Today was the Fifth and Final day of Dashboard Week, and the dataset is the AFL. That’s right AFL data! I was very excited to be doing a Dashboard on the AFL. The only downside is that it must be done within 6 hours (9am – 3pm), Dashboard, Blog and all.
The dataset can be found here:

I knew from the offset that today was going to be a huge challenge in time boxing. I decided that by:

  • 9.30am, I needed to have my story.
  • 11.30am, I needed to have all my Charts done.
  • 1.30pm, I needed to have all my formatting done.
  • 2.30pm, I needed to have the Blog completed (1.51pm at the time of typing this)

The Story

First, the story, and as the saying goes:

“If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”
― Sun Tzu, The Art of War

An important part of any challenge or competition is to know your opponent. I decided that my dashboard would be designed to analyze the opponent team, and identify their most threatening players.

Data Preparation

Firstly, a big thank you to David Bartolo for providing us with very clean data. I plugged the data straight into Tableau, no cleaning needed whatsoever.


A definition of all the key statistics can be found here:
Stats glossary: Every stat explained (


The Plan

Knowing that time was of the essence, I jotted down what I wanted on paper. Next, I decided to have the option for the user to choose their own team, and have the list of opponents to include the remaining 17 teams. Finally, I also wanted to have a list of Round numbers so that the user could analyze their opponent according to any round that they have played.

The Charts

For the charts, I ideally would have wanted more, but due to time constraints settled on 4. For the first chart, I wanted to show the user how the opponent team has performed using a waterfall chart to map wins/losses and margins (or percentage). In addition, the remaining 3 charts would be to analyze players by their position, Forward, Midfield and Defender (I did not choose ruck, because you couldn’t tag their ruck with any other player apart from your ruck).

The Execution

Next, I started to work on the charts and finished my last chart at around 11.40am. The Final 4 charts were: 1 waterfall chart, 1 scatter plot, and 2 bar charts.

The Formatting

After that, I started on formatting and dashboard actions and filters. Now this was very time consuming as I would encounter some minor issues with the charts that I needed to fix.

The Dashboard

My dashboard looked like this:


In conclusion, I built the dashboard so that you can analyze your opponent teams, to know which of their players are performing well and tag them accordingly. You can use the drop down parameter options to select the stats for the positions that you wish to view. I have colored in red the ‘dangerous’ players. Finally, there is the waterfall chart to show you the opponent teams’ performance for the year.

Feel free to explore my dashboard to get your own insights:
AFL | Tableau Public

The time of completion 2.25pm.

Check out the other days of Dashboard Week:
Dashboard Week – Day 1: Global Power Plants
Dashboard Week – Day 2: CPI and the 5 WHY’s
Dashboard Week – Day 3: Star Wars
Dashboard Week – Day 4: ABS Census


I hope you have enjoyed the post, and I will see you next time.

The Data School
Author: The Data School