In a nod to our friends at Saint Joseph’s university in Philadelphia, I picked March Madness data for week 12, highlighting the phenomenon that is US college basketball.

The original article focused on the challenges around predicting which teams will feature in the semi-final and final round of the tournament and provided a poor visualization of the data, which we challenged the #MakeoverMonday community to makeover.

For me as someone with zero knowledge about basketball, this week was a great opportunity to learn more about the sport and attempt to grasp what the fuss is all about. I think living in Europe will always limit my appreciation for the sports madness that can takeover the American psyche, but the vizzes this week at least gave me some insight into the passion that exists with people and the strong affiliation they have with their teams.

What stood out for me this week:

  • the creativity people used to tackle the topic of basketball. There were a number of really creative approaches to the data, which not just showed data in an interesting way but also showed how a topic can be reflected well through colours, shapes or the general alignment of data.
  • the variety in the vizzes from very simple ones that focused on one aspect of the data, over more elaborate stories which people used to share their passion for a certain team, to infographic style vizzes which helped the ‘rest of the world’ understand how the NCAA Division 1 Men’s Basketball tournament works, what the different regions are, how they go through various rounds and how the seeding system works. There were history lessons, team portraits and other approaches and I really enjoyed working my way through them
  • I appreciate the collaboration that happened and the willingness people showed to teach their fellow Makeover Monday community members about the topic, point them to articles, videos etc.
    This was encouraging because I sometimes worry people will see MakeoverMonday as a competition, but clearly people are willing and happy to help others understand the data, work with it and create great charts and visualizations to bring a topic to life.
  • there was a trend towards the use of simple vizzes in combination to get to the heart of the data story. For example, a number of people looked at the variance in seeding at different rounds of the tournament and used bar charts and barbell charts together with annotations and table calculations to provide a great overview and analysis of a topic that can be hard to understand.
  • more and more people are stating the data sources on their vizzes and are including hyperlinks!

Where we can all still improve:

For those who are always keen to keep improving, here are some of the issues I noticed this week and I hope that we can all focus on getting better at this stuff and that everyone feels able to provide feedback (in an appropriate, professional and constructive manner) so that new and established members in the community can all learn and help us achieve our mission of creating better charts and telling better data stories…

  • Packed bubble charts. I will talk about these until the day I die. In most cases, i.e. almost all of the time, packed bubble charts are useless and not a great way to visualize data. Also in most cases, a bar chart is a better option. We have previously written about this in detail and so have other people. If you absolutely LOVE packed bubble charts, please read up on their effectiveness. And until we’ve convinced you of their uselessness, maybe try out using them for navigation actions only, as a way to wean yourself slowly 🙂
  • Too much information/detail. Some vizzes packed all the data into a single chart, which made it very difficult to figure out what the author was trying to tell the audience, what the different marks showed and how that related back to the topic or the rest of the story.
    Tableau gives you a number of options for including and excluding detail as needed. It will take time and practice to get it right, but consider the following suggestions as you craft your viz:

    • could I remove detail from the actual viz and provide the information in a tooltip instead for those who want to ‘discover’ more?
    • do I need to put the same dimension or measure on size, colour and shape at the same time? Using multiple ways of conveying the same information can be confusing. If you have a bar chart showing the teams’ scores (via length/height of the bar) in a single round, adding the score to colour to shade the bar, plus size to determine the width of the bar as well doesn’t add any additional value but rather distracts. Keep it simple. Length is enough. Use colours (sparingly) for other aspects, e.g. upset vs no upset or above avg vs below avg, etc.
    • using labels for everything. Do you need them all? Would it suffice to highlight min/max or call out specific teams based on the story you’re trying to tell?
    • shapes and their sizes. This is a fine line to walk. Sometimes shapes can work really well in vizzes, but using many different shapes is confusing especially when they are very small in your viz. Add size to the mix and things get even more confusing. Analysing the size of shapes (unless it’s basically HUGE vs tiny) will be hard for the human eye in many cases.
      Be very critical when you review your viz – does it tell the story effectively, do you think someone who sees it for the first time will understand it AND gain insights from it?
  • Attention to detail. Yes, we suggest you spend 60min or less on MakeoverMonday, because we don’t expect people to develop advanced dashboards and labour-intensive vizzes. But at the same time, we love seeing work that is polished. You can take a simple bar chart from looking okay to looking top notch in a matter of minutes by applying some formatting changes, such as changing/removing gridlines, row and column borders, by updating the colour from default blue to grey (or any other single colour that’s easy on the eye), by fine tuning your tooltips and fonts, using effective white spaces, well-crafted titles and clever descriptions or annotations.
    Those details make a big difference and can be a great skill to improve if you’re feeling stuck or don’t know how to change things up with the vizzes you’re creating.
  • Viz types. In line with my point about packed bubble charts, I suggest you use the same subset of data (dimensions, measures, filters, etc.) and simply duplicate the sheet and turn each new sheet into a different chart that contains the same data. This technique will help you find which of the vizzes is better than the others at getting the point across.


With all that said, let’s look at this week’s favorites. It was difficult to narrow down the list because there were a larger number that I really liked for various reasons, which I called out at the beginning, but here we go…

Lindsey Poulter

Author: Lindsey Poulter
Link: Tableau Public

What I like:

  • Lindsey’s vizzes are always so clear and cleanly laid out. They don’t overwhelm me with information but let me discover information gradually with all the relevant detail provided where and when I need it
  • she picks up the question from the original article
  • the questions above each viz guide me in how to read the charts
  • the call outs are excellent and give me new insights – how about that 20 year gap for seed 11 finalists???
  • each viz is simple enough to understand quickly
  • the timeline uses size and colour of the circles effectively to show different seeds reaching the final four and I really like how the gridlines provide a contrast to the vertical lines and that Lindsey aligned the horizontal lines on the gridlines for her call outs
  • this is an outstanding example of being mindful of the end user, thinking about how they will view every part of the viz and using good design to guide them through the viz
Athan Mavrantonis

Author: Athan Mavrantonis
Link: Tableau Public

What I like:

  • great simple colour scheme that is used well throughout and highlights the important parts of the story
  • picks up the key question from the original article
  • Athan replicated the ‘bracket’ layout and features it at multiple points in his viz
  • the columns of his table layout show clearly that number 1 seeded teams still make the final and/or win the championship more often than any other seed
  • clean layout and good use of icons – they are basketballs but they’re not ‘annoying’ and in your face but a subtle way to change things from a simple circle into picking up the theme of the data
Adam Crahen

Author: Adam Crahen
Link: Tableau Public

What I like:

  • simplicity above everything else. I love how simple this highlight table is and the amount of information it provides on one small screen
  • the colours work well with the basketball theme
  • heading and subtitle are clear and concise
  • table headers and legend/source provide all the relevant information in a very concise form
  • very simple way to answer a big question
Josh Weston

Author: Josh Weston

What I like:

  • it’s fun and creative and this week’s topic left room for those aspects, so well done, Josh!
  • headings, call outs and viz choices work well to provide an ‘infographic’ type approach
  • very clean layout, simple colours that appear throughout the viz and work well on the white background
  • the key statements called out at the top are enforces through the vizzes below
Staticum & Sebastian Soto Vera

Author: Staticum & Sebastian Soto Vera
Link: Tableau Public

What I like:

  • the design intrigues me and I like the circular layout as a nod to the basketball theme
  • the authors went through iterations, collaborated and took feedback onboard before producing this final version
  • it teaches me (and others) more about the topic by providing an introduction to the tournament
  • the legend on the right-hand side helps me with reading and interpreting the information
  • I find it interesting to see the various regions all at once and seeing how the original number of teams shrinks the closer they get to the championship