DEMYSTIFYING SWIMMING TALENT Aylin Post
Demystifying swimming talent Aylin Post
The studies in this thesis were conducted within the Health in Context Research Institute of the University Medical Center Groningen (UMCG) and under auspices of the research program Smart Movements (SMART) at the Center of Human Movement Sciences, part of the University Medical Center Groningen, University of Groningen, the Netherlands. The printing of this thesis was financially supported by the Graduate School of Medical Sciences (GSMS) of the UMCG and the University of Groningen, InnoSportLab de Tongelreep and the Dutch Swimming Federation (KNZB). Paranymphs: Daniëlle Brouwer and Rianne van Raaij Cover design: ©evelienjagtman.com Layout: Ridderprint | www.ridderprint.nl Printed by: Ridderprint | www.ridderprint.nl © Copyright 2024 by Aylin Post All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic and mechanical, including photocopying, recording or any information storage or retrieval system, without written permission from the author.
Demystifying swimming talent Proefschrift ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen op gezag van de rector magnificus prof. dr. ir. J.M.A. Scherpen en volgens besluit van het College voor Promoties. De openbare verdediging zal plaatsvinden op woensdag 2 oktober 2024 om 14.30 uur door Aylin Kim Post geboren op 13 september 1992
Promotores Dr. M.T. Elferink-Gemser Prof. dr. C. Visscher Prof. dr. R.H. Koning Beoordelingscommissie Prof. dr. S. Cobley Prof. dr. M. Lenoir Prof. dr. K.A.P.M. Lemmink
TABLE OF CONTENTS Chapter 1 General introduction 7 Chapter 2 Multigenerational performance development of male and female top-elite swimmers: A global study of the 100 m freestyle event 17 Chapter 3 Interim Performance Progression (IPP) during consecutive season best performances of talented swimmers 37 Chapter 4 Pacing behavior development in adolescent swimmers: A largescale longitudinal data analysis 59 Chapter 5 The importance of reflection and evaluation processes in daily training sessions for progression toward elite level swimming performance 83 Chapter 6 Tracking performance and its underlying characteristics in talented swimmers: A longitudinal study during the junior-to-senior transition 109 Chapter 7 Growing up and reaching for the top: A longitudinal study on swim performance and its underlying characteristics in talented swimmers 143 Chapter 8 General discussion 175 Appendices Summary 194 Nederlandse samenvatting 198 Dankwoord 202 About the author 208 List of publications 210
Chapter 1 General introduction
8 Chapter 1 While champions thrive, a new wave of future stars quietly rises As a sport known for its close margins where differences of just 0.03 seconds can determine victory or defeat, competitive swimming has a long history of success in The Netherlands. With an impressive tally of 62 Olympic medals up to 2024 (Olympian Database, 2024), Dutch swimmers have firmly established themselves as a force to be reckoned with, even when competing against swimming giants like the USA and Australia. Yet, we should not take this rich tradition of achievement for granted. In a relatively small country like the Netherlands, the pool of potential world-leading swimmers is limited. Typically, there are only about 16 swimmers every four years who successfully navigate the long and challenging path that leads to participation in the Olympic Games (Olympian Database, 2024). Among them, a few get the opportunity to compete for top positions on the podium, following the footsteps of icons like Pieter van den Hoogenband, Inge de Bruijn and Ranomi Kromowidjojo. Given this context, and even in the midst of remarkable achievements in the past and present, it is important to direct our attention towards the future. Who will be the next generation to rise and uphold the nation’s high standard of performance, and how can we best guide them on the journey towards the elite level? In other words: if we aspire to maintain our strong standing on the global swimming stage, we must truly excel in our efforts of finding and nurturing the upcoming wave of swimming talent. Talent identification and development in Dutch swimming Starting at the age of twelve, the Royal Dutch Swimming Federation (KNZB) initiates its endeavor to identify talented swimmers, aiming to provide them with optimal learning environments to accelerate or realize their potential towards swimming expertise (KNZB, 2024; Williams & Reilly, 2000). These talent development programs are designed to support promising swimmers with various benefits, including expert coaching, improved facilities, and the chance to train alongside other talented peers (KNZB, 2024). Unfortunately, due to capacity limitations in the talent identification and development (TID) system, coaches must decide who receives additional developmental opportunities, a privilege limited to a small group of selected swimmers only (Till et al., 2020). The main source of information to make these selections are the swimmers’ season best times and how they rank nationally. This approach appears reasonable given that swimming is fundamentally about travelling a certain distance in the water as fast as possible (Barbosa et al., 2010a). Moreover, thanks to advancements in technology like electronic timekeeping and the availability of competition data online (Swimrankings, 2024; World Aquatics, 2024), collecting this information is both reliable and straightforward. However, while
9 General introduction 1 measuring swim performance in terms of fastest time is quite simple, figuring out which youth swimmers are most likely to succeed as adults is anything but that (Koz et al., 2012; Till et al., 2020; Güllich et al., 2014; Güllich et al., 2023; Schorer et al., 2017). The complexity of athlete development A major challenge in talent identification processes for coaches lies in the dynamic nature of athletes’ capabilities, which are not fixed (Baker et al., 2019; Simonton, 2001). Rather than following a consistent upward trajectory, athletes develop along an unpredictable pathway marked by rapid progressions, plateaus, and setbacks (Abbott et al., 2005; Baker et al., 2018). This has been exemplified by studies showing that swimmers who were leading in their age category shifted to lower positions later on and vice versa over time (Barreiros et al., 2014; Brustio et al., 2021). According to the Groningen Sport and Talent Model, the changes in swim performance are driven by changes in underlying performance characteristics linked to the athlete (Elferink-Gemser & Visscher, 2012). These include the rate of learning, training and maturation of anthropometric, physiological, technical, tactical and psychological characteristics. At the same time, the environment plays a crucial role too with parents, coaches and talent development programs creating opportunities to support athlete development, for example by providing resources that facilitate high-quality training (De Bosscher & De Rycke, 2017; Henriksen et al., 2010; Henriksen & Stambulova, 2023; Marinho et al. 2020). All these factors have the potential to interact uniquely for each individual and, moreover, they can also change as someone’s career progresses (Abbott et al., 2005; Simonton, 1999). Therefore, in our efforts to identify and nurture promising swimmers, it is critical to acknowledge and act upon the complex nature of athlete development (Phillips et al., 2010; Ribeiro et al. 2021). Rather than solely focusing on swim performance as a stand-alone measure, which is often the case, we should also uncover the underlying performance characteristics that have contributed to what we see today. These factors may include swimmers’ height, maximal swimming velocity, stroke index, proficiency in starts and turns, lower body power and the ability to train effectively and efficiently (Barbosa et al., 2010b; Jürimäe et al., 2007; Morais et al., 2017; Morais et al., 2019; Morais et al., 2021; Morais et al., 2022; Seffrin et al., 2022). Furthermore, it is important to get an understanding about a swimmer’s developmental process over time. Failing to include this more sophisticated approach could result in missing out on future swimming stars, misallocating limited resources away from the most promising swimmers or falling short of unlocking swimmers’ true potential. Such inefficiency and ineffectiveness is problematic at every level of the system - for The Netherlands as acknowledged swimming nation, but most of all, for those aspiring swimmers who count on us to help them in pursuing their dreams.
10 Chapter 1 Towards a more refined understanding The foundation of a more refined approach in TID rests upon a profound understanding of the pathway to swimming expertise, yet scientific knowledge in this matter is lacking. Many studies within competitive swimming have focused on isolated performance domains (such as biomechanics) and have been conducted cross-sectionally (capturing a singular moment in time), typically involving recreational or elite adults rather than talented youth swimmers (Costa et al., 2012; Morais et al., 2021). While such studies offer value for specific research inquiries, they fall short of providing insights into developmental trajectories linked to the elite level (Glazier, 2017). Longitudinal, multi-dimensional studies, on the other hand, are well-suited to detect developmental changes (Cobley & Till, 2017; Elferink-Gemser et al., 2018). Rather than relying on a single snapshot of performance, these studies track individuals over an extended period, evaluating multiple underlying performance characteristics in relation to their age and performance level. This approach empowers researchers to retrospectively analyze how swimmers who eventually reached the elite level progressed over time, as opposed to those who did not make it. Such examinations may uncover the defining factors and developmental patterns linked to senior success. These insights may provide science-based guidance to coaches and swimmers, and support informed decision-making processes in practice. Altogether, this may enhance the efficacy and efficiency of the TID system. Thesis objective and outline With the ambition to improve TID processes in swimming practice, this thesis aims to gain a deeper understanding of the pathway to swimming expertise. We specifically seek to make a meaningful contribution towards addressing the key characteristics and corresponding developmental patterns that set apart swimmers who succeed in their career from those who don’t, spanning various developmental stages. In this pursuit, our focus rests on studying swim performance (in terms of swim times) and underlying performance characteristics linked to the swimmer by using a longitudinal and multidimensional approach.. Within this exploration, swimming expertise will be defined in relation to the elite level, signifying a performance level that aligns with the fastest 50 swimmers worldwide. However, while this standard is suitable for identifying senior elite swimmers, it lacks effectiveness for junior swimmers as it overlooks the significant developmental differences between age groups. Yet, the ability to differentiate which juniors are on track to reach the elite level is essential in our endeavor to uncover the pathway to swimming expertise, a challenge intensified by the absence of general developmental patterns of elite swimmers throughout their careers.
11 General introduction 1 Therefore, the primary focus of the first part of this thesis is to gain a deeper understanding of the performance progression of elite swimmers. Chapter 2 specifically explores the development of season best times for swimmers who achieved 1) top elite, 2) elite, 3) sub-elite and 4) high-competitive status in adulthood. By retrospectively analyzing the developmental patterns dating back to the age of twelve, this study aims to offer insights into when these four performance groups begin to differentiate. Additionally, age-related benchmarks to identify junior swimmers progressing towards the elite level in subsequent studies will be provided. Building upon the results from Chapter 2, Chapter 3 delves into a more detailed examination of performance progression within a single season. This study investigates whether talented swimmers who ultimately made it to the elite level are characterized with different patterns of interim performance progression (IPP) during two consecutive season best performances compared those who did not. The results of this chapter shed light on both the rates and timing of progression within a season. The second part of this thesis centers on the contributing factors underlying swim performance, essentially examining the process leading up to the result. Within this context, Chapter 4 investigates the development of pacing behavior in talented swimmers, specifically disentangling the effects of age and experience and differentiating between those who reached the elite level and those who did not. Whether swimmers who are on track to reach the elite level apply self-regulation of learning (SRL) subprocesses more frequently in their daily training sessions compared with swimmers who are not on this track will be explored in Chapter 5. In Chapter 6, talented swimmers in the late-junior-toearly-senior transition (males aged 16-19; females aged 15-18) will be analyzed. This chapter explores whether swimmers who are on track to the elite level at early senior age (males aged 19; females aged 18) show higher levels and progression of swim performance and underlying performance characteristics including, anthropometrics, starts, turns, maximal swimming velocity, stroke index and lower body power, compared to lower-performing peers during this transition. Chapter 7 delves further back in time, investigating swimmers during their pubertal years (males aged 13-15; females aged 12-14). This study examines whether swimmers on track to the elite level at late junior age (males aged 16; females aged 15) demonstrate higher levels and progression on swim performance and underlying characteristics including, anthropometrics, maximal swimming velocity, stroke index and lower body power. In Chapter 8, the overall findings of this thesis are discussed, providing future directions and recommendations for swimming practice.
12 Chapter 1 References 1. Abbott, A., Button, C., Pepping, G. J., & Collins, D. (2005). Unnatural selection: talent identification and development in sport. Nonlinear dynamics, psychology, and life sciences, 9(1), 61–88. 2. Baker, J., Schorer, J., and Wattie, N. (2018). Compromising talent: Issues in identifying and selecting talent in sport. Quest 70, 48–63. https://doi.org/10.1080/00336297.2017.1333438 3. Baker, J., Wattie, N., & Schorer, J. (2019). A proposed conceptualization of talent in sport: The first step in a long and winding road. Psychology of Sport and Exercise, 43, 27-33. https://doi. org/10.1016/j.psychsport.2018.12.016 4. Barbosa, T. M., Bragada, J. A., Reis, V. M., Marinho, D. A., Carvalho, C., & Silva, A. J. (2010a). Energetics and biomechanics as determining factors of swimming performance: updating the state of the art. Journal of science and medicine in sport, 13(2), 262–269. https://doi.org/10.1016/j. jsams.2009.01.003 5. Barbosa, T. M., Costa, M., Marinho, D. A., Coelho, J., Moreira, M., & Silva, A. J. (2010b). Modeling the links between young swimmers' performance: energetic and biomechanic profiles. Pediatric exercise science, 22(3), 379–391. https://doi.org/10.1123/pes.22.3.379 6. Brustio, P. R., Cardinale, M., Lupo, C., Varalda, M., De Pasquale, P., & Boccia, G. (2021). Being a top swimmer during the early career is not a prerequisite for success: A study on sprinter strokes. Journal of science and medicine in sport, 24(12), 1272–1277. https://doi.org/10.1016/j. jsams.2021.05.015 7. Cobley, S. & Till, K. (2017). Longitudinal studies of athlete development: their importance, methods and future considerations. In J. Baker, S. Cobley, J. Schorer & N. Wattie (Eds.). Routledge Handbook of Talent Identification and Development in Sport (pp. 250-268). Routledge. https://doi. org/10.4324/9781315668017 8. Costa, M. J., Bragada, J. A., Marinho, D. A., Silva, A. J., & Barbosa, T. M. (2012). Longitudinal interventions in elite swimming: a systematic review based on energetics, biomechanics, and performance. Journal of strength and conditioning research, 26(7), 2006–2016. https://doi. org/10.1519/JSC.0b013e318257807f 9. De Bosscher, V., & De Rycke, J. (2017). Talent development programmes: a retrospective analysis of the support services of talented athletes in 15 nations. European Sport Management Quarterly, 17(5), 590-609. https://doi.org/10.1080/16184742.2017.1324503 10. Elferink-Gemser M.T., Visscher, C. (2012). Who are the superstars of tomorrow? Talent development in Dutch Soccer. In J. Baker, J. Schorer, S. Cobley (Eds), Talent identification and development in sport. International perspectives (pp. 95-105). Routledge 11. Elferink-Gemser, M. T., Te Wierike, S. C. M., & Visscher, C. (2018). Multidisciplinary longitudinal studies: A perspective from the field of sports. In K. A. Ericsson, R. R. Hoffman, A. Kozbelt, & A. M. Williams (Eds.), The Cambridge handbook of expertise and expert performance (2nd ed., pp. 271–290). Cambridge University Press. https://doi.org/10.1017/9781316480748.016 12. Güllich A. (2014). Selection, de-selection and progression in German football talent promotion. European journal of sport science, 14(6), 530–537. https://doi.org/10.1080/17461391.2013.858371 13. Güllich, A., Barth, M., Macnamara, B. N., & Hambrick, D. Z. (2023). Quantifying the Extent to Which Successful Juniors and Successful Seniors are Two Disparate Populations: A Systematic Review and Synthesis of Findings. Sports medicine (Auckland, N.Z.), 53(6), 1201–1217. https://doi. org/10.1007/s40279-023-01840-1
13 General introduction 1 14. Henriksen, K., Stambulova, N., & Roessler, K. K. (2010). Successful talent development in track and field: considering the role of environment. Scandinavian journal of medicine & science in sports, 20 Suppl 2, 122–132. https://doi.org/10.1111/j.1600-0838.2010.01187.x 15. Henriksen, K., & Stambulova, N. (2023). The social environment of talent development in youth sport. Frontiers in sports and active living, 5, 1127151. https://doi.org/10.3389/fspor.2023.1127151 16. Jürimäe, J., Haljaste, K., Cicchella, A., Lätt, E., Purge, P., Leppik, A., & Jürimäe, T. (2007). Analysis of swimming performance from physical, physiological, and biomechanical parameters in young swimmers. Pediatric exercise science, 19(1), 70–81. https://doi.org/10.1123/pes.19.1.70 17. Koz, D., Fraser-Thomas, J., & Baker, J. (2012). Accuracy of professional sports drafts in predicting career potential. Scandinavian journal of medicine & science in sports, 22(4), e64–e69. https://doi. org/10.1111/j.1600-0838.2011.01408.x 18. KNZB. (2024, February 25). Topsport, Topsport en talentontwikkeling. https://www.knzb.nl/ kennisartikelen/talentontwikkeling-wz 19. Marinho, D. A., Barbosa, T. M., Lopes, V. P., Forte, P., Toubekis, A. G., & Morais, J. E. (2020). The Influence of the Coaches' Demographics on Young Swimmers' Performance and Technical Determinants. Frontiers in psychology, 11, 1968. https://doi.org/10.3389/fpsyg.2020.01968 20. Morais, J. E., Silva, A. J., Marinho, D. A., Lopes, V. P., & Barbosa, T. M. (2017). Determinant Factors of Long-Term Performance Development in Young Swimmers. International journal of sports physiology and performance, 12(2), 198–205. https://doi.org/10.1123/ijspp.2015-0420 21. Morais, J. E., Marinho, D. A., Arellano, R., & Barbosa, T. M. (2019). Start and turn performances of elite sprinters at the 2016 European Championships in swimming. Sports biomechanics, 18(1), 100–114. https://doi.org/10.1080/14763141.2018.1435713 22. Morais, J. E., Barbosa, T. M., Forte, P., Silva, A. J., & Marinho, D. A. (2021). Young Swimmers' Anthropometrics, Biomechanics, Energetics, and Efficiency as Underlying Performance Factors: A Systematic Narrative Review. Frontiers in physiology, 12, 691919. https://doi.org/10.3389/ fphys.2021.691919 23. Morais, J. E., Barbosa, T. M., Nevill, A. M., Cobley, S., & Marinho, D. A. (2022). Understanding the Role of Propulsion in the Prediction of Front-Crawl Swimming Velocity and in the Relationship Between Stroke Frequency and Stroke Length. Frontiers in physiology, 13, 876838. https://doi. org/10.3389/fphys.2022.876838 24. Olympian Database. (2024, February 25). Olympian database. https://www.olympiandatabase. com/index.php?id=13492&L=1 25. Phillips, E., Davids, K., Renshaw, I., & Portus, M. (2010). Expert performance in sport and the dynamics of talent development. Sports medicine (Auckland, N.Z.), 40(4), 271–283. https://doi. org/10.2165/11319430-000000000-00000 26. Ribeiro, J., Davids, K., Silva, P., Coutinho, P., Barreira, D., & Garganta, J. (2021). Talent Development in Sport Requires Athlete Enrichment: Contemporary Insights from a Nonlinear Pedagogy and the Athletic Skills Model. Sports medicine (Auckland, N.Z.), 51(6), 1115–1122. https:// doi.org/10.1007/s40279-021-01437-6 27. Schorer, J., Rienhoff, R., Fischer, L., & Baker, J. (2017). Long-Term Prognostic Validity of Talent Selections: Comparing National and Regional Coaches, Laypersons and Novices. Frontiers in psychology, 8, 1146. https://doi.org/10.3389/fpsyg.2017.01146 28. Seffrin, A., DE Lira, C. A., Nikolaidis, P. T., Knechtle, B., & Andrade, M. S. (2022). Age-related performance determinants of young swimmers in 100- and 400-m events. The Journal of sports medicine and physical fitness, 62(1), 9–18. https://doi.org/10.23736/S0022-4707.21.12045-6
14 Chapter 1 29. Simonton, D. K. (1999). Talent and its development: An emergenic and epigenetic model. Psychological Review, 106(3), 435–457. https://doi.org/10.1037/0033-295X.106.3.435 30. Simonton, D. K. (2001). Talent development as a multidimensional, multiplicative, and dynamic process. Curr. Dir. Psychol. Sci. 10, 39–43. doi: 10.1111/1467-8721.00110 31. Swimrankings. (2024, February 25). Swim performance database. https://www.swimrankings.net 32. Till, K., & Baker, J. (2020). Challenges and [Possible] Solutions to Optimizing Talent Identification and Development in Sport. Frontiers in psychology, 11, 664. https://doi.org/10.3389/ fpsyg.2020.00664 33. Williams, A. M., & Reilly, T. (2000). Talent identification and development in soccer. Journal of sports sciences, 18(9), 657–667. https://doi.org/10.1080/02640410050120041 34. World Aquatics. (2024, February 25). Swim performance database. https://www.worldaquatics. com/competitions/
15 General introduction 1
17 Performance development of top-elite swimmers 2 Chapter 2 Multigenerational performance development of male and female top-elite swimmers: A global study of the 100 m freestyle event Post, A. K., Koning, R. H., Visscher, C., & Elferink-Gemser, M. T. (2020). Multigenerational performance development of male and female top-elite swimmers: A global study of the 100 m freestyle event. Scandinavian Journal of Medicine & Science in Sports, 30(3), 564-571. https://doi.org/10.1111/sms.13599
18 Chapter 2 Abstract Background The present study investigated longitudinally the performance development of a multigenerational sample of competitive swimmers. The aim of the study was to provide unique insight into the junior towards senior performance development of those few who reached top-elite level. Season Best Times (SBT) of 100m freestyle performance of international swimmers, (1,305 males, aged 12-26 and 1.841 females, aged 12-24) competing in at least five seasons between 1993 and 2018, were corrected for the prevailing world record (WR). Swim performance was defined as a relative measure: relative Season Best Time=(SBT/WR)*100. Based on rSBT, four performance groups were defined: top-elite, elite, sub-elite and high-competitive. Results Univariate analyses of variance showed that male top-elite swimmers outperformed highcompetitive swimmers from the age of 12, sub-elite swimmers from the age of 14 and elite swimmers from the age of 18 while female top-elite swimmers outperformed highcompetitive and sub-elite swimmers from the age of 12 and elite swimmers from the age of 14 (p <0.05). Frequency analysis showed that male top-elite swimmers for the first time achieved top-elite level between the 17 and 24 years old (mean age of 21) while female top-elite swimmers started to perform at top-elite level between the 14 and 24 years old ( mean age of 18). Conclusion Male and female top-elite swimmers are characterized by a high performance level from 12 years on and progressively outperform swimmers from similar age. However, this goes together with a large variety in the individual pathways towards top-elite level within and between sexes. Keywords Competitive swimming, sport performance, world record, talent, acquisition of expertise.
19 Performance development of top-elite swimmers 2 Introduction In the context of athlete development, the increase of sport performance of a youth athlete aiming to make it to the top is key (Ericsson et al., 1993). In a relatively short time, young athletes will have to continue improving their sport performance to reach excellence (Ericsson et al., 1993; Wiersma, 2000; Elferink-Gemser et al., 2011). Knowledge about general performance development of those who have made it to the top could provide important information for athletes, coaches and federations (Allen et al., 2014). A thorough understanding of performance development during an athlete’s career could facilitate the identification and development of talented athletes and could enable sport federations to target their support towards those athletes who have the greatest potential to make it to the top (Durand-Bush & Salmela, 2002). A fitting sport to investigate the performance development of youth athletes on their way to the top is competitive swimming. Competitive swimming is a time trial sport in which a swimmer tries to travel a certain distance in the water as fast as possible. It is a popular global sport with a high level of competition in which the gap between the gold medalist and the last finisher in international competition is constantly decreasing (Stanula et al.,2012). The key distance in competitive swimming is the 100m freestyle long course event, which has been on every Olympic program since 1904 (men) and 1912 (women). In this event, competition starts from an early age on and the competition level is high for both male and female swimmers (Swimrankings, 2018; FINA, 2018). Due to technological progressions like electronic timekeeping and online accessible repeated-measures competition data, retrospective studies on performance data of swimmers in the 100m freestyle event offer great opportunities to provide new insights for performance development in competitive swimming. The time-captured nature of competitive swimming comes with a strong emphasis on swim performance from a young age on. In practice, this is marked by the early selection of the fastest youth swimmers into athlete development programs based on their competitive performance times (KNZB, 2018). The underlying assumption behind this approach is that future winners can be identified on the basis of their junior swim performance (Baker et al., 2018). In this way, swim performance from a young age on is highly valued and considered as a serious predictor of success (KNZB, 2018). Nevertheless, the utility of talent identification on the basis of performance at early ages has been questioned by several researchers (Elferink-Gemser et al., 2011; Gulbin et al., 2013; Vaeyens et al., 2008; Règnier & Salmela, 1993). Specific for competitive swimming, research from Barreiros et al. (2014) has shown that the conversion rates of junior elite swimmers into senior elite swimmers are generally low. Moreover, one of the concerns of using this approach is the fixed focus on the swimmer’s current performance level
20 Chapter 2 rather than the swimmer’s potential performance level. This risks the exclusion of talented swimmers who may not be the fastest yet, but who may be so in the future (ElferinkGemser et al., 2011; Elferink-Gemser et al., 2018). Scientific-based knowledge about the general performance development of top-elite swimmers throughout their entire career may enlighten the value of this approach. Research on adult elite swimmers has given valuable insight into performance progression and the age of peak performance. The study of Pyne et al. (2004) showed that performance progression by ∼1.0% within a competition and ∼1.0% within the year leading up to the Olympics is necessary to stay in contention for a medal at the Olympic Games. Allen et al. (2014; 2015) modelled the career performances of Olympic top-16 swimmers and concluded that elite male swimmers achieve their peak performance at ∼24 (± 2) years while elite female swimmers achieve peak performance at ∼22 (± 2) years. The difference in age of peak performance between sexes can presumably be explained by the in general ∼2-year earlier onset of puberty in females compared with males (Baxter-Jones & Sherar, 2006). Given this information, a comparison of the performance development between young male and female swimmers is of considerable interest as differences in performance development between sexes may hold important implications for training and athlete development programs. Both aforementioned studies provide valuable information about performance development of senior elite swimmers during adulthood, however, insight regarding the performance development during their younger years relative to swimmers who did not reach elite level is lacking. Big data analyses over multiple generations could provide relevant information about how elite swimmers got to their high level of expertise. What characterizes their successful performance development over the years compared to those who did not make it to the top? The present study investigates the 100m freestyle performance development of a multigenerational sample of swimmers in order to provide more insight into the junior towards senior performance development of those few who reached top-elite level. Each research question is answered separately for male and female swimmers. The research questions we aim to answer are (1) From which age on do top-elite swimmers outperform swimmers from other performance groups (e.g. high-competitive, sub-elite and elite)? (2) From which age on do top-elite swimmers start to perform at high-competitive level, sub-elite level, elite level and top-elite level? The results of this study add value to both science and sport practice as it broadens the knowledge about general performance development of top-elite swimmers. It may function as a guideline for athlete development programs by providing scientific-based knowledge about the performance development of top-elite swimmers.
21 Performance development of top-elite swimmers 2 Methods Ethical approval All procedures used in the study were approved by the Local Ethical Committee of the University Medical Center Groningen, University of Groningen, The Netherlands (201900334) in the spirit of the Helsinki Declaration with a waiver of the requirement for informed consent of the participants given the fact that the study involved the analysis of publicly available data. Data collection The swimmers we selected for this study were international male and female swimmers with performance data on the 100m freestyle long course event. Performance data was obtained from Swimrankings (Swimrankings, 2018), a recognized public data source which records swimming race results. Performance data was collected from 113 countries across different parts of the world including Africa, America, Asia, Australia and Europe. We collected all available 100m freestyle long course results from Swimrankings’ database, which initially resulted in 2,683,412 observations between 1993 and 2018. Data processing Performance data from the 1st of January 2008 till the 1st of January 2010 were excluded from analysis. During that time, swimmers were allowed to wear newly introduced fullbody polyurethane swimsuits which led to a major benefit of the swimmers’ drag force reduction (Tiozzo et al., 2009; Toussaint et al., 2002; Tomikawa & Nomura, 2009). From the 1st of January 2010 onwards, FINA banned these suits. Swim performances over 180seconds were excluded from analysis to ensure a representative dataset. A total of 2,383,616 observations was remained. Based on swim dates, performance data were classified in swimming seasons. Each swimming season officially starts on the first of September of a calendar year and ends on the 31st of August of the next calendar year (1st of September 2018 till 31st of August 2019 corresponds to swimming season 2018/2019). Swimmers were classified in age categories based on their age on the 31st of December of the swimming season (a girl who is 14 years old on the 31st of December 2018 would be classified in age category 14 year for swimming season 2018/2019). Therefore, all ages mentioned in the present study refer to the age category in which a swimmer participated during the swimming season and not the calendar age of the swimmer. For each swimmer, we selected one Season Best Time (SBT) per swimming season which we used for further analysis. A total of 1,131,963 observations was remained.
22 Chapter 2 Inclusion criteria For the purpose of this study, it is important to outline the individual performance development from a young age on towards the adult age of peak performance (or beyond). Therefore only those swimmers who; 1) were between 12 and 24 years old (female) or between 12 and 26 years (male) old; 2) were in competition for at least 5 seasons; 3) had at least one SBT within the age category of 16 years or younger; and 4) had at least one SBT within the age category of 20 years (female) or 22 years (male) or older were included (Allen et al., 2014; 2015). This resulted in 5,636 individual swimmers (3,259 female, 2,377 male) with 40,063 SBT’s (22,239 female, 17,824 male) with an average of 7.6 ± 2.1 observations per swimmer. Defining swim performance and performance development The present study includes swim performances of multiple generations, necessitating the correction of evolution in a given sport (Stoter et al., 2019). The continuous increase in world-class performances at Olympic Games and World Championships clearly reflects the evolution in a sport, as well as the improvement of world records (Stanula et al., 2012; König et al., 2014). For example, at the 100m freestyle event, the world record for females has been improved from 54.48 seconds to 51.71 seconds with 2.9 seconds (~5.3%) from 1994 to 2017 (FINA) and for males from 48.42 seconds to 47.04 seconds (fastest time in textile) with 1.38 seconds (~2.9%). To correct for evolution in competitive swimming, we use a method to compare performance over multiple generations, introduced and validated by Stoter et al. (2019). First, each swimmer’s SBT per swimming season between 2018 and their earliest available competitive performance was determined. Second, SBT’s were related to the prevailing world record (WR) or the fastest time in textile of the corresponding sex. The prevailing WR is the official WR at the date the athlete swam the SBT. WRs from 2008 or 2009 were replaced by the prevailing fastest time in textile. The corrected SBT will be referred to as relative Season Best Time (rSBT) and is presented as a percentage of the world record or fastest time in textile. In this study, rSBT defines swim performance (see equation 1).
23 Performance development of top-elite swimmers 2 Defining performance levels and groups Four performance levels were defined; top-elite, elite, sub-elite and high-competitive. Each performance level was characterized by sex-specific limits to account for differences in competition level between males and females (Table 1). The limits were calculated as the mean of 5 rSBTs for the xth swimmer from either the 100m freestyle performance FINA World Ranking Lists of 2014-2018 (FINA, 2018) or the 100m freestyle performance National Ranking Lists of the Netherlands 2014-2018 (Swimrankings, 2018). The limits of the top-elite performance level were based on rSBTs of the 8th male and female swimmer of the FINA World Ranking List 2014-2018 (e.g. rSBT 8th male swimmer 2014 + rSBT 8th male swimmer 2015 + rSBT 8th male swimmer 2016 + rSBT 8th male swimmer 2017 + rSBT 8th male swimmer 2018 / 5) . The other limits were defined so that they represented the 50th male and female swimmer of the FINA World Ranking List 2018 (elite performance level) and the 8th and 50th male and female swimmer of the National Ranking List of the Netherlands of 2018 (sub-elite and high-competitive performance levels respectively). We determined each swimmer’s current performance group by allocating the rSBT of a given season to one of the four performance levels. For example, if a 16 year old boy has a rSBT of 108%, his current performance level corresponds with the limits of the high competitive performance group. Next, we determined each swimmer’s best performance group by allocating the best rSBT ever to one of the four performance levels, meaning that a swimmer either once or multiple times has reached this performance level at any age. For example, if a boy has a best rSBT ever of 105%, his best performance level corresponds with the limits of the sub-elite performance group. A swimmer’s current performance group is a dynamic variable and may change over time, whereas a swimmer’s best performance group remains static. Swimmers with a best rSBT ever outside the limits of the high competitive level (best rSBT>114.1% for males and best rSBT >114.6% for females) were excluded from further analysis (a total of 16,406 observations). Moreover, outliers were excluded (a total of 647 observations) using stem-and-leaf plot, as swimmers might have a poor season due to injury, illness or other reasons, which are not representative for the swim performance of swimmers in the corresponding performance group. Table 2 presents the male/female distribution and the number of observations (i.e., rSBTs per swimming season) for each performance group included for the analysis on swim performance. Table 1. Limits of performance levels for males and females separately. Males Females Top-elite rSBT <102.2% rSBT <102.8% Elite 102.2% <> rSBT < 104.0% 102.8% <> rSBT < 105.5% Sub-elite 104.0% <> rSBT < 107.9% 105.5% <> rSBT < 108.0% High-competitive 107.9% <> rSBT < 114.1% 108.0% <> rSBT < 114.6%
24 Chapter 2 Table 2. Total number of swimmers (N = 3,146) and observations (N = 23,010) for each performance group for the analysis on swim performance (rSBT). Males Females Individuals Observations Individuals Observations Top-elite 29 274 57 504 Elite 62 582 218 1,734 Sub-elite 394 3,265 378 2,786 High-competitive 820 6,059 1,188 7,806 Total 1,305 10,180 1,841 12,830 Defining first entry ages For top-elite swimmers only, we determined the first entry age of each performance level. The first entry age is the minimum age at which a swimmer for the first time achieved a higher performance level (e.g. performance level transition from sub-elite level to elite level). First entry ages for skipped performance levels (e.g. a performance level transition from sub-elite level to top-elite level) were not reported. Statistical analysis All data were analyzed for male and female swimmers separately using IBM SPSS Statistics 24 and R. Mean scores and standard deviations were calculated for swim performance (rSBT) for the four performance groups per age category. Per age category, a one-way independent analysis of variance (ANOVA) was used to examine group differences based on rSBT with performance group as independent variable. Planned contrasts were performed to determine differences between top-elite swimmers and swimmers of other performance groups per age category. A frequency analysis with first entry age as variable was executed for top-elite swimmers only. Mean scores and frequency distribution tables of first entry age were produced for the four performance levels (high-competitive level, sub-elite level, elite level and top-elite level). Statistical tests were executed for the age categories in which there were more than two observations in the top-elite performance group. For all tests, p <0.05 was set as significance. Results Differences in swim performance between top-elite swimmers and other performance groups Figure 1 illustrates the performance development of male and female swimmers on the 100m freestyle from age 12 to 26 (males) and 12 to 24 (females) specified for each of the four performance groups.
25 Performance development of top-elite swimmers 2 For males, there was a significant effect of best performance group on rSBT from age 12 till 26 (p <0.05). Planned comparisons between the top-elite performance group and other performance groups revealed that from the age of 12, top-elite swimmers performed better than high-competitive swimmers (t(273)=-2.643, p=0.009). From the age of 14, topelite swimmers performed better than sub-elite swimmers (t(6.169)=-3.516, p=0.012). From the age of 18, top-elite swimmers performed better than elite swimmers(t(909)=-2.051, p=0.041). For females, there was a significant effect of best performance group on rSBT from age 12 till 24 (p <0.05). Planned comparisons between the top-elite performance group and other performance groups revealed that from the age of 12, top-elite swimmers performed better high-competitive swimmers (t(430)=-4.034, p <0.001) and sub-elite (t(430)=-2.268, p=0.024). From the age of 14, top-elite swimmers performed better than elite swimmers (t(939)=-3.574, p <0.001). Figure 1. Performance development of male (left) and female (right) swimmers on the 100m freestyle from age 12 to 26 years specified for each of the four best performance groups
26 Chapter 2 The stages towards acquisition of top-elite performance level Figure 2 shows the first entry age per performance level of male and female top-elite swimmers. In other words, it presents the distribution in age categories at which male and female top-elite swimmers for the first time performed high-competitive, sub-elite, elite and top-elite level. For males, the first entry age in high-competitive level ranges between 14 and 18 years, in which the majority of the male top-elite swimmers entered high-competitive level at the age of 16. The first entry age of sub-elite level ranges between the 15 and 21 years. At least one male swimmer who reached top-elite level, started participating at the sub-elite level for the very first time at the age of 15, while at least one other top-elite swimmer was 21. The age ranges of sub-elite level are largely similar to the age ranges at elite level, however the age at which the majority of male top-elite swimmers started to perform at elite level (20 years), is fairly higher than the age at which the majority of male top-elite swimmers started to perform at sub-elite and high-competitive level (both 16 years). Top-elite level performances started from the age of 17 years on, in which at least one male swimmer entered top-elite level for the first time at 24 years old. The majority of males entered topelite level around the age of 21. For females, the first entry age in high-competitive level ranges between the 12 and 14 years, in which the majority of female top-elite swimmers entered high-competitive level at the age of 13. This is about three years earlier than their male counterparts. The first age of sub-elite level ranges between the 12 and 16 years. The majority of the female topelite swimmers reached sub-elite level for the first time when they were 15 years. The first female top-elite swimmer entered elite level when she was 13 years, however the majority started to perform at elite level at the age of 15. As in male top-elite swimmers, at least one female top-elite swimmer reached elite-level when she was 22 years. The range of first entry ages in female top-elite swimmers is widely spread at top-elite level. The first female top-elite level swimmer who entered top-elite level was only 14 years, however at least one female top-elite swimmer reached elite-level when she was 24 years. In between, no clear pattern was found for the majority of the swimmers.
27 Performance development of top-elite swimmers 2 Figure 2. The distribution in age categories at which male (N=29) and female (N=57) top-elite swimmers for the first time performed at high-competitive (HC), sub-elite, elite and top-elite level. Dots represent mean ages.
28 Chapter 2 Discussion The present study investigated the 100m freestyle performance development longitudinally (over at least 5 years) in a multigenerational (over more than 20 years) sample of competitive swimmers to provide unique insight into the junior towards senior performance development of those few who reached top-elite level. The main findings showed that (1) from 12 years on, top-elite swimmers progressively outperformed swimmers of similar age, and that (2) there is a wide variety in the age at which male and female top-elite swimmers start to perform at high competitive, sub-elite, elite and top-elite level. The findings of the present study concretize that successful performance development to the top is characterized by a high level of expertise from 12 years on. Male top-elite swimmers outperformed high-competitive swimmers from 12 years on, sub-elite swimmers from 14 years on and elite swimmers from 18 years, while female top-elite swimmers outperformed high-competitive and sub-elite swimmers from 12 years on and elite swimmers from 14 years on. This progressive trend not only characterizes the differences between performance groups, but also the variety within the top-elite performance group. For both male and female top-elite swimmers, it seems that the higher the performance level becomes, the more variety in the first entry age range exists. For example in female top-elite swimmers, the first entry age range expanded from two years (12-14 years) in high competitive level to ten years (14-24 years) at top-elite level. This means that at least one 14 year old female top-elite swimmer entered high-competitive level while at least one other female top-elite swimmer achieved at the same age top-elite level. Looking at the differences between male and female top-elite swimmers, we see that most of the female top-elite swimmers achieved the high-competitive, sub-elite, elite and top-elite level at a younger age compared to most of the male top-elite swimmers. For example, most female top-elite swimmers reached high-competitive level at the age of 13 whilst most male top-elite swimmers reached highcompetitive level at the age of 16. Together, these results point out crucial differences in the individual pathways of performance development towards top-elite level within and between male and female swimmers. Now, an intriguing question is which underlying performance characteristics (e.g. anthropometrical, technical, tactical, physiological and psychological characteristics) contribute to the successful performance development towards top-elite level. In here, it is important to consider that the underlying performance characteristics are influenced by maturation, learning and training (Elferink-Gemser & Visscher, 2012; Barbosa et al., 2015; Till et al., 2014) and that athletes always develop in and with their environment. The environment (e.g. parents, coaches, talent development programs, competition and training facilities) plays a crucial role in developing the underlying performance characteristics (Bloom, 1985; Phillips et al., 2010). For example, the popularity of a sport might influence national, regional and local selection procedures for talent identification and
29 Performance development of top-elite swimmers 2 development programs and the level of competition. Individual differences in underlying performance characteristics, environmental characteristics, timing and tempo of the growth spurt and the number and quality of training hours may harness possible explanations for differences in swim performance between performance groups and sexes and for the wide variation in developmental patterns between top-elite swimmers. Therefore, future, longitudinal studies following youth swimmers throughout their sports career, measuring underlying performance characteristics, mapping environmental characteristics and tracking their maturation, learning, training and level of swim performance, could potentially provide further insight into successful 100m freestyle performance development of top-elite swimmers (Elferink-Gemser et al., 2011; Kannekens et al., 2011). In here, the effect of age of selection on the performance development of those reaching top-elite level should be addressed as well. The present study is the first that investigated 100m freestyle performance development at such large scale. Following the method developed by Stoter et al. (2019), the present study defined swim performance as a relative measure instead of an absolute measure. The major strength of using a relative measure of swim performance (rSBT) is that it allows a more “fair” comparison of swim performance between and within swimmers. Therefore we were able to include swim performance over multiple generations which resulted in a big data set with multigenerational and longitudinal data. Consequently, we extended group sizes of populations characterized with smaller sample sizes (e.g. top-elite swimmers). This provided us the unique opportunity to investigate 100m freestyle performance development of top-elite, elite, sub-elite and high-competitive swimmers over more than 20 years. In a similar way, other sports with absolute performance measures (i.e. time-trial sports such as cycling or running) can be studied. However, when applying this method it is important to realize that a different classification of performance groups may lead to different outcomes (Swann et al., 2015). Hence, the present study carefully considered the definitions of topelite, elite, sub-elite and high-competitive swimmers and defined performance groups based on task- and sex-specific limits, meaningful for the sport for competitive swimming. With particular interest, the present study researched the performance development of topelite swimmers. In here, the sport science perspective of striving to find regularities and patterns that can be applied to a whole population (Leezenberg & de Vries, 2001) was mixed with the investigation of individual pathways, a highly relevant and valuable combination for research in elite sports since experts in sports are individuals who do not comply with regularities. The frequency analysis on the first entry age of top-elite swimmers at the four performance levels showed an innovative method to describe the individual pathways towards acquisition of top-elite performance level. By analyzing these individual pathways, we gathered insight into the mean age and general age ranges at which top-elite swimmers for the first time started to perform at high-competitive, sub-elite, elite and top-elite level.
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