The second version of the Supreme Court prediction model, significantly expanded to cover nearly 200 years of Supreme Court history. Published in PLoS ONE, this work represents a major advance in quantitative legal prediction.

Key Improvements

Extended coverage from 60 years to 200 years (1816-2015)
Analyzed 240,000+ justice votes and 28,000+ case outcomes
70.2% accuracy at the case outcome level
71.9% accuracy at the justice vote level
Outperforms baseline models by nearly 5% across the past century

Technical Innovation

The model employs a time-evolving random forest classifier with sophisticated feature engineering:

Temporal adaptation to account for changing legal landscapes
Generalized approach applicable to any time period
Out-of-sample validation ensuring robust predictions
Uses only pre-decision data for all predictions

Research Impact

Published as: 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

The model and data are fully open source, enabling:

Reproducible research
Further academic study
Practical applications in legal analytics

Authors

Developed in collaboration with:

Daniel Martin Katz (Illinois Tech - Chicago Kent College of Law)
Josh Blackman (South Texas College of Law Houston)