Dr. Hab., Professor V.Y. Karpov¹
M.V. Nekrasova²
Associate Professor R.B. Krasnov³
E.V. Garina^4
1,2Russian State Social University, Moscow
³Penza State University, Penza
⁴Sechenov First Moscow State Medical University, Moscow

Corresponding author: vu2014@mail.ru

Abstract

Objective of the study was to analyze the modern swimming speed control profiles in the youth 100m freestyle.

Methods and structure of the study. We rated the swimming speed in every 25m segment; stroke pace by five double stroke tests; and the stroke length by segmental swimming speed divided by the relevant stroke pace. The study was run in September-November 2019 at the Russian State Social University’s and Moscow State Medical University’s swimming facilities, with the 10-17 year-old swimmers (n= 26) sampled for the tests.

Results and conclusion. We found the 100-meter freestyle swimming speed control patterns fairly individual, with every analyzed age group found swimming faster the first 25 meters (within one-percent significance rate). This startup acceleration appears largely due to the jump start, since the water start was found of insignificant contribution to the segment 1 speed.

The study found wavelike variations and correlations of the segmental speeds, stroke length and stroke pace in the 100m freestyle in every age group. The findings on the age-specific segmental stroking patterns are recommended being taken in account by the junior swimmers’ training and competitive systems.          

The study found the first 25m segment speed in the 100m freestyle being the fastest in the junior sample, with every age group tested with the stroke pace sags on the distance, particularly in the final 25m segment. The 10-13-year-old group was tested with the stroke length gradually growing and then dropping in the final segment – versus the wavelike stroke length variation in the 14-17-year-old group. On the whole, the junior swimmers’ sample was tested with inconsistent segmental speed/ stroke pace/ stroke length variations.

Keywords: swimming parameters, junior athletes, various distance segments, front crawl.

Background. The ever-growing competitiveness of the modern swimming sport urges the sport community to look for the training system excellence models and tools [1, 2], with a special priority to the swimming speed control including the stroke pace and stroke length profiled by the distance segments [3, 4]. The sport research community, however, is still in need of special studies to analyze the swimming profiles in the youth 100m freestyle to find the best speed control model.

Objective of the study was to analyze the modern swimming speed control profiles in the youth 100m freestyle.

Methods and structure of the study. We rated the swimming speed in every 25m segment; stroke pace by five double stroke tests; and the stroke length by segmental swimming speed divided by the relevant stroke pace. The study was run in September-November 2019 at the Russian State Social University’s and Moscow State Medical University’s swimming facilities, with the 10-17 year-old swimmers (n= 26) sampled for the tests.

Results and discussion. We found the 100-meter freestyle swimming speed control patterns fairly individual, with every analyzed age group found swimming the first 25 meters faster (within one-percent significance rate). This startup acceleration appears largely due to the jump start, since the water start was found of insignificant contribution to the segment 1 speed.

We found the first 25m segment speed of the 10-11 year-olds averaging at 1.44 m/s (t = 17.36s); 12-13 year-olds at 1.63 m/s (t =15.34s); 14-15 year-olds at 1.78 m/s (t = 14.04s), and the 16-17 year-olds at 1.91 m/s (t =13.09s). The second 25m segment speed was found to fall in the 10-11 year-old group to 1.31 m/s (t = 19.08s); 12-13 year-old group to 1.50 m/s (t = 16.67s ), 14-15 year-old group to 1.56 m/s (t = 16.03s); and the 16-17 year-old group to 1.64 m/s (t = 15.24s). The third 25m segment (50-75m) speed was tested to significantly change as compared to the second segment, with the 12-13 and 16-17 year-old groups showing insignificant falls versus the 10-11 and 14-15 years old groups that were tested with insignificant growth. The fourth 25m segment (75-100m) speed was tested to significantly fall in every age group, particularly in the 10-11-year-old group (18.8%, p <0.01) followed by the 12-13 year-olds (17.3%, p <0.01), 14-15 year-olds (12.0%, p <0.01) and the 16-17 year-olds (3.8%, p <0.05). It may be concluded that the segmental speed was found age-specific.

Furthermore, we should mention the natural competitive progress in the 100m freestyle with age, as the 10-11 year-olds recorded 77.54s; 12-13 year-olds to 68.52s; 14-15 year-olds 63.22s; and the 16-17 year-olds 59.82s. The stroke pace was also tested to vary on a segment-specific basis, with every age group tested with the stroke pace falls, particularly in the final segment. It should be emphasized that the stroke pace was tested to fall with age, particularly in the first segment. The stroke pace contrast ratio (segment 1 to segment 4 speed ratio) was tested the highest in the 10-11-year-old group (K = 1.18), and lowest in the 16-17-year-old group (K = 1.08 units) – that may be interpreted as indicative of the stroke pace stability growing with age despite the significant drop in segment 4, with the one- and five-percent significant margins (versus segment 1) for the 10-15 and 16-17 year-olds, respectively.

The segmental stroke length analysis found the age variations as well, with the 10-13-year-olds stroke length tested to gradually grow from the start and then fall in segment four; the 14-17-year-olds stroke length showing a wavelike variation; and the 16-17-year-olds stroke length tested highly stable. Therefore, the 10-17 year-old sample was tested with the age- and segment-specific speed/ stroke length / stroke pace variations that need to be taken into account by the training system designers for competitive progress.

The speed variability analysis found the segmental speed control rates being age-specific, with the highest variation range in the 10-11-year-old group (V = 4.2-9.8%) and lowest range in the 16-17 year-old group (V = 3.5-6.4%). The variation was the shortest and highest for segments 1 and 4, respectively, albeit never exceeding 10%, i.e. fairly stable.

The stroke pace variability was the highest (V = 4.8-12.5%) in the 10-11 year-old group – that may be interpreted as indicative of some individuals in the group prioritizing stroke pace and the others stroke length. The 16-17-year-olds were the only group with the low variation range, with the highest and lowest variations typical for the final segment 4 and starting segment 1, respectively – that is the case for every age group in fact.

We analyzed the age-specific stroke length correlations with the distance segments, and found them growing with age. The segment 1 and 4 stroke length were found mostly uncorrelated, and only the 16-17-year-olds were tested with a significant (r = 0.566) correlation of the segmental stroke length. The segmental stroke pace correlations were higher, with eight values found correlated within one-percent significant margin and growing with age. The correlations were the highest for the adjacent segments; and only the 16-17-year-olds were tested with a significant correlation of segment 1 and 4 stroke length rates (r = 0.565).

On the whole, the segmental stroke length and stroke pace were found correlated for the relevant segments: see Table hereunder. Thus the 10-11-year-old group speed in segment 1 was found dominated by stroke pace (r = 0.637); with the stroke length / stroke pace parity achieved in segments 2 and 3 (r = 0.558 and r = 0.540, respectively); followed by stroke length domination in segment 4 (r = 0.638).

The 12-13-year-old group was specific in the stroke test rates, with segments 1 and 3 tested with the stroke length / stroke pace parity, segment 2 dominated by stroke pace, and segment 4 by stroke length. The 14-15-year-old group was tested with significant stroke pace / stroke length correlations with the segmental speed, with segments 1 and 3 dominated by stroke pace, and segments 2 and 4 by stroke length. And the 16-17-year-old group was tested with the stroke length domination in segments 2 and 4, and stroke pace domination in segment 1.

Table 1. Segmental speed correlations with the stroke length and stroke pace in the junior sample

Note: Shaded correlations significant with

        р<0.01                            р<0.05

On the whole, the study found wavelike variations and correlations of the segmental speeds, stroke length and stroke pace in the 100m freestyle in every age group. The findings on the age-specific segmental stroking patterns are recommended being taken in account by the junior swimmers’ training and competitive systems.          

Conclusion. The study found the first 25m segment speed in the 100m freestyle being the fastest in the junior sample, with every age group tested with the stroke pace sags on the distance, particularly in the final 25m segment. The 10-13-year-old group was tested with the stroke length gradually growing and then dropping in the final segment – versus the wavelike stroke length variation in the 14-17-year-old group. On the whole, the junior swimmers’ sample was tested with inconsistent segmental speed/ stroke pace/ stroke length variations.

References

1. Mironenko E.N. Increasing rowing performance in sports strokes based on differentiated use of speed and coordination exercises at body conditioning stage. PhD diss.. Omsk, 2003. 24 p.
2. Platonov V.N. Competitive swimming: how to succeed. V. 2. Мoscow: Sovetskiy sport publ.. 2012. 544 p.
3. Pogrebnoy A.I., Arishin A.V. Comparative analysis of stroke kinematics in elite swimmers. Vestnik AGU. 2016. No. 2 (178). pp. 103-107.
4. Yatsenko V.L. Swimming technique building methods for junior swimmers by means of directed influence on stroke kinematic structure. PhD diss.. Krasnodar,  2002. 34 p.