How we investigated Olympic sponsor torchbearers

Cross-posted from The Guardian Datablog:

Our investigation into Olympic torchbearer stories has unearthed some surprising choices by sponsor organisations. But how did we get to that point? The answer is a combination of curiosity, cynicism and knowing when to ask for help.

Here’s how it went:

When I stumbled across the section profiling the various torchbearers on the London2012 site (in search of route information and other Olympics data), I saw something that’s essential in telling stories with data: human beings. People always help bring data to life, and the people carrying the Olympic torch, I thought, would provide a human angle to the Olympics. Who exactly were the 8,000 torchbearers? What picture did they paint of modern Britain? How did their stories compare to the “inspirational” legend painted by the Olympic PR machine?

I also had a vague hunch. As sponsors play such a large role in the Olympics – from geographical “exclusion zones” where no other brands can be displayed to the renaming of stadia and signage – I guessed that they might get some of the allocation of “inspirational” torchbearers as well. But that was for later.

First I had to get the data: the torchbearers data was published on a series of pages which were not linked in any easy way. You could, however, navigate by location (notably: only UK regions were provided, despite dozens coming from as far afield as China, Korea, the US and Singapore), name (at least three characters), or date. This last option allowed us to compile a list of links and go through those.

The members of the Scraperwiki community – and Zarino Zappia in particular – chipped in and helped write a scraper that could gather the data effectively: firstly the basic data on each torchbearer (around 6,000), and later the linked ‘nomination stories’ (around 5,900).

With that data on hand, the first thing was to get a general overview.

In Excel I generated a ‘pivot table’ to total up counts of torchbearers’ ages – there seemed to be a lot of younger torchbearers, so I used an =IF formula to gather those into 10-year age bands. Indeed, it turned out that almost a quarter were in their teens.

I repeated the process for hometowns, and looked for outliers, discovering a 101-year-old torchbearer – two years older than the previously reported “oldest torchbearer” – who had only recently been named and not reported in the British media. That was my initial human angle, and I looked around for some more details on the individual to add some vital colour and context.

Next: a local angle. Local media often celebrate their community, so I thought I’d map the torchbearers of Nottingham, the most ‘newsworthy’ of the top ten hometowns.  This was done by simply importing the data into Fusion Tables after adding “, UK” to all the hometowns in Excel so that it didn’t place any of the points in other countries. Later I used a list of Nottinghamshire towns to add another dozen or so torchbearers from the region. All this took less than an hour.

With the simple colour stuff done, it was time to turn to the something a little harder.

The Scraperwiki API allows you to ask questions of data in a more direct way, using queries. This is much quicker than using Excel (if you know how to write a SQL query – or even just how to search for tips), and allows you to see the results as a HTML table, or download them as separate CSV spreadsheets.

I tried looking for mentions of Olympic sponsors: McDonalds, Coca Cola, and so on. Then I browsed through the stories to see if anything jumped out. The first to jump out was a torchbearer whose nomination story was, unusually, written in the first person and appeared to largely consist of being “engaged in the business of sport” alongside the rather vague hope that by doing so they might have “contributed to the development of sport in our country”.

This led me to focus my first efforts on the adidas dataset. And when I discovered that the same nomination story was actually being used by 7 of those torchbearers, I kept digging – the hunch being that, if they had all had the same story written for them, perhaps they shared something in common: for example, working for the company and being too busy to write their own story.

I managed to confirm that at least one did indeed work for adidas, as he had written on his LinkedIn profile about his nomination. Most of the others shared names with prominent and senior figures in the sports and retail business – and I passed those leads on to others in the Help Me Investigate Olympics network. Meanwhile, I wrote up the story of the adidas torchbearers, most of whose stories seemed to centre around their ability to sell product.

While others chased responses from the sponsors, I continued to dig. Many sponsors had given torchbearer places to employees and others who had spent their lives raising money, volunteering, or who had achieved inspiring things in sport – which made the exceptions all the more striking: for ArcelorMittal, a billionaire who drew parallels between himself and an Olympian, and his son: nominated for his work in overseeing a corporate merger. For other sponsors: managing directors, CEOs and COOs; a PR strategist and a mobile phone salesman. A Brazilian food and drink magnate and a Russian deputy editor. The leads kept coming.

Between the concerning stories were the quotidian ones: one BP nomination story appeared to suggest that the person was carrying the torch because of their hard work in marketing promotional items and “[cleaning] the forecourt all the time”; while Samsung’s Jonghyuk Jeong had “worked over 15 years at Samsung heavy industry shipbuilding yard in Geo Je do and he is doing hobby of taking a picture.”

But in the end these were the decoration around a much more serious story: of what exactly the Olympic torchbearers did represent, and how big corporations chose to interpret the ‘Olympic spirit’.

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