Understanding the Importance of Drag Coefficient Assessment for a Deeper Insight into the Hydrodynamic Profile of Swimmers.

The main objective of this study was to confirm that the passive drag coefficient is less dependent on swimming speed than the passive drag, Froude, and Reynolds numbers, even as swimming speed increases. The sample consisted of 12 young proficient non-competitive swimmers (seven males and five females: 20.4 ± 1.9 years). Passive drag was measured with a low-voltage isokinetic engine at 1.2, 1.4, 1.6 and 1.8 m/s. The frontal surface area was measured using digital photogrammetry. Passive drag showed significant differences with a strong effect size over the four towing speeds measured (F = 116.84, p < 0.001, η2 = 0.91) with a quadratic relationship with speed. The Froude and Reynolds numbers had similar trends, but with linear relationships. Conversely, the passive drag coefficient showed non-significant differences across the four towing speeds (F = 3.50, p = 0.062, η2 = 0.33). This strongly suggests that the passive drag coefficient should be the variable of choice for monitoring the hydrodynamic profile of swimmers rather than the absolute value of passive drag.

A Single Dose of Beetroot Juice not Enhance Performance during Intervallic Swimming Efforts.

Despite the numerous scientific evidence on the topic, there is no clear and consistent answer that clarifies the true effects of beetroot juice (BJ) supplementation on different types of physical performance. This study examined whether an acute intake of BJ improves swimming performance, physiological variables of anaerobic metabolism, or subjective measures during high-intensity interval exercise with incomplete rest in competitive swimmers. Eighteen competitive swimmers (nine females and nine males) participated in this cross-over randomized, placebo-controlled, double-blind and counterbalanced study. In two trials, swimmers ingested BJ (70 mL, 6.4 mmol/400 mg NO3-) or placebo (PLA) (70 mL, 0.04 mmol/3 mg NO3-) three hours before a 2×6×100 m maximal effort with 40 seconds rest between repetitions and three minutes between blocks. The 100 m times showed no differences between groups (p > 0.05), but there was an interaction between block×repetition×condition (F5 = 3.10; p = 0.046; ηp2 = 0.54), indicating that the BJ group decreased the time of the sixth repetition of block2 compared to block1 (p = 0.01). Lactate concentration showed no differences between conditions (p > 0.05), but there was a main effect of block (ηp2 = 0.60) and a block×repetition interaction (ηp2 = 0.70), indicating higher values in block2 and increasing values between repetitions in block1. The subjective scales, perception of exertion (RPE) and Total Quality Recovery (TQR), showed no effects of condition (p > 0.05), but BJ swimmers had a greater TQR in the last repetitions of each block. In conclusion, a single dose of BJ did not enhance intermittent swimming performance or modified the physiological (lactate and heart rate) or subjective (RPE and TQR) variables; although there was a possible positive effect on the exercise tolerance at the end of effort.

Optimizing performance and mood state in competitive swimmers through tapering strategies.

Tapering is a concept that is of great importance in relation to performance, due of its great effect on the psychological and physical condition of the swimmer. Therefore, the present study aims to investigate the effect of two-week of tapering characterized by a progressive training volume reduction on mood state and swimming performance in competitive swimmers. Twenty-four competitive male swimmers were randomly assigned into two groups. Experimental group (age = 16.9 ± 0.5 years) and control group (16.1 ± 0.4 years). The mood subscales (tension, depression, anger, fatigue, confusion and vigor), total mood disturbance and swimming performance (50-m front crawl) were measured in pre and posttest. Our findings revealed a significant improvement in mood subscales (20.8 to 47.8%), total mood disturbance (14.4%) and in swimming performance (3.5%) after 2 weeks of tapering training. A significant correlation was observed between the total mood disturbance and the 50 m front crawl (r = -0.63) only in the experimental group. It was concluded that a progressive reduction in training volume with a maintain of intensity could improve mood state and swimming performance. In addition, a change in total mood disturbance could affect swimming performance. Swimming coaches are advised to include tapering period according to the standards we mentioned earlier before competitive swimming to improve mental state, which helps the swimmers to overcome the negative influences of overtraining and therefore they can promote sprint-swimming performance.

Measurement of the active drag coefficient in front-crawl: A stroke-by-stroke analysis.

The purpose of this study was to understand the change in active drag coefficient (CDA) over successive stroke cycles in front-crawl and the relationship between swimming speed and CDA. Eighteen national competitive swimmers (nine girls and nine boys with a mean age of 14.91 ± 0.59 years) were recruited. Swimming speed, propulsion (Ftotal) and frontal surface area were measured to calculate the CDA. Swimming speed (F = 1.790, p = 0.182, η2 = 0.07) and CDA (F = 0.907, p = 0.413, η2 = 0.06) did not change significantly over time, but swimming speed showed a decrease between the second and third stroke cycle. On the other hand, the Ftotal changed significantly over time (F = 4.437, p = 0.019, η2 = 0.21). Swimming speed and CDA showed a linear and strong relationship (R2 = 63.8 %). A stroke-by-stroke analysis showed that national level swimmers were able to maintain their hydrodynamic profile during a front-crawl maximal trial. Thus, it can be argued that a decrease in swimming speed can be related to a decrease in Ftotal. Swimming speed and CDA showed an inverse and significant relationship, with lower values of CDA resulting in faster swimming speeds.

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.

Analysis of upper limb propulsion in young swimmers in front-crawl through Statistical Parametric Mapping.

This study aimed to: (i) verify the within-subject effect of the dominant and non-dominant upper limb propulsion during consecutive arm-pulls through discrete (average) and continuous analysis (SPM), and; (ii) compare young swimmers' propulsion between both upper limbs through discrete (average) and continuous analysis (Statistical Parametric Mapping - SPM). The sample consisted of 17 young male swimmers (age = 16.02 ± 0.61-years) who regularly participate in national and international level competitions. A set of kinematic and propulsion variables were measured during a 25-m maximal trial in front-crawl. Statistical analysis of propulsion was performed using discrete variables and through SPM. Swimming velocity showed a significant decrease over time. A significant interaction between the "time" (consecutive arm-pulls) and "side" (dominant vs. non-dominant) effects was observed in both statistical analyzes. Only the dominant upper limb demonstrated a significant "time" effect with a significant difference (p < 0.05) between the first and third arm-pulls. SPM indicated that the "time" effect was observed between the âˆ¼ 34% and âˆ¼ 42% of the arm-pull. The differences between the first and third arm-pull were verified between the âˆ¼ 32% and âˆ¼ 43% of the arm-pull. A non-significant "side" effect was verified in both analyzes. Therefore, SPM analysis provided more sensitive and accurate outputs than discrete analysis. This will allow coaches to design specific training drills focused on specific moments of the arm-pull.

Comparison of swimming velocity between age-group swimmers through discrete variables and continuous variables by Statistical Parametric Mapping.

The aim of this study was to compare the swimming velocity in front-crawl between age-group swimmers using discrete variables against Statistical Parametric Mapping (SPM). The sample consisted of 30 young male swimmers divided into three groups (each with 10 swimmers) based on their age (group #1: 13.60 ± 0.84 years; group #2: 15.40 ± 0.32 years; group 3: 16.39 ± 0.69 years). Swimmers performed three maximal trials in front-crawl. The best performance was used for analysis. Comparison of swimming velocity between groups was analysed using discrete variables and as a continuous variable (SPM). As a discrete variable, the mean swimming velocity showed a significant difference between groups (p < 0.05). Moreover, when analysed by SPM, swimming velocity showed a significant difference (p = 0.021) between the ~ 44% and ~ 51% of the stroke cycle (transition of the propulsion phases between sides). Post-hoc comparison revealed a significant difference between group #1 and group #3 only in SPM analysis. Researchers, coaches, and practitioners should know that both measurement approaches can be used simultaneously. However, SPM offers more sensitive and accurate results about the swimmers' stroke cycle.

Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach.

Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring. The aim of this study was to reduce the dimensionality of the internal and external load measures, by a principal component approach, to describe and explain the resultant equations for TL monitoring during a standard in-season microcycle in sub-elite youth football. Additionally, it is intended to identify the most representative measure for each principal component. A principal component analysis (PCA) was conducted with a Monte Carlo parallel analysis and VariMax rotation to extract baseline characteristics, external TL, heart rate (HR)-based measures and perceived exertion. Training data were collected from sixty sub-elite young football players during a 6-week training period using 18 Hz global positioning system (GPS) with inertial sensors, 1 Hz short-range telemetry system, total quality recovery (TQR) and rating of perceived exertion (RPE). Five principal components accounted for 68.7% of the total variance explained in the training data. Resultant equations from PCA was subdivided into: (1) explosiveness, accelerations and impacts (27.4%); (2) high-speed running (16.2%); (3) HR-based measures (10.0%); (4) baseline characteristics (8.3%); and (5) average running velocity (6.7%). Considering the highest factor in each principal component, decelerations (PCA 1), sprint distance (PCA 2), average HR (PCA 3), chronological age (PCA 4) and maximal speed (PCA 5) are the conditional dimension to be considered in TL monitoring during a standard microcycle in sub-elite youth football players. Current research provides the first composite equations to extract the most representative components during a standard in-season microcycle in sub-elite youth football players. Futures research should expand the resultant equations within training days, by considering other well-being measures, technical-tactical skills and match-related contextual factors.

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.

Association between the Upper Quarter Dynamic Balance, Anthropometrics, Kinematics, and Swimming Speed.

Besides recurrently assessed water-based parameters, there are also some individual characteristics that affect swimming performance that are not water related. In the past few years, dynamic balance has been associated with land sports performance. Conversely, evidence on this topic in swimming is scarce. The purpose of this study was to assess the association between on-land dynamic balance and swimming performance. Sixteen young adults and recreational swimmers were recruited for the present study (8 males 20.8 ± 2.0 years, and 8 females 20.1 ± 1.9 years). A set of anthropometric features were measured. The upper quarter Y-balance test was selected as a dynamic balance outcome, and swimming speed as the swimming performance indicator. The results showed a moderate and positive correlation between dynamic balance and swimming performance (p < 0.05). Speed fluctuation was highly and negatively related to swimming speed (p < 0.001), i.e., swimmers who had higher scores in the dynamic balance were more likely to deliver better performances. This suggests that in recreational swimmers, the stability and mobility of the upper extremity had a greater influence on swimming performance. Therefore, swimming instructors are advised to include dynamic balance exercises in their land-based training sessions to improve their swimmers' performance.

Identifying Differences in Swimming Speed Fluctuation in Age-Group Swimmers by Statistical Parametric Mapping: A Biomechanical Assessment for Performance Development.

The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a non-significant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers' stroke cycle can be learned by assessing swimming speed using both methods.

Interaction of Kinematic, Kinetic, and Energetic Predictors of Young Swimmers' Speed.

PURPOSE: The aim of this study was to assess the interaction of kinematic, kinetic, and energetic variables as speed predictors in adolescent swimmers in the front-crawl stroke. DESIGN: Ten boys (mean age [SD] = 16.4 [0.7] y) and 13 girls (mean age [SD] = 14.9 [0.9] y) were assessed. METHODS: The swimming performance indicator was a 25-m sprint. A set of kinematic, kinetic (hydrodynamic and propulsion), and energetic variables was established as a key predictor of swimming performance. Multilevel software was used to model the maximum swimming speed. RESULTS: The final model identified time (estimate = -0.008, P = .044), stroke frequency (estimate = 0.718, P < .001), active drag coefficient (estimate = -0.330, P = .004), lactate concentration (estimate = 0.019, P < .001), and critical speed (estimate = -0.150, P = .035) as significant predictors. Therefore, the interaction of kinematic, hydrodynamic, and energetic variables seems to be the main predictor of speed in adolescent swimmers. CONCLUSIONS: Coaches and practitioners should be aware that improvements in isolated variables may not translate into faster swimming speed. A multilevel evaluation may be required for a more effective assessment of the prediction of swimming speed based on several key variables rather than a single analysis.

Anthropometric Measures, Muscle Resistance, and Balance in Physically Active, Aged Adults.

OBJECTIVES: This study aimed to examine the relationship between age, body mass index, muscle strength, and balance in physically active, aged adults. METHODS: Eighty-five participants were recruited for this study, having an average age of 70.31 years (SD = 9.90), ranging from 50 to 92 years. Twenty-six (30.6%) participants were male and fifty-nine (69.4%) were female. The participants had an average body mass index of 27.30 kg/m2 (SD = 3.62), ranging from 20.32 to 38.58 kg/m2. Participants undertook the Timed-Up and Go to test balance, and the chair-stand test to assess lower body strength. Hierarchical regression analyses were conducted. Three models (Model 1, 2, and 3) were tested to assess their relationships with balance: M1-Lower body muscle strength; M2-Lower body muscle strength and body mass index; M3-Lower body muscle strength, body mass index, and age. RESULTS: All hierarchical models displayed significant variance. The third model explained 50.9% of the variance in dynamic balance, [F(3, 81) = 27.94, p < 0.001, R = 0.71, Ra2 = 0.51]. The difference in Ra2 between the first, second, and third models was statistically significant (p < 0.05). Age, body mass index, and lower body muscle strength had significant (p < 0.05) correlations with balance. In terms of the significant impact of each predictor, age had the strongest association with balance (p < 0.05). CONCLUSIONS: The results are useful to understand mechanisms or diagnose people at risk of fall.

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.

Race Analysis and Determination of Stroke Frequency - Stroke Length Combinations during the 50-M Freestyle Event.

The aims of this study were to: (1) analyze and compare the stroke kinematics between junior and senior elite male swimmers in every section of the race during the 50-m freestyle event, and; (2) identify stroke frequency (SF)-stroke length (SL) combinations on swim speed independently for junior and senior swimmers in each section of the 50-m freestyle event. Eighty-six junior swimmers (2019) and 95 seniors (2021) competing in the 50-m long course meter LEN Championships were analyzed. The t-test independent samples (p ≤ 0.05) were used to compare juniors and seniors. The SF and SL combinations on swim speed were explored using three-way ANOVAs. Senior swimmers were significantly faster in the 50-m race than juniors (p < 0.001). Speed presented the largest significant difference (p < 0.001) in section S0-15 m (start until the 15th meter mark) being seniors fastest. Both junior and senior swimmers revealed a significant categorization (p < 0.001) by stroke length and stroke frequency in each race section. It was possible to model several SF-SL combinations for seniors and juniors in each section. The fastest swim speed in each section, for seniors and juniors independently, was achieved by a SF-SL combination that may not be the fastest SF or the longest SL. Coaches and swimmers must be aware that despite the 50-m event being an all-out bout, several SF-SL combinations were observed (independently for juniors and seniors), and they differ between race sections.

Associations between Coronavirus and Immune Response, Cardiorespiratory Fitness Rehabilitation and Physical Activity: A Brief Report.

COVID-19 has serious effects on cardiorespiratory capacity. In this sense, physical activity has been identified as beneficial in the treatment of cardiorespiratory diseases due to its anti-inflammatory and immunosuppressive benefits. To date, no study has been found on cardiorespiratory capacity and rehabilitation in patients cured after COVID-19. Thus, this brief report aims to relate the benefits of physical activity to cardiorespiratory function after COVID-19. It is important to know how different levels of physical activity can be related to the different symptoms of COVID-19. In view of this, the objectives of this brief report were to: (1) explore the theoretical associations between COVID-19 symptoms and physical activity; (2) compare the cardiorespiratory function of non-COVID-19 participants and post-COVID-19 patients; and (3) propose a physical activity program to improve the cardiorespiratory fitness of post-COVID-19 patients. Thus, we note that moderate-intensity physical activity (i.e., walking) has a greater beneficial effect on immune function, whereas vigorous activity (i.e., marathon running) tends to temporarily reduce immune function through an imbalance of cytokine types I and II in the hours and days after exercise. However, there is no consensus in the literature in this regard, since other investigations suggest that high-intensity training can also be beneficial, not causing clinically relevant immunosuppression. Physical activity has been shown to be beneficial in improving the clinical conditions most frequently associated with severe COVID-19. Thus, it is possible to infer that physically active individuals seem to be less exposed to the dangers of severe COVID-19 compared to non-active individuals through the benefits of physical activity in strengthening the immune system and fighting infections. The current study demonstrates that physical activity appears to be beneficial in improving the clinical conditions most often associated with severe COVID-19.

Stability analysis and prediction of pacing in elite 1500 m freestyle male swimmers.

This study aimed to analyse the stability of elite male long-distance swimmers (1500 m), and to identify the main predictors related to the pace. The performance of 16 elite male swimmers (22.59 ± 2.10 years-old) participating in the 1500 m event at the 2016 (London) and 2018 (Glasgow) LEN European Aquatic Championships were analysed. The lap performance, clean swim performance, turn performance, and a set of stroke mechanics variables were assessed. The lap performance presented a significant and moderate variation with all laps included (p < 0.001) and deleting the first and last lap (p = 0.002). Swimmers were significantly faster in the first half in comparison of the second. The total turn also presented a significant and moderate variation. The hierarchical linear modelling retained the time (estimate = 0.0019, p = 0.007), stroke frequency (estimate = -27.49, p < 0.001) and stroke length (estimate = -6.55, p < 0.001) as the main predictors of the clean swim performance. By contrast to the analysis based on the lap performance, clean swim performance presented a non-significant variation. Coaches should be aware that stroke length maintenance could negatively affect the clean swim performance, whereas a small increase of stroke frequency may present a meaningful enhancement of the total race time.

Effects of Isometric Strength and Plyometric Training on Running Performance: A Randomized Controlled Study.

Purpose: The aim of the study was to compare the effects of isometric strength (IST) and plyometric training (PT) on endurance running performance. Methods: Twenty-six endurance runners (18 males and 8 females; age 36 ± 6 years, stature 1.69 ± 0.05 m body mass 61.6 ± 8.0 kg, VO2max 50.4 ± 5.8 ml·kg-1·min-1) completed the countermovement jump (CMJ), isometric mid-thigh pull (IMTP), 2.4 km run time trial (2.4kmTT), running economy test (RE) and a graded exercise test measures at baseline. They were then randomly assigned to three groups, the control (CON), PT or IST group, and completed the circuit, plyometric or isometric training, respectively, twice a week for 6 weeks, while still continuing to perform their planned running training. They then completed the same set of measures performed at baseline post-intervention. Results: Significant time x group interactions and time main effect were observed for 2.4kmTT (P = .002, ƞ2p = .45 and P < .001, ƞ2 =0.72), maximal aerobic speed (MAS) (P = .006, ƞ2p = .39), CMJ height (P < .001, ƞ2p = .55) and IMTP relative peak force (P = .001, ƞ2p = .50) in favor of PT and IST. Significant main effect for time was observed for 2.4kmTT (P < .001, ƞ2p = .72), RE (P = .048, ƞ2p = .17), VO2max (P = .047, ƞ2p = .18), MAS (P < .001, ƞ2p = .63), CMJ height (P < .001, ƞ2p = .51) and IMTP relative peak force (P < .001, ƞ2p = .58). Conclusion: In conclusion, both PT and IST were similarly effective at enhancing running endurance performance. However, IST resulted in greater improvement to RE.

The role of the biomechanics analyst in swimming training and competition analysis.

Swimming analysts aid coaches and athletes in the decision-making by providing evidence-based recommendations. The aim of this narrative review was to report the best practices of swimming analysts that have been supporting high-performance athletes. It also aims to share how swimming analysts can translate applied research into practice. The role of the swimming analyst, as part of a holistic team supporting high-performance athletes, has been expanding and is needed to be distinguished from the job scope of a swimming researcher. As testing can be time-consuming, analysts must decide what to test and when to conduct the evaluation sessions. Swimming analysts engage in the modelling and forecast of the performance, that in short- and mid-term can help set races target-times, and in the long-term provide insights on talent and career development. Races can be analysed by manual, semi-automatic or fully automatic video analysis with single or multi-cameras set-ups. The qualitative and quantitative analyses of the swim strokes, start, turns, and finish are also part of the analyst job scope and associated with race performance goals. Land-based training is another task that can be assigned to analysts and aims to enhance the performance, prevent musculoskeletal injuries and monitor its risk factors.