Tours are strange beasts. Anyone who has ever been on a Club Rugby tour can attest that pre-match preparation isn’t entirely conducive to peak performance.
Professional sport should be the opposite of this. Next time you are watching Cricket on TV and they cut to the pavilion balcony, count how many non-playing staff are on hand. I’m not criticising touring parties for being too large – I’ve no data to assess that on. My point is that lots of money is spent by governing bodies to ensure enough specialists are on hand to keep eleven cricketers playing at their best.
Here’s a theory – all this investment in the extra 1% is missing the wood for the trees. The tour scheduling is an unseen problem.
Recall the post-before-last regarding Home Advantage growing as a series goes on, and your correspondent having an effect with no obvious cause? Going through the archives of @Chrisps01’s blog was a possible clue to this – [link] – some analysis on rest periods between matches. A quick re-cut of the data and I could quantitatively look at this effect with two decades’ worth of data.
There’s a certain base advantage in the first Test of a series, which is kept at the same level if subsequent Tests are played back-to-back (ie with less than a seven day gap between matches). Away teams are at a much bigger disadvantage when there is a longer gap between Tests.
Think back to summer 2017 – on August 29th West Indies beat England by five wickets to square the series with just the Lord’s Test to come. On September 2nd & 3rd the full strength West Indies team toiled in a meaningless draw against Leicestershire. England rested. West Indies put up little resistance in the third Test, scoring just 300 runs over two innings.
Why might away teams struggle with longer gaps between Tests? Here’s how I rationalise it:
With very short gaps between Tests, both teams are fully focused on recovery and getting the XI back ready to play the next Test. Both teams are therefore doing the same things and so no team gains an advantage over the other.
Longer gaps between Tests mean tour matches for the away team, and (in the modern era) rest for the home team. Even if not all of the team are involved in a tour match, the focus of the touring party is likely to be distracted by a competitive fixture.
Players for the host team may get the opportunity to go home for a few days during a break in the series – the away team will still be living in hotels.
The data implies that the home team’s activities result in better performance in the next Test.
Touring teams should revisit their itinerary so they are best placed to compete throughout a series: plenty of rest, no meaningless mid-series tour matches.
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.
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.
Home advantage exists across many sports, and Cricket is no exception. Each sport has its own factors driving home advantage (1).
It’s a fascinating
theme, and I plan to explore it via a series of posts, building a picture of
Home advantage in Test Cricket.
In this first piece
we’ll start with the magnitude of home advantage, and look at how teams fare at
the start of a series in this era of condensed tours with limited match
practice.
Measuring Home Advantage
So how big is home advantage? Eight of the last ten Ashes series have been won by the hosts. Casting the net a bit wider, including all Tests since 2000, we can be a bit more precise and measure home advantage a number of ways:
Figure 1: Five measures of Home Advantage. All figures presented from the home team’s perspective. Tests since 1st Jan 2000, excluding Zimbabwe, Bangladesh, Afghanistan & Ireland
The key metric is the 14% difference in runs per wicket between home and away teams. All other effects are a consequence of that. Take a player with a theoretical average of 35 – at home he’ll average 37.4; away that drops to 32.6. Over the course of an average match the 17% difference translates to a 63 run total edge to the home team, which in turn means roughly twice as many home wins as away wins in matches & series.
The example of Rory Burns illustrates the effect of Away games: his county stats are excellent, but he has played six Tests, all away, and averages 25. It will take a while for his average to tick up from there, assuming he gets the opportunity. How much easier life could be if he’d started with a home series! I’ll wager that there are players whose careers stalled because they debuted away from home, and were lumbered with averages that would mark them as not-quite-good-enough. At present that’s just conjecture, it’s on the list for me to return to at a later date.
Home
advantage gets bigger as a series goes on
My intention was to look at series of 3+ Tests and show that tourists were coming unstuck in the first Test (fail to prepare, prepare to fail) and then acclimatising and improving. Easy piece of analysis, right? What follows are multiple attempts to show it, and finding the opposite effect: Home advantage gets bigger as a series goes on
Here’s the Test-by-Test view:
Figure 2: 1st Jan 2000 – 9th Feb 2019, Home advantage in series of at least three Tests. Percentage advantage refers to the differential in Runs per Wicket. Excluding Zimbabwe, Bangladesh, Afghanistan & Ireland. Note the large sample sizes.
Home advantage grows though a series. The increase is insignificant from first to second Test, before jumping for later Tests of the same series. This is marked by a significant decline in away runs per wicket in later Tests in a series. Scoring 2.2 runs fewer per wicket in the later Tests is roughly the equivalent of replacing Tim Southee with a breadstick (in terms of batting contribution).
What does that mean for results? Well, if you are planning to follow your team abroad, you’d be wise to go to the early Tests in the series:
Figure 3: Home Wins increase noticeably for the third (and subsequent) Tests in a series. 1st Jan 2000 – 9th Feb 2019. Excluding Zimbabwe, Bangladesh, Afghanistan & Ireland
Worth noting that the extra home wins later in the
series come from both fewer draws and fewer away wins.
Now let’s consider first Test home advantage compares to the rest of that series (by country):
Figure 4: Relative home advantage in the first Test of a 3+ Test series as compared to the rest of that series. UAE not treated as a home ground for Pakistan. Victories by wickets are translated to runs based on the average fourth innings score. Draws are recorded as nil. 1st Jan 2000 – 9th Feb 2019. Excluding Zimbabwe, Bangladesh, Afghanistan & Ireland
Generally, home advantage is actually weaker in the first Test than later matches. But note the ‘Gabba effect in Australia – this traditional series opener is especially suited to players with experience in Australian conditions. That’s the exception – in most cases, home teams have more success later in the series.
Still not convinced? One more chart, and if you’re still not convinced you can give me both barrels on twitter (@edmundbayliss) and tell me I’m wrong!
Figure 5: Home advantage by match of series. 1st Jan 2000 – 9th Feb 2019. Excluding Zimbabwe, Bangladesh, Afghanistan & Ireland
There’s a predictable trend in Figure 5: home advantage has grown over time.
Discussion
Let’s recap – home advantage is worth 12% in the first two Tests of a series, and 18% in the later Tests.
Why should this be? Three hunches:
Away teams find
themselves behind in the series; selectors panic. Perhaps a 21-year-old batsman
get picked, or an unbalanced side is selected in the hope of turning the tide. Keaton
Jennings being recalled to replace Foakes (a better batsman) in the recent West
Indies tour is a neat example of muddled thinking http://www.espncricinfo.com/story/_/id/25953448/jennings-foakes-england-chaos-two-tests-ashes
Modern players
don’t spend much time in home conditions, but built their technique there.
Playing a lengthy series allows home players to reintroduce tried and tested ways
of playing. Away teams don’t have that luxury, and can’t expect to make
technical changes mid-series.
Fatigue: a small
squad gets run into the ground by back to back matches.
So, there we have it – home advantage is significant and grows as a series goes on. More analysis is needed to establish why this is the case.
Dan Weston (@SAAdvantage) suggested that matches after the series had already been decided could be a factor that hadn’t been taken into account:
To exclude just the “Dead Rubber” games would distort the home advantage effect, because to do so would include only the early matches in those series (probably won convincingly by the home team). The right response is to ignore all matches in a series where that series ends in a “Dead Rubber”.
Figure 7: Home advantage measured in runs, both including and excluding series that are decided before the last match of the series. Note that excluding the one sided series reduces the sample size to roughly 300 Tests- so there’s a bit more volatility between first and second Tests in the series. This is likely to be by chance, rather than a genuine effect.
Excluding one-sided series shows lower home advantage (because it excludes big home wins when a visiting team can’t compete with a superior host team). The overall effect is the same though- home advantage gets markedly bigger in the later Tests.
Looking at 2016-2018 Test, County Championship and Second XI bowling data, and adjusting for the relative quality of that Cricket, we can rank the England qualified players.
I’ll use this for a 2019 preview a bit closer to the start of the season.
In the meantime, here’s a look at England selection. Given that County Cricket mainly takes place in April, May, August and September, it doesn’t necessarily replicate the conditions for home Tests in mid-summer (let alone away games).
2016-2018 Bowling records, selected England bowlers. Ranking amongst England qualified bowlers, 45+ Wickets.
It’s surprising just how far down the list Wood, Curran, Rashid and Ali are. While it’s hard to find good English spinners, the case for picking Wood and Curran (77 D1 Wickets at 33) is weaker.
There’s also support for Stokes taking on a greater share of the bowling, just as he did in the West Indies (sending down 29 overs per game).
Long-term number eleven batsmen (James Anderson, Makhaya Ntini and Trent Boult) have played enough innings that we can use their successes and failures to determine: what should the last pair do to maximise runs?
There are three main factors to be considered: proportion of balls faced by the number eleven, strike Rate for the number eleven, and strike rate for the senior batsman.
As flagged by Chrisps in the comments last week, we’d ideally also consider bowling and fielding strategies for each batsman, but sadly these aren’t publicly available.
STRATEGY 1: Manage the balls faced by the number eleven (take the single on the fourth ball of the over)
A number eleven gets out around every 20.8 balls. If they
face a full over, there is a 26% chance they’ll be dismissed. That means the
batting team should do whatever they can to keep the number eleven off strike,
right?
Yes and no. During Anderson, Ntini and Boult’s most fruitful partnerships, they faced an average 45% of the strike.
Figure 1 – Anderson, Ntini & Boult’s successful partnerships vs proportion of balls faced
When Root and Anderson put on 198 against India in 2014, Root was carefully managing the strike: Anderson only faced the first ball of the over seven times in 53 overs. He faced the last ball of the over 42 times out of 53. So there’s a contradiction – the number eleven is being protected, and yet facing nearly half the deliveries. What’s going on?
The answer lies in the way Root protected Anderson: taking a
single 20 times on the fourth ball of the over. By looking for a single on the
fourth ball of the over, there were two further chances to rotate strike that
over if Root didn’t find a gap. This gave Anderson just 41% of the strike, and
crucially only three risky overs where Anderson had to face all six balls.
If the fielding side counter this strategy by defending the single for the last three balls of every over, there should be plenty of boundary opportunities for the senior batsman.
Figure 2: How batsmen approach sharing the strike when a top seven batsman is joined by a number eleven (partnerships 50 runs or more, since 2000).
The data shows three viable options: take the single from the fourth ball onwards (eg. Root), gamble and go ballistic (Watling) or trust a naturally defensive number eleven to block out an over (Collymore and Sharma).
STRATEGY 2: Keep usual strike rate for the number eleven (three runs per over or more)
Figure 3 – Anderson, Ntini & Boult’s successful partnerships vs their Strike Rate
Well there’s a surprise – the hypothesis was that the number eleven should defend, protecting his wicket while the senior batsman scored the runs. The converse is true: batting normally or counter attacking is the key to success. This may be because batting normally allows the number eleven to get off strike, and means a balanced field rather than an attacking one (think square leg, mid-on, cover rather than three more catchers).
Not too much to say here, just that attacking batting is good. It’s hard to accrue a big last wicket partnership at three an over unless the number eleven can be trusted to soak up a lot of deliveries.
Conclusion – for best tenth wicket results, the number eleven should bat positively but normally. Meanwhile the senior batsman should play aggressively on the first three balls of each over, before looking to take a single from the fourth ball onwards. Only expose a number eleven to a full over if you want a nice not-out to boost your average!
A quick word on methodology – the sampled innings for Figures 1, 3 and 4 only include those where runs were scored, and where the senior batsman played more than 30 innings in their career. The Strike Rates exclude the delivery where the wicket fell – otherwise shorter innings would be disproportionately affected by the wicket ball in which no runs are scored. The “% outperform” metric is how often the runs scored exceeded the mean expected runs for that duo for a given innings, taking into account whether they are at home or away.
Sri Lanka won a thriller last week (link), chasing down a target of 304 with one wicket in hand. The unbroken last wicket stand of 78 came out of nowhere. If they had been opening the batting for England, this would have been the ninth highest of the last 100 partnerships.
How common are these monster scores?
Considering tenth wicket partnerships since 2000, the Mean score is 14.5 runs, the Median eight, and the mean duration is 25 balls. The chance of scoring 78 or more is roughly 100-1. [1]
Figure 1
That tells us that very high scores are rare, but what about the big scores – are there any patterns here?
Bias towards the first innings of the match
Most involve a top order batsman with the number eleven
Three blisteringly fast run-a-ball partnerships; most are significantly faster than the average 3.0 runs per over for Test Cricket in this era.
Figure 2
Modelling tenth wicket partnerships
If you have two openers that average 40, you can model the partnership as if it is one batsman that averages 40 – the distribution of scores will be the same. This holds true until you have batsmen with wildly different averages. What would you expect a partnership to yield when a top order batsman is left with a number eleven for company?
A model of expected average for a tenth wicket partnership was created, using the following inputs: each Batsman’s Career Average, Home/Away and the innings number within the match. Various combinations of the two batsmen’s averages were tested against the data since 2000. [2]
Results were tested in two ways. I) Measuring the mean square difference between expected and actual partnership, and II) Seeking a distribution where half the scores are above and half below the expected distribution
The best fit was that the partnership average is: Weaker batsman’s average + 20% of the difference between both batsmen’s averages.
Returning to Sri Lanka’s match winning partnership, Perera (Avg 35) and Fernando (Avg 7) would be expected to average (7) + (35-7)*0.2 = 12.6 for the tenth wicket. Adjust for it being the fourth innings, and being away from home, and the expected average drops below 10. Something else is missing – or that 78 partnership is still a miracle!
Figure 3 – Least likely tenth wicket partnerships
Strategy and Strike Rate
If the number eleven bats defensively, that gives more time for the senior batsman to score runs: the partnership for the tenth wicket is likely to be more lucrative.
Think Chris Martin – he averaged 2.5, but at a Strike Rate of 20 runs per 100 balls. Martin could expect to stick around for 12.5 balls. If he scored at a Strike Rate of 50, he would only last an average of 5.0 balls, and there would quickly be a marooned batsman at the other end.
Ignore Strike Rate and the 84 Chris Martin put on with Tim Southee in 2008 (link) was a one-in-27 million event. Adjust for bludgeoning Southee and circumspect Martin and that drops to 1,500-1.
There is an unquantified boost to the expected partnership through farming the strike to ensure the senior batsman faces more balls. Another increase comes through aggressive batting by the senior batsman. I will consider adding those factors to my Test Match Cricket Model, so it better reflects the reality of occasional monster last stands.
Conclusion
Expected value of the tenth wicket: Weaker batsman average + 20% of the difference between both batsmen’s averages.
A last wicket partnership is more successful if the number eleven defends, leaving the attacking batting to the senior batsman. If numbers ten and eleven are batting together, they should bat naturally.
More very high partnerships than my model expects, driven by attacking batting.
[1] 100-1 odds for an average last wicket pair. The 600-1 for Sri Lanka reflected the fourth innings, against a fantastic South African bowling attack.
[2] Note that only batsmen with more than 30 career innings were included and matches involving Bangladesh are excluded.
I’ve always been critical of selectors. Even as a little boy who couldn’t pronounce Sri Lanka (Scarry Lanka), I couldn’t get my head round Graham Gooch not being picked for England ‘A’ when he was dropped by England. My father said that the ‘A’ team was only for young players. That didn’t make sense to me.
I was reminded of that as England Lions got trousered by India ‘A’ this week. Having said nice things about England picking their best batsmen in the Test team, that praise won’t be repeated for Lions batting choices.
This piece showed expected Test averages based on the last three years of First Class and Second XI cricket. Using the same data, we can see where the Lions top seven rank among England’s batting options, and what they would expect to score at Test level.
The data shows Duckett and Pope are solid choices, who could easily find themselves playing in the 2019 Ashes. Billings and Mullaney are slightly left field based on 2016-18, but with stronger career records. Fair enough.
Holden and Gregory? Max Holden was England U19 captain, and is only 21 years old. Gregory has solid white ball numbers, and it could be that the one dayers were the main focus of this tour. I’d suggest that Holden and Gregory wouldn’t be in this squad based on domestic red ball performances.
Sam Hain is 248th on the redballdata.com ranking of England’s batting options, eight places below Toby Roland-Jones. Since 2016 his First Division runs have been at an average of 20 and Second Division runs at 33. Yes, he has a List A average of a stonking 58. Is he Joe Denly’s understudy for the number three role? No. Let’s just say he won’t be in my Fantasy Cricket team next year.
How did this top order perform? Between them they contributed 229-7 and 187-5 in the first “Test” and 99-7 & 145-7 in the second. A collective average of 19, which is just slightly worse than their expected average of 24 – this wasn’t an aberration, the England Lions are light on batting.
Why don’t teams pick their strongest ‘A’ team? Especially in the off season when it’s not like counties are being deprived of their best players. Are these two red ball games just an afterthought? The current selection should be so much better.
Putting predictions on this blog allows testing of prediction against results. In this post I’ll look back at what I said before England’s tour of the West Indies in 2019.
I was surprised how few concrete predictions were included in previous posts. Next series I may include player by player predictions, so there are more data points.
1.No reason to model Jennings’ expected Test average as anything other than 33.
❌ Jennings averaged 16. Though it was only four innings, it’s hard to see that prediction as a success! The extra data takes his expected average down to 32.
2. One spinner is the right choice
✅ Rashid’s match figures of 26-1-117-0 with the ball and 12 & 1 with the bat showed England the error of their ways.
3. History says expected average by bowing type Spin 32 Pace 26
✅ Actual averages: spin 35 pace 21, which reflects the quality of bowing on display – both teams have better quicks than spinners.
4. West Indies’ best chance will come if their fast bowlers can keep England under 225 in one innings
✅ Both West Indies victories included innings where England scored under 225. England won the third Test scoring 277 & 361-5. I don’t really like this kind of prediction though: Cricket is won by taking 20 wickets and scoring more runs than your opponent. How you do that is unimportant.
5. England 2019 are at about the level of the 2005 Ashes side, by having no weak links rather than being packed with world-beating batsmen.
❓Most would say that England’s batting was stronger in the past, but the current team has huge potential. My view is that England’s current batting is fragile because it is not that good, while some pundits would have you believe that England are afflicted by “amazing-but-collapse-too-often syndrome”.
6. England have a one in three chance of Whitewashing the West Indies.
⚠️ I stand by this prediction- though hard to appraise the success of this. Just because it didn’t happen doesn’t mean there wasn’t a 33% chance of it. Equally if it did happen that wouldn’t tell us much from one prediction.
There has recently been interest in Keaton Jennings’ average against pace. Two failures in Barbados have stoked this discussion. His average (26) in 16 Tests is below his expected average (33) based on County performances over the last three years. Generally, I would choose the big sample size (County Cricket) over the smaller sample size (Tests), and so rate his expected average at 33, not 26.
But – can we learn anything about technical flaws from Jennings’ Test performances to change that view? Specifically his average against pace:
Keaton Jennings‘ average against pace (16.90) is the lowest of any opener to have played more than 15 Tests, for games in which ball-by-ball data is available.
Wisden (Jan 26th 2019, via Twitter)
I’ve had a look at his performances over the last 3 years on the county circuit. The hypothesis is that there are some very good pace bowlers in County Cricket, and as an opener Jennings will face them (a middle order batsman might be able to make hay without facing much of the best bowlers).
The data supports this hypothesis – 68% of the time he faces at least one opening bowler with Test experience.
Keaton Jennings has played two of the last three seasons in Division 1, scoring 11 hundreds, and making runs in a variety of conditions (including April and September- when the deck is stacked in the bowler’s favour). His three year average isn’t amazing, but the key point is that one can’t look at the above data and conclude that Jennings has a problem against pace bowling.
As an aside, this piece is a reminder that I need to build a way to combine the Test performances to the First Class performances to ensure I’m using every available data point in appraising batsmen.
Conclusion: There is no reason to model Jennings’ expected Test average as anything other than 33. Plenty of people will disagree with that!
One of the benefits of twitter is hearing new ideas. Jonas (@cric_analytics) has suggested the third innings should pause when the lead reaches 300, then the fourth innings takes place.
That way, a team that’s winning doesn’t have to pointlessly bat until the lead is over 500, before crushing an inferior opponent. Here’s how Jonas puts it:
I’ve modelled how this would work in practice, with the aim of answering two questions:
Does this make the strong team more likely to win? (Probably)
Is the game over sooner? (Generally)
Here’s the summary from the single scenario I looked at:
Scenario: West Indies vs England, Bridgetown.
England have batted first and scored 360. West Indies slipped up and were bowled out for 210. We join the action at the lunch on day three. England lead by 150. Two versions of this were modelled: under the existing laws, and temporarily declaring the third innings if they score 150 more.
Let’s see what happens:
In 92% of cases England made it to 150 without being bowled out – and so, with a lead of 300, temporarily declared
West Indies scored under 300 83% of the time – so the third innings did not need to re-commence
When the West Indies scored more than 300, sometimes the game meandered to a bore draw because the West Indies couldn’t confidently declare
Here’s the distribution of match end times depending on which rules apply:
We can see that there’s a big shift towards Day 4 finishes under compulsory declaration at 300 – mainly from the team batting fourth being bowled out for less than 300.
Worth noting the result wasn’t significantly affected by the rules being used. This would be different in other scenarios – such as if there was less time in the game.
Conclusion – This could be very useful in county cricket (where matches are only 4 days long). Suggest more modelling is required (especially scenarios where the odds are shifted from the draw being favourite to a result being favourite). A trial in County Championship Division 2 would be fascinating.
Red Ball Data 