activity
20182021
most citedHybrid Machine Learning Forecasts for the UEFA EURO 2020

6 citations · 10 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20216 cited

Hybrid Machine Learning Forecasts for the UEFA EURO 2020

Andreas Groll, Lars Magnus Hvattum, Christophe Ley +4

Three state-of-the-art statistical ranking methods for forecasting football matches are combined with several other predictors in a hybrid machine learning model. Namely an ability…

stat.ML2020

Deducing neighborhoods of classes from a fitted model

Alexander Gerharz, Andreas Groll, Gunther Schauberger

In todays world the request for very complex models for huge data sets is rising steadily. The problem with these models is that by raising the complexity of the models, it gets mu…

stat.ML20193 cited

Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019

Andreas Groll, Christophe Ley, Gunther Schauberger +2

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…

stat.AP20191 cited

Prediction of the 2019 IHF World Men's Handball Championship - An underdispersed sparse count data regression model

Andreas Groll, Jonas Heiner, Gunther Schauberger +1

In this work, we compare several different modeling approaches for count data applied to the scores of handball matches with regard to their predictive performances based on all ma…

stat.AP2018

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

Andreas Groll, Christophe Ley, Gunther Schauberger +1

In this work, we compare three different modeling approaches for the scores of soccer matches with regard to their predictive performances based on all matches from the four previo…