Statistical Considerations When Measuring Absolute Reliability And Variability Of Vector Data In Sport Performance

Abstract

Vector-based data in sport performance include a magnitude and direction. Statistically speaking, they are interval in nature as they may be positive or negative. The coefficient of variation (CV) is a commonly reported measure of variability, but its use with vector data is questionable and may be contraindicated. Limits of agreement (LOA) and standard error of measurement (SEM) may be better alternatives for vector data such as acceleration. The purpose of this study was to demonstrate the issues with quantifying variability of vector data, while also evaluating the utility of commonly used measures. Acceleration data at three intervals from 0 to 27.4 m (0 to 90 ft) were calculated from publicly available sprint performance data from 310 athletes participating in the 2018 and 2019 Major League Baseball seasons. CV, LOA, and SEM were calculated to evaluate inter-season variability. Variability of the first two intervals was acceptable for all measures, but the final interval was unclear as the CV was quite large (50.78%), while the LOA and SEM were only slightly larger than the other interval values. The final interval includes both positive and negative acceleration, contraindicating the usage of the CV. LOA and SEM are more useful for vector data, showing that the final interval was more variable between trials than the others, but not to the extent portrayed by the CV. The CV likely should not be used with vector data unless it is known that the data does not cross zero. LOA and SEM are appealing alternatives for the CV and should be considered since they work with positive and negative data.