I couldn't find too much to do with this week's data set, so I ended up with some simple BANs and line charts that take the original and reorganize them a bit to make them more clear.

Resources:

1. Data set - https://data.world/makeovermonday/2021w32
2. Chart Guide - https://chart.guide/
3. Final Viz - https://bit.ly/mm2021w32

Last week I saw this really cool viz from Squawka Football on Twitter and wanted to see if I could rebuild it.

1889 ➡️ 2̶0̶1̶8̶ 2019

Watch the race to become the most successful club in English top-flight history unfold before you. Now updated. pic.twitter.com/4IprWQ16ZT

— Squawka Football (@Squawka) May 13, 2019

Given that this requires animation, I knew I needed a tool that supported this and I turned to Flourish. The data has to be structured in a very specific way, so I downloaded the data from Wikipedia, imported it into Alteryx for a bit of a massage, and spit it back out in the format Flourish required.

And voila! An animated viz of the history of English football champions from 1888-2018. Very little effort required + great animation = win!

The 2018-19 Premier League season kicks off tomorrow night with an enticing match between Manchester United and Leicester City. This reminded me about a viz I had created at the end of the last season as a way of practicing stepped lines in Tableau.

When I originally created this, I had to use table calcs to get the stepped lines to work, which can get complicated and is very time consuming. Now with stepped lines, it merely a matter of changing the lines type.

I decided to add in a couple of user options:

1. Which team do you want to highlight?
2. How do you want to compare the teams? By total points for the season of the final position in the table?
3. Not all teams have been in the EPL for all 17 years, so I provided an option to filter down to just the teams that have been in the EPL for the user specified number of years.

Today I had the incredible honor to talk to the English Institute of Sport and PGIR about data visualisation and why they should care about it...and I got to do it at the home of England Rugby.

Twickenham Stadium - Home of England Rugby

Twickenham is my home town, so I was able to walk to the stadium and rehearse my presentation along the way. The day started with a quick recap of their previous day, and this was the first chart I saw. "Oh boy, this might be a long day" was the first thought in my head. Deep breaths Andy. Deep breaths.


Surely Duncan did this on purpose for the benefit of the day. And yes, I did say something. From there, we went on a tour of the ground going through the tunnel onto the pitch.


Once on the pitch, we made our way to the coach's box.

View from the England coach's box

There, the team analysts talked to us about their gameday setup, where the coaches sit, how they pass information, etc. I found it fascinating how rugby teams use data in-game, right there with the coach, to make decisions, yet football doesn't allow any of that. How archaic!

We went back into the locker room to talk about the facilities available to the players and how they transmit all of their data post match.

England dressing rom

Team conditioning room

We then made our way upstairs so the analysts could learn about data visualisation. The rest of the day started with a talk by me titled

What is Data Visualisation? And Why Should You Care?

This was a new talk for me, with the focus on getting the team to understand the impact displaying data visually can have. I started with a couple of simple examples, we discussed why we do data analysis, talked about how they can get started, how to constructively evaluate visualisations, and a brief intro to the design process.

After this talk, four different analysts presented some of their work. Each table then took 10 minutes to discuss and write down what works and what could be improved based on what I had taught them. This worked like magic! I then gave my opinion in front of the whole group and suddenly a mere four hours after we started, we had a bunch of data viz snobs! I love it! It's so great to see the light bulb go off and especially to see them critiquing their own work.

The day concluded with a series of "appointments" where they could chat with me one-on-one about their work and get individual feedback. It reminded me a lot of the Tableau Doctor sessions at TC.

I made notes throughout the day and came up with a list of 10 key takeaways:

1. Include context - compared to what?
2. Where should the eye focus?
3. Highlight what's important
4. Be careful with stop light theme 
5. Include a call to action whenever possible
6. Pay attention to color usage
7. Know your audience
8. Does the audience now what to do next?
9. Effective chart types (boring is ok if it works)
10. White space is ok - it gives the viz room to breathe

Overall, an incredibly satisfying day. Thank you EIS for asking me to come! I'd be happy to do it again any time. For those interested, here's my presentation.

This week’s Makeover Monday takes a look at this donut chart from Mail Online:

We’re working again with a very simple data set this week and a chart that suffers many problems. Instead of listing them all out individually, I’ve used Tableau’s Story Points feature to walk you through the step-by-step makeover. In the end, it took me 10 steps to get to the final result that I’m satisfied with. To me, the story isn’t about the increase in wages for footballers, rather the increase in their wages compared to the average household.

If you love soccer, then it’s likely that you follow the EPL.  My favorite team?  ARSENAL!  Did you see the incredible goal by RVP Saturday against Everton? You may never see better technique and now he’s only one goal behind Thierry Henry’s team record for goals in a calendar year.  Please Lord, keep RVP healthy for a full season!

And if you love soccer and you love stats, then check out Soccer by the Numbers. Chris Anderson writes many quality posts and recently he blogged about points and relegation.  I wanted to take Chris’ ideas a step farther.  I needed a richer dataset than what Chris was able to provide, so I downloaded the final tables (i.e., standings) from the EPL back to the 2001-02 season from ESPNSoccernet. You can download the full dataset here.

I borrowed (or is it stole?) Steve Wexler’s technique for providing instructions (hover over the EPL logo to see what I mean).  There’s lots of interactivity in the viz, so first check out the instructions, then start clicking around. 

Answer this: How many teams have qualified for the Champions League with a negative goal differential?  Who were they? What else can you tell me about the team(s)?  Post your answer in the comments.

Let me start by saying I am a HUGE Arsenal fan. I catch every game that’s on TV here in the States. 

If you’re living under a rock and don’t watch football, the knockout stages of the UEFA Champions League begin Tuesday with Arsenal hosting Barcelona on Wednesday in the biggest tie of the round.  Catch the game on Fox Soccer Channel at 2:30pm ET.  If you want to see the game played at its absolute highest level, this is the game to watch.  It will be a beautiful sight.

Annually Deloitte publishes a list of the top 20 football clubs in the world based on revenue.  As always, the Guardian Datablog published a viz to go along with its article.  They published this absolutely hideous stacked bar chart.  Seriously, this is what they published.  How do you even know which team is which?  There is a fancy mouse-over feature.  This stuff kills me!

With Tableau, there are so many better ways to make this data interesting.  Here’s my take:

Interacting with the viz you can quickly see that:

1. The Barclays Premier League dwarfs the other leagues in all revenue types
2. The Barclays Premier League has seven of the top 20 teams (click on any of the league logos to filter the list of teams)
3. Real Madrid is a MUCH bigger club than its city neighbor Atletico de Madrid (350% bigger)
4. Manchester United is also a MUCH bigger club than its city neighbor Manchester City (229% bigger).  I hate them both, but Manchester City even more since they think they can buy themselves a title.  No chance with an Italian manager; the football is way too negative!
5. Arsenal dominates matchday revenue, thanks in large part to the spanking new Emirates Stadium (I can’t wait to see it some day).  I heard on TV today that they generate $3M every game
6. The top three clubs in terms of broadcasting revenue are all in Italy.  According to the NY Times, “Italian teams negotiate their own television contracts, with the top clubs like Inter Milan, A.C. Milan and Juventus garnering huge deals”, whereas it’s a shared revenue pool in the other leagues.  Heck, Real Madrid’s rank in broadcast revenue puts them at 17th, but their overall revenue has them at #1.
7. German clubs lead the way in commercial revenue.  I know virtually nothing about the Bundesliga other than their games are fun to watch and the chanting by the fan is endless

Does anything else stand out to you?

Go you Gooners!

The Guardian DataBlog is a great resource if you want to take random data sets and practice your visualization skills. One of the great aspects of this blog is that they provide all of the data; all you need to do is download them and start playing.

There was a blog post on December 3rd with the subtitle "Latest figures show more and more young people seeking treatment for cocaine addiction." The report in this post was concise and to the point: the number of people between the ages of 18-24 seeking treatment for cocaine use has skyrocketed between 2005 and 2009. I wanted to take their text-based summary and create visualizations (which is what they challenge their readers to do).

First, I wanted to understand the amount of drug use for all drugs.



A few observations quickly jump out:

1. 70% of the addicts are being treated for opiates or an opiates/crack cocktail. This should obviously be the focal point for reducing addiction rates.
   
   
2. It looks like there could have been some type of drug prevention or treatment program launched in 2006-2007. I would have to do some deeper research to find out, but this quick visualization leads you in that direction, which is exactly what rapid fire analysis is all about.
   
   
3. Female drug use is at its highest between the ages of 18-24, while men seek treatment between the ages of 25-29.

In the visualization for cocaine use only, I wanted to duplicate exactly what the blog post stated.



The facts stated are: