Insights on the Selection of the Coefficient of Variation to Assess Speed Fluctuation in Swimming.

The aim of this study was to compare swimming speed and speed fluctuations in front crawl between swimmers of different performance levels using discrete variables against statistical parametric mapping (SPM). The sample was composed of 34 male swimmers divided into three groups: (i) group #1-recreational swimmers; (ii) group #2-competitive swimmers aged 12 to 14 years; (iii) group #3-competitive swimmers aged 15 to 17 years. Swimming speed and speed fluctuations (calculated based on four different conditions) were used as discrete variables. Using these discrete variables, ANOVA one-way was used to verify differences between groups, and Bonferroni post-hoc correction for pairwise comparison whenever suitable. SPM (with similar statistical tests) was used to analyze the swimming speed and fluctuation as a continuous variable. Overall, both statistical approaches revealed significant differences (p < 0.001) in swimming speed and speed fluctuations. However, as discrete variables (in four different conditions), the speed fluctuation was not able to detect significant differences between groups #2 and #3. Conversely, SPM was more sensitive and did yield significant differences between these two groups. Therefore, researchers and coaches should be aware that the speed fluctuation as a discrete variable may not identify differences in swimming speed fluctuations when the average value between groups is marginal. On the other hand, SPM was more sensitive in analyzing all groups.

Relationship between swimming speed, intra-cycle variation of horizontal speed, and Froude efficiency during consecutive stroke cycles in adolescent swimmers.

The purpose of this study was to investigate the relationship between swimming speed, intra-cycle variation of horizontal speed of displacement (dv), and Froude efficiency (ηF) in front-crawl during three consecutive stroke cycles. The sample consisted of 15 boys aged 16.07 ± 0.77 years and 15 girls aged 15.05 ± 1.07 years. Swimming speed, dv and ηF were measured during a 25 m front-crawl trial. Three consecutive stroke cycles were measured. Swimming speed showed a non-significant stroke-by-stroke effect (F = 2.55, p = 0.087, η2 = 0.08), but a significant sex effect (F = 90.46, p < 0.001, η2 = 0.76). The dv and ηF had the same trend as the swimming speed for the stroke-by-stroke effect, but a non-significant sex effect (p > 0.05). The Spearman correlation matrix between swimming speed and dv, and swimming speed and ηF showed non-significant correlations for the three stroke cycles in both sexes. However, the tendency of the former was not always inverse, and the latter was not always direct. Coaches and swimmers need to be aware that lower dvs are not always associated with faster swimming speeds and vice-versa, and that ηF is a predictor of swimming speed, not dv.

Numerical and experimental methods used to evaluate active drag in swimming: A systematic narrative review.

Introduction: In swimming, it is necessary to understand and identify the main factors that are important to reduce active drag and, consequently, improve the performance of swimmers. However, there is no up-to-date review in the literature clarifying this topic. Thus, a systematic narrative review was performed to update the body of knowledge on active drag in swimming through numerical and experimental methods. Methods: To determine and identify the most relevant studies for this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was used. Results: 75 studies related to active drag in swimming and the methodologies applied to study them were analyzed and kept for synthesis. The included studies showed a high-quality score by the Delphi scale (mean score was 5.85 ± 0.38). Active drag was included in seven studies through numerical methods and 68 through experimental methods. In both methods used by the authors to determine the drag, it can be concluded that the frontal surface area plays a fundamental role. Additionally, the technique seems to be a determining factor in reducing the drag force and increasing the propulsive force. Drag tends to increase with speed and frontal surface area, being greater in adults than in children due to body density factors and high levels of speed. However, the coefficient of drag decreases as the technical efficiency of swimming increases (i.e., the best swimmers (the fastest or most efficient) are those with the best drag and swimming hydrodynamics efficiency). Conclusion: Active drag was studied through numerical and experimental methods. There are significantly fewer numerical studies than experimental ones. This is because active drag, as a dynamical phenomenon, is too complex to be studied numerically. Drag is greater in adults than in children and greater in men than in women across all age groups. The study of drag is increasingly essential to collaborate with coaches in the process of understanding the fundamental patterns of movement biomechanics to achieve the best performance in swimming. Although most agree with these findings, there is disagreement in some studies, especially when it is difficult to define competitive level and age. The disagreement concerns three main aspects: 1) period of the studies and improvement of methodologies; 2) discrimination of methodologies between factors observed in numerical vs. experimental methods; 3) evidence that drag tends to be non-linear and depends on personal, technical, and stylistic factors. Based on the complexity of active drag, the study of this phenomenon must continue to improve swimming performance.