LOADING

Type to search

Article Featured North America

Data-Driven Litigation Strategies: The value of legal analytics on your opposing counsel

Share

Approximately 41,000 civil lawsuits are filed each day in the United States. Ninety-seven percent of these cases circulate at the circuit and the county level. This represents a veritable goldmine of information about the litigation process. There is just one small problem. Very few states have integrated court systems, which means this information rarely emerges from one place. 

Each county authors its own procedures for collecting, cataloging, and publishing court documents. A clerk working in a courthouse somewhere in the middle of Georgia posts schedules on their website in WordPerfect, a software format rarely installed on most computers. Across the country, in a county nestled near San Francisco, the Superior Court of California publishes tentative rulings in PDF files every day. These files are organized by judge—one file for each judge. The files seem to disappear just as quickly as they appear.  

This is a treasure trove of information, but the formats are clunky and the interfaces are cumbersome. If an attorney wanted to sort through this information, they would have to construct their own databases, hand-coding each entry one-by-one. This would allow them to sort through different variables, whether it be the parties involved, the topics considered, or the motions filed. The process would be “super slow—to the point where you wouldn’t even do it,” says Huong Nguyen, an intellectual property attorney. 

This is the world in which AI-powered legal analytics adds value. Legal tech platforms like Trellis, Ravel Law, and Premonition are revolutionizing the ways attorneys conduct legal research. Trellis, for example, uses artificial intelligence to mine state court records, aggregating data in ways that allow users to search through—and collect information about—virtually any variable of their choosing. Typically, attorneys use these resources to uncover judicial tendencies by studying tentative rulings. “[C]ertain judges are re-using the same language over and over again, or according themselves to patterns, like focusing on the third factor in a four-factor test,” explains Daniel Lewis, the co-founder and CEO of Ravel Law. 

However, what is, perhaps, less well known is the fact that the same type of analysis can be performed on opposing counsel. Does your opposing counsel tend to lose motions because of the circumstances of a particular case? Or, does your opponent have a tendency to miss procedural deadlines? Does she or he tend to advance irrelevant arguments? What are the sources of case law towards which they tend to turn? For example, do they litigate trade secret cases as intellectual property matters or do they prefer to treat them as a contract disputes? By answering these questions, an attorney can learn to think like their opponent. 

Curating historical information about individual attorneys and law firms is a particularly daunting task. The difficulties are compounded by the fact that attorneys move from one law firm to another. Law firms add and delete named partners. As such, it can be hard to track legal entities across any given dataset. Luckily, Trellis has found ways to manage these challenges at the state trial court level, offering a service that allows attorneys to learn the ins-and-outs of the counsels involved in each case through a Google-like search algorithm. Attorneys can study how their opponents draft their motions and shape their case timelines. Similarly, Lex Machina’s Law Firms Comparator application enables a user to perform side-by-side comparisons of different law firms, displaying a range of case-specific data about win-rates, case timing, and damages history that can be crucial in determining whether or not to settle out-of-court.

Kirk C. Jenkins, chair of the Appellate Task Force at Sedwick, uses AI-powered legal analytics to study his opponents. “Scanning your opponent’s filings in cases in other jurisdictions can sometimes reveal useful admissions or contradictory positions,” he explains. “If your case is a putative class action, these searches can help determine at the earliest moment whether the named plaintiff has filed other actions, perhaps against other members of your client’s industry.” Did these earlier actions end in trials, settlements, or dismissals? When combined, all of this information can be used to give an attorney an idea about how aggressively their opponent will litigate a case. 

Jenkins uses these same tools to study his opposing counsel and the sitting judge on his matters. Have they frequently appeared in front of a particular judge? How experienced are they in different areas of the law? Do they tend to win more often than comparable law firms? All of these questions can be answered with legal analytics.

Even in family matters, where attorneys are less concerned about win rates than they are about case durations, such analysis can help an attorney set timelines and client expectations for pending litigation. For instance, Toby Unwin, the co-founder of Premonition, has helped clients identify how long divorce cases typically last in specific jurisdictions, curating lists of attorneys that consistently have high case durations. This is the kind of information that is critical in the development of case strategy. 

With each day that passes, the data behind legal analytics continue to grow. This information is no longer buried inside courthouse archives, stuck behind county clerks and cumbersome filing procedures. Because of legal tech innovators, it is now accessible to anyone with a computer and an Internet connection. The types of strategic insights enclosed in these datasets feel infinite, limited only by the types of questions we might think to ask. The answers to your questions about opposing counsel are there. What do you want to know? 

________

Nicole Clark is a business litigation and labor and employment attorney and the CEO and co-founder of Trellis Research. 

Photo by Lukas from Pexels

Tags: