Validity of a Local Positioning System during Outdoor and Indoor Conditions for Team Sports

Dose-Response Relationship for External Workload and Neuromsuclar Performance Over a Female, Collegiate, Basketball Season

ABSTRACT

Philipp, NM, Cabarkapa, D, Blackburn, SD, and Fry, AC. Dose-response relationship for external workload and neuromsuclar performance over a female, collegiate, basketball season. J Strength Cond Res 38(5): e253-e263, 2024-The aim of this study was to investigate the relationship between external workload exposure and changes in countermovement jump force-time characteristics over the course of an entire basketball season, in a sample of National Collegiate Athletic Association Division I, female, basketball players. Data for 12 players were retrospectively analyzed, with external workload being quantified by means of an exponentially weighted, acute, and chronic workload, as well as an acute:chronic workload ratio derived from an inertial measurement unit-based system worn by athletes for all practices and games during the regular season. Countermovement jumps were performed on a total of 26 test days over the span of the in-season competitive period. To statistically analyze these relationships, and to account for multiple observations of the same athletes in a data set, linear mixed-effects models with athlete identity (ID) intercept as the random effect were used. Study findings suggested that associations between external workload exposure and respective force-time characteristics after controlling for the random effect of athlete ID were dependent on the specific metric or metric subgroup used, as well as the type of workload exposure (e.g., acute vs. chronic). Force-time signatures from the braking phase (e.g., average braking force) seemed to be particularly associated with higher degrees of acute workload exposure, whereas strategy-based metrics such as countermovement depth showed significant associations with chronic workload exposure. Furthermore, model results suggested the importance of analyzing neuromuscular responses to external workload on an individual basis, rather than across an entire team. Findings might help practitioners in their selection process related to metrics of interest in monitoring neuromuscular fatigue and readiness.

A comparison of the physical demands generated by playing different opponents in basketball friendly matches

This study aimed to compare the physical demands of playing opponents of different skill levels in basketball. Eighteen men's college basketball players wore accelerometers to measure the relative accumulated acceleration load (AAL), estimated equivalent distance, and frequencies of sprint, jump, and exertion events during games against professional teams (Pro), teams at the same competition level (Collegiate), and teams comprising intra-team members in practice games (Scrimmage). Internal responses were calculated using the relative rating of perceived exertion (sRPE). A repeated measures analysis of variance, Bonferroni post-hoc tests, and standardized Cohen's effect sizes were calculated to compare the physical demands and internal responses across matches played against different levels of opponents. The results showed that in the game against the Pro, AAL (arbitrary units), sprint events (cases per min), and exertion events (cases per min) were significantly (p < .05) higher than those in games against the Collegiate and Scrimmage teams. As the competitive level of the opponents increased, the relative external load of the participants also increased. Conversely, internal responses measured using sRPE were lower after games against the Pro than those against the Collegiate. Internal and external loads may vary from each other depending on contextual factors.

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.

Worst-case scenario analysis of physical demands in elite men handball players by playing position through big data analytics

The physical demands of intermittent sports require a preparation based, by definition, on high-intensity actions and variable recovery periods. Innovative local positioning systems make it possible to track players during matches and collect their distance, speed, and acceleration data. The purpose of this study was to describe the worst-case scenarios of high-performance handball players within 5-minute periods and per playing position. The sample was composed of 180 players (27 goalkeepers, 44 wings, 56 backs, 23 centre backs and 30 line players) belonging to the first eight highest ranked teams participating in the European Men's Handball Championship held in January 2022. They were followed during the 28 matches they played through a local positioning system worn on their upper bodies. Total and high-speed distance covered (m), pace (m/min), player load (a.u.) and high-intensity accelerations and decelerations (n) were recorded for the twelve 5-min periods of each match. Data on full-time player average and peak demands were included in the analysis according to each playing position. A systematic three-phase analysis process was designed: 1) information capture of match activities and context through sensor networks, the LPS system, and WebScraping techniques; 2) information processing based on big data analytics; 3) extraction of results based on a descriptive analytics approach. The descriptive cross-sectional study of worst-case scenarios revealed an ~17% increment in total distance covered and pace, with a distinct ~51% spike in high-intensity actions. Significant differences between playing positions were found, with effect sizes ranging from moderate to very large (0.7-5.1). Line players, in particular, showed a lower running pace peak (~10 m/min) and wings ran longer distances at high speed (> 4.4 m/s) than the rest of the field players (~76 m). The worst-case scenario assessment of handball player locomotion demands will help handball coaches and physical trainers to design tasks that replicate these crucial match moments, thus improving performance based on a position-specific approach.

The Effects of Resistance Training on Muscular Fitness, Muscle Morphology, and Body Composition in Elite Female Athletes: A Systematic Review

BACKGROUND

Well programmed strength and conditioning training is an indispensable part of the long-term training process for athletes in individual and team sports to improve performance and prevent injuries. Yet, there is a limited number of studies available that examine the effects of resistance training (RT) on muscular fitness and physiological adaptations in elite female athletes.

OBJECTIVES

This systematic review aimed to summarize recent evidence on the long-term effects of RT or combinations of RT with other strength-dominated exercise types on muscular fitness, muscle morphology, and body composition in female elite athletes.

MATERIALS AND METHODS

A systematic literature search was conducted in nine electronic databases (Academic Search Elite, CINAHL, ERIC, Open Access Theses and Dissertations, Open Dissertations, PsycINFO, PubMed/MEDLINE, Scopus, and SPORTDiscus) from inception until March 2022. Key search terms from the MeSH database such as RT and strength training were included and combined using the operators "AND," "OR," and "NOT". The search syntax initially identified 181 records. After screening for titles, abstracts, and full texts, 33 studies remained that examined the long-term effects of RT or combinations of RT with other strength-dominated exercise types on muscular fitness, muscle morphology, and body composition in female elite athletes.

RESULTS

Twenty-four studies used single-mode RT or plyometric training and nine studies investigated the effects of combined training programs such as resistance with plyometric or agility training, resistance and speed training, and resistance and power training. The training duration lasted at least 4 weeks, but most studies used ~ 12 weeks. Studies were generally classified as 'high-quality' with a mean PEDro score of 6.8 (median 7). Irrespective of the type or combination of RT with other strength-dominated exercise regimens (type of exercise, exercise duration, or intensity), 24 out of 33 studies reported increases in muscle power (e.g., maximal and mean power; effect size [ES]: 0.23 < Cohen's d < 1.83, small to large), strength (e.g., one-repetition-maximum [1RM]; ES: 0.15 < d < 6.80, small to very large), speed (e.g., sprint times; ES: 0.01 < d < 1.26, small to large), and jump performance (e.g., countermovement/squat jump; ES: 0.02 < d < 1.04, small to large). The nine studies that examined the effects of combined training showed significant increases on maximal strength (ES: 0.08 < d < 2.41, small to very large), muscle power (ES: 0.08 < d < 2.41, small to very large), jump and sprint performance (ES: 0.08 < d < 2.41, small to very large). Four out of six studies observed no changes in body mass or percentage of body fat after resistance or plyometric training or combined training (ES: 0.026 < d < 0.492, small to medium). Five out of six studies observed significant changes in muscle morphology (e.g., muscle thickness, muscle fiber cross-sectional area; ES: 0.23 < d < 3.21, small to very large). However, one study did not find any changes in muscle morphology (i.e., muscle thickness, pennation angle; ES: 0.1 < d < 0.19, small).

CONCLUSION

Findings from this systematic review suggest that RT or combined RT with other strength-dominated exercise types leads to significant increases in measures of muscle power, strength, speed, and jump performance in elite female athletes. However, the optimal dosage of programming parameters such as training intensity and duration necessary to induce large effects in measures of muscular fitness and their physiological adaptations remain to be resolved in female elite athletes.

An Investigation of Surface EMG Shorts-Derived Training Load during Treadmill Running

The purpose of this study was two-fold: (1) to determine the sensitivity of the sEMG shorts-derived training load (sEMG-TL) during different running speeds; and (2) to investigate the relationship between the oxygen consumption, heart rate (HR), rating of perceived exertion (RPE), accelerometry-based PlayerLoadTM (PL), and sEMG-TL during a running maximum oxygen uptake (V˙O2max) test. The study investigated ten healthy participants. On day one, participants performed a three-speed treadmill test at 8, 10, and 12 km·h-1 for 2 min at each speed. On day two, participants performed a V˙O2max test. Analysis of variance found significant differences in sEMG-TL at all three speeds (p < 0.05). A significantly weak positive relationship between sEMG-TL and %V˙O2max (r = 0.31, p < 0.05) was established, while significantly strong relationships for 8 out of 10 participants at the individual level (r = 0.72-0.97, p < 0.05) were found. Meanwhile, the accelerometry PL was not significantly related to %V˙O2max (p > 0.05) and only demonstrated significant correlations in 3 out of 10 participants at the individual level. Therefore, the sEMG shorts-derived training load was sensitive in detecting a work rate difference of at least 2 km·h-1. sEMG-TL may be an acceptable metric for the measurement of internal loads and could potentially be used as a surrogate for oxygen consumption.

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.

Examination of the ZXY Arena Tracking System for Association Football Pitches

Modern analyses of football games require precise recordings of positions and movements. The ZXY arena tracking system reports the position of players wearing a dedicated chip (transponder) at high time resolution. The main issue addressed here is the quality of the system's output data. Filtering the data to reduce noise may affect the outcome adversely. Therefore, we have examined the precision of the data given, possible influence by sources of noise, the effect of the filtering, and the accuracy of the built-in calculations. The system's reported positions of the transponders at rest and during different types of movements, including accelerations, were recorded and compared with the true positions, speeds, and accelerations. The reported position has a random error of ≈0.2 m, defining the system's upper spatial resolution. The error in signals interrupted by a human body was of that magnitude or less. There was no significant influence of nearby transponders. Filtering the data delayed the time resolution. Consequently, accelerations were dampened and delayed, causing an error of 1 m for sudden changes in position. Moreover, fluctuations of the foot speed of a running person were not accurately reproduced, but rather, averaged over time periods >1 s. Results calculated from measured values appeared accurate and were readily reproduced in a spreadsheet output. In conclusion, the ZXY system reports the position with little random error. Its main limitation is caused by averaging of the signals.

Gaussian mixture modeling of acceleration-derived signal for monitoring external physical load of tennis player

Introduction: With the widespread use of wearable sensors, various methods to evaluate external physical loads using acceleration signals measured by inertial sensors in sporting activities have been proposed. Acceleration-derived external physical loads have been evaluated as a simple indicator, such as the mean or cumulative values of the target interval. However, such a conventional simplified indicator may not adequately represent the features of the external physical load in sporting activities involving various movement intensities. Therefore, we propose a method to evaluate the external physical load of tennis player based on the histogram of acceleration-derived signal obtained from wearable inertial sensors. Methods: Twenty-eight matches of 14 male collegiate players and 55 matches of 55 male middle-aged players wore sportswear-type wearable sensors during official tennis matches. The norm of the three-dimensional acceleration signal measured using the wearable sensor was smoothed, and the rest period (less than 0.3 G of at least 5 s) was excluded. Because the histogram of the processed acceleration signal showed a bimodal distribution, for example, high- and low-intensity peaks, a Gaussian mixture model was fitted to the histogram, and the model parameters were obtained to characterize the bimodal distribution of the acceleration signal for each player. Results: Among the obtained Gaussian mixture model parameters, the linear discrimination analysis revealed that the mean and standard deviation of the high-intensity side acceleration value accurately classified collegiate and middle-aged players with 93% accuracy; however, the conventional method (only the overall mean) showed less accurate classification results (63%). Conclusion: The mean and standard deviation of the high-intensity side extracted by the Gaussian mixture modeling is found to be the effective parameter representing the external physical load of tennis players. The histogram-based feature extraction of the acceleration-derived signal that exhibit multimodal distribution may provide a novel insight into monitoring external physical load in other sporting activities.

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

Purpose

The aim of this study was to analyze the within-week differences in external training intensity in different microcycles considering different playing positions in women elite volleyball players.

Methods

The training and match data were collected during the 2020–2021 season, which included 10 friendly matches, 41 league matches and 11 champions league matches. The players’ position, training/match duration, training/match load, local positioning system (LPS) total distance, LPS jumps, accelerations, decelerations, high metabolic load distance (HMLD), acute and chronic (AC) mean and AC ratio calculated with the rolling average (RA) method and the exponentially weighted moving average (EWMA) method, monotony and strain values were analyzed.

Results

All the variables except strain, Acc/Dec ratio and acute mean (RA) showed significant differences among distance to match days. Regarding the players’ positions, the only difference was found in the AC ratio (EWMA); in all microcycles, the middle blocker player showed workload values when compared with the left hitter, setter and libero.

Conclusion

Overall, the analysis revealed that the intensity of all performance indicators, except for strain, acc/dec and acute mean load (RA), showed significant differences among distance to match day with moderate to large effect sizes. When comparing players’ positions, the middle blocker accumulated the lowest loads. There were no significant differences among other positions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13102-022-00568-1.

Contextualizing Physical Data in Professional Handball: Using Local Positioning Systems to Automatically Define Defensive Organizations

In handball, the way the team organizes itself in defense can greatly impact the player's activity and displacement during the play, therefore impacting the match demands. This paper aims (1) to develop an automatic tool to detect and classify the defensive organization of the team based on the local positioning system data and check its classification quality, and (2) to quantify the match demands per defensive organization, i.e., defining a somehow cost of specific defensive organizations. For this study, LPS positional data (X and Y location) of players from a team in the Spanish League were analyzed during 25 games. The algorithm quantified the physical demands of the game (distance stand, walk, jog, run and sprint) broken down by player role and by specific defensive organizations, which were automatically detected from the raw data. Results show that the different attacking and defending phases of a game can be automatically detected with high accuracy, the defensive organization can be classified between 1-5, 0-6, 2-4, and 3-3. Interestingly, due to the highly adaptive nature of handball, differences were found between what was the intended defensive organization at a start of a phase and the actual organization that can be observed during the full defensive phase, which consequently impacts the physical demands of the game. From there, quantifying for each player role the cost of each specific defensive organization is the first step into optimizing the use of the players in the team and their recovery time, but also at the team level, it allows to balance the cost (i.e., physical demand) and the benefit (i.e., the outcome of the defensive phase) of each type of defensive organization.

Simplifying External Load Data in NCAA Division-I Men's Basketball Competitions: A Principal Component Analysis

The primary purpose was to simplify external load data obtained during Division-I (DI) basketball competitions via principal component analysis (PCA). A secondary purpose was to determine if the PCA results were sensitive to load demands of different positional groups (POS). Data comprised 229 observations obtained from 10 men's basketball athletes participating in NCAA DI competitions. Each athlete donned an inertial measurement unit that was affixed to the same location on their shorts prior to competition. The PCA revealed two factors that possessed eigenvalues >1.0 and explained 81.42% of the total variance. The first factor comprised total decelerations (totDEC, 0.94), average speed (avgSPD, 0.90), total accelerations (totACC, 0.85), total mechanical load (totMECH, 0.84), and total jump load (totJUMP, 0.78). Maximum speed (maxSPD, 0.94) was the lone contributor to the second factor. Based on the PCA, external load variables were included in a multinomial logistic regression that predicted POS (Overall model, p < 0.0001; AUC_centers = 0.93, AUC_guards = 0.88, AUC_forwards = 0.80), but only maxSPD, totDEC, totJUMP, and totMECH were significant contributors to the model's success (p < 0.0001 for each). Even with the high significance, the model still had some issues differentiating between guards and forwards, as in-game demands often overlap between the two positions. Nevertheless, the PCA was effective at simplifying a large external load dataset collected on NCAA DI men's basketball athletes. These data revealed that maxSPD, totDEC, totJUMP, and totMECH were the most sensitive to positional differences during competitions. To best characterize competition demands, such variables may be used to individualize training and recovery regimens most effectively.

Time-Motion Analysis by Playing Positions of Male Handball Players during the European Championship 2020

The aim of this study was to analyze time-motion characteristics of elite male handball players during the last European Championship 2020. A total of 414 players from 24 national teams were analyzed during 65 matches using a local positioning system (LPS) for the first time in a European Championship. Players (n = 1865) covered significantly (p < 0.001; ES = 0.48) more total distance in offense (1217.48 ± 699.33 m) and in all locomotion categories (p < 0.001) than in defense (900.96 ± 538.95 m), with a similar average total time on court (13.40 ± 8.19 min in offense and 13.27 ± 8.59 min; p > 0.05). The running pace was significantly higher in offense 96.53 ± 22.57 m/min than in defense 82.72 ± 43.28 m/min (p < 0.001; ES = 0.47). By playing positions, the Left Wing players covered significantly (p < 0.001) higher distances (2547.14 ± 1309.52) and showed longer playing time (32.08 ± 17.01). Center Back was the playing position that showed the highest global running pace (98.34 m/min). Players with higher running pace in offense (p < 0.001) were Left Backs (105.95 ± 25.20) and the Center Backs in defense (95.76 ± 48.90). There were no significant differences between winners and losers or between top ranked and lower ranked teams in terms of time played, distance covered, and running pace. Specific physical conditioning is necessary to maximize performance and minimize fatigue when performing in long tournaments.

Validation of Player and Ball Tracking with a Local Positioning System

The aim of this study was the validation of player and ball position measurements of Kinexon's local positioning system (LPS) in handball and football. Eight athletes conducted a sport-specific course (SSC) and small sided football games (SSG), simultaneously tracked by the LPS and an infrared camera-based motion capture system as reference system. Furthermore, football shots and handball throws were performed to evaluate ball tracking. The position root mean square error (RMSE) for player tracking was 9 cm for SSCs, the instantaneous peak speed showed a percentage deviation from the reference system of 0.7-1.7% for different exercises. The RMSE for SSGs was 8 cm. Covered distance was overestimated by 0.6% in SSCs and 1.0% in SSGs. The 2D RMSE of ball tracking was 15 cm in SSGs, 3D position errors of shot and throw impact locations were 17 cm and 21 cm. The methodology for the validation of a system's accuracy in sports tracking requires extensive attention, especially in settings covering both, player and ball measurements. Most tracking errors for player tracking were smaller or in line with errors found for comparable systems in the literature. Ball tracking showed a larger error than player tracking. Here, the influence of the positioning of the sensor must be further reviewed. In total, the accuracy of Kinexon's LPS has proven to represent the current state of the art for player and ball position detection in team sports.