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.
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.
I have a theory that openers are better than middle order batsmen with the same average. If someone averages 35 against the new ball, that has to be better than averaging 35 against the third change bowler using a 60 over old lump of leather.
Here’s Michael Carberry’s take on the unique challenges of opening:
As openers, we don’t have the luxury of being able to come in against the old ball where it’s doing less. You see it on the first morning of a match. Everyone’s prodding the wicket. ‘Oh yeah, this looks a belter’. It’s never a belter when you’re facing the new ball. If the ball is going to do something, generally you’re the one who’s going to get it.
If Carberry is right, once openers see off the new ball, their expected runs for the rest of the innings should be higher than their career average – they’ve done the hard part.
How to prove it though? The proper way would be to show what various batsmen average at differing stages of their innings, against particular bowlers, against both old and new ball, and when the bowler is in their first, second, third spells. That’s not complicated, but would be time consuming, starting from a ball by ball database of Test Cricket. I haven’t done that. Instead, I’ve looked at what happens to a batsman’s average once they are “in”. Previous analysis tells me a batsman is fully in by the time they are 30 not out.
The benefit from “getting your eye in” is worth about five runs onto a player’s average (ie. if you average 40, by the time you get to 30* your expected average goes up to 45).
Surprisingly, openers don’t get a further boost once they get to 30. This is odd – by the time an opener is on 30, 20 overs would have gone, the three best bowlers would have bowled six or seven overs and the ball would no longer be hooping round corners. I’ve definitely watched England play, rocking back and forth in my seat saying “if they can just get to 20 overs, see off the new ball, it will get easier. It’ll be all right”. Turns out that was piffle. It gets easier (c.12%), but it’s not a violent swing into the batsman’s favour.
Weaker middle order batsmen get the biggest benefit from getting to 30. I think that’s because they really are the easiest times to bat – 40+ overs into the innings, tired bowlers, etc. In other words, these players aren’t becoming relatively better once they are in – they just tend to be building an innings as conditions become more favourable.
Put the above analysis together, and I’ll give you a second hypothesis – collapses in red ball Cricket are partly because lower middle order batsmen’s averages flatter them. A batsman that averages 30 can make hay in helpful conditions – yet they only average 30. That must mean that they average less than 30 in challenging conditions. Maybe when the going gets tough, the middle order will disproportionately get blown away. Unfortunately for me, that hypothesis doesn’t show up in the numbers. Yet.
Since “Expected Innings Average” (EIA) is a non-standard metric, it’s worth explaining what it is and how I’ve derived it, else you’d have every reason to dismiss this as someone fitting the data to match their hypothesis.
EIA was calculated for every innings where a batsman scored over 30. Their runs in that innings (minus 30) were compared to their EIA to get a view of how their average (once they had got their eye in) compared to what one would expect from when they started their innings. Thus Benefit from getting eye in = Runs scored – EIA – 30.
To calculate EIA I started with the batsman’s career average. Then adjusted for the runs per wicket on that ground, then added or subtracted 8.5% depending on Home/Away. To adjust for not outs, I added the EIA to the not-out score.
For instance, when Virender Sehwag scored 319, his expected average was 49 (Career Average) * 1.2 (Ground adjustment for Chennai) * 1.17 (Innings Adjustment – for the 2nd innings of the match) * 1.085 (Playing at home) = 74. Conditions were favourable – but he still exceeded expectation by 245.
In case you aren’t a fan of the above, I also calculated the impact based on raw averages. It doesn’t reveal much. Just goes to show how important the context of an innings is: raw averages are just too simplistic.
Michael Carberry’s recent interview in Wisden is linked here.
Wind the clock back. The good old days. Specifically the noughties (or 2000s, or whatever). An opening batsman fulfilled the same role in Tests or ODIs. Hence their ODI and Test averages were similar, and you could use one to predict the other with a fair degree of confidence.
The correlation is so good that the names get all jumbled up on the straight line running from (20,20) to (50,50). Yes, there’s some Test specialists there (Cook, Strauss) but most of the 23 players that meet the criteria for inclusion behave as expected.
That correlation has broken down now.
There are three distinct types of player, reflected in the clustering in the chart:
Versatile elite batsmen (Warner, Iqbal) – just as good in either format, average over 40 in both.
Test specialists (Latham, Azhar Ali) – who are/were good enough to play in ODI Cricket, but averaged at least five lower in ODIs
ODI specialists (Hales, Guptill) – averaging under 30 in Tests.
I’m reminded of the film Titanic (1997) explaining the captain’s complacency: “26 years of experience working against him”. That line stuck with me – it’s easy to assume past trends will continue, and that you can use opening the batting in ODIs as a pathway into opening in Tests.
Not any more. Unless the player is good. And I mean really good, the best predictor I can see for successfully opening the batting in Tests is successfully opening the batting in red ball Cricket. Think about Jason Roy – ODI Average 43 as an opener, Test Average 19. I don’t think anyone is now expecting him to average 35 in Tests as an opener. Yet someone must have thought he could, else he wouldn’t have been picked.
redballdata.com – closing the stable door after the horse has bolted!
PS. This piece serves as another reminder to me to continually check that the trends I’ve seen still hold – else one day I could be the mug taking Fig.1 to a meeting, persuading everyone to pick the best ODI openers to open the batting in Tests.
In this piece I’ll look at which grounds are best for red ball batting, and use that to see what impact that has on averages: how much of a boost do Surrey’s batsmen get from playing at the Oval?
Beyond it being a spot of trivia, I can immediately see two reasons why this matters.
i. High scoring grounds harm the county’s league position
In County Cricket there are 16 points for a win, 5 for a draw and none for losing. A win and a loss is worth 16 points, while two draws is worth 10. Drawing is bad*.
And yet there are teams producing high scoring pitches, boosting the chances of a draw, and reducing their chances of picking up 16 points.
Compare Gloucestershire’s two home grounds since 2017: at Bristol (32 Runs per Wicket), W2 L4 D8. Cheltenham (28 Runs per Wicket), W4 L1 D2. Excluding bonus points, Cheltenham is worth an extra 5.4 points per match. While that’s an extreme example, and the festival only takes place in the summer months, there’s still the question “why make Bristol so good for batting”?
Maybe a deeper look at the data will reveal why Gloucestershire and Surrey don’t try to inject a bit more venom into Bristol and The Oval; for now it looks like an error.
*There’s an exception: a team that is targeting survival in Division 1 might choose to prepare a flat track and harvest batting points plus drawn match points in certain situations. For the other 15 counties, drawing is still bad.
ii. Averages should be adjusted to reflect where people play their Cricket.
When using data to rank county batsmen and bowlers, the one gap that I couldn’t quantify was the impact of how batting or bowling friendly each player’s home county is. With this data we can add an extra level of precision to each player’s ratings.
How would we do that? It would be wrong to simply take the difficulty of a player’s home ground as the adjustment – because there are also away games. The logical approach would be to take the average of that player’s home grounds (50%, weighted by the various home grounds that county uses) and the other teams in that division (50% weighting).
For instance, Olly Pope’s average is artificially inflated by 10% from being based at The Oval. That takes his rating (expected Division 1 average) down to 54.6 from the suspiciously strong 60.7.
Equally, Tom Abell clambers up the ranks of 2019’s County batsmen: his rating jumps 7.1% to 35.6 from 33.2. Not an extreme move, but a nice boost to go from 50th to 31st on the list.
This takes us one step closer to a ratings system that captures everything quantifiable. Before next season I’ll adjust the ratings of batsmen and bowlers to reflect this factor.
This is my first attempt at something difficult: finding the best players that aren’t regularly playing County Cricket, but that are good enough to do so. In theory there shouldn’t be very many players like this – because counties will know who their best players are.
I’ve used my database of bowling performances from 2016-19 in County Championship and 2nd XI Championship Cricket and picked out six that have promising data.
Time will tell how many of these players get regular first team cricket (and succeed) in 2020.
I’ve looked at players that have been selected for no more than three County Championship matches in 2019, for reasons other than injury.
Note that England’s Matt Parkinson only played four games for Lancashire in the 2019 County Championship, so might have made it onto a list like this, but he is unlikely to be under anyone’s radar now he’s in the Test squad.
This page contains expected County Championship Division One batting averages for all County Cricketers to have i) played during 2019; and ii) batted in at least 20 completed innings since 2016.
Performances in the Second Eleven Championship, County Championship and Test Cricket are included, though each performance is weighted according to the level being played at (so averaging 30 in Test Cricket is much better than averaging 40 in the Second Eleven Championship).
To give a better indication of current ability, and to partly adjust for age, ratings are weighted more heavily towards recent performances.
Ratings are shown if each player were playing in Division One – this ensures bowlers are compared on an apples-to-apples basis.
I’ll update this page periodically, as more games are played and more information is available on each player.
This version includes matches up to 29th September 2019.
Zak Crawley is an odd Test selection
Expected Division 1 average under 30
Only averaged 34 in 2019, after averaging 32 in Division 2 in 2018.
Even separately adjusting for age (he’s only 21), it’s hard to argue he’s currently better than Dent & Rhodes.
Ollie Pope is practically too good to be true
Expect his average to come down – he can’t possibly have an expected average exceeding 60.
Only 42 completed innings – barely a sufficient sample size to be included in the top 50 players.
Still, he’s easily worth a Test place.
Very few English batsmen are capable of consistently averaging over 40 in Division 1
Cook, Ballance, Northeast and Brown are the four England qualified batsmen who would be more likely than not to average over 40.
There’s more decent English openers than you may have been told elsewhere
Keaton Jennings, Mark Stoneman, Chris Dent and Will Rhodes could cover Burns and Sibley. And, if he could be coaxed out of Chelmesford, Cook.
England selectors might well be relieved that Cook has retired – imagine having to choose two out of Cook, Sibley and Burns to open the batting.
What do you think?
No doubt there’s plenty of themes and trends from the data that I’ve not mentioned – please do drop me a line through the contact page or @edmundbayliss on Twitter and let me know what you think.