http://journals.plos.org/plosone/article?
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RESEARCH ARTICLE
A general approach for predicting the
behavior of the Supreme Court of the United
States
Daniel Martin Katz1,2*, Michael J. Bommarito II1,2, Josh Blackman3
1 Illinois Tech - Chicago-Kent College of Law, Chicago, IL, United States of America, 2 CodeX - The Stanford
Center for Legal Informatics, Stanford, CA, United States of America, 3 South Texas College of Law Houston,
Houston, TX, United States of America
* [email protected]
Abstract
Building on developments in machine learning and prior work in the science of judicial pre-
diction, 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 that leverages 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 jus-
tice 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.
Introduction
As the leaves begin to fall each October, the first Monday marks the beginning of another term
for the Supreme Court of the United States. Each term brings with it a series of challenging,
important cases that cover legal questions as diverse as tax law, freedom of speech, patent law,
administrative law, equal protection, and environmental law. In many instances, the Court’s
decisions are meaningful not just for the litigants per se, but for society as a whole.
Unsurprisingly, predicting the behavior of the Court is one of the great pastimes for legal
and political observers. Every year, newspapers, television and radio pundits, academic jour-
nals, law reviews, magazines, blogs, and tweets predict how the Court will rule in a particular
case. Will the Justices vote based on the political preferences of the President who appointed
them or form a coalition along other dimensions? Will the Court counter expectations with an
unexpected ruling?
PLOS ONE | https://doi.org/10.1371/journal.pone.0174698 April 12, 2017 1 / 18
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Citation: Katz DM, Bommarito MJ, II, Blackman J
(2017) A general approach for predicting the
behavior of the Supreme Court of the United
States. PLoS ONE 12(4): e0174698. https://doi.
org/10.1371/journal.pone.0174698
Editor: Luı
´s A. Nunes Amaral, Northwestern
University, UNITED STATES
Received: January 17, 2017
Accepted: March 13, 2017
Published: April 12, 2017
Copyright: © 2017 Katz et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data and replication
code are available on Github at the following URL:
https://github.com/mjbommar/scotus-predict-v2/.
Funding: The author(s) received no specific
funding for this work.
Competing interests: All Authors are Members of
a LexPredict, LLC which provides consulting
services to various legal industry stakeholders. We
received no financial contributions from LexPredict
or anyone else for this paper. This does not alter
our adherence to PLOS ONE policies on sharing
data and materials.
DANIEL MARTIN KATZ, MICHAEL J.
BOMMARITO II & JOSH BLACKMAN,
A GENERAL APPROACH FOR
PREDICTING THE BEHAVIOR OF THE
SUPREME COURT OF THE UNITED
STATES, PLOS ONE 12.4 (2017):
E0174698.
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2017
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