Instead of focusing on bettering your 401K, big banks turned their efforts towards predicting who would win the World Cup. Goldman Sachs, UBS, and Dutch Bank were amongst the notable names. Some running as many as 1 million simulations and they all were wrong – none had the winner, France, even making it to the finals. Why were these financial institutions with robust machine learning tech so horribly off?
Two areas of failure: defining the “problem” and setting proper requirements.
Goldman Sachs analyzed data points about both teams and individual players. UBS based their assessment on a team’s custom “object skill level measurement” rating. They had Germany pegged as the winner with a 24% certainty. Dutch came at it a bit differently by judging a team’s market value, presuming that a team’s market worth is closely correlated to its success.
“It is difficult to assess how much faith one should have in these predictions… But the forecasts remain highly uncertain, even with the fanciest statistical techniques, simply because football is quite an unpredictable game. This is, of course, precisely why the World Cup will be so exciting to watch,” wrote Manav Chaudhary, Nicholas Fawcett and Sven Jari Stehn, the three economists behind Goldman Sachs’ project.
Do you think machine learning could have predicted this more closely? If so, how?
Does this type of fail make you second guess using tech for major life decisions? I.e. Investing, health decisions, etc.
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