Logit models for the probability of winning football games

Alves, Alessandro Martins; Mello, João Carlos Correia Baptista Soares de; Ramos, Thiago Graça; Sant’Anna, Annibal Parracho

doi:10.1590/s0101-74382011000300003

Cited by 7 publications

(3 citation statements)

References 13 publications

(5 reference statements)

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“…Logit models have been used widely in several fields, including medicine, biology, psychology, economics, insurance, politics, etc. Recent applications of the linear logit specification in statistics in sports are [13,14] in basketball, [15,16] for football, among others.…”

Section: Frequentist Estimationmentioning

confidence: 99%

A Comparative Study of Logistic Models Using an Asymmetric Link: Modelling the Away Victories in Football

2018

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Abstract:The target of this paper is to study the relevant factors affecting the victories away from home of football teams in order to fit the probability of winning an away match. The paper addressed the following research issues: (a) Is the identification of the significant variables underlying the results plausible? (b) Can information of these factors increase the probability of winning away from home and assist coaches in their decisions? Empirically, it is shown that there are more home victories and draws than away victories in the professional football leagues in Europe and this fact has to be taken into account. Thus, the classical logistic and Bayesian regression models do not seem to be adequate in this case and an asymmetric logistic regression model is therefore considered. This paper analyses 380 games played in the First Division of the Spanish Football League during the 2013-2014 season. Asymmetric logistic regression from a Bayesian point of view is chosen as the best model. This model detects new relevant factors undetected by standard logistic regressions. In view of the paper's findings, various practical recommendations were made in order to improve decision-making in this field. The Asymmetric logit link is a helpful device that can assist coaches in their game strategies.

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Section: Frequentist Estimationmentioning

confidence: 99%

A Comparative Study of Logistic Models Using an Asymmetric Link: Modelling the Away Victories in Football

2018

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“…Outros autores (Caldas, 1995;Alves et al, 2011) já haviam combinado algumas dessas técnicas, mas o presente trabalho foi o primeiro a combinar todas em um único modelo.…”

Section: Modelagemunclassified

Um modelo híbrido incorporando preferências declaradas e envoltória de dados aplicado ao transporte de cargas no Brasil

et al. 2016

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Article Info ResumoKeywords: transporte de carga análise envoltória de dados logito ordinal preferência declarada O presente trabalho visa identificar empresas que são eficientes em termos de transporte de carga no seu dia-a-dia e verificar os aspectos importantes na hora da tomada de decisão para a contratação do serviço de transporte de carga. Para fazer essa avaliação serão utilizadas diversas técnicas como a Analise Envoltória de Dados, a parte de preferência declarada e modelo logito ordinal. Por meio do uso da técnica do DEA, as eficiências serão classificadas em alta, média e baixa e esta classificação será a variável dependente do modelo logito ordinal e as variáveis independentes deste modelo serão as utilidades oriundas da preferencia delcarada e do modelo de maxdiff que avaliou algumas características que não estavam no modelo de preferência declarada. IntroduçãoEsse estudo visa construir um modelo para identificar a forma ideal de transporte de carga no Brasil, para pequenas e médias empresas. Objetiva, ainda, identificar os fatores que influenciam a escolha das empresas por um modo de transporte, além de avaliar a possibilidade da migração do transporte de carga do sistema rodoviário para o ferroviário. Para tanto, foram aplicados questionários aos responsáveis pela contratação do serviço de transporte de carga das empresas do cadastro do SEBRAE.O trabalho foi inspirado em Lima et al. (2011), cujo objetivo era identificar as companhias aéreas mais eficientes do ponto de vista do consumidor, a partir da seleção de 171 vôos em diversos horários, dividindo-os em tercis e realizando uma analise exploratória dos resultados. O presente estudo utilizou um modelo Logit Ordinal para explicar as empresas mais eficientes, bem como as principais características por elas indicadas para contratação do serviço de transporte de carga. Compreender os motivos da contratação do serviço fornece subsídios para a melhoria do setor de transporte.A infraestrutura de transporte de um país é a principal força motriz para o seu desenvolvimento econômico, pois a distribuição é fundamental para garantir o escoamento da produção industrial. O tamanho territorial brasileiro torna ainda mais importante o setor de transporte, pela necessidade de se fazer a distribuição e o transporte da produção industrial entre os centros consumidores das regiões do país.Segundo a CNT (2013), o principal meio de transporte de carga no Brasil é o rodoviário, que detém 60% de toda a carga transportada no país. Estudos na área de transporte (TEIXEIRA, 2007; SCHROEDER e CASTRO, 1996; WANKE e FLEURY, 2006) mostram que um estímulo a outros modos poderia gerar ganhos à economia nacional, indicando a necessidade de mudança da matriz de transporte no Brasil.O estudo indica que as empresas mais eficientes são as que valorizam tanto a segurança da carga quanto a facilidade para atingir o consumidor. O que corrobora o fato de não optarem pelo uso da ferrovia. Tal fato precisa ser considerado pelo governo brasileiro ao definir o financiamento de projetos pa...

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“…As the focus of our study is to assess whether team's performance indicators are able to predict the win of home team in a generic match, rather than estimating the strength of each team, a simple binomial logistic regression (BLR) model with only teams’ difference in covariates has been adopted, assuming therefore that these predictors would capture the main effects on the result of interest (home team win). The BLR model, which largely used in previous studies (Alves et al, 2011; Magel and Melnykov, 2014) and not only in the soccer context (Lisi and Zanella, 2017), has been extended by adding the ELO rating as predictor and two random effects that take into account the hierarchical structure of data (matches within seasons, seasons within countries).…”

Section: Introductionmentioning

confidence: 99%

Exploring and modelling team performances of the Kaggle European Soccer database

Carpita

Ciavolino

Pasca

2019

Statistical Modelling

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This study explores a big and open database of soccer leagues in 10 European countries. Data related to players, teams and matches covering seven seasons (from 2009/2010 to 2015/2016) were retrieved from Kaggle, an online platform in which big data are available for predictive modelling and analytics competition among data scientists. Based on both preliminary data analysis, experts’ evaluation and players’ position on the football pitch, role-based indicators of teams’ performance have been built and used to estimate the win probability of the home team with the binomial logistic regression (BLR) model that has been extended including the ELO rating predictor and two random effects due to the hierarchical structure of the dataset. The predictive power of the BLR model and its extensions has been compared with the one of other statistical modelling approaches (Random Forest, Neural Network, k-NN, Naïve Bayes). Results showed that role-based indicators substantially improved the performance of all the models used in both this work and in previous works available on Kaggle. The base BLR model increased prediction accuracy by 10 percentage points, and showed the importance of defence performances, especially in the last seasons. Inclusion of both ELO rating predictor and the random effects did not substantially improve prediction, as the simpler BLR model performed equally good. With respect to the other models, only Naïve Bayes showed more balanced results in predicting both win and no-win of the home team.

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Logit models for the probability of winning football games

Cited by 7 publications

References 13 publications

A Comparative Study of Logistic Models Using an Asymmetric Link: Modelling the Away Victories in Football

A Comparative Study of Logistic Models Using an Asymmetric Link: Modelling the Away Victories in Football

Um modelo híbrido incorporando preferências declaradas e envoltória de dados aplicado ao transporte de cargas no Brasil

Exploring and modelling team performances of the Kaggle European Soccer database

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