01192nam a2200169 a 4500 20260901030417.0 250101s2019 xx o 000 0 eng d Andreas Groll Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019 arXiv 2019 text online resource In 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. Open access — freely available to read. https://arxiv.org/pdf/1906.01131v1 Read the full paper (PDF) ERES 716 716 0 0 0 3 GEN MAIN MAIN SCICOMP 2026-09-01 0 YGE000977 2026-09-01 03:04:17 https://arxiv.org/pdf/1906.01131v1 2026-09-01 PAPER