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August 3, 2016

Does the weather and MLB baseball impact federal sentencing outcomes more than racial factors?

The seemingly somewhat kooky question in the title of this post is prompted by this seemingly somewhat kooky empirical paper now available via SSRN and authored by a group of data researchers and titled "Events Unrelated to Crime Predict Criminal Sentence Length." Here is the paper's abstract (with a key sentence emphasized to explain my post title query):

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.

Based on a quick scan of the paper, I came to the conclusion that one would need to have a pretty sophisticated understanding of both federal sentencing patterns and empirical methods to assess the soundness of the analysis here.  Still, the paper's penultimate paragraph reinforces that this analysis led to some notable conclusions (with my emphasis again added):

A justice system reasonably aspires to be consistent in the application of law across cases and to account for the particulars of a case. Our goal was to create a prediction model of criminal sentence lengths that accounts for non-judicial factors such as weather and sports events among the feature set. The feature weights offer a natural metric to evaluate the importance of these features unrelated to crime relative to case-specific factors. Using a Random Forest, we found several expected crime related features appearing within the top 10% most important features. However, we also found defendant characteristics (unrelated to the crime), sport game outcomes, weather, and location features all predictive of sentence length as well, and these features were, surprisingly, more predictive than the defendant’s race. Further investigating this predictive ability would be of interest to those studying the criminal justice system.

August 3, 2016 at 10:50 AM | Permalink

Comments

Location impacts the judge pool, the US attorney pool, and the mix of cases typically handled so it is hardly surprising that location is relevant. The other results, on the other hand, do seem random.

Posted by: ohwilleke | Aug 3, 2016 6:11:57 PM

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