🤖 AI Summary
This study addresses the challenges posed by the dynamic nature of team strength, high outcome randomness, and the critical role of draws in football matches—factors that traditional rating systems struggle to model effectively within football-specific contexts. To overcome these limitations, this work proposes an adaptive rating framework built upon Glicko-2, incorporating adjustments for goal margin, home-field advantage, structural shocks, and dominance weighting. An ordered logit model is integrated to explicitly capture the probabilities of win, draw, and loss outcomes. By combining dynamic Bayesian updating with Monte Carlo simulation, the proposed method not only enhances predictive accuracy for match results but also enables high-fidelity full-season simulations, significantly improving both contextual adaptability and the representation of uncertainty inherent in football competitions.
📝 Abstract
Football match outcome prediction is a challenging problem because team strength changes over time, match outcomes contain a high level of randomness, and draws play a central role in the result structure. Classical rating systems such as Elo provide simple and interpretable dynamic summaries of team ability, but they do not explicitly model uncertainty and often ignore football-specific contextual information. This paper proposes an adaptive Glicko-2-based rating framework for probabilistic football forecasting and leaguelevel season simulation. The proposed framework extends the standard Glicko-2 model by incorporating football-specific mechanisms, including margin-of-victory adjustment, dominance weighting, structural shocks, home advantage modelling, and an ordered-logit draw model. The framework estimates latent team strength dynamically, converts rating differences into win-draw-loss probabilities, and uses these probabilities to simulate the remaining part of a league season through Monte Carlo sampling.