Correction: In-season internal and external training load quantification of an elite European soccer team.

[This corrects the article DOI: 10.1371/journal.pone.0209393.].

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.

Identifying the ideal weekly training load for in-game performance in an elite Brazilian soccer team.

Introduction: The purpose of this study was to investigate the ideal training load to be applied during periods of fixture congestion to ensure an adequate dose-response effect for performance maintenance. Methods: Match performance data and corresponding pre-match training load sessions (both N = 498 match performance cases and training-block session cases) were collected (with the catapult system, VECTOR7) from 36 male professional soccer players (23.5 ± 5.2 years; 178 ± 4 cm; 75.5 ± 6.0 kg) belonging to the Brazilian First Division team during the 2022 season. The following data were collected in match and training sessions: jump, acceleration, deceleration, and change of direction (COD); running distance producing metabolic power at different intensities (>20, >20-35, >35-45, >45-55, and >55 W kg-1), total distance (m), relative distance (m/min), running distance at different speeds (>20, >25, and >30 km/h), number of sprints (running >25 km/h), and maximum speed (km/h). Mixed linear model (MLM), decision tree regression (DTR), and cluster K means model (SPSS v.26) approach were performed to identify the most critical variables (and their respective load) in the training sessions that could explain the athlete's match performance. Results: MLM and DTR regression show that training load significantly affects game performance in a specific way. According to the present data, an interference phenomenon can occur when a high load of two different skills (running in a straight line vs COD, deceleration, and jumping) is applied in the same training block of the week. The cluster approach, followed by a chi-squared test, identified significant associations between training load and athlete match performance in a dose-dependent manner. Discussion: The high load values described here have a beneficial effect on match performance, despite the interference between stimuli discussed above. We present a positive training load from a congested season from the Brazilian First Division team. The study suggests that an interference effect occurs when high physical training loads are applied to different specific physical skills throughout the season.

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.

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.

Detraining and Retraining Effects from a Multicomponent Training Program on the Functional Capacity and Health Profile of Physically Active Prehypertensive Older Women.

BACKGROUND: Resuming a physical exercise program after a period of cessation is common in older women. Monitoring the responses during this detraining (DT) and retraining (RT) may allow us to analyze how the body reacts to an increase and a reduction in physical inactivity. Therefore, we conducted a follow-up training, DT, and RT in prehypertensive older women to analyze the response to these periods. METHODS: Twenty-three prehypertensive older women (EG; 68.3 ± 2.8 years; 1.61 ± 0.44 m) performed 36 weeks of the multicomponent training program (MTP) followed by twelve weeks of DT plus eight weeks of RT. Fifteen prehypertensive older women (CG; 66.3 ± 3.2 years; 1.59 ± 0.37 m) maintained their normal routine. Functional capacity (FC), lipid, and hemodynamic profile were assessed before, during 24 and 36 weeks of the MTP, after 4 and 12 weeks of DT, and after 8 weeks of RT. RESULTS: After 24 weeks of the MTP, only SBP did not improve. Four weeks of DT did not affect lower body strength (30-CS), TC, or GL. Eight weeks of RT improved BP (SBP: -2.52%; ES: 0.36; p < 0.00; DBP: -1.45%; ES: 0.44; p < 0.02), handgrip strength (3.77%; ES: 0.51; p < 0.00), and 30-CS (3.17%; ES: 0.38; p < 0.04) compared with 36 weeks of the MTP. CONCLUSIONS: Eight weeks of RT allowed patients to recover the benefits lost with detraining, which after only four weeks affected them negatively, and the systematic practice of exercise contributed to greater regulation of BP since 24 weeks of the MTP proved not to be enough to promote positive effects of SBP.

Effects of applying a multivariate training program on physical fitness and tactical performance in a team sport taught during physical education classes.

Introduction: A multivariate training program could be a pedagogical choice to improve physical and tactical performance in a team sport taught during physical education classes at different levels of education. Thus, the aim of this study was to verify the effects of applying a multivariate training program on physical fitness and tactical performance during the teaching of a basketball didactic unit in basic and secondary education. Methods: Seventy-five students from a Portuguese school, with an average age of 15.02 ± 1.31 years, included forty-two students from basic school and thirty-three students from secondary school. The FITescola® test battery was used to assess physical fitness (i.e., sit-ups, push-ups, horizontal impulse, shuttle test, 40 m sprint, agility 4 × 10 m). The Game Performance Assessment Instrument (GPAI) was used to assess students' tactical performance for each player's game performance during a 20-minute 3 vs. 3 match. The GPAI variables were decision making index (DMI), skill execution index (SEI), support actions index (SI), and adaptability index (AI). During a basketball didactic unit teaching, the students were randomly divided into two groups, a control group that will not carry out the training program and an experimental group that will carry out a strength training program, high intensity explosive exercises and activities based on small-sided games (SSG) for 6 weeks. The two groups were evaluated in two moments: before the application of the training program and after the application of the training program regarding changes in physical fitness and tactical performance. The independent samples t-test (samples from two groups) and paired sample Test (for the same group) were applied for pre and post-assessment comparisons. Results: All indexes present significant differences between basic and secondary students in the pre- and post-assessment tests with small effects (t = -6.54 to -4.82, Δ = -27.57 to -0.16, p<0.05-p< 0.001, d = 0.78-1.05). Discussion: The results allow to conclude that in a school environment, a well-structured multivariate training program can effectively improve students' tactical skills, increasing their physical conditioning levels.

A comparison of load cell and pressure sensors to measure in-water force in young competitive swimmers.

The purpose of this study was to compare the in-water force of young competitive swimmers using tethered swimming and differential pressure sensors. Thirty-one swimmers (16 girls and 15 boys) were randomly assigned to perform two in-water tests. Swimmers completed two maximum bouts of 25 m front crawl with a differential pressure system and a 30 s maximum bout with an attached load cell (tethered-swimming). The peak force (FPEAK, in N) of dominant and non-dominant upper limbs was retrieved for further analysis. Comparison between methods revealed significant differences in all force variables (p ≤ 0.05) and the biases (mean differences) were large in girls (FPEAK dominant, 45.89 N; FPEAK non-dominant, 43.79 N) and boys (FPEAK dominant, 67.26 N; FPEAK non-dominant, 61.78 N). Despite that, simple linear regression models between the two methods showed significant relationships with a moderate effect in all variables for girls, whereas in boys a high and moderate effect was verified for FPEAK of dominant and non-dominant limbs (respectively). It seems that using pressure sensors and tethered swimming leads to different FPEAK values in young competitive, where correction factors are needed to compare data between both methods.

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.

Perspectives on Player Performance during FIFA World Cup Qatar 2022: A Brief Report.

Changing the date of the FIFA World Cup Qatar 2022 may represent a factor to consider for the expected performance of participating players. This was due to fixture congestion at the start of the season and expected weather conditions during the competition. Thus, the main purpose of this brief report was to critically analyze the potential impact of changing the competition date and weather conditions on players' performance. In addition, a brief description about the performance during the World Cup is also provided. For the research, the Web of Science, PubMed and SPORTDiscus databases were accessed using the primary keywords FIFA World Cup and World Soccer Cup associated with the secondary keywords match running performance, fixture congestion, fatigue and weather conditions. After applying inclusion and exclusion criteria, 52 articles were considered for analysis. The results seem to indicate that although changes were expected due to the modifications made (i.e., the competition date and scheduling congestion), the performance of the players seems not to have been affected in terms of the analyzed indicators. Furthermore, it seems possible to identify some patterns in the behavior of the teams that reached the most advanced stages of the competition.

From Entry to Finals: Progression and Variability of Swimming Performance at the 2022 FINA World Championships.

The aim of the present study was two-fold: (i) to analyze the progression and variability of swimming performance (from entry times to best performances) in the 50, 100, and 200 m at the most recent FINA World Championships and (ii) to compare the performance of the Top16, semifinalists, and finalists between all rounds. Swimmers who qualified with the FINA A and B standards for the Budapest 2022 World Championships were considered. A total of 1102 individual performances swimmers were analyzed in freestyle, backstroke, breaststroke, and butterfly events. The data was retrieved from the official open-access websites of OMEGA and FINA. Wilcoxon test was used to compare swimmers' entry times and best performances. Repeated measures ANOVA followed by the Bonferroni post-hoc test were performed to analyze the round-to-round progression. The percentage of improvement and variation in the swimmers' performance was computed between rounds. A negative progression (entry times better than best performance) and a high variability (> 0.69%) were found for most events. The finalists showed a positive progression with a greater improvement (~1%) from the heats to the semifinals. However, the performance progression remained unchanged between the semifinals and finals. The variability tended to decrease between rounds making each round more homogeneous. Coaches and swimmers can use these indicators to prepare a race strategy between rounds.

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.

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.

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.

Performance Tiers within a Competitive Age Group of Young Swimmers Are Characterized by Different Kinetic and Kinematic Behaviors.

The present study aimed to analyze swimmers' in-water kinetic and kinematic behaviors according to different swimming performance tiers within the same age group. An amount of 53 highly trained swimmers (girls and boys: 12.40 ± 0.74 years) were split up into 3 tiers based on their personal best performance (i.e., speed) in the 50 m freestyle event (short-course): lower-tier (1.25 ± 0.08 m·s-1); mid-tier (1.45 ± 0.04 m·s-1); and top-tier (1.60 ± 0.04 m·s-1). The in-water mean peak force was measured during a maximum bout of 25 m front crawl using a differential pressure sensors system (Aquanex system, Swimming Technology Research, Richmond, VA, USA) and defined as a kinetic variable, while speed, stroke rate, stroke length, and stroke index were retrieved and considered as kinematic measures. The top-tier swimmers were taller with a longer arm span and hand surface areas than the low-tier, but similar to the mid-tier. While the mean peak force, speed and efficiency differed among tiers, the stroke rate and stroke length showed mixed findings. Coaches should be aware that young swimmers belonging to the same age group may deliver different performance outcomes due to different kinetic and kinematic behaviors.

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.