Dynamics of variation of sports performance in light of Time Series based on Artificial Neural Networks in swimming

• Autorzy: Rejdych W. , Filip A. , Karpinski J. , Krawczyk M. , Jarosz J. , Socha T. , Maszczyk A.
• Języki publikacji: EN

Abstrakty

EN

Background. General availability of sports results, such as current world records, world rankings, and results of the Olympic Games, offers opportunities for the analysis variation in many different competitions and sports. Current trends in the progress in sports performance have been analysed based on e.g. freestyle swimming. The focus of this study is on the analysis of variability of sports results in swimming achieved by women and men in 11 Olympic Games in 1972-2012. Material and Methods. The analysis was based on the results of top eight finalists in all four events. Four 100m sprinting events (men's and women's) included in the program of the Olympic Games (freestyle, backstroke, breaststroke and butterfly swimming) were analysed. Results. The analyses showed that a statistically significant difference in women's real and model results was found for the most recent Olympics in Rio de Janeiro in 2016 for the 100m breaststroke swimming. Conclusions. The predicted results suggest that during the next Olympic Games in Tokyo, dynamics of progress in women's results is likely to be faster compared to men in three discussed events: 100m breaststroke, 100m butterfly and 100m backstroke. The above trend may not be observed in these events. Therefore, future research studies should be aimed to verify this tendency and the dynamics of progress in the results in breaststroke, backstroke and butterfly stroke.

• Wydawca: -
• Czasopismo: Baltic Journal of Health and Physical Activity. The Journal of Gdansk University of Physical Education and Sport
• Rocznik: 2018
• Tom: 10
• Numer: 2
• Opis fizyczny: p.25-33,ref.

Twórcy

autor

Rejdych W.

• Department of Individual Sports, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

autor

Filip A.

• Department of Statistics and Methodology, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

autor

Karpinski J.

• Department of Individual Sports, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

autor

Krawczyk M.

• Department of Individual Sports, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

autor

Jarosz J.

• Department of Individual Sports, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

autor

Socha T.

• Department of Individual Sports, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

autor

Maszczyk A.

• Department of Statistics and Methodology, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

Bibliografia

• [1] Nevill A, Whyte G, Holder R, Peyrebrune M. Are there limits to swimming world records? International J Sport Med. 2007;28:1012-1017. https://doi.org/10.1055/s-2007-965088
• [2] Barthelot G, Len S, Hellard P, et al. Technology & swimming: 3 steps beyond physiology. Mat Today. 2010;13:46-51. https://doi.org/10.1016/S1369-7021(10)70203-0
• [3] Haake S. The impact of technology on sporting performance in Olympic sports. J Sport Sci. 2009;27(13):1421-1431. https://doi.org/10.1080/02640410903062019
• [4] Neiva PH, Vilas-Boas JP, Barbosa TM, Silva AJ, Marinho DA. 13th FINA World Championships: Analysis of swimsuits used by elite male swimmers. J Hum Sport Exerc. 2011;6(1):87-93. https://doi.org/10.4100/jhse.2011.61.10
• [5] O’Connor L, Vozenilek J. Is it athlete or equipment? An analysis of the top swim performance from 1990 to 2010. J Strength Cond Res. 2011;25(12):3239-3241. https://doi.org/10.1519/JSC.0b013e3182392c5f
• [6] Swimming World Records. Long Course (50m) World Records. [Available at: www.fina.org/content/swimming-records, Accessed April 2018].
• [7] Ostrowski A, Strzała M, Stanula A, Juszkiewicz M, Pilch W, Maszczyk A. The role of training in the development of adaptive mechanisms in freedivers. J Hum Kinetics. 2012;32:197-210. https://doi.org/10.2478/v10078-012-0036-2
• [8] Maszczyk A, Roczniok R, Waśkiewicz Z, et al. Application of regression and neural models to predict competitive swimming performance. Percept Motor Skills. 2012;114(2):610-26. https://doi.org/10.2466/05.10.PMS.114.2.610-626
• [9] Grycmann P, Maszczyk A, Socha T, et al. Modelling analysis and prediction of women javelin throw results in the years 1946-2013. Biol Sport. 2015;32(4):345-350. https://doi.org/10.5604/20831862.1189201
• [10] Seiler S, De Koning JJ, Foster C. The fall and rise of the gender difference in elite anaerobic performance 1952-2006. Med Sci Sport Exerc. 2007;39:534-540. https://doi.org/10.1249/01.mss.0000247005.17342.2b
• [11] Stanula A, Maszczyk A, Roczniok R, et al. The Development and Prediction of Athletic Performance In Freestyle Swimming. J Hum Kinetics. 2012;2:97-107. https://doi.org/10.2478/v10078-012-0027-3
• [12] Liu Y, Paul S, Fu HF. Accomplishments and compromises in prediction research for world records and best performances in track and field and swimming. Measur Phys Educ Exerc Sci. 2012;6:167-182. https://doi.org/10.1080/1091367X.2012.700252
• [13] Smith DJ. A framework for understanding the training process leading to elite performance. Sport Med. 2003;33:1103-1126. https://doi.org/10.2165/00007256-200333150-00003
• [14] Costa MJ, Marinho DA, Reis VM, et al. Tracking the performance of world-ranked swimmers. J Sport Sci Med. 2010;9:411-417.
• [15] Trewin CB, Hopkins WG, Pyne DB. Relationship between world ranking and Olympic performance of swimmers. J Sport Sci. 2004;22:339-345. https://doi.org/10.1080/02640410310001641610
• [16] Balyi I, Hamilton A. Long term-planning of athlete development: the training to win phase. FHS. 1999;3:7-9.
• [17] Heazlewood T. Prediction versus reality: The use of mathematical models to predict elite performance in swimming and athletics at the Olympic games. J Sport Sci Med. 2006;5:541-547.
• [18] Pyne D, Mujika I, Reilly T. Peaking for optimal performance: research limitations and future directions. J Sport Sci. 2009;27:195-202. https://doi.org/10.1080/02640410802509136
• [19] Olympic Games swimming results. Meet list by type: Long Course (50m) Olympic Games. [Available at: https://www.swimrankings.net, Accessed April 2018].
• [20] Lord C. Aquatics 1908–2008 100 Years of Excellence in Sport. FINA; 2008.
• [21] Sobczyk M. Statystyka [Statistics]. Warszawa: Wydawnictwo Naukowe PWN; 2002. Polish.
• [22] Maszczyk A, Golas A, Pietraszewski P, Roczniok R, Zajac A, Stanula A. Application of Neural and regression models in sports results prediction. Procedia Soci Behavio Sci. 2014;117:482-487. https://doi.org/10.1016/j.sbspro.2014.02.249
• [23] Arellano R, Brown P, Cappaert J, Nelson R. Analysis of 50-m, 100-m, 200-m freestyle swimmers at the 1992 Olympic Games. J Appl Biomech. 1994;10:189-199. https://doi.org/10.1123/jab.10.2.189
• [24] 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(4):274-278. https://doi.org/10.1519/00124278-199611000-00012
• [25] Wolfrum M, Rust ChA, Rosemann T, Lepers R, Knechtle B. Changes in breaststroke swimming performances in national and international athletes competing between 1994 and 2011 – A comparison with freestyle swimming performances. BMC Sport Sci Med Rehab. 2014;6:18. https:// doi.org/10.1186/2052-1847-6-18
• [26] Tanaka H, Seals DR. Age and gender interactions in physiological functional capacity: Insight from swimming performance. J Appl Physiol. 1997;82:846-851. https://doi.org/10.1152/jappl.1997.82.3.846

Identyfikatory

DOI

10.29359/BJHPA.10.2.03