Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019 (Record no. 716)

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005 - DATE AND TIME OF LATEST TRANSACTION
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100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Andreas Groll
245 10 - TITLE STATEMENT
Title Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer arXiv
Date of production, publication, distribution, manufacture, or copyright notice 2019
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Content type term text
338 ## - CARRIER TYPE
Carrier type term online resource
520 ## - SUMMARY, ETC.
Summary, etc. 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.
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open access — freely available to read.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://arxiv.org/pdf/1906.01131v1">https://arxiv.org/pdf/1906.01131v1</a>
Link text Read the full paper (PDF)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Electronic resource (link)
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Total checkouts Barcode Date last seen Uniform resource identifier Price effective from Koha item type
      Available online General Yegates University Library Yegates University Library Science and Computing 09/01/2026   YGE000977 09/01/2026 https://arxiv.org/pdf/1906.01131v1 09/01/2026 Research paper — read online