In a recent post over at Concurring Opinions, Harry Surden (Colorado) concludes with the following prediction: "In the not too distant future, such data-driven approaches to engaging in legal prediction are likely to become more common within law. Outside of law, data analytics and machine-learning have been transforming industries ranging from medicine to finance, and it is unlikely that law will remain as comparatively untouched by such sweeping changes as it remains today."
If Surden is even partially correct we should expect to see data increasingly pressed into the service of a more sophisticated legal outcomes "prediction" business. Of course, the Katz, Bommarito, and Blackman paper's claim (discussed in Surden's post) for a 70.9% successful prediction rate needs to be placed into some context. Specifically, as many law profs and appellate litigators instinctively already know, simply by predicting a reversal one can correctly predict the outcome of a Supreme Court case with approximately 56-73% accuracy (for an extended discussion, click here). While a 70.9% prediction rate is important, when it comes to Supreme Court cases the correct baseline is not a Priest-Klein 50%.