The Development and Prediction of Athletic Performance in Freestyle Swimming

Open access

The Development and Prediction of Athletic Performance in Freestyle Swimming

This paper analyses the dynamics of changes between the performances of elite freestyle swimmers recorded at particular Olympic Games. It also uses a set of chronologically ordered results to predict probable times of swimmers at the 2012 Olympic Games in London. The analysis of past performances of freestyle swimmers and their prediction have revealed a number of interesting tendencies within separately examined results of men and women. Women's results improve more dynamically compared with men's. Moreover, the difference between women's and men's results is smaller, the longer the swimming distance. As both male and female athletes tend to compete more and more vigorously within their groups, the gap between the gold medallist and the last finisher in the final is constantly decreasing, which provides significant evidence that this sport discipline continues to develop.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Adamczyk JG. The estimation of the rest test usefulness in monitoring the anaerobic capacity of sprinters in athletics. Polish Journal of Sport & Tourism 2011; 18(3) 214.

  • Anderson M Hopkins W Roberts A and Pyne D. Ability of test measures to predict competitive performance in elite swimmers. Journal of Sports Sciences 2008; 26: 123-130.

  • Arellano R Brown P Cappaert J and Nelson R. Analysis of 50-m 100-m and 200-m Freestyle swimmers at the 1992 Olympic Games. Journal of Applied Biomechanics 1994; 10: 189-199.

  • Aspenes S Kjendlie PL Hoff J and Helgerud J. (2009) Combined strength and endurance training in competitive swimmers. Journal of Sport Science and Medicine 8 357-365

  • Busso T and Thomas L. Using mathematical modeling in training planning. Int J Sports Physiol Perf 2006; 1: 400-405.

  • Chatterjee S Laudato M. An analysis of world record times of men and women in Running Skating and swimming. Journal of Strength and Conditioning Research 1996; 10(4): 274-278.

  • Costa M et al. Tracking the performance of world-ranked swimmers. Journal of Sports Science and Medicine 2010; 9: 411-417.

  • Edelman-Nusser J Hohmann A Henneberg B. Modeling and prediction of competitive performance in swimming upon neural networks. European Journal of Sport Science 2002; 2(2): 1-10.

  • FINA. FINA swimming rules 2009-2013. Lausanne Switzerland: FINA 2009.

  • Fukuda DH. Smith AE. Kendall KL. Cramer JT. Stout J R. An Alternative Approach to the Army Physical Fitness Test Two-Mile Run Using Critical Velocity and Isoperformance Curves. Military Medicine 2012; 177 (2): 145-151

  • Heazlewood I and Lackey G. The use of mathematical models to predict elite athletic performance at the Olympic Games. In: Third Conference on Mathematics and Computers in Sport 30 September - 2 October 1996 Bond University Queensland Conference Proceedings. Ed: deMestre N. p. 185-206.

  • Heazlewood T. Prediction versus reality: The use mathematical models to predict elite performance in swimming and athletics at the Olympic Games. Journal of Sports Science and Medicine 2006; 5: 541-547.

  • Hellard P Avalos M Millet G Lacoste L Barale F and Chatard JC. Modeling the residual effects and threshold saturation of training: A case study of Olympic swimmers. Journal of Strength and Conditioning Research 19: 67-75 2005.

  • Maglischo EW. Swimming fastest. Champaign Illinois: 2003 Human Kinetics.

  • Mamen A. Laparidist C. van den Tillaar R. Precision in Estimating Maximal Lactate Steady State Performance in Running Using a Fixed Blood Lactate Concentration or a Delta Value from an Incremental Lactate Profile Test. International Journal of Applied Sports Sciences 2011; 23(1): 212.

  • O'Connor L Vozenilek J. Is it thlete or the equipment? An analysis of the top swim performance from 1990 to 2010. Journal of Strength and Conditioning Research 2011; 25(12): 3239-3241.

  • Mageean A L. Alexander RP. Mier CM. Repeated Sprint Performance in Male and Female College Athletes Matched for VO Relative to Fat Free Mass. International Journal of Exercise Science 2011; 4(4): 229-237.

  • Péronnet F and Thibault G. Mathematical analysis of running performance and world running records. Journal of Applied Physiology 1989; 67: 453-465.

  • Prendergast K. What do world running records tell us? Modern Athlete and Coach 1990; 28: 33-36.

  • Pyne D Trewin C and Hopkins W. Progression and variability of competitive performance of Olympic swimmers. Journal of Sports Sciences 2004; 22: 613-620.

  • Silva A et al. The use of neural network technology to model swimming performance. Journal of Sports Science and Medicine 2007; 6: 117-125.

  • Trewin C Hopkins W and Pyne D. Relationship between world ranking and Olympic performance in swimmers. Journal of Sports Sciences 2004; 22: 339-345.

Journal information
Impact Factor

IMPACT FACTOR 2018: 1.414
5-year IMPACT FACTOR: 1.858

CiteScore 2018: 1.60

SCImago Journal Rank (SJR) 2018: 0.644
Source Normalized Impact per Paper (SNIP) 2018: 0.941

Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 361 100 5
PDF Downloads 259 56 2