Everything You Know About Lawyer Selection is Wrong: Big Data Analyzes Litigation — Moneyball for Law?

emergentfutures:

Lawyer selection is much like the bad old days of baseball player selection, contends Premonition LLC., a Florida based Artificial Intelligence company targeting the legal industry. Premonition claims to know which attorneys usually win before which judges. People pick attorneys based on recommendations from friends, online reviews, because they’re friends, friends of friends, went to a particular law school, have nice offices, work for a renowned firm, an advertisement, they found them in the phone book, etc. Many publications run “Top Lawyer” lists, people who are recognized by their peers as being “the best”. Premonition analyzed the win rates of these attorneys, it turned out most were average. The only way that they stood out was a disproportionate number of appealed and re-opened cases, i.e. they were good at dragging out litigation. They discovered that even the law firms themselves were poor at picking litigators. 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!

If generally accepted methods for choosing a lawyer don’t work, what does? “Win Rate,” says Toby Unwin, inventor and Co-founder of the Premonition system. “The only item that affects the likely outcome of a case is the attorney’s prior win rate,

Moneyball for lawyers: prior win rate is all that matters.

Everything You Know About Lawyer Selection is Wrong: Big Data Analyzes Litigation — Moneyball for Law?

Written by

Founder, Work Futures. Editor, GigaOm. My obsession is the ecology of work, and the anthropology of the future.

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