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

18 Pages Posted: 9 Jul 2014 Last revised: 19 Jan 2017

See all articles by Daniel Martin Katz

Daniel Martin Katz

Illinois Tech - Chicago Kent College of Law; Bucerius Center for Legal Technology & Data Science; Stanford CodeX - The Center for Legal Informatics; 273 Ventures

Michael James Bommarito

273 Ventures; Licensio, LLC; Stanford Center for Legal Informatics; Michigan State College of Law; Bommarito Consulting, LLC

Josh Blackman

South Texas College of Law Houston

Date Written: January 16, 2017

Abstract

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time evolving random forest classifier which leverages some unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.

Keywords: United States Supreme Court, Machine Learning, Law and Social Science, Quantitative Legal Prediction, SCOTUS prediction, artificial intelligence and law, online learning, judicial prediction, random forest

JEL Classification: C45, K40

Suggested Citation

Katz, Daniel Martin and Bommarito, Michael James and Blackman, Josh, A General Approach for Predicting the Behavior of the Supreme Court of the United States (January 16, 2017). Available at SSRN: https://ssrn.com/abstract=2463244. or http://dx.doi.org/10.2139/ssrn.2463244

Daniel Martin Katz (Contact Author)

Illinois Tech - Chicago Kent College of Law ( email )

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273 Ventures ( email )

HOME PAGE: http://273ventures.com

Licensio, LLC ( email )

Okemos, MI 48864
United States

Stanford Center for Legal Informatics ( email )

559 Nathan Abbott Way
Stanford, CA 94305-8610
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Michigan State College of Law ( email )

318 Law College Building
East Lansing, MI 48824-1300
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Bommarito Consulting, LLC ( email )

MI 48098
United States

Josh Blackman

South Texas College of Law Houston ( email )

1303 San Jacinto Street
Houston, TX 77002
United States

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