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

About

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.

Interests

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

Recent 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.
Mar 3, 2022 4 min read R, worldfootballR, fotmob, soccer

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?
Jun 5, 2021 17 min read R, worldfootballR, sports analytics, soccer

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.
Apr 19, 2021 13 min read R, worldfootballR, soccer

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.
Jan 29, 2021 13 min read R, worldfootballR, soccer

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.
Jan 10, 2021 6 min read R, worldfootballR, soccer
See all posts

Projects

Explaining Player Valuations

A shiny app that measures the market value of individual stats by regressing them against player market valuations from Transfermarkt.

xG Performance App

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

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

Popular Topics

afl analytics basketball boomers data analysis data visualisation fifa fifa19 gganimate ggplot ggplot2 gt R R Package soccer soccer analytics sports analytics statistics tidyverse worldfootballR

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