I used to be a litigator at a large Los Angeles law firm, and the moment one of my cases was assigned to a judge, I did the first thing that most litigators do — I reflected on whether I had ever appeared before that judge and, if so, tried to recall whatever takeaways, sometimes hazy, I could muster. If I was completely unfamiliar with a judge, I usually called or emailed a colleague in a hunt for anecdotal information that might help prepare me for litigating a case before that judge. Sound inefficient (and insufficient)? Well, it is. And it is precisely why I created Gavelytics, a research tool designed to significantly reduce guesswork and empower litigators to base their strategic decisions on hard data.
Litigators loathe uncertainty. As do I. Which is why I hired a team of software engineers, attorneys and data scientists to answer the question: how can I acquire enough information about my judge’s past behavior to effectively evaluate how she might rule on critical issues in my case? Gavelytics is the result of this rigorous inquiry. Offering a detailed analytics database on California civil trial judges, Gavelytics helps attorneys build their litigation strategies around statistical analyses of judicial tendencies.
Imagine if litigators could anticipate how a judge would rule on a pivotal motion. Or make a data-supported decision regarding whether to disqualify a judge at the outset of litigation. Or adjust settlement posture based on the judge’s tendencies in bench trials or on summary judgment motions. With Gavelytics, litigators can accomplish this and much more. To be of any value to litigators, judicial analytics data must enable tactical or strategic decisions that litigators would be unable to make absent such data. Such “actionable” judicial analytics data help litigators: