How data analytics has transformed cricket

I’ve never been much of a sports viewer, but headlines have always been my hook. So, while the records Sri Lankan bowler Muttiah Muralitharan set in the 1990s and 2000s meant little to me, I was absorbed by the news reports of the three rounds of biomechanical testing he underwent to prove that his “doosra” wasn’t bending the rules. I still have no idea how many wickets he took, but I do remember that the machine took 250 photos per second and provided a 3D rendering of his bowling action. It seemed so cutting-edge, so unprecedented, so automated a way to measure what I’d always thought was a matter of personal skill and achievement. It was among the early demonstrations of data analysis in sports, and I, for one, had never seen anything like it.

Now of course, we all have all sorts of trackers strapped to our wrists to tell us how much energy we’ve expended walking to the fridge for a snack. Naturally, then, for professional athletes, data gathering is at a whole other level with tracking software and machine learning transforming the game. Data analysts are now integral to sports teams, generating statistical breakdowns to explain ball speeds, angles of delivery and each player’s strengths and weaknesses. Coaches don’t just impart wisdom and technique, they optimise play based on these analyses. We take a deep dive into the world of cricket analytics, which has not only changed the way athletes perform but also how teams are picked, and the ways fans watch the sport. We don’t just support a home team or fall in love with a player’s form and grace anymore; we pick and choose depending on projections that give the likelihood of success. Even in fantasy leagues, it’s data that’s the MVP.

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