01347nam a2200181 a 450000500170000000800400001710000180005724500740007526400160014933600090016533800200017452006940019450600460088885600660093494200090100099900130100995201430102220260901030417.0250101s2019    xx     o     000 0 eng d1 aAndreas Groll10aHybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019 1barXivc2019  atext  aonline resource  aIn this work, we combine two different ranking methods together with several other predictors in a joint random forest approach for the scores of soccer matches. The first ranking method is based on the bookmaker consensus, the second ranking method estimates adequate ability parameters that reflect the current strength of the teams best. The proposed combined approach is then applied to the data from the two previous FIFA Women's World Cups 2011 and 2015. Finally, based on the resulting estimates, the FIFA Women's World Cup 2019 is simulated repeatedly and winning probabilities are obtained for all teams. The model clearly favors the defending champion USA before the host France.0 aOpen access — freely available to read.40uhttps://arxiv.org/pdf/1906.01131v1yRead the full paper (PDF)  cERES  c716d716  001040738GENaMAINbMAINcSCICOMPd2026-09-01l0pYGE000977r2026-09-01 03:04:17uhttps://arxiv.org/pdf/1906.01131v1w2026-09-01yPAPER