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This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women's freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA -7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs -0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training.

Objective: This study aimed to identify Key Performance Indicators (KPIs) for men's swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men's individual 100 m races of the 2019 European Short-Course Swimming Championships., Results: Duration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs - 2.3% to + 2.3%), Breaststroke (0% bias, LOAs - 0.9% to + 0.9%), Butterfly (0% bias, LOAs - 1.2% to + 1.2%), and Freestyle (0% bias, LOAs - 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes., (© 2024. The Author(s).)

The aims of the study were to provide benchmarks and normative data for 100 m, 200 m, and 400 m short-course individual medley (IM) races, investigate differences between the various swimming strokes and turns involved in IM, and quantify the effect and contribution of various race sections on swimming performance. All IM races (n = 320) at the 2019 European Short-Course Swimming Championships were video monitored and digitized with interrater reliability described by a mean intra-class correlation coefficient of 0.968. Normative data were provided for the eight finalists of each event (FINA points = 886 ± 37) and the eight slowest swimmers from each event (FINA points = 688 ± 53). Contribution and effects of race sections on swimming performance were investigated using stepwise regression analysis based on all races of each event. Regression analysis explained 97-100% of total variance in race time and revealed turn time ( β ≥ 0.53) as distinguishing factor in short-course IM races in addition to swim velocity ( β ≥ -0.28). Start time only affected 100 m ( β ≥ 0.14) and 200 m ( β ≥ 0.04) events. Fastest turn times were found for the butterfly/backstroke turn. Breaststroke showed slowest swim velocities and no difference between fastest and slowest 100 m IM swimmers. Therefore, breaststroke may provide largest potential for future development in IM race times. Correlation analyses revealed that distance per stroke ( r ≥ -0.39, P < 0.05) rather than stroke rate ( r ≤ -0.18, P > 0.05) is a performance indicator and may be used by coaches and performance analysts to evaluate stroke mechanics in male IM swimmers despite its more complex assessment. Performance analysts, coaches, and swimmers may use the present normative data to establish minimal and maximal requirements for European Championship participation and to create specific drills in practice., (© Journal of Sports Science and Medicine.)

Objectives: To provide normative data and establish percentile curves for long-course (50 m pool length) swimming events and to compare progression of race times longitudinally for the various swimming strokes and race distances., Design: Descriptive approach with longitudinal tracking of performance data., Methods: A total of 2,884,783 race results were collected from which 169,194 annual best times from early junior to elite age were extracted. To account for drop-outs during adolescence, only swimmers still competing at age of peak performance (21-26 years) were included and analyzed retrospectively. Percentiles were established with z-scores around the median and the Lambda-Mu-Sigma (LMS) method applied to account for potential skewness. A two-way analysis of variance (ANOVA) with repeated measure and between-subject factor was applied to compare race times across the various events and age groups., Results: Percentile curves were established based on longitudinal tracking of race times specific to sex, swimming stroke, and race distance. Comparing performance progression, race times of freestyle sprint events showed an early plateau with no further significant improvement (p > 0.05) after late junior age (15-17 years). However, the longer the race distance, the later the race times plateaued (p < 0.05). Female swimmers generally showed an earlier performance plateau than males. Backstroke and freestyle showed an earlier performance plateau compared to the other swimming strokes (p < 0.05)., Conclusions: Performance progression varied between sex, swimming strokes, and race distances. Percentile curves based on longitudinal tracking may allow an objective assessment of swimming performance, help discover individual potentials, and facilitate realistic goal setting for talent development., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: To establish reference data on required competition age regarding performance levels for both sexes, all swimming strokes, and race distances and to determine the effect of competition age on swimming performance in the context of other common age metrics. In total, 36,687,573 race times of 588,938 swimmers (age 14.2 ± 6.3 years) were analyzed. FINA (Fédération Internationale de Natation) points were calculated to compare race times between swimming strokes and race distances. The sum of all years of race participation determined competition age., Results: Across all events, swimmers reach top-elite level, i.e. > 900 FINA points, after approximately 8 years of competition participation. Multiple-linear regression analysis explained up to 40% of variance in the performance level and competition age showed a stable effect on all race distances for both sexes (β = 0.19 to 0.33). Increased race distance from 50 to 1500 m, decreased effects of chronological age (β = 0.48 to - 0.13) and increased relative age effects (β = 0.02 to 0.11). Reference data from the present study should be used to establish guidelines and set realistic goals for years of competition participation required to reach certain performance levels. Future studies need to analyze effects of transitions between various swimming strokes and race distances on peak performance., (© 2022. The Author(s).)

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.)

While talent development and the contributing factors to success are hardly discussed among the experts in the field, the aim of the study was to investigate annual variation in competition performance (AVCP), number of races per year, and age, as potential success factors for international swimming competitions. Data from 40'277 long-course races, performed by all individual female starters (n = 253) at the 2018 European Swimming Championships (2018EC) for all 10 years prior to these championships, were analyzed. Relationships between 2018EC ranking and potential success factors, i.e., AVCP, number of races per year, and age, were determined using Pearson's correlation coefficient and multiple linear regression analysis. While AVCP was not related to ranking, higher ranked swimmers at the 2018EC swam more races during each of the ten years prior to the championships (P < 0.001). Additionally, older athletes were more successful (r = -0.42, P < 0.001). The regression model explained highly significant proportions (P < 0.001) and 43%, 34%, 35%, 49% of total variance in the 2018EC ranking for 50m, 100m, 200m, and 400m races, respectively. As number of races per year (β = -0.29 --0.40) had a significant effect on ranking of 50-400m races, and age (β = -0.40 --0.61) showed a significant effect on ranking over all race distances, number of races per year and age may serve as success factors for international swimming competitions. The larger number of races swum by higher ranked female swimmers may have aided long-term athlete development regarding technical, physiological, and mental skill acquisitions. As older athletes were more successful, female swimmers under the age of peak performance, who did not reach semi-finals or finals, may increase their chances of success in following championships with increased experience., Competing Interests: The authors have declared that no competing interests exist.

The purpose of this study was (1) accurately estimate longitudinal relationships between decimal age (i.e., chronological and relative) and performance in Australian female 100 m (N = 765) and 200 m (N = 428) Breaststroke swimmers (10-18 years); and (2) determine whether corrective adjustment procedures could remove Relative Age Effects (RAEs) in an independent sample of age-matched 100 m (N = 2,491) and 200 m (N = 1,698) state/national level Breaststroke swimmers. In Part 1, growth curve modelling quantified longitudinal relationships between decimal age and swimming performance. In Part 2, relative age distributions (Quartile 1-4) for "All", "Top 25%" and "10%" of swimming times were examined based on raw and correctively adjusted swim times for age-groups. Based on raw swim times, finding identified RAE effect sizes increased in magnitude (small-medium) with selection level ("All"-"Top 25%") in 12-14 years age-groups for both events. However, when correctively adjusted swim performances were examined, RAEs were primarily absent across all age-groups and selection levels. Using longitudinal reference data, corrective adjustment procedures removed relative age advantages in female youth Breaststroke performance. Removing the influence of relative age-related differences is predicted to improve the accuracy of identifying genuinely skilled youth swimmers.

Born, DP, Stöggl, T, Petrov, A, Burkhardt, D, Lüthy, F, and Romann, M. Analysis of freestyle swimming sprint start performance after maximal strength or vertical jump training in competitive female and male junior swimmers. J Strength Cond Res 34(2): 323-331, 2020-To investigate the freestyle swimming sprint start performance before and after 6 weeks of maximal strength compared with vertical jump training. With a between-group repeated-measure design, 21 junior swimmers (12 female and 9 male) competing in national and international championships performed 2 weekly sessions of either maximal strength (heavy-loaded back squat and deadlift exercise) or vertical jump training (unloaded box jumps) for 6 weeks during the precompetition phase of the seasonal main event. Session ratings of perceived exertion were used to compare the load of both training programs. Before and after the training period, sprint start performance was investigated on a starting block equipped with force plates synchronized to a 2-dimensional motion capture system. Total training load did not differ between the 2 groups. Sprint start performance and most kinematic and kinetic parameters remained unaffected. In pooled data of the U17 swimmers, however, 5-m, 15-m, and 25-m split times were improved with maximal strength (p = 0.02, 0.03, and 0.01), but not with vertical jump training (p = 0.12, 0.16, and 0.28). Although there was no global effect, focus on the subgroup of U17 swimmers showed an improved sprint start performance with 2 sessions of maximal strength training integrated into a 16-hour training week. Although outcomes of the conditioning program seemed to be affected by the training history and performance level of the athletes involved, strength and conditioning coaches are encouraged to introduce maximal strength training at a young age.

Objectives: (1) Generate accurate estimates of the relationship between decimal age (i.e., chronological and relative) with swimming performance based on longitudinal data. (2) Determine whether corrective adjustment procedures can remove Relative Age Effects (RAEs) from junior/youth swimming., Design: Longitudinal and repeated years of cross-sectional performance data were examined., Methods: (1) Participants were 553 male 100m Freestyle swimmers (10-18 years) who participated in ≥five annual events between 1999-2017. Growth curve modelling quantified the relationship between age and swimming performance, permitting corrective adjustment calculations. (2) Participants were N=2141 male 100m Freestyle swimmers (13-16 years) who swam at state/national events in 2015-2017. Relative age distributions for 'All', 'Top 50%', '25%' and '10%' of swimming times were examined based on raw and correctively adjusted swim times. Chi-square, Cramer's V and Odds Ratios (OR) determined whether relative age (quartile) inequalities existed according to age-groups, selection level and correctively adjusted swim times., Results: Based on raw swim times, for 'All' swimmers RAEs was evident at 13 and 14 years-old and dissipated thereafter. But, RAE effect sizes substantially increased with selection level, with large-medium effects between 13-15 years-old (e.g., 15 years - Top 50% Q1v Q4 OR=2.28; Top 10%=6.02). However, when correctively adjusted swim times were examined, RAEs were predominantly absent across age-group and selection levels., Conclusions: With accurate longitudinal reference data, corrective adjustment procedures effectively removed RAEs from 100m Freestyle swimming performance, suggesting the potential to improve swimming participation experience and performance evaluation., (Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Objectives: To determine the prevalence, magnitude and transient patterning of Relative Age Effects (RAEs) according to sex and stroke event across all age-groups at the Australian National age swimming Championships., Design: Repeated years of cross-sectional participation data were examined., Methods: Participants were 6014 unique male (3185) and female (2829) swimmers (aged 12-18 years) who participated in Freestyle (50, 400m) and/or Breaststroke (100, 200m) at the National age swimming Championships between 2000-2014 (inclusive). RAE prevalence, magnitude and transience were determined using Chi-square tests and Cramer's V estimates for effect size. Odds Ratios (OR) and 95% Confidence Intervals (CI) examined relative age quartile discrepancies. These steps were applied across age-groups and according to sex and each stroke event., Results: Consistent RAEs with large-medium effect sizes were evident for males at 12-15 years of age respectively, and with large-medium effects for females at 12-14 respectively across all four swimming strokes. RAE magnitude then consistently reduced with age across strokes (e.g., Q1 vs. Q4 OR range 16year old males=0.94-1.20; females=0.68-1.41). With few exceptions, by 15-16 years RAEs had typically dissipated; and by 17-18 years, descriptive and significant inverse RAEs emerged, reflecting overrepresentation of relatively younger swimmers., Conclusions: Performance advantages associated with relative age (and thereby likely growth and maturation) are transient. Greater consideration of transient performance and participation in athlete development systems is necessary. This may include revising the emphasis of sport programmes according to developmental stages and delaying forms of athlete selection to improve validity., (Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Purpose: To investigate performance progression from early-junior to peak performance age and compare variety in race distances and swimming strokes between swimmers of various performance levels. Methods: Using a longitudinal data analysis and between-groups comparisons 306,165 annual best times of male swimmers (N = 3897) were used to establish a ranking based on annual best times at peak performance age. Individual performance trajectories were retrospectively analyzed to compare distance and stroke variety. Performances of world-class finalists and international- and national-class swimmers (swimming points: 886 [30], 793 [28], and 698 [28], respectively) were compared across 5 age groups—13–14, 15–16, 17–18, 19–20, and 21+ years—using a 2-way analysis of variance with repeated measures. Results: World-class finalists are not significantly faster than international-class swimmers up to the 17- to 18-year age group (F2|774 = 65, P <.001, η p 2 =.14) but specialize in short- or long-distance races at a younger age. World-class breaststroke finalists show faster breaststroke times compared to their performance in other swimming strokes from an early age (P <.05), while world-class freestyle and individual medley finalists show less significant differences to their performance in other swimming strokes. Conclusions: While federation officials should aim for late talent selection, that is, not before the 17- to 18-year age group, coaches should aim to identify swimmers' preferred race distances early on. However, the required stroke variety seems to be specific for each swimming stroke. Breaststroke swimmers could aim for early and strong specialization, while freestyle and individual medley swimmers could maintain large and very large stroke variety, respectively. [ABSTRACT FROM AUTHOR]

The aim was to provide benchmarks and investigate contribution of start and turn performances in the European Short-Course Swimming Championship. Over all race distances, 932 individual races of male competitors were video captured and the start and turn performances were analysed. Start and turn performances contributed up to 26% and 56% of the total race time. Analysis of variance showed that the 15 m start times were faster for Freestyle and Butterfly (p < 0.001) compared to the other swimming strokes. In-water starts (Backstroke) were slower at the 5 m mark compare to on-block starts (p < 0.001). Tumble turns were faster than open turns (p < 0.001). Multiple linear regression analysis explained 82–97% of total variance in the race results with a decreasing effect of start performance for the longer distance races. Turn performance affected the results across all race distances (p < 0.001). The benchmarks and percentiles provide comparative data for swimmers of different performance levels. Considering the large contribution of start and turn performance to race time and the high effect in the regression model, training regimes that are mainly based on conditioning of free-swimming should be reconsidered. [ABSTRACT FROM AUTHOR]

Objective: It is heavily discussed whether larger variety or specialization benefit elite performance at peak age. Therefore, this study aimed to determine technical (number of different swimming strokes) and physiological (number of different race distances) variety required to become an international-class swimmer (> 750 swimming points) based on 1′522′803 race results. Results: Correlation analyses showed lower technical variety in higher ranked swimmers (P < 0.001), yet with small effects (0.11–0.30). However, Poisson distribution revealed dose-time-effects and specified number of swimming strokes required during each age group. Specifically, freestyle swimmers showed highest chances when starting to compete in three to four swimming strokes but reduced their variety to three swimming strokes at the ages of 12/13yrs with another transition to two swimming strokes at the ages of 19/21yrs (female/male swimmers, respectively). Although both sexes showed similar specialization pattern throughout their career, earlier specialization was generally evident in female compared to male swimmers. At peak performance age, freestyle was most frequently combined with butterfly. Swimmers who either kept competing in all five swimming strokes or focused on only one at the beginning of their careers showed lowest probability of becoming an international-class swimmer. Physiological variety increased during junior age but declined again to three race distances towards elite age. [ABSTRACT FROM AUTHOR]

Inter-individual differences in maturation-associated development can lead to variations in physical performance, resulting in performance (dis)advantages and maturation selection bias within youth sport systems. To address such bias and account for maturational differences, Maturation-based Corrective Adjustment Procedures (Mat-CAPs) could be beneficial. The present study aimed to: (1) determine maturity timing distributions in youth female swimming; (2) quantify the relationship between maturation status and 100-m front-crawl (FC) performance; (3) implement Mat-CAPs to remove maturational influences upon swimming performance. For Aim 1 and 2, participants were 663 female (10–15 years) swimmers who participated in 100-m FC events at Australian regional, state, and national-level competitions between 2016–2020 and underwent anthropometric assessment (mass, height and sitting height) to estimate maturity timing and offset. For Aim 3, participants aged 10–13 years were categorised into maturity timing categories. Maturity timing distributions for Raw ('All', 'Top 50%' and 'Top 25%') and Correctively Adjusted swim times were examined. Chi-square, Cramer's V and Odds Ratios determined the presence of maturation biases, while Mat-CAPs identified whether such biases were removed in targeted age and selection-groups. Results identified that between 10–13 years, a significantly higher frequency of 'early' maturers was apparent, although tapered toward higher frequencies of 'Late-normative' maturers by 14–15 years. A curvilinear relationship between maturity-offset and swim performance was identified (R2= 0.51, p<0.001) and utilised for Mat-CAPs. Following Mat-CAPs application, maturity timing biases evident in affected age-groups (10–13 years), and which were magnified at higher selection levels ('Top 50%' & '25%' of swim performances) were predominantly removed. Findings highlight how maturation advantages in females occurred until approximately 13 years old, warranting restricted Mat-CAPs application. Mat-CAPS has the potential to improve female swimmer participation experiences and evaluation. [ABSTRACT FROM AUTHOR]

To provide percentile curves for short-course swimming events, including 5 swimming strokes, 6 race distances, and both sexes, as well as to compare differences in race times between cross-sectional analysis and longitudinal tracking, a total of 31,645,621 race times of male and female swimmers were analyzed. Two percentile datasets were established from individual swimmers' annual best times and a two-way analysis of variance (ANOVA) was used to determine differences between cross-sectional analysis and longitudinal tracking. A software-based percentile calculator was provided to extract the exact percentile for a given race time. Longitudinal tracking reduced the number of annual best times that were included in the percentiles by 98.35% to 262,071 and showed faster mean race times (P < 0.05) compared to the cross-sectional analysis. This difference was found in the lower percentiles (1st to 20th) across all age categories (P < 0.05); however, in the upper percentiles (80th to 99th), longitudinal tracking showed faster race times during early and late junior age only (P < 0.05), after which race times approximated cross-sectional tracking. The percentile calculator provides quick and easy data access to facilitate practical application of percentiles in training or competition. Longitudinal tracking that accounts for drop-out may predict performance progression towards elite age, particularly for high-performance swimmers. [ABSTRACT FROM AUTHOR]

The aim of the study was to investigate the effect of start and turn performances on race times in top-elite female swimmers and provide benchmarks for all performance levels, all swimming strokes, and all race distances of the European Short-Course Championships (EC). The individual races (n = 798) of all female competitors (age: 20.6 ± 3.9 years, FINA points: 792 ± 78) were video-monitored for subsequent analysis of start and turn performances. Benchmarks were established across all competitors of each event based on the 10th, 25th, 50th, 75th, and 90th percentiles. Start and turn performances contributed up to 27.43% and 56.37% to total race time, respectively. Mechanistic analysis revealed that the fastest swimmers had the lowest contribution of the acyclic phases to race time. Therefore, relative to their faster race times, these swimmers were even faster during starts and turns. Multiple linear regression analysis showed large effects of turn performance on 50, 100, 200, 400, and 800 m race times (β = 0.616, 0.813, 0.988, 1.004, and 1.011, respectively), while the effect of start performance continuously decreased the longer the race distance. As turn performance may be the distinguishing factor in modern short-course races, benchmarks should be used to set goals and establish training guidelines depending on the targeted race time. [ABSTRACT FROM AUTHOR]