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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]

Objectives: To compare performance progression and variety in race distances of comparable lengths (timewise) between pool swimming and track running. Quality of within-sport variety was determined as the performance differences between individual athletes' main and secondary race distances across (top-) elite and (highly-) trained swimmers and runners. Methods: A total of 3,827,947 race times were used to calculate performance points (race times relative to the world record) for freestyle swimmers (n = 12,588 males and n = 7,561 females) and track runners (n = 9,230 males and n = 5,841 females). Athletes were ranked based on their personal best at peak performance age, then annual best times were retrospectively traced throughout adolescence. Results: Performance of world-class swimmers differentiates at an earlier age from their lower ranked peers (15-16 vs. 17-20 year age categories, P < 0.05), but also plateaus earlier towards senior age compared to runners (19-20 vs. 23 + year age category, P < 0.05), respectively. Performance development of swimmers shows a logarithmic pattern, while runners develop linearly. While swimmers compete in more secondary race distances (larger within-sport variety), runners specialize in either sprint, middle- or long-distance early in their career and compete in only 2, 4 or 3 other race distances, respectively. In both sports, sprinters specialize the most (P < 0.05). Distance-variety of middle-distance swimmers covers more longer rather than sprint race distances. Therefore, at peak performance age, (top-) elite female 200 m swimmers show significantly slower sprint performances, i.e., 50 m (P < 0.001) and 100 m (P < 0.001), but not long-distance performances, i.e., 800 m (P = 0.99) and 1,500 m (P = 0.99). In contrast, (top-) elite female 800 m middle-distance runners show significantly slower performances in all their secondary race distances (P < 0.001). (Top-) elite female athletes specialize more than (highly-) trained athletes in both sports (P < 0.05). Conclusions: The comparison to track running and lower ranked swimmers, the early performance plateau towards senior age, and the maintenance of a large within-sport distance variety indicates that (top-) elite sprint swimmers benefit from greater within-sport specialization. [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]

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).)