Package: footBayes 2.1.0

footBayes: Fitting Bayesian and MLE Football Models

This is the first package allowing for the estimation, visualization and prediction of the most well-known football models: double Poisson, bivariate Poisson, Dixon-Coles, negative binomial, Skellam, student_t, diagonal-inflated bivariate Poisson, and zero-inflated Skellam. It supports both maximum likelihood estimation (MLE, for 'static' models only) and Bayesian inference. Team abilities can be static or dynamic over weeks or seasons, with alternative specifications of the evolution variance: a common variance (Owen, 2011), variance inflation after the summer break (Koopman and Lit, 2015) <doi:10.1111/rssa.12042>, and weighted dynamic models with commensurate priors (Macrì-Demartino, Egidi and Torelli, 2026) <doi:10.1093/jrsssc/qlag032>. Historical team strengths can be estimated through a Bayesian Bradley-Terry-Davidson model and used as a covariate. For Bayesian methods, it incorporates several techniques: MCMC sampling with Hamiltonian Monte Carlo, variational inference using either the Pathfinder algorithm or Automatic Differentiation Variational Inference (ADVI), and the Laplace approximation. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package. The model construction relies on the most well-known football references, such as Dixon and Coles (1997) <doi:10.1111/1467-9876.00065>, Karlis and Ntzoufras (2003) <doi:10.1111/1467-9884.00366> and Egidi, Pauli and Torelli (2018) <doi:10.1177/1471082X18798414>.

Authors:Leonardo Egidi [aut, cre], Roberto Macrì Demartino [aut], Vasilis Palaskas [aut]