Should Buttler bat up the order in ODIs?

Having a model for ODI Cricket is great when it comes to considering selection, or gambling, but it’s challenging to come up with further practical uses. Fortunately, some recent tweets about batting orders gave me an idea – using the model to suggest the optimum batting order.

England have batsmen with averages and strike rates to get excited about. The current top six is Roy, Bairstow, Root, Morgan, Stokes and Buttler.

Jos Buttler’s career strike rate is 120. He once scored a century in sixty-six balls. If England get a good start, at what point should they push Buttler up the order so he isn’t watching from the pavilion when he could be swishing sixes? He has finished “not out” in 23 of his 116 innings – and could have contributed more in each of those matches if he had been on the field earlier.

Firstly, let’s consider how Buttler has performed by batting position:

Fig 1 – Buttler’s performances batting in ODI Cricket, up to 13/07/2019 by position in the batting order.

The more excitable among us would conclude that six is Buttler’s weakest position, and he has to bat at four or five based on the above averages and strike rates. Personally (and somewhat arbitrarily), I’d like a 20 innings sample size before concluding. All the table above says is there’s no compelling reason why Buttler can’t bat anywhere in the middle order.

So what number should Buttler bat? Using a model of ODI cricket, simulating England batting against their own bowlers at Chester-le-Street*, we can predict performance for England’s usual batting order and compare that to Buttler jumping up two places to number four.

The Duckworth-Lewis method tells us that the way batsmen play at each stage in the innings is a function of how many wickets have fallen. The hypothesis is that the earlier the second wicket falls, the more conservatively England will bat, and thus the less useful it is to promote Buttler. It would actually be counterproductive, because if he’s out he’s not around to score quickly at the end of the innings.

Scanario time: we join the action at the fall of the second wicket, Roy and Bairstow the men out.

Fig 2 – modelled impact of moving Buttler to bat at four rather than six. x-axis represents the over in which the second wicket falls.

The chart shows that promoting the swashbuckling Buttler too early has a slightly adverse impact on expected runs (he’s not the person you want at the crease as you rebuild – hold him back). If the second wicket falls any time after 20 overs, it is beneficial to move Buttler up to number four. The later in the innings the second wicket falls, the more important it is to promote Buttler.** That said, the benefit is less than one run for overs 20-30, so if the batsmen are concerned a fluid batting order could cause them to underperform, coaches should take heed.

Note that the benefit starts to shrink very late in the innings – as the number four will only face a handful of balls anyway.

To put the previous chart into context, here’s a comparison between the two scenarios:

Fig 3 – Modelled median runs scored after the fall of the second wicket. x-axis shows different stages in the innings when the second wicket falls.

The two curves are very similar. If your eyes (and/or phone resolution) are up to it, you’ll see that the blue line (Buttler in at six) underperforms the orange line, especially in the latter stages of the innings.

England have an analyst with a ball-by-ball ODI model. Has he already done this analysis and are England already applying it? Consider the evidence of the World Cup Group Stages:

  • #6 vs Australia. Comfortably chasing 224 when the second wicket fell with the score on 147 in the 20th over. Morgan bats at four, England don’t lose another wicket.
  • #4 vs New Zealand. Second wicket falls in the 31st over. Buttler promoted to four.
  • #6 vs India. Second wicket falls in the 31st over. Third wicket falls in the 32nd over. Buttler bats at six? Takes revenge on the ball, scoring 20 from eight balls.
  • #6 vs Australia. England 53-4 in the 14th over when Buttler comes to the crease. Couldn’t realistically hold him back any longer.
  • #6 vs Sri Lanka. England three down inside 20 overs, Buttler held back to number six.
  • #5 vs Afghanistan. England 169-2 after 29.5. Morgan goes in, hits 148 (71). Buttler doesn’t get a turn until the third wicket falls in the 47th over.
  • #4 vs Bangladesh. 205-2 (31.3). Buttler promoted to four.
  • #6 vs Pakistan. England three down with just 86 on the board. Buttler comes in when the fourth wicket falls.
  • #6 vs South Africa. 111-3 (19.1). England play it safe and Buttler bats at six. Fair enough.

England’s strategy broadly follows the recommendations in this post (and therefore what an ODI simulator would recommend). Two exceptions: against Afghanistan and India. It would be fascinating to know why Buttler batted at four against New Zealand, but not (in similar circumstances) against India.

We can conclude that with their current batting order, England should move Jos Buttler up the batting order if the second wicket falls after the 30th over. A word of caution – the 90million balls I modelled were for this specific scenario, and not the general case. If you would like me to consider another scenario, please do get in touch via the “Contact” page or @edmundbayliss on twitter.

*If I had my time again I wouldn’t have had England playing against themselves and at a ground with high ODI batting averages. Regrettably, I neglected to update those inputs after a World Cup game there. If the modelled runs in this piece feel high to you, that’s why.

**It’s human nature to pick one reason for an outcome. “If the coach had just tweaked the order, we would have put enough on the board”. It’s seldom that clear cut. These batting order changes are worth up to three runs, very much in the “extra one percent” territory.

World Cup Bowling: Postmortem

Admittedly there’s a game to go, so this is a mid-mortem of how bowling has driven success.

Today (11th July), Australia’s fifth bowler was a combination of Steve Smith and Marcus Stoinis. Joint figures of 3-0-34-0 did not help their team’s cause when trying to defend 223. A canny side would try to pick off the ten overs Australia have to find from their weaker bowlers. Are the Australians particularly vulnerable here?

How effectively have the all-rounders bowled in the 2019 Cricket World Cup, and what can we learn from this?

All rounders – aggregate bowling performances at the 2019 Cricket World Cup. Sorted by bowling average. England and New Zealand players highlighted. Note the scarcity of averages under 35.

Bear in mind that the average runs per wicket across the tournament was 33.5, most all rounders under-performed the average by at least 10%.

Now to assess Cricket World Cup 2019 bowling on a country by country basis:

CWC 2019 bowling records split by Bowlers and All Rounders, by country. Note the core bowling units all average between 24 and 30. Averages for all rounders are higher, while Australia’s all rounders are an outlier: averaging 50 at an economy rate of 6.2.

Firstly, the semi-finalists.

Australia struggled with their fifth bowler through the World Cup. Maxwell bowled 49 wicketless overs, and all five all rounders went for over a run a ball. Combine that with the weakness in the number eight batting slot, and you can see why expectations for Australia were low coming into the tournament.

Given Starc’s relentless 27 wickets at 19, it was surprising that Australia’s front line bowlers averaged as much as 30 with the ball – Lyon/Coulter-Nile/Zampa conceded 697 to pick up just 12 wickets. This attack was the weakest performing of the four semi-finalists, which makes it incredible that they won 70% of their matches. Well batted Warner and Finch.

There’s a tight grouping for New Zealand, India and England. If India have a weakness it was that Hardik Pandya’s bowling averaged 45 over nine matches. With a career average of 41, that puts pressure onto the rest of the attack. Taken individually, a spell like 10-0-55-1 (his semi-final performance) is disappointing but acceptable. However India’s problem is that that’s near his average, and opponents can expect low risk runs. If India had a stronger fifth bowler New Zealand may not have accrued 239 runs.

Of course selectors have to balance batting and bowling – it’s just that England have Stokes and Woakes so don’t really need to concern themselves with that conundrum. Similarly, New Zealand have Neesham and Williamson.

Next we look at the sides that didn’t make it to the semis:

CWC 2019 bowling records split by Bowlers and All Rounders, by country – teams eliminated at the group stage only. Circles represent front line bowlers, crosses are all rounders.

Funny how a cold look at the data changes your perspective. I hadn’t realised all of Sri Lanka, Afghanistan, Bangladesh and West Indies averaged over 39 with the ball. Little wonder their collective record was W8 – L29: if you let a team get to 150-3, they’ll bat you out of the game.

A word on Shakib Al Hasan – his bowling figures don’t stand out (he ended with 11 wickets at 36). His batting more than made up for it though (606 runs at 87). A great combination of fantastic batting and sending down more than nine overs each innings.

It wasn’t the bowling that let South Africa down. The need to find replacements for Amla and Duminy is pressing.

Pakistan have the greatest discrepancy between the specialist bowlers and the all-rounders. Shadab Khan (tournament figures 2-188) and Imad Wasim (2-189) repeatedly let teams off the hook. Some may be surprised that Khan, who in his last ODI batted at number nine, is listed as an all-rounder. His batting average says he is, yet his strike rate and boundary hitting say otherwise. Time will tell.

What have we learned? Five teams at this tournament had successful front line bowlers. The teams contesting the final on Sunday could also rely on their all rounders getting wickets; that sets England and New Zealand apart from the others.

Bowling averages at the 2019 Cricket World Cup correlated with winning rates

England Batsmen at Lord’s

There are suspicions afoot that England have an ODI weakness at the home of Cricket.

CricViz’s analysis is here. In a nutshell, England struggle when the ball does a bit. Lord’s is a prime example of that, hence England have lost two of their last five games there and are vulnerable. It’s a neat piece of work.

And yet… Cricket is an individual sport masquerading as a team one. “England” as a batting lineup is a myth. In this piece I’ll explore the expected top seven for the game on 25th June 2019 and their track record in white ball cricket at Lord’s.

Firstly, ODI records.

Fig 1- ODI Records of selected England players at Lord’s

We can eliminate Bairstow, Root and Morgan from our enquiries. They have done well. Also, it’s Morgan’s home ground – surely he is familiar enough with conditions to not be at a disadvantage?

Note how Roy and Hales have been something of a flop at Lord’s. They aren’t playing tomorrow so we can put them to one side. That leaves Vince, Stokes, Buttler, Ali & Woakes under the spotlight. None of them have played a T20I at Lord’s but we can look at their Test Match record.

Fig 2: Test Records

Stokes has a decent red ball record at Lord’s. Not the same discipline, will let you make your own mind up.

List A records – note the very small sample size. Because Stokes, Buttler, Ali & Woakes all play in the North group, they rarely get the chance to play at Lord’s. Can’t read much into this.

How about the 20-20 record?

Oh. As far as I can tell none of Stokes / Buttler / Ali / Woakes have batted in a 20-20 at Lord’s. Vince has, and it hasn’t gone well.

What can we conclude? Firstly, county players generally stick to their half of the country when it comes to white ball Cricket, and many will only have strapped on their coloured pads in a minority of England’s grounds. Secondly, the jury is still out on Stokes / Buttler / Ali at Lord’s. More data please! Finally, over six white ball innings and four Test innings Vince has 151 runs at 15.1 – that’s not good.

Cricketing Barbarians

Rugby Union has a representative team called the Barbarians. They are something of an oddity in the professional era – an invitational team who play against international sides in exhibition matches. These are high scoring, free flowing matches that encourage an attacking and entertaining spectacle. It’s a great antidote to the win-at-all-costs culture that has come with professionalism.

Cricket has a significant gap between the top ODI teams and the rest. Matches between these sides will rarely be balanced – since 2015 there have been 69 games between the top eight sides and Non-World-Cup (NWC) teams. The NWCs have won just five and tied one. There were likely some pretty dull days among the 63 defeats. The perception is that it’s hard to sell these games, so boards would rather have long series between the top teams than host a “minnow”.

How about we use the first paragraph as a solution to the second? Picture an invitational side, the best of the NWC teams*, playing an exhibition 50 over match at Lord’s against an England XI to start the international summer. No stats or averages up for grabs. No wider context, apart from the love of Cricket and a desire to grow the game by giving the best-of-the-rest a chance to show what they can do.

It has been tried before, a three day warm up game in 2012. England scraped a three wicket win in a balanced contest. A shortened red ball practice match might not be the right format – it’s unlikely to pull in the crowds in the way an exhibition 50 over game could.

Marketing these games would not be taxing – take a leaf out of the Barbarians book and have big name guest coaches (such as Kumar Sangakkara). The Baa-Baas have a nice touch where each player wears their own club socks to complement the black and white hoops of the Barbarians kit. This MCC NWC XI could do something similar with Helmets**.

Numbers time

Since this is redballdata.com, we’d better have some stats to support the idea that a composite Barbarian team would be more successful than individual countries.

This isn’t intended to be a comprehensive review of who the best NWC players are – more an indicative view of how players have fared against the best ODI sides. Bertus de Jong is a useful source on Associate Cricket, if you’d like to know more.

Here’s some candidates to be on display for this theoretical XI, based on performances against the World Cup teams since 2015. It would be easy to find a competitive top six from these players, ideally not just drawing players from the 11-13th best teams (Ireland, Scotland and Zimbabwe). Note that no individual team can field seven players who have averaged over 27 against the big names.

Fig 1: ODI Batting 2015-2019, NWC vs World Cup Teams. Ranked by average. Excludes players that retired before 1/1/18.

As for bowling, not many great averages, but these Economy rates would keep the NWC XI in the game. As for individual countries, Zimbabwe have plenty of bowling which could challenge the top ODI teams. Ireland and Scotland don’t have that depth.

Fig 2: ODI Bowling 2015-2019, NWC vs World Cup Teams. Ranked by average. Excludes players that retired before 1/1/18.

This Barbarian concept could work. The hosts would get a spectacle and something a little different for the fans, while giving the Associates & Affiliates a chance for their best players to gain experience of competing against a top team.

*If this comes across as condescending to the sides ranked 11-20, it isn’t intended to. The world is getting smaller, and if Cricket doesn’t widen its popularity, richer sports will. Think of this proposal as a means to an end, building towards bilateral series.

** It may not surprise you to learn that I don’t work in Marketing.

Further Reading

The Cricketer magazine flagged the best players missing from the 2019 World Cup in an article here.

Here are some highlights of an England vs Barbarians fixture in 2018.

Left arm pace in ODIs – where are all the part-timers?

Like most sports fans my weekends can include shouting at the radio. Unlike most sports fans I’m usually het up about statistics, not necessarily the performances on the field.

Last weekend it was claimed that the Indian middle order has a weakness against left arm pace in ODIs. I won’t name the individual that said it, because they are an excellent commentator and this isn’t intended to be a criticism of them.

What’s wrong with that claim? Left arm pace bowlers are normally front line bowlers, so are better than the average bowler. That means that when it is said that “X does badly against left arm quicks” we really mean “X is less good against the better bowlers”. Of course they are, we all are!

Time for a couple of charts. Firstly, there’s a clear distinction between performance of front line bowlers (ie. those that bowl on average more than six overs per innings) and the “change” bowlers:

Fig 1: Bowling records for the 10 World Cup teams this decade.

Note the key difference in average between front line and backup bowlers – 12.1 runs per wicket. It’s likely that the backup bowlers bowl in the middle overs, so flattering their Economy Rates compared to the bowlers trusted to finish the innings.

Sampling the data in any way that includes a greater proportion of front line bowlers will give metrics that indicate batsmen are struggling. For instance, by only measuring performance against left arm pace bowlers!

Next, the same view as above, but with left arm pace bowlers. Note how high the average overs per innings are for left arm pace bowlers. There’s a full list of bowlers at the end of this piece.

Fig 2: Bowling records for the 10 World Cup teams this decade. Split between left arm pace and others.

Left arm pace bowlers average 12% less than other bowlers (admittedly while conceding runs 3% faster). For analysis of the advantages left arm pace bowlers have, refer to this Cricinfo article http://www.espncricinfo.com/magazine/content/story/851399.html

But why wouldn’t there be part-time Left arm quicks? It could be margin of error: bowling over the wicket to a right hander, straying onto the pads is risky while conservatively keeping a consistent line outside the off stump takes bowled and LBW out of the equation.

Summing up, we can draw two conclusions. When considering performance against a sub group of bowlers, one needs to adjust for the quality of that sub-group. The smaller the sub-group, the more careful you need to be. Also, expect all batsmen to average 12% less against left arm pace bowlers in ODIs.

Appendix

Fig 3: All left arm pace bowlers to have played ODI Cricket for the 10 world cup teams since 2010.

There’s barely a part-timer in the group. Only Anderson, Franklin, Udana, Reifer averaged less than six overs per innings – and Raymon Reifer has only played two games!

Still reading? Here’s another example to make the point: imagine a naïve Cricket Analyst for a Test team at the end of last Century. Crunching the numbers they see that most batsmen underperform against leg spin, so recommend the selectors fast track a ‘leggie’. The unfortunate Analyst didn’t notice that there weren’t very many leg spinners out there. All they’ve really discovered is that it wasn’t easy to face Shane Warne, Mushtaq Ahmed or Stuart Macgill. That’s not especially insightful: right data, wrong conclusion.

Matchups and Opportunity Cost

There’s a theory (which I just invented) that you could listen to old radio broadcasts of Cricket and be able to judge the date by the buzzwords of the era. For 2019, it’s “Matchups”: pitting bowlers against the optimum batsmen to stifle run scoring and take cheap wickets.

Matchups seem like a plausible proposition – get enough data, find some patterns, check you’ve got a decent sample size and out will pop some options to consider. Note the need for a plausible proposition (ie. not “Roy struggles against the flipper in the top of the hour when the bowling is from the North-West”).

There are three issues I have with the use of Matchups.

Firstly, they aren’t publicly available – if a pundit refers to X having a weakness against a particular type of bowling, the viewer/listener has no way of knowing if that’s a fact or an opinion. In times gone by, we could accept that all such utterances were opinions, and who better to go to for opinions than people who report on the game for a living? The balance has shifted – so now when hearing “Bairstow struggles against spin early in the innings”, it could be opinion, bad data*, or a solid piece of analysis. There’s something unsatisfying about that.

Secondly, we don’t know if Matchups work. If each one is a hypothesis, it should be easy to aggregate them in order to compare results and expectation. I expect much of this is – understandably – happening behind closed doors. My hunch is also that many Matchups evaporate as statistical flukes, so are of no benefit. If you’re aware of a rigorous assessment of Matchups, please do drop me a line on twitter or via the Contact page on this site.

Finally, and of relevance to the Cricket World Cup, there’s an opportunity cost associated with changing bowling plans. Especially in ODIs where bowlers need rest during an innings.

Let’s explore that Opportunity Cost – what are the downsides of opening with spin? We can expect more teams to open with spin against England after Bairstow fell first ball against Imran Tahir. Here’s how South Africa used their bowling resources that day:

Fig 1: Overs bowled by each player

Early wickets have a big impact on expected score – but one cannot fully appraise the impact of opening with Tahir without taking all factors into account.

  • Rabada didn’t get the new ball. He then had to condense 10 overs into 44, rather than across 50 – does that impact the pace he can bowl?
  • After 24 overs, with the score on 131-3, Faf du Plessis threw the ball to JP Duminy. Five of the next eight overs were bowled by Duminy and Markram. On this occasion it worked – 5-0-30-0 is not too bad. But it’s the big picture that matters, not one innings.
  • Pretorius only bowled seven overs, Phehlukwayo eight. Without a medium pacer or second spinner than can bowl 10 overs in a row, once a team opens with spin, they are probably going to underuse their fourth and fifth bowler.

What are the factors to consider when weighing up whether to open with a spinner in a four pace / one spin attack?

  1. Will it work? What is the increase in chance of a wicket versus the default option?
  2. What are the relative strengths of your sixth (and possibly seventh) best bowlers, compared to your fourth and fifth?
  3. How fit is the bowler who won’t now be opening? Are you confident they can bowl 10 out of 44 overs? How many days since your last game?

What have we learned? The value of a Matchup is the expected gain from one pairing over another, less the downsides of changing the bowling order to accommodate using a specific bowler at a particular time.

* A word on bad data: Andrew Strauss averaged 91.5 against Mitchell Johnson in Tests. It’s a nice piece of trivia, but it’s only based on Strauss scoring 183-2 against Johnson. I doubt this would have much predictive power. Using that as a basis of prediction is roughly the equivalent of writing off Graham Gooch after he bagged a pair on debut.

Further reading: Cricmetric.com claims to have Matchup data for Batsmen vs Bowlers – I’ve no reason to doubt their data.

What’s a dropped catch worth in ODI Cricket?

Jason Roy dropped the ball today. I didn’t see it, but apparently it was rather an easy catch. Pakistan went on from 135-2 (24 overs) to finish 348-8, a score just out of England’s reach. The final winning margin was 14 runs.

What did that drop do to Pakistan’s expected score? Here’s the simulations for the two scenarios: 136-2 (24.1) and 135-3 (24.1)

Fig 1: Two scenarios for the 145th ball of Pakistan’s Innings: Out or one run scored.

If Hafeez had been out, the mean score was 350, while the dropped catch increased the mean score to 377. That’s a 27 run impact.

Can we break that down?

  • Firstly, the runs scored on that ball. Value = one run. Easy.
  • Secondly, the reduced run rate as a new batsman plays themselves in. According to some analysis I’ve done on how batsmen play themselves in, that’s worth four runs (Hafeez had faced 12 balls by this point, so would have been just starting to accelerate).
  • The rest of the impact (22 runs) comes from two factors: more conservative batting as Pakistan from having fewer wickets in hand, and the increased chance of getting bowled out (and thus not using all their overs).

To generalise, the cost of a dropped catch would be a function of:

  • Runs scored on that ball
  • Whether the surviving batsman is set
  • How long left in the innings (the wicket affects the value of future deliveries. Thus the later in the innings a wicket falls, the lower the value of that wicket)
  • How many wickets the batting team has in hand (does the wicket cause more defensive batting)? In this case, being three wickets down after half the innings still leaves plenty of scope for aggressive batting so doesn’t have as big an impact as it could.
  • Strike Rate and Average of the reprieved batsman relative to the rest of the team (dropping Wahab Riaz is better than dropping Babar Azam).

Interesting topic. I might come back to this when other people drop sitters.

World Cup Scheduling is Bangladesh’s friend

Had a brief look at the Cricket World Cup fixtures, didn’t see anything of interest – with 40 odd days to play nine matches each, there would be plenty of time between games so no need to take fatigue into account.

Actually, the fixture list has some unnecessary oddities. There are two occasions when a team plays twice in three days.

Afghanistan follow their match against India on 22nd June with one against Bangladesh on the 24th.

Then India have their own congestion when their 30th June game against England is followed by – you’ve guessed it- Bangladesh on the 2nd July.

Individual performances in the Royal London One Day Cup shows that when bowlers have had less rest than the batsmen they are bowling to, the batsmen get an boost. This was particularly clear cut when teams played twice in three days.

Bangladesh may have a better chance of making the semi finals than the 18% implied by the bookies.

Fantastic boundaries and when to find them

Using a ball-by-ball database of 2019 ODIs, I’ve looked at boundary hitting through the innings. This was to refresh my ODI model, which was based on how people batted in 2011.

Fig 1: Boundary hitting by over. ODIs between the top nine teams, Q1 2019

Key findings:

  • First 10 over powerplay: 10% of balls hit for four, c.2% sixes. Just two fielders outside the ring.
  • Middle overs 10 – 40: c. 8% balls hit for four, c. 2% sixes. Four fielders outside the ring limits boundary options. Keeping wickets in hand mean batsmen don’t risk hitting over the top, though if wickets in hand the six hitting rate starts to pick up from the 30th over.
  • Overs 40-45: Six hitting reaches 5%. No increase in the number of fours: five boundary riders give bowlers plenty of cover.
  • Overs 46-50: Boundary rate c.18% with boundaries of both types picking up.

These probabilities have been added to the model, which now makes some sense and isn’t claiming a 6% chance England score 500!

An early view of what the model thinks for Thursday’s Cricket World Cup opener – if England bat first 342 is par. 69% chance England get to 300, 20% chance of England getting to 400. I can believe that, it is The Oval after all.

The ODIs they are a’changing

My ODI model was built in those bygone 260-for-six-from-50-overs days. Having dusted it off in preparation for the Cricket World Cup it failed its audition: England hosted Pakistan recently, passing 340 in all four innings. Every time, the model stubbornly refused to believe they could get there. Time to revisit the data.

Dear reader, the fact that you are on redballdata.com means you know your Cricket. Increased Strike Rates in ODIs are not news to you. This might be news to you though – higher averages cause higher strike rates.

Fig 1: ODI Average and Strike Rate by Year. Top 9 teams only. Note the strength of correlation.

Why should increasing averages speed up run scoring? Batsmen play themselves in, then accelerate*. The higher your batsmen’s averages, the greater proportion of your team’s innings is spent scoring at 8 an over.

Let’s explore that: Assume** everyone scores 15 from 20 to play themselves in, then scores at 8 per over. Scoring 30 requires 32 balls. Scoring 50 needs 46 balls, while hundreds are hit in 84 balls. The highest Strike Rates should belong to batsmen with high averages.

Here’s a graph to demonstrate that – it’s the top nine teams in the last ten years, giving 90 data points of runs per wicket vs Strike Rate

Fig 2: Runs per over and runs per wicket for the first five wickets for the top nine teams this decade, each data point is one team for one year. Min 25 innings.

Returning to the model, what was it doing wrong? It believed batsmen played the situation, and that 50-2 with two new batsmen was the same as 50-2 with two players set on 25*. Cricket just isn’t played that way. Having upgraded the model to reflect batsmen playing themselves in, now does it believe England could score 373-3 and no-one bat an eyelid? Yes. ODI model 3.0 is dead. Long live ODI model 4.2!

Fig 3: redballdata.com does white ball Cricket. Initially badly, then a bit better.

Still some slightly funny behaviour, such as giving England a 96% chance of scoring 200 off 128 or a 71% chance of scoring 39 off 15. Having said that, this is at a high scoring ground with an excellent top order. Will keep an eye on it.

In Summary, we’ve looked at how higher averages and Strike Rates are correlated, suggested that the mechanism for that is that over a longer innings more time is spent scoring freely, and run that through a model which is now producing not-crazy results, just in time for the World Cup.

*Mostly. Batsmen stop playing themselves in once you are in the last 10 overs. Which means one could look at the impact playing yourself in has on average and Strike Rate. But it’s late, and you’ve got to be up early in the morning, so we’ll leave that story for another day.

**Bit naughty this. I have the data on how batsmen construct their innings, but will be using it for gambling purposes, so don’t want to give it away for free here. Sorry.