Test vs County Cricket Averages

“Coach woulda put me in fourth quarter, we would’ve been state champions. No doubt. No doubt in my mind.”

Napoleon Dynamite (2004)

It’s often assumed that we cannot compare Test and first class batting performances – the old comparing ‘apples to oranges’ conundrum. But if we can quantify the relative values of the different formats, we can compare like with like.

Looking at batting performance of players who’ve played across multiple formats in English* domestic cricket (2016-2018), one can assess the relative difficulty of each tier. My analysis found that it’s 19% harder to bat in Test Cricket than it is in Division 1.

If a player averages 40 in Division 1 – the data says you could expect him to average 31 in Test cricket, 44 in Division 2, and 54 in the 2nd XI.

That tells us that you’d need to consistently average over 55 in Division 2 to average 40 in test cricket – hence so few England players being pulled from those ranks in recent years.

It also means that Hildreth (who I’ve previously thought of as an England option as he averages 41 in Division 1) would be expected to average 32 in Tests, and therefore isn’t the batsman we are looking for.

A few examples of 2016-2018 Division 1 and Test averages:

Note that only Root and Buttler underperformed in Division 1 relative to Test Cricket.

At this point its worth going into the assumptions – professionally I’m always keen to show where the data ends and the judgement begins. The data can tell us performances for each player who crosses tiers. Judgement needs to be applied to appraise that data and turn it into a single factor.

Some options:

  • Jonas (@cric_analytics) has looked at minimum 10 innings in both competitors – the downside of this is that it excludes valid data points. For instance, Ben Stokes scored 226 @ 28.3 in D1 in the last 3 years – 10 runs below his test average. That should count to the total, even if it’s a small sample. Jonas reckoned a 20% gap between Test and County cricket – slightly wider than my data suggests.
  • Include all overlap – the risk is that this is skewed by a few high/low scores from one-test wonders against weak/strong opponents. This gives a mere 2% difference between Test and D1.
  • Overseas players included: this gave an 8% gap between D1 and Test – but playing away from home knocks 10% off batting average, so this is not a fair comparison. To put it another way, Pujara playing for Yorkshire averaged 14, because every game was an away game.
  • I have used relative performance for English players with >4 completed innings in each format, and weighted the overall result according to the lower of the completed innings in each format. For instance, Ben Stokes has played 8 completed D1 innings, but 46 Test innings – so the overall result is weighted with a factor of 8 because of Stokes’ performances, while Dawid Malan played 36 D1, 26 Test innings, so is more useful for this exercise and receives a weighting of 26.

Adjusting for the level individuals are playing at, allows comparison of players in different tiers. In future posts I’ll look at some implications of this data:

  1. 2nd XI players with the potential to be First Class batsmen
  2. England’s best available batsmen
  3. Overseas players: who has & hasn’t succeeded – will look at any trends in the data.
  4. It’ll take more number crunching, but I’m interested in linking First Class / List A performance- to see how well correlated they are, and use that to gauge quality of players for which limited data is available (there are a lot of players with a handful of FC games behind them – too few completed innings to fairly appraise them

*I know it’s English and Welsh. Sorry Glamorgan. There isn’t an easy word for English and Welsh, so I’ll use English as shorthand for English and Welsh.

The Journey Begins

Thanks for joining me!

“You’d better listen to her, because the Pentagon does”

Top Gun (1986)

A bit about me before I get into the numbers:

It’s easy to have an opinion, and particularly easy to broadcast that view online. Filtering out the noise is a challenge.

So why should anyone care what I think about cricket?

Well, my cv for starters- Masters degree in Physics from Oxford (4th year was focused on simulations of Earth’s atmosphere), then qualified as an accountant, spent 2 years in Banking Front Office (where I cut my teeth on excel modelling), and after a further role in Banking Finance I’m now working for a FTSE-100 retailer, doing modelling and strategy.

It’s not quite the Pentagon, but you should listen to me, because some people at a FTSE-100 retailer do.

In 2011 I built a test match simulator – which could predict the outcome of an innings from a given starting point, based on ball by ball bowler vs batsman probabilities, and running the simulated innings enough times to get a reasonable sample (>1,000). This was mainly for gambling, and it works.

Later I expanded this to cover the two white ball formats, though the 50 over model has always received more attention than the 20-20 one – I don’t mind 20-20, but I struggle to love it.

With a full time job, and a young family, cricket data comes third on the list – and that means I will focus on red ball cricket. There’s a lot of professionals who have got further than me in 20-20, and I’m not going to stand out by splitting my efforts across 3 formats.

Let’s see if I can come up with some original thoughts, and some predictions which stand the test of time.

Ed Bayliss, Dec 2018.