Don’t Blame the Data

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

Jason Zivkovic

Jason Zivkovic

Data Scientist

Reece Group


My name is Jason Zivkovic and I’m a Data Scientist from Melbourne, Australia, working at the Reece Group.

Some of my passions include web scraping, package and app development, statistical modeling, sports analytics and data visualisation.

The bulk of my programming is done using the R programming language, with some Python thrown in.

What you will find here

This blog will be mainly about R programming, mainly through sports, 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.

Importantly, all views expressed here are my own.


  • Supervised and Unsupervised Machine Learning
  • Web Scraping
  • Shiny App Development
  • Dev Ops
  • Data Visualisation
  • Sports Analytics

Recent Posts

Rating the Difficulty of the Big 3's Grand Slam Wins

Introduction As the current (2023) Australian Open came to a close with Novak Djokovic winning a record equaling 22nd Grand Slam, the expected discourse on who is the best player of all time is back on the agenda.

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.


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, 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.


worldfootballR R Package

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

chessR R Package

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

nblR R Package

An R package developed to aid in the extraction of the Australian National Basketball League (NBL) data

bettRtab R Package

An R package API wrapper to extract data from the betting company TAB

xG Performance App

A shiny app to analyse the goal scoring performance of teams in the big five leagues against xG