Moneyball Lawyering

Charles J. Snyder, UCLA Law

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65 Ark. L. Rev. --- (2013)

Abstract

Michael Lewis’s now-famous book Moneyball seeks to answer “an innocent question: how did one of the poorest teams in baseball, the Oakland Athletics, win so many games?” Throughout the book, readers come to see that Billy Beane and the A’s did it largely by ignoring or rejecting baseball’s conventional wisdom, relying instead on statistical measures of performance and value.

This data-driven approach illustrates what psychologists and behavioral economists have been reporting in recent decades: while most of our judgments and actions are appropriate most of the time, our decision-making is systematically sub-optimal in particular circumstances. We naturally struggle with complex decisions, generalize from unrepresentative datasets, and are especially bad at assigning probabilities and forecasting future events. And if that weren’t distressing enough, what’s worse is that most of us are ignorant to our own cognitive deficits, exhibiting unfounded confidence in our judgments.

Beane and the A’s avoided many of these pitfalls by removing or mediating human influence in their decision-making process, relying instead on formulas populated with good data. This paper argues that litigators, who like GMs are barraged with “conventional wisdom” and exhibit systematic cognitive biases, could benefit from an objective, data-driven approach to decision-making similar to Beane’s. Specifically, while recognizing that neither is a panacea, this paper argues that a modified regression or Bayesian approach to strategic decision-making in litigation could reduce the impact of cognitive biases, thereby producing better results. The paper also identifies ways in which lawyers can mine available data to improve performance. In the end, as it was for the A’s, this paper is about finding better ways to win.