Predicting Breaststroke and Butterfly Stroke Results in Swimming Based on Olympics History
Abstract
:1. Introduction
2. Materials and Methods
2.1. Subjects
2.2. Statistical Analysis
3. Results
3.1. 100 m Breaststroke
3.2. 200 m Breaststroke
3.3. 100 m Butterfly
3.4. 200 m Butterfly
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Mountjoy, M.; Junge, A.; Alonso, J.M.; Clarsen, B.; Pluim, B.M.; Shrier, I.; van den Hoogenband, C.; Marks, S.; Gerrard, D.; Heyns, P.; et al. Consensus statement on the methodology of injury and illness surveillance in FINA (aquatic sports). Br. J. Sports Med. 2016, 50, 590–596. [Google Scholar] [CrossRef] [PubMed]
- Pelayo, P.; Alberty, M. The history of swimming research. In World Book of Swimming: From Science to Performance; Nova Publishers: Hauppauge, NY, USA, 2011; ISBN 9781616682026. [Google Scholar]
- Lippi, G.; Banfi, G.; Favaloro, E.J.; Rittweger, J.; Maffulli, N. Updates on improvement of human athletic performance: Focus on world records in athletics. Br. Med. Bull. 2008, 87, 7–15. [Google Scholar] [CrossRef] [PubMed]
- Nevill, A.M.; Whyte, G.P.; Holder, R.L.; Peyrebrune, M. Are there limits to swimming world records? Int. J. Sports Med. 2007, 28, 1012–1017. [Google Scholar] [CrossRef]
- Stanula, A.; Maszczyk, A.; Roczniok, R.; Pietraszewski, P.; Ostrowski, A.; Zajac, A.; Strzała, M. The development and prediction of athletic performance in freestyle swimming. J. Hum. Kinet. 2012, 97–107. [Google Scholar] [CrossRef] [PubMed]
- Berthelot, G.; Sedeaud, A.; Marck, A.; Antero-Jacquemin, J.; Schipman, J.; Saulière, G.; Marc, A.; Desgorces, F.D.; Toussaint, J.F. Has Athletic Performance Reached its Peak? Sport. Med. 2015, 45, 1263–1271. [Google Scholar] [CrossRef] [Green Version]
- O’Connor, L.M.; Vozenilek, J.A. Is it the athlete or the equipment? An analysis of the top swim performances from 1990 to 2010. J. Strength Cond. Res. 2011, 25, 3239–3241. [Google Scholar] [CrossRef]
- Seifert, L.; Leblanc, H.; Chollet, D.; Sanders, R.; Persyn, U. Breaststroke kinematics. In World Book of Swimming: From Science to Performance; Nova Publishers: Hauppauge, NY, USA, 2011; pp. 135–152. ISBN 9781616682026. [Google Scholar]
- Barbosa, T.M.; Fernandes, R.J.; Morouco, P.; Vilas-Boas, J.P. Predicting the intra-cyclic variation of the velocity of the centre of mass from segmental velocities in butterfly stroke: A pilot study. J. Sport. Sci. Med. 2008, 7, 201–209. [Google Scholar]
- De Jesus, K.; de Jesus, K.; Figueiredo, P.A.; Gonca̧lves, P.; Vilas-Boas, J.P.; Fernandes, R.J. Effects of fatigue on kinematical parameters during submaximal and maximal 100-m butterfly bouts. J. Appl. Biomech. 2012, 28, 599–607. [Google Scholar] [CrossRef]
- Barbosa, T.; Fernandes, R.; Keskinen, K.L.; Colaço, P.; Cardoso, C.; Silva, J.; Vilas-Boas, J.P. Evaluation of the energy expenditure in competitive swimming strokes. Int. J. Sports Med. 2006, 27, 894–899. [Google Scholar] [CrossRef] [Green Version]
- Strzała, M.; Krężałek, P.; Kaca, M.; Głąb, G.; Ostrowski, A.; Stanula, A.; Tyka, A. Swimming speed of the breaststroke kick. J. Hum. Kinet. 2012, 35, 133–139. [Google Scholar] [CrossRef]
- Strzała, M.; Stanula, A.; Krężałek, P.; Ostrowski, A.; Kaca, M.; Głąb, G. Butterfly Sprint Swimming Technique, Analysis of Somatic and Spatial-Temporal Coordination Variables. J. Hum. Kinet. 2017, 60, 51–62. [Google Scholar] [CrossRef] [Green Version]
- FINA, R. FINA Swimming Rules: 2017–2021. Fed. Int. Natat. 2017, 1, 15. [Google Scholar]
- Maglischo, E. Swimming Fastest; Human Kinetics: Champaign, IL, USA, 2003. [Google Scholar]
- Pop, C. The Modern Olympic Games—A Globalised Cultural and Sporting Event. Proc. Soc. Behav. Sci. 2013, 92, 728–734. [Google Scholar] [CrossRef] [Green Version]
- Jensen, R.D.; Christiansen, A.V.; Henriksen, K. The Olympic Games: The Experience of a Lifetime or Simply the Most Important Competition of an Athletic Career? Phys. Cult. Sport. Stud. Res. 2014, 64, 41–52. [Google Scholar] [CrossRef]
- Pyne, D.B.; Trewin, C.B.; Hopkins, W.G. Progression and variability of competitive performance of Olympic swimmers. J. Sports Sci. 2004, 22, 613–620. [Google Scholar] [CrossRef] [PubMed]
- Trewin, C.B.; Hopkins, W.G.; Pyne, D.B. Relationship between world-ranking and Olympic performance of swimmers. J. Sports Sci. 2004, 22, 339–345. [Google Scholar] [CrossRef] [PubMed]
- Issurin, V.; Kaufman, L.; Lustig, G.; Tenenbaum, G. Factors affecting peak performance in the swimming competition of the Athens Olympic Games. J. Sports Med. Phys. Fitness 2008, 48, 1–8. [Google Scholar]
- Leroy, A.; Gey, S. Functional data analysis in sport science: Example of swimmers’ progression curves clustering. Appl. Sci. 2018, 8, 1766. [Google Scholar] [CrossRef] [Green Version]
- Costa, M.J.; Marinho, D.A.; Reis, V.M.; Silva, A.J.; Marques, M.C.; Bragada, J.A.; Barbosa, T.M. Tracking the performance of world-ranked swimmers. J. Sport. Sci. Med. 2010, 9, 411–417. [Google Scholar]
- Hellard, P.; Avalos, M.; Millet, G.; Lacoste, L.; Barale, F.; Chatard, J.C. Modeling the residual effects and threshold saturation of training: A case study of olympic swimmers. J. Strength Cond. Res. 2005, 19, 67–75. [Google Scholar] [CrossRef] [Green Version]
- Chınurum, J.; OgunjImi, L.O.; O’Neill, C.B. Gender and Sports in Contemporary Society. J. Educ. Soc. Res. 2014, 4, 25. [Google Scholar] [CrossRef] [Green Version]
- Oliver, K.L.; Hamzeh, M. “The boys won’t let us play”: Fifth-Grade mestizas challenge physical activity discourse at school. Res. Q. Exerc. Sport 2010, 81, 38–51. [Google Scholar] [CrossRef]
- Hannon, J.; Soohoo, S.; Reel, J.; Ratliffe, T. Gender stereotyping and the influence of race in sport among adolescents. Res. Q. Exerc. Sport 2009, 80, 676–684. [Google Scholar] [CrossRef]
- FINA. Swimming Results. Available online: www.fina.org/swimming/results (accessed on 6 April 2021).
- Zheng, J. A policy analysis of the development of elite swimming in China between 2000 and 2012: A national team perspective. Int. J. Hist. Sport 2017, 34, 1247–1274. [Google Scholar] [CrossRef]
- Gerrard, D. Drug misuse in sport: A historical perspective. N. Z. Med. J. 2015, 128, 16–18. [Google Scholar] [PubMed]
- Psycharakis, S.G.; Cooke, C.B.; Paradisis, G.P.; O’Hara, J.; Phillips, G. Analysis of selected kinematic and physiological performance determinants during incremental testing in elite swimmers. J. Strength Cond. Res. 2008, 22, 951–957. [Google Scholar] [CrossRef] [PubMed]
- Sánchez, J.; Arellano, R. Stroke index values according to level, gender, swimming style and event race distance. In Proceedings of the 20 International Symposium on Biomechanics in Sports, Cáceres, Spain, 1–5 July 2002; pp. 56–59. [Google Scholar]
- Knechtle, B.; Dalamitros, A.A.; Barbosa, T.M.; Sousa, C.V.; Rosemann, T.; Nikolaidis, P.T. Sex differences in swimming disciplines—Can women outperform men in swimming? Int. J. Environ. Res. Public Health 2020, 17, 3651. [Google Scholar] [CrossRef] [PubMed]
- Hunter, S.K. Sex differences in human fatigability: Mechanisms and insight to physiological responses. Acta Physiol. 2014, 210, 768–789. [Google Scholar] [CrossRef] [Green Version]
- Fukuda, D.H.; Smith, A.E.; Kendall, K.L.; Cramer, J.T.; Stout, J.R. An alternative approach to the army Physical Fitness test two-mile run using critical velocity and isoperformance curves. Mil. Med. 2012, 177, 145–151. [Google Scholar] [CrossRef] [Green Version]
- Heazlewood, T. Prediction versus reality: The use of mathematical models to predict elite performance in swimming and athletics at the Olympic Games. Proc. J. Sports Sci. Med. 2006, 5, 541–547. [Google Scholar]
- Peronnet, F.; Thibault, G. Mathematical analysis of running performance and world running records. J. Appl. Physiol. 1989, 67, 453–465. [Google Scholar] [CrossRef] [Green Version]
- Chatterjee, S.; Laudato, M. An analysis of world record times of men and women in running, skating, and swimming. J. Strength Cond. Res. 1996, 10, 274–278. [Google Scholar] [CrossRef]
- Aspenes, S.; Kjendlie, P.L.; Hoff, J.; Helgerud, J. Combined strength and endurance training in competitive swimmers. J. Sport. Sci. Med. 2009, 8, 357–365. [Google Scholar]
- Robazza, C.; Pellizzari, M.; Bertollo, M.; Hanin, Y.L. Functional impact of emotions on athletic performance: Comparing the IZOF model and the directional perception approach. J. Sports Sci. 2008, 26, 1033–1047. [Google Scholar] [CrossRef]
- Zajac, A.; Cholewa, J.; Poprzecki, S.; Waśkiewicz, Z.; Langfort, J. Effects of sodium bicarbonate ingestion on swim performance in youth athletes. J. Sport. Sci. Med. 2009, 8, 45–50. [Google Scholar]
- Neptune, R.R.; McGowan, C.P.; Fiandt, J.M. The influence of muscle physiology and advanced technology on sports performance. Annu. Rev. Biomed. Eng. 2009, 11, 81–107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sedeaud, A.; Marc, A.; Schipman, J.; Schaal, K.; Danial, M.; Guillaume, M.; Berthelot, G.; Toussaint, J.F. Secular trend: Morphology and performance. J. Sports Sci. 2014, 32, 1146–1154. [Google Scholar] [CrossRef]
- Siders, W.A.; Lukaski, H.C.; Bolonchuk, W.W. Relationships among swimming performance, body composition and somatotype in competitive collegiate swimmers. J. Sports Med. Phys. Fit. 1993, 33, 166–171. [Google Scholar]
- König, S.; Valeri, F.; Wild, S.; Rosemann, T.; Rüst, C.A.; 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. [Google Scholar] [CrossRef] [Green Version]
- Reardon, C.; Creado, S. Drug abuse in athletes. Subst. Abuse Rehabil. 2014, 95. [Google Scholar] [CrossRef] [Green Version]
- DIfiori, J.P.; Green, G.; Meeuwisse, W.; Putukian, M.; Solomon, G.S.; Sills, A. Return to sport for North American professional sport leagues in the context of COVID-19. Br. J. Sports Med. 2021, 55, 417–421. [Google Scholar] [CrossRef] [PubMed]
- Sarto, F.; Impellizzeri, F.M.; Spörri, J.; Porcelli, S.; Olmo, J.; Requena, B.; Suarez-Arrones, L.; Arundale, A.; Bilsborough, J.; Buchheit, M.; et al. Impact of Potential Physiological Changes due to COVID-19 Home Confinement on Athlete Health Protection in Elite Sports: A Call for Awareness in Sports Programming. Sport. Med. 2020, 50, 1417–1419. [Google Scholar] [CrossRef] [PubMed]
- Reardon, C.L.; Bindra, A.; Blauwet, C.; Budgett, R.; Campriani, N.; Currie, A.; Gouttebarge, V.; McDuff, D.; Mountjoy, M.; Purcell, R.; et al. Mental health management of elite athletes during COVID-19: A narrative review and recommendations. Br. J. Sports Med. 2021. [Google Scholar] [CrossRef] [PubMed]
Stroke | Distance | Sex | Place | Values of the Univariate Linear Regression Model | Prediction of the Results for the 2021 Olympics | |||||
---|---|---|---|---|---|---|---|---|---|---|
F | df | r | r2 | Prediction Value | Confidence Interval 95% | |||||
LL | UL | |||||||||
Breaststroke | 100 m | Men | 1st | 215.15 * | 1. 10 | 0.98 | 0.96 | 0:56.96 | 0:56.27 | 0:57.65 |
final | 256.49 * | 1. 10 | 0.98 | 0.96 | 0:58.12 | 0:57.52 | 0:58.73 | |||
8th | 113.13 * | 1. 9 | 0.96 | 0.93 | 0:59.16 | 0:58.32 | 0:59.99 | |||
Women | 1st | 129.77 * | 1. 10 | 0.96 | 0.93 | 1:03.41 | 1:02.37 | 1:04.45 | ||
final | 83.96 * | 1. 10 | 0.95 | 0.89 | 1:04.44 | 1:03.02 | 1:05.86 | |||
8th | 57.31 * | 1. 9 | 0.93 | 0.86 | 1:05.65 | 1:03.87 | 1:07.43 | |||
200 m | Men | 1st | 60.57 * | 1. 10 | 0.93 | 0.86 | 2:04.80 | 2:02.46 | 2:07.14 | |
final | 156.49 * | 1. 10 | 0.97 | 0.94 | 2:05.10 | 2:03.21 | 2:06.99 | |||
8th | 144.26 * | 1. 8 | 0.97 | 0.95 | 2:06.12 | 2:03.87 | 2:08.38 | |||
Women | 1st | 63.86 * | 1. 10 | 0.93 | 0.86 | 2:15.75 | 2:12.30 | 2:19.21 | ||
final | 82.89 * | 1. 10 | 0.94 | 0.89 | 2:17.99 | 2:14.91 | 2:21.06 | |||
8th | 77.32 * | 1. 10 | 0.94 | 0.87 | 2:20.12 | 2:16.92 | 2:23.33 | |||
Butterfly | 100 m | Men | 1st | 96.48 * | 1. 10 | 0.95 | 0.91 | 0:49.85 | 0:49.16 | 0:50.54 |
final | 231.19 * | 1. 10 | 0.98 | 0.96 | 0:50.26 | 0:49.75 | 0:50.77 | |||
8th | 216.85 * | 1. 10 | 0.98 | 0.96 | 0:50.51 | 0:49.90 | 0:51.12 | |||
Women | 1st | 69.59 * | 1. 10 | 0.94 | 0.87 | 0:54.66 | 0:53.50 | 0:55.83 | ||
final | 186.93 * | 1. 10 | 0.97 | 0.95 | 0:55.78 | 0:55.06 | 0:56.50 | |||
8th | 236.34 * | 1. 9 | 0.98 | 0.96 | 0:56.73 | 0:56.06 | 0:57.41 | |||
200 m | Men | 1st | 109.31 * | 1. 10 | 0.96 | 0.92 | 1:51.21 | 1:50.05 | 1:52.40 | |
final | 107.46 * | 1. 10 | 0.96 | 0.91 | 1:52.40 | 1:51.04 | 1:53.76 | |||
8th | 114.65 * | 1. 10 | 0.96 | 0.92 | 1:53.64 | 1:52.21 | 1:55.07 | |||
Women | 1st | 44.95 * | 1. 10 | 0.9 | 0.82 | 2:02.18 | 2:00.03 | 2:04.33 | ||
final | 77.93 * | 1. 10 | 0.94 | 0.87 | 2:04.02 | 2:02.20 | 2:05.84 | |||
8th | 45.89 * | 1. 10 | 0.91 | 0.82 | 2:05.29 | 2:02.52 | 2:08.06 |
Event | OG | Women | Men | Difference [s] | Difference [%] | |
---|---|---|---|---|---|---|
Breaststroke | 100 m | Rio 2016 | 1:04.93 | 0:57.13 | 7.80 | 13.65 |
Tokyo 2021 | 1:03.41 | 0:56.96 | 6.45 | 11.32 | ||
200 m | Rio 2016 | 2:20.30 | 2:07.46 | 12.84 | 10.07 | |
Tokyo 2021 | 2:15.75 | 2:04.80 | 10.95 | 8.77 | ||
Butterfly | 100 m | Rio 2016 | 0:55.48 | 0:50.39 | 05.09 | 10.10 |
Tokyo 2021 | 0:54.66 | 0:49.85 | 04.81 | 9.65 | ||
200 m | Rio 2016 | 2:04.85 | 1:53.36 | 11.49 | 10.14 | |
Tokyo 2021 | 2:02.18 | 1:51.21 | 10.97 | 9.86 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hołub, M.; Stanula, A.; Baron, J.; Głyk, W.; Rosemann, T.; Knechtle, B. Predicting Breaststroke and Butterfly Stroke Results in Swimming Based on Olympics History. Int. J. Environ. Res. Public Health 2021, 18, 6621. https://doi.org/10.3390/ijerph18126621
Hołub M, Stanula A, Baron J, Głyk W, Rosemann T, Knechtle B. Predicting Breaststroke and Butterfly Stroke Results in Swimming Based on Olympics History. International Journal of Environmental Research and Public Health. 2021; 18(12):6621. https://doi.org/10.3390/ijerph18126621
Chicago/Turabian StyleHołub, Maciej, Arkadiusz Stanula, Jakub Baron, Wojciech Głyk, Thomas Rosemann, and Beat Knechtle. 2021. "Predicting Breaststroke and Butterfly Stroke Results in Swimming Based on Olympics History" International Journal of Environmental Research and Public Health 18, no. 12: 6621. https://doi.org/10.3390/ijerph18126621
APA StyleHołub, M., Stanula, A., Baron, J., Głyk, W., Rosemann, T., & Knechtle, B. (2021). Predicting Breaststroke and Butterfly Stroke Results in Swimming Based on Olympics History. International Journal of Environmental Research and Public Health, 18(12), 6621. https://doi.org/10.3390/ijerph18126621