Technology 7 June 2019 By Sam WongThe FIFA Women’s World Cup kicks off today in France, with 24 teams competing for football’s biggest prize. While there have been many attempts to use statistical models to predict results in men’s football, the women’s game has received less attention from statisticians so far. One team, led by Andreas Groll at TU Dortmund University in Germany, has used machine learning to create a probabilistic forecast for the tournament. Read more: Why video-assisted referees won’t stop World Cup errors The model combines three state-of-the-art forecasting methods. First, it estimates each team’s ability based on results from the last eight years, with more recent results weighted more heavily. It generates a second ability estimate for each team based on odds from 18 bookmakers, taking into account their profit margins. Finally, it uses machine learning to combine the two ability estimates along with a broad range of other data, including FIFA rankings and socioeconomic factors, such as the country’s population and GDP, as well as which team is playing at home. Some of these factors are more relevant than others, but the model learns which are most important by comparing the data with results from the last…