By Shira Zucker
In the wake of more mass layoffs at top law firms, mounting calls to retire the billable hour, and freezes on associate hiring, an emerging subset of tech startups is attempting to transform the crisis of traditional law firm management into a lucrative business opportunity. These companies market big data services and software products to law firms for the purpose predicting legal outcomes and driving legal strategy, an approach that some commentators have dubbed “Moneyball lawyering.”
The term “Moneyball” references sabermetrics and the use of statistical data to analyze and recruit baseball players, as described in Michael Lewis’s 2003 book by the same name and popularized by a 2011 film adaptation. Billy Bean, maverick General Manager of the small-market Oakland Athletics, became a household name for creatively employing these techniques to compete against larger, more moneyed teams—to remarkable, if not sustained, success. Now several companies are utilizing computer-driven statistical comparisons to predict wins and losses in the context of litigation. Take, for example, Lex Machina, which utilizes legal stats to assess and predict patent litigation outcomes by party, venue, and judge. Parties could use this information to inform crucial tactical decisions, such as when to petition for venue transfer. Another company, Picture It Settled, applies similar algorithms to predict negotiation paces and outcomes. Negotiators could use this resource to ensure they are not leaving money on the table. The tools are designed to supplement rather than supplant the instincts of experienced attorneys.
Predictive analytics could help law firms conserve billable hours, reduce emotional decisionmaking, identify more lucrative strategies, and lower monthly usage bills from legal research databases. Still, the “Moneyball” analogy is in many ways an awkward fit.
Google “moneyball for lawyers” and you’ll quickly discover that a wealth of consultants have adopted the catchphrase to dress up run-of-the-mill marketing tactics and peddle them to law firms. Of course, many law firms, like other businesses, already utilize statistical data to improve client satisfaction, identify niche markets, and better promote services.
More to the point, Bean relied upon the “Moneyball” system to compete with big-budget teams that—unlike the Oakland As—were positioned to cherry-pick the most sought-after players. In contrast, Lex Machina sells its stat-crunching services for a premium (at least, for now) and boasts big-fish clients like Wilson Sonsini Goodrich & Rosati; Fenwick & West; and Orrick, Herrington & Sutcliffe. Considering these facts, “Moneyball lawyering” services may do more to strengthen the advantages of big firms over small shops and solos than they do to level the playing field. And, if prices come down and the services become more accessible, competitive potential may suffer. Consider that once the cat was out of the bag about “Moneyball“ as a sports team management strategy, the Oakland As had to compete against teams that employed the same tactics. If law firms begin to rely upon comparable tactics, those employing the tools may see diminishing advantages over competitors. Finally, while winning cases and getting the biggest settlements are high markers of success, they are only components of overall legal value.
Still, “Moneyball lawyering” purveyors may prove to be valuable players in developing the future of law firm business strategy.