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Dashboard Week (Day 2) – Sports Day

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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After a challenging day 1, our next challenge was to analyze a sports dataset from the SportsVizSunday repository. The objective was to work with data from a less familiar domain and find a strong story with meaningful insights. I had chosen to analyze the Boxing dataset. The dataset contains information about different titles, boxing weight divisions, a list of boxers, the time period they held the title and other relevant data.

  • Choosing the dataset

Our coaches suggested that we focus our analysis to a specific portion of the dataset (in my case, it was to look at the performance of one boxer) since we only had one day to work on our dashboards. I chose to look at the journey of one of the most renowned boxers in the the World Boxing Association (WBA) – Muhammad Ali. I confined the analysis to only the heavyweight category and the time period that Muhammad Ali was active in boxing.

  • Clean and Analyze the data

Now that I have the dataset, the next step is to clean the data and look for any inconsistencies across the WBA dataset. Fortunately, the dataset was clean and required no data prep/cleaning in Alteryx except for one missing value (the number of days Muhammad Ali held a title). I solved this issue by calculating the difference between when he won the title and when it got removed. Now, we have all the data to load into Tableau.

  • Data Visualization in Tableau

The final step is to build the dashboard. Every dashboard needs to have a strong story with supporting insights. The story here was about Muhammad Ali’s boxing career in the WBA and how he compared against the other boxers. More importantly, was he the Greatest of his time?

The key insights that I focused on were:

    • Timeline of all the boxers who held the WBA title.
    • How many times did they defend their WBA title?
    • Who held on to the WBA title the longest?

Since I had limited data, I supplemented my analysis with information about the various matches and their details. After the story and insights were ready, I built the below dashboard, and we could see the final picture. Muhammad Ali clearly dominated all the charts and proved he was the greatest of his time.

 

Icon attribute: <a href="https://storyset.com/business">Business illustrations by Storyset</a>

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Dashboard Week (Day 2) – Sports Day

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. 

After a challenging day 1, our next challenge was to analyze a sports dataset from the SportsVizSunday repository. The objective was to work with data from a less familiar domain and find a strong story with meaningful insights. I had chosen to analyze the Boxing dataset. The dataset contains information about different titles, boxing weight divisions, a list of boxers, the time period they held the title and other relevant data.

  • Choosing the dataset

Our coaches suggested that we focus our analysis to a specific portion of the dataset (in my case, it was to look at the performance of one boxer) since we only had one day to work on our dashboards. I chose to look at the journey of one of the most renowned boxers in the the World Boxing Association (WBA) – Muhammad Ali. I confined the analysis to only the heavyweight category and the time period that Muhammad Ali was active in boxing.

  • Clean and Analyze the data

Now that I have the dataset, the next step is to clean the data and look for any inconsistencies across the WBA dataset. Fortunately, the dataset was clean and required no data prep/cleaning in Alteryx except for one missing value (the number of days Muhammad Ali held a title). I solved this issue by calculating the difference between when he won the title and when it got removed. Now, we have all the data to load into Tableau.

  • Data Visualization in Tableau

The final step is to build the dashboard. Every dashboard needs to have a strong story with supporting insights. The story here was about Muhammad Ali’s boxing career in the WBA and how he compared against the other boxers. More importantly, was he the Greatest of his time?

The key insights that I focused on were:

    • Timeline of all the boxers who held the WBA title.
    • How many times did they defend their WBA title?
    • Who held on to the WBA title the longest?

Since I had limited data, I supplemented my analysis with information about the various matches and their details. After the story and insights were ready, I built the below dashboard, and we could see the final picture. Muhammad Ali clearly dominated all the charts and proved he was the greatest of his time.

 

Icon attribute: <a href="https://storyset.com/business">Business illustrations by Storyset</a>

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