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Using Analytics and Predictive Modeling In Legal
For decades, in-house practitioners have chosen outside litigation counsel through instinct, personal networks and anecdotal experience, often defaulting to a familiar name, a trusted former colleague, or a firm remembered for a handful of “big wins.” More recently, some have attempted to bring data into the process, but the data most often used has been backward-looking: win–loss records, billing histories, or motion grant rates. While useful, those metrics rarely correlate with future performance in the unique circumstances of a new matter. Today, however, forward-looking general counsel are beginning to adopt the same kind of predictive analytics that have already reshaped other high-stakes industries.
Professional baseball and financial services may not appear to share obvious markers of success, yet in both, modern achievement hinges on the ability to detect patterns and correlations hidden within massive stores of data. Michael Lewis’ “Moneyball” popularized this idea more than 20 years ago, revealing how moving beyond surface-level statistics to predictive models of future performance gave early adopters a decisive competitive edge. What began in baseball as a fringe experiment is now firmly embedded in the front offices of every major professional team. Finance has undergone a similar transformation. For decades, portfolio managers relied on past returns, anecdotal signals, and accumulated experience — much as many lawyers still do today. Now, forward-looking forecasts built from thousands of variables consistently outperform traditional methods, uncovering risks and opportunities invisible to even the most seasoned professionals. The lesson across both industries is clear: Once predictive analytics demonstrate their value, they evolve quickly from disruptive innovation to indispensable infrastructure. Litigation now stands on the cusp of the same transformation.
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