Adam Kareen | Software Developer
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An in-depth analysis of Australian Football League (AFL) data. This repository contains data, tools and code for exploring and analysing AFL match and player statistics, as well as historical odds data.

Table of Contents


The AFL Data Analysis project provides a comprehensive platform for examining and deriving insights from AFL match and player data. Whether you're a sports enthusiast, a tipper, a data scientist, or a student, this repository offers valuable resources for diving into the world of Australian Rules Football. In addition, historical odds data from 2009 to the present is available for tipping and betting purposes.

The repository currently stores match scores data from 1897 to 2020, In depth statistics on all players who have ever played in the VFL/AFL and historical odds data from 2009 to 2021. The data is stored in CSV format for easy access and analysis.


  • Analyze match statistics to gain insights into team performance.
  • Explore player data to assess individual player performance.
  • Visualize AFL data for a better understanding of the game.
  • Historical odds data for tipping and betting analysis.
  • Data cleaning and preprocessing tools for ease of analysis.
  • Jupyter notebooks with example analyses and visualizations.


  1. Clone the repository:

    git clone
                    cd AFL-Data-Analysis
  2. Install the required dependencies:

    pip install -r requirements.txt


I regularly update the CSV data files in the "data" directory with the latest AFL match and player data. But you can also do your own data scraping using the provided scripts in the "scripts" directory. These scripts are written in Python and use the Beautiful Soup library for web scraping.

To start analyzing AFL data, you can use the provided Jupyter notebooks in the "notebooks" directory. These notebooks cover a wide range of analyses and visualizations related to AFL match and player data.


  • <data_storage_directory>: Specify the directory where the data will be stored.
  • <option>: Choose one of the following options:
    • matches <start year> <end year>: Collect match scores data within the specified year range.
    • players <start year> <end year>: Gather player statistics data within the specified year range.
    • all_players: Collect data for all players.

Please note that the available data ranges from 1897 to the current year.


  • To scrape match scores data from 1897 to 2020 and store it in the "data/match_scores" directory:

    python data/match_scores matches 1897 2020
  • To gather player statistics data from 1990 to 2023 and save it in the "data/player_stats" directory:

    python data/player_stats players 1990 2023
  • To collect data for all players and store it in the "data/all_players" directory:

    python data/all_players all_players

Data Sources

This project uses publicly available AFL data sources, including match scores, player statistics, and historical odds data. The data sources are as follows:

Data Guide

Match Spreadsheet Column Names

Category Columns
Game Info Cols Year, Round, Home Team, Away Team, Date
Home Team Stats Cols Home Team Goals (1st, 2nd, 3rd, 4th), Home Team Behinds (1st, 2nd, 3rd, 4th), Home Team Total Goals, Home Team Total Behinds, Home Team Total Score
Away Team Stats Cols Away Team Goals (1st, 2nd, 3rd, 4th), Away Team Behinds (1st, 2nd, 3rd, 4th), Away Team Total Goals, Away Team Total Behinds, Away Team Total Score
Winning Info Cols Winning Team, Margin
Misc Info Cols Attendance, Venue

Player Spreadsheet Column Names

Abbreviation Description
# Jumper
GM Games played
KI Kicks
MK Marks
HB Handballs
DI Disposals
DA Disposal average
GL Goals
BH Behinds
HO Hit outs
TK Tackles
RB Rebound 50s
IF Inside 50s
CL Clearances
CG Clangers
FF Free kicks for
FA Free kicks against
BR Brownlow votes
CP Contested possessions
UP Uncontested possessions
CM Contested marks
MI Marks inside 50
1% One percenters
BO Bounces
GA Goal assist
%P Percentage of game played
SU Sub (On/Off)
Subbed on
Subbed off


Contributions to AFL Data Analysis are welcome! If you have ideas for improvements, new analyses, or data sources, please open an issue or submit a pull request. We appreciate your input and help in making this project even better.