Australian Football League Teams Performance Analysis

In this project, we aim to provide some insights on the AFL team performance throughout the past nine years (2012 - 2021).

Please see the dashboard here.

Team Members 👨🏻‍💻👩🏻‍💻👩🏻‍💻👩🏻‍💻

• Ishaan Nigam
• Nga Phu
• Samra Vatan Parast
• Serena Zhao

Datasets

Please add password for postgresql before running the code in jupyter notebook

We extracted our data by performing API calls, we used Squiggle API. The Squiggle API offers public access to raw data about AFL games and predictions made by popular online models. Query types we used are as per below;

• Teams query
• Games query
• Tips query

ETL Process

• Extract:Datasets were extracted using Squiggle API calls.

• Transform: Using Jupyter Notebook and pandas, we cleaned and reorganised the data according to our needs.

• Load: Considering the normalised and relational structure of our data, PostgreSQL was our database of choice.

Data Engineering - Architecture Diagram

We created our table schemas and configured primary key and foreign keys to create relationships between our tables.

The tool we used is this step was Quick Database Diagrams (QuickDBD)

Data Visualisation

We draw insights from our data using JavaScript and HTML.

Libraries we used are as follows;

• D3.js: D3 is a JavaScript library for manipulating documents based on data.
• AnyChart: AnyChart is a lightweight and robust JavaScript charting library.
• Plotly: Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration.

Final Dashboard