Don’t Blame the Data

If you don’t agree with it, please, please don’t blame the data.

What you will find here

This blog will be mainly about sport, but will touch on other parts of life also, using data visualisations to tell the story. What I can promise though is it will be filled with my own selfish pleasures. I hope they align with my readers’ interests.

Some of the sports that will be included include Australian Rules Football (AFL), Basketball, American Football, World Football (Soccer) and many more.

The one constant is that the data will tell the story.

If you don’t agree with it, please, please don’t blame the data.

Jason Zivkovic

Jason Zivkovic

Data Scientist

Reece Group

About Me

My name is Jason Zivkovic and I’m a Data Scientist at the Reece Group.

Growing up as a sports-mad child and teen, statistics were always in my world. But with the love of sport blinding me, I didn’t even realise it was a love of statistics and data. Then, after meandering (much to my mother’s chagrin) through my early work life, and dabbling with different careers, I discovered edX and DataCamp and the programming-focused data analysis short courses they had available. I started taking them and my love of the data was rekindled! Life was made a lot easier in that I had two managers in a row who’s thirst for analytics was as prominent as mine and they gave me the freedom to learn, explore and discover endless possibilities. To them I will be forever greatful.

Kaggle was where I first started dabbling in small analyses - the datasets are as diverse as any wide-eyed newbie could wish for. I still have a look from time to time and think I should write another kernel. I got some great help from the Kaggle community and the code bases there are really good for any new starter. You can find my profile here.


  • Data Visualisation
  • Sports Analytics
  • Supervised and Unsupervised Machine Learning

Recent Posts

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.

All roads lead to gganimate

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.