mle_foot {footBayes}

Fit football models with Maximum Likelihood

Description

ML football modelling for the most famous models: double Poisson, bivariate Poisson, Skellam and student t.

Usage

mle_foot(data, model, predict, ...)

Arguments

data

A data frame, or a matrix containing the following mandatory items: season, home team, away team, home goals, away goals.

model

The type of model used to fit the data. One among the following: "double_pois", "biv_pois", "skellam", "student_t".

predict

The number of out-of-sample matches. If missing, the function returns the fit for the training set only.

...

Optional arguments for MLE fit algorithms.

Details

See documentation of stan_foot function for model details. MLE can be obtained only for static models, with no time-dependence. Likelihood optimization is performed via the BFGS method of the optim function.

Value

MLE and 95% profile likelihood deviance confidence intervals for the model's parameters: attack, defence, home effect and goals' correlation.

Author(s)

Leonardo Egidi [e-mail]

References

Baio, G. and Blangiardo, M. (2010). Bayesian hierarchical model for the prediction of football results. Journal of Applied Statistics 37(2), 253-264.

Egidi, L., Pauli, F., and Torelli, N. (2018). Combining historical data and bookmakers' odds in modelling football scores. Statistical Modelling, 18(5-6), 436-459.

Gelman, A. (2014). Stan goes to the World Cup. From "Statistical Modeling, Causal Inference, and Social Science" blog.

Karlis, D. and Ntzoufras, I. (2003). Analysis of sports data by using bivariate poisson models. Journal of the Royal Statistical Society: Series D (The Statistician) 52(3), 381-393.

Karlis, D. and Ntzoufras,I. (2009). Bayesian modelling of football outcomes: Using the Skellam's distribution for the goal difference. IMA Journal of Management Mathematics 20(2), 133-145.

Owen, A. (2011). Dynamic Bayesian forecasting models of football match outcomes with estimation of the evolution variance parameter. IMA Journal of Management Mathematics, 22(2), 99-113.

Examples

## Not run: 
require(tidyverse)
require(dplyr)

data("italy")
italy <- as_tibble(italy)
italy_2008<- italy %>%
   dplyr::select(Season, home, visitor, hgoal,vgoal) %>%
   dplyr::filter( Season=="2008")

mle_fit <- mle_foot(data  = italy_2008,
                   model = "double_pois")

## End(Not run)