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 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.
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.
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…
On the 24th of August, 2019, the Australian Men’s Basketball team, the Boomers, created history when they were able to enact their own David and Golliath moment, taking down Team USA for the first time ever.
Introduction There is a lot of talk about crowd behaviour and crowd issues with the modern day AFL. I personally feel the crowd issues are a case of business-as-usual; I’ve been going to AFL games on-and-off for close to three decades and the crowd behaviour is no different today as it was 20 years ago… just now everyone with a phone and Twitter account is a journo.