Civil Litigation Data
Who's suing whom, for how much, and how it settled -- the dataset every litigation funder wants.
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Find Me This Data →Overview
What Is Civil Litigation Data?
Civil litigation data encompasses court filings, verdicts, settlements, and case outcomes that track who is suing whom, for what amounts, and how disputes resolve. This dataset is compiled from court records across state and federal jurisdictions, spanning tort cases, contract disputes, personal injury claims, and commercial litigation. The data includes jury awards, settlement figures, case timelines, judicial decisions on motions, and appeal outcomes—essential intelligence for litigation funders, law firms, and legal outcome prediction tools. Historically, comprehensive litigation datasets have been limited; the longest assembled time series spans 40 years of tort verdicts from California and Illinois counties, revealing trends in damage awards and case resolution patterns. Access to bulk court data remains fragmented: the Big Three legal research vendors (LexisNexis, Westlaw, Bloomberg Law) jealously guard private repositories with high user fees and restrictive licensing, while state and federal courts do not provide bulk data freely or uniformly.
Market Data
40 years
Longest Civil Verdict Time Series
Source: RAND Institute for Civil Justice
Bulk court data not widely available from U.S. state or federal courts
Data Coverage Gap for Outcome Prediction
Source: Cambridge University Press
Answers for same judge motion opinions ranged 9–32 depending on legal research product
Judge Opinion Disagreement Across Platforms
Source: Cambridge University Press
Who Uses This Data
What AI models do with it.do with it.
Litigation Funders
Assess settlement and award patterns to evaluate risk and ROI on case funding decisions, identify high-probability disputes worth financing.
Law Firms & Defense Counsel
Use litigation outcome prediction tools to evaluate case strategy, estimate settlement ranges, and guide client negotiations based on comparable verdicts and judicial patterns.
Legal Analytics & Prediction Tool Developers
Build machine learning models for outcome prediction, case evaluation systems, and trial strategy tools that require historical verdict and settlement data for model training.
Insurance Carriers
Analyze settlement trends and damages to underwrite litigation risk, reserve accurately, and guide defense strategy in high-risk cases.
What Can You Earn?
What it's worth.worth.
Court Record Licensing (Bulk Historical Data)
Varies
Big Three vendors charge high user fees for access to private court document repositories; pricing not disclosed publicly but tied to firm size and jurisdiction scope.
Litigation Outcome Prediction Tools
Varies
Proprietary tools used by large firms (e.g., Morgan & Morgan, Dentons) are internally developed; commercial offerings charge based on case volume and feature access.
eDiscovery & Expert Witness Support
Varies
Forensic data analysis, metadata authentication, and expert declarations charged on engagement basis for high-risk civil litigation support.
What Buyers Expect
What makes it valuable.valuable.
Data Accuracy & Consistency
Buyers demand standardized court records with validated judgment amounts, settlement figures, and case outcomes. Significant disagreement between legal research platforms on basic facts (e.g., judge motion counts) undermines confidence in predictions.
Comprehensive Coverage Across Jurisdictions
Data must span multiple state and federal courts with consistent reporting periods. Lower court civil filings are essential for capturing mass litigation (eviction, benefits appeals) where case volumes are highest but data quality is lowest.
Temporal Depth & Longitudinal Continuity
Predictive models require long time series to identify trends in damages, settlement rates, and judicial behavior. Short datasets make it difficult to distinguish real patterns from year-to-year variability in awards.
Rich Metadata & Case Context
Buyers need detailed case attributes: practice area, judge identity, claim type, defendant profile, and procedural history. Without these, outcome predictions lack explanatory power and strategic utility.
Companies Active Here
Who's buying.buying.
Developed proprietary 'Google-style' internal analytics to evaluate actionable data points from personal injury settlements and court proceedings at scale.
Spun off independent analytics lab and venture firm to fund development in litigation outcome prediction and AI-enabled legal tools.
Maintain private repositories of court documents and judicial decisions; guard data with high user fees and restrictive licensing to serve outcome prediction and litigation support products.
Commercial tool that predicts case outcome and settlement probability using litigation data analytics.
FAQ
Common questions.questions.
Why is civil litigation data hard to access?
Bulk court data is not widely or freely available from U.S. state or federal courts. The Big Three legal research vendors have invested heavily in compiling private troves of court documents and decisions, and they guard these resources with high user fees, restrictive terms, and threatened litigation to prevent data sharing. This creates a significant barrier for smaller firms and access-to-justice initiatives.
What makes litigation data reliable for outcome prediction?
Reliable outcome prediction requires abundant historical data, stable legal rules, and high case volumes. However, even the Big Three suffer from data problems: a recent study found that answers about the same judge's motion opinions ranged from 9 to 32 depending on which legal research product was used. This disagreement on basic facts is an existential problem for prediction accuracy.
How far back does civil litigation data go?
The longest assembled time series of civil jury verdicts spans 40 years, compiled by the RAND Institute for Civil Justice from tort cases in San Francisco County, CA and Cook County, IL. This depth is rare; most jurisdictions lack conveniently available civil case filing data before 2005, making long-term trend analysis difficult.
Who are the main buyers of this data?
Litigation funders, personal injury law firms, defense counsel, insurance carriers, and legal tech developers are the primary users. Large firms like Morgan & Morgan and Dentons have built proprietary internal analytics systems to extract actionable insights from settlement and verdict patterns, while smaller firms and legal aid organizations struggle to access comprehensive data.
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