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.

Introduction

Specializing late and maintaining a large variety of sport disciplines supposedly increase the chances of top performances at elite age, limit burnout risk and reduce injury incidences (1–3). However, evidence is limited to review articles and only a low number of original investigations (4). Additionally, many findings originate from game sports (5), which profit from skill transfer between disciplines, i.e., tactical positioning and decision-making (6), and limit their implications for cgs- (centimeter-gram-second) and individual sports. Furthermore, accuracy of retrospective questionnaires on sport participation during junior age are limited to the participants' accurate memory and only assess quantity of sport variety (3, 7, 8). Assessment of the quality of within-sport variety, i.e., performance differences between the athletes' main and secondary events, may further improve knowledge of specialization and variety during long-term athlete development (LTAD).

The quality of within-sport variety can be assessed by comparing performance over various race distances. Swimming conditions are standardized during competitions, i.e., specified water temperature, limited current, exact pool length and wave breaking lane ropes (9). This allows the comparison of race results from various venues and championships to analyze variety in race distances and throughout an athlete's entire swimming career. As such, previous studies found that swimmers typically enter the sport early and, on average, accumulate 8 years of competition participation before reaching (top-) elite level (10, 11), but maintain a larger skill variety within their sport (12). Competing in more than one swimming stroke and race distance as a child may indeed improve success and medal chances at adult age, particularly for freestyle events, which provide up to six race distances from 50 m to 1,500 m (13, 14).

However, large within-sport variety may also limit performance progression, as the ever-evolving landscape of swimming and continuously improving world records (15–17) require new training and development strategies to meet the distance-specific biomechanical and physiological requirements (18, 19). Sprint swimmers in particular may benefit a more intense, race-pace specific, and less voluminous training approach to optimally transfer high stroke frequencies into propulsion, develop neuro-muscular abilities, explosivity, speed and power for start and turn performances (20–23). Compared to the over-distance oriented approach in swimming, track runners typically follow training regimes that are characterized by more under-distance training: fewer hours per week with higher training intensities (24–27). This approach may be used to reduce overuse injuries, e.g., fatigue fractures, which are associated with impact forces and high volumes of running (28). Furthermore, the majority of disciplines in track-and-field involve explosive movement patterns, which typically require under-distance oriented training regimes with higher exercise intensities (25, 27, 29).

Additionally, track runners do not maintain the same within-sport variety as swimmers, but instead specialize in either sprint-, middle- and long-distance races (12, 27, 30). Despite their very different approaches to training, pool swimming and track running share common race lengths in competition (timewise, Table 1) as well as common physiological and metabolic demands for sprint, middle- and long-distance events (33–37). Therefore, comparing LTAD and evolution of within-sport distance variety between track runners and pool swimmers may provide new insights into training and development strategies, particularly for the neuro-muscular adaptations of sprint swimmers.

Table 1

Table 1. World record times [mm:ss.00] of long-course (50 m pool length) freestyle swimming and outdoor (400 m track length) track running events (31, 32).

The aim of the present study was 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 assessed between the athletes' main and secondary race distances as well as between various performance levels. Track runners were expected to show lower variety and higher specialization on and around their main race distances.

Methods

Subjects

A total of 3,827,947 race times of freestyle pool swimmers (n = 12,588 males and n = 7,561 females) and track runners (n = 9,230 males and n = 5,841 females) representing 213 countries were obtained from the official databases of the European Aquatics (38) and World Athletics associations (39) and used for the present study. The data set of the male swimmers has previously been used as part of another study (12), however, to provide a comprehensive data set and comparison to the male runners' race data, two additional years (2022 and 2023) and another performance level (regional-class swimmers) have been added since the mentioned publication. Additionally, we have further developed the statistical model from repeated measure analysis of variance (ANOVA) to linear mixed model analysis (LMM). As only publicly available data are included and were analyzed anonymously, explicit written consent from the athletes was not required. The study protocol received prior approval from the institutional review board of the Swiss Federal Institute of Sport Magglingen (Reg.-Nr. 222_LSP_Born_03_2024) and adheres to the ethical standards outlined in the Declaration of Helsinki by the World Medical Association regarding research with human subjects.

Data collection

To compare performance progression and variety in race distances of comparable lengths (timewise, refer to Table 1) and to determine factors leading to world-class performances at peak performance age in swimming and track running regarding performance progression and quality of within-sport distance variety, only swimmers and runners still competing at peak performance age were included in the present study. Hence, all swimmers and track runners at peak performance age were extracted from the 2023 to 2016 databases and ranked based on their personal best in each particular race distance at peak performance age. Then, each individual athlete's annual best times for all race distances were retrospectively extracted until early junior age (13–14 year age category), i.e., also including the 2015–2006 databases.

Although, individual athletes may perform at the highest international level at a younger age, previous literature has shown that, on average, swimmers and track runners reach their peak performance aged 23–30 years (40, 41). Additionally, the recently introduced U23 European swimming championships, which is intended as a transition phase between international junior and senior championships (42), further supports the 23 year cut-off age that distinguishes between developing and peak performing swimmers and runners. Consistent with the Olympic events, which high-performance swimmers and runners aim for, only long-course swimming races (50 m pool length) and outdoor running races (400 m track) were considered for the present study. Freestyle provides the largest range in swimming race distances at Olympic swimming competitions (50 m to 1,500 m) and, therefore, provides a reasonable data base for the comparison with track running. Hence, only freestyle swimming races are included in the present study.

Data analysis

All race times were converted to performance points according to the official method of the world governing body in swimming. The point system expresses race times relative to the current world record (which equals 1,000 points) and allows the comparison of performances across various race distances (9), and thus, the comparison between sports. With swimming as the main point of interest for the present study, the swimming-specific point system was used for both sports to cluster swimmers and runners into different performance levels. Those performance levels were based on previous recommendations (43) and each individual athlete's personal best at peak performance age: world-class finalists (>850 performance points), international-class (850-750 performance points), national-class (750-650 performance points) and regional-class (650-550 performance points). Athletes with fewer than 550 performance points were excluded from the data analysis.

Quality of within-sport variety was then compared across six race distances (refer to Table 1) for both swimmers and runners. Performances of the athletes' main race distance (the particular race distance on which the ranking at peak performance age was based) were compared with their individual performances in the other (secondary) race distances. For the sake of clarity and to make the data tsunami of the present study more accessible for the reader, only two performance groups were used to assess the quality of variety and comparison between the athletes' main and secondary race distances: (top-) elite (1,000-750) and (highly-) trained (750-550 performance points) swimmers and runners (44). To assess longitudinal development, annual best times were averaged over two years and compared across the 13–14, 15–16, 17–18, 19–20, 21–22 and 23–30 year age categories.

The “pandas” library (version 1.5.1, pandas-dev/pandas, Zenodo, Genève, Switzerland) in Python (version 3.9.7, Python Software Foundation, Beaverton, USA) was used to compute performance points, establish rankings at peak performance age, and extract annual best times retrospectively for all included swimmers and runners. The subsequent data processing was carried out with Microsoft Excel (version 2209, Microsoft Corporation, Redmond, WA, USA). As part of the present interdisciplinary study, the data collection and analysis were led by an experienced data scientist, holding a master's degree and PhD. Scripts and procedures were validated by the other scientists involved in the project.

Statistical analysis

All data are presented as mean ± standard deviation and were analyzed with the Jamovi software package version 2.3.28.0 (Jamovi Project 2022, retrieved from https://www.jamovi.org). A diagonal straight line in the Q-Q plot and a Gaussian distribution in the histogram confirmed normally distributed standardized residuals (45). Non-normally distributed data were subjected to logarithmic transformation. Linear mixed model (LMM) analysis was used to compare performance levels (850 vs. 750 vs. 650 vs. 550 or international vs. national) across the age categories (13–14 vs. 15–16 vs. 17–18 vs. 19–20 vs. 21–22 vs. 23+) as fixed factors and athletes' performance points for the particular age category as dependent variable. Subject was added as the random factor to account for missing values in the time series. Fixed intercepts and restricted maximum likelihood (REML) were employed. Bonferroni's correction was used to correct post hoc tests for multiple pairwise comparisons. An alpha error of 0.05 determined significant differences. Since maturational growth rates are different between sexes, all analyses were conducted separately for men and women (46).

Results

Performance progression

Performance progression of pool swimmers shows a logarithmic pattern: a steeper incline during the younger age categories and a flattening off towards peak performance age. In contrast, track runners show a linear development pattern (Figure 1). The earlier performance plateau in swimmers occurs between 17 and 22 years of age, while runners typically progress until the 23 + age category (P < 0.05, refer to Figure 1 and Table 2). While performance plateaus earlier in long-distance compared to sprint- and middle-distance swimmers, this is not the case for runners. However, performance of both female regional-class swimmers and runners declined towards the 23 + age category. In male regional-class athletes, this performance decline towards the 23 + age category is particularly evident in sprint swimmers, i.e., 50 m (P < 0.001) and 100 m (P = 0.007), as well as sprint, middle- and long-distance runners, i.e., 200 m (P < 0.001), 400 m (P = 0.012), 1,500 m (P < 0.001) and 5,000 m (P < 0.001). Furthermore, female swimmers' performances differentiate between the performance levels at a younger age compared to female track runners. Specifically, female world-class finalists start showing significantly faster race times compared to international-class athletes at a younger age in swimming compared to running (P < 0.05). Similar differences in differentiation between the two sports can also be seen in male athletes (Table 2).

Figure 1

Figure 1. Progression of performance points [a.u.] over the age categories [years] of world-class finalists (850), international- (750), national- (650) and regional-class (550) female pool swimmers and track runners. Athletes were ranked based on their personal best at peak performance age in the particular race distance. Annual best times across the age categories were retrospectively extracted across all race distances and compared between performance levels and age categories with linear mixed model analysis. Significant differences are indicated compared to world-class finalists (*) and the previous age category (#).

Table 2

Table 2. Progression of performance points [a.u.] over the various age categories [years] of world-class finalists (850), international- (750), national- (650) and regional-class (550) male pool swimmers and track runners.

Quality of within-sport distance variety

While swimmers typically compete across all race distances, runners specialize in either sprint, middle- or long-distance early in their career and compete in only two, four or three other race distances, respectively. In both sports, sprinters specialize more than middle- and long-distance athletes. As such, (top-) elite female 50 m swimmers show significantly slower performances in their 200 m (P < 0.001), 400 m (P < 0.001), 800 m (P < 0.001) and 1,500 m races (P = 0.029) at peak performance age, while (top-) elite female 200 m sprint runners do not even compete over race distances of more than 800 m. (Top-) elite female 800 m and 1,500 m swimmers show significantly slower performances in their 50 m (P < 0.001), 100 m (P < 0.001) and 200 m (P < 0.001) races at peak performance age. The variety of middle-distance swimmers covers more long- rather than sprint race distances. As such, 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) (23 + age category). Runners are more specialized, as (top-) elite female 800 m middle-distance runners show significantly slower performances in all their secondary race distances, i.e., 200 m, 400 m, 1,500 m, 3,000 m (all P < 0.001; Figures 2, 3).

Figure 2

Figure 2. Progression of performance points [a.u.] of (top-) elite (1,000-750 performance points) and (highly-) trained female freestyle pool swimmers (750-550 performance points) across the various age categories [years]. Annual best times of the main race distance (dotted lines) were retrospectively extracted and compared to the individual swimmers’ annual best times of their secondary race distances using linear mixed model analysis, with ^{50, 100, 200, 400, 800} and ¹⁵⁰⁰ indicating the significant difference to the specific secondary race distance. For the sake of clarity, significant differences to the previous age category (#) are only indicated for the main race distance.

Figure 3

Figure 3. Progression of performance points [a.u.] of (top-) elite (1,000-750 performance points) and (highly-) trained female track runners (750-550 performance points) across the various age categories [years]. Annual best times of the main race distance (dotted lines) were retrospectively extracted and compared to the individual runners’ annual best times of their secondary race distances using linear mixed model analysis, with ^{200, 400, 800, 1500, 3000} and ⁵⁰⁰⁰ indicating significant differences to specific secondary race distances. For the sake of clarity, significant differences to the previous age category (#) are only indicated for the main race distance.

(Top-) elite female athletes specialize more than (highly-) trained female athletes in both sports. While (top-) elite swimmers still compete across all six race distances at peak performance age (23 + age category), they show more significantly slower secondary race distances compared to their main race distance than (highly-) trained swimmers (P < 0.05). Specifically, (top-) elite female 100 m swimmers show significantly slower 50 m (P = 0.002), 200 m (P = 0.008), 400 m (P < 0.001) and 800 m (P < 0.001) race times, while (highly-) trained female 100 m swimmers only show significantly slower 50 m (P < 0.001) and 400 m (P < 0.001) race times at peak performance age. The same effect of greater specialization in higher ranked athletes is apparent in male swimmers. (Top-) elite female runners generally compete in a lower number of secondary race distances compared to (highly-) trained runners. For instance, (top-) elite female 400 m runners also compete in 200 m and 800 m races, while (highly-) trained 400 m runners compete in 200 m–1,500 m races. (Top-) elite female 5,000 m runners compete over all race distances from 800 m to 5,000 m, while (highly-) trained 5,000 m runners compete over all race distances from 400 m to 5,000 m. Male runners showed the opposite trend, with (top-) elite 800 m, 1,500 m and 5,000 m runners competing in more secondary race distances than (highly-) trained runners (Tables 3, 4).

Table 3

Table 3. Progression of performance points [a.u.] of (top-) elite (1,000-750 performance points) and (highly-) trained male freestyle pool swimmers (750-550 performance points) across the various age categories [years].

Table 4

Table 4. Progression of performance points [a.u.] of (top-) elite (1,000-750 performance points) and (highly-) trained male track runners (750-550 performance points) across the various age categories [years].

Discussion

The main findings of the present study are that swimmers' performances differentiate between world-class athletes and their lower ranked peers at an earlier age compared to that of runners. Due to the logarithmic development pattern, performance of swimmers plateaus earlier towards senior age compared to that of runners, which show a linear development pattern. Swimmers also show a greater within-sport distance variety and compete across all six race distances, whereas runners focus on only three to five race distances. While sprinters specialize most in both sports, within-sport variety of middle-distance swimmers covers more long-distance races rather than sprints. Additionally, (top-) elite swimmers specialize more than (highly-) trained swimmers.

Performance development

Swimmers show a larger within-sport variety and compete in more events alongside their main race distance than runners, who specialize more and compete in fewer secondary race distances. As swimming technique is a dominant key performance indicator and important contributing factor to swimming performance, swimmers are able to compete over a greater variety of race distances within their specific swimming stroke than runners (14). The large contribution of swimming technique may also explain the logarithmic development pattern. Quick learning experience of technical elements may add to the physical development and allow for the steep performance curve at young age, which has also previously been shown in backstroke swimmers (47). In contrast, track runners primarily rely on physical fitness, and thus, develop linearly with later differentiation between performance levels (27, 29). The present findings are also supported by previous sprint data of track runners, which showed a similar linear development pattern after 10 years of age (48).

Neuro-muscular aspects of sprint swimming

The present study shows that sprint swimmers specialize more than middle- and long-distance swimmers. Additionally, specialization affects performance level, as (top-) elite show earlier and greater specialization compared to (highly-) trained sprint swimmers. This higher degree in specialization may be due to the specific technical, metabolic and neuro-muscular demands that are required for world-class success (18, 19, 49). The neuro-muscular abilities have gained particular importance over the last decade, as they enhance in-water force production (50, 51) as well as start and turn performances, which have been shown to be distinguishing factors for swim races (52–55). As such, the push-off from a solid base during starts and turns allows swimmers to translate their full capacity of maximal strength and power into propulsion (20, 56). Therefore, the early introduction to dry-land training and strength-and-conditioning regimes is particularly important for sprint swimmers (21, 57) and may explain the earlier and higher degree of specialization in (top-) elite compared to (highly-) trained sprint swimmers. Furthermore, the scientific literature supports the early introduction of children and adolescents to strength training and shows that both sexes gain substantial strength even before puberty (58, 59). Additionally, establishing a solid technical foundation in regard to dry-land exercises and barbell lifting techniques during early stages of their swimming careers prepares sprinters for heavy lifts and minimizes injury risk at late junior and senior age (59, 60).

Technical elements

In addition to the physiological and neuro-muscular specificities of sprint races, technical elements should be considered when discussing differences in specialization pattern. Swimming is traditionally considered an endurance sport, which requires high training volumes to maximize aerobic capacity and ingrain movement patterns specific to swimming technique (26). The relatively low density of water requires a progressively increasing hand velocity during the arm stroke, with minimal adjustments affecting optimal usage of the hydrodynamic lift, to allow for effective water-based movement (61, 62). Based on the fact that swimming involves different conversion of metabolic to mechanical power than land-based movement patterns, i.e., running (19), large training volumes are commonly prescribed to maximize the technical learning experience (23, 26). This aerobic culture of the sport may provide good development opportunities for middle-distance swimmers, whose distance variety covers more long-distance rather than sprint race. However, the higher velocities during sprint races require a higher cadence, which swimmers must translate into propulsion without losing traction, control of the water resistance and hydrodynamic lift during the in-water phase of the arm stroke (63–65). Additionally, the high cadence of sprint swimming does not allow for a completely relaxed arm and strict high elbow position during the overwater phase of the arm stroke. Instead, sprinter swing their arm forward with a more extended elbow and show fewer intra-cyclic gliding phases compared to middle- and long-distance swimmers (66, 67). This sprint-specific swimming technique, e.g., underwater hand trajectories, arm stroke, shoulder and hip roll at a high cadence (65, 68), are unlikely to develop during long-slow distance training. Instead, aerobic sessions should only be used to provide sufficient ability to recovery and to improve resilience to maximize volume of velocity-specific sets, i.e., 15–25 m bouts at and above race-pace (24, 69).

Aerobic aspects and over-distance training

Throughout the development process, junior swimmers are typically developed from longer to shorter race distances, due to the over-distance oriented training approach (26, 70). In contrast, track sprinters (runners) aim for shorter sprints at younger ages and progress to longer race distances throughout adolescence in many countries, i.e., 60 m (U14), 80 m (U16) and 100 m [U18 and older (71, 72)]. While maximal sprint velocity and basic speed abilities can be developed from an early age in an athlete's career, insights from other endurance sports show that the aerobic capacity develops over consecutive years of training and that resilience for longer race distances increases with ages (73–75). Due to the technical challenge of translating a high stroke rate into propulsion (63–65), sprint swimmers in particular may benefit from an early focus on sprint speed, while their aerobic capacity could develop over the years of training (73, 75).

Other disciplines in the sport

Training regimes are also affected by the other disciplines in the sport. Since most track and field disciplines rely on explosive strength, i.e., jumping, throwing and sprinting, the general athletic education is more oriented towards strength, speed and power. In contrast, with race times of more than one minute, the majority of swimming events rely mostly on aerobic energy contribution (33, 34), which explains the over-distance oriented training approaches (26). However, with the increasing importance of the acyclic elements, i.e., start and turn (52, 53, 55), placing more focus on dry-land training strategies from an early stage in swimming careers may help develop specific key performance indicators (21, 57). Future research should determine optimal ratios between dry-land and specific pool-based training, as well as volume and intensity during in-water sessions (21, 57, 76). This is also important in regard to the earlier performance plateaus evident in swimmers compared to runners, which indicates that key performance indicators related to swimming-specific physical fitness have not yet reached their full potential. Earlier and greater specialization on specific race distances and further development of strength and conditioning regimes may improve swimming performances and even world records in the future.

Study limitations

The present study is based on race results that only allow retrospective analyses of performance pattern. By clustering performance groups, i.e., world-class finalists, international-, national- and regional-class athletes, in two sports, we aimed to discover the most promising development strategies, draw parallels and highlight differences between swimming and running. However, although development strategies of world-class athletes may have worked well in the past, they may not be the ideal for the future. Thus, the results of the present study can only provide a starting point for the development of new training strategies.

Infrastructural aspects need to be considered as well and may affect the development of optimal swimming technique (77). Compared to track runners, who have large track-and-field grounds and the option to complete aerobic sets, i.e., long-slow distance training, outside the running track, swimming pools are less spacious training facilities. In some countries, swimming clubs and teams have to share pools with the public and lane use is limited. Particularly at junior age, before swimmers train in national training centers (24, 70), sprint talents cannot be placed in a separate training group—due to infrastructure constraints—and often train in the same lane as middle-distance and long-distance swimmers. Hence, the volume-oriented programs do not allow for optimal training differentiation and development of sprint abilities. Even with knowledge of optimal development strategies, infrastructural aspects need to be considered and will affect knowledge transfer into the daily training and competition routine of coaches and athletes.

Conclusion

Although, sprinters specialize more than middle- and long-distance swimmers, as do (top-) elite compared to (highly-) trained swimmers, the comparison to similar race distances (timewise) in track running indicates that swimmers have a larger within-sport distance variety compared to runners. The early performance plateau towards senior age, the large within-sport distance-variety and high reliance of key performance indicators on technical elements indicate that swimmers may not yet use their full physiological potential.

Since the majority of events favors aerobic and over-distance training, the landscape of competitive swimming does not provide an optimal environment for the development of sprint swimmers. The comparison with track running indicates that sprint swimmers may require an even earlier and higher degree of specialization with more attention to explosivity-oriented training regimes and frequent race-pace specific training sessions.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by institutional review board of the Swiss Federal Institute of Sport Magglingen (Reg.-Nr. 222_LSP_Born_03_2024). The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee waived the requirement of written informed consent for participation as only publicly available data are included and were analyzed anonymously. Explicit written consent from the athletes was not required.

Author contributions

D-PB: Conceptualization, Data curation, Formal Analysis, Investigation, Funding acquisition, Visualization, Writing – original draft. MR: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Visualization, Writing – original draft. JL: Conceptualization, Data curation, Formal Analysis, Writing – review & editing. DZ: Data curation, Writing – review & editing. AF: Conceptualization, Data curation, Investigation, Writing – review & editing. JR-N: Conceptualization, Data curation, Formal Analysis, Investigation, Visualization, Writing – original draft.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.

The study was financed by the research grants of Swiss Olympic (national governing body of sports) and the Swiss Swimming Federation [ZI70B1020071 RLS 3M/SwissAqua/KPI]. However, the funder had no role in the data collection, data analysis, or data interpretation, nor any right to approve or disapprove publication of the final manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

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References

1. Myer GD, Jayanthi N, Difiori JP, Faigenbaum AD, Kiefer AW, Logerstedt D, et al. Sports specialization, part II: alternative solutions to early sport specialization in youth athletes. Sports Health. (2016) 8:65–73. doi: 10.1177/1941738115614811

PubMed Abstract | Crossref Full Text | Google Scholar

2. Myer GD, Jayanthi N, Difiori JP, Faigenbaum AD, Kiefer AW, Logerstedt D, et al. Sport specialization, part I: does early sports specialization increase negative outcomes and reduce the opportunity for success in young athletes? Sports Health. (2015) 7:437–42. doi: 10.1177/1941738115598747

PubMed Abstract | Crossref Full Text | Google Scholar

3. Orsolic M, Barbaros P, Novak D. What makes A grand slam champion? Early engagement, late specialization and timely transition from having fun to dedication. Front Sports Act Living. (2023) 5:1213317. doi: 10.3389/fspor.2023.1213317

PubMed Abstract | Crossref Full Text | Google Scholar

4. Mosher A, Fraser-Thomas J, Baker J. What defines early specialization: a systematic review of literature. Front Sports Act Living. (2020) 2:596229. doi: 10.3389/fspor.2020.596229

PubMed Abstract | Crossref Full Text | Google Scholar

5. Barth M, Gullich A, Macnamara BN, Hambrick DZ. Predictors of junior versus senior elite performance are opposite: a systematic review and meta-analysis of participation patterns. Sports Med. (2022) 52:1399–416. doi: 10.1007/s40279-021-01625-4

PubMed Abstract | Crossref Full Text | Google Scholar

6. Causer J, Ford PR. Decisions, decisions, decisions": transfer and specificity of decision-making skill between sports. Cogn Process. (2014) 15:385–9. doi: 10.1007/s10339-014-0598-0

PubMed Abstract | Crossref Full Text | Google Scholar

7. Güllich A. Many roads lead to Rome–developmental paths to Olympic gold in men’s field hockey. Eur J Sport Sci. (2014) 14:763–71. doi: 10.1080/17461391.2014.905983

Crossref Full Text | Google Scholar

8. Charbonnet B, Schmid MJ, Orencik M, Van Niekerk E, Conzelmann A. The road to excellence in women’s football: a retrospective cohort study over the last 30 years with Swiss national players. Sci Med Footb. (2023):1–12. doi: 10.1080/24733938.2023.2279531

PubMed Abstract | Crossref Full Text | Google Scholar

9. World Aquatics. Swimming Points and Rules. Lausanne: Fédération Internationale de Natation (2023). Available online at: https://www.worldaquatics.com/swimming/points and https://www.worldaquatics.com/rules (Accessed July 27, 2023).

Google Scholar

10. Post AK, Koning RH, Visscher C, Elferink-Gemser MT. Multigenerational performance development of male and female top-elite swimmers-A global study of the 100 m freestyle event. Scand J Med Sci Sports. (2020) 30:564–71. doi: 10.1111/sms.13599

PubMed Abstract | Crossref Full Text | Google Scholar

11. Born DP, Stäcker I, Romann M, Stöggl T. Competition age: does it matter for swimmers? BMC Res Notes. (2022) 15:82. doi: 10.1186/s13104-022-05969-6

PubMed Abstract | Crossref Full Text | Google Scholar

12. Born DP, Bjorklund G, Lorentzen J, Stoggl T, Romann M. Specialize early and select late: performance trajectories of world-class finalists and international- and national-class swimmers. Int J Sports Physiol Perform. (2024) 19:164–72. doi: 10.1123/ijspp.2023-0171

PubMed Abstract | Crossref Full Text | Google Scholar

13. Staub I, Zinner C, Bieder A, Vogt T. Within-sport specialisation and entry age as predictors of success among age group swimmers. Eur J Sport Sci. (2020) 20:1160–7. doi: 10.1080/17461391.2019.1702107

PubMed Abstract | Crossref Full Text | Google Scholar

14. Stewart AM, Hopkins WG. Consistency of swimming performance within and between competitions. Med Sci Sports Exerc. (2000) 32:997–1001. doi: 10.1097/00005768-200005000-00018

PubMed Abstract | Crossref Full Text | Google Scholar

15. Mazzilli F. Body height and swimming performance in 50 and 100 m freestyle Olympic and world championship swimming events: 1908–2016. J Hum Kinet. (2019) 66:205–13. doi: 10.2478/hukin-2018-0068

PubMed Abstract | Crossref Full Text | Google Scholar

16. Knechtle B, Dalamitros AA, Barbosa TM, Sousa CV, Rosemann T, Nikolaidis PT. Sex differences in swimming disciplines-can women outperform men in swimming? Int J Environ Res Public Health. (2020) 17:1–17. doi: 10.3390/ijerph17103651

Crossref Full Text | Google Scholar

17. Konig S, Valeri F, Wild S, Rosemann T, Rust CA, Knechtle B. Change of the Age and performance of swimmers across world championships and Olympic games finals from 1992 to 2013—a cross-sectional data analysis. Springerplus. (2014) 3:652. doi: 10.1186/2193-1801-3-652

PubMed Abstract | Crossref Full Text | Google Scholar

18. Fernandes RJ, Carvalho DD, Figueiredo P. Training zones in competitive swimming: a biophysical approach. Front Sports Act Living. (2024) 6:1363730. doi: 10.3389/fspor.2024.1363730

PubMed Abstract | Crossref Full Text | Google Scholar

19. Zamparo P, Cortesi M, Gatta G. The energy cost of swimming and its determinants. Eur J Appl Physiol. (2020) 120:41–66. doi: 10.1007/s00421-019-04270-y

PubMed Abstract | Crossref Full Text | Google Scholar

20. West DJ, Owen NJ, Cunningham DJ, Cook CJ, Kilduff LP. Strength and power predictors of swimming starts in international sprint swimmers. J Strength Cond Res. (2011) 25:950–5. doi: 10.1519/JSC.0b013e3181c8656f

PubMed Abstract | Crossref Full Text | Google Scholar

21. Bishop C, Cree J, Read P, Chavda S, Edwards M, Turner A. Strength and conditioning for sprint swimming. Strength Cond J. (2013) 35:1–6. doi: 10.1519/SSC.0000000000000019

Crossref Full Text | Google Scholar

22. Seifert L, Chollet D, Bardy BG. Effect of swimming velocity on arm coordination in the front crawl: a dynamic analysis. J Sports Sci. (2004) 22:651–60. doi: 10.1080/02640410310001655787

PubMed Abstract | Crossref Full Text | Google Scholar

23. Nugent F, Comyns T, Kearney P, Warrington G. Ultra-short race-pace training (USRPT) in swimming: current perspectives. Open Access J Sports Med. (2019) 10:133–44. doi: 10.2147/OAJSM.S180598

PubMed Abstract | Crossref Full Text | Google Scholar

24. Pollock S, Gaoua N, Johnston MJ, Cooke K, Girard O, Mileva KN. Training regimes and recovery monitoring practices of elite British swimmers. J Sports Sci Med. (2019) 18:577–85.31427881

PubMed Abstract | Google Scholar

25. Haugen T, Sandbakk O, Seiler S, Tonnessen E. The training characteristics of world-class distance runners: an integration of scientific literature and results-proven practice. Sports Med Open. (2022) 8:46. doi: 10.1186/s40798-022-00438-7

PubMed Abstract | Crossref Full Text | Google Scholar

26. Nugent FJ, Comyns TM, Warrington GD. Quality versus quantity debate in swimming: perceptions and training practices of expert swimming coaches. J Hum Kinet. (2017) 57:147–58. doi: 10.1515/hukin-2017-0056

PubMed Abstract | Crossref Full Text | Google Scholar

27. Haugen T, Sandbakk O, Enoksen E, Seiler S, Tonnessen E. Crossing the golden training divide: the science and practice of training world-class 800- and 1500-m runners. Sports Med. (2021) 51:1835–54. doi: 10.1007/s40279-021-01481-2

PubMed Abstract | Crossref Full Text | Google Scholar

28. Warden SJ, Davis IS, Fredericson M. Management and prevention of bone stress injuries in long-distance runners. J Orthop Sports Phys Ther. (2014) 44:749–65. doi: 10.2519/jospt.2014.5334

PubMed Abstract | Crossref Full Text | Google Scholar

29. Haugen T, Seiler S, Sandbakk O, Tonnessen E. The training and development of elite sprint performance: an integration of scientific and best practice literature. Sports Med Open. (2019) 5:44. doi: 10.1186/s40798-019-0221-0

PubMed Abstract | Crossref Full Text | Google Scholar

30. Brustio PR, Rainoldi A, Boccia G. Two is better than one: successful world-class sprinters compete in two disciplines. J Funct Morphol Kinesiol. (2023) 8:52. doi: 10.3390/jfmk8020052

PubMed Abstract | Crossref Full Text | Google Scholar

31. World Athletics. World Records. Monaco: World Athletics (2024). Available online at: https://worldathletics.org/records/by-category/world-records (Accessed April 08, 2024).

Google Scholar

32. World Aquatics. World Records. Lausanne: World Aquatics (2024). Available online at: https://www.worldaquatics.com/swimming/records (Accessed April 08, 2024).

Google Scholar

33. Spencer MR, Gastin PB. Energy system contribution during 200- to 1500-m running in highly trained athletes. Med Sci Sports Exerc. (2001) 33:157–62. doi: 10.1097/00005768-200101000-00024

PubMed Abstract | Crossref Full Text | Google Scholar

34. Duffield R, Dawson B, Goodman C. Energy system contribution to 400-metre and 800-metre track running. J Sports Sci. (2005) 23:299–307. doi: 10.1080/02640410410001730043

PubMed Abstract | Crossref Full Text | Google Scholar

35. Duffield R, Dawson B, Goodman C. Energy system contribution to 100-m and 200-m track running events. J Sci Med Sport. (2004) 7:302–13. doi: 10.1016/s1440-2440(04)80025-2

PubMed Abstract | Crossref Full Text | Google Scholar

36. Massini DA, Almeida TAF, Macedo AG, Espada MC, Reis JF, Alves FJB, et al. Sex-specific accumulated oxygen deficit during short- and middle-distance swimming performance in competitive youth athletes. Sports Med Open. (2023) 9:49. doi: 10.1186/s40798-023-00594-4

PubMed Abstract | Crossref Full Text | Google Scholar

37. Ribeiro J, Figueiredo P, Sousa A, Monteiro J, Pelarigo J, Vilas-Boas JP, et al. VO(2) kinetics and metabolic contributions during full and upper body extreme swimming intensity. Eur J Appl Physiol. (2015) 115:1117–24. doi: 10.1007/s00421-014-3093-5

PubMed Abstract | Crossref Full Text | Google Scholar

38. Swimrankings.Net. (2024). Available online at: https://www.swimrankings.net/ (Accessed February 15, 2024).

Google Scholar

39. World Athletics. Stats Zone. Monaco: World Athletics (2024). Available online at: https://worldathletics.org/stats-zone (Accessed February 15, 2024).

Google Scholar

40. Allen SV, Vandenbogaerde TJ, Hopkins WG. Career performance trajectories of Olympic swimmers: benchmarks for talent development. Eur J Sport Sci. (2014) 14:643–51. doi: 10.1080/17461391.2014.893020

PubMed Abstract | Crossref Full Text | Google Scholar

41. Haugen TA, Solberg PA, Foster C, Moran-Navarro R, Breitschadel F, Hopkins WG. Peak age and performance progression in world-class track-and-field athletes. Int J Sports Physiol Perform. (2018) 13:1122–9. doi: 10.1123/ijspp.2017-0682

PubMed Abstract | Crossref Full Text | Google Scholar

42. European Aquatics. European U23 Swimming Championships. Nyon, Switzerland: Ligue Européenne de Natation (2023). Available online at: https://www.len.eu/get-to-know-the-european-u23-swimming-championships/ (Accessed March 07, 2024).

Google Scholar

43. Ruiz-Navarro JJ, Lopez-Belmonte O, Gay A, Cuenca-Fernandez F, Arellano R. A new model of performance classification to standardize the research results in swimming. Eur J Sport Sci. (2022) 23(4):478–88. doi: 10.1080/17461391.2022.2046174

PubMed Abstract | Crossref Full Text | Google Scholar

44. Mckay AKA, Stellingwerff T, Smith ES, Martin DT, Mujika I, Goosey-Tolfrey VL, et al. Defining training and performance caliber: a participant classification framework. Int J Sports Physiol Perform. (2022) 17:317–31. doi: 10.1123/ijspp.2021-0451

PubMed Abstract | Crossref Full Text | Google Scholar

45. Field A. Discovering Statistics Using IBM SPSS Statistics. Los Angeles: Sage Publications Ltd (2013).

Google Scholar

46. Shapiro JR, Klein SL, Morgan R. Stop ‘controlling’ for sex and gender in global health research. BMJ Glob Health. (2021) 6:1–3. doi: 10.1136/bmjgh-2021-005714

Crossref Full Text | Google Scholar

47. Alshdokhi K, Petersen C, Clarke J. Improvement and variability of adolescent backstroke swimming performance by age. Front Sports Act Living. (2020) 2:46. doi: 10.3389/fspor.2020.00046

PubMed Abstract | Crossref Full Text | Google Scholar

48. Romann M, Cobley S. Relative age effects in athletic sprinting and corrective adjustments as a solution for their removal. PLoS One. (2015) 10:e0122988. doi: 10.1371/journal.pone.0122988

PubMed Abstract | Crossref Full Text | Google Scholar

49. Sanders RH, Takagi H, Vilas-Boas JP. How technique modifications in elite 100 m swimmers might improve front crawl performances to podium levels: swimming ‘chariots of fire’. Sports Biomech. (2023) 22:1532–51. doi: 10.1080/14763141.2021.1998590

PubMed Abstract | Crossref Full Text | Google Scholar

50. Crowley E, Harrison AJ, Lyons M. The impact of resistance training on swimming performance: a systematic review. Sports Med. (2017) 47:2285–307. doi: 10.1007/s40279-017-0730-2

PubMed Abstract | Crossref Full Text | Google Scholar

51. Miras-Moreno S, Lopez-Belmonte O, Garcia-Ramos A, Arellano R, Ruiz-Navarro JJ. Which strength manifestation is more related to regional swimmers’ performance and in-water forces? Maximal neuromuscular capacities versus maximal mechanical maintenance capacity. Int J Sports Physiol Perform. (2024) 19(6):608–19. doi: 10.1123/ijspp.2023-0475

PubMed Abstract | Crossref Full Text | Google Scholar

52. Arellano R, Ruiz-Navarro JJ, Barbosa TM, Lopez-Contreras G, Morales-Ortiz E, Gay A, et al. Are the 50 m race segments changed from heats to finals at the 2021 European swimming championships? Front Physiol. (2022) 13:797367. doi: 10.3389/fphys.2022.797367

PubMed Abstract | Crossref Full Text | Google Scholar

53. Born DP, Romann M, Stöggl T. Start fast, swim faster, turn fastest: section analyses and normative data for individual medley. J Sports Sci Med. (2022) 21:233–44. doi: 10.52082/jssm.2022.233

PubMed Abstract | Crossref Full Text | Google Scholar

54. Polach M, Thiel D, Krenik J, Born DP. Swimming turn performance: the distinguishing factor in 1500 m world championship freestyle races? BMC Res Notes. (2021) 14:248. doi: 10.1186/s13104-021-05665-x

PubMed Abstract | Crossref Full Text | Google Scholar

55. Morais JE, Barbosa TM, Silva AJ, Veiga S, Marinho DA. Profiling of elite male junior 50 m freestyle sprinters: understanding the speed-time relationship. Scand J Med Sci Sports. (2022) 32:60–8. doi: 10.1111/sms.14058

PubMed Abstract | Crossref Full Text | Google Scholar

56. Tor E, Pease DL, Ball KA. Key parameters of the swimming start and their relationship to start performance. J Sports Sci. (2015) 33:1313–21. doi: 10.1080/02640414.2014.990486

PubMed Abstract | Crossref Full Text | Google Scholar

57. Nugent FJ, Comyns TM, Warrington GD. Strength and conditioning considerations for youth swimmers. Strength Cond J. (2018) 40:31–9. doi: 10.1519/ssc.0000000000000368

Crossref Full Text | Google Scholar

58. Burt LA, Naughton GA, Greene DA, Courteix D, Ducher G. Non-elite gymnastics participation is associated with greater bone strength, muscle size, and function in pre- and early pubertal girls. Osteoporos Int. (2012) 23:1277–86. doi: 10.1007/s00198-011-1677-z

PubMed Abstract | Crossref Full Text | Google Scholar

59. Granacher U, Lesinski M, Busch D, Muehlbauer T, Prieske O, Puta C, et al. Effects of resistance training in youth athletes on muscular fitness and athletic performance: a conceptual model for long-term athlete development. Front Physiol. (2016) 7:164. doi: 10.3389/fphys.2016.00164

PubMed Abstract | Crossref Full Text | Google Scholar

60. Behm DG, Young JD, Whitten JHD, Reid JC, Quigley PJ, Low J, et al. Effectiveness of traditional strength vs. power training on muscle strength, power and speed with youth: a systematic review and meta-analysis. Front Physiol. (2017) 8:423. doi: 10.3389/fphys.2017.00423

PubMed Abstract | Crossref Full Text | Google Scholar

61. Vilas-Boas JP, Ramos RJ, Fernandes RJ, Silva AJ, Rouboa AI, Machado L, et al. Hydrodynamic analysis of different finger positions in swimming: a computational fluid dynamics approach. J Appl Biomech. (2015) 31:48–55. doi: 10.1123/jab.2013-0296

PubMed Abstract | Crossref Full Text | Google Scholar

62. Van Houwelingen J, Schreven S, Smeets JB, Clercx HJ, Beek PJ. Effective propulsion in swimming: grasping the hydrodynamics of hand and arm movements. J Appl Biomech. (2017) 33:87–100. doi: 10.1123/jab.2016-0064

PubMed Abstract | Crossref Full Text | Google Scholar

63. Valkoumas I, Gourgoulis V, Aggeloussis N, Antoniou P. The influence of an 11-week resisted swim training program on the inter-arm coordination in front crawl swimmers. Sports Biomech. (2023) 22:940–52. doi: 10.1080/14763141.2020.1770324

PubMed Abstract | Crossref Full Text | Google Scholar

64. Valkoumas I, Gourgoulis V. Sprint resisted swimming training effect on the swimmer’s hand orientation angles. J Biomech. (2024) 164:111991. doi: 10.1016/j.jbiomech.2024.111991

PubMed Abstract | Crossref Full Text | Google Scholar

65. Samson M, Monnet T, Bernard A, Lacouture P, David L. Kinematic hand parameters in front crawl at different paces of swimming. J Biomech. (2015) 48:3743–50. doi: 10.1016/j.jbiomech.2015.07.034

PubMed Abstract | Crossref Full Text | Google Scholar

66. Seifert L, Leblanc H, Chollet D, Delignieres D. Inter-limb coordination in swimming: effect of speed and skill level. Hum Mov Sci. (2010) 29:103–13. doi: 10.1016/j.humov.2009.05.003

PubMed Abstract | Crossref Full Text | Google Scholar

67. Nikodelis T, Kollias I, Hatzitaki V. Bilateral inter-arm coordination in freestyle swimming: effect of skill level and swimming speed. J Sports Sci. (2005) 23:737–45. doi: 10.1080/02640410400021955

PubMed Abstract | Crossref Full Text | Google Scholar

68. Andersen JT, Sinclair PJ, Mccabe CB, Sanders RH. Kinematic differences in shoulder roll and hip roll at different front crawl speeds in national level swimmers. J Strength Cond Res. (2020) 34:20–5. doi: 10.1519/JSC.0000000000003281

PubMed Abstract | Crossref Full Text | Google Scholar

69. Aspenes ST, Karlsen T. Exercise-Training intervention studies in competitive swimming. Sports Med. (2012) 42:527–43. doi: 10.2165/11630760-000000000-00000

PubMed Abstract | Crossref Full Text | Google Scholar

70. Lang M, Light R. Interpreting and implementing the long term athlete development model: english swimming Coaches’ views on the (swimming) LTAD in practice. Int J Sports Sci Coach. (2010) 5:389–402. doi: 10.1260/1747-9541.5.3.389

Crossref Full Text | Google Scholar

71. Swiss Athletics. Rules. Ittigen: Swiss Athletics Federation (2024). Available online at: https://swiss-athletics.ch/ (Accessed April 25, 2024).

Google Scholar

72. European Athletics. Regulations. Lausanne: European Athletics (2024). Available online at: https://www.european-athletics.com/competitions/competition-regulations (Accessed April 25, 2024).

Google Scholar

73. Ross A, Leveritt M. Long-term metabolic and skeletal muscle adaptations to short-sprint training: implications for sprint training and tapering. Sports Med. (2001) 31:1063–82. doi: 10.2165/00007256-200131150-00003

PubMed Abstract | Crossref Full Text | Google Scholar

74. Allen SV, Hopkins WG. Age of peak competitive performance of elite athletes: a systematic review. Sports Med. (2015) 45:1431–41. doi: 10.1007/s40279-015-0354-3

PubMed Abstract | Crossref Full Text | Google Scholar

75. Walther J, Haugen T, Solli GS, Tonnessen E, Sandbakk O. From juniors to seniors: changes in training characteristics and aerobic power in 17 world-class cross-country skiers. Front Physiol. (2023) 14:1288606. doi: 10.3389/fphys.2023.1288606

PubMed Abstract | Crossref Full Text | Google Scholar

76. Cuenca-Fernandez F, Boullosa D, Ruiz-Navarro JJ, Gay A, Morales-Ortiz E, Lopez-Contreras G, et al. Lower fatigue and faster recovery of ultra-short race pace swimming training sessions. Res Sports Med. (2023) 31:21–34. doi: 10.1080/15438627.2021.1929227

PubMed Abstract | Crossref Full Text | Google Scholar

77. De Bosscher V. A mixed methods approach to compare elite sport policies of nations. A critical reflection on the use of composite indicators in the SPLISS study. Sport in Society. (2018) 21:331–55. doi: 10.1080/17430437.2016.1179729

Crossref Full Text | Google Scholar