Why Cricket Science?
The year is 2001. The place is Ranchi, a small town in India. A short, bespectacled, eight year old boy is playing cards with his friend. But these aren’t ordinary playing cards. They each have a picture of a sportsman and at least a dozen numbers on them. The boy isn’t doing so well —his opponent seems to better know the ‘right’ numbers to call out from the cards.
Those were cricket slam cards. The boy was me. I didn’t know it then, but I would end up spending a lot of my free time on numbers and cricket.
India has a strong affinity for the sport of cricket. Cricket has a strong affinity for numbers and statistics. As a math-obsessed Indian, it wasn’t long before I had a light-bulb moment of diving deeper into this interest.
Like any sport, cricket generates a lot of numbers. The outcome of each delivery, the runs scored by a batsman, the winning margin in a match, the winning percentage of a team, and so on. I have applied statistics and probability to such data, created mathematical models of the game, utilized machine learning to gain novel insights, analyzed spectrograms of time series, coded a library of functions and simulators, and more. This has been ongoing for about fifteen years, on and off. It has been mostly ‘off’ for the last couple of years though, and that has motivated me to finally begin sharing my work. I hope that someone is able to pick up from where I’ve left off. A deeper part of me hopes that the process will spur me to resume this labor of love.
Through these upcoming stories, we will learn about applying principles of data science, noodling with scientific hypotheses, numerical programming, and of course — about the beautiful patterns concealed within cricket. If you are new to the game, I suggest watching this 3-minute video or, if you are one of the rare breed of internet users with more time to spare, this 17-minute episode of Explained.
How will this be different from any other cricket analysis blog? My choice of the word “science” is very deliberate. We will not merely be calculating averages and standard deviations. Instead, we will utilize the scientific method: formulate hypotheses, analyze data, create mathematically sound theories, and then use those theories to make testable predictions. What emerges is a fascinating series of results and ideas. It will be my goal to make you as excited about it as it is to me.
