Exploring and modelling team performances of the Kaggle European Soccer database

DOI10.1177/1471082X18810971OpenAlexW2911213127MaRDI QIDQ5142229

Maurizio Carpita, Enrico Ciavolino, Paola Pasca

Publication date: 30 December 2020

Published in: Statistical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/1471082x18810971

zbMATH Keywords

binomial logistic regression (BLR) modelcomparison of classification modelsKaggle European Soccer (KES) databaseprediction of match resultsrole-based player performance indicatorsstatistical learning models

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (7)

Observed heterogeneity in players' football performance analysis using PLS-PM ⋮ The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models ⋮ Integration of model-based recursive partitioning with bias reduction estimation: a case study assessing the impact of Oliver's four factors on the probability of winning a basketball game ⋮ A robust method for clustering football players with mixed attributes ⋮ Clustering of variables methods and measurement models for soccer players' performances ⋮ Sports analytics in the NFL: classifying the winner of the superbowl ⋮ Discovering associations between players' performance indicators and matches' results in the European Soccer Leagues

Uses Software

• R
• S-PLUS
• caret
• aplore3
• MEMSS

Cites Work

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• Statistical modeling: The two cultures. (With comments and a rejoinder).
• Extended ordered paired comparison models with application to football data from German Bundesliga
• Training and assessing classification rules with imbalanced data
• Bayesian modelling of football outcomes: using the Skellam's distribution for the goal difference
• Modelling soccer matches using bivariate discrete distributions with general dependence structure
• An Introduction to Statistical Learning
• Applied Logistic Regression

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