Cricket is a complex game – about as difficult to get a full hang of as a 21-year old trying to learn English from scratch. It takes a while, and a lot of practice. Even then, many people (I know) still have difficulty getting all the rules right. As a result, there are a lot of numbers that emerge after each game – so many runs scored in different directions, so many balls bowled at so-so speeds, at so-so lengths to so many batsmen, so many partnerships each lasting so many balls, so many successful and not-so-successful fielding positions, etc. In short, cricket is a statistics-heavy game, perhaps heavier than baseball itself. So if baseball had sabermetrics, what does cricket have?
Nothing official in place, for starters. Cricketing sabermetrics isn’t new, but it isn’t prevalent either – the reasons are too many to be dealt with here, but not the least of them is that cricket is also more complex than baseball. Building a statistical framework to encompass all of its nuances is difficult. So, a simplified version of cricketing sabermetrics – one making a lot of assumptions – assessing only the batsmen’s performance during Ashes 2013 caught my interest. Satyam Mukherjee, a post-doctoral fellow at the Kellogg School of Management, had used complex network analysis to figure out why Clarke, Trott and Bell were the better players during the tournament, and he establishes it with mathematical proof.
His work also raises a lot of questions on the relevance of such mechanisms in modern sport. Read my piece on this work for The Hindu.