Source: data from [olympics.com](http://olympics.com) website. Tools: Office 365 (Excel + Powerpoint)
The data might be beautiful, but that definition paragraph in the bottom half of each page is NOT:
*Number of medals in excess/fewer than what would have…..*
I tried to read it like 4 times and still can’t understand what they’re trying to say. I’m sure, given enough time I could but I have a life so suck it map.
i’m curious how these categories were decided. was there some sort of unsupervised learning/clustering, or were they just made manually? because it seems weird to group higher/stronger with more precise. i’d think precision would either go by itself or get lumped in with the ‘artistic’ category.
Add boxing and judo to this category, because in many cases it is the referees’ subjective view that decides the outcome of a match.
Not gonna go to their impact in basketball, which for a while now has been plagued by FIBA delegating inexperienced or inept referees to events like the World cup and the Olympics due to some ongoing dispute with Euroleague (which contracts most of the best referees outside the NBA), and as a result we often get “artistic” impression that may not even be aligned with the game rules to have an impact of the match outcome as well.
How do some countries have negative medals?
What conclusion am I supposed to draw from this? I honestly have no idea. It seems like you just massaged the data and made some maps. What am I missing?
So… did you intentionally leave the UK out of this or what?
![[OC] Success at Paris 2024: a categorical breakdown](https://www.europesays.com/wp-content/uploads/2024/08/b7erndi0rfid1.png)
7 comments
Source: data from [olympics.com](http://olympics.com) website. Tools: Office 365 (Excel + Powerpoint)
The data might be beautiful, but that definition paragraph in the bottom half of each page is NOT:
*Number of medals in excess/fewer than what would have…..*
I tried to read it like 4 times and still can’t understand what they’re trying to say. I’m sure, given enough time I could but I have a life so suck it map.
i’m curious how these categories were decided. was there some sort of unsupervised learning/clustering, or were they just made manually? because it seems weird to group higher/stronger with more precise. i’d think precision would either go by itself or get lumped in with the ‘artistic’ category.
Add boxing and judo to this category, because in many cases it is the referees’ subjective view that decides the outcome of a match.
Not gonna go to their impact in basketball, which for a while now has been plagued by FIBA delegating inexperienced or inept referees to events like the World cup and the Olympics due to some ongoing dispute with Euroleague (which contracts most of the best referees outside the NBA), and as a result we often get “artistic” impression that may not even be aligned with the game rules to have an impact of the match outcome as well.
How do some countries have negative medals?
What conclusion am I supposed to draw from this? I honestly have no idea. It seems like you just massaged the data and made some maps. What am I missing?
So… did you intentionally leave the UK out of this or what?