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Batting Outside My Crease

I’ve long admitted to being a numbers-led or analysis-first vizzer. I marvel at many of the wonderfully creative and artful outputs the gifted community produce on a regular basis and acknowledge that I’m never going to give them a run for their money. I’m comfortable, though, that providing analysis through vizzes is my niche and where I can best hone my skills. Every so often, however, it’s useful to operate outside of one’s comfort zone.

A few Saturdays ago I picked up the newspaper and ran through my usual ritual. Separate the sections, isolate the sports and skim it. I find that later in the week I’ll never come back to the sports news since it’s almost immediately outdated, even that same day since the world of sport moves on so quickly, so this is where I start. Other obligations being what they are, though, this probably amounts to about five minutes over lunch, culminating at half-reading the back-page article, by Barney Ronay (@barneyronay).

That week, it was an ode to Steve Smith, Australia’s cricket captain. Through partisan fandom, I’m hard-wired to dislike Steve Smith, but Barney builds a compelling case for recognising Smith’s quality, having reached a number of milestones at a relatively early age. The article is littered with statistics but all written in prose, and it felt like a prime candidate for visualising, and a personal challenge.

I took a pen to the article and highlighted the tidbits about his career that I felt could be represented in charts:

  • became the seventh fastest batsman to pass 5,000 runs in Test cricket
  • finished on 178 not out, the highest score by an Australian in India
  • averages more than 45 on every continent
  • scores the same weight of runs home and away
  • has a hundred against every opponent
  • averages 71 in 42 Tests

These all seemed like obtainable stats to source, and I started (and finished) at cricinfo.com, dipping into their tools to recreate the stats that Barney quoted. All the while, I had in my mind David Krupp’s recent MakeoverMonday contribution (image below), and felt I should make an attempt at something in a broadly similar style without completely imitating.

Charlie Hutcheson recently took this viz apart, and the amount of floating content scared the wits out of me, so I went to the other extreme – a 100% tiled infographic. Baby steps! I also wanted to see if I could create something that was quite atypical by my standards, so experimented with some quite garish colours in Aussie green and gold.

There are a couple of differences I had to introduce between the article and this viz. First of all, his batting average isn’t above 45 on each continent, since in Europe its 43.3. But his lowest Strike Rate is 45.1, which I presume is what Barney actually meant, so for the avoidance of doubt I published both on the viz. It also wasn’t clear whether the ‘weight of runs’ was his contribution relative to the rest of his team, or his average home/away. I’ve presumed the latter.

The data in the viz is frozen in time as at the date of Barney’s article on 17th March and won’t update to show his drop in performance after the forthcoming Ashes series this winter (as if).

I like the idea of taking unvisualised data from an article like this and may try to repeat it if I find similar articles with accessible data sources. Though I accept that might limit me to a handful of sports. I will confess that I’m not delighted with the output, but it still pushed me in a different direction and taught me a few lessons I hope to be able to apply elsewhere.

Front page image of Steve Smith taken under the Creative Commons licence from wiki.

About The Author

Mark Edwards

A statistician at heart, Mark’s approach is always numbers-led. Already visualising data in other side-projects, Mark was introduced to the world of Tableau in 2016, when he and Pablo started working together in financial services. A keen participant in social Tableau challenges, Mark is building his skills and appreciation of powerful visuals, discovering interesting and untapped data sets, a path that has already led to a new career and a range of further opportunities.

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