Introduction Do you find your love of football and curious mind converging to the point where you want to dig a little into the data to confirm if what your eyes are seeing is in fact what’s happening, but you just don’t know how to get started, or where to get data from?
The data for this post has been extracted from fbref.com, using the worldfootballR R stats package. To install the package, run devtools::install_github("JaseZiv/worldfootballR").
To extract the data for the post, the below code was run on Monday the 19th of April.
Football (soccer) as often referred to as “the beautiful game”, is made all the more beautiful because of the intricacies of playing styles between different nations.
This piece aims to begin exploring some of these differences in the playing style of the top five European domestic leagues.
This post continues a series of posts that aims to showcase the new worldfootballR R package for extracting world football (soccer) data from popular data site fbref.com.
This post will aim to analyse expected goals and actual goals, primarily focusing on Liverpool.
Introduction This piece is the first part in a series of posts I will be releasing that will look to analyse how many wins teams should’ve won given their performances over the season and compare them to their actual wins achieved.
This post aims to introduce you to animating ggplot2 visualisations in r using the gganimate package by Thomas Lin Pedersen.
The post will visualise the theoretical winnings I would’ve had, had I followed the simple model to predict (or tip as it’s known in Australia) winners in the AFL that I explained in this post.
I feel like I always overthink footy tipping. During each round, I make myself believe I have some sort of secret sauce and conjure up visions in my head of nailing a solid roughy… and then fall flat half way through the season and give up…