Posts

Efficiently get Fotmob match IDs with worldfootballR

With worldfootballR recently including a wrapper for extracting Fotmob data, I thought it might be a good time to write a small post on how to get historical match IDs fairly efficiently.

ANALYSE FOOTBALL (SOCCER) DATA IN R WITH ZERO R EXPERIENCE

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?

Historically Bad Melbourne Victory

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.

Passing in the Top Five European Domestic Leagues

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.

Expected Goals and Liverpool - An Intro to worldfootballR

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.

Liverpool's Earlier Subsititutions - an Introduction to worldfootballR

With the creation of the worldfootballR R package (a new R package to aid in the extraction of Football (Soccer) data from fbref), I will be trying to highlight ways the package can be used.

Applying Pythagorean Expectation to Pro Sports - An Intro

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.

Playing List Ages at the Pointy End

This is the second installment at my look at the age distributions of each AFL team for the 2020 season. The fist post (which can be found here), looked at the impact that the statistic chosen to report out the age profiles can have, specifically using the mean versus the median.

The Agony and the Ecstasy of my first open source contribution

For the last year or so, I’ve had this desire to contribute to an open source R package, but like a lot of people, I found the thought of tackling the task frightening.

Analysing AFL Team Age... Properly!

After seeing my beloved Hawthorn Hawks tweet out an article on their website regarding player ages for each team, it got me riled up that the media love to cite Champion Data’s “average age” as their measure.