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Racial segregation has long been a problem in communities across the country. One approach to help understand such an important issue is to attempt to describe it quantitatively. Many metrics have been developed, all with various strengths and weaknesses, but none fully capture the nuances of this complicated issue. This work provides an overview of four of the mathematical approaches that have been developed to study segregation, explains how they function using small examples, and compares and contrasts their effectiveness in various situations. We then focus on segregation in Los Angeles (LA) County, including a detailed exploration of the most recent score proposed by authors Sousa and Nicosia, which conducts a random walk and outputs the number of steps it takes to reach all racial classes in the system. While we found there is a difference between the average step lengths of LA County vs. an unbiased null model, attempts to standardize outputs erases crucial data, and compressing this issue into one score is not representative of its complexity. This suggests that future exploration should attempt to study segregation more comprehensively rather than distilling an incredibly complicated and important issue into a single statistic. More work is needed to quantitatively represent the complexities of racial segregation in an effective matter.