# Applying Force Plate Technology to Inform Human Performance Programming in Tactical Populations
## 3 Profile Testing for Physical Capabilities
### Case Study Scenario 1: Profiling  
The occupational requirements in tactical populations consisting of both exceptional physical performances and sedentary tasks require a wide range of physical capabilities. Thus, tactical fitness test batteries may consist of select/non-select decisions for occupational specialties based on the minimum required scores across a wide range of fitness domains (i.e., strength and endurance), such as the physical fitness test (PFT), combat fitness test (CFT), LawFit, or FireFit [75; 76; 77]. Even if testing is not used as minimum requirements or to predict performances for specific occupational specialties, the utilization of percentile scores allows the practitioner to understand weak areas that need improvement or strong areas that need maintained (i.e., categories as--Excellent, Good, Average, Below Average, Poor). The latter example is likely to be the most common scenario for force plate assessments, at least until enough data are gathered and analyzed to determine \"likely and unlikely\" scenarios for occupational performance or injury risk. Until then, claims of high injury risk based on performance cut-offs are deemed inappropriate. In this example case study, a group of (fictional) firefighters perform an entire annual testing battery consisting of strength measures (loaded countermovement jump height and absolute and relative IMTP peak force), power measures (CM) and DJ height), reactive strength indexes (RSI from DJ), and movement strategies (DJ peak force when landing and CMJ asymmetries in mean concentric force and countermovement depth). Firefighter 1 (Figure 3) was slightly below the average (when compared to the group, which is gender  
Figure 2: Example of a normal distribution and classifications for Z-score and percentiles. and occupationally specific) relative strength measures but was above the average for movement strategies, which indicates the need to continue strength training with the additional attention on movement patterns during explosive tasks. Firefighter 2 (Figure 3) was below average on all strength and power measures, which suggests this firefighter is either experiencing undue fatigue or lacks fundamental strength and power capacities. Thus, the impending training program(s) should consider individual fatigue levels, as well as training all facets of a force-velocity profile [78]. Firefighter 3 (Figure 3) was well above-average for all strength measures and CMJ performances. However, Firefighter 3 was slightly below average in DJ performances and had low reactive strength capabilities, which suggests the need for explosive movements in their training program to induce adaptations for improving the ability to quickly decelerate and reaccelerate. Since Firefighter 3 also showed above-average CMJ asymmetries and shallower countermovement depths, their training should also be aimed to improve movement competencies. Conversely, Firefighter 4 (Figure 3) performed poorly on unloaded and loaded CMJs. Considering the above-average DJ height and average relative strength, Firefighter 4 may require coaching efforts aimed to improve jumping strategies and the addition of velocity and power-focused training efforts (i.e., velocity-based training and pyometrics). Notice the importance of considering strength and power capabilities when making training recommendations, as weaker individuals would improve ballistic power performances from strength training more than power training alone [79]. Nonetheless, by visualizing these individual performances compared to group averages, using Z-scores for each metric displayed in radar plots (also applicable to other visualization strategies such as conditionally formatted tables, time series comparisons), practitioners can identify areas for improvement for each individual or group of individuals (Table 2). Figure 3: Displayed are the results of force plate assessments of four firefighters (1, purple; 2, green; 3, blue; 4, red) with the results described in case study 1.