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

Question

1. What are the benefits of using coefficient of variation for absolute reliability measurement in sport performance?

2. What are the issues associated with measuring variability of vector data in sport performance and what are the alternatives for assessing variability and absolute reliability?

3. How was MLB sprint performance data evaluated?

4. What statistical measures were used in the study?

Accepted Answer

The coefficient of variation (CV) is a popular measure of absolute reliability in sport performance. It is a ratio of the standard deviation (SD) to the mean, often represented as a percentage. The CV is beneficial because it is easy to calculate, requiring only the mean and SD. It allows for comparisons between sample data and only requires one trial to compute, unlike other reliability assessments that require a minimum of two trials. This makes it a convenient and efficient tool for evaluating reliability in sport performance studies.

Accepted Answer

Despite its popularity and ease of production, the coefficient of variation (CV) should not be applied in every situation, especially when dealing with vectors in sport performance. Vectors have both magnitude and direction, and this is indicated by the value being positive or negative. The CV requires data to be measured on the ratio scale, not interval, which means it is often scalar data from a sport performance standpoint. However, vector data such as force, acceleration, velocity, or power are vectors and are not appropriate for CV. This issue has been demonstrated previously using temperatures. The CV is calculated using a measure of dispersion (sd) and a measure of central tendency (mean), and vector data may have larger measured CVs than scalar data due to the inclusion of a vector direction. Other options for measuring absolute reliability and variability include limits of agreement (LOA) and the standard error of measurement (SEM). LOA demonstrates the amount of variability expected between trials to a 95% confidence interval, while the SEM estimates the noise between trials of a test and is an indicator of precision. The SEM can be calculated using the standard deviation and a previously calculated reliability measure such as the intraclass correlation coefficient (ICC). The resulting SEM is the value where 34% of the error variance will lie above and below the measured value, indicating the amount of precision within one standard deviation. The SEM minimum detectable change at the 95% confidence limit (SEM mdc95) indicates the amount of difference required to be considered an actual change because it is past what might be credited to measurement error. However, the SEM assumes that data are homoscedastic, which means that the chance for error is the same regardless of measure magnitude. If proportional bias is present, a log transformation may be required, making interpretation more difficult. Therefore, alternatives such as LOA and SEM should be considered for assessing variability and absolute reliability in sport performance, as they retain the original unit of measure and provide more applicable results.

Accepted Answer

The MLB sprint performance data from the 2018 and 2019 seasons was evaluated using acceleration data from 0 to 27.432 m (0 to 90 ft) at three intervals. Only 'competitive' sprints qualify for this data set, excluding sprints of two or more bases on hits that were not home runs and sprints from home to first on softly hit balls. The average of the best 66% of each athlete's sprints were calculated and made publicly available, with a minimum of 10 competitive sprints required for inclusion in the data set.

Accepted Answer

The study utilized various statistical measures, including coefficient of variation (CV), standard error of measurement (SEM), intraclass correlation coefficient (ICC), and SEM mdc95. These measures were calculated using data from each season. ICC was determined using a function in the irr package, which incorporated an ANOVA model, type of consistency, and two raters (season). The other statistics were derived from specific equations. Additionally, Bland-Altman LOA plots were included to illustrate the interpretation of LOA at specific intervals.

Accepted Answer

The homogeneity of variance was not violated at any of the 3 acceleration intervals between the two seasons of the 310 baseball players. Therefore, the Standard Error of Mean (SEM) and/or Limit of Agreement (LOA) would be more appropriate than the Coefficient of Variation (CV) with this sample. However, CVs are still shown for comparison purposes in this study. The final interval, a3, appears to be the most variable with the largest CV values (mean of 50.8%). Figure 1 demonstrates the amount of overlap in each season's data distribution, the amount of dispersion for each interval, and that the 3rd interval demonstrates that both positive and negative acceleration values are included. Table 3 shows the results of all measures of variability and absolute reliability (CV, LOA, SEM, and SEM mdc95). Figure 2 depicts the limits of agreement for each season's acceleration data during the final interval, where 95% of data likely vary between 0.34 m/s 2 below and 0.27 m/s 2 above actual values.

Accepted Answer

The appropriate statistical measures for interval data include LOA, SEM, and SEM mdc95. LOA demonstrates the amount of data variation between trials or seasons, while SEM values indicate the percentage of variability within a certain range. SEM mdc95 provides a more direct interpretation by indicating the minimum difference required to be considered a significant change beyond measurement error. These measures are more suitable for interval data as they account for both positive and negative values, unlike the CV, which is inappropriate for such data. The choice of critical value for LOA and SEM mdc95 can be adjusted based on the desired confidence level. These measures are particularly useful in athlete monitoring to distinguish between signal and noise. However, it's important to note that the trial data in this study were separated by seasons, which may not be suitable for test-retest reliability assessments due to increased variability over time. The focus of this study was to highlight the issues associated with using common variability and absolute reliability measures, such as the CV, for interval data.

Accepted Answer

CV, or coefficient of variation, is contraindicated in sport performance because it requires ratio or scalar data. This limitation makes it unsuitable for vector data, such as force, acceleration, or velocity. When using CV with vector data, practitioners must ensure that the data does not vary on both sides of zero. Due to these constraints, alternative measures like SEM (Standard Error of Measurement) and LOA (Limit of Agreement) are recommended. These alternatives can handle positive and negative data and maintain the initial unit of measure, making them easier to interpret. Additionally, the SEM mdc95 (Standard Error of Measurement at 95% confidence interval) is particularly useful in athlete monitoring situations, as it helps distinguish actual change from noise or error.

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

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Most frequently asked questions

1. What are the benefits of using coefficient of variation for absolute reliability measurement in sport performance?

2. What are the issues associated with measuring variability of vector data in sport performance and what are the alternatives for assessing variability and absolute reliability?

3. How was MLB sprint performance data evaluated?

4. What statistical measures were used in the study?

5. Is SEM or LOA more appropriate for this sample?

6. What are the appropriate statistical measures for interval data?

7. Why is CV contraindicated in sport performance?

Citations

Between-Session Reliability of Athletic Performance and Injury Mitigation Measures in Female Adolescent Athletes in the United States

References

R: A language and environment for statistical computing.

Statistical methods for assessing agreement between two methods of clinical measurement

Measures of reliability in sports medicine and science.

Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM.

Statistical Methods For Assessing Measurement Error (Reliability) in Variables Relevant to Sports Medicine

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