34B. Looking at nervous nineties numbers another way
Rohit Sharma does slow down in his nervous nineties. However, does it really affect the team?
Sometimes, looking at numbers from a different angle can completely change the story. Don’t take that statement too literally. I’m not asking you to twist or turn your head. However, when you normalise numbers, or look at ratios differently, put together numbers you wouldn’t normally put together or look at cuts of data that you don’t normally look at, there’s a whole new set of insights that can be unleashed.
Last week we saw that Indian batsmen continue to slow down in their nineties, and identified Rohit Sharma as the batsman who does it a lot. We also saw that Australian batsmen slow down more for their hundreds than Indian batsmen (on average), though Steve Smith speeds up in his nineties.
Now, can we look at this another way. Essentially, Rohit Sharma bats at a strike rate of 122 in his 80s, and this drops to 90 runs per 100 balls in his 90s. This 30 point drop in strike rate seems like a lot, and seems to confirm Matthew Hayden’s and Glenn Maxwell’s hypotheses from all those years ago that Indian batsmen are more concerned about their personal milestones than maximising their teams’ runs (never mind that Warner, Finch and Shaun Marsh do it more nowadays).
However, does Rohit Sharma’s slowing down affect India’s batting that much? In fact, after the last edition I got a lot of angry comments (including one by a former Test cricketer) that essentially said that it is okay that Sharma slows down for his hundreds because when he gets them he gets big ones (including three doubles), and so the balls wasted in getting to his 100 get made up for by his massive scoring later on.
That got me thinking - how many balls does Rohit Sharma really waste in slowing down for his century? And that demands some inversion of the numbers I presented in the last edition.
If Sharma were scoring at 122 per 100 balls, that means he would take 8.2 balls to hit 10 runs. By slowing down to a strike rate of 90, he takes 11.1 balls to get through his 90s. Essentially, Rohit Sharma’s slowing down in his 90s costs his team three balls!
At the time when Maxwell made his comment in early 2016, Sharma was scoring at 151 in his 80s and 98 in his 90s, implying that he was taking 10.2 balls to get through his 90s rather than 6.6, costing his team close to four balls. I’m not sure if that was sufficient to account for India’s defeats in the first two games of that series (though Australia won the first one with exactly four balls to spare).
That said, Chris Gayle, who we saw slows down from 238 to 77 (which seems humongous), basically wastes 9 balls in chasing his 100, which perhaps seems a bit much.
I’ve reproduced here the last table from last week’s newsletter with the statistic presented in the form of “balls wasted” (rather than “difference in strike rates”).
It’s fashionable nowadays to say that data can “tell the truth”. However, as we can see in this post and its predecessor, the kind of message that you can tell using the data depends on the kind of message that you want to tell. In other words, it is not very hard to massage data to produce the message you want it to tell.
Maybe you should keep this in mind the next time you are trying to use data “science”!