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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.

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.

Soccer players perform a variety of training drills to develop the physical, technical and tactical qualities required for match-play. The role of coaches in prescribing training suggests that players may not always meet physical targets set by conditioning staff. To quantify the physical outputs elicited by different training drill types, 183 professional soccer players were monitored over 8 seasons using Microelectromechanical Systems during normal training, yielding 65,825 drill observations [362 ± 341 observations·player−1]. Linear mixed models assessed the influence of drill type, head coach and playing position on physical output. Drills lasted ~14 min, eliciting total distances and high speed running of ~1000 m and 40 m, respectively. Conditioning drills elicited substantially greater relative high-speed running [18.8 ± 27.2 m.min−1] and Sprint [3.5 ± 9.4 m.min−1] distances than all other drill types. The proportion of training drill types used and external outputs elicited per drill were affected by the head coach. Midfielders recorded the highest total distance [77.3 ± 36.1 m.min] and PlayerLoad™ [8.29 ± 3.54] of any playing position, whilst the lowest outputs were recorded by goalkeepers. This study provides reference data for practitioners when seeking to manipulate training prescription to achieve physical output targets whilst also meeting the team’s technical and tactical objectives.