# data[].attributes.attachment_caption
The histogram on the left y axis compares the results of Monte-Carlo simulation (light blue) with the observations (yellow). RV (circles) stands for radial-velocity, and TTV (triangles) stands for transit-timing variation, both methods of detecting exoplanets. Good luck figuring out the rest.

# data[].attributes.body
Describes the size and composition of various planets that have been detected in other solar systems. Sourced from: https://www.pnas.org/doi/10.1073/pnas.1812905116

# data[].attributes.headline
Intelligence Rating Vs Attractiveness Ratings Between regions and ethinicity

# data[].attributes.subject
Intelligence Rating Vs Attractiveness Ratings Between regions and ethinicity

# data[].attributes.body
https://journals.sagepub.com/doi/full/10.1177/25152459211045334

# data[].attributes.attachment_caption
"the difference is a factor of two, but the KFC logo has four times the area of the Starbucks logo" - https://eagereyes.org/blog/2008/linear-vs-quadratic-change 

# data[].attributes.headline
making mcdonalds look unproportionally look huge

# data[].attributes.subject
making mcdonalds look unproportionally look huge

# data[].attributes.body
originally from https://www.princeton.edu/~ina/infographics/index.html

# data[].attributes.body
"Lord of the Rings" chart, prominently displayed at the top, uses lines to show the journeys and encounters of different characters throughout the films.

# data[].attributes.headline
What percent of gamers have 'x' CPU cores

# data[].attributes.subject
What percent of gamers have 'x' CPU cores

# data[].attributes.body
https://www.reddit.com/r/dataisugly/comments/1bp8lfj/yaxis_is_a_bit_funky/

# data[].attributes.body
https://theconversation.com/three-graphs-that-show-a-global-slowdown-in-covid-19-deaths-135756

# data[].attributes.body
Source: The numbers of the Lotus, like a star (microsiervos.com)

# data[].attributes.body
Source: https://twitter.com/reina_sabah/status/1291509085855260672

# data[].attributes.wish_content.attachment_props.text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me

# data[].attributes.wish_content.attachment_props.original_text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me https://t.co/sy9NHW9oTK

# data[].attributes.attachment_link.embed_code
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me pic.twitter.com/sy9NHW9oTK
— Sabah Ibrahim (@reina_sabah) August 6, 2020

# data[].attributes.attachment_link.metadata.attachment_props_source.data.text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me https://t.co/sy9NHW9oTK

# data[].attributes.attachment_link.metadata.attachment_props.original_text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me https://t.co/sy9NHW9oTK

# data[].attributes.attachment_link.metadata.attachment_props.text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me

# data[].attributes.headline
Montgomery County Ride On Proposed Routes

# data[].attributes.subject
Montgomery County Ride On Proposed Routes

# data[].attributes.body
Source: https://experience.arcgis.com/experience/92145b6c04e0408c97a4f2df6c36aae8

# data[].attributes.attachment_caption
"the difference is a factor of two, but the KFC logo has four times the area of the Starbucks logo" - https://eagereyes.org/blog/2008/linear-vs-quadratic-change 

# data[].attributes.headline
making mcdonalds look unproportionally look huge

# data[].attributes.subject
making mcdonalds look unproportionally look huge

# data[].attributes.body
originally from https://www.princeton.edu/~ina/infographics/index.html

# data[].attributes.headline
The 1972 Metropolitan Transportation Authority revised map of the rapid transit facilities of New York City Transit Authority by Vignelli, Massimo

# data[].attributes.subject
The 1972 Metropolitan Transportation Authority revised map of the rapid transit facilities of New York City Transit Authority by Vignelli, Massimo

# data[].attributes.body
https://www.davidrumsey.com/luna/servlet/detail/RUMSEY~8~1~278315~90051706:The-Metropolitan-Transportation-Aut?qvq=q:pub_list_no%3D%229891.000%22;lc:RUMSEY~8~1&mi=2&trs=3

Not the original (Which was a physical image); used Archive.org to find a copy of the image from a map archive.

# data[].attributes.attachment_link.title
The Metropolitan Transportation Authority revised map of the rapid transit facilities of New York City Transit Authority

# data[].attributes.attachment_link.description
New York City Transit Authority; Vignelli, Massimo (1931-2014); The Metropolitan Transportation Authority revised map of the rapid transit facilities of New York City Transit Authority. New York City Transit Authority, 1972. (Cover title) New York Subway guide : New York City Transit Authority.; 9891.003; Color map, 45x45, folded to 18x9.

# data[].attributes.body
"Lord of the Rings" chart, prominently displayed at the top, uses lines to show the journeys and encounters of different characters throughout the films.

# data[].attributes.headline
Pie chart of super smash bros ultimate results

# data[].attributes.subject
Pie chart of super smash bros ultimate results

# data[].attributes.attachment_caption
This graphs makes it seem like there are more viewers then TV's owned in households but doesn't take into account people going out to bars or restaurants to watch. Many other ways to watch without a tv in the household.

# data[].attributes.body
Source: https://ecampusontario.pressbooks.pub/bio16610w18/chapter/how-graph-misrepresents-data/

# data[].attributes.headline
Energy Generation by Fuel Type for Suppliers of Apple Inc.

# data[].attributes.subject
Energy Generation by Fuel Type for Suppliers of Apple Inc.

# data[].attributes.body
https://www.bloomberg.com/graphics/2023-opinion-apple-supply-chain-climate-change/

# data[].attributes.body
Source: https://www.nytimes.com/2022/01/06/opinion/omicron-covid-us.html

# data[].attributes.body
Source: The numbers of the Lotus, like a star (microsiervos.com)

# data[].attributes.attachment_caption
original caption"Scroll down to see a list of the Unicode v12.0 emoji ranked in order of how frequently they are used."

# data[].attributes.body
source: https://home.unicode.org/emoji/emoji-frequency/

# data[].attributes.body
Source: https://www.livescience.com/45083-misleading-gun-death-chart.html

# data[].attributes.attachment_link.readable_text
As you may have seen, Luke Bornn set Twitter on fire yesterday (to the tune of nearly 500 RTs) re-posting something that Sam Ventura mentioned previously on why radar charts are bad.

A reminder, blatantly plagiarized from @stat_sam, of why radar plots are misleading. Eye focuses on area, not length. pic.twitter.com/Dk3gcn1GD1

- Luke Bornn (@LukeBornn) May 17, 2017

Obviously, a lot of eyes turned toward me, since it is probably my fault they exist at all in soccer/football, and possibly my fault they have crept into other sports. Daryl Morey then managed to do a drive-by on my career so far, posting this tweet

@LukeBornn @stat_sam No analytics person worth his salt uses radar plots or pie charts or 2 Y axis. Probably more if I think on it. All super misleading.

- Daryl Morey (@dmorey) May 17, 2017

Which I don't think was calling my entire analytics career into question, but could be interpreted as such. THANKS DARYL. I am pleased to note that at least I don't use pie charts or 2 Y axes. Anyway, none of this is personal to me and please don't assume I took it as such. I do have incredible respect for Luke, Daryl, and Sam though, so I thought this topic was actually worth revisiting. In addition to hot takes, that thread under Luke's tweet generated a lot of great discussion.

The fact that lots of people have reactions to this type of work is a good thing, not a bad one. Anyway... many smart, analytically savvy people hate radars mostly for the reasons explained in that thread.

They can be misleading. Ordering of variables matters. There are more precise, accurate ways to convey the data.

The thing is, I knew all of this before I started down this road.

My stuff used to just feature tables of numbers. Then I spent the better part of six months doing a deep dive into data vis before I ever spat out a silly radar. And yet, some might say despite my education, I still did it.

Why?

It's obviously the result of a choice, not of ignorance. Before I'm tried and hanged for data visualization crimes against humanity, I'd at least like a chance to mount my defence. Often when someone allegedly smart (that's me) continues to do something somewhat controversial in the face of some serious criticism, there are things we can learn.
Learn to Communicate
I have been to a lot of analytics conferences at this point, and the biggest point of emphasis on the sports side is always communication is key. You need to understand your audience (usually coaches, sometimes executives), and take steps to deliver your analysis in a form and language that they can accept. Rephrase that a bit, and you end up with:

Audience. Dictates. Delivery.

In order to succeed, you need to take account of the audience you are pitching to and give them something they can understand. Even better, give them something they want to understand. (It helps if it's pretty.) In soccer/football circa 2014, the fanbase had no real statistical knowledge. The media was just glomming on to the idea that maybe stripping out penalties from goalscoring stats made sense, assists might be vaguely interesting, and the concept of rate stats wasn't completely insane.

I'm not being glib here, this was how it was.

" xG " (or Expected Goals) was seriously weird and controversial and people seemed to think, presumably via the result of someone else's misguided analysis, that possession had something to do with the probable final score. In situations like this, visuals go a long way toward opening the conversation. If you show a table of numbers to a coach who isn't already on board, you're dead.

Bar charts?

Only mostly dead.

Radars?

Interesting... Tell me more.

The same was true of the general public. Radars grabbed people in a way almost nothing else did. I think part of that is related to the fact that various soccer/football video games had used spider charts for a long time already, so they were somewhat familiar. Math = bad. Familiar = less scary = good.
Right, we have a vis style that grabs attention - can I fix the flaws?
Rewinding, when faced with a cool visualisation framework that would allow us to talk about player stats in an accessible way - something ALMOST NO ONE WAS DOING IN SOCCER at the time - I set about seeing if I could correct radars for some flaws. Major flaws with radars:

Order of variables matters

Area vs length issue means potential misinterpretation

Axes represent different independent scales

So what did I do?

Added the 95th/5th percentile cutoffs to normalize for population. Suddenly axes weren't really on independent scales, even if it seemed like they were

Broke the stats we care about for different positions into their own templates

Clustered similar element stats together. Shooting over here. Passing over here. Defensive over here, etc.

One thing I was also clear about up front was that I wanted to include actual output numbers, not just percentiles. This was another choice about audience impact. Sports quants mostly care about percentiles. Normal fans barely cared at all about numbers, so percentiles would be even more abstract.

Plus no one had ever done percentile work for most of the stats in football.

What is a high number of dribbles per game? No one knows. Putting percentile info made even less sense then, because we were just starting to have conversations about basic stats. Going back to my youth collecting baseball cards, I wanted people to be able to talk and argue about Messi vs Ronaldo from a stats perspective, and the only way to make that happen was to have some actual numbers on the vis. I don't even know if this was successful, but it was a design impetus that was constantly in my head.
Impact vs Accuracy
Most of the people ranting about radar charts on Twitter yesterday are pretty hardcore quants. To many of them, sacrificing precision for anything is strictly verboten. The problem with this perspective for me was: radars aren't for you. Hell, radars aren't even for me. I work in the database, and my conclusions are largely drawn from that perspective.

The minor inaccuracy issues of radars don't affect my work.

BUT I wanted to talk to a resistant public about soccer stats, and this enabled discussion. I needed to talk to coaches about skill sets and recruitment, and this was a vital way of bringing statistics into that discussion while comparing potential recruits to their own players.

As I designed them, radars exist to help you open the door with statistical novices, and from that perspective they have been wildly successful. Even in 2017, football/soccer doesn't have the volume of knowledgeable fans that basketball and baseball have in the U.S. We also don't have coaches who are comfortable with almost any statistical discourse, although that is definitely changing in the last year.
Actual, practical feedback
So a funny thing happened on the way to the boardroom: In football, radars became accepted as a default visualization type. I've visited a number of clubs who just incorporated the work as part of a basic suite of soccer vis, only occasionally to my chagrin.

" My coaches love these. They want us to do physical stats in this form because they feel like they are easy to understand." " This is cool. I like the way the shapes become recognizable as you use them more, and clearly indicate different types of player. "

At Brentford, we took two non-stats guys, taught them the basics of interpretation, and churned through over 1000 potential recruits in a year. Football isn't like American sports. Players can come from a ridiculous number of vectors, and radars were the best, most easily understandable unit of analysis I could find.

Combine no money, huge squad needs, and limited recruitment personnel, and the only way we could hope to succeed was via efficiency and volume. They were not the end of the analysis. In fact, for recruits we liked, they only comprised a tiny portion of the evaluation cycle. From a volume perspective though, radars were the most used form of evaluation in the process.

On StatsBomb IQ, our analytics platform, even non-recruitment people seem to be taking a deep dive in a way they never have before. One person researched nearly 1000 players and teams over the course of the first two weeks, just because they liked learning about the game and the stats in this way.

In my own opinion, having researched many alternatives, I feel like radars are the fastest way to get a handle on what skill set a player may or may not have, and include some basic statistical context.
You can't prevent misuse of statistics
This tweet from Luke yesterday made me laugh

@stat_sam In case people think this is well-known, last month I met a pro soccer team using radar plot area as a global metric to value players...

- Luke Bornn (@LukeBornn) May 17, 2017

Two words: competitive advantage. If you're going to take the research for free and apply it while failing to understand how to actually use it, you deserve what you get. I'm 98% certain this club never talked to me, or else I would have forcefully steered them away from that type of analysis. And the problem is, you can't prevent people from doing bad analysis on any type of stats.

Single numbers?

Mostly useless.

Thinking the wrong stats are important?

Happens all the time, even from smart, highly educated individuals. Bad interpretations of basic visualizations? Check newspapers almost every day. Bad/useless visualization? So many, it's a surprise we don't all walk around with our eyes bleeding.

Look, everyone makes mistakes in their jobs.

We try to be objective, but everyone in stats and analytics also makes mistakes. Daryl Morey drafted Joey Dorsey, even though understanding age curves and competition cohorts is a pretty basic concept. Soccer stats once thought possession% was important. Pep Guardiola apparently thought he could win a Premier League with a bunch of fullbacks over 30. I said nice things about Luke Bornn.

It happens to the best of us.

The fact of the matter is, unless you are there talking to the users every day, you can't prevent people from taking your work and potentially using it poorly. It doesn't matter if that work is in tables or numbers, or bar charts, or radars, or fans, or code, or in a shot map, or whatever. The interpretation, application, and execution of analysis will remain more important than simply having the information from now until the end of time.
What next?
The bashing of radars is almost a yearly event at this point, and like I said at the start, I concede that there are flaws in the vis style that even my adjustments haven't completely overcome. With that in mind - and despite the fact that customers actually seem very happy with our current style of vis - we will probably add an alternate form of player data vis to StatsBomb IQ by the end of the year.

I'm not sure exactly what it will be, but as football clubs move from novice to intermediate to advanced statistical analysis, precision will become more important and I want us to stay ahead of that curve. In the meantime, I hope my defense of radars above has at least explained why I made this horrible, unforgiveable visualisation choice in 2014 and continued to stick with it over the years. Communication and opening doors to talk about stats in football with coaches, analysts, and owners remains the most important hurdle we have to overcome.

Radars start a conversation.

They get a reaction.

And for whatever reason, football people are often more comfortable talking about and digesting them than almost any other vis type I have encountered.

Maybe that will change in the future. Until then radars remain a pretty damned good visualisation for displaying most of the different elements in player skill sets*, which is the most important conversation topic we have in player recruitment.

Ted Knutson
ted@statsbomb.com
@mixedknuts

*In my opinion at least, and provided you correct for their flaws and educate your users.

# data[].attributes.attachment_link.title
Revisiting Radars - StatsBomb | Data Champions

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Ted Knutson discusses the recent focus on Radars and the evolution of data visualisation in the football analytics industry.

# data[].attributes.body
Source:

https://nftbooks.info/

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Mortality in seven countries attributed to COVID-19 (January 23 to April 20, 2020) Danny Dorling

# data[].attributes.headline
2020 COVID-19 Mortality Rates in Seven Countries

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2020 COVID-19 Mortality Rates in Seven Countries

# data[].attributes.body
Source: https://theconversation.com/three-charts-that-show-where-the-coronavirus-death-rate-is-heading-137103

# data[].attributes.headline
Horrible Godawful Useless Nutrition graphic Ive stared at this for 5 minutes and havent learned a thing

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Horrible Godawful Useless Nutrition graphic Ive stared at this for 5 minutes and havent learned a thing

# data[].attributes.body
Source: https://generalassemb.ly/blog/

# data[].attributes.body
source: https://public.tableau.com/app/profile/zak.saucede/viz/HowintenseisTheDataSchool/DataSchoolTraining

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Source: https://twitter.com/reina_sabah/status/1291509085855260672

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As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me

# data[].attributes.wish_content.attachment_props.original_text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me https://t.co/sy9NHW9oTK

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As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me pic.twitter.com/sy9NHW9oTK
— Sabah Ibrahim (@reina_sabah) August 6, 2020

# data[].attributes.attachment_link.metadata.attachment_props_source.data.text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me https://t.co/sy9NHW9oTK

# data[].attributes.attachment_link.metadata.attachment_props.original_text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me https://t.co/sy9NHW9oTK

# data[].attributes.attachment_link.metadata.attachment_props.text
As an Indian woman, I can confirm that too much of my time is spent hiding behind a rock praying the terrifying gang of international giant ladies and their Latvian general don't find me

# data[].attributes.attachment_caption
The histogram on the left y axis compares the results of Monte-Carlo simulation (light blue) with the observations (yellow). RV (circles) stands for radial-velocity, and TTV (triangles) stands for transit-timing variation, both methods of detecting exoplanets. Good luck figuring out the rest.

# data[].attributes.body
Describes the size and composition of various planets that have been detected in other solar systems. Sourced from: https://www.pnas.org/doi/10.1073/pnas.1812905116

# data[].attributes.headline
Intelligence Rating Vs Attractiveness Ratings Between regions and ethinicity

# data[].attributes.subject
Intelligence Rating Vs Attractiveness Ratings Between regions and ethinicity

# data[].attributes.body
https://journals.sagepub.com/doi/full/10.1177/25152459211045334

# data[].attributes.headline
What percent of gamers have 'x' CPU cores

# data[].attributes.subject
What percent of gamers have 'x' CPU cores

# data[].attributes.body
https://www.reddit.com/r/dataisugly/comments/1bp8lfj/yaxis_is_a_bit_funky/

# data[].attributes.attachment_link.readable_text
Pokemon GO has taken the world by storm. With over 100 million downloads and an average of 24 million active users, Pokemon GO is the biggest mobile game ever!

To catch Pokemons in the wild and become the very best (Pokemon theme lyrics for the candid reader), people are looking for the best tips and tricks on the web. As a proof, here the Google Trends chart for searches related to "Pokemon" over the last 90 days.

We notice the sharp increase of interest starting from the 6th of July 2016, the official launch date of Pokemon GO.

The number of Youtube videos about Pokemon are also naturally growing and becoming viral: Today, I invite you to immerse yourself in the marvelous universe of Pokemon videos: from the classics to the very weird and deleted videos...

Pokemon data viz

How did you create this network?

This network is created by scraping all the Youtube playlists containing the words: "Pokemon" or "Pokemon GO". Here, each node represents a video, the links are created if videos follow each other on a playlist.

The first step is to read the content of the Youtube playlist search and to extract from the result pages the links to the associated playlists along with useful information such as the title, creator, date of creation, channel name, channel id, etc. All this data is stored in a database.

Then for each playlist, we need to actually fetch all videos information and their order in the playlist. Similarly to the playlist, I also gather the video title, creator, date alongside the number of views, likes, dislikes, etc.

Because people tend to put the same videos in different playlists, the network is composed of independent sets of videos (the vermicellis) but has a strongly connected core with the most popular videos. Due to their popularity, the videos are put in a lot of playlists which will make them even more popular afterward. Let's call this phenomenon the popularity spiral.

I see what you did there, it's a Pokeball!

I'm probably sure you think I have created this Pokeball shape on purpose!

For those who don't know, a Pokeball is an essential item for Pokemon trainers as it allows to catch and hold Pokemon around your belt. If you want to know more about Pokeballs have a look at this nicely illustrated post.

A Pokeball

Well, the funny thing is that the structure of the network itself, with all the tendrils and the well-connected core, was automatically rendered in a Pokeball shape using the excellent Scalable Force Directed Placement (SDFP) graph layout algorithm.

Ok, you did not explicitly create the Pokeball shape, what about the colors?

I have to plead guilty here, the principal colors: red, white and blue were selected on purpose to accentuate the Pokeball effect.

However,the color intensity depends on the total number of views of the each spaghetti! More formally for the scientific reader, it actually depends on the number of views of each weakly-connected components of the graph. The same technique cannot be used for the core because it is the largest component of the graph, or in layman terms, a bowl of spaghetti that cannot be separated.

To bring more depth and actually dissociate what's happening in the center, I used a community detection algorithm to reveal and color communities of related videos. This technique is commonly used to recommend items to users. In the case of Youtube, it's the recommended videos on the right of the video player.

Let's go interactive!

Many people have asked me if I could make my networks interactive, especially in the case of my art exhibition around networks. I am proud to introduce my first interactive 3D network on this blog.

Introducing the interactive Pokemon video network

Open 3D Pokemon video network
Just move with your mouse, scroll to zoom double-click to open the associated video. The z-axis is dependent on the number of views of each video. The most viewed videos are closer to the screen.

Update: The Pokemon network has evolved, read all about it in my Evolving Pokemon Network article.

‍

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Kirell Benzi - Gotta Catch 'Em All - A Pokemon Dataviz

# data[].attributes.attachment_link.description
Pokemon GO has taken the world by storm. With over 100 million downloads and an average of 24 million active users, Pokemon GO is the biggest mobile game ever! To catch Pokemon in the wild and become the very best (Pokemon theme lyrics for the candid reader), people are looking for the best tips and tricks on the web.

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The above visualization is a 3d visualization for the aviation data. There are different kind of filters to represent the data. The visualization shows the frequencies for the data. The connections are represented with the lines.

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Source:

https://icao.maps.arcgis.com/home/webscene/viewer.html?webscene=85f462213e544283aa68c5f6e1eff845

# data[].attributes.body
https://theconversation.com/three-graphs-that-show-a-global-slowdown-in-covid-19-deaths-135756

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2013 Major League Soccer Team Salary Breakdown.

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2013 Major League Soccer Team Salary Breakdown.

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This chart shows a 2013 salary breakdown for Major League Soccer teams, with each team represented by a composite bar that indicates all its players' salaries, colored by position.

# data[].attributes.body
Source: Painting with Numbers by Randall Bolten (painting-with-numbers.com)

Original article (CBS fixed it): Marijuana use and support for legal marijuana continue to climb - CBS News

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Green means low. And gray means low. Also gray means medium. Stay safe!

# data[].attributes.subject
Green means low. And gray means low. Also gray means medium. Stay safe!

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https://www.reddit.com/r/dataisugly/comments/vokfkz/green_means_low_and_gray_means_low_also_gray/

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Percentage of Marijuana Use by High Schoolers Compared to Hot Dog Sales in the US

# data[].attributes.subject
Percentage of Marijuana Use by High Schoolers Compared to Hot Dog Sales in the US

# data[].attributes.body
Source: https://www.reddit.com/r/dataisugly/comments/lyw7pl/i_have_no_idea_what_is_trying_to_be_conveyed/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

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https://www.mediamatters.org/fox-friends/dishonest-fox-chart-overstates-comparison-welfare-full-time-work-500-percent

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https://www.reddit.com/r/dataisbeautiful/comments/1btb63o/oc_visualised_paragraph_embeddings_by_character/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button