Profiling of elite male junior 50 m freestyle sprinters: Understanding the speed-time relationship

Case Report: Differences in self-selected pacing in 20, 40, and 60 ironman-distance triathlons: a case study

Background

Triathletes are pushing their limits in multi-stage Ironman-distance triathlons. In the present case study, we investigated the pacing during 20, 40, and 60 Ironman-distance triathlons in 20, 40, and 60 days, respectively, of one professional IRONMAN® triathlete.

Case study

Event 1 (20 Ironman-distance triathlons in 20 days), Event 2 (40 Ironman-distance triathlons in 40 days), and Event 3 (60 Ironman-distance triathlons in 60 days) were analyzed by discipline (swimming, cycling, running, and overall event time), by Deca intervals (10 days of consecutive Ironman-distance triathlons) and additional data (sleep duration, body mass, heart rate in cycling and running). To test differences between Events and Deca intervals within the same discipline, T-tests (2 groups) or one-way ANOVAs (3 or more groups) were used.

Results

Swimming splits were fastest in Event 1, (ii) cycling and running splits were fastest in both Event 2 and 3, (iii) overall speed was fastest in Event 3, (iv) sleep duration increased during Event 2 but decreased in Event 3, (v) body mass decreased in Event 2, but increased in Event 3 and (vi) heart rate during cycling was similar in both Event 2 and 3. In contrast, heart rate during running was greater in Event 3.

Conclusion

In a professional IRONMAN® triathlete finishing 20, 40, and 60 Ironman-distance triathlons in 20, 40, and 60 days, respectively, split performances and both anthropometrical and physiological changes such as body mass and heart rate differed depending upon the duration of the events.

Sprinting to the top: comparing quality of distance variety and specialization between swimmers and runners

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.

Race analysis in swimming: understanding the evolution of publications, citations and networks through a bibliometric review

The aim of this study was to conduct a scoping and bibliometric review of swimming articles related to race analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant studies. Articles on race analysis in swimming published between 1984 and December 31, 2023 were retrieved from the Web of Science database. 366 records were screened and a total of 74 articles were retained for analysis. Until 2012, there were some time intervals with no or few publications. From 2012, there was a clear upward trend in publications and citations. This theme was led by the United States of America, Australia, and Spain. Australia and Spain maintain their status as the countries with the most publications. The analysis of author collaborations revealed two clusters with Spanish authors, and the remaining clusters are composed of Portuguese, Swiss, and Australian authors. With this bibliometric review, it has been possible to understand the evolution of the articles published on race analysis in swimming, the countries and the authors that have contributed most to this topic over the years. The prediction model shows that the number of articles and citations on this topic will continue to increase over the next 10 years (until 2034).

Effects of anthropometrics, thrust, and drag on stroke kinematics and 100 m performance of young swimmers using path-analysis modeling

The aim of this study was to understand the interactions between anthropometric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty-five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in regional and national competitions were recruited. The 100 m freestyle performance was chosen as the variable to be predicted. A series of anthropometric (hand surface area-HSA), kinetic (thrust and active drag coefficient (CDA )), and kinematic (stroke length (SL); stroke frequency (SF), and swimming speed) variables were measured. Structural equation modeling (via path analysis) was used to develop and test the model. The initial model predicted performance with 90.1% accuracy. All paths were significant (p < 0.05) except the thrust-SL. After deleting this non-significant path (thrust-SL) and recalculating, the model goodness-of-fit improved and all paths were significant (p < 0.05). The predicted performance was 90.2%. Anthropometrics had significant effects on kinetics, which had significant effects on kinematics, and consequently on the 100 m freestyle performance. The cascade of interactions based on this path-flow model allowed for a meaningful prediction of the 100 m freestyle performance. Based on these results, coaches and swimmers should be aware that the swimming predictors can first meaningfully interact with each other to ultimately predict the 100 m freestyle performance.

Relationship between swimming speed, intra-cycle variation of horizontal speed, and Froude efficiency during consecutive stroke cycles in adolescent swimmers

The purpose of this study was to investigate the relationship between swimming speed, intra-cycle variation of horizontal speed of displacement (dv), and Froude efficiency (ηF) in front-crawl during three consecutive stroke cycles. The sample consisted of 15 boys aged 16.07 ± 0.77 years and 15 girls aged 15.05 ± 1.07 years. Swimming speed, dv and ηF were measured during a 25 m front-crawl trial. Three consecutive stroke cycles were measured. Swimming speed showed a non-significant stroke-by-stroke effect (F = 2.55, p = 0.087, η2 = 0.08), but a significant sex effect (F = 90.46, p < 0.001, η2 = 0.76). The dv and ηF had the same trend as the swimming speed for the stroke-by-stroke effect, but a non-significant sex effect (p > 0.05). The Spearman correlation matrix between swimming speed and dv, and swimming speed and ηF showed non-significant correlations for the three stroke cycles in both sexes. However, the tendency of the former was not always inverse, and the latter was not always direct. Coaches and swimmers need to be aware that lower dvs are not always associated with faster swimming speeds and vice-versa, and that ηF is a predictor of swimming speed, not dv.

Analysis of upper limb propulsion in young swimmers in front-crawl through Statistical Parametric Mapping

This study aimed to: (i) verify the within-subject effect of the dominant and non-dominant upper limb propulsion during consecutive arm-pulls through discrete (average) and continuous analysis (SPM), and; (ii) compare young swimmers' propulsion between both upper limbs through discrete (average) and continuous analysis (Statistical Parametric Mapping - SPM). The sample consisted of 17 young male swimmers (age = 16.02 ± 0.61-years) who regularly participate in national and international level competitions. A set of kinematic and propulsion variables were measured during a 25-m maximal trial in front-crawl. Statistical analysis of propulsion was performed using discrete variables and through SPM. Swimming velocity showed a significant decrease over time. A significant interaction between the "time" (consecutive arm-pulls) and "side" (dominant vs. non-dominant) effects was observed in both statistical analyzes. Only the dominant upper limb demonstrated a significant "time" effect with a significant difference (p < 0.05) between the first and third arm-pulls. SPM indicated that the "time" effect was observed between the ∼ 34% and ∼ 42% of the arm-pull. The differences between the first and third arm-pull were verified between the ∼ 32% and ∼ 43% of the arm-pull. A non-significant "side" effect was verified in both analyzes. Therefore, SPM analysis provided more sensitive and accurate outputs than discrete analysis. This will allow coaches to design specific training drills focused on specific moments of the arm-pull.

Tracking performance and its underlying characteristics in talented swimmers: a longitudinal study during the junior-to-senior transition

The present study strived to gain a more profound understanding of the distinctions in development between swimmers who are considered to be on track to the senior elite level compared to those who are not. Longitudinal data of 29 talented sprint and middle-distance swimmers (12 males; 17 females) on season best performances (season best times) and underlying performance characteristics (anthropometrics, starts, turns, maximal swimming velocity, stroke index [SI, an indirect measure of swimming efficiency] and lower body power) were collected over four swimming seasons (median of n = 3 seasons per swimmer). Based on their season best performance at early senior age (males aged 18-19; females aged 17-18), some swimmers were considered to be on track to reach the elite level (referred to as high-performing seniors; 6 males and 10 females), whereas others were not (referred to as lower-performing seniors; 6 males and 7 females). Retrospectively studying these swimmers (males and females separately), we found that all high-performing seniors were already on track to the elite level at late junior age (males aged 17; females aged 16), evidenced with faster season best performances throughout their transition compared to their lower-performing peers (p < 0.05). Independent sample t-tests revealed that high-performing seniors significantly outscored their lower-performing peers on maximal swimming velocity (males and females), starts and turns (males), SI (females) and lower body power (females) at late junior age (p < 0.05). Additionally, multilevel models showed faster rates of development for high-performing seniors on turns and maximal swimming velocity (males), and SI (females) compared to lower-performing peers during the junior-to-senior transition (p < 0.05). Particularly, the higher initial levels of swim performance and underlying characteristics at late junior age as well as the ability to keep progressing on season best performances (males and females), turns and maximal swimming velocity (males), and SI (females) during the junior-to-senior transition, may be crucial factors in the attainment of swimming expertise.

Using Statistical Parametric Mapping as a statistical method for more detailed insights in swimming: a systematic review

Swimming is a time-based sport and hence strongly dependent from velocity. Most studies about swimming refer to velocity as discrete variable, i.e., 0-D (no time dimension). However, it was argued that using swimming velocity as a continuous variable (1-D, with time dimension) with Statistical Parametric Mapping (SPM) can bring deeper and detailed insights about swimming performance. Therefore, the aim of this study was to perform a systematic review about the current body of knowledge of using Statistical Parametric Mapping in a swimming context. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant articles. After screening, nine articles related to Statistical Parametric Mapping (SPM) analysis in swimming were retained for synthesis. Results showed that four articles (44.4%) aimed to understand the kinematics, isokinetic joint torque or electromyographic (EMG) pattern of the swimmer's shoulder either on land or during front crawl trials. Two articles (22.2%) focused on understanding the swimming velocity while performing the breaststroke stroke. One article (11.1%) analyzed the swimmers' propulsion at front-crawl stroke, another one (11.1%) compared swimming velocity during a complete stroke cycle in young swimmers of both sexes as a discrete variable and as a continuous variable. Also, one article (11.1%) analyzed the underwater undulatory velocity. In an EMG context, some findings verified in SPM are not possible to be discovered with traditional 0-D statistical methods. Studies about swimming velocity (breaststroke, freestyle, and underwater undulatory velocity) and propulsion (front-crawl) also highlighted the SPM advantages in comparison to traditional statistical methods. By using SPM, researchers were able to verify specifically where within the stroke cycle significant differences were found. Therefore, coaches can get more detailed information to design specific training drills to overcome hypothetical handicaps.

Race Analysis and Determination of Stroke Frequency - Stroke Length Combinations during the 50-M Freestyle Event

The aims of this study were to: (1) analyze and compare the stroke kinematics between junior and senior elite male swimmers in every section of the race during the 50-m freestyle event, and; (2) identify stroke frequency (SF)-stroke length (SL) combinations on swim speed independently for junior and senior swimmers in each section of the 50-m freestyle event. Eighty-six junior swimmers (2019) and 95 seniors (2021) competing in the 50-m long course meter LEN Championships were analyzed. The t-test independent samples (p ≤ 0.05) were used to compare juniors and seniors. The SF and SL combinations on swim speed were explored using three-way ANOVAs. Senior swimmers were significantly faster in the 50-m race than juniors (p < 0.001). Speed presented the largest significant difference (p < 0.001) in section S0-15 m (start until the 15^th meter mark) being seniors fastest. Both junior and senior swimmers revealed a significant categorization (p < 0.001) by stroke length and stroke frequency in each race section. It was possible to model several SF-SL combinations for seniors and juniors in each section. The fastest swim speed in each section, for seniors and juniors independently, was achieved by a SF-SL combination that may not be the fastest SF or the longest SL. Coaches and swimmers must be aware that despite the 50-m event being an all-out bout, several SF-SL combinations were observed (independently for juniors and seniors), and they differ between race sections.

EFFECTS OF SPRINT INTERVAL TRAINING ON ATHLETES' SPEED

ABSTRACT Introduction: The sprint is a track and field event that combines strength and speed. It has a cyclical nature. Each muscle group involved in the starter completes hundreds of contractions. Maximum strength and the fastest muscle contraction are the best combinations. This is the explosive power of runners. Objective: Analyze the effects of sprint interval training on the speed of track and field athletes. Methods: In a random sampling, 30 college athletes were selected for the investigation. These runners were divided into experimental and control groups. The experimental group adopted the sprint interval training method with tire traction. The control group did not engage in this physical exercise. The speed training load of the athlete was gradually increased during the experimental period, lasting 10 weeks. Competitors performed short-distance tests after the physical training. Results: After ten weeks, the experimental group's performance on 30 meters was significantly improved (P<0.05). There was a statistical increase in acceleration in the 50m start (P<0.01). There was no significant difference between the experimental and control groups in the 30-meter starting speed (P>0.05). The experimental group significantly improved the 50m performance (P<0.05). Conclusion: Sprint interval training is effective in increasing the speed of athletes. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Pacing Behaviour Development and Acquisition: A Systematic Review

BACKGROUND

The goal-directed decision-making process of effort distribution (i.e. pacing) allows individuals to efficiently use energy resources as well as to manage the impact of fatigue on performance during exercise. Given the shared characteristics between pacing behaviour and other skilled behaviour, it was hypothesized that pacing behaviour would adhere to the same processes associated with skill acquisition and development.

METHODS

PubMed, Web of Science and PsycINFO databases between January 1995 and January 2022 were searched for articles relating to the pacing behaviour of individuals (1) younger than 18 years of age, or (2) repeatedly performing the same exercise task, or (3) with different levels of experience.

RESULTS

The search resulted in 64 articles reporting on the effect of age (n = 33), repeated task exposure (n = 29) or differing levels of experience (n = 13) on pacing behaviour. Empirical evidence identifies the development of pacing behaviour starts during childhood (~ 10 years old) and continues throughout adolescence. This development is characterized by an increasingly better fit to the task demands, encompassing the task characteristics (e.g. duration) and environment factors (e.g. opponents). Gaining task experience leads to an increased capability to attain a predetermined pace and results in pacing behaviour that better fits task demands.

CONCLUSIONS

Similar to skilled behaviour, physical maturation and cognitive development likely drive the development of pacing behaviour. Pacing behaviour follows established processes of skill acquisition, as repeated task execution improves the match between stimuli (e.g. task demands and afferent signals) and actions (i.e. continuing, increasing or decreasing the exerted effort) with the resulting exercise task performance. Furthermore, with increased task experience attentional capacity is freed for secondary tasks (e.g. incorporating opponents) and the goal selection is changed from achieving task completion to optimizing task performance. As the development and acquisition of pacing resemble that of other skills, established concepts in the literature (e.g. intervention-induced variability and augmented feedback) could enrich pacing research and be the basis for practical applications in physical education, healthcare, and sports.

Race analysis of the men's 50 m events at the 2021 LEN European Championships

The aim of this study was to: (i) characterise the stroke kinematics' stability of the male swimmers competing in the four 50 m events at the 2021 European Championships, and; (ii) understand the speed-time relationship in the four race events. All male swimmers who participated in the 50 m events (backstroke: 78 swimmers; breaststroke: 79 swimmers; butterfly: 89 swimmers; freestyle: 95 swimmers) were evaluated. In each swimming stroke swimmers were split in two groups (better and poorer performances). Significant variances (p < 0.05) were observed in both groups in all variables and for all swimming strokes. Swimming speed was the variable with the highest variance in both groups and strokes. Overall, better swimmers presented a low to high normative stability, and poorer swimmers a moderate-to-high. Speed-time curve fitting for all swimming strokes and groups suggested a cubic relationship. It can be considered that elite male swimmers racing 50 m sprint events at major competitions present an all-out trend. The present data provide coaches with substantial information about the main trend in the 50 m sprint events, specifically in each section of the race.

Are the 50 m Race Segments Changed From Heats to Finals at the 2021 European Swimming Championships?

This study explored in the 50 m races of the four swimming strokes the performance parameters and/or technical variables that determined the differences between swimmers who reach the finals and those who do not. A total of 322 performances retrieved from the 2021 Budapest European championships were the focus of this study. The results of the performances achieved during the finals compared to the heats showed that the best swimmers did not excel during the heats, as a significant progression of performance was observed in most of the strokes as the competition progressed. Specifically, combining men and women, the swimmers had in freestyle a mean coefficient of variation (CV) of ∼0.6%, with a mean range of performance improvement (∆%) of ∆ = ∼0.7%; in breaststroke a mean CV of ∼0.5% and ∆ = −0.2%; in backstroke a mean CV of ∼0.5% and ∆ = −0.6%, and; in butterfly a mean CV of ∼0.7% and ∆ = −0.9%. For all strokes, it was a reduction of the underwater phase with the aim of increasing its speed. However, this result was not always transferred to the final performance. In any case, most of the swimmers tried to make improvements from the start of the race up to 15 m. Furthermore, the swimmers generated an overall increase in stroke rate as the rounds progressed. However, a decrease in stroke length resulted and, this balance appeared to be of little benefit to performance.

Understanding the Role of Propulsion in the Prediction of Front-Crawl Swimming Velocity and in the Relationship Between Stroke Frequency and Stroke Length

Introduction: This study aimed to: 1) determine swimming velocity based on a set of anthropometric, kinematic, and kinetic variables, and; 2) understand the stroke frequency (SF)–stroke length (SL) combinations associated with swimming velocity and propulsion in young sprint swimmers.

Methods: 38 swimmers (22 males: 15.92 ± 0.75 years; 16 females: 14.99 ± 1.06 years) participated and underwent anthropometric, kinematic, and kinetic variables assessment. Exploratory associations between SL and SF on swimming velocity were explored using two two-way ANOVA (independent for males and females). Swimming velocity was determined using multilevel modeling.

Results: The prediction of swimming velocity revealed a significant sex effect. Height, underwater stroke time, and mean propulsion of the dominant limb were predictors of swimming velocity. For both sexes, swimming velocity suggested that SL presented a significant variation (males: F = 8.20, p < 0.001, η² = 0.40; females: F = 18.23, p < 0.001, η² = 0.39), as well as SF (males: F = 38.20, p < 0.001, η² = 0.47; females: F = 83.04, p < 0.001, η² = 0.51). The interaction between SL and SF was significant for females (F = 8.00, p = 0.001, η² = 0.05), but not for males (F = 1.60, p = 0.172, η² = 0.04). The optimal SF–SL combination suggested a SF of 0.80 Hz and a SL of 2.20 m (swimming velocity: 1.75 m s⁻¹), and a SF of 0.80 Hz and a SL of 1.90 m (swimming velocity: 1.56 m s⁻¹) for males and females, respectively. The propulsion in both sexes showed the same trend in SL, but not in SF (i.e., non-significant variation). Also, a non-significant interaction between SL and SF was observed (males: F = 0.77, p = 0.601, η² = 0.05; females: F = 1.48, p = 0.242, η² = 0.05).

Conclusion: Swimming velocity was predicted by an interaction of anthropometrics, kinematics, and kinetics. Faster velocities in young sprinters of both sexes were achieved by an optimal combination of SF–SL. The same trend was shown by the propulsion data. The highest propulsion was not necessarily associated with higher velocity achievement.