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This study examined the impact of various smoothing techniques on acceleration data obtained from a Global Navigation Satellite System (GNSS) device during accelerating and decelerating movements, resembling those commonly observed in team sports. Eight participants performed six different accelerating and decelerating movements at different intensities and starting speeds for a total of 46 trials each. The movements were collected concurrently at 10 Hz using a GNSS device (Vector S7, Catapult Sports) at 100 Hz using a motion analysis system (Vicon). Acceleration data were smoothed using (I) a fourth-order Butterworth filter (cut-off frequencies ranging from raw to 4.9 Hz), (II) exponential smoothing (smoothing constant ranging from 0.1 to 0.9), and (III) moving average (sliding window ranging from 0.2 s to 2.0 s). To determine the ability of a GNSS to quantify acceleration, a variety of measurement indices of validity were obtained for each movement and each smoothing technique. The fourth-order Butterworth filter with a cut-off frequency of 2 Hz (mean bias 0.00 m·s−2, 95% LoA ± 1.55 m·s−2, RMSE 0.79 m·s−2) showed the strongest relationship with the Vicon data. These results indicate that this smoothing technique is more accurate than those currently used and accepted on GNSS devices in the sports science community. [ABSTRACT FROM AUTHOR]

This study aimed to measure the differences in commonly used summary acceleration metrics during elite Australian football games under three different data processing protocols (raw, custom-processed, manufacturer-processed). Estimates of distance, speed and acceleration were collected with a 10-Hz GNSS tracking technology device from fourteen matches of 38 elite Australian football players from one team. Raw and manufacturer-processed data were exported from respective proprietary software and two common summary acceleration metrics (number of efforts and distance within medium/high-intensity zone) were calculated for the three processing methods. To estimate the effect of the three different data processing methods on the summary metrics, linear mixed models were used. The main findings demonstrated that there were substantial differences between the three processing methods; the manufacturer-processed acceleration data had the lowest reported distance (up to 184 times lower) and efforts (up to 89 times lower), followed by the custom-processed distance (up to 3.3 times lower) and efforts (up to 4.3 times lower), where raw data had the highest reported distance and efforts. The results indicated that different processing methods changed the metric output and in turn alters the quantification of the demands of a sport (volume, intensity and frequency of the metrics). Coaches, practitioners and researchers need to understand that various processing methods alter the summary metrics of acceleration data. By being informed about how these metrics are affected by processing methods, they can better interpret the data available and effectively tailor their training programs to match the demands of competition. [ABSTRACT FROM AUTHOR]

This study identified and profiled the types of player actions of Australian football players executed during high acceleration and/or deceleration efforts and identified how the duration exceeding the acceleration or deceleration threshold influenced the identified executed player actions. The commencing speed of the acceleration and deceleration efforts were also assessed. Acceleration and deceleration efforts of 22 Australian football players monitored with a 10-Hz GNSS device across two matches were analysed. Synchronised video footage of the matches was used to identify the player actions executed during acceleration and deceleration efforts. Linear mixed models were conducted to examine the relationship between commencing speed, player action, and duration exceeding the effort threshold. 'Linear movement' and 'jump' were the most frequently executed player actions for high accelerations and 'stop' and 'being tackled' for high decelerations. Significant differences were observed in commencing speed between player actions for the high deceleration efforts. Furthermore, it was found that a higher commencing speed was associated with an increased duration exceeding the deceleration threshold. These findings will help practitioners and researchers with selecting minimum-duration criteria for acceleration and deceleration efforts and provide an understanding of the types of player actions executed during these efforts. [ABSTRACT FROM AUTHOR]

Purpose: Sprints and accelerations are popular performance indicators in applied sport. The methods used to define these efforts using athlete-tracking technology could affect the number of efforts reported. This study aimed to determine the influence of different techniques and settings for detecting high-intensity efforts using global positioning system (GPS) data. Methods: Velocity and acceleration data from a professional soccer match were recorded via 10-Hz GPS. Velocity data were filtered using either a median or an exponential filter. Acceleration data were derived from velocity data over a 0.2-s time interval (with and without an exponential filter applied) and a 0.3-second time interval. High-speed-running (≥4.17 m/s²), sprint (≥7.00 m/s²), and acceleration (≥2.78 m/s²) efforts were then identified using minimum-effort durations (0.1-0.9 s) to assess differences in the total number of efforts reported. Results: Different velocity-filtering methods resulted in small to moderate differences (effect size [ES] 0.28-1.09) in the number of high-speed-running and sprint efforts detected when minimum duration was <0.5 s and small to very large differences (ES -5.69 to 0.26) in the number of accelerations when minimum duration was <0.7 s. There was an exponential decline in the number of all efforts as minimum duration increased, regardless of filtering method, with the largest declines in acceleration efforts. Conclusions: Filtering techniques and minimum durations substantially affect the number of high-speed-running, sprint, and acceleration efforts detected with GPS. Changes to how high-intensity efforts are defined affect reported data. Therefore, consistency in data processing is advised. [ABSTRACT FROM AUTHOR]

Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads--The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future. [ABSTRACT FROM AUTHOR]

The need to quantify aspects of training to improve training prescription has been the holy grail of sport scientists and coaches for many years. Recently, there has been an increase in scientific interest, possibly due to technological advancements and better equipment to quantify training activities. Over the last few years there has been an increase in the number of studies assessing training load in various athletic cohorts with a bias toward subjective reports and/or quantifications of external load. There is an evident lack of extensive longitudinal studies employing objective internal-load measurements, possibly due to the cost- effectiveness and invasiveness of measures necessary to quantify objective internal loads. Advances in technology might help in developing better wearable tools able to ease the difficulties and costs associated with conducting longitudinal observational studies in athletic cohorts and possibly provide better information on the biological implications of specific external-load patterns. Considering the recent technological developments for monitoring training load and the extensive use of various tools for research and applied work, the aim of this work was to review applications, challenges, and opportunities of various wearable technologies. [ABSTRACT FROM AUTHOR]

Athlete-tracking devices that include global positioning system (GPS) and microelectrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete-tracking devices and to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal, and data-filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision, and software/firmware versions in any published research. In addition, details of inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance. [ABSTRACT FROM AUTHOR]

This study assessed the interchangeability between 10-Hz multi-GNSS GPS devices (Vector®) and two optical tracking systems (TRACAB® and Second Spectrum®). The agreement between data from the optical tracking systems when processed with manufacturer and GPS-filtered software was also assessed. Thirty players competing in the English Premier League were monitored using three different tracking systems across five matches. To determine the interchangeability between systems, player movement variables including, total distance, high-speed running distance (19.8–25.2 km·h−1), sprinting distance (>25.2 km·h−1), efforts >19.8 km·h−1 and maximal speed were compared. Equations were formed using linear regression and linear mixed-effects models to allow interchangeability of player movement variables between systems. Over half of the variance of most interchangeability equations were explained and associated with very strong positive correlations (r > 0.72). Small to huge differences were found between systems for most player movement variables. Data of optical tracking systems had decreased values in speed variables >19.8 km·h−1 when processed through GPS software. This study provides equations for practitioners to interchange player movement variables between TRACAB, Second Spectrum and Vector GPS systems with reduced error. This will enable practitioners to combine and share data captured with different tracking systems to analyse and improve their training. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the influence of score line, level of opposition, and timing of substitutes on the activity profile of rugby sevens players and describe peak periods of activity. Methods: Velocity and distance data were measured via 10-Hz GPS from 17 international-level male rugby sevens players on 2-20 occasions over 4 tournaments (24 matches). Movement data were reported as total distance (TD), high-speed-running distance (HSR, 4.17-10.0 m/s), and the occurrence of maximal accelerations (Accel, ≥2.78 m/s²). A rolling 1-min sample period was used. Results: Regardless of score line or opponent ranking there was a moderate to large reduction in average and peak TD and HSR between match halves. A close halftime score line was associated with a greater HSR distance in the 1st minute of the 1st and 2nd halves compared with when winning. When playing against higher-compared with lower-ranked opposition, players covered moderately greater TD in the 1st minute of the 1st half (difference = 26%; 90% confidence limits = 6, 49). Compared with players who played a full match, substitutes who came on late in the 2nd half had a higher average HSR and Accel by a small magnitude (31%; 5, 65 vs 34%; 6, 69) and a higher average TD by a moderate magnitude (16%; 5, 28). Conclusions: Match score line, opposition, and substitute timing can influence the activity profile of rugby sevens players. Players are likely to perform more running against higher opponents and when the score line is close. This information may influence team selection. [ABSTRACT FROM AUTHOR]

This study examines differences in weekly load between the first (FT) and the under 19 team (U19) within a professional football setting. Data were collected in 11 FT and 9 U19 players (2016-2017 season). FT data was divided into weeks with (FT-M1) or without (FT-M0) a mid-week match. Indicators were total distance (TD) and TD at 12-15, 15-20, 20-25 and >25 km‧h-1 and were analysed as external load (m), intensity (m‧min-1) and load monotony (a.u.). TD-based load was higher for U19 compared to FT-M0 (very likely moderate) and FT-M1 (likely large). Differences at higher velocities were substantially less (trivial to possibly small), with TD >25 km‧h-1 being lower than FT-M0 (very likely moderate) and FT-M1 (likely small). All intensity indicators were lower for U19 (likely small to almost certainly large). Load monotony was higher compared to FT-M1 (possibly small to almost certainly very large). Compared to FT-M0, monotony was higher for TD (possibly very large) and TD >25 km‧h‑1 (possibly moderate) but lower for TD 12-15 (possibly small) and 15-20 km‧h‑1 (likely moderate). So, despite higher weekly external loads at low velocity for elite youth players, external intensity and load variation increases when these players may transition to professional football. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the occurrence of Repeated High-Acceleration Ability (RHAA) bouts in elite youth football games using 10-Hz GPS devices and two relative thresholds derived from players' actual maximal acceleration. Thirty-six outfield soccer players (age 14.9 ± 0.6 years) participated in the study. Players wore 10-Hz GPS units during 41 official games. High accelerations were defined as efforts commencing above a threshold corresponding to 70 % (T70 %) or 80 % (T80 %) of the average 5-m acceleration obtained during a 40-m sprint test; RHAA bouts were defined as ≥ 3 efforts with ≤ 45 s recovery between efforts. Results were analysed via generalised linear mixed model and magnitude-based inferential statistics. On average, 8.0 ± 4.6 and 5.1 ± 3.5 bouts were detected in an entire game using T70 % and T80 %, respectively. When all positions were analysed together, there was a verylikely small difference in the number of RHAA bouts between first and second half for T70 % and T80 %, respectively. RHAA bouts occur frequently in elite youth football, with small differences between halves and between playing positions within the first or second half in most variables assessed. [ABSTRACT FROM AUTHOR]

This study investigated the effects of successive matches on match-running in elite under-23 soccer players during an international tournament. Match-running data was collected using a semi-automated multi-camera tracking system during an international under-23 tournament from all participating outfield players. Players who played 100% of all group stage matches were included (3 matches separated by 72 h,n = 44). Differences in match-running performance between matches were identified using a generalised linear mixed model. There were no clear effects for total, walking, jogging, running, high-speed running and sprinting distance between matches 1 and 3 (effect size (ES); −0.32 to 0.05). Positional analysis found that sprint distance was largely maintained from matches 1 to 3 across all positions. Attackers had a moderate decrease in total, jogging and running distance between matches 1 and 3 (ES; −0.72 to −0.66). Classifying players as increasers or decreasers in match-running revealed that match-running changes are susceptible to individual differences. Sprint performance appears to be maintained over successive matches regardless of playing position. However, reductions in other match-running categories vary between positions. Changes in match-running over successive matches affect individuals differently; thus, players should be monitored on an individual basis. [ABSTRACT FROM PUBLISHER]

We compared the match activity profiles of elite footballers from Australian football (AF), rugby league (RL) and soccer (SOC), using identical movement definitions. Ninety-four elite footballers from AF, RL or SOC clubs in Australia participated in this study. Movement data were collected using a 5-Hz global positioning system from matches during the 2008–2011 competitive seasons, including measures of velocity, distance, acceleration and bouts of repeat sprints (RS). Australian footballers covered the greatest relative running distances (129 ± 17 m.min−1) compared to RL (97 ± 16 m.min−1) and SOC (104 ± 10 m.min−1) (effect size [ES]; 1.0–2.8). The relative distance covered (4.92 ± 2.10 m.min−1vs. 5.42 ± 2.49 m.min−1; 0.74 ± 0.78 m.min−1vs. 0.97 ± 0.80 m.min−1) and the number of high-velocity running (0.4 ± 0.2 no.min−1vs. 0.4 ± 0.2 no.min−1) and sprint (0.06 ± 0.06 no.min−1vs. 0.08 ± 0.07 no.min−1) efforts between RL and SOC players were similar (ES; 0.1–0.3). Rugby league players undertook the highest relative number of accelerations (1.10 ± 0.56 no.min−1). RS bouts were uncommon for all codes. RL and SOC players perform less running than AF players, possibly due to limited open space as a consequence of field size and code specific rules. While training in football should be code specific, there may be some transference of conditioning drills across codes. [ABSTRACT FROM AUTHOR]