Addressing Information Asymmetry in Legal Disputes through Data-Driven Law Firm Rankings

Abstract

Legal disputes are on the rise, contributing to growing litigation costs. Parties in these disputes must select a law firm to represent them, however, public rankings of law firms are based on reputation and, we find, have little correlation with actual litigation outcomes, giving parties with more experience and inside knowledge an advantage. To enable litigants to make informed decisions, we present a novel dataset of 310,876 U.S. civil lawsuits and we apply an algorithm that generalizes the Bradley-Terry model to assess law firm effectiveness. We find that our outcome-based ranking system better accounts for future performance than traditional reputation-based rankings, which often fail to reflect future legal performance. Moreover, this predictability decays to zero as the number of interactions between law firms increases, providing new evidence to the long-standing debate about whether litigation win rates approach 50\% as information asymmetry diminishes. By prioritizing empirical results, our approach aims to provide a more equitable assessment of law firm quality, challenging existing prestige-focused metrics, and levels the playing field between litigants.