Extended ordered paired comparison models with application to football data from German Bundesliga

DOI10.1007/s10182-014-0237-1zbMath1443.62537OpenAlexW2036008110MaRDI QIDQ1621966

Gerhard Tutz, Gunther Schauberger

Publication date: 12 November 2018

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://epub.ub.uni-muenchen.de/17911/1/TRTutzSchauberger151.pdf

zbMATH Keywords

Bradley-Terry modelpenalized estimationpaired comparison systems

Mathematics Subject Classification ID

Applications of statistics (62P99) Paired and multiple comparisons; multiple testing (62J15)

Related Items (7)

Analysis of the importance of on-field covariates in the German Bundesliga ⋮ Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures ⋮ The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models ⋮ Default priors for the intercept parameter in logistic regressions ⋮ How often does the best team win? A unified approach to understanding randomness in North American sport ⋮ Exploring and modelling team performances of the Kaggle European Soccer database ⋮ Subject-specific modelling of paired comparison data: A lasso-type penalty approach

Uses Software

• R
• VGAM
• glmnet
• prefmod
• BradleyTerry2
• Fahrmeir
• gvcm.cat
• CasANOVA
• ordBTL
• catdata

Cites Work

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• The analysis of rank ordered preference data based on Bradley-Terry type models
• A log-linear approach for modelling ordinal paired comparison data on motives to start a PhD programme
• Simultaneous Factor Selection and Collapsing Levels in ANOVA
• A State-Space Model for National Football League Scores
• Modelling the Effect of Subject-Specific Covariates in Paired Comparison Studies with an Application to University Rankings
• Dynamic Stochastic Models for Time-Dependent Ordered Paired Comparison Systems
• Analysis of Ordinal Paired Comparison Data
• Quasi-variances
• Multivariate statistical modelling based on generalized linear models.
• A new look at the statistical model identification

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