When I missed my moment in the sun

Going through an old piece I’d written for Mint, while conducting research for something I’m planning to write, I realise that I’d come rather close to staking claim as a great election forecaster. As it happened, I just didn’t have the balls to stick my neck out (yes, mixed metaphors and all that) and so I missed the chance to be a hero.

I was writing a piece on election forecasting, and the art of converting vote shares into seat shares, which is tricky business in a first past the post system such as India. I was trying to explain how the number of “corners of contests” can have an impact on what seat share a particular vote share can translate to, and I wrote about Uttar Pradesh.

Quoting from my article:

An opinion poll conducted by CNN-IBN and CSDS whose results were published last week predicted that in Uttar Pradesh, the Bharatiya Janata Party is likely to get 38% of the vote. The survey reported that this will translate to about 41-49 seats for the BJP. What does our model above say?

If you look at the graph for the four-cornered contest closely (figure 4), you will notice that 38% vote share literally falls off the chart. Only once before has a party secured over 30% of the vote in a four-cornered contest (Congress in relatively tiny Haryana in 2004, with 42%) and on that occasion went on to get 90% of the seats (nine out of 10).

Given that this number (38%) falls outside the range we have noticed historically for a four-cornered contest, it makes it unpredictable. What we can say, however, is that if a party can manage to get 38% of the votes in a four-cornered state such as Uttar Pradesh, it will go on to win a lot of seats.

As it turned out, the BJP did win nearly 90% of all seats in the state (71 out of 80 to be precise), stumping most election forecasters. As you can see, I had it all right there, except that I didn’t put it in that many words – I chickened out by saying “a lot of seats”. And so I’m still known as “the guy who writes on election data for Mint” rather than “that great election forecaster”.

Then again, you don’t want to be too visible with the predictions you make, and India’s second largest business newspaper is definitely not an “obscure place”. As I’d written a long time back regarding financial forecasts,

…take your outrageous prediction and outrageous reasons and publish a paper. It should ideally be in a mid-table journal – the top journals will never accept anything this outrageous, and you won’t want too much footage for it also.

In all probability your prediction won’t come true. Remember – it was outrageous. No harm with that. Just burn that journal in your safe (I mean take it out of the safe before you burn it). There is a small chance of your prediction coming true. In all likelihood it wont, but just in case it does, pull that journal out of that safe and call in your journalist friends. You will be the toast of the international press.

So maybe choosing to not take the risk with my forecast was a rational decision after all. Just that it doesn’t appear so in hindsight.

Airline pricing is strange

While planning our holiday to al-Andalus during my wife’s Easter break (starting later this week), we explored different options for flights from different destinations in al-Andalus to Barcelona before we confirmed our itinerary.

As it turned out, it was cheapest (by a long way) to take a flight back from Malaga to Barcelona on Good Friday (meaning we were “wasting” three days of Priyanka’s vacation – which we were okay with), and so we’ve booked that.

Now, Vueling (Iberia’s low cost version where we’ve booked our tickets) sends me an email offering credits of €40 per passenger if we could change our flight from Friday to Saturday (one day later). In other words, it turns out now that the demand for Friday flights is so much more than that for the Saturday flight that Vueling is willing to refund more than half the fare we’ve paid so that we can make the change!

I don’t know what kind of models Vueling uses to predict demand but it seems to me now that their forecasts at the time we made our booking (3 weeks back) were a long way off – that they significantly underestimated their demand for Friday and overestimated demand for Saturday! If this is due to an unexpected bulk booking I wouldn’t blame them, else they have some explaining to do!

And “special occasions” such as long weekends, and especially festivals such as Good Friday, are a bitch when it comes to modelling, since you might need to hard code some presets for this, since normal demand patterns will be upset for the entire period surrounding that.

PS: Super excited about the upcoming holiday. We’re starting off touristy, with a day each in Granada and Cordoba. Then some days in Sevilla and some in Malaga. If you have any recommendations of things to do/see/eat in these places, please let me know! Thanks in advance.