Within-week differences in external training load demands in elite volleyball players

External Load Variability in Elite Futsal: Positional Demands and Microcycle Structuring for Player Well-Being and Performance

The aim of this study was to compare the external load of each session along competitive microcycles on an elite futsal team, considering the positions and relationships of the players. The external load of 10 elite players from a First Division team in the Spanish Futsal League (age 27.5 ± 7 years, height 1.73 ± 0.05 m, weight 70.1 ± 3.8 kg) were recorded across 30 microcycles. The players' external loads were monitored using OLIVER devices. To analyse the external load, Levene's test was conducted to assess the homogeneity of variances, followed by one-way analysis of variance (ANOVA) to identify differences in dependent variables across the different microcycle days and player positions. Regarding external load during the microcycle, the day with the lowest external load was MD-1, and the days with the highest external load were MD-3 and MD-4. In addition, considering playing positions, pivots exhibited the lowest loads throughout the microcycle, whereas wingers and defenders exhibited the highest loads, depending on the variables analysed. By providing reference values from elite contexts, this study offers practical insights for S&C coaches to optimize microcycles. Furthermore, it contributes to workload management strategies within sport science and public health frameworks, promoting sustainable performance and athlete wellness in futsal.

Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis

Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study. Recordings of external load (speed, distance, and acceleration/deceleration data) and internal load (heart rate) were obtained during all training sessions and official matches over a 4-year period. Machine learning model training and evaluation features were calculated for each of nine different metrics for a 28-day period prior to the injury and an equal-length baseline epoch. The acute surge in the values of each workload metric was quantified by the deviation of maximum values from the average, while the variations of cumulative workload over the last four weeks preceding injury were also calculated. Seven features were selected by the model as prominent estimators of injury incidence. Three of the features concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90-100% of maximum). The four cumulative load features were (total distance, high speed and sprint running distance and training load score). The accuracy of the muscle injury risk assessment model was 0.78, with a sensitivity of 0.73 and specificity of 0.85. Our model achieved high performance in injury risk detection using a limited number of training load variables. The inclusion, for the first time, of heart rate related variables in an injury risk assessment model highlights the importance of physiological overload as a contributor to muscle injuries in soccer. By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition.

Quantifying internal and external training loads in collegiate male volleyball players during a competitive season

BACKGROUND

The long-term monitoring of internal and external training load is crucial for the training effectiveness of athletes. This study aims to quantify the internal and external training loads of collegiate male volleyball players during the competitive season. The internal and external training load variables were analyzed across mesocycles and playing positions.

METHODS

Fourteen participants with age of 20.2 ± 1.3 years, height of 1.81 ± 0.05 m, and body weight of 70.8 ± 5.9 kg were recruited. The data were collected over a 29-week period that was divided into four mesocycles: preparation 1 (P1, weeks 1-7), competition 1 (C1, weeks 8-14, including a 5-day tournament in week 14), preparation 2 (P2, weeks 15-23), and competition 2 (C2, weeks 24-29, including a 6-day tournament in week 29). Each participant wore an inertial measurement unit and reported the rating of perceived exertion in each training session. The internal training load variables included weekly session rating of perceived exertion, acute: chronic workload ratio, and training monotony and strain. The external training load variables included jump count and height and the percentage of jumps exceeding 80% of maximal height.

RESULTS

C2 had the highest average weekly internal training load (3022 ± 849 AU), whereas P2 had the highest average weekly acute: chronic workload ratio (1.46 ± 0.13 AU). The number of weekly jumps in C1 (466.0 ± 176.8) was significantly higher than in other mesocycles. Weekly jump height was significantly higher in C1, P2, and C2. Internal training load was positively correlated with jump count (ρ = 0.477, p < 0.001). Jump count was negatively correlated with jump height (ρ = -0.089, p = 0.006) and the percentage of jumps exceeding 80% of maximal height (ρ = -0.388, p < 0.001). The internal and external training load variables were similar among different playing positions.

CONCLUSION

The participants exhibited significantly higher internal training load in C2 and higher jump height after P1. A high jump count was associated with higher internal training load and lower jump height. Excessive jumps may result in fatigue and reduce height.

Causal inference did not detect any effect of jump load on knee complaints in elite men's volleyball

The aim was to determine how jump load affects knee complaints in elite men's volleyball. We collected data from four men's premier league volleyball teams through three seasons in a prospective cohort study (65 players, 102 player-seasons). Vert inertial measurement devices captured the jump load (jump frequency and jump height) from 21 088 daily player sessions, and knee complaints were reported in 3568 weekly OSTRC-O questionnaires. Mixed complementary log-log regression models described the probability of (i) experiencing symptoms if players were currently asymptomatic, (ii) worsening symptoms if players had symptoms, and (iii) recovery from knee complaints. Based on our causal assumptions, weekly jump load was modeled as the independent variable, adjusted for age (years), weight (kg), position on volleyball team, and past jump load. No certain evidence of an association was found between weekly jump load and probability of (i) knee complaints (p from 0.10 to 0.32 for three restricted cubic splines of load), (ii) worsening symptoms if the player already had symptoms (p from 0.11 to 0.97), (iii) recovery (p from 0.36 to 0.63). The probability of knee complaints was highest for above-average weekly jump load (~1.2% for an outside hitter with mean age and height) compared with low loads (~1%) and very high loads (→ ~ 0%). The association between jump load and knee complaints risk remains unclear. Small differences in risk across observed jump load levels were observed. It would likely require substantially increased sample sizes to detect this association with certainty.

Comparing Two Methods of Acute: Chronic Workload Calculations in Girls’ Youth Volleyball

Monitoring training load using acute:chronic workload ratio (ACWR) enables coaches to maximize fitness potential while mitigating injury risks by maintaining an optimal ACWR range. There are two methods of determining ACWR: rolling average (RA) and exponentially weighted moving average (EWMA). This study aimed to (1) compare weekly changes in kinetic energy (KE) output in female youth athletes (n = 24) during the high school (HSVB) and club volleyball (CVB) seasons and (2) evaluate the agreement in RA and EWMA ACWR calculations during the HSVB and CVB seasons. Weekly load was measured using a wearable device, and RA and EWMA ACWRs were calculated using KE. The HSVB data showed spikes in ACWR at the onset of the season and during one week mid-season (p = 0.001–0.015), but most weeks were in the optimal ACWR range. The CVB data had greater weekly variations throughout the season (p < 0.05), and many weeks were outside of the optimal ACWR range. There were moderate correlations between the two ACWR methods (HSVB: r = 0.756, p < 0.001; CVB: r = 0.646, p < 0.001). Both methods can be used as a monitoring tool for consistent training like that in HSVB, but more research is needed to investigate appropriate methods for an inconsistent season like that of CVB.