The supreme court forecasting project: Legal and political science approaches to predicting Supreme Court decisionmaking

This Essay reports the results of an interdisciplinary project comparing political science and legal approaches to forecasting Supreme Court decisions. For every argued case during the 2002 Term, we obtained predictions of the outcome prior to oral argument using two methods-one a statistical model that relies on general case characteristics, and the other a set of independent predictions by legal specialists. The basic result is that the statistical model did better than the legal experts in forecasting the outcomes of the Term's cases: The model predicted 75 % of the Court's affirm/reverse results correctly, while the experts collectively got 59. 1 % right. These results are notable, given that the statistical model disregards information about the specific law or facts of the cases. The model's relative success was due in large part to its ability to predict more accurately the important votes of the moderate Justices (Kennedy and O'Connor) at the center of the current Court. The legal experts, by contrast, did best at predicting the votes of the more ideologically extreme justices, but had difficulty predicting the centrist justices. The relative success of the two methods also varied by issue area, with the statistical model doing particularly well in forecasting "economic activity" cases, while the experts did comparatively better in the "judicial power" cases. In addition to reporting the results in detail, the Essay explains the differing methods of prediction used and explores the implications of the findings for assessing and understanding Supreme Court decision making.