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footBayes

★ 59 Open GitHub ↗

An R package for many football models

// repository documentation

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# footBayes <img src="man/figures/logo.png" width = 180 align="right" />

[![CRAN Version](https://www.r-pkg.org/badges/version/footBayes)](https://cran.r-project.org/package=footBayes)
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The goal of `footBayes` is to propose a complete workflow to:

-   Fit the most well-known football models, including the double Poisson, bivariate Poisson, Skellam, and Student‑t distributions. It supports both maximum likelihood estimation (MLE) and Bayesian inference. 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.

-   Visualize the teams' abilities, the model checks, the rank-league reconstruction;

-   Predict out-of-sample matches.

## Installation

Starting with version **2.0.0**, `footBayes` package requires installing the R package [`cmdstanr`](https://mc-stan.org/cmdstanr/) (not available on CRAN) and the command-line interface to Stan: [`CmdStan`](https://mc-stan.org/users/interfaces/cmdstan.html). 
For a step-by-step installation, please follow the instructions provided in [Getting started with CmdStanR](https://mc-stan.org/cmdstanr/articles/cmdstanr.html).

You can install the released version of `footBayes` from CRAN with:

``` r
install.packages("footBayes", type = "source")
```

Please note that it is important to set `type = "source"`. Otherwise,
the ‘CmdStan’ models in the package may not be compiled during
installation.

Alternatively to CRAN, you can install the development version from
GitHub with:

``` r
# install.packages("devtools")
devtools::install_github("leoegidi/footBayes")
```

## Example

In what follows, a quick example to fit a Bayesian double Poisson model
for the Italian Serie A (seasons 2000-2001, 2001-2002, 2002-2003),
visualize the estimated teams’ abilities, and predict the last four
match days for the season 2002-2003:

``` r
library(footBayes)
library(dplyr)
```

``` r 
# Dataset for Italian Serie A
data("italy")
italy <- as_tibble(italy)
italy_2000_2002 <- italy %>%
  dplyr::select(Season, home, visitor, hgoal, vgoal) %>%
  filter(Season == "2000" | Season == "2001" | Season == "2002")

colnames(italy_2000_2002) <- c("periods",
                               "home_team",
                               "away_team",
                               "home_goals",
                               "away_goals")

# Double poisson fit (predict last 4 match-days)
fit1 <- stan_foot(data = italy_2000_2002,
                  model = "double_pois",
                  predict = 36,
                  iter_sampling = 200,
                  chains = 2) 
```

The results (i.e., attack and defense effects) can be investigated using
``` r 
print(fit1, pars = c("att", "def"))
```

To visually investigate the attack and defense effects, we
can use the `foot_abilities` function
``` r 
foot_abilities(fit1, italy_2000_2002) # teams abilities
```

To check the adequacy of the Bayesian model the function `pp_foot` provides posterior predictive plots
``` r 
pp_foot(fit1, italy_2000_2002) # pp checks
```
<img src="man/figures/readme_pp_foot.png" width="80%" style="display: block; margin: auto;" />

Furthermore, the function `foot_rank` shows the final rank table and the plot with the predicted points
``` r 
foot_rank(fit1, italy_2000_2002) # rank league reconstruction
```

In order to analyze the possible outcomes of the predicted matches, the function `foot_prob` provides a table containing the home win, draw and away win probabilities for the out-of-sample matches
``` r 
foot_prob(fit1, italy_2000_2002) # out-of-sample posterior pred. probabilities
```

For more and more technical details and references, see the vignette!

footBayes

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