T20 batting: running out of steam

When should you consolidate? I’ve devised a general rule to calculate when the batting team should slow down and conserve wickets.

Let’s recap the current state of T20 International batting. Teams usually end their innings with wickets in hand. Since 2016 the average first innings score is 166-6*. Teams rarely get bowled out, having enough batting depth to attack throughout, even if a wicket falls early on.

Looking at this another way, on average it takes more than 120 balls to bowl a team out. The openers can go out and play naturally, expecting the top seven to do the business. The number eight batsman averages only three balls per game. I think of limited overs batting in terms of “Expected Balls”: how long would you expect it to take to bowl this team out if they were batting normally? For example, England bat deep, expecting to last 172 balls before being bowled out – this gives them licence to attack in a game that’s only 120 balls long.

~~~

The more you get in the first innings, the higher your chance of victory. But get greedy, take too many risks, and you may fall short of a middling score that might have been enough. Any approach to batting has a range of possible outcomes. The goal is to pick the approach that maximises expected win %. How do you do that?

Here’s one example – consider a binary choice where number four in the first innings can either bat normally or anchor (Strike Rate down 10%, Average 20% higher). According to my model, for this current England T20 team, pre innings or at 0-1 anchoring is not optimal. 0-2 it’s marginal. It’s only worthwhile if you’ve slumped to 0-3. Which, coincidentally, is the point at which England’s expected balls drops below 120 (ie. they run the risk of the median innings not lasting 20 overs). This makes intuitive sense: tailor your batting aggression so you almost (but not quite) get bowled out.

Note this assumes England are playing against an equally talented team – hence win % pre game is 50%

The general rule: bat normally unless Expected Balls < Balls Remaining.

A recent example – England were 34-3 (5.3) – which looks precarious, but the Bairstow-Stokes-Morgan middle order meant it was more-likely-than-not that England would bat all 20 overs, and have a reasonable chance of chasing their 180 target. England won the game in the 20th over. Maybe that “lose three wickets in the powerplay, lose the game” maxim is outdated as T20 averages improve. For England, Expected Balls exceeded Balls Remaining, even having lost three wickets in the powerplay.

But this is too simplistic. Not everyone can strike at 150: you can’t expect fireworks from every tail. Here’s the strike rates of the top 10 T20I teams over the last five years. Numbers 9-11 just aren’t as good. I think of teams “Running out of steam” when all the quick scorers are out.**

“Running Out Of Steam” depends of the composition of one’s batting order. England currently have Jofra Archer at number nine. Deep. West Indies aren’t so lucky – Keemo Paul bats at eight with a domestic career SR of 107 – so they Run Out Of Steam at six down.

My hunch is that cricketers know what their tail is like, and how likely it is that tail will be exposed, and bat accordingly. Take another recent example – WI T20 #1 – at 59-5 (5.1) West Indies were vulnerable. One more wicket and they were done for. So Pollard and Allen consolidated, taking 37 from the next five overs. A rain interruption meant the innings was reduced to 16 overs. With just six overs left – it was time to attack, lifting the score to 180 by the end of the innings. Subsequent discussion focussed on the impressive assault, missing the responsible consolidation period that made it possible.

~~~

Here’s the “Balls to Run Out Of Steam”*** for the Top 8 T20I sides, based on their most recent XI

As at December 2020

This tells us that England, Australia and Pakistan have the capacity to score more quickly than each player’s career record (ie. if they bat naturally, they are wasting resources by being too conservative). If wickets fall, that should be reassessed****.

Note Sri Lanka put out a particularly weak XI in their last game. Numbers four and below would expect to strike at below 130 – way off the pace. Hence they run out of hitters unusually quickly.

Teams should tailor their aggression, aiming to not quite run out of steam. To do this, throughout the innings the batting team should compare EBTROOS to balls remaining, adjusting the EBTROOS as wickets fall.

Just imagine those clipboards showing live updates of Expected Balls To Run Out Of Steam, and Optimum Strike Rate. (Screenshot from Sky Sports)

Footnotes

*Top 10 teams against each other. Sorry Luxembourg.

**I wish I was good at writing. Spent ages trying to come up with a better name for it than “running out of steam”. Ideas welcome.

***BTROOS = Balls To Run Out Of Steam. This is clunky stuff.

**** There’s an added complexity which I’ll keep for the footnotes: median innings length is not the same as balls per wicket. The difference is only 4% at the start of the innings, but gets bigger as fewer wickets are left. Here’s the same table, but with Median Balls To Run Out Of Steam*****. MBTROOS = Median Balls To Run Out Of Steam. Perhaps MBTROOS could rhyme with albatross. Anyway, here’s the MBTROOS for the latest England T20 lineup:

MBTROOS for England. Note 3 down after the powerplay would mean MBTROOS 86, which are two balls more than the 84 remaining. So keep attacking!

The perils of batsmen switching counties

In a recent article for “County Cricket Matters” magazine, I looked at the impact of changing counties on a batsman’s average. You can buy it here.

The main conclusion was that a transfer tends to negatively impacts batting.

A surprising observation was that younger players are more adaptable and may improve, while the over thirties rarely benefit from a move. Small sample sizes, so just a tantalising hypothesis for now.

This data covered 2016-2019. The curtailed 2020 season was too short to extend the analysis, so we’ll have to wait for 2021 to see if I’m onto something.

P.S. Much of my analysis considers trends such as these. Since we’ve had two years’ since this blog began, at some point I’ll check if those trends continued. Trends that continue after you’ve noticed them are much more valuable than mere things-that-have-been-true-lately.

The ODI “who is winning?” formula

Modelling a chase is hard. I was looking for a rule of thumb: a quick calculation that could support the monte-carlo simulation I run. And here it is:

Decimal odds of chasing team winning = 1 + (Required Runs/Expected Runs)^8

Jonas (@cric_analytics)

Jonas gave the example of Australia needing 145 more to win an ODI against England. He thought Australia could on average expect to score 110 from their last 20 overs. Australia’s decimal odds were thus 1+(145/110)^8 = 10.1 (or roughly a 10% chance of winning).

To successfully unpack (or steal!) the formula, the element that needs a bit of thought is “Expected Runs”. We can use Duckworth-Lewis, combined with ground data to give an approximation. 20 overs & 5 wickets left meant 38.6% of resources remaining. On a 285 par pitch, that’s the 110 Expected Runs that Jonas calculated.

Taking the formula one step further, “Expected Runs” can be adjusted for the quality of the batting and bowling teams to give a more precise calculation for a specific run chase. I have added this expanded formula to my model to better understand who is winning and why.

Here’s an example of what this looked like when Australia were 222-5, needing another 81 from the last 10 overs (third ODI, 16th Sept 2020):

Aus 222-5 (40). Maxwell 74* Carey 77*. Target 303.

The raw formula gave Australia a 22% chance with 26.1% of resources remaining (Expected Runs = 69, on the basis that a normal par score is 264 – that may be an underestimate as scores keep rising). However Old Trafford slightly favours the batsmen, and England’s attack is sub par – lifting Australia to 32%.

My model had Australia at a 42% chance – the extra 10% coming from the strength of Australian batting, the two batsmen being set, and any other differences between my model’s Monte Carlo simulation and Jonas’ formula. The right hand column is the output of my model, and the penultimate column is the one that goes haywire if something is wrong: a useful check.

What’s the message? Firstly, if the model is working, I can see who is winning during a chase and why. Secondly, matchups and other complexity have made my model something of a “black box” – Jonas’ formula will be a useful check that my model isn’t off piste.

Rating the Blast teams … Lancashire’s batting is better than it looks

I rated the county 20-20 batting in one hour of analysis. Reckon I need to understand 20-20 eventually.

My starting position: scoring quickly is good but getting out is bad. Thus the best teams will score quickly, with high “balls per dismissal”.

Here’s how that looks for this year’s teams (rated using the most commonly used XI this year):

Quarter final qualifiers in green, eliminated teams in red. Only the top eight batsmen’s SR has been included as the tail rarely bats, but the whole team has been included in the BPD calculation.

Averages matter

Surrey and Gloucestershire score just as quickly as Yorkshire and Kent, but with higher balls per wicket they are less likely to fail – so are more consistent and better batting units.

I had heard that averages don’t matter in 20-20: I think they do.

A team has to be confident of lasting 120 balls. I don’t know how many balls you need to expect the unit to survive before getting bowled out in 120 becomes unlikely – maybe 180? Only three teams on the right of this chart are at that level. Once all teams are there then wickets cease to be a limiting factor, and it’s all about strike rate.

Lancashire – skewed by strong bowling

Bottom right should contain bad teams: trundling to 140 and losing.

Yet Lancashire won five games this summer with a team where no-one has a four year SR over 135. They even scored 190 (SR 158) against Durham. What’s going on?

The key is that they are a strong bowling team that often have easy chases. They thus play within themselves to secure the win. This makes their players look like plodders. Yet batting first they score 177 on average over the last two years. While chasing that drops to 129.*

Lancashire’s true position on the chart would be somewhere up and to the left. Repeating this chart with first innings data would help.

Here’s my attempt at a Boston Consulting Group** view of T20 team batting:

Stars: good teams. Dogs: bad teams. Question marks: Could be bad teams, could be players looking slow from chasing small totals. Roller-coasters: Might score 200 one game, 105 all out the next.

I’ve only looked at the batting, but I feel like this view might have some predictive power. 2/6 Dogs qualified, 1/5 Roller-coasters, 2/3 Question Marks, 3/4 Stars. The three Stars were the three group winners.

*Data up to 19th Sept 2020.

** Boston Consulting Group suggested in 1970 that companies could consider any product as being one of four types in a market (Star, Dog, Question Mark, Cash Cow). I’ve ripped off their idea to try to look like I know about business as well as cricket.

Conferences: what we can learn from the 2020 Bob Willis Trophy

This year’s Bob Willis Trophy was entertaining. I’m glad the counties and the ECB made it work. It was the right format for a condensed season, and the Essex / Somerset final should get the attention it deserves.

The setup looks like it will be broadly maintained next season. The expected structure is ten games in three conferences to sort teams into three divisions, four further games within those divisions, and a final for each division.

While there are merits to this structure, there are also risks: specifically mismatches and the dilution of talent. I’m going to show you the key weaknesses in the format by looking at the 2020 tournament.

1. Mismatches

Two divisions ensures a smaller step from CCD1 to Test cricket. Yet under the new structure, a good chunk of the ten games at the conference level (ie. the first part of the season) will be against weaker teams.

I can show you this with data – comparing how players did this year versus their four-year domestic record. Because there was no mixing between groups, we can look at each conference separately:

Note that a negative impact for bowlers means you would expect them to record a lower average (eg. a bowler who would average 30 in CC D1 would expect to average 18.9 in the BWT North group).

I estimate the overall standard was 19% lower than CC D1, with conditions 22% harder for batting (perhaps pitches were understandably not in as good nick as usual, perhaps batsmen need more preparation than bowlers). That’s a big step up to Test cricket.

Is it sensible to establish a competition where Simon Harmer’s expected average is 14? This year was unusual; longer term the red and white ball England squads need to be available for county duty as much as possible, else the cricketing value of the championship will be diminished. My prediction – a year or two of the best bowlers averaging 10-15 would drive Test selectors to look to the IPL and CPL for performances against the best (if their heads aren’t already turned in that direction).

Of course, exceptional circumstances meant squads were unusually depleted (no overseas players; many foreign-based cricketers were unavailable until the Blast; England’s red and white ball teams were otherwise engaged). Surrey’s squad was often just a list of their available players. Even without that, the mixing means the best aren’t playing the best as often.

2. Dilution of talent

Generally, Test batsmen have been drawn from CC D1. They’ll face a variety of top bowlers there. There are still great bowlers in D2 (James Anderson and Mohammad Abbas, for example), but it’s less consistent.

An example- this summer’s Central group was definitely light in the spin department. Here’s the top 10 spinners by wickets taken:

Nothing against these players – but would a summer in the Central division give a batsman confidence he could tour India with reliable technique against spin?

The wider point – there’s no guarantee a conference structure will provide the rounded challenges to turn a good player into an great one.

**

County cricket has to serve three purposes: to maintain tradition, to entertain, and to help the Test team flourish. The two division structure wasn’t perfect, but ticked all three boxes. The new conference structure can’t afford to fall down on the last point. For a fuller appraisal, I recommend George Dobell’s piece in this month’s Cricketer magazine.

Top innings bowler in Tests

Cricket has quirks. Look at it through another lens; learn something new. Today’s angle is wicket-taking. What kinds of bowlers are the leading wicket takers in a particular innings, and what implications does that have?

Since 2010. First five bowlers used only

No surprises here. Let’s go deeper.

Pace bowlers by innings and by position

Since 2010. First five bowlers used only

Opening bowlers are the best pace bowlers. They also get a boost in the first morning on a fresh pitch. In later innings, they are still favourites among pace bowlers (though the spinners start to get a look-in).

First change bowlers are about two-thirds as likely to be leading wicket taker in an innings. Probably two factors – they aren’t quite as good as openers, and (more significantly), they don’t get the new ball, or first crack at the tail. In later innings their chances improve, probably because those innings are shorter, squeezing out the part time bowlers.

The fourth bowler is just as good as the third, until the second half of the match. The fifth bowler gets a reasonable go in the first innings, but not much action in the second. They are surprisingly unlikely to take the most wickets in an innings – it’s kind of a four horse race.

Pace bowlers by Ground

We haven’t the data to do much by ground, but one stadium gets two Tests a year: Lord’s.

Lord’s since 2010. First five bowlers used only

There’s a 75% chance one of the two openers is leading wicket taker in the first innings at Lord’s – batting is hard in that first hour. The change bowlers don’t really get a look in.

Until models are seriously good, they won’t always beat local knowledge. To get that I need domestic stats. Must build that database at some point.

Spin bowlers by Country by innings

Never cut a spinner. Or back one to be leading wicket taker in the first innings in the SENA countries (South africa, England, New Zealand, Australia). The general trend of a gentle increase in top-wicket-taker probability from 19% in the first innings to 26% in the last is not seen worldwide. In India, Sri Lanka and Bangladesh, a front line spinner should rarely be available at longer odds than 3-1 in any innings.

(Also a reminder that spinners go from pointless to prized over the course of five days in England).

Individual players

If ye grind yer [gunpowder] too fine, it’ll blow yer bloody head off, then nobody’ll know who’s best shot; thee or me

Sharpe’s Siege

Do bowlers have preferred grounds? Almost certainly. Does it show up objectively in the data? Maybe, if you know what to look for. I’ve checked just one example: Stuart Broad. Using the data since 2010, Old Trafford was clearly his favourite ground, while The Oval was a graveyard. However, look just outside this horizon, and in Broad’s only previous Old Trafford Test he took 0-79, while in his earlier Oval Tests returned 11 wickets at 19. Regression to the mean? Maybe.

What have we learned?

Nothing earth shattering – hints at how bowling position impacts performance, a reminder that grounds can have peculiar characteristics, another reminder that the usefulness of spin through a match varies by country, and my squeamishness about small sample sizes was reinforced when looking at Broad’s record.

Call this one “knowledge consolidation” – kicking the tyres of what I think I know, and nodding approvingly at the result.

James Anderson and the timescales of chance

Cricviz put out a tweet recently showing how James Anderson’s Expected Average has steadily improved over his career. That consistency is in contrast to volatility in his actual average. In this piece I’ll explore why Expected Average may be more reliable than actual averages.

Background

It’s worth recapping what Expected Average is. Cricviz use ball tracking to build a database of many deliveries, so know for a given ball (perhaps an 82mph full away-swinger) how many runs would on average be scored, and the likelihood of taking a wicket. Expected Runs divided by Expected Wickets is Expected Average*.

Why is Expected Average compelling? It captures that which is under the bowler’s control. The trajectory of a ball, its seam position, pace and spin are controllables. Everything else is outside the bowler’s sphere of influence (who are you bowling to? Do they edge it to first slip, edge it for four, or miss it completely?) Expected Average always rewards good bowling, unlike the fickle real world.

Expected Average beats actual average

Returning to Anderson: looking at his bowling average year-by-year you’d say he blows hot and cold. Seven years averaging under 24, but five years averaging over 30. Contrast that with his (very stable) Expected Average. Using xA the volatility disappears – it tells a different story: Anderson has been a consistent bowler, improving steadily to become the player he is today. I find that easier to believe.

The two metrics (actual average and Expected Average) can be reconciled by assuming that actual average is a function of Expected Average and luck. Using my formula for error bars [1 standard deviation = xA * xW(-0.5)], we get the following chart, which shows that Anderson’s ups and downs might be quite normal (ie. two thirds of the time the blue line is between the grey lines).

How can we get past the impact of luck? Look back at the formula. Uncertainty scales with the inverse square root of wickets. More matches, more wickets, less of a look in for Lady Luck. Let’s consider a rolling four year horizon, where luck is corralled into a +/-2 impact on average (below). Average and Expected Average nicely aligned. Expected Average bypasses the need for luck to iron itself out over time: it’s a better metric. Everything wrapped up in a neat little package.

Limitations of Expected Average

So far, so predictable. Now the fun part. Here’s a hypothesis for you: Expected Average is incomplete as it misses the impact of “setting a batsman up”.

A googly pitching on the fourth stump is a fine ball. But isn’t it better after a sharply turning leg break that beat the outside edge? Or an inswinger after four well targeted outswingers?

Did you notice how Anderson has outperformed his Expected Average on a rolling four year basis recently? What if that’s not noise, but rather a master craftsmen conditioning a batsman to play the last ball rather than the next one? And knowing from experience just what to bowl? That would manifest itself as Anderson taking wickets with balls that are better than they appear in isolation on a highlights package.

There could be a number of other causes (attacking fields / batsmen playing him on reputation), and it’s a bit rich me trying to throw shade on Cricviz’s metrics by mining the jeepers out of the data in one tweet. Still, food for thought. I’d give you something concrete, but that will take much more hoovering up the trail of breadcrumbs Cricviz leave through their twitter account and blogs.

The future of Expected Average

Cricviz will take their time building trust in Expected Averages. They have a tough concept to sell if they want xW xA xR to enter the lexicon: most of what we see (and debate) is noise. Signal takes years. People won’t want to hear that because it’s boring, counter-intuitive and at odds with standard narratives.

Expect them to make the case for luck’s impact slowly. They’ve done the right thing having strong communicators on board. Here’s an example, suggesting that Anderson’s ups and downs this summer were just luck. That’s something we can believe: a run of fortune ruining a week. The harder message to land will be impact of luck on a series or even a year. Discretion being the better part of valour, Cricviz didn’t actually state Anderson’s xA in the piece, just that it was better than his actual average. They waited until the numbers aligned before quoting xA.

The data revolution will be televised. It just might take a while to convince everyone it happened.

*I can’t be sure this is their exact definition, but it’s close enough.

Using judgement when rating cricketers

Yesterday Zak Crawley scored 171*. 

Before the West Indies series I said “he would be a bold and wrong selection, going against the publicly available data… If he succeeds, I will give them credit.” Time for me to issue a credit to the England selectors.

Moving swiftly on, with Crawley and Ben Stokes outperforming their ratings it’s time for a rethink. There’s qualitative data I’ll start using: the judgement of others. We’re in uncharted waters; we’re Going Beyond Stats.

I appraise batsmen based on four years of data (six if they rarely play). It’s a stable method. I don’t have meaningless volatile opinions (like a certain former England captain). There are limitations though. Injuries, new techniques, changing roles can all have an effect. My ratings system is too slow (look at Bairstow – his stellar 2016 is still boosting his rating).

Adding the 171* to my database would boost Crawley’s expected Test average by three runs (going from 26 to 29). Is that enough? Is he better than that?

Rob Key previously said “Crawley is young and his numbers will improve. You just have to watch him bat to know that.” Actually I can’t: I don’t trust my eye. But if people I respect are saying a player is better than their stats, maybe there’s something in that.

With a base average of 29, your chances of 171* (in the first innings, at home, against a strong attack) are around one in 150. In other words, yesterday shouldn’t have happened. Much more likely for a player with a base average of 35 – it’s a one in 60 shot.

After a finite number of innings there is uncertainty about how good a player is. I can tell you roughly how good a player has been, give or take a bit. Luck is a factor, moreso the less data you have. Error bars give a range of possible ratings for a player, with a 95% chance their level of ability lies somewhere in that range.

Let’s jam those two concepts together to add a third step to my ratings framework:

  1. Rate player based on stats
  2. Add error bars to that rating, based on the number of dismissals.
  3. NEW: Adjust the rating within the plausible range based on the views of people you trust.

Oh, and “the views of people you trust” can include selectors. For instance, if someone had rubbish data but is batting at four, then selectors are implicitly telling us the person is better than their stats. We should use that information.

What does that mean for Crawley?

  1. Rating 29
  2. Margin of error +/- 7 (after 83 dismissals in the last three years).
  3. Boost rating by 5.0 to reflect the excitement and that England choose to bat him at three.

Crawley is now expected to average 34 in Tests. This recognises that he is probably at the upper end of the range of plausible averages (22 to 36) because he is trusted to bat at three, and is highly regarded.

I will do the same exercise with all county cricketers to redefine their red ball ratings to incorporate the role they are given. However, there are about to be several meaningless games in the Bob Willis Trophy, so that may have to wait for next year.

I’ll also do that with England’s Test batsmen. For instance, Stokes at 40 +/- 12 might be re-rated at 44 (based on two year record, and pundit judgement).

One innings doesn’t change everything, but I’ve seen enough outliers to want to try something new. Where does this leave us, now data is being tinkered with, and we lose the safety of being moored to pure stats? I draw the parallel with the astronomers maintaining the Earth as the centre of the solar system, as more data made that harder to believe. Models became contorted to fit the data, before being binned. Is that happening here? Time will tell. #OnOn.

Test bowling averages by month in England

Spinners averages are influenced by time of year and innings number. I’ve had a go at quantifying these effects, and looked at the (smaller) impact on pace bowlers. Along the way I’ll make a couple of observations on fourth innings chases.

Starting with spinners, it won’t surprise you to hear that early season English conditions are unfavourable. The first innings average for spinners before August is 59. Spinners are little use in May and June, when pitches are damper and less worn. Twirlymen are generally passengers in the first innings of a match.

Spinners since 2010, first five bowlers used. Later innings have lower averages, likewise for later in the summer.

Spin bowlers average 15% less in August/September than May/June. While spinners are generally more effective in the fourth innings, it’s July when they are matchwinners. Averaging 22, with 49 wickets (so a decent sample size). Next consider the same view but for pace bowlers:

Pace bowlers since 2010, first five bowlers used. Fourth innings has lower averages, likewise for later in the summer.

Much less variation with the seasons for quick bowlers; this graph is much greener, pace bowlers are always a good thing. Averages dropping about 10% from spring to summer. A pronounced dip in the fourth innings (23) against averaging 30 in the first three innings. The sweet spot data point, again, is that fourth innings in July.

Combining spinners and pace bowlers, over the last decade the fourth innings yields 19 runs per wicket in July, compared to 28 in other months. The reason is somewhat counter-intuitive – pitches start out best for batting in July. The higher averages in the first three innings at that time of year mean the fourth innings in July start on average 20 overs later than at other times of year. Cracks, footmarks, the works. Just 3 out of 21 fourth innings chases were successful in July. Conversely, batting last in May/June was successful 5 times out of 13: a 250 chase is achievable.

***

Here’s a handy guide for captains of when to bowl spin in England. When do spinners average less than pace? (Or “Should I bowl my spinner if I have a fresh pace bowler?”)

In England, since 2010, performances of first five bowlers used.

I’m being somewhat glib (pace is less effective with an old ball and July 31 is not magically different from August 1), but teams do get this wrong. Just because spin is most effective in the fourth innings doesn’t mean any spinner is the best option. West Indies failed to defend 191 in the fourth innings in May 2012. Marlon Samuels (off spin, part time) bowled ten overs for 51 – Fidel Edwards only got eight overs.

What about the middle overs of the fourth innings? We can have a go at “spin or pace in the fourth innings with an old ball,” by comparing outcomes for spinners to the fourth bowler used (if they are a pace bowler). On this measure spin wins with an average of 25 against 27 for the fourth bowler used. Meanwhile opening bowlers have more fun in the fourth innings in England, averaging 22.

***

When slow bowlers look ineffective (early in the summer and early in every match), that’s because they are ineffective. They are still helpful in the fourth innings, but don’t give them the new ball. Spinners are probably overused and overselected in Tests in England.

Keeping things topical: Yasir Shah (Test average 30) should seldom bowl overs that could be Mohammad Abbas’s (Test average 21).

England vs Pakistan Preview – August 2020

An impressive batting unit supported by an exciting bowling attack versus an impressive batting unit supported by an exciting bowling attack.

What will decide the series? Here’s my usual pre-series ramble, with stats on the Pakistan squad at the bottom.

  1. All rounders. England are running out fast. Debateable whether England will even have an all rounder for the first Test. Some names to mull: Ben Stokes (is he fit to bowl?), Joe Denly (dropped), Moeen Ali (dropped), Chris Woakes (struggling with the bat), Sam Curran (is he good enough at either discipline)? As for Pakistan, Shadab Khan averages 34 with the bat from five Tests, but only 27 in First Class – more a number seven than a six. Pakistan will be gambling either way: a five man attack lengthens the tail, an inexperienced four man attack has nowhere to hide.
  2. Pitch preparation. England would be stronger if they could confidently not pick a spinner (Bess didn’t contribute a lot with the ball against West Indies). Pakistan are itching to play two spinners. Why would the Old Trafford groundsman produce a deck that turns? Worth noting in the two Manchester Tests this summer, spin averaged 52 while pace was around half that at 27.
  3. Naseem Shah and Kashif Bhatti. Pakistan’s batting is solid, enough talent that they can cover if any one of them goes full Shai Hope. For all the excitement, I’m uncertain about their bowling. Mohammad Abbas is a banker (Test average 21, and the same average in two devastating seasons at Leicestershire). Shaheen Shah Afridi has 30 Test wickets, so has some track record. Yasir Shah has a proven record – it’s just a bit mediocre (averaging 34 in the last four years). One of Naseem Shah or Bhatti thus has to step up. The signs are good – both average 17 in domestic cricket in the last four years (Bhatti has 125 wickets, the younger N. Shah has only 26).
  4. Rest. West Indies clearly don’t read this blog (or The Daily Telegraph), else they wouldn’t have knackered their bowlers playing three back-to-back Tests. A three Test BTB series is more like a tournament than a traditional Test series: you’ve got to manage bowler workload. The easiest way to do that is to pick the best team for the first game, then half the pace bowlers miss the second Test, and the others miss the third. Sohail Khan is good enough to rotate in – but are the other Pakistan squad bowlers Test standard? With England’s squad depth, their edge will get bigger as the series goes on.
  5. England’s Ashes tunnel vision. Picking Crawley and Bess with an eye on December 2021 is silly. When the West Indies series got real, Crawley was dropped and Bess didn’t get a chance to bowl. Pakistan will be only too happy if England’s team sheet has a number three with a First Class average of 31, and an off spinner for Pakistan’s right handed middle order to milk. Bess isn’t a bad player, it’s just that England would have a better chance of winning playing an extra pacer.

England start as favourites. Burns, Sibley, Root, Stokes are a fine core of a batting order, and there’s healthy bowling options. If Stokes can bowl, a balanced England team playing on increasingly familiar territory should be too strong.

Get to know the Pakistan squad: Stats

Batting:
Note through this lens Babar Azam isn’t the standout batsman.
Bowling:
Note the domestic four year averages of Abbas, N.Shah and Bhatti.