Events Unrelated to Crime Predict Criminal Sentence Length

Abstract

In United States District Courts for federal criminal cases, prison sentence length guidelines are established by the severity of the crime and the criminal history of the defendant. In this paper, we investigate the sentence length determined by the trial judge, relative to this sentencing guideline. Our goal is to create a prediction model of sentencing length and include events unrelated to crime, namely weather and sports outcomes, to determine if these unrelated events are predictive of sentencing decisions and evaluate the importance weights of these unrelated events in explaining rulings. We find that while several appropriate features predict sentence length, such as details of the crime committed, other features seemingly unrelated, including daily temperature, baseball game scores, and location of trial, are predictive as well. Unrelated events were, surprisingly, more predictive than race, which did not predict sentencing length relative to the guidelines. This is consistent with recent research on racial disparities in sentencing that highlights the role of prosecutors in making charges that influence the maximum and minimum recommended sentence. Finally, we attribute the predictive importance of date to the 2005 U.S. Supreme Court case, United States v. Booker, after which sentence length more frequently fell near the guideline minimum and the range of minimum and maximum sentences became more extreme.