Effects of exceeding stroke frequency of maximal effort on hand kinematics and hand propulsive force in front crawl

Which Phases of the Stroke Cycle Are Propulsive in Front Crawl Swimming?

Purpose: The aim of this study was fourfold: (1) to quantify acceleration, velocity, and phase overlap for each phase of the stroke cycle (SC) during 200 m front crawl; (2) for each variable, to identify any differences between the four SC phases; (3) to investigate changes in variables during the 200 m; (4) to explore any association between performance and each variable. Methods: Ten swimmers performed a 200 m maximum swim. Four SCs were analyzed, one for each 50 m, using three-dimensional methods. Each SC was split into four phases: entry, pull, push, and recovery. Center of mass (CM) acceleration; maximum, minimum, and average CM velocity; phase duration, and, overlap of a phase of one arm with each phase of the opposite arm were calculated. Results and Conclusion: Phase velocities were positively correlated with performance and decreased during the 200 m. The acceleration data showed high within and between-swimmer variability. When the entry of one arm overlapped with the pull, and sometimes push, phase of the opposite arm, it was propulsive for the whole body. The pull was the slowest phase and overlapped predominantly with the opposite arm's recovery. The push phase was often propulsive for the whole body, regardless of the overlaps with the other arm, and together with the entry were the fastest phases. The recovery of each arm was mostly resistive for the whole body, except the short period of overlap with the opposite arm's push phase.

Comparison of foot pressure distribution and foot kinematics in undulatory underwater swimming between performance levels

This study aimed to elucidate the foot kinematics and foot pressure difference characteristics of faster swimmers in undulatory underwater swimming (UUS). In total, eight faster and eight slower swimmers performed UUS in a water flume at a flow velocity set at 80% of the maximal effort swimming velocity. The toe velocity and foot angle of attack were measured using a motion capture system. A total of eight small pressure sensors were attached to the surface of the left foot to calculate the pressure difference between the plantar and dorsal sides of the foot. Differences in the mean values of each variable between the groups were analysed. Compared to the slower swimmers, the faster swimmers exhibited a significantly higher swimming velocity (1.53 ± 0.06 m/s vs. 1.31 ± 0.08 m/s) and a larger mean pressure difference in the phase from the start of the up-kick until the toe moved forward relative to the body (3.88 ± 0.65 kPa vs. 2.66 ± 1.19 kPa). The faster group showed higher toe vertical velocity and toe direction of movement, switching from lateral to medial at the time of generating the larger foot pressure difference in the up-kick, providing insight into the reasons behind the foot kinematics of high UUS performance swimmers.

Effects of anthropometrics, thrust, and drag on stroke kinematics and 100 m performance of young swimmers using path-analysis modeling

The aim of this study was to understand the interactions between anthropometric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty-five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in regional and national competitions were recruited. The 100 m freestyle performance was chosen as the variable to be predicted. A series of anthropometric (hand surface area-HSA), kinetic (thrust and active drag coefficient (CDA )), and kinematic (stroke length (SL); stroke frequency (SF), and swimming speed) variables were measured. Structural equation modeling (via path analysis) was used to develop and test the model. The initial model predicted performance with 90.1% accuracy. All paths were significant (p < 0.05) except the thrust-SL. After deleting this non-significant path (thrust-SL) and recalculating, the model goodness-of-fit improved and all paths were significant (p < 0.05). The predicted performance was 90.2%. Anthropometrics had significant effects on kinetics, which had significant effects on kinematics, and consequently on the 100 m freestyle performance. The cascade of interactions based on this path-flow model allowed for a meaningful prediction of the 100 m freestyle performance. Based on these results, coaches and swimmers should be aware that the swimming predictors can first meaningfully interact with each other to ultimately predict the 100 m freestyle performance.

Role of kicking action in front crawl: the inter-relationships between swimming velocity, hand propulsive force and trunk inclination

This study aimed to investigate the essential role of the kicking action in front crawl. To achieve this objective, we examined the relationships of the hand propulsive force and trunk inclination with swimming velocity over a wide range of velocities from 0.75 m·s-1 to maximum effort, including the experimental conditions of arm stroke without a pull buoy. Seven male swimmers performed a 25 m front crawl at various speeds under three swimming conditions: arm stroke with a pull buoy, arm stroke without a pull buoy (AWOB) and arm stroke with a six-beat kick (SWIM). Swimming velocity, hand propulsive force and trunk inclination were calculated using an underwater motion-capture system and pressure sensors. Most notably, AWOB consistently exhibited greater values than SWIM for hand propulsive force across the range of observed velocities (p < 0.05) and for trunk inclination below the severe velocity (p < 0.05), and these differences increased with decreasing velocity. These results indicate that 1) the kicking action in front crawl has a positive effect on reducing the pressure drag acting on the trunk, thereby allowing swimmers to achieve a given velocity with less hand propulsive force, and 2) this phenomenon is significant in low-velocity ranges.

Kinematic and kinetic parameters to identify water polo players' eggbeater kick techniques

This study aimed to clarify the kinematic and kinetic parameters that identify the technical differences in the eggbeater kick. Twelve water polo players performed the eggbeater kick, and its kinematics were recorded by a motion capture system. Pressure distributions around the feet were measured by sixteen pressure sensors attached to the dorsal and plantar surfaces of the feet, from which the resultant fluid force acting on the feet and the vertical component of the force (i.e., propulsive force) were estimated. Repeated-measures analysis of variance (including post hoc test) results showed that the pressure difference, due to negative pressure on the dorsal side of the foot, around the first toe was significantly larger than the other foot segments (difference of up to 7 kN/m2, P < 0.01). Moreover, cluster analysis (including Fisher information) results showed that the kinetic (fluid force and pressure) data had a major influence on clustering; the highest Fisher information was 10.42 for the mean propulsive force. Among the kinematic foot parameters, the influence of the foot angle data on clustering was large, suggesting its importance as a technical parameter of the eggbeater kick in relation to the kinetic data.

How do swimmers control their front crawl swimming velocity? Current knowledge and gaps from hydrodynamic perspectives

The aim of this study was to review the literature on front crawl swimming biomechanics, focusing on propulsive and resistive forces at different swimming velocities. Recent studies show that the resistive force increases in proportion to the cube of the velocity, which implies that a proficient technique to miminise the resistive (and maximise the propulsive) force is particularly important in sprinters. To increase the velocity in races, swimmers increase their stroke frequency. However, experimental and simulation studies have revealed that there is a maximum frequency beyond which swimmers cannot further increase swimming velocity due to a change in the angle of attack of the hand that reduces its propulsive force. While the results of experimental and simulation studies are consistent regarding the effect of the arm actions on propulsion, the findings of investigations into the effect of the kicking motion are conflicting. Some studies have indicated a positive effect of kicking on propulsion at high swimming velocities while the others have yielded the opposite result. Therefore, this review contributes to knowledge of how the upper-limb propulsion can be optimised and indicates a need for further investigation to understand how the kicking action can be optimised in front crawl swimming.Abbreviations: C: Energy cost [kJ/m]; Ė: Metabolic power [W, kJ/s]; Fhand: Fluid resultant force exerted by the hand [N]; Ftotal: Total resultant force [N] (See Appendix A); Fnormal: The sum of the fluid forces acting on body segments toward directions perpendicular to the segmental long axis, which is proportional to the square of the segmental velocity. [N] (See Appendix A); Ftangent: The sum of the fluid forces acting on body segments along the direction parallel to the segmental long axis, which is proportional to the square of the segmental velocity. [N] (See Appendix A); Faddmass: The sum of the inertial force acting on the body segments due to the acceleration of a mass of water [N] (See Appendix A); Fbuoyant: The sum of the buoyant forces acting on the body segments [N] (See Appendix A); D: Fluid resistive force acting on a swimmer's body (active drag) [N]; T: Thrust (propulsive) force acting in the swimming direction in reaction to the swimmer's actions [N]; Thand: Thrust force produced in reaction to the actions of the hand [N]; Tupper_limb: Thrust force produced in reaction to the actions of the upper limbs [N]; Tlower_limb: Thrust force produced in reaction to the actions of the lower limbs [N]; Mbody: Whole-body mass of the swimmer [kg]; SF: Stroke frequency (stroke number per second) [Hz]; SL: Stroke length (distance travelled per stroke) [m]; v: Instantaneous centre of mass velocity of the swimmer [m/s]; V - : Mean of the instantaneous centre of mass velocities in the swimming direction over the period of the stroke cycle [m/s]; a: Centre of mass acceleration of the swimmer [m/s2]; V - hand: Mean of the instantaneous magnitudes of hand velocity over a period of time [m/s]; Ẇtot: Total mechanical power [W]; Ẇext: External mechanical power [W]; Ẇd: Drag power (mechanical power needed to overcome drag) [W, Nm/s]; α: Angle of attack of the palm plane with respect to the velocity vector of the hand [deg]; ηo: Overall efficiency [%]; ηp: Propelling efficiency [%]; MAD-system: Measuring Active Drag system; MRT method: Measuring Residual Thrust method.

Using Statistical Parametric Mapping as a statistical method for more detailed insights in swimming: a systematic review

Swimming is a time-based sport and hence strongly dependent from velocity. Most studies about swimming refer to velocity as discrete variable, i.e., 0-D (no time dimension). However, it was argued that using swimming velocity as a continuous variable (1-D, with time dimension) with Statistical Parametric Mapping (SPM) can bring deeper and detailed insights about swimming performance. Therefore, the aim of this study was to perform a systematic review about the current body of knowledge of using Statistical Parametric Mapping in a swimming context. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant articles. After screening, nine articles related to Statistical Parametric Mapping (SPM) analysis in swimming were retained for synthesis. Results showed that four articles (44.4%) aimed to understand the kinematics, isokinetic joint torque or electromyographic (EMG) pattern of the swimmer's shoulder either on land or during front crawl trials. Two articles (22.2%) focused on understanding the swimming velocity while performing the breaststroke stroke. One article (11.1%) analyzed the swimmers' propulsion at front-crawl stroke, another one (11.1%) compared swimming velocity during a complete stroke cycle in young swimmers of both sexes as a discrete variable and as a continuous variable. Also, one article (11.1%) analyzed the underwater undulatory velocity. In an EMG context, some findings verified in SPM are not possible to be discovered with traditional 0-D statistical methods. Studies about swimming velocity (breaststroke, freestyle, and underwater undulatory velocity) and propulsion (front-crawl) also highlighted the SPM advantages in comparison to traditional statistical methods. By using SPM, researchers were able to verify specifically where within the stroke cycle significant differences were found. Therefore, coaches can get more detailed information to design specific training drills to overcome hypothetical handicaps.

Agreement between Different Methods to Measure the Active Drag Coefficient in Front-Crawl Swimming

The aim of this study was to analyze the agreement of the active drag coefficient measured through drag and propulsion methods. The sample was composed of 18 swimmers (nine boys: 15.9 ± 0.9 years; nine girls: 15.3 ± 1.2 years) recruited from a national swimming team. The velocity perturbation method was used as the drag measurement system and the Aquanex system as the propulsion system. For both sexes combined, the frontal surface area was 0.1128 ± 0.016 m2, swim velocity 1.54 ± 0.13 m.s-1, active drag 62.81 ± 11.37 N, propulsion 68.81 ± 12.41 N. The level of the active drag coefficient agreement was calculated based on the mean values comparison, simple linear regression, and Bland Altman plots. The mean data comparison revealed non-significant differences (p > 0.05) between methods to measure the active drag coefficient. Both the linear regression (R2 = 0.82, p < 0.001) and Bland Altman plots revealed a very high agreement. The active drag coefficient should be the main outcome used in the interpretation of the swimmers' hydrodynamic profile, because it is less sensitive to swimming velocity. Coaches and researchers should be aware that the active drag coefficient can also be calculated based on propulsion methods and not just based on drag methods. Thus, the swimming community can now use different equipment to measure the hydrodynamics of their swimmers.

Body composition and anthropometrics of young male swimmers in relation to the tethered swimming and kinematics of 100-m front crawl race

BACKGROUND

The aim was to analyze the relationship of body mass and predicted muscle mass of body segments on swimming kinematics and tethered swimming indices, and further assess the influence of those indices on 100-m front crawl performance of adolescent male swimmers.

METHODS

In 19 volunteer swimmers (age: 13.5±0.44 years, height: 168.6±7.77 cm, body mass: 56.9±10.57 kg), the predicted muscle mass of body segments was assessed with bioelectrical impedance analysis. The kinematic indices of swimming (stroke rate - SR, stroke length - SL, and stroke index - SI) were calculated from a video recording of a 100-m front crawl race. The strength indices (maximum and average value of force, average impulse per single cycle, force decline) were collected in a 30-second front crawl tethered swimming test.

RESULTS

The average tethered swimming force was positively correlated with surface swimming speed (0.505; P≤0.05). Indices of SL, SI were influenced by average impulse per single cycle (0.58, 0.55; P≤0.05), and further the SI was strongly correlated with most specified speed indices of the 100-m race (0.59; P≤0.05).

CONCLUSIONS

It can be stated that the ability of force development in a single stroke, owing to strong interrelation with SI, is a good predictor in talent identification among young swimmers.

Rapid Change in the Direction of Hand Movement to Increase Hand Propulsion During Front Crawl Swimming

This study aims to investigate the difference in hand acceleration induced by rapid changes in hand movement directions and propulsion between fast and slow groups of swimmers during front crawl swimming. Twenty-two participants, consisting of 11 fast and 11 slow swimmers, performed front crawl swimming at their maximal effort. Hand acceleration and velocity and the angle of attack were measured using a motion capture system. The dynamic pressure approach was used to estimate hand propulsion. In the insweep phase, the fast group attained significantly higher hand acceleration than the slow group in the lateral and vertical directions (15.31 [3.44] m·s-2 vs 12.23 [2.60] m·s-2 and 14.37 [1.70] m·s-2 vs 12.15 [1.21] m·s-2), and the fast group exerted significantly larger hand propulsion than the slow group (53 [5] N vs 44 [7] N). Although the fast group attained large hand acceleration and propulsion during the insweep phase, the hand velocity and the angle of attack were not significantly different in the 2 groups. The rapid change in hand movement direction could be considered in the technique of underwater arm stroke, particularly in the vertical direction, to increase hand propulsion during front crawl swimming.

Manipulation of Stroke Rate in Swimming: Effects on Oxygen Uptake Kinetics

The study aimed to assess the effect of different front crawl stroke rates (SRs) in the oxygen uptake (̇VO₂) kinetics and ̇VO₂ peak, the total time to exhaustion (TTE), and blood lactate concentration ([La]) at 95% of the 400-m front crawl test (T400) mean speed (S400). Twelve endurance swimmers performed a T400 and four trials at 95% of the S400: (i) free SR, (ii) fixed SR (100% of the average free SR trial), (iii) reduced SR (90% of the average free SR trial), and (iv) increased SR (110% of the average free SR trial). ̇VO₂ was accessed continuously with breath-by-breath analysis. The results highlighted: (i) the time constant at increased SR (13.3±4.2 s) was lower than in the reduced SR condition (19.5±2.6 s); (ii) the amplitude of the primary phase of ̇VO₂ kinetics in the fixed SR (44.0±5.8 ml·kg^-1·min^-1) was higher than in the increased SR condition (39.5±6.4 ml·kg^-1·min^-1); and (iii) TTE was lower in the fixed SR (396.1±189.7 s) than the increased SR condition (743.0±340.0 s). The results indicate that controlled SR could be considered a swimming training strategy, focusing on physiological parameters overload.

Using Statistical Parametric Mapping to Compare the Propulsion of Age-Group Swimmers in Front Crawl Acquired with the Aquanex System

Understanding the difference in each upper limb between age groups can provide deeper insights into swimmers’ propulsion. This study aimed to: (1) compare swimming velocity and a set of kinematical variables between junior and juvenile swimmers and (2) compare the propulsion outputs through discrete and continuous analyses (Statistical Parametric Mapping—SPM) between junior and juvenile swimmers for each upper limb (i.e., dominant and non-dominant). The sample was composed of 22 male swimmers (12 juniors with 16.35 ± 0.74 years; 10 juveniles with 15.40 ± 0.32 years). A set of kinematic and propulsion variables was measured at maximum swimming velocity. Statistical Parametric Mapping was used as a continuous analysis approach to identify differences in the propulsion of both upper limbs between junior and juvenile swimmers. Junior swimmers were significantly faster than juveniles (p = 0.04, d = 0.86). Although juniors showed higher propulsion values, the SPM did not reveal significant differences (p < 0.05) for dominant and non-dominant upper limbs between the two age groups. This indicates that other factors (such as drag) may be responsible for the difference in swimming velocity. Coaches and swimmers should be aware that an increase in propulsion alone may not immediately lead to an increase in swimming velocity.

Quantification of swimmers' ability to apply force in the water: the potential role of two new variables during tethered swimming

This study aimed 1) to examine variables that may quantify the ability to apply force in the water and 2) to test their relationship with free swimming performance. Sixteen regional-level swimmers participated in this study. Average (Favg) and maximum (Fmax) forces were measured for 30 s arm stroke tethered swimming in a flume at zero and 1.389 m/s water flow speeds. The maximum and average force's relative changes (ΔFmax and ΔFavg, respectively) were calculated between tethered swimming at zero and 1.389 m/s water flow speeds. Free swimming speeds were obtained from 25, 50, and 100 m front crawl trials, and were correlated with ΔFmax and ΔFavg. A negative correlation was found between ΔFmax and 25, 50 and 100 m speeds (r =  -0.84, r = -0.74, r = -0.55; p < 0.05, respectively) and ΔFavg correlated negatively with 25 and 50 m speeds (r = -0.63, r = -0.54; p < 0.05, respectively), but it did not correlate with 100 m swimming speed. The relative change in force could be used to quantify the ability to apply force in the water. This could aid coaches to understand if changes in swimmers' ability to apply force in the water contribute to improvements in performance.

The Effect of a Coordinative Training in Young Swimmers' Performance

This study investigated the effects of a coordinative in-water training. Total 26 young swimmers (16 boys) were divided in a training group (that performed two sets of 6 × 25-m front crawl, with manipulated speed and stroke frequency, two/week for eight weeks) and a control group. At the beginning and end of the training period, swimmers performed 50-m front crawl sprints recorded by seven land and six underwater Qualisys cameras. A linear mixed model regression was applied to investigate the training effects adjusted for sex. Differences between sex were registered in terms of speed, stroke length, and stroke index, highlighting that an adjustment for sex should be made in the subsequent analysis. Between moments, differences were noticed in coordinative variables (higher time spent in anti-phase and push, and lower out-of-phase and recovery for training group) and differences between sex were noticed in performance (stroke length and stroke index). Interactions (group * time) were found for the continuous relative phase, speed, stroke length, and stroke index. The sessions exerted a greater (indirect) influence on performance than on coordinative variables, thus, more sessions may be needed for a better understanding of coordinative changes since our swimmers, although not experts, are no longer in the early learning stages.

Understanding the Role of Propulsion in the Prediction of Front-Crawl Swimming Velocity and in the Relationship Between Stroke Frequency and Stroke Length

Introduction: This study aimed to: 1) determine swimming velocity based on a set of anthropometric, kinematic, and kinetic variables, and; 2) understand the stroke frequency (SF)–stroke length (SL) combinations associated with swimming velocity and propulsion in young sprint swimmers.

Methods: 38 swimmers (22 males: 15.92 ± 0.75 years; 16 females: 14.99 ± 1.06 years) participated and underwent anthropometric, kinematic, and kinetic variables assessment. Exploratory associations between SL and SF on swimming velocity were explored using two two-way ANOVA (independent for males and females). Swimming velocity was determined using multilevel modeling.

Results: The prediction of swimming velocity revealed a significant sex effect. Height, underwater stroke time, and mean propulsion of the dominant limb were predictors of swimming velocity. For both sexes, swimming velocity suggested that SL presented a significant variation (males: F = 8.20, p < 0.001, η² = 0.40; females: F = 18.23, p < 0.001, η² = 0.39), as well as SF (males: F = 38.20, p < 0.001, η² = 0.47; females: F = 83.04, p < 0.001, η² = 0.51). The interaction between SL and SF was significant for females (F = 8.00, p = 0.001, η² = 0.05), but not for males (F = 1.60, p = 0.172, η² = 0.04). The optimal SF–SL combination suggested a SF of 0.80 Hz and a SL of 2.20 m (swimming velocity: 1.75 m s⁻¹), and a SF of 0.80 Hz and a SL of 1.90 m (swimming velocity: 1.56 m s⁻¹) for males and females, respectively. The propulsion in both sexes showed the same trend in SL, but not in SF (i.e., non-significant variation). Also, a non-significant interaction between SL and SF was observed (males: F = 0.77, p = 0.601, η² = 0.05; females: F = 1.48, p = 0.242, η² = 0.05).

Conclusion: Swimming velocity was predicted by an interaction of anthropometrics, kinematics, and kinetics. Faster velocities in young sprinters of both sexes were achieved by an optimal combination of SF–SL. The same trend was shown by the propulsion data. The highest propulsion was not necessarily associated with higher velocity achievement.

Wearables in Swimming for Real-Time Feedback: A Systematic Review

Nowadays, wearables are a must-have tool for athletes and coaches. Wearables can provide real-time feedback to athletes on their athletic performance and other training details as training load, for example. The aim of this study was to systematically review studies that assessed the accuracy of wearables providing real-time feedback in swimming. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were selected to identify relevant studies. After screening, 283 articles were analyzed and 18 related to the assessment of the accuracy of wearables providing real-time feedback in swimming were retained for qualitative synthesis. The quality index was 12.44 ± 2.71 in a range from 0 (lowest quality) to 16 (highest quality). Most articles assessed in-house built (n = 15; 83.3%) wearables in front-crawl stroke (n = 8; 44.4%), eleven articles (61.1%) analyzed the accuracy of measuring swimming kinematics, eight (44.4%) were placed on the lower back, and seven were placed on the head (38.9%). A limited number of studies analyzed wearables that are commercially available (n = 3, 16.7%). Eleven articles (61.1%) reported on the accuracy, measurement error, or consistency. From those eleven, nine (81.8%) noted that wearables are accurate.

Relationship Between Hand Kinematics, Hand Hydrodynamic Pressure Distribution and Hand Propulsive Force in Sprint Front Crawl Swimming

Purpose

This study investigated the relationship between hand kinematics, hand hydrodynamic pressure distribution and hand propulsive force when swimming the front crawl with maximum effort.

Methods

Twenty-four male swimmers participated in the study, and the competition levels ranged from regional to national finals. The trials consisted of three 20 m front crawl swims with apnea and maximal effort, one of which was selected for analysis. Six small pressure sensors were attached to each hand to measure the hydrodynamic pressure distribution in the hands, 15 motion capture cameras were placed in the water to obtain the actual coordinates of the hands.

Results

Mean swimming velocity was positively correlated with hand speed (r = 0.881), propulsive force (r = 0.751) and pressure force (r = 0.687). Pressure on the dorsum of the hand showed very high and high negative correlations with hand speed (r = −0.720), propulsive force (r = −0.656) and mean swimming velocity (r = −0.676). On the contrary, palm pressure did not correlate with hand speed and mean swimming velocity. Still, it showed positive correlations with propulsive force (r = 0.512), pressure force (r = 0.736) and angle of attack (r = 0.471). Comparing the absolute values of the mean pressure on the palm and the dorsum of the hand, the mean pressure on the dorsum was significantly higher and had a larger effect size (d = 3.71).

Conclusion

It is suggested that higher hand speed resulted in a more significant decrease in dorsum pressure (absolute value greater than palm pressure), increasing the hand propulsive force and improving mean swimming velocity.

Adaptability in Swimming Pattern: How Propulsive Action Is Modified as a Function of Speed and Skill

The objectives of this study were to identify how spatiotemporal, kinetic, and kinematic parameters could (i) characterize swimmers' adaptability to different swimming speeds and (ii) discriminate expertise level among swimmers. Twenty male participants, grouped into (a) low-, (b) medium-, and (c) high-expertise levels, swam at four different swim paces of 70, 80, 90% (for 20 s), and 100% (for 10 s) of their maximal speed in a swimming flume. We hypothesized that (i) to swim faster, swimmers increase both propulsion time and the overall force impulse during a swimming cycle; (ii) in the frequency domain, expert swimmers are able to maintain the relative contribution of the main harmonics to the overall force spectrum. We used three underwater video cameras to derive stroking parameters [stroke rate (SR), stroke length (SL), stroke index (SI)]. Force sensors placed on the hands were used to compute kinetic parameters, in conjunction with video data. Parametric statistics examined speed and expertise effects. Results showed that swimmers shared similarities across expertise levels to increase swim speed: SR, the percentage of time devoted to propulsion within a cycle, and the index of coordination (IdC) increased significantly. In contrast, the force impulse (I ⁺) generated by the hand during propulsion remained constant. Only the high-expertise group showed modification in the spectral content of its force distribution at high SR. Examination of stroking parameters showed that only high-expertise swimmers exhibited higher values of both SL and SI and that the low- and high-expertise groups exhibited similar IdC and even higher magnitude in I ⁺. In conclusion, all swimmers exhibit adaptable behavior to change swim pace when required. However, high-skilled swimming is characterized by broader functional adaptation in force parameters.