|The Devil’s Advocate™|
From time to time our more creovative™ thought leaders may fly excitable kites in the theory that law firms are using big data to pursue “Moneyball-style” strategies on their lawyers and during recruitment to gain insights about potential lawyer performance.
(1) Emerging baseball players are experienced playing actual baseball, with years of public data about the minutiae of their performance. Law grads haven’t advised on deals or prosecuted cases. They have taken exams, and what data there is, is de minimis: grades.
(2) Lateral hires do have experience, but there is no publicly available data about their performance. Their employers may have lots of data in the form of time records, but they don’t share it.
(3) The nature of data for baseball are homogeneous, bounded, complete and suitable for statistical analysis. Baseball is a zero-sum, complete, defined game. Players have defined roles. You can model and correlate player inputs to game outcomes.
(4) You can't do that for legal practice. Every “game” is a complex adaptive system. There is no guaranteed outcome. There may be no outcome. The outcome may be completely unexpected.
(5) The “Moneyball strategy” was to mine that performance data to extract *counterintuitive* insights — that, for example, your “homer” rate, or foot-speed, is far less important than your unglamorous “get to first base” rate.
(6) But what even are the performance data that law firms would be looking at to run a “Moneyball strategy”? What are the game outcomes, and what performance inputs can be correlated with them? Number of 6 minute units put down to conference calls? Drafting security waterfalls?
What law firms really care about recoverable time: the proportion of a fee-earner’s chargeable time they actually manage to charge clients with. This is not new — law firms have always done this — and it is not a “Moneyball strategy”.
One hardly needs sophisticated data analytics to conclude “the top university graduates don’t make the best private practice lawyers”. Given how different the disciplines are, it should require data analysis to conclude anything else.