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Context: Accelerometer peak impact accelerations are being used to measure player physical demands in contact sports. However, their accuracy to do so has not been ascertained. Purpose: To compare peak-impact-acceleration data from an accelerometer contained in a wearable tracking device with a 3-dimensional motion-analysis (MA) system during tackling and bumping. Methods: Twenty-five semielite rugby athletes wore a tracking device containing a 100-Hz triaxial accelerometer (MinimaxX S4, Catapult Innovations, Australia). A single retroreflective marker was attached to the device, with its position recorded by a 12-camera MA system during 3 physical-collision tasks (tackle bag, bump pad, and tackle drill; N = 625). The accuracy, effect size, agreement, precision, and relative errors for each comparison were obtained as measures of accelerometer validity. Results: Physical-collision peak impact accelerations recorded by the accelerometer overestimated (mean bias 0.60 g) those recorded by the MA system (P < .01). Filtering the raw data at a 20-Hz cutoff improved the accelerometer's relationship with MA data (mean bias 0.01 g; P > .05). When considering the data in 9 magnitude bands, the strongest relationship with the MA system was found in the 3.0-g or less band, and the precision of the accelerometer tended to reduce as the magnitude of impact acceleration increased. Of the 3 movements performed, the tackle-bag task displayed the greatest validity with MA. Conclusions: The findings indicate that the MinimaxX S4 accelerometer can accurately measure physical-collision peak impact accelerations when data are filtered at a 20-Hz cutoff frequency. As a result, accelerometers may be useful to measure physical collisions in contact sports. [ABSTRACT FROM AUTHOR]

This study assessed the criterion validity of a magnetic, angular rate, and gravity (MARG) sensor to measure countermovement jump (CMJ) performance metrics, including CMJ kinetics before take-off, in elite basketballers. Fifty-four basketballers performed 2 CMJs on a force platform with data simultaneously recorded by a MARG sensor located centrally on the player’s back. Vertical accelerations recorded from the MARG sensor were expressed relative to the direction of gravity. Jumps were analyzed by a blinded assessor and the best jump according to the force platform was used for comparison. Pearson correlation coefficients (r) and mean bias with 95% ratio limits of agreement (95% RLOA) were calculated between the MARG sensor and the force platform for jumps performed with correct technique (n = 44). The mean bias for all CMJ metrics was less than 3%. Ninety-five percent RLOA between MARG- and force platform–derived flight time and jump height were 1 ± 7% and 1 ± 15%, respectively. For CMJ performance metrics before takeoff, impulse displayed less random error (95% RLOA: 1 ± 13%) when compared with mean concentric power and time to maximum force displayed (95% RLOA: 0 ± 29% and 1 ± 34%, respectively). Correlations between MARG and force platform were significant for all CMJ metrics and ranged from large for jump height (r = 0.65) to nearly perfect for mean concentric power (r = 0.95). Strong relationships, low mean bias, and low random error between MARG and force platform suggest that MARG sensors can provide a practical and inexpensive tool to measure impulse and flight time–derived CMJ performance metrics. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the validity of an accelerometer to measure average acceleration values during high-speed running. Thirteen subjects performed three sprint efforts over a 40-m distance (n = 39). Acceleration was measured using a 100-Hz triaxial accelerometer integrated within a wearable tracking device (SPI-HPU; GPSports). To provide a concurrent measure of acceleration, timing gates were positioned at 10-m intervals (0-40 m). Accelerometer data collected during 0-10 m and 1 0-20 m provided a measure of average acceleration values. Accelerometer data was recorded as the raw output and filtered by applying a 3-point moving average and a 1 0-point moving average. The accelerometer could not measure average acceleration values during high-speed running. The accelerometer significantly overestimated average acceleration values during both 0-10 m and 10-20 m, regardless of the data filtering technique (p < 0.001). Body mass significantly affected all accelerometer variables (p < 0.10, partial η² = 0.091-0.219). Body mass and the absence of a gravity compensation formula affect the accuracy and practicality of accelerometers. Until GPSports-integrated accelerometers incorporate a gravity compensation formula, the usefulness of any accelerometer-derived algorithms is questionable. [ABSTRACT FROM AUTHOR]