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Dashboard Week: Day 4 Big Bash League

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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The final dashboard week challenge was using the Big Bash League data for the T20 cricket league in Australia. I was really excited about this one as I don’t like cricket. I love it!

The data had ball-by-ball data from 2011-2021 and match data for the same period. This was great and I set off making a host of measures like economy rates and strike rates etc. The data seemed pretty clean, and I began looking for stories. I noticed that the most successful team was Sydney Sixers and the least successful was Sydney Thunder. That seemed like a good story to examine the why of this tale of one city. I began collating the batting and bowling data for the 2 teams to see where potential differences may be.

Once I had done this, I couldn’t really see any major differences between the two teams, so the reason likely lies outside the data provided here. I spent so long looking for stories that weren’t there and creating measures to try and test that I nearly missed the 3 pm deadline. Anyway, I managed to produce a fairly simple dashboard, but the dataset is something I plan to play around with once we finish a couple of upcoming client projects.

 

dashboard

 

My general reflections on Dashboard Week were that it was fun and useful to have a number of tasks using different software from Power BI, Tableau, Alteryx and Tableau Prep. I enjoyed the variety of having a scraping task, a spatial task, survey data and some sports data. The time pressure wasn’t really too bad, apart from the last day (mainly due to fatigue I think). I do enjoy working with data like we had this week rather than sales and profit-based data. Glad it’s over though!

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Dashboard Week: Day 4 Big Bash League

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here

The final dashboard week challenge was using the Big Bash League data for the T20 cricket league in Australia. I was really excited about this one as I don’t like cricket. I love it!

The data had ball-by-ball data from 2011-2021 and match data for the same period. This was great and I set off making a host of measures like economy rates and strike rates etc. The data seemed pretty clean, and I began looking for stories. I noticed that the most successful team was Sydney Sixers and the least successful was Sydney Thunder. That seemed like a good story to examine the why of this tale of one city. I began collating the batting and bowling data for the 2 teams to see where potential differences may be.

Once I had done this, I couldn’t really see any major differences between the two teams, so the reason likely lies outside the data provided here. I spent so long looking for stories that weren’t there and creating measures to try and test that I nearly missed the 3 pm deadline. Anyway, I managed to produce a fairly simple dashboard, but the dataset is something I plan to play around with once we finish a couple of upcoming client projects.

 

dashboard

 

My general reflections on Dashboard Week were that it was fun and useful to have a number of tasks using different software from Power BI, Tableau, Alteryx and Tableau Prep. I enjoyed the variety of having a scraping task, a spatial task, survey data and some sports data. The time pressure wasn’t really too bad, apart from the last day (mainly due to fatigue I think). I do enjoy working with data like we had this week rather than sales and profit-based data. Glad it’s over though!

Share this post