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Purpose: Agility in young soccer players has long been associated with physical attributes like strength, speed, and power. The aims of this study were to investigate the relationship between cognitive factors and agility performance in this unique population. Methods: 39 young soccer players age 13.56 (SD 0.58) years were assessed for leg-muscle function (jump tests) and maturation status (peak height velocity). The Y-Agility Test, which included decision making in players, was used to measure performance. Cognitive factors such as perceptual skills and decision making were evaluated with the Y-Agility Test. Results: The linear regression showed an absence of significance between the Y-Agility Test and drop-jump contact (P = .283), Y-Agility Test and drop-jump flight (P = .185), Y-Agility Test and squat jump (P = .868), and Y-Agility Test and countermovement jump (P = .310). The linear mixed-model analyses suggested a difference between early-average maturers (P = .009) and early-late maturers (P = .005) but did not show a difference between average-late subjects (P = 1.000). Drop-jump flight did not show a difference in maturation (early-average P = 1.000; early-late P = 1.000; average P = 1.000). Squat-jump performance did not demonstrate any significance (early-average P = .618; early-late P = 1.000; P = 1.000). Countermovement-jump performance did not show any significance (early-average P = 1.000; early-late P = 1.000; average-late P = .492). Finally, agility performance does not show any significance between maturation levels (early-average maturer P = .450; early-late P = 1.000; average-late P = .830). Conclusion: Agility in young soccer players appears to follow a nonlinear trajectory, with cognitive factors possibly playing a more significant role than previously thought.

Purpose: To compare power output and pacing between maximal 1500- and 2000-m on-water rowing performances. Methods: Twenty-six (female n = 4, male n = 22) international rowers, across 6 boat classes, completed maximal 1500- and 2000-m on-water races, separated by 24 to 48 hours. Crew combinations and seat orders remained consistent between races. Peach PowerLine instrumentation measured power output and stroke rate. Differences in completion time, mean power output (MPO), percentage prognostic velocity (PPV; percentage of world record velocity in each boat class), stroke rate, and pacing variance were assessed using linear mixed modeling. Results: Compared with 2000-m, completion times were 90.4 (6.1) seconds shorter over 1500 m (−24.7% [0.7%]). Both MPO (P = .255, ${\eta }_{\mathrm{p}}^2=.06$ ) and PPV (P = .340, ${\eta }_{\mathrm{p}}^2=.18$ ) were not different between distances. Broadly, crews adopted a reverse-J-shaped pacing across both distances, demonstrating a reduced variance over 1500 m (P = .035, ${\eta }_{\mathrm{p}}^2=.62$ ). Percentage change in MPO from 2000 to 1500 m demonstrated a strong negative association with pacing variance over 1500 m (R ² = .74, P = .027). Conclusions: International rowing crews did not increase MPO or PPV when racing maximally over 1500 m compared to 2000 m. Comparable strategies were adopted over both distances, with less variance in pacing observed over 1500 m. The crews that demonstrated greater increases in MPO over the shorter race employed a flatter pacing strategy. To improve 1500-m on-water performance, rowers may need to adopt a more even pacing approach.

Objective: To analyze the influence of congested and noncongested fixture periods during 2 seasons in a professional male football team on soft-tissue injury incidence and external load. Methods: Thirty-three professional football players from a Portuguese Liga I team participated in this study. Weekly external load and soft-tissue injury rate and burden of 2 consecutive seasons (2021–22 and 2022–23) were analyzed. Results: Total soft-tissue injury rate and burden for the 2 seasons were 3.9 and 3.2 injuries per 1000 hours and 71.8 and 60.5 days per 1000 hours for congested and noncongested periods, respectively. No significant differences were observed between congested and noncongested periods. Total high-speed running, sprint distance, distance above 80% and 90% of maximal velocity, and meters accelerating and decelerating above 2 m/s² were significantly higher for noncongested weeks. Match accelerations and decelerations above 3 m/s² were higher during congested periods and training during noncongested periods. No differences between the 2 periods were observed for the total number of accelerations and decelerations above 3 m/s². Overall, physical outputs per week were higher for training during noncongested weeks, whereas matches during congested periods registered higher external load. Conclusions: No effect of a congested schedule was observed on soft-tissue injury rates and injury burden. Higher match exposure during congested periods increased external load performed per week, and during noncongested periods, training load was superior to congested weeks.

Purpose: This study aimed to examine the impact of different rest periods between short sprint interval training (SSIT) trials on the physiological and performance adaptations of female volleyball players. Methods: Twenty-four trained college athletes volunteered to participate in this study and were randomly assigned to 3 SSIT groups with different work-to-rest ratios (1:2 [5-s run:10-s rest], 1:4 [5-s run:20-s rest], and 1:6 [5-s work:30-s rest]). Before and after 6-week training, physiological parameters (maximum oxygen uptake, first and second ventilatory thresholds, and peak and mean power output) and physical performance measures (ie, countermovement vertical jump, 10-m sprint, and T-test change-of-direction speed) were evaluated. Results: After the training period, all groups improved (P = .001) their sport-related performance and physiological parameters, ranging from moderate to very large effect sizes. Comparative analysis of the magnitude of training effects indicated that the 1:6 SSIT group had in a significantly greater change in countermovement vertical jump (P = .007), 10-m sprint (P = .014), peak power output (P = .019), and mean power output (P = .05) compared with 1:2 SSIT group. By contrast, the 1:2 SSIT group demonstrated significantly (P = .022) greater changes in maximum oxygen uptake than the 1:6 SSIT group. However, the change-of-direction speed and changes in first and second ventilatory thresholds were the same among the groups (P > .05). Conclusions: When performing SSIT, longer rest intervals are suitable for physical and anaerobic performance, and shorter rest periods are appropriate for enhancing the cardiorespiratory fitness of female volleyball players’ performance.

Purpose: To profile the training characteristics of an elite team pursuit cycling squad and assess variations in training intensity and load accumulation across the 36-week period prior to a world-record performance at the 2018 Commonwealth Games. Methods: Training data of 5 male track endurance cyclists (mean [SD]; age 21.9 [3.52] y; 4.4 [0.16] W·kg⁻¹ at anaerobic threshold; 6.2 [0.28] W·kg⁻¹ maximal oxygen uptake 68.7 [2.99] mL kg·min⁻¹) were analyzed with weekly total training volume and heart rate, power output, and torque intensity distributions calculated with reference to their 3:49.804 min:s.ms performance requirements for a 4-km team pursuit. Results: Athletes completed 543 (37) h⁻¹ of training across 436 (16) sessions. On-bike activities accounted for 69.9% of all training sessions, with participants cycling 11,246 (1139) km⁻¹ in the training period of interest, whereas 12.7% of sessions involved gym/strength training. A pyramidal intensity distribution was evident with over 65% and 70% of training, respectively, performed at low-intensity zone heart rate and power output, whereas 5.3% and 7.7% of training was performed above anaerobic threshold. The athletes accumulated 4.4% of total training volume at, or above, their world-record team pursuit lead position torque (55 N·m). Conclusions: These data provide updated and novel insight to the power and torque demands and load accumulation contributing to world-record team pursuit performance. Although the observed pyramidal intensity distribution is common in endurance sports, the lack of shift toward a polarized intensity distribution during taper and competition peaking differs from previous research.

Purpose: To compare the training characteristics of an elite team pursuit cycling squad in the 3-month preparation phases prior to 2 successive world-record (WR) performances. Methods: Training data of 5 male track endurance cyclists (mean [SD]; age 23.4 [3.46] y; body mass 80.2 [2.74] kg; 4.5 [0.17] W·kg⁻¹ at LT₂; maximal aerobic power 6.2 [0.27] W·kg⁻¹; maximal oxygen uptake 65.9 [2.89] mL·kg⁻¹·min⁻¹) were analyzed with weekly total training volume by training type and heart rate, power output, and torque intensity distributions calculated with reference to the respective WRs’ performance requirements. Results: Athletes completed 805 (82.81) and 725 (68.40) min·wk^–1 of training, respectively, in each season. In the second season, there was a 32% increase in total track volume, although track sessions were shorter (ie, greater frequency) in the second season. A pyramidal intensity distribution was consistent across both seasons, with 81% of training, on average, performed below LT₁ power output each week, whereas 6% of training was performed above LT₂. Athletes accumulated greater volume above WR team pursuit lead power (2.4% vs 0.9%) and torque (6.2% vs 3.2%) in 2019. In one athlete, mean single-leg-press peak rate of force development was 71% and 46% higher at mid- and late-phases, respectively, during the preparation period. Conclusions: These findings provide novel insights into the common and contrasting methods contributing to successive WR team pursuit performances. Greater accumulation of volume above race-specific power and torque (eg, team pursuit lead), as well as improved neuromuscular force-generating capacities, may be worthy of investigation for implementation in training programs.

Purpose: Prediction of athlete wellness is difficult—or, many sports-medicine practitioners and scientists would argue, impossible. Instead, one settles for correlational relationships of variables gathered at fixed moments in time. The issue may be an inherent mismatch between usual methods of data collection and analysis and the complex nature of the variables governing athlete wellness. Variables such as external load, stress, muscle soreness, and sleep quality may affect each other and wellness in a dynamic, nonlinear, way over time. In such an environment, traditional data-collection methods and statistics will fail to capture causal effects. If we are to move this area of sport science forward, a different approach is required. Methods: We analyzed data from 2 different soccer teams that showed no significance between player load and wellness or among individual measures of wellness. Our analysis used methods of attractor reconstruction to examine possible causal relationships between GPS/accelerometer-measured external training load and wellness variables. Results: Our analysis showed that player self-rated stress, a component of wellness, seems a fundamental driving variable. The influence of stress is so great that stress can predict other components of athlete wellness, and, in turn, self-rated stress can be predicted by observing a player’s load data. Conclusion: We demonstrate the ability of nonlinear methods to identify interactions between and among variables to predict future athlete stress. These relationships are indicative of the causal relationships playing out in athlete wellness over the course of a soccer season.