Today marked the last day of the dashboard week for DSAU 17, meaning an end to the daily dashboard’s for now.

The assignment given was to find and tell a story using a Big Bash League, or BBL, data set from 2011 – 2021.

Let’s dive into it.

The Idea

To begin with, I jumped straight into looking at some stats for bowlers. I wasn’t aware that BBL had a Player of the Match award, and so looked at that. From here, I knew I wanted to investigate whether this was an accurate ranking of the best bowlers in BBL.


I decided to look into wickets taken, and this was a good indicator that maybe PotM wasn’t accurate, as SA Abbott had a noticeable jump up. However, this may be due to longevity so the investigation continued.

I then looked into creating a run economy measure to use. I decided to see what state bowler’s performed worse in, with the intention to have it filtered on the dashboard. This would be a good indicator for the top bowlers.

I then tracked this over time of the data set, so we could see how the average run economy changes over time.

I finally prepared some BANs to use, so we could see at a broad level before filtering to one of our top 10 contenders.

Result & Insight

After putting this onto a dashboard, we see the final result below.

If I filter through these, we actually see that AJ Tye, who’s at the bottom of the PotM awards has the one of the lowest run rates, and highest strike rates for bowlers. This would make him my pick for the greatest BBL bowler.

Thanks for going through my process of building a BBL dashboard!

The Data School
Author: The Data School