>Seed total for men’s sweet 16 dreams

It's teams not dreams but I suppose you see what you want to see lol

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Your infographic says "UBS and Credit Suisse together hold combined assets of over 7x Swiss GDP", but the Reuters article you link to says "The banks, two of the most systemically relevant in global finance, hold combined assets of up to 140% of Swiss gross domestic product..."

I think this is because you're using assets under management and total assets interchangeably, which doesn't seem correct. UBS's 2022 annual report lists its total assets as $1.1 trillion, which is different from the $4.5 trillion of assets that it manages.

Great visualization and smart to start the axis at 40 since that’s the lowest possible total. A data label might be a good edition. I’m trying to compare this year to that 2010-15 batch that is similar. Cool chart!

Sum of Seeds is my new daycare business

I believe it is 232. If all the 13,14,15, and 16 seeds advanced.

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What’s the highest possible?

Never count out the little guy.

Seed total for men’s sweet 16 dreams… sounds… like bad word choice.

There are 16 teams left in the NCAA Men’s Basketball tournament. This chart is a proxy for frequency and/or magnitude of upsets (higher seed beating a lower seed) that have occurred at this stage since 2000.

The lowest possible aggregate seed total is 40 (each 1, 2, 3, and 4 seeds advance to sweet 16). The average since 2000 is 72. The year 2021 saw a record high total of 94. This year the total is 78.

Data: NCAA.com

Tool: Excel

I have students who, last semester, were given instruction by the college advisors, enrollment specialists, and librarians on how to use it for “searches.” So yes. There is enough time for college students to have already been introduced to this in a bad way and need to be retrained/retaught how to effectively use it without copy/pasting output.

They won't be 3rd anymore after they realize losses from all the horrible swaps CS was engaged in..

People don't have to deal with data size and scalability issue? For me, I found it quite a pain having to make sure data fits the machine for in memory analytics. And handle OOM even when using parallel engine like Apache Spark.

Moral of the story : quit while you are ahead.

It’s the same entity. It’s the “City and County of San Francisco” so the city data by definition includes spending and debt that would normally be accrued at the county level. So not an apples to apples comparison.

https://en.wikipedia.org/wiki/Consolidated_city-county

Thanks for the feedback! If you click the link to the chart (https://app.openaxis.com/visualizations/13172) and hit "new chart" under it, you can correct the visualization and repost it!

If you check out the data set here (https://app.openaxis.com/visualizations/13172), you'll see that the chart is referencing San Francisco as a city

A legend that uses values as keys? That's so silly. What if two of the values were the same?

UBS knocked Fidelity Investments off the podium with their acquisition of Credit Suisse, bringing their total assets under management (AUM) to $5.9T - over 7x the GDP of Switzerland.

Sources: ADVRatings Reuters

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Tools: Figma

Used APIs from Youtube, Twitter, Tiktok, and Google analytics I used used Python/Django and D3.js graphs

Wow all my gripes in one place

17% color coding the pie but not the labels

12% not ordering the slices by size nor chronologically

37% no sample size and no sources

53% percentages in this comment not adding up to 100%

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