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.
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.
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.
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
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: