Discerning excellence from mediocrity in swimming: New insights using Bayesian quantile regression

References

• Ackland, T. R., & Bloomfield, J. (1996). Stability of human proportions through adolescent growth. Australian Journal of Science and Medicine in Sport, 28(2), 57–60.
   PubMedGoogle Scholar
• Barbosa, T. M., Bartolomeu, R., Morais, J. E., & Costa, M. J. (2019). Skillful swimming in age-groups is determined by anthropometrics, biomechanics and energetics. Frontiers in Physiology, 10, 73. doi: 10.3389/fphys.2019.00073
   PubMed Web of Science ®Google Scholar
• Bompa, T. (1994). Theory and methodology of training. Toronto: Kendall/Hunt Publishing Company.
   Google Scholar
• Bürkner, P. C. (2018). Advanced Bayesian multilevel modeling with the R package brms. The R Journal, 10(1), 395–411. doi: 10.32614/RJ-2018-017
   Google Scholar
• Chatard, J. C., Lavoie, J. M., & Lacour, J. R. (1992). Swimming skill cannot be interpreted directly from the energy cost of swimming. In D. Maclaren, T. Reilly, & A. Lees (Eds.), Biomechanics and medicine in swimming VI (pp. 173–180). London: E & FN Spon.
   Google Scholar
• Duche, P., Falgairette, G., Bedu, M., Lac, G., Robert, A., & Coudert, J. (1993). Analysis of performance of prepubertal swimmers assessed from anthropometric and bio-energetic characteristics. European Journal of Applied Physiology and Occupational Physiology, 66, 467–471. doi: 10.1007/BF00599623
   PubMed Web of Science ®Google Scholar
• Federation Internationale De Natation (FINA). (2014). Talent identification programmes. [cited 18 September 2014]. Retrieved from http://www.fina.org/
   Google Scholar
• Geladas, N. D., Nassis, G. P., & Pavlicevic, S. (2005). Somatic and physical traits affecting sprint swimming performance in young swimmers. International Journal of Sports Medicine, 26, 139–144. doi: 10.1055/s-2004-817862
   PubMed Web of Science ®Google Scholar
• Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2019). R-squared for Bayesian regression models. The American Statistician, 73(3), 307–309. doi: 10.1080/00031305.2018.1549100
   Web of Science ®Google Scholar
• Heinlein, S. A., & Cosgarea, A. J. (2010). Biomechanical considerations in the competitive swimmer’s shoulder. Sports Health, 2, 519–525. doi: 10.1177/1941738110377611
   PubMedGoogle Scholar
• Hollander, P., de Groot, G., van Ingen Schenau, G. J., Kahman, R., & Toussaint, H. M. (1988). Contribution of the legs in front crawl swimming. In V. B. E. Ungerechts, K. Reischle, & K. Wilke (Eds.), Swimming science (pp. 39–43). Champaign, IL: Human Kinetics.
   Google Scholar
• Issurin, V. B. (2017). Evidence-based prerequisites and precursors of athletic talent: A review. Sports Medicine, 47(10), 1993–2010. doi: 10.1007/s40279-017-0740-0
   PubMed Web of Science ®Google Scholar
• Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. doi: 10.1080/01621459.1995.10476572
   Web of Science ®Google Scholar
• Koenker, R. (2005). Quantile regression (Econometric society monographs). Cambridge: Cambridge University Press.
   Google Scholar
• Lätt, E., Jürimäe, J., Mäestu, J., Purge, P., Rämson, R., Haljaste, K., … Jürimäe, T. (2010). Physiological, biomechanical and anthropometrical predictors of sprint swimming performance in adolescent swimmers. Journal of Sports Science & Medicine, 9, 398–404.
   PubMed Web of Science ®Google Scholar
• Mclean, S. P., & Hinrichs, R. N. (1998). Sex differences in the centre of buoyancy location of competitive swimmers. Journal of Sports Sciences, 16(4), 373–383. doi: 10.1080/02640419808559365
   PubMed Web of Science ®Google Scholar
• Merlise, A. C. (2018). BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling, R package version 1.5.3.
   Google Scholar
• Merlise, A. C., Ghosh, J., & Littman, M. L. (2011). Bayesian adaptive sampling for variable selection and model Averaging. Journal of Computational and Graphical Statistics, 20(1), 80–101. doi: 10.1198/jcgs.2010.09049
   Web of Science ®Google Scholar
• 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 Performance, 12, 198–205. doi: 10.1123/ijspp.2015-0420
   PubMed Web of Science ®Google Scholar
• Morais, J. E., Silva, A. J., Marinho, D. A., Seifert, L., & Barbosa, T. M. (2015). Cluster stability as a new method to assess changes in performance and its determinant factors over a season in young swimmers. International Journal of Sports Physiology Performance, 10, 261–268. doi: 10.1123/ijspp.2013-0533
   PubMed Web of Science ®Google Scholar
• Nevill, A. M., Oxford, S. W., & Duncan, M. J. (2015). Optimal body size and limb length ratios associated with 100-m personal-best swim speeds. Medicine & Science in Sports & Exercise, 47, 1714–1718. doi: 10.1249/MSS.0000000000000586
   PubMed Web of Science ®Google Scholar
• Nevill, A. M., Yassine, N., Myers, T. D., Sammoud, S., & Chaabene, H. (2020). Key somatic variables associated with, and differences between the 4 swimming strokes. Journal of Sports Science, 1, 8.
   Google Scholar
• R Core Team. (2019). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
   Google Scholar
• Sammoud, S., Nevill, A. M., Negra, Y., Bouguezzi, R., Chaabene, H., & Hachana, Y. (2018a). 100-meter breaststroke swimming performance in youth swimmers: The predictive value of anthropometrics. Pediatric Exercise Science, 2017; 1(30), 3393–3401.
   Google Scholar
• Sammoud, S., Nevill, A. M., Negra, Y., Bouguezzi, R., Chaabene, H., & Hachana, Y. (2018b). Allometric associations between body size, shape, and 100-m butterfly speed performance. Journal of Sports Medicine Physical Fitness, 58(5), 630–637. doi: 10.23736/S0022-4707.17.07480-1
   PubMed Web of Science ®Google Scholar
• Shahbazi-Moghadam, M. (2008). Determination of arms and legs contribution to propulsion and percentage of coordination in breaststroke swim. In ISBS-Conference Proceedings Archive, 2008; 1,1.
   Google Scholar
• Slaughter, M. H., Lohman, T. G., Boileau, R. A., Horswill, C. A., Stillman, R. J., VanLoan, M. D., & Bemben, D. A. (1988). Skinfold equations for estimation of body fatness in children and youth. Human Biology, 60, 709–723.
   PubMed Web of Science ®Google Scholar
• Sprague, H. A. (1976). Relationship of certain physical measurements to swimming speed. Research Quarterly, 47, 810–814.
   PubMedGoogle Scholar
• Stan Development Team. (2018). RStan: the R interface to Stan. R package version 2.18.2. Retrieved from http://mc-stan.org/
   Google Scholar
• Stewart, A., Marfell-Jones, M., Olds, T., & de Ridder, H. (2011). International standards for anthropometric assessment. Lower Hutt: ISAK; 57–72.
   Google Scholar
• Toussaint, H. M., & Beek, P. J. (1992). Biomechanics of competitive front crawl swimming. Sports Medicine, 13(1), 8–24. doi: 10.2165/00007256-199213010-00002
   PubMed Web of Science ®Google Scholar
• Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413–1432. doi: 10.1007/s11222-016-9696-4
   Web of Science ®Google Scholar
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. doi: 10.1080/00031305.2016.1154108
   Web of Science ®Google Scholar
• Yang, Y. W., Wang, H. J., & He, X. M. (2016). Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood. International Statistical Review = Revue internationale De Statistique, 84, 327–334.
   Web of Science ®Google Scholar