# Applying Force Plate Technology to Inform Human Performance Programming in Tactical Populations
## 2 Important Data Considerations for Implementing Force Plate Testing  
The most important step in preparation for using force plate data to inform training program prescriptions is identifying the database management system that will store the raw data, conduct calculations, and build visualizations. It is common for high frequency (1000 Hz) force plate systems to provide a wealth of meaningful data from the force-time curve, resulting in upwards of 100+ calculated variables. This, understandably so, may look overwhelming at first sight, but a properly designed (and commercially available) data management system provides the ability to make this vast data easily accessible, visualizable, and actionable, as well as overlay additional important contextual data such as training load. Figure 1 displays a dashboard example of an executive summary of CMJ testing utilizing Fusion Sport Smartabase Athlete Management System (AMS). In the absence of a data management tool (such as an AMS), the data becomes very cumbersome to manage and can lead to a decreased likelihood of analyzing historical trends and managing large datasets. Although deeper evaluations of data may be necessary, the simplicity and automaticity of displays on dashboards can improve the buy-in from all users by seeing the testing results being implemented immediately. Indeed, the results from force plate data are only as reliable as the depth of standardization of the testing conditions and parameters, which are disseminated in a previous review [41]. In line with standardization recommendations (i.e., time of day, warm-up, instructions, tools), the order of assessments should be consistent across testing sessions, and testing should linearly progress from the least to most fatiguing assessments to limit the influence of the prior test on the next. Fatigue accumulation during testing may also be reduced by allowing short rest periods between trials (10-30 s) and between assessments (45-120 s). The duration of rest must be kept consistent across testing sessions and will be dictated by the number and intensity of trials performed for each assessment. Typically, averaging 2-3 trials provides more reliable and accurate depictions of the current state the individual is in [59]. However, if multiple trials are not permitted by schedule, single trials are sufficient for detecting neuromuscular fatigue [46] and should be used, at the very least. Figure 1: A Fusion Sport Smartabase dashboard built to display comparisons of individuals to their average historical values (identifying those increasing, maintaining, and decreasing in power production capabilities by utilizing jump height Z-scores based on intra-individual variation), others within the group (leaderboard comparisons to the group average), and as a percentage of their all-time personal best record (100% highlighted in green would indicate a personal record while red indicates very low performances compared to historical records). Highlighting groups of individuals with red flag alerts from decreased performances can be used to easily identify and make appropriate adjustments to individual training regimens. This data and names have been synthetically generated for visual purposes. A standardized and supervised dynamic warm-up protocol must be implemented to ensure maximal performances during testing. Examples are provided in Table 1 below. The warm-up protocols are strategically standardized to help ensure maximal effort is being attained at each trial within and across testing sessions over time. Failure to maximally potentiate the assessed musculature (e.g., lower extremity muscles contributing to CMJ performance) causes the inability to decipher whether variations in power output are due to neuromuscular fatigue or poor effort, which lends inaccurate interpretations of data.