Cricket Statistics Request: Brittleness Index

To all numbers-minded cricket bloggers (i.e., Devanshu Mehta): can you design a statistical index that tracks a batting line-up’s brittleness? Definition: hard, but liable to break up.

After watching the Indian line-up over the past few months (in England, and in ODIs against the West Indies), it occurs to me that despite its obvious formidable powers, it can still be broken in half. This isn’t a new story; before the team’s No. 1 Test ranking, India did display an alarming tendency to collapse in a heap for no apparent reason. I don’t how you’d rigorously measure this trend — something that allows for high averages (as India has), but also a high likelihood to collapse (defined by wickets falling over a short number of overs) and a collection of low scores (i.e., if one does badly, most in the top six do badly). V.V.S. Laxman will probably ruin the curve here, so you might consider excluding him. 

I can only hope that when this team heads down to Australia, Sachin Tendulkar and Virender Sehwag and Gautam Gambhir will put aside whatever blues have been dogging them and help Dravid and Laxman.


2 thoughts on “Cricket Statistics Request: Brittleness Index

  1. This Indian line-up is brittle in parts, and then really non-brittle in others. It’s like a candy bar with parts made of diamond and other parts made of pure gold. The ’90s line-up was pure gold. Very shiny, very expensive, but brittle.

  2. Russ says:

    DB, it depends how you define brittleness, statistically. Keeping in mind differences in scores might be pitch or opposition related. I have a funky graph that seems to imply brittleness:

    Teams ought to, really, have a roughly normal distribution of scores. But they often have double humps, implying a tendency to collapse in hard times and bully in good. The percentage difference between the two humps would be a god measure of brittleness, but I don’t know how to find those humps, statistically. (Though if I was minded I could probably work it out).

    It is more interesting to discuss batsmen than teams. But I’ve never found a good way of determining if a player is better than expected under pressure; not least because good players tend to shift the curve (and there is always a lot of luck involved).

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