Getting your eye in across formats

As ever shorter forms of Cricket proliferate, fast starters with the bat are becoming more valuable (just ask Dane Vilas). Let’s take stock of how players start across the three formats.

Tests, ODIs, and 20-20 are essentially the same game. Each match starts on a fresh pitch, with different ball, bowlers and climatic conditions to the last game. It takes more than one over to get one’s eye in, so batsmen are simultaneously settling in against multiple bowlers no matter the flavour of Cricket they are playing. The key differences which might impact how batsmen settle in are the white ball deviating less than the red ball, and the intent of batsmen differing.

How do we measure how settled a batsman is in each format in a given stage of the innings? In Tests we can use a batsman’s average ball-by-ball. However, in white ball Cricket Strike Rate is the prize (a cynic might suggest the curves suit my story so I’ve cherry picked).

The first thing to note is that these curves are quite similar – 20-20 batsmen really do take time to go through the gears. I’ll wager the slow acceleration is not through lack of effort. Players will be going as fast as they think they can: accelerating faster risks their wicket.

Data sources

Normally, this site is based on numbers I’ve crunched. This piece is an exception – while the ODI analysis is my own, I’ve based the 20-20 curve on this piece from sportdw.com. An aside – I looked at this back in 2017, and the bookmakers weren’t getting their “what happens next ball” odds right. Wish I’d been in a position to capitalise, but with a young family such activity was very much on the back burner. Anyway, it’s a fine post from sportdw.com, so take a look.

For Test matches I’ve used the analysis mentioned in my last post, Bayesian survival analysis of batsmen in Test Cricket. I had to make some assumptions to take Stevenson & Brewer’s player specific data and create a general case. Any errors in that process are mine – if you’d like to know about “getting your eye in” for Test Cricket, please read their work.

The current state

Let’s think about this behaviour from a theoretical perspective: in any innings a team are looking to make the best use of their resources. In Tests that’s as simple as maximising runs per innings: pick players that score the most runs per wicket, regardless of whether they start badly then get good, or quickly settle but then don’t improve much. Appraising Test players is easy – just look at the average*!

ODIs and 20-20s need to manage two scarce resources: wickets and balls. In 20-20 the number of overs is more of a constraint than in one day Cricket, which is to the advantage of faster starters. After 12 balls a 20-20 batsman is at full speed, while an ODI player is only scoring at 90% of their career SR.

What happens next

Data on 20-20 is everywhere. That’s partly why I don’t focus on it: the people that analyse 20-20 for a living are doing a great job. Seeing what’s available publicly, I can only imagine what depth of analysis the richest franchises have locked away on their laptops! I would be very surprised if there aren’t “getting your eye in” curves for each batsman, and teams optimising batting orders to get the fast starters into the right roles, alongside the high-average-slow-start-but-quick-when-they-get-going types.

Personally, I like a blunt approach – and assume all players play themselves in in the same way. Partly this is because I lack data – so have to make assumptions else I’d never have a model. Ultra short form games derived from Cricket will test that approach, and we will learn more about Cricket over the next couple of years.

Comparison with baseball

Baseball is a little like Cricket. Yet batters are up and running after just a handful of pitches. Why? A combination of there being only one pitcher, the ball not bouncing (so the ground matters less), and long breaks between innings mean that there’s limited benefit from repeated plate appearances in a game. Adding the yellow line for baseball shows just how similar the curves for Cricket’s three formats are. For now.

*For consistency I should say “factor in how many innings they have played, whether they were home or away, their record in each competitor adjusted for difficulty, what number they were batting, the grounds they were playing on, how old they are, which attacks they played against, what innings of the match each innings was” – but my writing barely flows at the best of times; that detour would not have been helpful. Hopefully you know what I meant.

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