Goldman Sachs used AI to simulate World Cup outcomes
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Loading...It's a sentiment Goldman Sachs seems to have taken to heart when compiling its forecast for the 2018 FIFA World Cup, which is set to kick off on June 14.
The firm used machine learning to run 200,000 models, mining data on team and individual player attributes, to help forecast specific match scores. Goldman then simulated 1 million possible variations of the tournament in order to calculate the probability of advancement for each squad.
The tournament graph shows how Goldman sees the World Cup unfolding. Note that the numbers next to each nation — which dictate whether they advance over their opponent — represent the predicted, unrounded number of goals scored in each possible iteration of the tournament, based on machine learning results applied to countless scenarios.
Here are the key takeaways from Goldman's data:
Brazil is expected to win its sixth World Cup title, defeating Germany in the final by an unrounded score of 1.70 to 1.41.
While France has better overall odds of lifting the trophy than Germany, its expected meeting with Brazil in the semi-finals has it falling short of the title match.
England is expected to make it to the quarter-final stage, where Goldman says they'll lose to Germany.
Spain and Argentina are forecast to underperform, both losing in the quarter-finals.
Russia isn't expected to make it out of the group stage at all, despite its role as tournament host.
Saudi Arabia will be the surprise team that advances out of the group stage, ahead of host nation Russia.
"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.
With all of that established, football remains a highly unpredictable sport, with many more variables in play than even Goldman could assess. Which is why the authors of the report offer disclaimer at the end.
Source: Bloomberg Pro Terminal
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