Here’s the Goldman explanation in more detail:
“We feed data on team characteristics, individual players and recent team performance into four different types of machine learning models to analyze the number of goals scored in each match. The models then learn the relationship between these characteristics and goals scored, using the scores of competitive World Cup and European Cup matches since 2005. By cycling through alternative combinations of variables, we get a sense of which characteristics matter for success and which stay on the bench. We then use the model to predict the number of goals scored in each possible encounter of the tournament and use the unrounded score to determine the winner.”
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