# Applying Force Plate Technology to Inform Human Performance Programming in Tactical Populations
## 3 Profile Testing for Physical Capabilities
### Creating Normative Data and Percentile Scores  
Normative data sets are critical when comparing individual or group performances to the typical distribution of performance values and must be assessed for a given population (gender, age, specialty, etc.). This allows for the identification of below or above-average performances but requires a large randomly selected sample from a specific population. For that reason, it is not appropriate to compare values from a sample in one population to normative data from another population (i.e., across vastly different occupational specialties). Indeed, more data is needed to better understand not just tactical personnel overall but specific occupational specialties within tactical groups who require specific and potentially different physiological demands.  
Practitioners are encouraged to indicate high and low performers within their group by calculating and comparing standard scores (i.e., inter-individual: Z-scores, STEN scores, and percentile rankings). Standard scores also allow more direct comparisons among performance metrics of differing units (i.e., comparing magnitude changes in power output and impulse) and are more likely to resonate with personnel by demonstrating relative changes in performances. The scores align with standard deviations to indicate the distance an individual's performance is from the group's average at a single time point or their own performances over time (Figure 2). For example, a Z-score of 0 equates to the group average, while a Z-score of +2 would equate to a performance better than approximately 97% of all scores (mean = 50% + 34% (+1 standard deviation) + 13% (+2 standard deviations). Whereas a Z-score of \(-\)0.5 would equate to one standard deviation below the mean of a performance, \(\sim\)34% of all performers (or 34% of one's own previous performances across a specific time period), and indicate a red flag alert to monitor the individual and promote a prescriptive training and/or recovery plan. Percentiles (and other standard scores) are linked to standard deviations above and below the mean (if a normal distribution exists, Figure 2), but custom weighted classifications may also be created based on well-established \"norms\" for a group. For example, if rates of attrition or injury increase when performances are below a certain threshold and successes are noted when performances are above a threshold, practitioners may use these standardized classifications to suggest low, average, and high performers (again, assuming a normal distribution). Regardless, the classifications should remain consistent unless new data emerges warranting the need for change. Context should also be taken for each metric, as positive changes to standard scores (i.e., jump height, forces, and power) may indicate improved performance, while negative changes to other variables indicate improved performance (i.e., duration metrics, eccentric to concentric ratios). Further, some metrics follow a bell shape, where a large change in either positive or negative directions may be a red flag for poor performance or are dependent on changes to other metrics (i.e., countermovement depth).