Record of my publicly available work

A few people who I’ve spoken to as part of my job hunt have asked to see some “detailed descriptions” of work that I’ve done. The other day, I put together an email with some of these descriptions. I thought it might make sense to “document” it in one place (and for me, the “obvious one place” is this blog). So here it is. As you might notice, this takes the form of an email.

I’m putting together links to some of the publicly available work that i’ve done.
1. Cricket
I have a model to evaluate and “tell the story of a cricket match”. This works for all limited overs games, and is based on a dynamic programming algorithm similar to the WASP. The basic idea is to estimate the odds of each team winning at the end of each ball, and then chart that out to come up with a “match story”.
And through some simple rules-based intelligence, the key periods in the game are marked out.
The model can also be used to evaluate the contributions of individual batsmen and bowlers towards their teams’ cause, and when aggregated across games and seasons, can be used to evaluate players’ overall contributions.
Here is a video where I explain the model and how to interpret it:
The algorithm runs live during a game. You can evaluate the latest T20 game here:
Here is a more interactive version , including a larger selection of matches going back in time.
Related to this is a cricket analytics newsletter I actively wrote during the World Cup last year. Most Indians might find this post from the newsletter interesting:
2. Covid-19
At the beginning of the pandemic (when we had just gone under a national lockdown), I had built a few agent based models to evaluate the risk associated with different kinds of commercial activities. They are described here.
Every morning, a script that I have written parses the day’s data from and puts out some graphs to my twitter account  This is a daily fully automated feature.
Here is another agent based model that I had built to model the impact of social distancing on covid-19.
tweetstorm based on Bayes Theorem that I wrote during the pandemic went viral enough that I got invited to a prime time news show (I didn’t go).
3. Visualisations
I used to collect bad visualisations.
I also briefly wrote a newsletter analysing “good and bad visualisations”.
4. I have an “app” to predict which single malts you might like based on your existing likes. This blogpost explains the process behind (a predecessor of ) this model.
5. I had some fun with machine learning, using different techniques to see how they perform in terms of predicting different kinds of simple patterns.
6. I used to write a newsletter on “the art of data science”.
In addition to this, you can find my articles for Mint here. Also, this page on my website  as links to some anonymised case studies.

I guess that’s a lot? In any case, now I’m wondering if I did the right thing by choosing “skthewimp” as my Github username.