ABSTRACT
The purposes of this study were as follows: 1) to classify competitive swimmers on the basis of their stroke rate (SR) and stroke length (SL) by applying a factor analysis and a cluster analysis, 2) to derive discriminant functions by applying a multiple discriminant analysis of the stroke characteristics, and 3) to compare the race patterns of groups classified according to the results of the cluster analysis. 94 male swimmers who had participated in 100-m freestyle events held at the All Japan Swimming Championship from 2000 to 2004 were selected as subjects. The average value of their swimming velocity, SR, and SL in four swimming phases (15-25 m, 25-45 m, 57.5-75 m, and 75-95 m) were measured from videos captured. The subjects were classified according to their SR and SL by applying a factor analysis and a cluster analysis. Results showed that the competitive swimmers could be classified into Type1-4. Type1 showed high values of SR throughout the race, Type2 showed high values of SR in the first half and high values of SL in the second half, Type3 showed high values of SL in the first half and high values of SR in the second half, and Type4 showed high values of SL throughout the race. Although there was no difference in swimming records among the four groups, each group had different race patterns. These results suggest that the knowledge of race patterns might be useful to swimmers or coaches for planning race strategies.
ABSTRACT
The aim of this study was to investigate the relationship between changes in swimming velocity (SV), stroke rate (SR), stroke length (SL) and muscle activities during a 4×50m swimming test to simulate a 200m freestyle race. A total of 20 male collegiate swimmers participated in this study. The electromyography (EMG) signals of 11 muscles, 7 muscles in the upper half of the body and 4 muscles in the lower half, were recorded with surface electrodes. SV, SR and SL were analyzed for each 50m (S1, S2, S3 and S4) from side view recordings of swimmers taken with an underwater camera.SV and SR for S1 were significantly higher than for S2 and S3. SL for S1 was significantly longer than for S3. The averaged EMGs (aEMGs) for S1 were significantly higher than for S2 or for S3 in 5 muscles (flexor carpi ulnaris, biceps brachii, triceps brachii, deltoideus posterior and rectus femoris). There were no significant changes between S3 and S4 in SV, SR, SL and aEMG of all muscles except pectoralis major which showed the highest aEMG in S4. Significant correlations were observed between changes of (Δ) aEMG and ΔSV, especially in upper half muscles (<i>r</i>=0.485-0.939, <i>p</i><0.05).These results suggested that decline in muscle activities of the five muscles mentioned earlier caused a decrease in SV from S1 to S3; and an increase in muscle activity of the pectoralis major led preventing a decline in SV by maintaining the SR in S4.