Most trial lawyers go into closing arguments with a number in their head and a hope in their chest. Sean Claggett, founder of Claggett & Sykes Trial Lawyers, goes in with a forecast.
Earlier this year, Claggett tried a wrongful death case in Provo, Utah, a jurisdiction widely considered inhospitable to large plaintiff verdicts. Every attorney he talked to said the same thing: you can't get a big verdict there. Claggett's data put the top of the realistic range around $78 million. The jury came back at $81 million.
That kind of precision was the product of a methodology Claggett and his co-authors John and Alicia Campbell have spent a decade building, testing, and refining, documented in their book JuryBall: The Big Data Revolution Is Here. They call it big data litigation, a way to know what a case is worth before trial, and Claggett says it's starting to show up behind most of the largest verdicts in the country.
Plaintiff attorneys make two high-stakes decisions on every case: what it's worth, and whether to try it. Most make those calls on experience, intuition, and the conventional wisdom of their jurisdiction.
The problem, Claggett argues, is that experience alone produces a sample size too small to be reliable. A lawyer who has tried fifty cases thinks they know what a case is worth, but fifty verdicts can't separate pattern from noise. You need hundreds before the flukes cancel out and the real number emerges.
Ask for too much and the jury punishes the overreach; ask for too little and you may win but leave your client short. Either error costs the client money, and the attorney rarely realizes it happened.
Claggett's answer is to widen the sample. A big data study takes a real case and runs it past hundreds of online participants who act as stand-in jurors: they review the evidence from both sides and deliver verdicts. Those verdicts cluster into a statistically grounded range for what a live jury would award, replacing a gut estimate with a number the data supports.
A big data study begins long before participants ever see the case materials. The team writes the strongest possible version of both sides. The defense statement carries every argument that would hurt the plaintiff at trial, presented at its most compelling. The statement incorporates videos, animations, and medical records.
The goal is to give the defense their "best day" in the simulation, because a weak defense statement produces a fantasy instead of a forecast.
The team then recruits participants online and sends them through the full presentation. Validity checks confirm that real people, not bots, are answering. After reviewing the materials, participants answer structured questions about liability and damages.
A standard big data study runs 300 to 700 participants. John and Alicia Campbell run the research operation, with analytical support from Cornell professors.
The 60-to-70-page report includes demographic breakdowns and word clouds showing how plaintiff and defense jurors actually describe each party. In a JuryBall CLE session, Claggett walked through one study where, in a case involving an eleven-year-old killed in a crosswalk, defense jurors described the child as "careless" and "reckless," while plaintiff jurors called the same child "kind" and a "victim." That's voir dire intelligence you can't get any other way.
In Espana v. Republic Services, Claggett's team tested three damages asks ($25 million, $50 million, and $75 million) and found win rates nearly identical across all three: 69%, 72%, 70%. Then they tested one variable: whether to include an element where the girl had promised her family she'd buy her mom a house when she grew up. Removing that ask at $75 million jumped the win rate from 70% to 77% and the expected average verdict from $29 million to $38 million. The actual verdict: $38.6 million.
The story was sound. It just wasn't the right ask for that jury. The number has to match the harm, and the data tells you where the match is.
Once the responses are collected, the first step is removing the noise at the extremes. In every panel, there are people whose answers reflect ideology rather than evidence, who would give everything or nothing regardless of the facts. They won't survive voir dire in a real courtroom, so they don't belong in the data. The methodology filters them out, and what remains are the jurors you'll actually face: people whose decisions track the strength of the case in front of them.
Four numbers matter:
In every trial Claggett has tried with this method, the verdict has never landed below the 25th percentile, occasionally coming in just above the 75th. In 95% of cases, it falls between the midpoint and the average. His benchmark: a win rate above 70% is a case worth taking to verdict; at 72%, he considers the defense position untenable.
From there, Claggett aims for what he calls the "Goldilocks Zone": the ask that creates the highest win rate and the best financial result for the plaintiff. It's the number the evidence supports and the data validates. The Espana numbers above are the Goldilocks Zone in practice.
For settlement, the target is different: Claggett aims between the median and the mean, what a fair result would look like if the case were tried a hundred times. Best day is for the courtroom; fair value is for the table.
One finding surprises most attorneys: geography matters far less than they assume. The conventional wisdom says certain venues are simply hostile to large verdicts, but Claggett's nationally randomized studies predict outcomes across widely varying jurisdictions, and recent verdicts in rural North Carolina and South Carolina, including a record-breaking $101 million, bear that out. People's worldviews, he argues, are now shaped by the same information environment whether they live in rural Iowa or New York City.
Case selection. Knowing the likely verdict range before filing changes the calculus on which cases are worth pursuing and which settlement offers deserve serious consideration. Claggett now passes on cases that once might have seemed worth taking, because he has a clearer picture of what they're actually worth.
Trial preparation. Once the big data study establishes the range, live focus groups (typically three before a major trial) test the opening statement, voir dire questions, order of proof, and even visual presentation details. The data tells you what your best day looks like; the focus groups tell you whether you're delivering it.
Negotiation. Walking into mediation with a data-backed number changes the dynamic entirely. When the Provo defense opened at $1 million against a case the data valued at $78 million, Claggett declined and walked.
Claggett and the Campbells have run the method across more than 1,500 studies and hundreds of thousands of participants. Every trial Claggett has tried using it has landed within the range the data predicted, never below the floor.
For plaintiff attorneys who want to understand the methodology in depth, Claggett and the Campbells teach it annually at JuryBall Vegas and in Madrid, Spain. Their book JuryBall: The Big Data Revolution Is Here covers the foundational research. A second book, The Evolution of Data, is currently in progress.
The value of a case is knowable. Guessing at it costs clients money they were owed.
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Adam Ramirez, Managing Editor of The Tort Report, previously edited and covered law and business at Bloomberg Law, Forbes, and Thomson Reuters.
