Researchers have quantified swimming races for several decades to provide objective information on race strategy and characteristics. The purpose of the present review was to summarize knowledge established in the literature and current issues in swimming race analysis. A systematic search of the literature for the current narrative review was conducted in September 2020 using Web of Science, SPORTDiscus (via EBSCO), and PubMed. After examining 321 studies, 22 articles were included in the current review. Most studies divided the race into the start, clean swimming, turn, and/or finish segments; however, the definition of each segment varied, especially for the turn. Ideal definitions for the start and turn-out seemed to differ depending on the stroke styles and swimmers’ level. Many studies have focused on either 100 m or 200 m events with the four strokes (butterfly, backstroke, breaststroke, and freestyle). Contrastingly, there were few or no studies for 50 m, long-distance, individual medley, and relay events. The number of studies examining races for short course, junior and Paralympic swimmers were also very limited. Future studies should focus on those with limited evidence as well as race analysis outside competitions in which detailed kinematic and physiological analyses are possible.

The present study aimed to compare muscle coordination strategies of the upper and lower limb muscles between beginners and elite breaststroke swimmers. Surface electromyography (EMG) of eight muscles was recorded in 16 swimmers (8 elite, 8 beginners) during a 25 m swimming breaststroke at 100% of maximal effort. A decomposition algorithm was used to identify the muscle synergies that represent the temporal and spatial organisation of muscle coordination. Between-groups indices of similarity and lag times were calculated. Individual muscle patterns were moderately to highly similar between groups (between-group indices range: 0.61 to 0.84). Significant differences were found in terms of lag time for pectoralis major (P < 0.05), biceps brachii, rectus femoris and tibialis anterior (P < 0.01), indicating an earlier activation for these muscles in beginners compared to elites (range: −13.2 to −3.8% of the swimming cycle). Three muscle synergies were identified for both beginners and elites. Although their composition was similar between populations, the third synergy exhibited a high within-group variability. Moderate to high indices of similarity were found for the shape of synergy activation coefficients (range: 0.63 to 0.88) but there was a significant backward shift (−8.4% of the swimming cycle) in synergy #2 for beginners compared to elites. This time shift suggested differences in the global arm-to-leg coordination. These results indicate that the synergistic organisation of muscle coordination during breaststroke swimming is not profoundly affected by expertise. However, specific timing adjustments were observed between lower and upper limbs.

Background and aim: To identify kinematic variables related to short course 100 m breaststroke performance. Methods: An automatic race analysis system was utilized to obtain start (0–15 m), turn (5 m before the wall until 10 m out), finish (95–100 m), and clean swimming (the rest of the race) segment times as well as cycle rate and cycle length during each swimming cycle from 15 male swimmers during a 100 m breaststroke race. A bivariate correlation and a partial correlation were employed to assess the relationship between each variable and swimming time. Results: Turns were the largest time contributor to the finishing time (44.30 ± 0.58%), followed by clean swimming (38.93 ± 0.50%), start (11.39 ± 0.22%), and finish (5.36 ± 0.18%). The finishing time was correlated (p < 0.001) with start segment time (r = 0.979), clean swimming time (r = 0.940), and 10 m turn-out time (r = 0.829). The clean swimming time was associated with the finishing time, but cycle rate and cycle length were not. In both start and turns, the peak velocity (i.e., take-off and push-off velocity) and the transition velocity were related to the segment time (r ≤ −0.673, p ≤ 0.006). Conclusions: Breaststroke training should focus on: (I) 15 m start with generating high take-off velocity, (II) improving clean swimming velocity by finding an optimal balance between cycle length and rate, (III) 10 m turn-out with maintaining a strong wall push-off, and (IV) establishing a high transition velocity from underwater to surface swimming.

The Polar OH1 is an optical heart rate (HR) sensor which can be used on different parts of the body. The purpose of the study was to evaluate the validity of the OH1 as well as a wrist worn heart rate device (Polar M600) during swimming. Twenty-six well-trained competitive swimmers performed a regular training session including different swimming intensities. During the training the swimmers wore a H10 HR sensor with Polar Pro strap (H10) underneath the swim suit, a Polar OH1 optical HR sensor (OH1) underneath the swimming cap at the temple, and a sports watch with optical HR sensor, Polar M600 smart watch (M600) on the wrist. No difference in HRmax, HRmean and HRmin between H10 and OH1 were evident. The HRmax and HRmean obtained by the M600 was significantly lower than the obtained by H10 and OH1 (p < 0.05). The ICC showed mostly excellent agreements between H10 and OH1 and poor to good agreements between H10 and M600. Bland-Altmann plot for M600 vs. H10 indicates upper and lower limits of agreement of -53.0 to 33.9 beats per minute. For OH1 vs. H10 the upper and lower limits of agreement were -26.9 to 24.7 beats per minute. The Polar OH1 optical HR sensor is a valid tool to monitor HR of different intensities during swimming whereas the Polar M600 smart watch as a wrist worn device is less accurate.

The purpose of this study was to establish the relationships between 50 m sprint swimming performance and variables acquired from a swimming load-velocity profile established by semi-tethered butterfly swimming. Twelve male elite swimmers participated in the present study and performed 50 m sprint and semi-tethered butterfly swimming with different loads. The mean velocity among all upper-limb cycles was obtained from the 50 m swimming (race velocity), and maximum load and velocity were predicted from the load-velocity profile established by the semi-tethered swimming test. There was a very large correlation (r=0.885, p<0.01) and a high intra-class correlation (0.844, p<0.001) between the race velocity and the predicted maximum velocity. Significant correlations were also observed between the predicted maximum load and the 50 m time as well as the race velocity (r=− 0.624 and 0.556, respectively, both p<0.05), which imply that an ability to achieve a large tethered swimming force is associated with 50 m butterfly performance. These results indicate that the load-velocity profile is a useful tool for predicting and assessing sprint butterfly swimming performance.

The purpose of the present study was to investigate differences in a 100 m breaststroke time-trial between elite and sub-elite swimmers. Elite and sub-elite male swimmers (seven each; 772.1 ± 35.2 and 610.6 ± 24.7 FINA point, respectively) performed 100 m breaststroke, which was recorded by a multi-camera system that provided the mean and time-series velocity data in the glide, pull-out, and clean-swimming segments. The mean velocity in each segment was compared between the groups using an independent-samples t-test (for the 1^st lap) and two-way mixed-design ANOVA (for the 2^nd—4^th laps), which suggested a larger mean clean-swimming (in all laps; 7–11% difference) and glide (in the 2^nd and 3^rd lap; about 13% difference) velocity for the elite swimmers. The time-series data displayed faster velocity in elite swimmers than in the sub-elite group during the first part (up to 40% time) of the glide segment (p < 0.05). Differences in the clean-swimming segment between the groups were observed (p < 0.001) apart from the first 5–15% time of the segment. No differences in the pull-out and at the beginning of the clean-swimming imply that coaches and swimmers should not assume that a good clean-swimming technique also guarantees fast velocity in these segments.