The billable hour has plenty of enemies, but Wendy Rubas wasn't always among them.

As an associate at McDermott Will & Emery's Chicago office from 1999 to 2004, she felt that, even if it was a taskmaster, it served another purpose—a measure of her value to the firm.

Even before there was a billable hour target to hit, Rubas' grades in law school served as a barometer of her hard work. Moving her way through the great sorting of law school and Big Law associate life, there was always a reliable metric to tell her where she stood.

Of all the changes that would occur when Rubas moved in-house to a Chicago-based health-care system in 2004, perhaps none was as destabilizing as losing a cut-and-dry number to prove her worth. In business meetings at her hospital, doctors discussed how many lives they had saved and businesspeople showed how much spending they had cut. Winning a summary judgment didn't seem to stack up.

“I didn't have any goal that is expected and nothing to say I'm meeting it,” Rubas said. “It's not defined. So I started to just define it.”

Rubas is now the general counsel of VillageMD, a tech-focused company helping primary care physicians transition to a new value-based payment system. In her nearly 15 years as an in-house lawyer, she has gone further than most toward capturing and modeling data that shows her clients the costs associated with legal events.

Wendy Rubas, general counsel of VillageMD.

Through automated reports, she has wound up tracking the relative value of legal work. And that has taken her to an even more unexpected place: changing the way she thinks about what lawyers should be doing. For instance, she's not all that interested in spending time negotiating software licenses or nondisclosure agreements. They rarely are “a cause of loss,” she says.

“What would happen if you signed a terrible NDA?” she asks, before whispering the answer: “Probably nothing. So what are we doing all this negotiating for? That's the behavior change that has been the most radical for me.”

Rubas says, in order for this idea to proliferate through the legal market, there needs to be a common language for legal work and outcomes that can be shared across industries and law firms to provide a detailed, comparable understanding of what is happening in legal departments and law firms across the country. That would help the industry know what it should be spending its time on, rather than finding places it where it can spend it.

This idea is separately picking up steam in Big Law firms, corporate legal departments and technology companies. Creating a data-tracking system that could measure legal events and outcomes—in addition to the costs associated with doing legal work—could have radical consequences for the industry. The long-term impact of a legal market that competes on the quality of outcomes rather than quantity of effort could be an upending of the traditional metrics that lawyers like Rubas have long relied on to validate their places in the legal ecosystem: namely, law school and law firm brand names. When clients can judge outcomes, performance is more likely to be rewarded than credentials.

But first, a reliable data-capturing system must be put in place, which raises a few questions. What would that system measure—and how? Who would do the measuring? Answers are slowly coming into view. And beyond that horizon, the profession will confront the question that may be at the heart of it all: Will lawyers be willing to overcome professional biases to apply statistics-based decision-making to their practices?

StatCast for Lawyers

One of the most visible examples of how a new data-tracking system can change an industry's self-evaluation comes, strangely enough, from the baseball diamond.

Babe Ruth may have made the home run famous, but it took a century after he first stepped into the batter's box for baseball teams to properly value the home run. Major League Baseball teams hit 6,105 home runs in 2017, setting an all-time record and blowing away the previous high of 5,693 set in 2000, the heart of the steroid era.

There are a number of reasons for this increasingly feast-or-famine approach, including theories that baseballs themselves have been “juiced,” but most anybody in the game will credit the introduction of a slew of new data points and statistics gathered by a system called StatCast that MLB brought online in 2015. StatCast is a series of cameras and sensors in all 30 baseball stadiums that measure the physics of the sport.

As with any data point, these new numbers mean nothing until they're put into context. Blending a mixture of all its measurements, StatCast knows how frequently any batted ball will be caught by a defender. That is a statistic now known, simply, as “catch probability.” It is a finite number that hitters attempt to minimize. The way to do that, the numbers show, is to hit the ball higher and harder. It's not so much a focus on home runs that has resulted in their spike, but rather, in the new language of baseball, a focus on the ball's “exit velocity” off the bat.

Andrew Baker, a senior director at HBR Consulting who has helped law firms and clients like Rubas develop data-capturing systems, says he sees a number of similarities between baseball and the legal profession. He expects there would be similar changes to lawyers' behaviors if there were a better way to measure the impact of their work.

“We're silly to assume that there wouldn't be an insight like [exit velocity] in legal,” Baker says. “You have a guild that has operated in almost exactly the same way for a very long time, and it has done so pretty much exclusively relying on subjective measurements. There are no studies that suggest how we practice law right now is optimal.”

Persuasion Rate

Make no mistake, practicing law is nowhere near as amenable to data analysis as baseball. There are no home runs for lawyers. Even if there were, they would differ from litigation to regulatory to transactional practices by each matter.

But by no means is the law incompatible with data analysis. Physics-tracking cameras will play no role in that analysis, but one technology that will is computers' increasing ability to understand text. That is the technology underlying LexisNexis Context. Launched in late 2018, Context has analyzed rulings from each U.S. district court judge on 100 different types of motions. The analysis can tell lawyers how likely a given judge is to grant or deny a motion for dismissal, as well as how frequently the judge cites any given case in deciding that motion.

Judge William Alsup of the U.S. District Court for the Northern District of California, for instance, has ruled on 864 motions to dismiss, granting 52 percent and tossing 25 percent of them. In doing so, he is most likely to cite Bell Atlantic v. Twombly (425 appearances), a watershed 2007 U.S. Supreme Court case that raised the bar a plaintiff must meet in order to proceed.

Nik Reed of Ravel Law.

The data gives lawyers insights that they previously could only access by anecdote and through speaking with a judge's clerk, says Nik Reed, co-founder of Ravel Law, which LexisNexis purchased and refitted to create Context.

“If you read 1,000 documents, you're probably not going to notice that one case was cited 50 times,” Reed says. “But that's something that computers are really good at spotting.”

Applying a similar analysis to individual lawyers' briefs could be the context that turns that data into something powerful enough to change purchasing decisions. Matching up lawyers' briefs with judges' decisions could tell clients how frequently a lawyer's case citations are persuasive for any given judge. “Persuasion rate” could be the new defining measure of a litigator's success.

“When the technology is there, when the process is there and when the data is right, it shows that the possibility is ­really limitless,” says Mark Koussa, director of product ­management at LexisNexis.

Lawsuits as Data Points

LegalMation is another company hard at work turning legal documents into data points. Today, the company's software can generate automated responses to lawsuits in as little as two minutes. The product is being used by companies as big as Walmart and law firms including Ogletree, Deakins, Nash, Smoak & Stewart.

But spitting out responses to complaints is just one product that LegalMation's software generates. Its larger ability is to turn lawsuits into data points that will allow for a new level of analysis that James Lee, the company's co-founder, compares to a “digital fingerprint” for a lawsuit. It is called an “entity relationship” report that tracks about 500 sortable data points.

For a personal injury lawsuit, it can determine what body part is involved in an injury, how many times a defendant has been accused of the same wrongful act, whether a substance is said to have caused a slip-and-fall and more.

The software also can decipher aspects of a lawsuit that don't seem as straightforward. For instance, it can tell when a client issued an apology as a result of the alleged behavior. With enough data, it could determine whether apologies are helpful or harmful as it relates to payouts.

Lee says he believes this type of analysis will ultimately change the competitive landscape for law firms, allowing firms and clients alike to anonymize data as a way to create benchmarks in the industry. Clients could make purchasing decisions based on that data.

“That is what this is all about: getting better, more precise information to everybody in the marketplace, so we can all become better lawyers and deliver better outcomes,” Lee says.

A Standard Taxonomy

While efforts to turn legal documents into data could introduce new ways of analyzing legal issues, there also is a broader effort underway to turn the actual work lawyers do into data that is comparable across law firms. But firms may not be tracking the most useful data to best conduct that analysis.

Most of today's legal data pertains to time and billing. That data is often next to useless, many lawyers say, because bad time-keeping habits and the current system of task-based coding is not granular enough to measure what actually happened in a given matter.