2022
DOI: 10.1080/02640414.2022.2054535
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Techniques to derive and clean acceleration and deceleration data of athlete tracking technologies in team sports: A scoping review

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Cited by 9 publications
(6 citation statements)
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“…For example, Lacome et al (37) and Akenhead et al (1) observed that MSS and maximum acceleration biases were larger with raw data vs. smoothed (moving average) data during linear sprinting tasks. In accordance with previous literature (21), a low-pass Butterworth filter was the most frequently reported filtering technique with both radar and GNSS data in this review. However, as mentioned, and important to note, only 2 GNSS studies reported all specific filtering specifications (e.g., digital, low-pass, dual-pass, or 1 Hz cutoff Butterworth; Table 3).…”
Section: Discussionsupporting
confidence: 84%
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“…For example, Lacome et al (37) and Akenhead et al (1) observed that MSS and maximum acceleration biases were larger with raw data vs. smoothed (moving average) data during linear sprinting tasks. In accordance with previous literature (21), a low-pass Butterworth filter was the most frequently reported filtering technique with both radar and GNSS data in this review. However, as mentioned, and important to note, only 2 GNSS studies reported all specific filtering specifications (e.g., digital, low-pass, dual-pass, or 1 Hz cutoff Butterworth; Table 3).…”
Section: Discussionsupporting
confidence: 84%
“…More recent studies have since shown improvements in agreement outcomes (CVs <5%) across metrics with improved intrasystem and intersystem reliability using 10 Hz GNSSs (11,14,22). Although measures of speed are achieving good agreement with higher sampling rates, present results suggest that GNSS is still suboptimal for quantifying acceleration metrics (21,58). It has been suggested that low sampling rates limit the ability to determine changes in velocity during the initial portions of the sprint where acceleration is likely the highest.…”
Section: Discussionmentioning
confidence: 70%
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