N.B.: The results and research available in this repository are superseded by mjbommar/scotus-predict-v2 and arXiv:1612.03473.

N.B.: Unless you intend to replicate the prior paper results from 2014, all future citations and research should be directed towards mjbommar/scotus-predict-v2 and arXiv:1612.03473.

Predicting the Behavior of the Supreme Court of the United States: A General Approach

• Title: Predicting the Behavior of the Supreme Court of the United States: A General Approach
• Authors: Daniel Martin Katz, Michael J Bommarito II, Josh Blackman
• Paper URL: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244
• Blog URL: http://lexpredict.com/portfolio/predicting-the-supreme-court/

Paper Abstract

Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized,and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).

Source Description

The source and data in this repository allow for the reproduction of the results in this paper.

Data Description

The data used in this paper is available from the Supreme Court Database (SCDB).

Version

The latest version of this v1 model was released in October 2015. All future work on Marshall+ will be conducted on mjbommar/scotus-predict-v2, which has a latest release date of December 2016.