As advanced functional apparel (e.g. wearable technology) continues to develop and permeate the consumer market, sizing and fit for the human body have become obstacles to consumer accessibility and garment functionality. This study develops sizing and design strategies for an advanced functional compression garment for the lower leg through an investigation of anthropometric geometric variability of the North American civilian population (using the CAESAR database). We extracted six lower leg measurements - ankle, calf, and knee circumferences as well as knee-to ankle, kneeto- calf, and ankle-to-calf lengths - from a sample of CAESAR three-dimensional body scans (n = 160) and ran descriptive statistics to quantify lower leg variability. We then arranged the sample population separately using six different grouping variables - body mass index (BMI), height, weight, knee-to-ankle length, ankle circumference, calf circumference, and knee circumference - and conducted an analysis of variance (ANOVA) using each sorting algorithm to determine which variable(s) produced the most distinct groups (quartiles) for the anthropometric dimensions of interest (e.g., lower leg circumferences/lengths). The results conclude that sorting by BMI does not produce statistically discrete sizes; however, sorting by ankle circumference does (p < 0.05). Furthermore, length was found to be independent from circumference and vary consistently between ankle-based size groups. We conclude with sizing and design strategies for future development of advanced functional garments to aid in the transition from research to industry.