A probabilistic model for explaining the points achieved by a team in football competition. Forecasting and regression with applications to the Spanish competition

Pérez Sánchez, José María Dávila Cárdenes, Nancy Gómez-Déniz, Emilio

Descripción:

In the last decades, a lot of research papers applying statistical methods for analysing sports data have been published. Football, also called soccer, is one of the most popular sports all over the world organised in national championships in a round robin format in which the team reaching the most points at the end of the tournament wins the competition. The aim of this work is to develop a suitable probability model for studying the points achieved by a team in a football match. For this purpose, we built a discrete probability distribution taking values, zero for losing, one for a draw and three for a victory. We test its performance using data from the Spanish Football League (First division) during the 2013-14 season. Furthermore, the model provides an attractive framework for predicting points and incorporating covariates in order to study the factors affecting the points achieved by the teams.

Cobertura:

eng

Títol:

A probabilistic model for explaining the points achieved by a team in football competition. Forecasting and regression with applications to the Spanish competition

Materia:

Covariate, football data, forecasting, regression, sport statistics, truncated distribution, weighted distribution

Formato:

application/pdf

Autor:

Pérez Sánchez, José María
Dávila Cárdenes, Nancy
Gómez-Déniz, Emilio

Tipo:

SORT:ART
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article

Fecha:

2019

Editor:

Universitat Rovira i Virgili
Universitat de Lleida
Universitat de Girona
Universitat Pompeu Fabra
Universitat Politècnica de Catalunya
Universitat Autònoma de Barcelona
Universitat de Barcelona
Institut d'Estadística de Catalunya

Autor/es de la URV:	Pérez Sánchez, José María Dávila Cárdenes, Nancy Gómez-Déniz, Emilio
Año de publicación de la revista:	2019

Información