MSS). However this approach has received limited attention in swimming cohorts, and therefore, the aim of this research was to determine the association between ASR and CS with a mixed energy system performance (200 m freestyle) in trained swimmers. We further examined whether these associations could be strengthened with the addition of physiological markers of aerobic and anaerobic capacity. METHODS: Trained male (n=8) and female (n=9) swimmers completed three maximal freestyle swims (50, 200, and 400 m) on separate days, with the 50 m (MSS) and 400 m (MAS) speeds used for the determination of ASR and CS. Arterial oxygen saturation, blood lactate concentration, peak oxygen consumption (VO2peak), blood bicarbonate concentration, and blood pH were measured pre- and post-trial. Linear regression models were adopted to assess the best predictors of 200 m freestyle performance, with multivariate analysis of variance used to assess the impact of distance on all variables. RESULTS: MSS was the strongest predictor of 200 m performance (R2 = 0.83), followed by CS (R2 = 0.69, whilst ASR could only provide moderate predictive capabilities (R2 = 0.36). However, the addition of CS, ASR and ∆pH to MSS in our optimal regression model accounted for 95% of variance in 200 m freestyle performance, decreasing mean absolute error from 2.41 s to 1.46 s. The removal of physiological data (∆pH) from the model had only a trivial impact on predictive capabilities (R2 = 0.94). There was a large and significant effect (η2>0.14) of trial distance on all physiological and performance variables; however, post hoc analysis revealed no significant difference for VO2peak. CONCLUSION: In this investigation, MSS provided the strongest single predictor of 200 m freestyle performance, while the addition of ASR and CS substantially improved the model. Markers of aerobic and anaerobic capacity did not meaningfully improve the model. Modelling of ASR and CS provides a practical alternative to the collection of physiological variables in swimming cohorts for identifying performance potential in this mixed energy system event.
Read CV Bryce LaniganECSS Paris 2023: OP-AP39
INTRODUCTION: The physiological assessment of swimmers has been widely examined, however there is no consensus on the best methodologies to assess fundamental physiological characteristics. For example, whilst it has been proposed that maximal blood lactate accumulation rate (VLamax) is a useful index of glycolytic power, literature investigating methodologies to estimate VLamax is limited. In swimming specifically, currently only four academic articles examine VLamax, each presenting their own novel methodology. Recently, it was found that a 25-m maximal swim effort presented the highest VLamax compared to 35 and 50-m distances (1). This finding aligns with the initial proposed duration of approximately 10-seconds of maximal effort for a VLamax estimation, and validates a 25-m distance for VLamax assessment in swimmers (1). The reliability and sensitivity to training of VLamax derived from the 25-m test however, has not been established. METHODS: Nineteen state-to-national level swimmers completed a 25-m VLamax test on three consecutive mornings across three consecutive weeks. The first test of each week was conducted following 48-hours of rest to investigate reliability. The test was also conducted 12- and 36-hours post an anaerobic training set to determine the sensitivity of VLamax to anaerobic stimuli. On all occasions, resting blood lactate was measured immediately prior to entering the pool. Swimmers commenced from a push start and were asked to produce their fastest possible velocity across a 25-m distance. Blood lactate was measured 30-s post-test and then every minute until the concentration reached a plateau or decreased. VLamax was calculated as the difference between maximal and resting blood lactate concentrations, divided by the difference between the test duration and the duration during which it is assumed that lactate production has not yet begun (1). Reliability of VLamax was assessed by typical error (TE), coefficient of variation (CV) and intra-class correlation coefficients (ICC). Generalised linear mixed modelling was conducted to understand the sensitivity of VLamax to anaerobic training stimuli. RESULTS: The VLamax TE was 0.072 mmol·L-1·s-1 (95% CI: 0.051-0.118 mmol·L-1·s-1) with a CV of 19.9% (95% CI: 14.8-30.2%) and ICC of 0.847 (95% CI: 0.609-0.936). VLamax was significantly reduced at 12- (ß = -0.216, p = 0.016) and 36-hours (ß = -0.216, p = 0.014) post anaerobic training compared to baseline. CONCLUSION: VLamax derived from a 25-m maximal swim is sensitive to changes in glycolytic power within a training cycle. The reliability of the VLamax swim test is deemed ‘good’, however interpretations on anaerobic readiness within a training cycle or glycolytic power profile should be made with respect to the TE and CV. Future investigations into the magnitude of change in VLamax between different physiological profiles are encouraged. REFERENCE: 1. Mavroudi et al. (2023) Maximal Lactate Accumulation Rate at Three Sprint Swimming Distances. Sports (Basel).11(4):87.
Read CV Karla BulteECSS Paris 2023: OP-AP39