The kids aren’t alright

Let’s look at the English First Class matches between Universities (technically the six University Centres of Cricketing Excellence) and Counties. These are vastly mismatched. The 2019 results make depressing reading for fans of university sport: UCCEs played 18 won 0 drawn 11 lost 7. County batsmen averaged 52 runs per wicket, while the students managed a paltry 15. If the UCCEs had been playing in the County Championship, they would have picked up a mere four batting points in over a season’s worth of matches.

It’s quite telling that over the last three years, only three student bowlers came out averaging under 35.

Fig 1- UCCE bowling performances against Counties in First Class Cricket, 2017-19. Overall bowling figures aggregated by player

Let’s not beat about the bush – the Universities were hazed by Counties that weren’t even at full strength. At first glance you might conclude that we can’t learn anything from these matches. Don’t be so defeatist! We have an opportunity to test how much better batsmen become when competing against players from a couple of rungs down the sporting ladder. It has always puzzled me: what should I model when an average player faces great bowling?

The method I’ve used is to compare individual batsmen’s performances in University matches against expected performance in County Championship Division 1. Since there aren’t that many University matches, we’ll need to group players by expected average to get meaningful sample sizes. We will also use three years’ worth of matches.

For the expected averages of each County batsman I’ve already done the legwork- see https://twitter.com/EdmundBayliss/status/1112335412658401280 and https://twitter.com/EdmundBayliss/status/1108509473591775233.

Here are the results:

Fig 2. The orange line represents the expected averages for each group of players (eg. “Very Low” are players who average below 20). The blue line shows the average vs Universities for that cohort, while the Grey bar (right hand scale) shows the ratio between actual and expected average.

Some interesting findings:

  • Overall “multiplier” (ie. boost to batsman’s average from facing University level bowling) 1.73 – a batsman who averages 30 in D1 would average 52 against UCCEs.
  • The University matches can distort First Class averages, especially for players with limited Caps. For instance, George Hankins averages 25 in FC Cricket, but strip out University matches and that drops to 23. Ateeq Javid’s 25 also drops to 23 when you exclude the 143 against Loughborough. Thus “First Class” average is reliable for county regulars, but fringe players will play a higher proportion of their innings against students. In these cases, “First Class” average should be disregarded in favour of a blended measure of County Championship & Second XI matches.
  • Batsmen with the lowest averages get the biggest boost– this could be because County Cricket pits them against deliveries which they aren’t good enough to defend. Put them against easier bowling and their technique is up to it, so they flourish.
  • Both the “Good” batsmen (who average 30-40 in D1) and the “Very Good” batsmen become excellent averaging 60+ against Universities. Why the plateau at 60? This is possibly caused by batsmen that “Retire Out”– which will affect the highest scoring (ie. best) players more. The concept of “Retired Out” is another reason UCCE matches distort FC averages.
  • Players ranked “Good” or above scored 29 hundreds in 131 completed innings. That’s a Century every 4.5 innings. Quite a mismatch between bat and ball.
  • It’s hard to appraise fringe County players, because of the low number of matches played. Ideally, scores from the University matches could be incorporated into my database in the same way 2nd XI matches have been (by adjusting for the difficulty of the opposition). However, the above tells us that the standard is too low and variable – so disregarding the data is the safest approach. This means that raw First Class averages are potentially suspect, and county selection should not be based on performances against the Universities – no matter how tempting it is. A fine example of selection being driven by University matches is Eddie Byrom being picked by Somerset on the back of 115* against Cardiff UCCE. He made 6 & 14 against Kent, and hasn’t played since.

Conclusion

Based on the above, there’s no evidence to say that top batsmen become impossible to get out when they play against weaker bowlers. A reasonable approximation is that Division 1 batsmen would average 72% more when playing against Universities.

When modelling expected average for a given batsman and bowler, the following rule of thumb is sufficient: Expected average = (batsman average / mean batsman average) * (mean bowler average / bowler average).

PS. Fitting the University Matches into the English summer

What place do the UCCE matches have in the cricketing calendar? Tradition is important. Personally, I would like these matches to continue. What’s needed is a window where the best players are unavailable (as these matches are of limited use to them).

In their wisdom, the ECB have established a 38 day window called “the Hundred”. I propose a change to the calendar – instead of the University matches, the 50 over competition should be the curtain raiser for summer. Half the group games could take place in early April, with the other half happening at the start of “the Hundred” window. This would be followed by two weeks of UCCE matches.

This would ease some of the congestion in the fixture calendar, and make a more logical use of county squads and grounds while we wait for “the Hundred” to finish. It would also mean full strength squads playing some 50 over Cricket, so England have some chance of being competitive in future World Cups.

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.