“Principal Component Analysis” for shoes

OK, this is not a technical post. This is more in the realm of “life hacks“. It has everything to do with an observation I made a couple of months back, and how that has helped significantly combat decision fatigue.

I currently own eight pairs of shoes, which is perhaps a lifetime high. And lifetime high means that I was spending a lot of time each time I went out on which shoe to wear.

I have two pairs of open shoes, which I can’t wear for long periods of time, but are convenient in terms of time spent in wearing and taking off. I have two pairs of “semi-formal” ankle-high shoes – one an old pair that refuse to wear out, and another a rather light new one with sneaker bottoms. There are two pairs of “formal shoes”, one black and one brown. And then there are two sneakers – one pair of running shoes and one more general-purpose “fancy” one (this last one looks great with jeans, but atrocious with chinos, which I wear a lot of).

The running shoes have resided in my gym bag for the last nine months, and I use them exclusively indoors in the gym. So they’re “sorted”.

The problem I was facing was that among my seven other pairs of shoes I would frequently get confused on which one to wear. I would have to evaluate the fit with the occasion, how much I would have to stand (I need really soft-bottom shoes if I’ve to stand for a significant period of time), what trousers I was wearing and all such. It became nerve-wracking. Also, our shoe box, which was initially designed for two people and now serves three, placed its own constraints.

So as I somehow cut through the decision fatigue and managed to wear some shoes while stepping out of home, I noticed that a large proportion of the time (maybe 90%) I was wearing only three pairs of shoes. The other shoes were/are still good and I wouldn’t want to give them away, but I found that three shoes would serve the purpose on most occasions.

This is like in principal component analysis, where a small number of “components” (linear combination of variables) predict most of the variance in all the variables put together. In some analysis, you simply use these components rather than all the variables – that rather simplifies the analysis and makes it more tractable.

Since three pairs of shoes would serve me on 90% of the occasions, I decided it was time to take drastic action. I ordered a set of shoe bags from Amazon, and packed up four pairs of shoes and put them in my wardrobe inside. If I really need one of those four, it means I can put the effort at that point in time to go get that from inside. If not, it is rather easy to decide among the three outside on which one to wear (they’re rather dissimilar from each other).

I no longer face much of a decision when I’m stepping out on what shoes to wear. The shoe box has also become comfortable (thankfully the wife and daughter haven’t encroached on my space there even though I use far less space than before). Maybe sometime if I get really bored of these shoes outside, I might swap some of them with the shoes inside. But shoe life is much more peaceful now.

However, I remain crazy in some ways. I still continue to shop for shoes despite owning a lifetime high number of pairs of them. That stems from the belief that it’s best to shop for something when you don’t really need it. I’ll elaborate more on that another day.

Meanwhile I’m planning to extend this “PCA” method for other objects in the house. I’m thinking I’ll start with the daughter’s toys.

Wish me luck.