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Give Me a Break: How to Leverage Verdict Data to Make Better Settlement Decisions

By Nicole Clark.

“Who hurt you?” shouts a billboard for the Razavi Law Group in Los Angeles. The city is awash with gigantic advertisements in this style, each hawking the services of a local personal injury attorney. Our skyline, visually invaded by a collection of stern-looking men in dark suits, highlights the saturated nature of personal injury law. “[A]t the end of the day,” explains Ardy Pirnia of the Pirnia Law Group, “if you want to generate leads, the only way to do it is to be in everyone’s face.”


There is, however, a curious whiplash that occurs whenever cases move from highway billboards to downtown courtrooms. As the business of personal injury law gives way to its practice, one preference is to keep the legal matter private. That is, to prevent the case from ever having to face a jury. This is particularly true for slips and falls, where blame-the-plaintiff biases can easily run rampant. “Of all the cases we handle as plaintiffs’ personal-injury lawyers,” begins Teresa Johnson, an attorney at Kramer Holcomb Sheik, “none are met with more eye rolls and a ‘give me a break’ than when the judge tells the potential panel ‘this is a slip and fall matter.’”


And yet, something is missing between these two observations about showing face and hiding it. We are left wondering about what happens in the interim of a lawsuit. What is there to know about the events that unfold between the acquisition of a case and its settlement? And, more importantly, how are attorneys leveraging verdict analytics every step of the way to make these litigation decisions?

Impermissibly Speculative

Outcome prediction is an important part of practicing law. Clients expect their attorneys to provide them with accurate assessments of the potential consequences of any major legal decision. These assessments, which typically take place at the beginning of the litigation process, allow clients to strategize how they would like to navigate through a specific legal matter. Over the past decade, a variety of legal analytics platforms have emerged to help attorneys generate these predictions, producing qualitative and quantitative insights into how a case is likely to unfold.


Many of these platforms started by following the logics of conventional research. They provided their users with the tools needed to conduct element-focused analyses. By studying previous motions filed with the court, users were positioned to better understand how likely their action would survive a motion to dismiss or a motion for summary judgment. Take, as an example, Teresa Johnson, the personal injury attorney mentioned above. On January 7, 2017, her client, Jorge Perez, visited Hibachi Buffet for lunch. During his visit, he left his table and walked along a hallway to the restaurant’s restroom. Upon returning to his table, he slipped and fell, fracturing his left patella. Perez subsequently filed a lawsuit against the restaurant, alleging that an employee, making his or her way to the kitchen, had negligently dripped water along the hallway’s tile floor. A single question sat at the center of the lawsuit. How did the floor become wet? According to photographs and witness testimony, the water spill was ten inches long and extended past the restroom and into the kitchen. However, the plaintiff’s allegations that an employee had spilled water while taking dirty dishes to the kitchen for washing was vehemently denied by the restaurant. According to Hibachi Buffet, there was no evidence showing that any of its employees spilled liquid on the floor. The plaintiff’s story was, according to the defense, impermissibly speculative.


With access to an easily searchable database of prior decisional law, attorneys can anticipate the processes their judge would need to follow in his or her assessment of a claim for impermissible speculation, breaking down a cause of action into its constituent elements and determining how the known facts of a case would need to be applied to each element. With Trellis, for example, an attorney can use Smart Search to uncover tentative rulings on the topic of “impermissible speculation.” She would quickly learn that, in the past, what distinguishes permissible inference from bald speculation is the existence, in the record, of a single fact that could support the inference. In other words, decisional law makes clear that what is important is that “an evidentiary leap is tethered to the evidentiary record” (Siewe v. Gonzales, 480 F.3d 160, 169). Our attorney now knows what she will need to highlight in order to affirm her case. But what should happen next? Calling Bluffs

While archives of decisional law are crucial, they don’t necessarily provide a lot of decision-making guidance, especially when it comes to formulating litigation strategies. Decisional analysis can tell us little about how a jury will respond to a specific type of action or a specific type of argument. Recognizing this limitation, legal analytics platforms have started integrating verdict data into their systems, amending their archives of case law, legal petitions, and judicial rulings to also include information related to outcomes and settlement awards, particularly for cases where judicial officers never issue formal opinions.


As one of the top causes of unintentional injuries, slips and falls can result in astronomical expenses, ranging from medical bills to lost wages. But every slip and fall case is unique. Some will settle at the onset of litigation. Some will make it all the way to trial. And others will settle days—maybe even hours—before trial begins. With access to verdict data, attorneys can begin to anticipate the trajectories of different settlement strategies, identifying the range of possible outcomes for each decision.


I want to return to the Hibachi Buffet, this time with verdict analytics. At the beginning of his lawsuit, Perez demanded $499,999 from the owners of the restaurant. Let’s imagine, for a moment, that I’m defending Hibachi Buffet in this slip-and-fall case. How should I advise the restaurant? I might start by pulling verdict data for a randomized sample of cases filed by my opposing counsel. On Trellis, each of these entries contains a description of the case, the initial demands and offers presented, the verdict type, the jury vote, the jury composition, and the final outcome. It only takes a few minutes to sift through this data, which tells me that two-thirds of the cases in my sample resulted in jury verdicts. I now know that my opposing counsel has few qualms about taking a case all the way to trial, spending whatever it might cost to pursue an action in court. I can also see that all of the juries in this sample found in favor of the plaintiff when represented by my opposing counsel. With this information, I can begin to better assess the advantages of pushing my client to increase its settlement offer. After all is said and done, it might not be worthwhile to roll the dice on a jury with this particular opponent. One month before trial, Hibachi Buffet presented Perez with a settlement offer of $250,001. There is a world of information embedded within this event. But what does it really tell us? What can we learn by tracking this information? At a general level, verdict analytics help attorneys identify the monetary amounts at stake in the settlement process. The settlement of personal injury cases can get stalled by monetary demands that far exceed the reasonable value of a case. However, by comparing verdict data from similar cases, we can quickly get a feel for the amounts different types of parties have been willing to accept in order to resolve different types of matters. At a more specific level, we can also look into the timing of these settlement offers. Is there something telling about the defendant’s last minute effort to settle the case out of court? Does the amount of the settlement offer index anything about the defendant’s confidence level before trial?


A Jury Box Verdict

 On October 7, 2019, a jury for the Los Angeles County Superior Court found in favor of Jorge Perez. Concluding that Hibachi Buffet was negligent in the maintenance of its premises, all twelve jurors agreed to award the plaintiff $850,000 in damages. The amount, although less than the restaurant’s $1 million insurance policy limit, far exceeded the $250,001 settlement presented to Perez. This case study is a reminder that while jury verdicts are few and far between, they are incredibly important. Each year, juries at the state and federal level decide the outcomes of billions of dollars. They also set the standards that influence future legal behavior, as jury verdicts determine the value of legal disputes in ways that can influence the choices of future plaintiffs, defendants, and their attorneys. Even though we may never know with any degree of absolute certainty how a case will unfold in front of a jury box, verdict analytics has brought a new level of transparency to the risks and rewards of the settlement process, the details of which would otherwise have remained hidden from the public record.


About the Author Nicole Clark is CEO and co-founder of Trellis.

Business litigation and labor and employment attorney.


Trellis is an AI-powered legal research and analytics platform that gives state court litigators acompetitive advantage by making trial court rulings searchable, and providing insights into the

patterns and tendencies of your opposing counsel, and your state court judges.


To search verdicts on Trellis, visit today.


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