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.

Interests

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

Recent Posts

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.

Projects

worldfootballR R Package

An R package developed to aid in the extraction of world football (soccer) data from fbref.com

chessR R Package

An R package developed to aid in the extraction and analysis of data from popular online chess platforms

Contact