How do you get Steve Smith out?

Steve Smith is the leading run-scorer in Tests in 2019, despite giving the rest of the field a 9 month head start. Steve Smith has 9 consecutive 75+ scores in Ashes test (the next best is 4). Other than Don Bradman, Steve Smith has the most Test centuries against England.

The superlatives about Smith following his first innings double century against England, followed by a rapid fire 82 off 92 balls in the second innings, in the fourth Test at Old Trafford have come thick and fast.

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However, following his feats at Edgbaston in the first Test, England may have been thinking they had found his kryptonite. Before the second Test at Lords, England dropped Moeen Ali in favour of the left-arm finger spinner Jack Leach; left-arm finger spinners apparently having marginally better success than other types of bowler (a tactic that has been tried before against a dominant batsman). Then, during the Test Jofra Archer felled him during a fiery spell, resulting in Smith’s absence from the second innings and the third Test.

The evidence from Old Trafford suggests that whatever England thought would work actually doesn’t (or didn’t). Smith looked largely untroubled against various short-pitched assaults by Archer (who was looking increasing ragged and even sulky during a long first innings) and, although nicking one to slip off Leach during this first innings when he was on 118 (later given not out due to a front foot no-ball), similarly against Leach (albeit Smith was out to Leach in the second innings, when pushing towards a declaration).

So what does the data say about how you get Smith out?

Certainly, left-arm finger spinners have had a measure of success against Smith. Of his 105 dismissals in Test cricket, 18 (18.17%) have come when facing left-arm finger spinners. Further, Rangana Herath (5 dismissals) and Ravindra Jadeja (4 dismissals) are 2 of the 8 most successful bowlers (measured by wickets taken) against Smith.

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However, all of Herath and Jadeja’s dismissals of Smith arose in Test matches played in the subcontinent. Herath also ranks 10th in terms of the bowlers with the most Test wickets, with Jadeja (198 wickets in 43 Test matches, and aged just 30) likely to climb well into the top 20. So, if you are a generational left-arm finger spinner playing against Smith in Asia you are likely to have a measure of success. If you are not, the data does not endorse the strategy of selecting a left-arm finger spinner to play against Smith.

The data also does not bear out any real weakness of Smith against express pace. Umesh Yadav (4 dismissals over 14 innings), Steve Finn (3 dismissals over 9 innings), Morne Morkel (3 dismissals over 10 innings) and Kagiso Rabada (3 dismissals over 12 innings) have had a measure of success, but the real insight to be had is the mode of dismissal that gets Smith out at a relatively higher rate.

Across all dismissals in Test cricket, catches by the wicket-keeper accounts for 16.27% of all dismissals. However, Smith is dismissed as a result of catches by the wicket-keeper 22.9% of the time.

Bowlers ranging from Yasir Shah (2 caught behind dismissals over 6 innings) to Chris Tremlett (2 caught behind dismissals over 8 innings), and Kagiso Rabada (2 caught behind dismissals over 12 innings) to Tim Bresnan (2 caught behind dismissals over 14 innings) have had success against Smith via this method.

Particularly revealing is the success that South African fast bowlers have had. Smith has been dismissed 12 times in Tests by South African fast bowlers, on 4 occasions (33.33% of the time; i.e. double the historic mean) via catches by the wicket-keeper. Smith’s average (mean runs) is also a relatively modest 41.53 against South Africa, less than 2/3 of his overall average.

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Smith is not the type of batsman who takes the game away from the opposition via rapid scoring. His strike rate (mean runs per 100 balls) is a relatively sedate 56.21, which is not at the level of say a Brian Lara (60.51) or Matthew Hayden (60.10), both of whom also average over 50 in Test cricket (albeit not close to Smith’s average). In short, Smith grinds you down, but he doesn’t decimate you in a session or two.

The take away? No one is suggesting it is easy getting Smith out, or that the chosen tactic is likely to work all the time. However, the data tells us that disciplined fast bowling on or about off stump with appropriately set fields is likely to give yourself the best chance of getting him out, nicking off behind or perhaps elsewhere in the cordon, while remaining relatively safe from having the game taken away from you at rapid pace.

All data used in this post was obtained from the following websites:

The curious case of Mitchell Starc

The use of data, and data analytics, is exploding world-wide. From business to science to politics, data is being gathered and analysed to improve outcomes (competitive and overall). In fact, renowned futurist Yuval Noah Harari thinks that humankind is adopting a new creed which will eventually replace religions and other socio-economic systems such as capitalism, which he terms “Dataism”.

Data analytics has also had a presence in professional sport for some time.  Made famous by the movie “Moneyball”, data analytics is relied on significantly in MLB, the NBA and professional football leagues in the United States and Europe. The use of data analytics is also increasing in NFL, NHL, and cricket.

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Cricket, like baseball, has always been a sport in which data (statistics) has been ingrained. The growth of t20 leagues across the globe (and particularly the IPL) has resulted in a growing attention to the importance of data, the question of what is meaningful data, and the use of this in order to assess and recruit players, analyse opposing teams and plot strategies.

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The prevalence of data analytics in sports such as MLB and the NBA has meant that data analytics has also made its way into the sport’s media, including via dedicated blogs.

Although from time to time data analytics is touched on in cricket media, for example when evaluating players in t20 league auctions, as far as I’m aware there is no blog dedicated to data analytics in cricket. So, I thought I would try my hand at this.

I envisage that this blog will look for the data that explains current situations or trends in the game. Other times, it may use data to suggest how things could be done differently. But, if I find something that involves the analysing of cricket data in order to make conclusions about that data (the basic definition of data analytics), and I find it interesting, it will find its way in here! Oh, and the name? A nod to the most famous cricket statistic of all.

Given the hype around the Ashes at the moment, I thought I would focus first on this, and particularly the curious case of Mitchell Starc’s non-selection.

On one view, it has been quite the fall from grace for Starc. With a record 27 wickets at the recent World Cup, and following years of consistent Test cricket, many may have thought that Starc would be one of the first picked for the Ashes tests.

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Clearly, this has not proved to be the case. Starc has carried the drinks for three test in a row, with Pat Cummins, James Pattinson, Peter Siddle and Josh Hazlewood preferred to him. As Langer has explained the decision, in reference to why Hazlewood was selected over Starc:

 …we know that the style of play we want to play against England, at his best he should execute those plans really well.

He hits a great length, he’s usually pretty miserly in his economy rate so that’s what gave him the edge…

Even after the Jofra Archer barrages at Lords and Headingley Langer has remained steadfast. When asked whether the Australians would select Starc or at least return fire in the bouncer war he responded:

I’m sure the bouncer will still be part of every bowler’s armoury, if it helps us get batsmen out then we’ll use it. Otherwise we’ll keep sticking to the plan.

So what is the data on which this plan is based?

In short, and as alluded to by Langer, it is Starc’s economy rate, his mean runs per over.

From previous unsuccessful Ashes tours, extending back to 2001, the Australians have learned the importance of parsimony. In England, there is generally some assistance for the bowler in the pitch or the weather conditions, and there is less of a need to blast teams out. Further, grounds are small and outfields are fast. In short, Australia bowling sides should approach their task in a slightly different manner to which they would approach it on home soil; with the focus on building pressure while relying on the natural assistance offered. The wickets will come, the key is to build the pressure in order to allow for this and not to leak too many runs in the process.

To illustrate this point, here are the mean run-rates at each of the five Ashes venues (over the history of Test matches played at these venues):

Edgbaston – 2.98

Lords – 2.88

Headingley – 2.83

Old Trafford – 2.71

The Oval – 2.81

This can be contrasted with the five major test venues in Australia:

Gabba – 3.14

WACA – 3.32

Adelaide Oval – 3.09

MCG – 2.91

SCG – 2.88

Of course, the changed pace of test cricket in the last 10 years means that these records are, to some extent, redundant.

Further, they are an aggregate run-rate, i.e. across all innings in the Test match. They are therefore skewed by losing teams.

Finally, they include all types of bowlers, including spinners who historically have had higher mean run-rates.

So, honing these statistics: 1) in the last 10 years: 2) for winning teams; 3) and for fast bowlers only (obviously at an overall loss of sample size), the mean run-rate at each of the five Ashes venues is as follows:

Edgbaston – 2.90

Lords – 2.66

Headingley – 3.19

Old Trafford – 3.16

The Oval – 2.93

A take-away? Economy rates for fast bowlers need to be relatively low in England for teams to win.

How, then, do the five key Australian pacemen compare? Well, in Test matches in England their mean economy rates are as follows:

Pat Cummins – 2.71

Peter Siddle – 3.07

Josh Hazlewood – 3.29

James Pattinson – 3.03

Mitchell Starc – 3.45

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Australia don’t have a plug and play medium-fast all-rounder, who can keep one end tight while more attacking fast bowlers rotate at the other end (although Nathan Lyon is sometimes asked to play this role). So, they need their fast bowlers to be economical. Of the five listed above, Starc is the outlier, and historically concedes well above the mean run-rate needed to achieve success at Australia’s five Ashes venues.

Of course, the discrepancy can’t be overplayed. Over a 20 over spell in a day, the difference between say Siddle and Starc is only 7.6 runs. However, with a lower “average” (mean runs per wicket) in England (29.21 compared to 31.24), albeit with a slightly worse strike-rate (Siddle takes 2.74 balls more than Starc to get a wicket in England), the selection is more than understandable.

Now, if Australia’s batsmen could just hold up their end of the bargain, the data-influenced strategy just might work…

All data used in this post was obtained from the following websites:

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