Can the legal system be Moneyballed?

A Florida-based AI firm has released some very interesting revelations about lawyers and their rates of prevailing in lawsuits. The firm, Premonition, has determined that lawyers — even those on ‘top lawyer lists’, or considered by peers to be among the best — generally have average results. The company also discovered that lawyers and law firms don’t actually keep track of win/loss data: they mostly track fees and billing proportions. One indicative statistic kind of jumps out as a condemnation of the industry:

In a study of the United Kingdom Court of Appeals, it found a slight negative correlation of -0.1 between win rates and re-hiring rates, i.e. a barrister 20% better than their peers was actually 2% less likely to be re-hired!

The industry is so self blind that it actually penalizes lawyers that perform better.

Toby Unwin, the co-founder of Premonition says that the only thing that correlates with win rate in court cases is a history of winning. ‘The only item that affects the likely outcome of a case is the attorney’s prior win rate, preferably for that case type before that judge,’ he stated in the company’s press release.

The Premonition system is a web crawler that applies AI to scrape court results so that win/loss data for lawyers can be analyzed. This is a function of many factors, such as the relationship between the lawyer and the judge. This is like the example in Moneyball — the story about Billy Beane’s Oakland A’s,  where the team analyzed how batters fared against specific other pitchers, or how frequently they got on base in general — except Premonition is analyzing how lawyers do when in front of specific judges and specific kinds of litigation.

Most interesting is that Premonition states that they have found no correlation between win rate and billing rate. The best results appear to come from small firms and soloists that they call ‘strip mall superstars’.

I make no claim about this technology, but what I find interesting is that the lack of transparency that makes it basically impossible for the average person or business to make informed judgments about which litigator to hire for a court case. It seems obvious that AI/analytic tools like Premonition should be of immense value, and therefore the marketplace for such tools should lead to a Moneyball like disruption in the field of law. This is also a condemnation for the larger law firms, who aren’t apparently tracking their lawyers in a sensible way relative to what matters to their clients.

 

Come watch MoneyBall tonight with us

You can join us at the premier of Moneyball, featuring Brad Pitt, Philip Seymour Hoffman and Jonah Hill today, Friday, Sept. 23, at the Sundance Kabuki theater on Fillmore Street in San Francisco. There are 30 tickets reserved for GigaOM readers. Time to play ball!

Predicting the Unpredictable

yummy_wineAfter graduating from college, I left the barren Arizona desert for Manhattan to take my first job. It didn’t take long for my new Manhattanite friends to inform me that it was time to upgrade to wine from beer, so I enrolled in a wine-tasting class. But while it was great fun, I don’t think that I was any better at assessing the quality of wine after I’d completed the class than I was going in, though I was much better at faking it.
It wasn’t until years later that I discovered the secret, and it came via a Princeton economist. Understanding the fact that wine is an agricultural product, and as such is dramatically affected by weather, Orley Ashenfelter used decades of weather data and auction prices to come up with this equation for Bordeaux wines:

“Wine quality = 12.145 + 0.00117 winter rainfall + 0.0614 average growing season temperature — 0.00386 harvest rainfall”

Assessing wine is considered an art rather than a science, but oftentimes creativity is about applying a little science to art — as Orley did by taking into account weather and auction data. In an effort to inspire entrepreneurs to also turn ill-practiced art into science, below I share a few other examples. Read More about Predicting the Unpredictable