Monthly Archives: January 2012

Introducing… What Size Am I

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For many women, there are few things more frustrating than trying on clothes. To put it in terms that my (mostly male) coder friends will understand: debugging CSS doesn’t come close to the blood-boiling irritation of trying to work out whether you are a size 8, a size 10, or both. Because, yes, you can be one size for tops and another for skirts, all in the same shop.

It may surprise men reading this to learn that there is no agreement on what makes a size 10. Shops differ. A lot. When I am shopping on the high street, I take each item into the changing room in two or three different sizes. When shopping online, I’m sure you can see that this is even more of a problem.

Anyway, here is my attempt to help make sizing a bit easier for female customers, inspired by this New York Times article about the madness. The Times pointed out the problem, but they didn’t turn it into a solution – that’s what I’ve tried to do here, having noticed that most stores do publish their own size details online.

And so: presenting “What Size Am I?”, a web app to help women in the UK and the US find clothes that fit.

Here is a screenshot:

What Size Am I? page

As a female hacker, this combines two of my main interests in life: clothes and nice tech. If you’re using a modern browser with SVG support, you should be able to enter your bust, waist and hip measurements in inches or cm, and see an interactive graph of where you fit, from roomy Jaeger to tiny Reiss. If you’re using IE8 or below, you’ll just see a table (sorry IE-using folks).

I’ve also included the closest fits of all (using an admittedly blunt least-squares metric), because it’s helpful to know a shop or two where you’re guaranteed to find things that fit. Currently that’s the kind of knowledge only gained after a lot of Saturday afternoons struggling with a lot of zips.

While working on this, I noticed some interesting trends. Firstly, all stores size in evenly spaced increments – because they are using fitting models rather than individual models for each size – but different stores aim for different markets.

Some retailers seem to cover pretty much every widely available size – in the UK, these include Gap, Marks & Spencer, Monsoon, and Next:

What Size Am I? page

Others are unashamedly aimed at what I call the “fashionable midget” end of the market, like TopShop, Banana Republic and Kate Middleton’s beloved Reiss:

What Size Am I? page

Secondly, I assumed that the fashionable-midget and pricier stores would size smaller, but that’s not actually true. Counter-intuitively, a size 10 in upmarket Whistles, Zara, or Reiss is actually quite a lot larger than a size 10 in ASOS, Monsoon, or M&S.

I think that’s because the “whole of market” stores have larger gaps between their sizes. Or it might be vanity sizing, because Whistles, Reiss et al probably have wealthier, older customers. Who knows?

Thirdly, this is really best shown by comparing sizes with your own body shape, but it’s possible to see the different body types that different shops fit. Compare LK Bennett (light blue) with TopShop (dark blue):

What Size Am I? page

The light blue curves are much, well, curvier than the dark blue. LK Bennett is cut for the strongly hourglass, and slightly pear-shaped: TopShop is more up-and-down.

Broadly and unscientifically speaking, M&S, Karen Millen and French Connection look the most pear-shaped to me: Banana Republic and Warehouse look best for the top-heavy: LK Bennett and Zara are cut for a fitted waist, while Oasis and TopShop appear least curvy overall.

This is pleasing, because it confirms the suspicions I’ve held for a long time. I hope you find the tool useful: if you see anything I could do better, please let me know in the comments.

PS: OMG, D3 FTW

Building this has been an excuse to play with D3.js, the JavaScript library formerly known as Protovis, which I use to draw the chart. D3 is awesome: many thanks to Mike Bostock for building it and making it open source.

Forget 5.9% – some train fares rise today by four times inflation

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Today, rail fares go up by an inflation-busting average of 5.9%, to howls of outrage from commuters and groups like Passenger Focus. But what many people don’t realise is that 5.9% is just an average.

And while Passenger Focus came across individual fare increases of up to 11%, I have scraped data from National Rail Enquiries and found that some anytime fares rise today by as much as 20% – that’s four times inflation – while others have fallen by as much as 45%.

Roll up, roll up to play the great rail fares lottery! (Bad luck if you live on Merseyside, where everyone seems to be a loser this year.)

Travelling from Moorfields to Chester at peak time? Oh no! Your anytime fare with Merseyrail has rocketed by 20% overnight, from £5.15 to £6.20. Liverpool to Southport? Ouch! The Merseyrail anytime fare is up 19%, from £4.65 to £5.50. Peak-time London to Warwick with Chiltern? Bad luck! Your fare rises by 9.8% today, from £51 to £56.

But peak-time Gatwick Airport to Southampton? DRRRRRIIIING – you’ve hit the jackpot: Southern’s anytime fare has bizarrely fallen by 45%, from £26.90 to £14.90. What’s going on?

You can find my raw data here – I scraped the fare increase in Anytime tickets on every end-to-end route listed in the NAPTAN database. I chose Anytime tickets because they are unregulated fares, and hence not subject to the RPI-plus-1% average limit imposed by the Chancellor. However, Passenger Focus’s good work has found large variation in regulated (off-peak and season) fares too – buying train tickets really is a lottery.

The 5.9% figure is a high-level average produced by ATOC, which regulates the train operators. When I rang them, ATOC told me the 5.9% figure is an average of an average, across all operators and all available potential routes, and all regulated and unregulated fares.

Clearly, with such wild variation between operators and regions, we need much better comparative data. I drew a graph showing the average rise I found in each operator’s fares, which showed large differences. I also compared the variation in fare increases, which produced some interesting geographical patterns (I’m looking at you in particular, Southern).

However, I’ve decided not to publish these for the moment, because my data only covers Anytime tickets and end-to-end routes, not every available journey, and it has holes in it, having been scraped. To compare prices properly, we really need to know how often each ticket is bought, but that data isn’t public.

At some point, I’ll try a proper analysis with the National Fares Manual and librailfare. In the meantime – here’s hoping your train fare hasn’t gone up too much, and happy new year!