Big data couldn't get the World Cup results right

Goldman Sachs' statistical model for the World Cup sounded impressive: The investment bank mined data about the teams and individual players, used artificial intelligence to predict the factors that might affect game scores and simulated 1 million possible evolutions of the tournament. The model was updated as the games unfolded, and it was wrong again and again. It certainly didn't predict the final between France and Croatia. The failure to accurately predict the outcome of soccer games is a good opportunity to laugh at the hubris of elite bankers, who use similar complex models for investment decisions. Tom Pair, founder of the Upper Left Opportunities Fund, a hedge fund, tweeted recently: "Of course, past data don't always predict the future; Goldman Sachs never tells clients to make decisions solely on the basis of its models' findings. And in any case, the model only generated probabilities of winning a game and advancing, and no team was given more than an 18.5 per cent chance of winning the World Cup. The moral of the story is probably that buzz-generating technologies such as big data and AI don't necessarily make statistical forecasting more accurate."