3,134 research outputs found
On Elo based prediction models for the FIFA Worldcup 2018
We propose an approach for the analysis and prediction of a football
championship. It is based on Poisson regression models that include the Elo
points of the teams as covariates and incorporates differences of team-specific
effects. These models for the prediction of the FIFA World Cup 2018 are fitted
on all football games on neutral ground of the participating teams since 2010.
Based on the model estimates for single matches Monte-Carlo simulations are
used to estimate probabilities for reaching the different stages in the FIFA
World Cup 2018 for all teams. We propose two score functions for ordinal random
variables that serve together with the rank probability score for the
validation of our models with the results of the FIFA World Cups 2010 and 2014.
All models favor Germany as the new FIFA World Champion. All possible courses
of the tournament and their probabilities are visualized using a single Sankey
diagram.Comment: 22 pages, 7 figure
Introducing LASSO-type penalisation to generalised joint regression modelling for count data
In this work, we propose an extension of the versatile joint regression framework for bivariate count responses of the R package GJRM by Marra and Radice (R package version 0.2-3, 2020) by incorporating an (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation of the penalty. The method enables variable selection and the corresponding estimates guarantee shrinkage and sparsity. Hence, this approach is particularly useful in high-dimensional count response settings. The proposal’s empirical performance is investigated in a simulation study and an application on FIFA World Cup football data
Generalised joint regression for count data: a penalty extension for competitive settings
We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package GJRM and allows for modelling linear and non-linear dependence through the use of several copulae. Moreover, the parameters of the marginal distributions of the count responses and of the copula can be specified as flexible functions of covariates. Motivated by competitive settings, we also discuss an extension which forces the regression coefficients of the marginal (linear) predictors to be equal via a suitable penalisation. Model fitting is based on a trust region algorithm which estimates simultaneously all the parameters of the joint models. We investigate the proposal’s empirical performance in two simulation studies, the first one designed for arbitrary count data, the other one reflecting competitive settings. Finally, the method is applied to football data, showing its benefits compared to the standard approach with regard to predictive performance
Generalised joint regression for count data
We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package GJRM and allows for modelling linear and non-linear dependence through the use of several copulae. Moreover, the parameters of the marginal distributions of the count responses and of the copula can be specified as flexible functions of covariates. Motivated by competitive settings, we also discuss an extension which forces the regression coefficients of the marginal (linear) predictors to be equal via a suitable penalisation. Model fitting is based on a trust region algorithm which estimates simultaneously all the parameters of the joint models. We investigate the proposal’s empirical performance in two simulation studies, the first one designed for arbitrary count data, the other one reflecting competitive settings. Finally, the method is applied to football data, showing its benefits compared to the standard approach with regard to predictive performance
Identifying Optimal Technical and Tactical Performance Characteristics in Australian Football
This study identified the optimal technical and tactical performance characteristics of Australian football teams. The application of machine learning approaches identified the key indicators of successful AFL teams. The main findings of this research provide an evidence-base for key stakeholders to inform their training and match day decisions
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