Swimming researchers are, on regular
basis, trying to identify and understand
the factors that can predict
swimming performance. For such
aim most of the research designs
adopted are cross sectional in nature.
There are few longitudinal researches
in swimming science

Barbosa, Tiago M.

Bragada, José A.

Costa, M.J.

Marinho, D.A.

Silva, A.J.

Biblioteca Digital do IPB

Mário J Costa 1,4, José A Bragada 1,4, Daniel A Marinho 2,4, António J Silva 3,4 , Tiago M Barbosa 1,4  TRACKING THE FREESTYLE PERFORMANCE  FROM CHILDREN TO ADULT AGE  Costa, M.J., Marinho, D.A., Reis, V.M., Silva, A.J., Bragada, J.A., Barbosa, T.M. (2010a). Stability and prediction of 100-m Breaststroke performance during the elite swimmers career. In: PL Kjendlie, RK Stallman and J Cabri (Eds.), Biomechanics and Medicine in Swimming XI (pp. 272-273). Norway.  Costa, M.J., Marinho, D.A., Reis, V.M., Silva, A.J., Marques, M.C., Bragada, J.A. et al., (2010b). Tracking the performance of world-ranked swimmers. Journal of Sports Science and Medicine, 9, 411-417. Fleiss, J.L. (1981). Statistical methods for rates and proportions (2nd ed.). New York: John Wiley.  Hopkins, W.G., Pike, J.C. & Nottle, C. (2010). Overall trends and individual trajecto-ries of swimming performance in a decade of new Zealand National Championships. In: PL Kjendlie, RK Stallman and J Cabri (Eds.), Biome-chanics and Medicine in Swimming XI, (pp. 72). Norway. Malina, R.M. & Bouchard, C. (1991). Grouth, maturation and physical activity. Champaign, IL: Human Kinetics. Malina RM. Adherence to physical activity from childhood to adulthood: a per-spective forma tracking studies. Quest 2001; 53:346-355. Pyne, D., Trewin, C., Hopkins, W. (2004) Pro-gression and variability of competi-tive performance of Olympic swim-mers. Journal of Sports Sciences 22, 613-620. Sokolovas, G. (1998). Biological maturation of swimmers. In: KL Keskinen, PV Komi, AP Hollander (Eds.), Proceedings of the VIIIth International Symposium on Bio-mechanics and Medicine in Swimming (pp. 315-319). Finland. Stewart, A., Hopkins, W. (2000) Consistency of swimming performance within and between competitions. Medicine and Science in Sports Exercise 32, 997-1001. 1 Polytechnic Institute of Bragança, Bragança, Portugal  2 University of Beira Interior, Covilhã, Portugal  3 University of Trás-os-Montes and Alto Douro, Vila Real, Portugal  4 Research Centre in Sports, Health and Human Development, Vila Real, Portugal  SUBJECTS: Portuguese elite male swimmers where chosen to be the main focus of our study. It was con-sidered as inclusion criteria to be a Portuguese male top-50 swimmer, for short course during the 2006-2007 season in the 50-m, 100-m, 200-m freestyle events . On the contrary, an exclusion criteria was considered: (i) to be a swimmer from the Portu-guese top-50, but authors did not have access to season best perform-ance in some of the chronological ages; (ii) to be a swimmer from the Portuguese top-50 but not having swum the event at least one time per season from 12 to 18 years for some reason; (iii) to be a swimmer from the Portuguese top-50 but not being at least 18 years-old. So, an overall of 124 elite male swimmers  and 868 race times were analyzed. STUDY DESIGN: We made a retro-spective performance data analysis of elite male swimmers during seven consecutive seasons. Portuguese male’s Top-50 in the 2006-2007 sea-son was consulted to verify the swimmers included in it. So, the Por-tuguese National Swimming Federa-tion allowed us to collect the best of-ficial results between 12 and 18 years old from each swimmer identi-fied in the Top-50. When suitable or appropriate performance times were also collected from public swimming database (www.swimrankings.net). STATISTICAL PROCEDURES: The nor-mality of the distributions was as-sessed with the Shapiro-Wilk test. Longitudinal assessment was made based on two approaches: (i) mean stability; (ii) normative stability. For mean stability, mean plus one stan-dard deviation and quartiles were computed. Data variation was ana-lyzed with ANOVA repeated meas-ures followed by a post-hoc test (Bonferroni test). The normative sta-bility was analyzed with the Cohen’s Kappa (K) and the Pearson Correla-tion Coefficient. The qualitative in-terpretation K value was made ac-cording to Fleiss (1981) suggestion: (i) excellent if K > 0.75; (ii) moderate if 0.40 < K < 0.75 and; (iii) low if K < 0.40.  Qualitatively stability based on Pearson Correlation was considered to be: (i) high if r ≥ 0.60; (ii) moder-ate if 0.30 < r < 0.60 and; (iii) low if r < 0.30, as suggested by Malina (2001).        The results pointed out a perform-ance enhancement from children to adult age in all Freestyle events. The performance stability and prediction based on overall career period was low. However, coaches should set the age of 16 years-old as a determi-nant chronological point, were the ability to predict the adult perform-ance level increases strongly.  CONCLUSION   REFERENCES  Swimming researchers are, on regu-lar basis, trying to identify and un-derstand the factors that can predict swimming performance. For such aim most of the research designs adopted are cross sectional in na-ture. There are few longitudinal re-searches in swimming science. Moreover, most of these longitudinal studies dedicate their data analysis for a short time period, i.e., less than one season (e.g., Stewart & Hopkins, 2000) or, for a time period between one and five seasons (e.g., Pyne et al., 2004; Costa et al., 2010b). On the other hand, the number of longitudi-nal designs analysing swimming per-formance for a longer time period, e.g., more than five consecutive sea-sons still scarce. Some interesting conclusions were verified when adopting those longer time frames. Regarding the national level breast-strokers performances in the 100-m event for seven consecutive seasons, the age of 16 was considered a mile-stone, as the stability increased strongly starting in that age (Costa et al., 2010a). Analyzing the overall trends and individual trajectories of swimming performance for a dec-ade, Hopkins et al. (2010) reported that the New Zealand swimmer’s age for best performance was 18.9 ± 1.5 years and 18.7 ± 2.5 years for boys and girls respectively. So, it appears to exist a lack of consistent or mas-sive knowledge from the stability and variation of the swimming per-formance during a long time period namely from children to adult age. The purpose of this study was to track and analyze the freestyle per-formance stability throughout the elite swimmer’s career from 12 to 18 years old.   INTRODUCTION  Figure 1 presents the performance variation throughout the seven con-secutive seasons in the freestyle events analyzed. ANOVA revealed significant variations in the swim-ming performance in the 50-m [F1,35 = 769.88; P < 0.01, power = 1.00], 100-m [F1,43 = 3326.19; P < 0.01, power = 1.00] and 200-m [F1,43 = 16272.81; P < 0.01, power = 1.00]. Bonferroni post-hoc tests verified significant differences (P < 0.01) be-tween all ages for all events, except between the 17 and the 18 years. The overall stability based on K val-ues was low in all events analyzed: 50-m (K = 0.22 ± 0.05), 100-m (K = 0.27 ± 0.05) and 200-m ( 0.23 ± 0.05). So, based on overall tracking    METHODS  values of the seven consecutive seasons, a low swimming performance stability and prediction can be considered. Table 1 presents the Pearson Correlation Coefficient values for pair wised seasons between 12 and 18 years old. Doing an analysis based on the adult performance season (i.e., 18 years old), there is a trend to stability become high from 15 to 16 years in the 50-m (r = 0.72) and 100-m (r = 0.68); from 16 to 17 in the 200-m (r = 0.78).  Several authors reported for world level swimmers, that the age interval between 15 and 16 is determinant to achieve the best individual performance in long dis-tance events (Malina & Bouchard, 1991; Sokolovas, 1998).   RESULTS AND DISCUSSION RESULTS AND DISCUSSION  Figure 1. Mean (+1 SD) variation of swimming performance throughout seven con-secutive seasons in the freestyle events analyzed.     Table 1. Pearson Correlation Coefficients throughout all season’s analyzed   ACKNOWLEDGMENTS  From Mário J. Costa to the Portuguese Science and Technology Foundation (FCT) for the PhD grant (SFRH/BD/62005/2009).  50-m 12 13 14 15 16 17 13 0.84** 1         14 0.75** 0.90** 1       15 0.62** 0.73** 0.81** 1     16 0.48** 0.48** 0.58** 0.61** 1   17 0.16 0.25 0.35* 0.33 0.74** 1 18 0.31 0.26 0.32 0.28 0.75** 0.87** 100-m 12 13 14 15 16 17 13 0.83** 1         14 0.67** 0.87** 1       15 0.34* 0.56** 0.83** 1     16 0.13 0.36* 0.61** 0,80** 1   17 0.08 0,10 0.33* 0.57** 0.81** 1 18 -0.62 -0.23 0.16 0.37* 0.68** 0.84** 200-m 12 13 14 15 16 17 13 0.83** 1         14 0.51** 0.68** 1       15 0.23 0.43** 0.75** 1     16 -0.08 0.18 0.47** 0.71** 1   17 -0.10 0.10 0.29 0.56** 0.56** 1 18 0.10 0.21 0.34* 0.54** 0.48** 0.78** * P < 0.05; ** P < 0.01 

Tracking the freestyle performance from children to adult age

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Tracking the freestyle performance from children to adult age

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