Algorithm predicts US Supreme Court decisions 70% of time

On this heat map, scholars plotted each justice serving since the '50s and for each year added a shaded box. The more green the cell, the more predictable the justice was that year. The method performed well at predicting certain justices and not as well on others, with a 70.9 percent accuracy rating overall.

Josh Blackman's Blog

A legal scholar says he and colleagues have developed an algorithm that can predict, with 70 percent accuracy, whether the US Supreme Court will uphold or reverse the lower-court decision before it.

"Using only data available prior to the date of decision, our model correctly identifies 69.7 percent of the Court’s overall affirm and reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes," Josh Blackman, a South Texas College of Law scholar, wrote on his blog Tuesday.

While other models have achieved comparable accuracy rates, they were only designed to work at a single point in time with a single set of nine justices. Our model has proven consistently accurate at predicting six decades of behavior of thirty Justices appointed by thirteen Presidents. It works for the Roberts Court as well as it does for the Rehnquist, Burger, and Warren Courts. It works for Scalia, Thomas, and Alito as well as it does for Douglas, Brennan, and Marshall. Plus, we can predict Harlan, Powell, O’Connor, and Kennedy.

Given that there isn't much wagering action out there for Supreme Court decisions, Blackman says there's other real-world applications, like helping high court litigators develop strategies to overcome the model.