Who Gets an Olympic Medal | #MakeoverMonday Week 7

At the time of writing the 2018 Winter Olympic Games are in full force, so it seems only natural that the #MakeoverMonday topic for Week 7 of this year is record level results of Winter Games medal wins.

I have to say that I was particularly excited to dive into this data set.  Here’s what a few rows of data look like:

I always find with this level of data there are so many interesting things that can be done that it gets really hard to focus.  The trouble is that all of the rows are interesting, so as a creator I’m immediately drawn to organizing “all the data” and want put to it ALL on display.  And that’s where the first 20 minutes of my development were headed.

I’d started with a concept of showing all the medals and more specifically showing the addition of new sports over time.   As I was building, the result was quite clearly going to be a giant poster form viz.  Not what I was going for.

To move past that my mind shifted to female sports at the Winter Olympics.  And if you look through the data set you’ll see there are some interesting points.  Specifically that it took about 60 years for women to get to a similar number of events/medals as men.  (yellow = men, purple = women, gray = mixed)

I spent some time stuck on this – thinking through how I could segment by different sports and to extract out some of the noise of the different years and come up with a slope chart.  Ultimately I found myself disappointed with all of these pursuits – so my thoughts shifted.

So I switched gears and stumbled on this chart:

Which as you look through it is REALLY interesting.  I had just watched the Opening Ceremonies and knew there were 91 delegations (countries) represented in 2018.  To know that in 2014 the number was probably similar, yet only 26 reached a podium seemed to be a sticking point in my mind.

So – that led to a quick adventure over to http://www.olympic.org to add context to the number of countries represented at the games over the years.  They actually have really nice summary pages for each set of games that made gathering data simple.  Here’s a snapshot of 1980 – Lake Placid:

Using the ribbon of information at the bottom I went about collecting and enriching the data set.  Because what was missing from our original #MakeoverMonday data was the NULLs.

Sufficiently enriched I was able to come up with a calculation for the percentage of delegations medalling at each set of games.  Of course I suspected that this would not be close to 100%, only by virtue of knowing that we’ve got 91 delegations in 2018.  Here’s the chart:

So – now the story is unfolding, but I wanted to take it a few steps further.  My main beef: I want to also see how many additional delegations are bringing athletes to the games.  Specifically at the first data point I’d think that it was a small number of countries because the game were new. Essentially the opportunity for medalling would perhaps be greater.  Hence settling on what ended up being my final submission for the week:

click to view on Tableau Public

What are you looking at?  Medals are clearly parsed out into Gold, Silver, and Bronze.  Each bar represents a Winter Games.  The width of the bar is the # of countries/delegations, the height of the bar is the % of countries who medalled in that respective color.  I concede in this that eliminating the dimensionality of medals may have made for a more consolidated view, but I selfishly wanted to use the different colors.

Here’s the non-medalled version:

Less abstracted, more analytically presented:

Ultimately for the sake of the exercise I went with continuous bar sizing representing the number of delegations at each Winter Games.  And my “why” is because this isn’t often seen and within the confines of this visualization it would be a great usage.  Explaining this aloud should facilitate easy cognition.  The wider bars means more countries participating (reinforced by our general knowledge of the games).  And then the height of the bars can cleanly represent the percentage of those getting medals.  Plus – per usual – the tooltip divulges all this in well articulated detail.  (++ bars allow for chronology of time)

I’m quite pleased with this one.  Maybe because I am the designer, but I was delighted with the final representation both from a visual perspective and an analytical presentation perspective.  There is a certain amount of salience in having both the bars gets larger over time (and repeating that 3 times) and the colors of the medals being represented within a single worksheet.

#MakeoverMonday Week 12 – All About March Madness

This week’s Makeover Monday topic was based on an article attempting to provide analysis into why it is harder for people to correctly pick their March Madness brackets. The original visualization is this guy:

With most Makeover Monday approaches I like to review the inspiration and visualization and let that somewhat decide the direction of my analysis. In this case I found that completely impossible. For my own sake I’m going to try and digest what it is I’m seeing/interpreting.

  • Title indicates that we’re looking at the seeds making the final 4
  • Each year is represented as a discrete value
  • I should be able to infer that “number above column represents sum of Final Four seeds” by the title
  • In the article it says in 2008 that all the seeds which made it to the final 4 were #1 – validates my logical assumption
  • Tracking this down further, I am now thinking each color represents a region – no idea which colors mean what – I take that back, I think they are ranked by the seed value (it looks like the first instance of the best seed rank is always yellow)
  • And then there’s an annotation tacked on % of Final Four teams seeded 7th or lower for two different time periods
    • Does 5.2% from 1985 to 2008 equal: count the blue bars (plus one red bar) with values >=7 – that’s 5 out of (24 * 4) = 5/96 = 5.2%
    • Same logic for the second statistic: 7 out of (8 * 4) = 21.9%

And then there’s the final distraction of the sum of the seed values above each bar.  What does this accomplish?  Am I going to use it to quickly try and calculate an “average seed value” for each year?  Because my math degree didn’t teach me to compute ratios at the speed of thought – it taught me to solve problems by using a combination of algorithms and creative thinking.  It also doesn’t help me with understanding interesting years – the height of the stacked bars does this just fine.

So to me this seems like an article where they’ve decided to take up more real estate and beef up the analysis with a visual display.  It’s not working and I’m sad that it is a “Chart of the Day.”

Now on to what I did and why.  I’ll add a little preface and say that I was VERY compelled to do a repeat of my Big Game Battle visualization, because I really like the idea of using small multiples to represent sports and team flux.  Here’s that display again:

Yes – you have to interact to understand, but once you do it is very clear.  Each line represents a win/loss result for the teams.  They are then bundled together by their regions to see how they progressed into the Superbowl.  In the line chart it is a running sum.  So you can quickly see that the Patriots and Falcons both had very strong seasons.  The 49ers were awful.

So that was my original inspiration, but I didn’t want to do the same thing and I had less time.  So I went a super distilled route of cutting down the idea behind the original article further.  Let’s just focus on seed rank of those in the championship.  To an extent I don’t really think there’s a dramatic story in the final 4 rankings – the “worst” seed that made it there was 11th.  We don’t even know if that team made it further.

In my world I’ve got championship winners vs. losers with position indicating their seed rank.  Color represents the result for the team for the year and for overall visual appeal I’ve made the color ramp.  To help orient the reader, I’ve added min/max ranks (I screwed this up and did pane for winner, should have been table like it is for loser, but it looks nice anyway).  I’ve also added on strategic years to help demonstrate that it’s a timeline.  If you were to interact, you’d see the name of the team and a few more specifics about what it is you’re looking at.

The reality of my takeaway here – a #1 seed usually wins.  Consistently wins, wins in streaks.  And there’s even a fair amount of #1 losers.  If I had to make a recommendation based on 32 years of championships: pick the #1 seeds and stick with them.  Using the original math from the article: 19 out of 32 winners were seed #1 (60%) and 11 out of 32 losers were seed #1 (34%).  Odds of a 1 being in the final 2 across all the years?  47% – And yes, that is said very tongue in cheek.