Big banks like Goldman Sachs spectacularly failed to predict the World Cup winner — here’s why


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  • Five major banks predicted the outcome of the World Cup in Russia, and all but one got it wrong.
  • Goldman Sachs forecast that Brazil would lift the trophy, while UBS backed Germany.
  • Banks were afflicted by the sheer unpredictability of this year’s tournament, which saw Croatia make it all the way to the final.

The 2018 World Cup, which ended Sunday, was an almost unequivocal success. The soccer was scintillating, there was little trouble away from the pitch, and the controversial VAR replay system largely delivered as promised.

As with any World Cup, numerous players rose to the occasion, with a breakout performance from France’s Kylian Mbappe among the highlights.

Among those that didn’t perform, however, were big financial institutions. Before the tournament, banks including Goldman Sachs, ING, and UBS all took a stab at a forecasting what would happen, generally by applying techniques traditionally used in financial analysis and economic modeling to soccer.

All but one of them — Nomura — got the tournament wrong.

Of the five major banks to forecast a winner before the tournament, two chose Spain, one chose Germany, and one chose Brazil. Goldman Sachs even changed its modeling several times during the tournament and still managed to get it wrong. Only the Japanese lender Nomura managed to correctly predict that France would leave Russia as the world champion.

So what went wrong? To understand where lenders slipped up, it is first important to look at exactly how they tried to make their forecasts and what they got wrong. Each bank is different, so we’ll look at some of them individually.

Goldman Sachs

Goldman Sachs used perhaps the most interesting form of modeling, employing machine learning to run 200,000 models, mining data on team and player attributes, to help forecast specific match scores. The bank then simulated 1 million variations of the tournament to calculate the probability of advancement for each squad.

“We are drawn to machine learning models because they can sift through a large number of possible explanatory variables to produce more accurate forecasts than conventional alternatives,” a group of strategists from Goldman’s international research team wrote in a client note before the tournament.

“We capture the stochastic nature of the tournament carefully using state-of-the-art statistical methods and we consider a lot of information in doing so,” they added.

“England meets Germany in the quarters, where Germany wins; and Germany meets Brazil in the final, and Brazil prevails,” the bank said in its first forecasts, as evidenced by the chart below:

Screen Shot 2018 07 18 at 15.46.09

Screen Shot 2018 07 18 at 15.46.09

Screen Shot 2018 07 18 at 15.46.09

Goldman Sachs

In reality, Germany was eliminated at the group stage, England made it all the way to the semifinals, and Brazil lost to Belgium in the quarterfinals. Just 11 of the teams Goldman expected to make the last 16 progressed from the group stage, only half of the predicted quarterfinalists made it that far, and just one predicted semifinalist got that far.

Goldman even forecast that the eventual runner-up, Croatia, would be eliminated from the tournament at the group stage, exiting at the expense of Iceland.

After the group stage, Goldman updated its predictions but continued to miss the mark, with its final forecast — delivered ahead of the semifinals matches, England versus Croatia and Belgium versus France — saying we’d see a final between England and Belgium. The final, of course, was played between Croatia and France.

Goldman did caveat its failures before the tournament, saying “forecasts remain highly uncertain, even with the fanciest statistical techniques, simply because football is quite an unpredictable game.”

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