Legal Technology

Data on judges' tendencies featured in new predictive analytics tool from Bloomberg Law

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The increasingly crowded field of predictive judicial analytics tools has a new player.

On Tuesday, Bloomberg Law officially launched its Litigation Analytics tool, which tracks historical and statistical data relating to judges to provide insight into their tendencies.

Among the questions Litigation Analytics seeks to answer: How long does a certain judge take to issue certain rulings? How often are they overturned on appeal? How do they rule on certain dispositive motions?

In an interview with ABA Journal, Darby Green, Commercial Product Director for Bloomberg Law Litigation Solutions, Litigation Analytics, says that Litigation Analytics is the natural by-product of Bloomberg’s access to tons of legal and corporate data, as well as its vast research capabilities. “At heart, we really are a data company,” Green says. “When you’re talking about litigation analytics, you’re really taking about analyzing dockets and caselaw. Bloomberg Law is in the enviable position of having access to both of those data sets.” Green says Litigation Analytics sprouted out of Bloomberg LP’s company database, which has access to information from over 70,000 public companies and 3.5 million private companies. “We can see which law firms represent which companies,” Green says. “What’s really new here is that we now have the ability to search by judge.”

According to a Bloomberg Law press release, the Litigation Analytics tool covers four analytical areas: motion outcomes, appeal outcomes, average length of time until resolution and case types. The tool also keeps track of which lawyers appear most often before specific judges.

For instance, Bloomberg Law conducted a sample analysis (PDF) of five high-profile federal judges in the U.S. Based on the data the Litigation Analytics tool examined, Bloomberg Law determined that, among other things, Jesse Furman. U.S. District Judge for the Southern District of New York granted motions to dismiss in full 60 percent of the time—nearly twice as often as Judge Amy St. Eve for the Northern District of Illinois, while Judge Jack Weinstein for the Eastern District of New York denied motions to dismiss in full at 41.9 percent—a higher rate than the other four (the other judges analyzed were Judge Leonard Stark for the District of Delaware and Judge Susan Illston for the Northern District of California).

The tool, which covers all active federal district court judges going back to 2007, is fully integrated with Bloomberg Law’s legal research capabilities—a feature that, according to Bob Ambrogi at LawSites, sets it apart from its competitors, including Lex Machina, Premonition, Ravel Law and Judicial Perspectives from ALM.

Meanwhile, Green believes that Bloomberg Law is getting into the field at just the right time.

“I believe we’re at an inflection point right now,” Green says. “Companies and lawyers are primed to start using predictive analytics more and more. Any lawyer will tell you that prior behavior is not a guarantee of future behavior, but it can help make you better informed as you make decisions.”

Green adds that predictive analytics will allow corporate counsel to better frame what their litigation budgets should look like and give lawyers access to information so that they may better evaluate their cases and forecast their costs.

As for what the future holds for Litigation Analytics, Green says that Bloomberg Law is already working on adding features and capabilities.

“We’re already hard at work at normalizing data on individual attorneys—that will, very likely, be the first real enhancement to this that we’ll see in the coming months,” Green says. She also says that Bloomberg Law hopes to add more motion types and date ranges, and are looking at adding bankruptcy and appellate judges. “Overall, we want to help people use this product to better predict their outcomes and how long cases will take, as well as extrapolate costs. We want to create comfort for people as we move into this new frontier of predictive analytics.”

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