Litigation Analytics

Litigation, or judicial, analytics applies data analytics to litigation and docket data, such as judges' rulings on motions and appeals, settlement amounts, case duration, and the track records of opposing counsel and parties, to inform litigation strategy. Typical uses include assessing how likely a particular judge is to grant a given type of motion, anticipating whether and for how much a case is likely to settle, and evaluating an opposing firm's or attorney's litigation history before a matter proceeds. The underlying docket data is generally processed using a combination of machine learning and natural language processing, and results can vary meaningfully between platforms depending on coverage, how missing or mislabeled docket entries are handled, and whether a tool searches full filing text or only docket entry titles. Business development and recruiting teams also use these tools, comparing a firm's litigation footprint against competitors or assessing a lateral hire's client relationships. Generative AI is beginning to extend this category from static reports toward synthesized, multi-step analysis, for example cross-referencing a judge's recent rulings with an opposing counsel's motion history to draft a strategy memo for attorney review.