English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we compare three different modeling approaches for the scoresof soccer matches with regard to their predictive performances based on allmatches from the four previous FIFA World Cups 2002 - 2014: Poisson regressionmodels, random forests and ranking methods. While the former two are based onthe teams' covariate information, the latter method estimates adequate abilityparameters that reflect the current strength of the teams best. Within thiscomparison the best-performing prediction methods on the training data turn outto be the ranking methods and the random forests. However, we show that bycombining the random forest with the team ability parameters from the rankingmethods as an additional covariate we can improve the predictive powersubstantially. Finally, this combination of methods is chosen as the finalmodel and based on its estimates, the FIFA World Cup 2018 is simulatedrepeatedly and winning probabilities are obtained for all teams. The modelslightly favors Spain before the defending champion Germany. Additionally, weprovide survival probabilities for all teams and at all tournament stages aswell as the most probable tournament outcome.

Prediction of the FIFA World Cup 2018 - A random forest approach with an emphasis on estimated team ability parameters