Podcast: Can Appellate Outcomes Be Predicted?
Kirk Jenkins likes to know the odds. As an undergrad at Harvard, the Chicago-based Sedgwick partner built regression models to predict decisions…
February 23, 2017 at 12:54 PM
2 minute read
Kirk Jenkins likes to know the odds. As an undergrad at Harvard, the Chicago-based Sedgwick partner built regression models to predict decisions by the Federal Reserve Bank. So it's no surprise that now, as a lawyer, he is among the early believers in the value of legal data analytics. And he has a message for the rest of the profession: there's no going back.
“Fundamentally, this is about moving the mindset of lawyers towards thinking about litigation the way that clients do,” Jenkins says in a new podcast with Law.com. “You hear a lot that clients don't like litigation because it's outrageously expensive. Well, there's some truth to that. But it would be more precise to say that clients worry about litigation because it's a risk that hasn't been quantified.”
Jenkins has compiled years of decisions by courts in Illinois and California, slicing the metrics every which way to create models that he contends give businesses a greater measure of predictability in litigation at every stage. He also blogs about trends he spots in the data, breaking down trivia like which California Supreme Court justices write the longest majority opinions in civil cases.
In this interview, he explains how his model works, offers predictions on several important California Supreme Court cases, and explains why he sees analytics as the future.
- Want more podcasts? Check out An Insider's Take on Litigation Funding
Ben Hancock can be reached at [email protected]. On Twitter: @benghancock
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