Reliability of Wearable Inertial Measurement Units to Measure Physical Activity in Team Handball

Tracking What Truly Matters: The Contribution of Game Load, Recovery, and Well-Being to Basketball Performance

PURPOSE

This study aimed to explore the relationships of game load, recovery status, and well-being with performance efficiency in basketball players across different performance levels.

METHODS

Data from 16 professional male basketball players across 18 official basketball games were analyzed utilizing principal component analysis to reduce dimensionality and identify key factors related to game load, perceived recovery, and well-being associated with performance. Quantile regression was employed to assess the impact of these factors on performance efficiency at different quantiles (10th, 50th, and 90th).

RESULTS

Six factors were extracted. The first factor, composite load (a combined measure of the external physical loads and session rating of perceived exertion experienced by players), explained 42% of the variance, followed by well-being (15%), composite jump load (focused on jump-related movements, 11%), PlayerLoad per minute (5%), muscle soreness (4%), and recovery status (4%). Quantile regression analyses revealed that composite load had a significant positive effect in the ordinary least-squares estimate (P < .001) and at the 10th (P = .044) and 50th quantiles (P < .001). Composite jump load had significant effects in the ordinary least-squares estimate (P < .001) and the 50th quantile (P = .003). PlayerLoad per minute had significant effects in the ordinary least-squares estimate (P < .001) and in both the 10th (P < .001) and the 50th quantiles (P < .001). In addition, well-being was significant at the 50th quantile (P < .001), whereas muscle soreness and recovery showed no significant effects.

CONCLUSION

Composite load and PlayerLoad per minute consistently had positive associations with game performance, particularly for players at low and medium performance levels.

Interunit Reliability of Firstbeat Sport Sensors as Accelerometer-Based Tracking Devices in Basketball

PURPOSE

The aim of this study was to assess the interunit reliability of the movement load (ML) derived from the Firstbeat Sport sensors during basketball training sessions.

METHODS

Eight professional male basketball players (age 25.5 [4.7] y, stature 199 [8] cm, body mass 96.6 [7.8] kg, body fat 10.0% [2.2%]) were recruited to participate in this study. A total of 50 individual training sessions were recorded on one or multiple occasions (range: 1-21 individual sessions for each player) while players were wearing 2 Firstbeat Sport sensors firmly affixed to their chest roughly at the base of the sternum via textile straps. ML was calculated via Firstbeat Sports software for the entire basketball training sessions and for the following basketball-specific activity types: 5-on-5 full court (5v5, n = 30), 5-on-5 half-court plus 2 courts back and forth (5v5 1/2 + 2, n = 26), and 5-on-5 half-court (5v5 1/2, n = 26).

RESULTS

Linear mixed models revealed no statistical differences in ML data recorded using different sensors during the entire training sessions and the selected basketball-specific activity types (all P > .05, trivial). The coefficient of variation calculated as percentage and intraclass correlation coefficient calculated for the entire training sessions and basketball-specific activity types ranged from 2.51% to 5.97% and from .98 to 1.00, respectively.

CONCLUSIONS

The ML derived from the Firstbeat Sport sensor showed acceptable interunit reliability when considering the full training sessions and basketball-specific activities in professional basketball. Overall, basketball practitioners can use Firstbeat Sport sensors interchangeably to monitor the external load of professional basketball players during training sessions.

Applying a Cluster-Analysis Approach to Monitor Training Load in Male Volleyball During the Preseason Period

PURPOSE

This study aimed to (1) classify the external-load measures carried out during the preseason period by male volleyball players via cluster technique identifying the most important external-load measures and (2) assess the differences between clusters in internal-load variables.

METHODS

Twenty-two male Division 1 and 2 volleyball players (mean [SD] age 21.2 [3.0] y, stature 186.4 [6.0] cm, body mass 80.0[10.5 kg]) were recruited for this study. Players' external (jump, player load, acceleration, deceleration, and change of direction) and internal (percentage of peak heart rate, summated heart-rate zones, and session rating of perceived exertion) loads were monitored during 5 weeks of the preseason period for both Division 1 and Division 2 teams. External-load measures were classified via a 2-step cluster analysis followed by predicting importance analysis, while differences in internal-load measures between clusters were analyzed using linear mixed models.

RESULTS

The 3 identified clusters classified the sessions in high (C1, 30.1%) moderate (C2, 31.8%), and low (C3, 38.1%) load. Predicting importance analysis found jump as the main cluster predictor (predicting value = 1), followed by player load (predicting value = 0.73). An effect of cluster was found on each internal-load measure (P < .001), with post hoc analyses showing lower values in C3 compared with C1 and C2 (P < .05, effect sizes ranges from small to moderate).

CONCLUSIONS

Volleyball coaches can adopt a monitoring system including cluster analysis to classify the preseason training sessions' load having a higher consideration for jump and player load as the main external-load measures.

The reliability of a commonly used (CatapultTM Vector S7) microtechnology unit to detect movement characteristics used in court-based sports

This two-part study evaluated the inter- and intra-unit reliability of Catapult Vector S7 microtechnology units in an indoor court-sport setting. In part-one, 27 female netball players completed a controlled movement series on two separate occasions to assess the inter- and intra-unit reliability of inertial movement analysis (IMA) variables (acceleration, deceleration, changes of direction and jumps). In part-two, 13 female netball players participated in 10 netball training sessions to assess the inter-unit reliability of IMA and PlayerLoadTM variables. Participants wore two microtechnology units placed side-by-side. Reliability was assessed using intraclass correlation coefficient (ICC), coefficient of variation (CV) and typical error (TE). Total IMA events showed good inter-unit reliability during the movement series (ICC, 1.00; CV, 3.7%) and training sessions (ICC, 0.99; CV, 4.5%). Inter-unit (ICC, 0.97; CV, 4.7%) and intra-unit (ICC, 0.97; CV, 4.3%) reliability for total IMA jump count was good in the movement series, with moderate CV (7.7%) during training. Reliability decreased when IMA counts were categorised by intensity and movement type. PlayerLoadTM (ICC, 1.00; CV, 1.5%) and associated variables revealed good inter-reliability, except peak PlayerLoadTM (moderate) and PlayerLoadSLOW (moderate). Counts of IMA variables, when considered as total and low-medium counts, and PlayerLoad variables are reliable for monitoring indoor court-sports players.

Description and Classification of Training Drills, Based on Biomechanical and Physiological Load, in Elite Basketball

The aim of this study was to understand and describe the physiological and biomechanical demands of various tasks used in basketball training and, subsequently, to provide a practical application of these tasks in a typical training week. Twelve basketball players had their external load variables monitored across 179 training sessions (2896 samples) using local positioning system technology. These variables included total distance covered, distance covered at various intensity levels, accelerations, decelerations, PlayerLoad™, and explosive efforts. The analysis revealed significant differences in both physiological and biomechanical loads across various drills. Specifically, tasks with more space and fewer defenders, such as 3v0 full court, impose higher physiological loads compared to tasks with less space and more defenders, like 5v5 full court. The difference in physiological load between these tasks was statistically significant (p < 0.05) with a moderate effect size (ES: -0.60, 95% CI: [-0.99, -0.22]). In terms of biomechanical load, drills with increased defensive pressure, such as 5v5 full court, exhibited significantly higher values compared to less specific drills, such as 5v0 full court, with a very large effect size (ES: 1.37, 95% CI: [1.04, 1.70], p < 0.01). Additionally, comparisons between 5v5 full court and 3v0 full court for biomechanical load produced a very large effect size (ES: 1.67, 95% CI: [1.37, 1.97], p < 0.01), indicating a substantial difference in load demands. The results indicate that tasks with more space and fewer defenders impose higher physiological loads, while those with less space and more defenders increase the biomechanical load. For training design, it is recommended to schedule tasks with a higher biomechanical load at the beginning of the session and those with a physiological orientation toward the end. Understanding the distinct demands of different drills can help coaches structure training sessions more effectively to optimize player load and performance development throughout the week.

Is Match Playing Time a Potential Tool for Managing Load in Women's Handball?

ABSTRACT

García-Sánchez, C, Navarro, RM, Nieto-Acevedo, R, and de la Rubia, A. Is match playing time a potential tool for managing load in women's handball? J Strength Cond Res 38(12): e769-e775, 2024-The aim of this study was to explore the external load experienced by female handball players according to the time played in official matches (≥40, <40, and ≤20 minutes) and in relation to playing positions (backs, pivots, and wings). Twenty-two female players from the Spanish second Division were monitored across 13 matches. Total distance covered, high-speed running distance (HSR), accelerations, decelerations, and PlayerLoad were collected in absolute and relative values using a local positioning system (WIMU PRO, Realtrack Systems SL, Almería, Spain). One-way ANOVA with partial eta-squared and Cohen's d were used to determine the differences between playing time groups and between playing positions. This study revealed that players with more match playing time had a higher absolute values in all external load variables ( p < 0.001; very large effects), except in HSR where only wings showed higher values according to the time played ( p < 0.001; very large effects). However, these differences between playing time groups disappear when the external load values were normalized according to the effective playing time ( p > 0.05). In relation to playing positions, wings covered more HSR/minutes than the other playing positions regardless of the playing time ( p < 0.05; large effects). In addition, backs performed more ACC/minutes than wings in the high playing time group and more than pivots in the low playing time group ( p < 0.05, moderate effects). In addition, backs performed more DEC/minutes than wings in the low playing time group ( p < 0.05, moderate effects). Therefore, handball coaches should consider the playing time as an effective tool to optimize the weekly load distribution.

Training and Game Loads Across Noncongested and Congested Weekly Microcycles During the Regular Season in a Semiprofessional Women's Basketball Team

PURPOSE

To quantify and compare loads encountered in individual training sessions and games during noncongested and congested weeks in semiprofessional women basketball players.

METHODS

Using an observational, longitudinal design, 12 players from the same team had their external (PlayerLoad, relative PlayerLoad, and total and high-intensity inertial movement analysis variables) and internal load (session rating of perceived exertion [sRPE], sRPE-load, percentage of heart rate peak, and modified summated-heart-rate-zones load) monitored across a regular season. Training and game data were categorized into noncongested (0-1 game) and congested weeks (2-3 games). Linear mixed models and Cohen d effect sizes were used for analyses.

RESULTS

Comparisons between training sessions revealed higher (P < .05, d = 1.35-5.33) PlayerLoad, total inertial movement analysis, sRPE, and sRPE-load during training session 1 than training session 2 in congested weeks. Comparisons between training sessions and games revealed higher (P ≤ .001, d = 1.10-1.66) sRPE and sRPE-load during games than training sessions 1 and 2 in noncongested weeks, alongside higher (P ≤ .001, d = 1.87-3.55) sRPE during game 1 than training sessions 1 and 2 in congested weeks. Comparisons between games revealed higher (P < .05, d = 0.57-2.82) loads in game 3 during congested weeks compared with all other games.

CONCLUSIONS

Training appeared to be tapered in congested weeks, likely to account for upcoming increases in game loading, but remained relatively consistent across sessions during noncongested weeks. Individual game loads remained relatively consistent but were noticeably increased when a third game was played in the week.

Quantifying jumps and external load in netball using VERT inertial measurement units

This study assesses the relationship between an inexpensive and an established inertial measurement device for the measurement of movement and jumps in netball. Twenty-five female netballers participated in this study (11 elite and 14 sub-elite). Two inertial measurement devices (Catapult ClearSky T6 and VERT Classic) were worn simultaneously during a training session. The 'Kinetic Energy' output provided by the VERT device was compared to the total PlayerLoad collected by the ClearSky T6 device. Automated total jump counts were also compared across devices. A correlation of r = 0.76 (95% confidence interval [CI]; 0.52 to 0.89) was identified between total PlayerLoad and 'Kinetic Energy'. Similarly, a correlation of r = 0.81 (95% CI; 0.61 to 0.91) was identified for the relationship between ClearSky T6 jump count and VERT jump count. Mean bias of 1.7 jumps (95% CI; -2.5 to 5.8) with 95% limits of agreement (-17.9 to 21.3) were found when comparing the jump counts between the ClearSky T6 and VERT devices. The correlations found in this study indicate that the VERT devices may be an accessible alternative for sub-elite athletes; however, the wide confidence intervals and limits of agreement warrant caution.

Athlete Monitoring Systems in Elite Men's Basketball: Challenges, Recommendations, and Future Perspectives

Athlete monitoring systems (AMSs) provide a centralized platform for integrating, processing, analyzing, and graphing various monitoring data to help coaches manage the rigorous demands of elite men's basketball players, who frequently participate in high-stress games with minimal recovery time. This review synthesizes current challenges in deploying AMSs, underscores their role in injury prevention and performance optimization, and discusses technological advances that could enhance their utility. Key challenges include selecting appropriate monitoring methods based on human and financial resources, accuracy of data collection, real-time data processing, and personalization of training regimens. Due to the weaknesses and limitations of each monitoring method, it is recommended that both objective (e.g., external load data, heart rate measures, and biomarkers) and subjective (athlete-reported outcome measures) monitoring data be integrated into an AMS to provide a holistic insight of the athlete's health and readiness. In addition, decision support systems integrated into an AMS can help coaches quickly gain an overview of their players' current condition and make informed decisions about daily load and recovery management. In this context, future perspectives suggest the potential for AMSs to incorporate predictive analytics and artificial intelligence to further enhance decision-making processes in elite men's basketball. Our findings underscore the need for continued innovation and rigorous validation of AMS technologies to ensure they meet the evolving demands of professional sports environments.

Neuromuscular Load in Professional Women's Handball: Segmentation of the Player Load and the Impacts at Group and Individual Level

Handball is a team sport characterised by physical interaction with other opponents. This interaction produces a high load on the players that can manifest itself in various ways, from discomfort to prolonged injuries due to tears caused by excessive load. In order to establish correct protocols for application in women's teams, context- and gender-specific reference data must be available. For this reason, the present research aims to find out how women's teams in European competitions prepare for decisive matches during the match week, analysing the load in a segmented way and the level of specificity that should be achieved in training. Ex post facto research was used in which a total of 17 players belonging to a women's first division handball team in Spain participated. The variables player load and impacts extracted from the use of Wimu ProTM inertial devices were analysed. The results showed a high neuromuscular load in players at this competitive level, especially in the variable impacts, reaching values per session of up to 1000 impacts. The individuality analyses show that the load varies significantly depending on the subject, which is why it is considered essential to establish protocols for strength work and load control in the most specific way possible.

Football Movement Profile-Based Creatine-Kinase Prediction Performs Similarly to Global Positioning System-Derived Machine Learning Models in National-Team Soccer Players

PURPOSE

The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) -derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models.

METHODS

Four hundred forty-four national-team soccer players (under 15 [U15] to senior) were monitored during training sessions and matches using GPS. CK was measured every morning from whole blood. The players had 19.3 (18.1) individual GPS-CK pairs, resulting in a total of 8570 data points. Machine learning models were built using (1) GPS-derived or (2) FMP-based parameters or (3) the combination of the 2 to predict the following days' CK value. The performance of general and individual-specific prediction models was compared. The performance of the models was described by R2 and the root-mean-square error (RMSE, in units per liter for CK values).

RESULTS

The FMP model (R2 = .60, RMSE = 144.6 U/L) performed similarly to the GPS-based model (R2 = .62, RMSE = 141.2 U/L) and the combination of the 2 (R2 = .62, RMSE = 140.3 U/L). The prediction power of the general model was better on average (R2 = .57 vs R2 = .37) and for 73% of the players than the individualized model.

CONCLUSIONS

The results suggest that FMP-based CK-prediction models perform similarly to those based on GPS-derived metrics. General machine learning models' prediction power was higher than those of the individual-specific models. These findings can be used to monitor postmatch recovery strategies and to optimize weekly training periodization.

Quarterly fluctuations in external and internal loads among professional basketball players

Purpose

This study aims to explore the variations in external and internal loads on a quarter-by-quarter basis among professional Chinese basketball players. It emphasizes the crucial impact of these variations on optimizing athletic performance and match strategies.

Method

An observational longitudinal study design was employed, involving sixteen male players from the National Basketball League during the 2024 season in China. Data collection was facilitated through the use of Catapult S7 devices for measuring external loads and session ratings of perceived exertion (sRPE) for assessing internal loads. Linear mixed-effects models were utilized for the statistical analysis to identify differences in workload intensities across game quarters based on player positions. The Pearson correlation coefficient was used to examine the relationship between external and internal load throughout the game.

Results

The analysis uncovered significant positional differences in workload intensities across game quarters. Guards were found to have a higher PlayerLoad™ (PL) per minute in the first quarter, while centers demonstrated an increase in high-intensity accelerations and jumps in the fourth quarter. Furthermore, a significant moderate correlation between sRPE and PL was observed across all game quarters, indicating a link between physical exertion and athletes' perceptions of effort.

Conclusion

The study offers new insights into the dynamic physical demands faced by basketball players and the importance of using both objective and subjective measures for a comprehensive assessment of athlete performance and wellbeing. The findings underscore the interconnectedness of physical exertion and athlete perception, providing a foundation for future research and practical applications in the field of basketball science.

The burdens of sitting on the bench - comparison of absolute and relative match physical load between handball players with high and low court time and implications for compensatory training

Wearables quantify the activity in team sports and indicate that players experience peak physical loads during competitions. Accordingly, players with limited court time in competitions will miss important training stimuli. The present study aimed to quantify these gaps in physical load in professional handball players. Activity of all players competing in the 2021/2022 Bundesliga (Germany) was tracked using Kinexon LPS sensors. Gaps in physical load were quantified comparing the 25% of appearances with the highest (HIGH; 51.8 ± 5.2 mins) and lowest court times (LOW; 10.1 ± 4.3 mins). Distances, accumulated acceleration, jumps, sprints, impacts, accelerations, and decelerations were analysed as absolute and relative (per minute) outcomes. Players were grouped into wings, backcourts, and pivots. Unpaired t-tests between HIGH and LOW were performed (p < .05), and effect sizes were calculated (Cohen´s d). Analyses revealed significant effects of court time on activity. While absolute activity increased for HIGH, relative activity increased for LOW (p < .05). In addition, effect sizes revealed position-specific gaps in physical load, particularly for acyclic activities (jumps, accelerations). Gaps in physical load resulting from limited court time are highly position-specific. Our observations may provide benchmarks for the position-specific calibration of compensatory training.

Effect of Level of Competition and Drill Typology on Internal and External Load in Male Volleyball Players During the Preseason Period

PURPOSE

This study aimed at evaluating the effect of level of competition and drill typology on loads during the preseason period in male volleyball players.

METHODS

Internal (percentage of peak heart rate [HR] and summated HR zone) and external (PlayerLoad per minute, total and high accelerations per minute [tACCmin and hACCmin], decelerations per minute [tDECmin and hDECmin], and jumps per minute [tJUMPmin and hJUMPmin]) loads were monitored across a 5-week preseason period in 12 Division 1 (age: 22.5 [3.9] y; stature: 188 [6.2] cm; body mass: 85 [11.6] kg; training experience: 9.4 [4.2] y) and 12 Division 2 (age: 20.7 [2.9] y; stature: 186 [6.2] cm; body mass: 77.8 [9.6] kg; training experience: 5.6 [2.3] y) male volleyball players. Furthermore, differences in load were assessed for each drill typology (warm-up, conditioning, technical, tactical, and integral).

RESULTS

No effects (P > .05) of level of competition on the internal (except for summated HR zone, P = .05) and external loads (except for tJUMPmin, P = .002) were found. Differently, drill typologies showed an effect (P < .001) on all the investigated internal- and external-load measures. The main post hoc results revealed higher (P < .05) percentage of peak HR, summated HR zone, PlayerLoad per minute, and tACCmin in warm-up and conditioning drills, while higher (P < .05) hDECmin and hJUMPmin were found in tactical and integral drills.

CONCLUSIONS

These results suggest that volleyball coaches use warm-up and conditioning drills when aiming at increasing the internal loads, PlayerLoad per minute, and tACCmin, while tactical and integral drills should be preferred to enhance the number of hDECmin and hJUMPmin.

Athlete External Load Measures Across a Competitive Season in High School Basketball

ABSTRACT

Askow, AT, Jennings, W, Jagim, AR, Fields, JB, Beaudoin, RG, Sanchez, GM, Weeks, JE, Oliver, JM, and Jones, MT. Athlete external load measures across a competitive season in high school basketball. J Strength Cond Res 37(11): 2206-2212, 2023-The purpose of this retrospective analysis was to quantify in-season external load and to determine if relationships existed between load metrics and basketball performance. Eleven male high school varsity basketball athletes (n = 11; mass 80.5 ± 9.6 kg, height 190.2 ± 9.4 cm, age 17.6 ± 0.7 years) were monitored across a season. PlayerLoad (PL), PL per minute (PL·min -1 ), total jumps, and explosive movements (EMs) were quantified using a commercially available local positioning unit. Basketball-specific performance metrics, including points scored, points allowed, point differentials, and shooting percentages for each quarter and game, were compiled. Data were analyzed using repeated-measure analysis of variance to evaluate differences in load by starting status, session type, game outcome, and game type. Pearson's correlation coefficients were used to assess relationships between load metrics and basketball performance. Statistical significance was set at p < 0.05. The mean values across 23 games for PL, PL·min -1 , total jumps, and EMs were 457 ± 104 AU, 10.9 ± 1.6 AU, 42.6 ± 9.6, and 46.7 ± 7.2, respectively. Relationships were observed ( p < 0.05) between PL and points scored ( r = 0.38) and free throw percentage ( r = 0.21). Further relationships were observed between PL·min -1 and free throw shooting percentage ( r = -0.27), and between points scored and total jumps ( r = 0.28), and EMs ( r = 0.26). Notable differences in game demands were observed for playing status. Meaningful differences in measures of external load were observed between each quarter of play, with the highest measures evident in quarters 1 and 3. Guards and forwards experienced minimal differences in external load during gameplay, and game outcome did not result in differences. Higher point totals corresponded with higher PL, total jumps, and EM.

Individual-Specific Relationship Between External Training and Match Load and Creatine-Kinase Response in Youth National Team Soccer Players

BACKGROUND

Previous studies have examined the relationship between external load and creatine-kinase (CK) response at the team level. This study aimed to build individualized CK prediction models for elite youth national team soccer players.

HYPOTHESIS

The CK response of youth soccer players can be categorized as being sensitive to micromovements (MM), high-velocity (HV) parameters, or the combination of both, measured during training sessions and matches.

STUDY DESIGN

Prospective cohort study.

LEVEL OF EVIDENCE

Level 4.

METHODS

A total of 25 U16-U17 youth national team soccer players were monitored during training sessions and matches using global positioning system (GPS) units. Individual CK values were measured every morning from whole blood. The data set consisted of 57 ± 17 individual datapoints per player. Individual prediction models were used to examine the relationship between external load and consecutive CK changes. Numerous models were built for each player using MM, HV parameters, or the combination of both. The performance of the models was described by the R2 and the root mean square error (RMSE, U/l for CK values).

RESULTS

The MM models were superior for 8 players (R2 = 0.68; RMSE = 113 U/l), followed by HV (8 players; R2 = 0.69; RMSE = 88 U/l) and the combined models (2 players; R2 = 0.64; RMSE = 141 U/l). For the remaining 7 players, the R2 of the models was <0.5. The recovery time between efforts was more important in the HV model.

CONCLUSION

Players could be categorized on sensitivity to MM, HV movements, or the combination of both.

CLINICAL RELEVANCE

These findings can be used to individualize postmatch recovery strategies and to optimize weekly training periodization to maximize match performance.

Physical match demands of four LIQUI-MOLY Handball-Bundesliga teams from 2019-2022: effects of season, team, match outcome, playing position, and halftime

Introduction

Due to the development in team handball, there is a need to optimize the physical capacities of team handball players for which knowledge of the physical match demands is essential. The aim of this study was to investigate the physical match demands of four LIQUI-MOLY Handball-Bundesliga (HBL) teams across three seasons with respect to the effects of season, team, match outcome, playing position, and halftime.

Methods

A fixed installed local positioning system (Kinexon) was used, collecting 2D positional and 3D inertial measurement unit data at 20 and 100 Hz, respectively. The physical match demands were operationalized by basic (e.g., distance, speed, and acceleration) and more advanced variables (e.g., jumps, throws, impacts, acceleration load, and metabolic power). A total of 347 matches (213 with an additional ball tracking) were analyzed from four teams (one top, two middle, and one lower ranked) during three consecutive seasons (2019-2022). One-way ANOVAs were calculated to estimate differences between more than two groups (e.g., season, team, match outcome, playing position). Mean differences between halftimes were estimated using Yuen's test for paired samples.

Results

Large effects were detected for the season (0.6 ≤ ξ ^ ≤ 0.86 ), team (0.56 ≤ ξ ^ ≤ 0.72 ), and playing position (0.64 ≤ ξ ^ ≤ 0.98 ). Medium effects were found for match outcome (ξ ^ ≤ 0.36 ) and halftime (ξ ^ ≤ 0.47 ).

Conclusion

For the first time, we provide a comprehensive analysis of physical match demands in handball players competing in the LIQUI-MOLY Handball-Bundesliga. We found that physical match demands differ on that top-level with up to large effect sizes concerning the season, team, match outcome, playing position, and halftime. Our outcomes can help practitioners and researchers to develop team and player profiles as well as to optimize talent identification, training, regeneration, prevention, and rehabilitation procedures.

Physical Demands during Official Competitions in Elite Handball: A Systematic Review

An understanding of physical demands during official competitions is essential to achieving the highest performance in handball. The aim of this systematic review was to summarise the available scientific evidence associated with physical demands during official competitions in elite handball according to playing positions, competition level and gender. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 17 studies were selected after a systematic search and selection process of three digital databases: PubMed, Web of Science and Sport Discus. The quality of the selected studies was evaluated using the Strengthening the Reporting of Observational Studies in Epidemiology checklist; the average score was 18.47 points. The sample consisted of 1175 handball players, of whom 1042 were men (88.68%) and 133 were women (11.32%). The results show that an elite handball player covered on average 3664.4 ± 1121.6 m during a match. The average running pace was 84.8 ± 17.2 m∙min-1. The total distance covered was largely greater in national competitions (4506.7 ± 647.9 m) compared with international competitions (2190.3 ± 1950.5 m) (effect size (ES) = 1.2); however, the running pace did not present any significant difference between the international or national level (ES = 0.06). In regard to gender, the total distance covered was moderately greater in female competitions (4549.1 ± 758.6 m) compared with male competitions (3332.6 ± 1257.7 m) (ES = 0.9), and the running pace was largely greater in female competitions (110.5 ± 7.2 m∙min-1) compared with male competitions (78.4 ± 19.7 m∙min-1) (ES = 1.6). In relation to playing position, backs and wings covered a moderately greater total distance (ES = 0.7 and 0.6) and slightly more meters per minute (ES = 0.4 and 0.2) than pivots. Moreover, the technical activity profile differed between playing positions. Backs performed moderately more throws than pivots and wings (ES = 1.2 and 0.9), pivots exhibited largely more body contact than backs and wings, and wings performed moderately more fast breaks (6.7 ± 3.0) than backs (2.2 ± 2.3) (ES = 1.8). Therefore, this research study provides practical applications for handball coaches and strength and conditioning professionals with respect to designing and implementing more individualised training programmes to maximise performance and reduce injury risk.

A holistic analysis of collective behaviour and team performance in Australian Football via structural equation modelling

Despite awareness of the importance of quantifying technical, tactical, and physical characteristics of match play, few studies have examined the structural relationship of these aspects in professional sport. Accordingly, this study concurrently examined these components in relation to quarter outcome (n = 272) in Australian Football. The study followed a retrospective longitudinal case study design where one teams' cooperative passing network, skill counts, physical loads, and spatiotemporal behaviours during official Australian Football League games were collected from a period spanning four seasons (2016-2019). A principal components analysis (PCA) and structural equation modelling were used to explore the structural relationships between components and examine the influence on quarter outcome as determined by the point differential (quarter margin). Scoring opportunity and ball movement had direct associations with quarter margin, while unpredictability, uncontested behaviour and physical behaviour did not. Negative associations between uncontested behaviour and scoring opportunity suggest that elevated high-pressure success and a lack of synchrony may positively influence scoring opportunity, a determinant of quarter margin. Further, negative associations between physical behaviour and ball movement suggest that with less physical work, a team's collective ability to transfer possession between teammates is facilitated, offering an interesting dichotomy between skill and physical demands of Australian Football. While hundreds of different metrics are available, the present study was the first to concurrently examine the influence of a variety of match play components on performance outcomes in Australian Football. These results may provide direction for coaches and practitioners when contemplating practice design, tactical strategies, or the development of behaviour through specific training exercises. Game plans and training drills that focus on optimising attacking and low-pressure ball movement coupled with high levels of mutual interaction between teammates may be beneficial for performance.

Relationship Between Training Workloads, Match Workloads, and Match Performance in Elite Netball

PURPOSE

This study examined the relationships between training workloads, game workloads, and match performance in an elite netball team.

METHODS

Ten elite female netball athletes were monitored over a complete season. Training and game external workloads were determined through inertial movement units and expressed as absolute PlayerLoad (PL) and change of direction (COD). Monthly workload and training efficiency index were also calculated, which used internal workloads (session rating of perceived exertion and summated heart-rate zones). Game performance was assessed through a performance analysis statistic algorithm called NetPoints. To account for the influence of team game workloads on each other, the average workload for midcourt positions (avgMC) was calculated for each game. Data for each athlete were transformed into z scores, and linear mixed modeling was used to build models to examine the relationships between workloads and game performance.

RESULTS

Monthly PL, training efficiency index PL, and avgMC PL were statistically significant (P < .05) and positively related to game PL (z = 0.20-0.35, P < .001-.02). For game COD, statistically significant positive relationships were found between monthly COD (z = 0.29 [0.11], P = .01) and avgMC COD (z = 0.21 [0.09], P = .03). The models for NetPoints found significant negative relationships with monthly PL (z = 0.46 [0.12], P < .001) and COD (z = -0.36 [0.11], P = .01).

CONCLUSIONS

Higher monthly workloads are related to higher game workload; however, they are also related to decreases in match performance. Therefore, netball practitioners should consider that increases to training workload in a 4-week period prior to a game can influence game workloads and performance.

External Load Analysis in Beach Handball Using a Local Positioning System and Inertial Measurement Units

Beach handball is a young discipline that is characterized by numerous high-intensity actions. By following up on previous work, the objective was to perform in-depth analyses evaluating external load (e.g., distance traveled, velocity, changes in direction, etc.) in beach handball players. In cross-sectional analyses, data of 69 players belonging to the German national or prospective team were analyzed during official tournaments using a local positioning system (10 Hz) and inertial measurement units (100 Hz). Statistical analyses comprised the comparison of the first and second set and the effects of age and sex (female adolescents vs. male adolescents vs. male adults) and playing position (goalkeepers, defenders, wings, specialists, and pivots) on external load measures. We found evidence for reduced external workload during the second set of the matches (p = 0.005, ηp2 = 0.09), as indicated by a significantly lower player load per minute and number of changes in direction. Age/sex (p < 0.001, ηp2 = 0.22) and playing position (p < 0.001, ηp2 = 0.29) also had significant effects on external load. The present data comprehensively describe and analyze important external load measures in a sample of high-performing beach handball players, providing valuable information to practitioners and coaches aiming at improving athletic performance in this new sport.

In-Season Session Training Load Relative to Match Load in Professional Ice Hockey

ABSTRACT

Allard, P, Martinez, R, Deguire, S, and Tremblay, J. In-season session training load relative to match load in professional ice hockey. J Strength Cond Res 36(2): 486-492, 2022-The aim of this study was to describe match load and intensity across player positions and match periods, the distribution of pregame training load and intensity over training days before a match, by player position, and the cumulative weekly training load over a season. Fifty professional ice hockey players, which at one time or another were part of the roster for the Laval Rocket during the 2017-18 season of the American Hockey League, participated in this study. External load was monitored in every training session and match over the season using portable inertial measurement units. Data are reported in absolute and relative values to a match. Defensemen have a lower intensity during matches than forwards (-1.70 OIL·min-1) while load is similar across position. The first period shows a higher relative load (+5.28% relative on-ice load [OIL]) while the third has a lower relative intensity (-2.91% OIL·min-1). Defensemen seem to train at a systematic higher relative intensity than wingers and centers (+8.34% relative OIL·min-1). Finally, the weekly training load remains relatively constant throughout the season (equivalent of 3.56 ± 1.69 matches played per week). Our results support the need for player monitoring in ice hockey using an individualized approach.

Impact of Match Type on Exercise Volume and Intensity of Semi-Professional Basketball Referees During a Competitive Season

Identification of the exercise volume and intensity of basketball referees, across competitive match types based on competition sex and level, would assist in developing appropriate training regimes. Purpose: The aims of this study were to quantify and compare the exercise volume and intensity encountered by semiprofessional basketball referees during different match types across a competitive season. Method: Sixteen referees participated and were monitored for absolute and relative PlayerLoad (PL) using microsensors during 25 semiprofessional matches (12 men's, 13 women's, 17 regular season, eight playoff). Comparisons between match types (men's vs. women's; regular season vs. playoff) were examined via independent t-tests or Mann-Whitney U tests, and repeated measures ANOVA with the magnitude of differences calculated via effect size. Results: Referees experienced an absolute PL of ~365 arbitrary units (AU) per match that was similar for men's, women's, regular season, and playoff matches during a competitive season. Relative PL was significantly greater (5.35 ± 0.82 vs. 4.26 ± 0.59 AU.min^-1, p < .001, effect size = 1.57) during women's matches with a greater proportion of each quarter and match spent within PL band 2 compared to men's matches (11.6 ± 2.2 vs. 8.8 ± 1.4%, p < .001, effect size = 1.53), likely due to a reduced number of referees officiating. Minimal differences existed between the regular season and playoff matches for absolute PL, relative PL, and proportion of quarter/match time within PL bands. Conclusion: The current study confirmed that referees experienced considerable exercise volume and intensity per match during a competitive season with these influenced primarily by referee configuration. The current results provide crucial information to develop effective training regimes for referees during a competitive season.

External workload intensity in cricket fast bowlers across maximal and submaximal intensities: Modifying PlayerLoad and IMU location

Workload is a commonly accepted risk factor for injury among fast bowlers, however many methods exist to characterise workload. Recently, automated intensity-sensitive measures like PlayerLoad have been used to improve the estimation of workload in fast bowlers. The purpose of this study was to determine whether similar variables could be extracted from a single inertial measurement unit (IMU) that highly correlate with intensity, according to release speed. Eight elite and pre-elite bowlers participated in the study, with each bowler bowling one over each at 60%, 80% and 100% intensity and repeating this across two sessions (36 balls per participant). IMUs were placed on the upper-back and non-bowling wrist and maximum PlayerLoad from each delivery (PL_max) was compared to the accumulated value across each delivery (PL_acc). The strongest correlation with release speed was with PL_acc from the non-bowling wrist (R = 0.74), followed by PL_acc from the upper-back (R = 0.65) and PL_max from the upper back (R = 0.60). Consequently, an improved estimation of the intensity at which bowlers are working at could be gained by examining accumulated PlayerLoad values from an IMU on the non-bowling wrist.

Weekly Training Demands Increase, but Game Demands Remain Consistent Across Early, Middle, and Late Phases of the Regular Season in Semiprofessional Basketball Players

PURPOSE

To compare weekly training, game, and overall (training and games) demands across phases of the regular season in basketball.

METHODS

Seven semiprofessional, male basketball players were monitored during all on-court team-based training sessions and games during the regular season. External monitoring variables included PlayerLoad™ and inertial movement analysis events per minute. Internal monitoring variables included a modified summated heart rate zones model calculated per minute and rating of perceived exertion. Linear mixed models were used to compare training, game, and overall demands between 5-week phases (early, middle, and late) of the regular season with significance set at P ≤ .05. Effect sizes were calculated between phases and interpreted as: trivial, <0.20; small, 0.20 to 0.59; moderate, 0.60 to 1.19; large, 1.20 to 1.99; very large, ≥2.00.

RESULTS

Greater (P > .05) overall inertial movement analysis events (moderate-very large) and rating of perceived exertion (moderate) were evident in the late phase compared with earlier phases. During training, more accelerations were evident in the middle (P = .01, moderate) and late (P = .05, moderate) phases compared with the early phase, while higher rating of perceived exertion (P = .04, moderate) was evident in the late phase compared with earlier phases. During games, nonsignificant, trivial-small differences in demands were apparent between phases.

CONCLUSIONS

Training and game demands should be interpreted in isolation and combined given overall player demands increased as the season progressed, predominantly due to modifications in training demands given the stability of game demands. Periodization strategies administered by coaching staff may have enabled players to train at greater intensities late in the season without compromising game intensity.

Relationship Between External and Internal Load Measures in Youth Beach Handball

PURPOSE

Assessing the relationship between external load (EL) and internal load (IL) in youth male beach handball players.

METHODS

A total of 11 field players from the Lithuanian U17 beach handball team were monitored across 14 training sessions and 7 matches. The following EL variables were assessed by means of inertial movement units: PlayerLoad™, accelerations, decelerations, changes of direction, and jumps and total of inertial movements. IL was assessed objectively and subjectively using the summated heart rate zones and training load calculated via session rating of perceived exertion, respectively. Spearman correlations (ρ) were used to assess the relationship between EL and IL. The interindividual variability was investigated using linear mixed models with random intercepts with IL as dependent variable, PlayerLoad™ as the independent variable, and players as random effect.

RESULTS

The lowest significant (P < .05) relationship was for high jumps with objective (ρ = .56) and subjective (ρ = .49) IL. The strongest relationship was for PlayerLoad™ with objective (ρ = .9) and subjective (ρ = .84) IL. From the linear mixed model, the estimated SD of the random intercepts was 19.78 arbitrary units (95% confidence interval, 11.75-33.31); SE = 5.26, and R2 = .47 for the objective IL and 6.03 arbitrary units (95% confidence interval, 0.00-7330.6); SE = 21.87; and R2 = .71 for the subjective IL.

CONCLUSIONS

Objective and subjective IL measures can be used as a monitoring tool when EL monitoring is not possible. Coaches can predict IL based on a given EL by using the equations proposed in this study.

Monitoring external load in elite male handball players depending on playing positions

Monitoring workload is critical for elite training and competition, as well as preventing potential sports injuries. The assessment of external load in team sports has been provided with new technologies that help coaches to individualize training and optimize their team's playing system. In this study we characterized the physical demands of an elite handball team during an entire sports season. Novel data are reported for each playing position of this highly strenuous body-contact team sport. Sixteen world top players (5 wings, 2 centre backs, 6 backs, 3 line players) were equipped with a local positioning system (WIMU PRO) during fourteen official Spanish first league matches. Playing time, total distance covered at different running speeds, and acceleration variables were monitored. During a handball match, wings cover the greater distance by high-speed running (> 5.0 m·s-1): 410.3 ± 193.2 m, and by sprint (> 6.7 m·s-1): 98.0 ± 75.4 m. Centre backs perform the following playing position that supports the highest speed intensities during the matches: high-speed running: 243.2 ± 130.2 m; sprint: 62.0 ± 54.2 m. Centre backs also register the largest number of high-intensity decelerations (n = 142.7 ± 59.5) compared to wings (n = 112.9 ± 56.0), backs (n = 105.2 ± 49.2) and line players: 99.6 ± 28.9). This study provides helpful information for professional coaches and their technical staff to optimize training load and individualize the physical demands of their elite male handball players depending on each playing position.

Physical workload and fatigue pattern characterization in a top-class women's football national team: a case study of the 2019 FIFA Women's World Cup

BACKGROUND

With the growing scientific interest in women's football it is critical to understand the match demands and fatigue patterns during a top-class women's competition.

METHODS

Physical characteristics and performance of top-class women football matches during the 2019 FIFA Women's World Cup was investigated from data collected using global positioning system for 21 outfield players during the tournament.

RESULTS

Relative total distance (TD; m·min^-1) was moderately lower (P≤0.05, ES:0.6) in the final match (96.1±5.0 m·min^-1) compared to the first (104.1±5.9 m·min^-1) match. Performance in relative total high-speed running (THSR; m·min^-1) declined -27.5% (P≥0.05) throughout the tournament following peak performance in the third match (M3) (5.3±1.7 m·min^-1). Match performance between halves was reduced -25.4±0.13% (P≤0.05, ES:1.2-2.3) across all measures in M3. Relative high-intensity running (HIR; 25.9±3.5 m·min^-1) and THSR (9.8±1.5 m·min^-1) were greater (P≤0.05, ES:0.2-0.7) for forwards, while midfielders performed greater (P≤0.05, ES:0.6) relative total distance (102.0±5.5 m·min^-1), compared to central defenders (HIR: 16.1±3.7 m·min^-1; THSR: 3.1±1.6 m·min^-1; TD: 92.5±5.7 m·min^-1). Reductions in relative HIR between halves (-10.9%), within the first half (-31.0%), and the start and end of the match (-36.9%) were most pronounced for midfielders (P≤0.05, ES:0.6-2.3). Across all matches, relative THSR performance was reduced, with moderate to large differences (P≤0.001, ES: 0.7-0.8) observed between halves, within the first half, and throughout the match.

CONCLUSIONS

In conclusion, specific fatigue patterns observed within and between matches throughout the women's World Cup, may be influenced by playing positions, the rank of the opposition, as well as the stage of the tournament.

A Novel Accelerometry-Based Metric to Improve Estimation of Whole-Body Mechanical Load

While the Player Load is a widely-used parameter for physical demand quantification using wearable accelerometers, its calculation is subjected to potential errors related to rotational changes of the reference frame. The aims of this study were (i) to assess the concurrent validity of accelerometry-based Player Load against force plates; (ii) to validate a novel metric, the Accel’Rate overcoming this theoretical issue. Twenty-one recreational athlete males instrumented with two triaxial accelerometers positioned at the upper and lower back performed running-based locomotor movements at low and high intensity over six in-series force plates. We examined the validity of the Player Load and the Accel’Rate by using force plates. Standard error of the estimate was small to moderate for all tested conditions (Player Load: 0.45 to 0.87; Accel’Rate: 0.25 to 0.95). Accel’Rate displayed trivial to small mean biases (−1.0 to 6.1 a.u.) while the Player Load displayed systematic very large to extremely large mean biases (17.1 to 226.0 a.u.). These findings demonstrate a better concurrent validity of the Accel’Rate compared to the Player Load. This metric could be used to improve the estimation of whole-body mechanical load, easily accessible in sport training and competition settings.

Specific Absolute Velocity Thresholds during Male Basketball Games Using Local Positional System; Differences between Age Categories

The aim of this study was (I) to establish absolute specific velocity thresholds during basketball games using local positional system (LPS) and (II) to compare the speed profiles between various levels of competitions. The variables recorded were total distance (TD); meters per minute (m·min); real time (min); maximum speed (Km h−1), distance (m), percentage distance, and percentage duration invested in four speed zones (standing–walking; jogging; running; and high-speed running). Mean and standard deviation (±SD) were calculated, and a separate one-way analysis of variance was undertaken to identify differences between competitions. TD (3188.84 ± 808.37 m) is covered by standing–walking (43.51%), jogging (36.58%), running (14.68%), and sprinting (5.23%) activities. Overall, 75.22% of the time is invested standing–walking, jogging (18.43%), running (4.77%), and sprinting (1.89%). M·min (large effect size), % duration zone 2 (moderate effect size); distance zone 4 (large effect size), and % distance zone 4 (very large effect size) are significantly higher during junior than senior. However, % distance zone 1 (large effect size) and % duration zone 1 (large effect size) were largely higher during senior competition. The findings of this study reveal that most of the distance and play time is spent during walking and standing activities. In addition, the proportion of time spent at elevated intensities is higher during junior than in senior competition.

Movement intensity demands between training activities and competition for elite female netballers

The aim of this study was to assess the differences in movement intensity demands between training activities and competition match-play in elite netball. Twelve elite female netballers (mean ± SD, age = 25.9 ± 5.1 years; height = 178.6 ± 8.9 cm, body mass = 71.1 ± 7.1 kg) competing in Australia's premier domestic netball competition participated. Data were collected across the season from all pre-season training sessions (n = 29), pre-season practice matches (n = 8), in-season training sessions (n = 21), in-season practice matches (n = 5), and competition matches (n = 15). Linear mixed-effects models assessed differences in PlayerLoad™ per minute and metreage per minute between activity types (Specialist, Skill Drills, Set-piece, Match Scenarios, Practice Match-play, and Competition Match-play) for positional groupings (Defenders, Midcourters, and Goalers). Competition Match-play resulted in higher (p < 0.05) PlayerLoad™ than all training activity types, with the largest magnitudes of difference between Specialist-Competition (d = 0.44-0.59; small to medium) and Skill Drills-Competition (d = 0.35-0.63; small to medium) for all positional groups. The smallest difference was found between Match Scenarios-Competition (d = 0.12-0.20; trivial to small) and Practice Match-play-Competition (d = 0.12-0.14; trivial). Competition Match-play also resulted in higher (p < 0.05) metreage per minute than Specialist (d = 0.23-0.53; small to medium), Skill Drills (d = 0.19-0.61; trivial to medium) and Set-piece (d = 0.05-0.31; trivial to small). Training activity demands in order of least to most similar to competition were specialist, skill drills, set-piece, match scenarios, and practice match-play. We provide data that enables coaches and physical preparation staff to incorporate progressions into their training session designs that can replicate the movement intensity demands of competition in training.

Quantification of Training Load Relative to Match Load of Youth National Team Soccer Players

BACKGROUND

Previous studies have examined the training load relative to match load in club settings. The aims of this study were to (1) quantify the external training load relative to match load in days before a subsequent international game and (2) examine the cumulative training load in relation to match load of U-17 national team field soccer players.

HYPOTHESIS

Volume and intensity load parameters will vary between trainings; the farthermost trainings have the highest load gradually decreasing toward the match.

STUDY DESIGN

Prospective cohort study.

LEVEL OF EVIDENCE

Level 4.

METHODS

External training load data were collected from 84 youth national team players using global positioning technology between 2016 and 2020. In the national team setting, training load data were obtained from 3 days before the actual match day (MD-3, MD-2, MD-1 days) and analyzed with regard to the number of days up to the game. Volume and intensity parameters were calculated as a percentage of the subsequent match load.

RESULTS

Significant differences were found between MD-1 and MD-2, as well as between MD-1 and MD-3 for most volume parameters (P < 0.01; effect sizes [ESs] 0.68-0.99) and high-intensity distance (P < 0.002; ES 0.67 and 0.73) and maximum velocity (P < 0.002; ES 0.82) as intensity parameters. Most cumulative values were significantly different from total duration (P < 0.001, common language ES 0.80-0.96).

CONCLUSION

The training volume gradually decreased as match day approached, with the highest volume occurring on MD-3. Intensity variables, such as maximum velocity, high-intensity accelerations, and meterage per minute were larger in MD-1 training relative to match load. Training volume was lowest in MD-1 trainings and highest in MD-3 trainings; intensity however varies between training days.

CLINICAL RELEVANCE

The findings of this study may help to understand the special preparational demands of international matches, highlighting the role of decreased training volume and increased intensity.

Factors Influencing Creatine Kinase Response in Youth National Team Soccer Players

BACKGROUND

Previous studies have examined the relationship between external training load and creatine kinase (CK) response after soccer matches in adults. This study aimed to build training- and match-specific CK prediction models for elite youth national team soccer players.

HYPOTHESIS

Training and match load will have different effects on the CK response of elite youth soccer players, and there will be position-specific differences in the most influential external and internal load parameters on the CK response.

STUDY DESIGN

Prospective cohort study.

LEVEL OF EVIDENCE

Level 4.

METHODS

Forty-one U16-U17 youth national team soccer players were measured over an 18-month period. Training and match load were monitored with global positioning system devices. Individual CK values were measured from whole blood every morning in training camps. The dataset consisted of 1563 data points. Clustered prediction models were used to examine the relationship between external/internal load and consecutive CK changes. Clusters were built based on the playing position and activity type. The performance of the linear regression models was described by the R² and the root-mean-square error (RMSE, U/L for CK values).

RESULTS

The prediction models fitted similarly during games and training sessions (R² = 0.38-0.88 vs 0.6-0.77), but there were large differences based on playing positions. In contrast, the accuracy of the models was better during training sessions (RMSE = 81-135 vs 79-209 U/L). Position-specific differences were also found in the external and internal load parameters, which best explained the CK changes.

CONCLUSION

The relationship between external/internal load parameters and CK changes are position specific and might depend on the type of session (training or match). Morning CK values also contributed to the next day's CK values.

CLINICAL RELEVANCE

The relationship between position-specific external/internal load and CK changes can be used to individualize postmatch recovery strategies and weekly training periodization with a view to optimize match performance.

Not All About the Effort? A Comparison of Playing Intensities During Winning and Losing Game Quarters in Basketball

PURPOSE

To compare peak and average intensities encountered during winning and losing game quarters in basketball players.

METHODS

Eight semiprofessional male basketball players (age = 23.1 [3.8] y) were monitored during all games (N = 18) over 1 competitive season. The average intensities attained in each quarter were determined using microsensors and heart-rate monitors to derive relative values (per minute) for the following variables: PlayerLoad, frequency of high-intensity and total accelerations, decelerations, changes of direction, jumps, and total inertial movement analysis events combined, as well as modified summated-heart-rate-zones workload. The peak intensities reached in each quarter were determined using microsensors and reported as PlayerLoad per minute over 15-second, 30-second, 1-minute, 2-minute, 3-minute, 4-minute, and 5-minute sample durations. Linear mixed models and effect sizes were used to compare intensity variables between winning and losing game quarters.

RESULTS

Nonsignificant (P > .05), unclear-small differences were evident between winning and losing game quarters in all variables.

CONCLUSIONS

During winning and losing game quarters, peak and average intensities were similar. Consequently, factors other than the intensity of effort applied during games may underpin team success in individual game quarters and therefore warrant further investigation.

Minimal Agreement between Internal and External Training Load Metrics across a 2-wk Training Microcycle in Elite Squash

This study investigated the relationships between internal and external training load metrics across a 2-week 'in-season' microcycle in squash. 134 on-court and 32 off-court 'conditioning' sessions were completed by fifteen elite squash players with an average (±SD) of 11 ± 3 per player. During every session, external load was captured using a tri-axial accelerometer to calculate Playerload; i.e., the instantaneous rate of change of acceleration across 3-dimensional planes. Internal load was measured using heart rate (HR), global (sRPE) and differential RPE (dRPE-Legs, dRPE-Breathing). Additionally, HR was used to calculate Banister's, Edward's and TEAM TRIMPs. Across 166 training sessions, Playerload was moderately correlated with TRIMP-Banister (r = 0.43 [95% CI: 0.29-0.55], p < 0.001) and TRIMP-Edwards (r = 0.50 [0.37-0.61], p < 0.001). Association of Playerload with TRIMP-TEAM (r = 0.24 [0.09-0.38], p = 0.001) was small. There was a moderate correlation between sRPE and Playerload (r = 0.46 [0.33-0.57], p < 0.001). Association of sRPE was large with TRIMP-Banister (r = 0.68 [0.59-0.76], p = 0.001), very large with TRIMP-Edwards (r = 0.79 [0.72-0.84], p < 0.001) and moderate with TRIMP-TEAM (r = 0.44 [0.31-0.56], p < 0.001). Both dRPE-Legs (r = 0.95 [0.93-0.96], p < 0.001) and dRPE-Breathing (r = 0.92 [0.89-0.94], p < 0.001) demonstrated nearly perfect correlations with sRPE and with each other (r = 0.91 [0.88-0.93], p < 0.001). Collection of both internal and external training load data is recommended to fully appreciate the physical demands of squash training. During a training microcycle containing a variety of training sessions, interpreting internal or external metrics in isolation may underestimate or overestimate the training stress a player is experiencing.

Comparing Weekly Training and Game Demands According to Playing Position in a Semiprofessional Basketball Team

PURPOSE

To compare weekly training and game demands according to playing position in basketball players.

METHODS

A longitudinal, observational study was adopted. Semiprofessional, male basketball players categorized as backcourt (guards; n = 4) and frontcourt players (forwards/centers; n = 4) had their weekly workloads monitored across an entire season. External workload was determined using microsensors and included PlayerLoad™ (PL) and inertial movement analysis variables. Internal workload was determined using heart rate to calculate absolute and relative summated-heart-rate-zones workload and rating of perceived exertion (RPE) to calculate session-RPE workload. Comparisons between weekly training and game demands were made using linear mixed models and effect sizes in each positional group.

RESULTS

In backcourt players, higher relative PL (P = .04, very large) and relative summated-heart-rate-zones workload (P = .007, very large) were evident during training, while greater session-RPE workload (P = .001, very large) was apparent during games. In frontcourt players, greater PL (P < .001, very large), relative PL (P = .019, very large), peak PL intensities (P < .001, moderate), high-intensity inertial movement analysis events (P = .002, very large), total inertial movement analysis events (P < .001, very large), summated-heart-rate-zones workload (P < .001, very large), RPE (P < .001, very large), and session-RPE workload (P < .001, very large) were evident during games.

CONCLUSIONS

Backcourt players experienced similar demands between training and games across several variables, with higher average workload intensities during training. Frontcourt players experienced greater demands across all variables during games than training. These findings emphasize the need for position-specific preparation strategies leading into games in basketball teams.

Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices

BACKGROUND

Measuring the physical work and resultant acute psychobiological responses of basketball can help to better understand and inform physical preparation models and improve overall athlete health and performance. Recent advancements in training load monitoring solutions have coincided with increases in the literature describing the physical demands of basketball, but there are currently no reviews that summarize all the available basketball research. Additionally, a thorough appraisal of the load monitoring methodologies and measures used in basketball is lacking in the current literature. This type of critical analysis would allow for consistent comparison between studies to better understand physical demands across the sport.

OBJECTIVES

The objective of this systematic review was to assess and critically evaluate the methods and technologies used for monitoring physical demands in competitive basketball athletes. We used the term 'training load' to encompass the physical demands of both training and game activities, with the latter assumed to provide a training stimulus as well. This review aimed to critique methodological inconsistencies, establish operational definitions specific to the sport, and make recommendations for basketball training load monitoring practice and reporting within the literature.

METHODS

A systematic review of the literature was performed using EBSCO, PubMed, SCOPUS, and Web of Science to identify studies through March 2020. Electronic databases were searched using terms related to basketball and training load. Records were included if they used a competitive basketball population and incorporated a measure of training load. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration # CRD42019123603), and approved under the National Basketball Association (NBA) Health Related Research Policy.

RESULTS

Electronic and manual searches identified 122 papers that met the inclusion criteria. These studies reported the physical demands of basketball during training (n = 56), competition (n = 36), and both training and competition (n = 30). Physical demands were quantified with a measure of internal training load (n = 52), external training load (n = 29), or both internal and external measures (n = 41). These studies examined males (n = 76), females (n = 34), both male and female (n = 9), and a combination of youth (i.e. under 18 years, n = 37), adults (i.e. 18 years or older, n = 77), and both adults and youth (n = 4). Inconsistencies related to the reporting of competition level, methodology for recording duration, participant inclusion criteria, and validity of measurement systems were identified as key factors relating to the reporting of physical demands in basketball and summarized for each study.

CONCLUSIONS

This review comprehensively evaluated the current body of literature related to training load monitoring in basketball. Within this literature, there is a clear lack of alignment in applied practices and methodological framework, and with only small data sets and short study periods available at this time, it is not possible to draw definitive conclusions about the true physical demands of basketball. A detailed understanding of modern technologies in basketball is also lacking, and we provide specific guidelines for defining and applying duration measurement methodologies, vetting the validity and reliability of measurement tools, and classifying competition level in basketball to address some of the identified knowledge gaps. Creating alignment in best-practice basketball research methodology, terminology and reporting may lead to a more robust understanding of the physical demands associated with the sport, thereby allowing for exploration of other research areas (e.g. injury, performance), and improved understanding and decision making in applying these methods directly with basketball athletes.

The Validity and Reliability of Wearable Microtechnology for Intermittent Team Sports: A Systematic Review

BACKGROUND

Technology has long been used to track player movements in team sports, with initial tracking via manual coding of video footage. Since then, wearable microtechnology in the form of global and local positioning systems has provided a less labour-intensive way of monitoring movements. As such, there has been a proliferation in research pertaining to these devices.

OBJECTIVE

A systematic review of studies that investigate the validity and/or reliability of wearable microtechnology to quantify movement and specific actions common to intermittent team sports.

METHODS

A systematic search of CINAHL, MEDLINE, and SPORTDiscus was performed; studies included must have been (1) original research investigations; (2) full-text articles written in English; (3) published in a peer-reviewed academic journal; and (4) assessed the validity and/or reliability of wearable microtechnology to quantify movements or specific actions common to intermittent team sports.

RESULTS

A total of 384 studies were retrieved and 187 were duplicates. The titles and abstracts of 197 studies were screened and the full texts of 88 manuscripts were assessed. A total of 62 studies met the inclusion criteria. Additional 10 studies, identified via reference list assessment, were included. Therefore, a total of 72 studies were included in this review.

CONCLUSION

There are many studies investigating the validity and reliability of wearable microtechnology to track movement and detect sport-specific actions. It is evident that for the majority of metrics, validity and reliability are multi-factorial, in that it is dependent upon a wide variety of factors including wearable technology brand and model, sampling rate, type of movement performed (e.g., straight line, change of direction) and intensity of movement (e.g., walk, sprint). Practitioners should be mindful of the accuracy and repeatability of the devices they are using when making decisions on player training loads.

Validity and Reliability of a New Device to Measure Type of Actions in Indoor Sports

The purpose of this study was to analyze the validity and the reliability of the intensity ranges, number of actions and changes of direction measured by a commercial inertial measurement unit. Eleven elite youth futsal players performed a circuit with different type of displacements as sprinting, running at low-medium intensity, standing up and changes of direction. Data recorded by the Overtraq system were compared with video-analyzer during the six trials of each player. Standard error mean, Intraclass Correlation Coeficient and Coefficient of variation, were calculated to analyze the reliability of the device, as well as the Root Mean Square Error and Confidence Interval with correlation of Pearson for its validity. The results reported good validity for three intensity ranges (R2>0.7) with high reliability (Intraclass Correlation Coeficient: 0.8-0.9), especially for high intensity actions (Intraclass Correlation Coeficient: 0.95, Coefficient of Variation: 3.06%). Furthermore, the validity for the number of different actions was almost perfect (96.3-100%), with only small differences regarding changes of activity (mean error: 2.0%). The Overtraq system can be considered as a valid and reliable technology for measuring and monitoring actions at different intensities and changes of direction in futsal, likewise common actions for other indoor sports.

How does playing position affect fatigue-induced changes in high-intensity locomotor and micro-movements patterns during professional rugby union games?

AbstractWe questioned whether changes in high-intensity locomotor and micro-movements patterns between the first and second part of each half depend on playing position in the 2014-2015 European rugby union championship winning team. Thirty-three rugby players were grouped according to five playing positions. Players were equipped with micro-electromechanical system including a GPS sampling at 10 Hz and high temporal resolution micro-sensors during 17 Top14 and 7 European games. High-speed movements (HSM), high-intensity accelerations (HIA), repeated high-intensity efforts (RHIE), and high-intensity micro-movements (HIMM) were subsequently compared between four 20-min game periods. No significant group × time interactions were observed for any locomotor variables (p > 0.283). Irrespectively of playing position, the number of HSM (p = 0.019), decreased from 0-20 min to 60-80 min as well as from 40-60 to 60-80 min (p < 0.001) with HIA (p = 0.020) and RHIE (p < 0.001). Significant group × time interaction was found for HIMM (p = 0.03) with a significant decrease observed in back row forwards from 0-20 to 60-80 min periods (-17.5%; ES = 0.6; p = 0.031). In elite rugby union, fatigue-induced changes during the last 20 min are independent from playing positions in high-intensity locomotor patterns in contrary to HIMM. Training drills that include specific RHIE (high-speed and HIA efforts) may be useful to postpone match-related fatigue.

The Relationships Between External and Internal Workloads During Basketball Training and Games

PURPOSE

To investigate the relationships between external and internal workloads using a comprehensive selection of variables during basketball training and games.

METHODS

Eight semiprofessional, male basketball players were monitored during training and games for an entire season. External workload was determined as PlayerLoad™: total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events. Internal workload was determined using the summated-heart-rate zones and session rating of perceived exertion models. The relationships between external and internal workload variables were separately calculated for training and games using repeated-measures correlations with 95% confidence intervals.

RESULTS

PlayerLoad was more strongly related to summated-heart-rate zones (r = .88 ± .03, very large [training]; r = .69 ± .09, large [games]) and session rating of perceived exertion (r = .74 ± .06, very large [training]; r = .53 ± .12, large [games]) than other external workload variables (P < .05). Correlations between total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events and internal workloads were stronger during training (r = .44-.88) than during games (r = .15-.69).

CONCLUSIONS

PlayerLoad and summated-heart-rate zones possess the strongest dose-response relationship among a comprehensive selection of external and internal workload variables in basketball, particularly during training sessions compared with games. Basketball practitioners may therefore be able to best anticipate player responses when prescribing training drills using these variables for optimal workload management across the season.

Effects of two exercise programmes on joint position sense, dynamic balance and countermovement jump in male amateur football players. A randomised controlled trial

Introduction: The injury prevention and warm-up exercises programmes improve physical performance and injury ratio, but it is poorly investigated in amateur football. Objectives: To assess the effects of two warm-up multi-station programmes (IAI-Programme and FIFA11+) through JPS, LSDT and CMJ. Study design: Randomised controlled trial. Methods: 36 football players were randomised into 2 groups: IAI-Programme (n = 18) and FIFA11+ (n = 18) and performed the intervention protocol for 6 weeks. JPS, LSDT and CMJ were measured at baseline, after 6, 10 and 18 weeks (from baseline). The inter-group and intra-group differences were assessed by repeated-measures analysis of variance test (ANOVA). Results: Significant differences between groups were found after 18 weeks in the absolute angular error (-2.18[-4.33,-0.047], d = 0.69, p < 0.05) of the JPS and in the CMJ (p = 0.001, ŋ²p=,0.298) in favour of IAI-Programme when compared to FIFA11 +. No significant differences between groups were found in the LSDT. There were also intra-group differences observed in the LSDT in both groups. Conclusions: IAI-Programme can provide sensitive benefits with respect to the proprioceptive ability of knee flexion and CMJ than FIFA11 +. Both IAI-Programme and FIFA11+ present improvements in the dynamic postural control measured by the LSDT.

The Impact of Contextual Factors on Game Demands in Starting, Semiprofessional, Male Basketball Players

PURPOSE

To quantify and compare external and internal game workloads according to contextual factors (game outcome, game location, and score-line).

METHODS

Starting semiprofessional, male basketball players were monitored during 19 games. External (PlayerLoad™ and inertial movement analysis variables) and internal (summated-heart-rate-zones and rating of perceived exertion [RPE]) workload variables were collected for all games. Linear mixed-effect models and effect sizes were used to compare workload variables based on each of the contextual variables assessed.

RESULTS

The number of jumps, absolute and relative (in min-1) high-intensity accelerations and decelerations, and relative changes-of-direction were higher during losses, whereas session RPE was higher during wins. PlayerLoad™ the number of absolute and relative jumps, high-intensity accelerations, absolute and relative total decelerations, total changes-of-direction, summated-heart-rate-zones, session RPE, and RPE were higher during away games, whereas the number of relative high-intensity jumps was higher during home games. PlayerLoad™, the number of high-intensity accelerations, total accelerations, absolute and relative decelerations, absolute and relative changes-of-direction, summated-heart-rate-zones, sRPE, and RPE were higher during balanced games, whereas the relative number of total and high-intensity jumps were higher during unbalanced games.

CONCLUSIONS

Due to increased intensity, starting players may need additional recovery following losses. Given the increased external and internal workload volumes encountered during away games and balanced games, practitioners should closely monitor playing times during games. Monitoring playing times may help identify when players require additional recovery or reduced training volumes to avoid maladaptive responses across the in-season.

Peak Match Demands in Young Basketball Players: Approach and Applications

Background: The aim of this study is to describe the peak match demands and compare them with average demands in basketball players, from an external load point of view, using different time windows. Another objective is to determine whether there are differences between positions and to provide an approach for practical applications. Methods: During this observational study, each player wore a micro technology device. We collected data from 12 male basketball players (mean ± SD: age 17.56 ± 0.67 years, height 196.17 ± 6.71 cm, body mass 90.83 ± 11.16 kg) during eight games. We analyzed intervals for different time windows using rolling averages (ROLL) to determine the peak match demands for Player Load. A separate one-way analysis of variance (ANOVA) was used to identify statistically significant differences between playing positions across different intense periods. Results: Separate one-way ANOVAs revealed statistically significant differences between 1 min, 5 min, 10 min, and full game periods for Player Load, F (3,168) = 231.80, η_p² = 0.76, large, p < 0.001. It is worth noting that guards produced a statistically significantly higher Player Load in 5 min (p < 0.01, η_p² = −0.69, moderate), 10 min (p < 0.001, η_p² = −0.90, moderate), and full game (p < 0.001, η_p² = −0.96, moderate) periods than forwards. Conclusions: The main finding is that there are significant differences between the most intense moments of a game and the average demands. This means that understanding game demands using averages drastically underestimates the peak demands of the game. This approach helps coaches and fitness coaches to prepare athletes for the most demanding periods of the game and present potential practical applications that could be implemented during training and rehabilitation sessions.

External Workload Can Be Anticipated During 5 vs. 5 Games-Based Drills in Basketball Players: An Exploratory Study

This study determined whether external workload could be anticipated during 5 vs. 5 games-based drills in basketball. Thirteen semi-professional, male basketball players were monitored during 5 vs. 5 training drills across the season. External workload was determined using PlayerLoad™ (AU∙min-1). The reference workload for each drill was calculated across all sessions, using bootstrapping. The bootstrap mean workload and 95% confidence intervals (CI) were then calculated for session 1, sessions 1-2, and continued for remaining sessions (1-3, 1-4, etc.), and were compared with those of the reference workload. The minimum sessions to anticipate workload for each drill was identified when the first normative value fell within ±5% or ±10% of the reference workload 95% CI. The minimum sessions were then tested to determine the accuracy to which workload could be anticipated. Three to four sessions were needed to anticipate workload within ±5%, while 2-3 sessions were needed to anticipate workload within ±10%. External workload was anticipated in 0-55% of future sessions using an error range of ±5%, and in 58-89% of sessions using an error range of ±10%. External workload during 5 vs. 5 games-based drills can be anticipated in most sessions using normative values established during a short-term monitoring period with an error range of ±10%.

Game schedule congestion affects weekly workloads but not individual game demands in semi-professional basketball

To quantify and compare workloads encountered by basketball players during individual games played across 1-, 2-, and 3-day periods in the same week, and during weeks where 1, 2, and 3 games are scheduled. Eight semi-professional male players were monitored. External workload was determined as absolute and relative (·min-1) PlayerLoad (PL), and total and high-intensity jumps, accelerations, decelerations, and changes of direction (COD). Internal workload was determined as absolute and relative summated heart rate zones (SHRZ), session-rating of perceived exertion (sRPE), and RPE. Game workloads were tabulated considering the order in which they were scheduled weekly (game 1, 2, or 3), and each week considering the number of games scheduled (1, 2, or 3 games). Analysing weekly workloads, duration was higher during 3-game than 1- and 2-game weeks (P <0.05, ES = 6.65-18.19). High-intensity decelerations and COD were higher during 3-game than 1-game weeks (P <0.05, ES = 1.26-1.55). Absolute PL, jumps, accelerations, decelerations, COD, and high-intensity jumps and accelerations were higher during 3-game than 1- and 2-game weeks (P <0.05, ES = 0.69-2.63). Absolute SHRZ and sRPE were higher during 3-game than 1- and 2-game weeks (P <0.05, ES = 0.86-2.43). Players completed similar individual game workloads regardless of the number of games played on consecutive days in the week. Workloads were similar during 1- and 2-game weeks, while the addition of a third game significantly increased the overall weekly workloads encountered.

Match performance in a reference futsal team during an international tournament - implications for talent development in soccer

Anecdotal evidence suggests that futsal can aid talent development for 11 a-side soccer through the potential for transfer of the game’s skills and constraints. However, additional scientific research is necessary to enhance understanding of the technical and physical demands of futsal and the potential returns for soccer player development. Accordingly, this study examined selected characteristics of physical (internal and external loads) and technical performance in international futsal match-play in order to study the potential of these for skill transfer and aiding soccer player development. Performance was investigated in futsal players (n = 16, 25.74±4.71 years) belonging to a national team during an international tournament. Pre-tournament fitness testing determined maximum heart rate (MHR) via the Yo-Yo IR1 test (194.6±11.1 beats min^-1) to aid interpretation of internal load via heart rate measures during match-play. External load (accelerations and deceleration events) was measured using an inertial movement unit. Finally, post-tournament analysis of selected technical events was performed. Results reported a mean heart rate value during ‘court time’ of 164.7±22.3 beats min^-1, which as a percentage of participants’ MHR was 87.7%±4.4%, and a mean peak MHR of 98.3±2.5%. Results showed 2.16±0.25 accelerating (>1.5 m·s^-2) and 2.78±0.13 decelerating (>1.5 m·s^-2) events per player per minute. Team statistics included 647 passing, ~51 dribbling and 78 set play events per team per game. Match analyses showed that 77.3% of ball receptions were completed with the sole of the foot. Assessment of two-footedness showed 80.1±16.7% of individual possessions used the dominant foot to receive and 84.1±10.7% to pass the ball thereby displaying strong foot dominance. These results have quantified characteristics of elite futsal match-play that are no doubt intrinsically related to the environmental and task constraints of the sport. In addition to enhancing understanding of futsal, there are potential learning returns and implications for skills transfer and the development of soccer players.

Individual Response to External Training Load in Elite Football Players

PURPOSE

To investigate within-player effect, between-player effect, and individual response of external training load from player tracking devices on session rating of perceived exertion training load (sRPE-TL) in elite football players.

METHODS

The authors collected sRPE-TL from 18 outfield players in 21 training sessions. Total distance, high-speed running distance (>14.4 m/s), very high-speed running distance (>19.8 m/s), PlayerLoad™, PlayerLoad2D™, and high-intensity events (HIE > 1.5, HIE > 2.5, and HIE > 3.5 m/s) were extracted from the tracking devices. The authors modeled within-player and between-player effects of single external load variables on sRPE-TL, and multiple levels of variability, using a linear mixed model. The effect of 2 SDs of external load on sRPE-TL was evaluated with magnitude-based inferences.

RESULTS

Total distance, PlayerLoad™, PlayerLoad2D™, and HIE > 1.5 had most likely substantial within-player effects on sRPE-TL (100%-106%, very large effect sizes). Moreover, the authors observed likely substantial between-player effects (12%-19%, small to moderate effect sizes) from the majority of the external load variables and likely to very likely substantial individual responses of PlayerLoad™, high-speed running distance, very high-speed running distance, and HIE > 1.5 (19%-30% coefficient of variation, moderate to large effect sizes). Finally, sRPE-TL showed large to very large between-session variability with all external load variables.

CONCLUSIONS

External load variables with low intensity-thresholds had the strongest relationship with sRPE-TL. Furthermore, the between-player effect of external load and the individual response to external load advocate for monitoring sRPE-TL in addition to external load. Finally, the large between-session variability in sRPE-TL demonstrates that substantial amounts of sRPE-TL in training sessions are not explained by single external load variables.