Unfortunately, the Supreme Court did not find this argument to be persuasive, ruling instead that the question of partisan gerrymandering is “nonjusticiable”—outside their jurisdiction. Subsequent rulings, such as Abbott v. League of United Latin American Citizens, give little hope that the Supreme Court will impede future gerrymandering.

A much better mathematical method to detect gerrymandering, known as Markov chain Monte Carlo (MCMC) sampling, has been percolating throughout the research literature, and was brought before the Supreme Court in the 2019 case Rucho v. Common Cause. Although it is not possible to compare a contested map against all possible maps, MCMC uses a computational technique called a “random walk” to generate a representative sample of legal electoral district maps by repeatedly making small arbitrary changes to possible district boundaries. Mathematicians, serving as expert witnesses for the plaintiffs and weighing in as amicus curie, argued that if a specific map is an outlier from the rest of samples in terms of political advantage, it indicates possible gerrymandering. The mathematicians found that maps proposed by the 2012 and 2016 North Carolina legislatures fell at the extreme ends of bell curves generated from MCMC-sampled maps, based on measures such as the number of Democrats elected and the number of Democratic voters in specific districts.