Football Elo Application

This morning, I discovered the Club Elo Ratings, and promptly proceeded to analyse Liverpool FC’s performance over the years based on these ratings, and then correlated the performance by manager.

Then, playing around with the data of different clubs, I realised that there are plenty more stories to be told using this data, and they are best told by people who are passionate about their respective clubs. So the best thing I could do is to put the data out there (in a form similar to what I did for Liverpool), so that people can analyse how their clubs have performed over the years, and under different managers.

Sitting beside me as I was doing this analysis, my wife popped in with a pertinent observation. Now, she doesn’t watch football. She hates it that I watch so much football. Nevertheless, she has a strong eye for metrics. And watching me analyse club performance by manager, she asked me if I can analyse manager performance by club!

And so I’ve added that as well to the Shiny app that I’ve built. It might look a bit clunky, with two unrelate graphs, one on top of the other, but since the two are strongly related, it makes sense to have both in the same app. The managers listed in the bottom dropdown are those who have managed at least two clubs in the Premier League.

If you’re interested in Premier League football, you should definitely check out the app. I think there are some interesting insights to be gleaned (such as what I presented in this morning’s post).

Single Malt Recommendation App

Life is too short to drink whisky you don’t like.

How often have you found yourself in a duty free shop in an airport, wondering which whisky to take back home? Unless you are a pro at this already, you might want something you haven’t tried before, but don’t want to end up buying something you may not like. The names are all grand, as Scottish names usually are. The region might offer some clue, but not so much.

So I started on this work a few years back, when I first discovered this whisky database. I had come up with a set of tables to recommend what whisky is similar to what, and which single malts are the “most unique”. Based on this, I discovered that I might like Ardbeg. And I ended up absolutely loving it.

And ever since, I’ve carried a couple of tables in my Evernote to make sure I have some recommendations handy when I’m at a whisky shop and need to make a decision. But then the tables are not user friendly, and don’t typically tell you what you should buy, and what your next choice should be and so on .

To make things more user-friendly, I have built this app where all you need to enter is your favourite set of single malts, and it gives you a list of other single malts that you might like.

The data set is the same. I once again use cosine similarity to find the similarity of different whiskies. Except that this time I take the average of your favourite whiskies, and then look for the whiskies that are closest to that.

In terms of technologies, I’ve used this R package called Shiny to build the app. It took not more than half an hour of programming effort to build, and most of that was in actually building the logic, not the UI stuff.

So take it for a spin, and let me know what you think.