![](https://static.wixstatic.com/media/13a7a4_c95aeb3ea43749238d63d882de80a64c~mv2.jpg/v1/fill/w_1920,h_1631,al_c,q_90,usm_0.66_1.00_0.01,enc_avif,quality_auto/13a7a4_c95aeb3ea43749238d63d882de80a64c~mv2.jpg)
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The Excel Unicorn Marathon "Top 10"
Project Type
Web scraping | Top "N" | Data Visualization
Date
December 2022
Location
Cape Town, South Africa
The Problem:
- When you have bucket-loads of complex data, how do you narrow down the meaningful insights effectively and efficiently?
The Solution:
- A useful tool is the "Top N" records from the data - where N is any number you'd like to investigate.
- To demonstrate this, let's look at the "Top 10" filters of my favourite sport - running!
- I wrote a PowerBI query to web-scrape all road running data from 1963 to 2022 from www.worldathletics.org.
- This resulted in a unique database of 77 300 data points, where the "Top 10" records were filtered for: Key Metrics, Race Winners, Nationality, and Venue.
- From here we can easily track the top runners of our time, the quickest marathon running times, which courses are the the most conducive for a quick finish, and who are highest ranked runners.
The Data Tool:
- Data preparation and visualization in Power BI.
![Marathon Dashboard Image](https://static.wixstatic.com/media/baee4d_6c781adca6f04b40a030435ec82ccc35~mv2.png/v1/fill/w_1,h_1,q_90/baee4d_6c781adca6f04b40a030435ec82ccc35~mv2.png)
![Marathon Dashboard Image](https://static.wixstatic.com/media/baee4d_6c781adca6f04b40a030435ec82ccc35~mv2.png/v1/fit/w_170,h_170,q_90/baee4d_6c781adca6f04b40a030435ec82ccc35~mv2.png)
![Marathon Top 10](https://static.wixstatic.com/media/baee4d_59ec8527480b434b90218938c7666932~mv2.png/v1/fill/w_1,h_1,q_90/baee4d_59ec8527480b434b90218938c7666932~mv2.png)
![Marathon Top 10](https://static.wixstatic.com/media/baee4d_59ec8527480b434b90218938c7666932~mv2.png/v1/fit/w_470,h_667,q_90/baee4d_59ec8527480b434b90218938c7666932~mv2.png)