Could Moneyball data predict the Supreme Court decision in Miller?

As the 11 Supreme Court justices deliberate on Miller this yuletide, many legal experts and political pundits will try to second-guess the decision. But what if judicial data could provide some insight before the judgment is delivered?

2016 has certainly thrown up many shocks and while a majority of the legal commentariat predict another heavy defeat for the UK government, there are unsubstantiated press reports specifically predicting that the Supreme Court’s justices are ‘heading for split 7-4 decision’ in favour of Gina Miller.

Across the Atlantic our American cousins are increasingly analysing millions of court rulings to more accurately predict how judges might find on future cases, rather than being forced to rely on unnamed ‘government sources’. For example, a recent article in the Wall Street Journal explained how, based on a judge’s reputation, new tools offer lawyers statistics on the likelihood of their case being dismissed.

Companies such as Ravel Law are at the forefront of developing such soothsaying tech and use machine learning to spot connections between cases and patterns in how judges rule. While using the system, litigator Eric Olson learned how one particular judge ‘made it clear she does not like sports analogies’.

Kirk Jenkins, a partner at international firm Sedgwick, also found that if judges at the Illinois Supreme Court ask you more questions than your opponent, ‘your odds drop like a rock’.

So, does the grilling of James Eadie QC by Lord Wilson, as to whether Britain could leave the EU by royal prerogative, indicate a knockout blow to the government? Or could the justices’ warm exchange with Lord Pannick QC about the pronunciation of De Keyser be lulling many into a false sense of security?