Moneyballing Litigation: Parallels between GCs and GMs

Updated: 2 days ago

As Michael Lewis of Moneyball fame once noted: “One absolutely cannot tell, by watching, the difference between a .300 hitter and a .275 hitter. The difference is one hit every two weeks.”[i] His point is both simple and profound: initial impressions can be deceiving, and if you want to draft players that are going to perform better than the opposition, you’re going to have to do some math.

Looking back over the last 20 years, I’ve concluded that managing litigation as a GC was a lot like being a GM in baseball. Both roles require you to pick individuals and teams that perform under pressure and in contests where a lot of money is at stake. Michael Lewis’s point--that you can’t tell the difference between the efficacy of players just by looking at them--resonates with me. It resonates because I know the same is true of both witnesses and lawyers. Appearances can be, and often are, deceiving. Sometimes profoundly so.

Baseball’s history of grappling with data is fascinating. For most of its history, important draft decisions in baseball were made based on superficial factors that trumped objective data. As Mr. Lewis puts it: the game “was an example of how unscientific culture responds, or fails to respond, to the scientific method.” That’s changed a lot over the last 20 years, mostly because a handful of teams with skinny budgets started using that hard data to make key draft decisions. And the decisions they made based on objective data (while inexplicable and counterintuitive to many) enabled them to win against teams with far larger budgets that wielded money as a weapon (albeit a blunt one). Money is the reason why the subtitle to Moneyball is “The Art of Winning an Unfair Game.”

Litigation, as we know, is also an unfair game. Disparities in resources are leveraged to create advantages that have nothing to do with justice and everything to do with economics. Interestingly, like baseball teams 20 years ago, many litigation teams today (and I’m including witnesses and attorneys in my definition of “teams”) are still chosen based on superficial factors. Attorneys with great jazz hands at marquee firms are often hired over less expensive attorneys that are measurably better, at least based on the metrics that matter. Put simply, litigation drafting decisions are often made the same way that Major League Baseball drafting decisions were once made before GMs started scrutinizing the data.

But data exists. In fact, in the context of litigation, there exists a vast trove of unexamined data out there that can be leveraged to make better decisions that yield better results for less money. But (almost) no one is looking at it. Consider the following thought experiment:

You have a big case and important decisions to make, among them, who is going to testify on your behalf as your 30(b)(6) witness. Now, picking a witness can be hard. It’s a decision with consequences, many of them profound. Nevertheless, you’ve identified two potential candidates that have testified on similar topics before. Both appear poised and professional but like those .300 and .275 hitters, you know you can’t tell which has performed better just by looking at them. Still, decisions must be made. Before you make yours, however, someone offers you a sealed envelope containing a single statistic: under oath, one of your witnesses has historically delivered responses that have been, on average, eight words in length while the other has an average of 17 words. Now, here’s the question: before you make your decision, do you want to open that envelope and find out which is which?

Second hypothetical. Your corporate witness is about to be deposed by a seasoned litigator on topics related to damages. How well or how poorly that goes will impact your case, possibly driving up or driving down the cost of settlement significantly. You know you can’t control what that witness will be asked, but you can decide which of two attorneys will defend them. Again, before you make your decision, someone offers you an envelope containing a single data point: one of those lawyers offers objections 29% more frequently than the other. Do you want to see what’s in that envelope?

Final hypothetical. A week before your witness is deposed someone offers you an envelope that contains statistics on the attorney that will question them: 1) the average length of their depositions; 2) the questions they habitually ask witnesses (broken down by witness type); 3) the frequency with which their questions are objected to by defending counsel; and 4) the nature of those objections (e.g., do they misstate the evidence, ask questions eliciting privileged information, etc.). The list goes on. Would that data help you prepare your witness and the defending attorney?

My guess is that if you’re meaningfully involved in litigation decisions, then you have an opinion on these questions. Certainly, I do. And by now, I’m sure you’ve already arrived at my point: the data in those hypothetical envelopes exists. Moreover, you’ve likely had access to it (in the unmined form of depositions) for years. You just haven’t used it because it’s scattered like confetti across cases and firms and attorneys and witnesses and time. The difficulties aside, for folks on both sides of “the v.,” that data contains a treasure trove of actionable insights, which is why my team is busy mining it. And soon, we will start to leverage it in ways that will make some people really nervous.

[i] Michael Lewis, Moneyball, quoting bill James.

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