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

Daniel Martin Katz

Illinois Tech - Chicago Kent College of Law

Michael James Bommarito II

LexPredict, LLC; Bommarito Consulting, LLC; Chicago-Kent College of Law - Illinois Institute of Technology; Michigan State College of Law

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 )

565 W. Adams St.
Chicago, IL 60661-3691
United States

HOME PAGE: http://www.danielmartinkatz.com/

Michael James Bommarito II

LexPredict, LLC ( email )

MI
United States

HOME PAGE: http://lexpredict.com

Bommarito Consulting, LLC ( email )

MI 48098
United States

HOME PAGE: http://bommaritollc.com

Chicago-Kent College of Law - Illinois Institute of Technology ( email )

565 W. Adams St.
Chicago, IL 60661-3691
United States

Michigan State College of Law ( email )

318 Law College Building
East Lansing, MI 48824-1300
United States

Josh Blackman

South Texas College of Law Houston ( email )

1303 San Jacinto Street
Houston, TX 77002
United States

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