RESUMEN
This study investigated the relationships between load-velocity profiling and 50 m breaststroke performance. Twenty-seven male swimmers qualified for the national championship participated and performed a 50 m breaststroke trial with a multicamera system. The total race time (t50 m), forward velocity during surface swimming (v50 m), stroke length, and stroke frequency were obtained from the automatic post-processing of the system. Afterwards, the participants performed semi-tethered swimming with three external loads using a robotic resistance device. The average velocity from three stroke cycles was plotted as a function of the corresponding load. The theoretical maximum velocity (v0) and load (L0), L0 normalized to body mass, steepness of the regression line (slope), and active drag (AD) were calculated. The main findings were moderate to large correlations of two 50 m race variables (t50 m and v50 m) with v0, L0, and AD (t50 m range: r = -.444 to r = -.619, p = .020 to p = .001), (v50 m range: r = .451 to r = .568, p = .018 to p = .002). This shows the importance of applying maximum propulsive force to achieve high swimming performance and that load-velocity profiling is an indicator of 50 m breaststroke performance. Load-velocity measurements over time can also monitor velocity, strength, and drag-minimizing abilities, explaining performance changes and training effects.
RESUMEN
INTRODUCTION: Active drag in swimming is a critical variable that affects swimmers' performance as well as the physiological load, but it is challenging for practitioners to assess this variable. This study aimed to assess if the load-velocity profiling method can be used as an indicator of active drag. METHODS: A total of 419 swimmers performed three semi-tethered swimming trials in their speciality among the four competitive strokes with different external loads. Linear regression between external load and swimming velocity, as well as the external load relative to the body mass and swimming velocity, were established. The active drag and drag coefficient of each swimmer were calculated using a velocity perturbation method. RESULTS: There were significant correlations of the active drag with the absolute slope (r ≥ 0.713, p < 0.001) and relative slope (r ≥ 0.538, p < 0.001) in all four strokes and both sexes. A multiple regression analysis exhibited that the primary determinant of these relationships was the drag coefficient (semi-partial correlation ≥0.404, p < 0.001). The effects of the height and body mass index (BMI) on the relationship between the drag and the absolute slope were small (0.195 ≤ semi-partial correlation ≤0.248, p < 0.001) in both cases, which became either non-significant (height: p ≥ 0.282) or trivial (BMI: -0.099 ≤ semi-partial correlation ≤ -0.073, p ≤ 0.009). CONCLUSIONS: These results indicate that the absolute load-velocity slope is a strong indicator of the active drag, and the relative slope is useful when indirectly assessing the drag coefficient.