Comparative Analysis of Multivariate Statistical Methods for Predicting Football Match Outcomes using Real Data - Bonsel, Job Paul Maria; Čabla, Adam, 1985-; Helman, Karel; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2024)
This thesis evaluates the predictive accuracy of six multivariate statistical models for forecasting football match outcomes within the English Premier League, comparing them against betting odds. The models examined include the Bradley-Terry model with an Additive Feature Differences (AFD) extension, Multinomial Logistic Regression (MLR), Bayesian Networks (BN), Decision Trees, Poisson regression, and Weibull count models. Using data from the 2006/2007 to 2018/2019 seasons sourced from www.football-data.co.uk and www.sofifa.com, the study incorporates both traditional performance metrics and FIFA video game ratings to assess team strengths. Performance evaluation is conducted through Ranked Probability Score (RPS) and Classification Accuracy, utilizing k-fold cross-validation. Results indicate that the Bradley-Terry AFD and MLR models perform most effectively, closely rivaling the sophisticated betting models which include real-time data adjustments. The study also highlights the tendency of models to overestimate home wins and the potential of FIFA ratings as predictive tools. This research provides insights into the capabilities of statistical models in sports analytics and suggests directions for future enhancements, particularly in integrating novel data sources to improve the prediction of football match outcomes.
Vedoucí práce: Adam Čabla
Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2024
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2024
Způsob přístupu: Internet
Keywords: data analysis and modeling [obor dipl. práce]; diplomové práce; master's theses