Hi there and welcome to my public profile!
My name is Jason, and I'm a Data Scientist at the Reece Group. I now find myself doing less on "the tools" and more in the leadership space, so my personal projects are a nice way to keep some of my technical skills sharp!
- worldfootballR - A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat and fotmob
- nblR - To quickly and efficiently load Australian basketball statistics from the NBL
- euroleagueR - To quickly and efficiently load Euroleague basketball statistics
- bettRtab - An R client to interact with the API of Australian betting company TAB
- chessR - An R package designed to extract and analyse chess game data played on Lichess and chess.com
- Expected vs Actual Season Performance for the Big Five Euro Leagues
- Explaining Player Valuations
- worldfootballR Scouting App
Getting started with R and worldfootballR
This post takes the reader on the steps necessary to download and install R and worldfootballR and get started with no coding experience:
https://www.dontblamethedata.com/blog/extract-data-using-worldfootballr/
Predicting AFL crowd attendance
A post pre-COVID that attempted to predict the size of AFL crowds: https://www.dontblamethedata.com/blog/building-a-linear-regression-model-in-r-to-predict-afl-crowds/
Finding the AFL 'Bandwagon supporters' teams
A tongue-in-cheek attempt to quantify the term 'bandwagon jumpers' of team supporters in the AFL: https://www.dontblamethedata.com/blog/using-data-to-determine-which-afl-fans-jump-onthe-bandwagon/
FIFA19 data deep dive
An early analysis I did on Kaggle that took a deep look at player data from the popular game series FIFA to help players identify players to target for career leagues: https://www.kaggle.com/jaseziv83/clustering-to-help-club-managers
NCAA Men's March Madness
Another Kaggle EDA for the annual March Madness competition run by Kaggle: https://www.kaggle.com/jaseziv83/a-recent-deep-look-at-the-men-s-ncaab