Predicting what will happen in England’s Test tour of UAE is a difficult task. Will we see a run feast, or perhaps death by spin? A Joe Root masterclass, or maybe a seamer-inspired show of English bowling strength?

The memories of 2012 are fresh. England arrived as the number one-ranked Test team. Unbeaten in nine series, they had risen from seventh to first in that list in little more than two years. Ashes winners down under and World T20 champions, a win in alien conditions against a talented, if fragile, Pakistan team seemed very much achievable.

However, England were humbled in all three Tests, coming out second best in a series that was defined by the bowlers. The expected attritional slog never materialised – batsmen struggled from the outset, with England’s 58-5 at lunch on day one of the opener setting the tone.

Azhar Ali scored 251 runs in his five innings, but no other batsman from either side averaged more than 40 in the series. On supposedly batsman-friendly wickets, the England batting unit misfired spectacularly.

The tourists’ opening day collapse was followed by a slump to 87-7 in the second innings and a failure to chase 145 in the second Test. They followed that by earning the unwelcome accolade of becoming the second team to lose a Test after dismissing the opposition for below 100 on day one.

The bowling attack functioned well. Stuart Broad took 13 wickets at an average of 20.4, Monty Panesar 14 at 21.6, Graeme Swann 13 at 25.1 and James Anderson nine at 27. However, the batting gave little respite – England were in the field on every day of the series.

Saeed Ajmal (24 wickets at 14.7 average) and Abdur Rehman (19 wickets at 16.7) were rampant. On low, skiddy pitches they bowled quickly for spinners, often touching 60 mph, testing both edges of the bat.

The pitches brought the stumps into play throughout, and a combination of excellent bowling and the new DRS system contributed to a record number of LBWs – the 43 in this three-match series is the joint-most ever recorded in a Test series.

However, the tour as a whole was not a disaster for England. They bounced back in the ODI series to deliver a whitewash of their own, and learned enough to seal a historic Test series triumph in India the following winter, the first by an England team in nearly 30 years.

So what can we learn from the last tour?

Attack the Stumps

Bowling straight in UAE gains much more reward than other host nations. Far more batsmen are dismissed bowled and LBW than the worldwide average, and in particular in comparison to Tests held in England.

Dismissals in Tests by venue

This table shows that nearly half of all dismissals in UAE are bowled or LBW, compared to less than a third in England. In the 2012 series 22.6% of the balls bowled would have gone on to hit the stumps. By way of comparison, in the fourth Ashes Test at Trent Bridge this summer, 9.1% would have struck the timbers.

The prominence of spin is obviously a major factor here – over half the overs bowled in the UAE are bowled by spinners, whereas in England it is about a quarter. Spinners bowl more balls that would hit the stumps, whilst the lower bounce of the pitches means that both seamers and spinners can hit the stumps from shorter lengths.

A Spinner’s Length

In general, balls that are hitting the stumps in Test cricket have a considerably lower average than those that don’t. For spinners, about 25% of balls bowled would go on to hit the stumps, and these take their wickets at an average of 17.4; the balls that are going to miss the stumps average nearly three times as much.

Stumps - Spinners% BallsAverage

What the Pakistani spinners did particularly well in 2012 was to bowl quicker, dragging their lengths back a little whilst still attacking the stumps. Monty Panesar was able to perform a similar role for England when he was selected for the second Test.

 Average SpeedStumpsAverage LengthBatViz Predicted AverageSeries Average

The spin bowling in this series was of a very high standard. Spinners normally average around 36 in Tests, so for BatViz to be predicting averages in the 20s the size of the challenge facing batsmen is evident. The actual Series averages show how much batsmen struggled to cope, with all the spinners having greater success than was expected.

For comparison, here are the statistics of spinners in the Ashes Test at Cardiff. They bowled slower and fuller, and were less able to attack the stumps.

 SpeedStumpsAverage Length

Pakistan start as favourites

With their strong batting and bowling line-ups and traditional strength in familiar conditions, it is no surprise that WinViz favours Pakistan at the series outset.

1st Test28%21%51%


The first Test at Abu Dhabi is moving on quickly. You have just checked WinViz and it has Pakistan at 42%, England at 35%, and the draw at 23%. You then look at PredictViz and it is showing an England win by a handful of runs. Surely something has gone wrong? One tool is saying Pakistan are going to win, the other that England are.

There has not been a technical meltdown; the CricViz tools are working correctly. Remember, we are not predicting a Pakistan victory. In fact, we think Pakistan are more likely to not win (58%), than win (42%). Therefore, it should not be a huge surprise that the average result is not a Pakistani win.

But wouldn’t you expect the average result to come from the most likely outcome?

Well, sometimes it does, sometimes it doesn’t. There are a couple of concepts to understand before looking at the details of why this happens.

First of all, percentiles: If you are in the 40th percentile for height, you would expect 40 out of 100 people to be your height or shorter. If you were in the 95th percentile for height then you would expect only five in every 100 people to be taller than you.

Similarly, if we look at the possible range of scores for a Test team we can use percentiles to measure how likely a team is to make a certain score. For example, let’s look at an average team batting second in a Test match. A score of 420 is in their 70th percentile. 30% of the time they will score more than 420.

Secondly, let’s take a look at probability spaces. Imagine we each roll a six-sided die and add the two numbers together. There are 36, equally likely outcomes, which we can illustrate in a diagram:

roll 1

As you can see, the total eight occurs five times, so we can say that the probability of getting a total of eight is five in every 36, or 5/36.

We are now going to play a game. The rules are as follows:

1.) If your score is greater than or equal to mine, then you win
2.) If my score is greater than yours, I win.
3.) However, if we both throw a 4 or higher, then it is a draw.

How likely is each of the three results? We can look at the probability space to tell us:

roll 2

So, in 15 of the 36 possibilities you win, so you will win 5/12 of the time. I will win 12 out of 36 or a third of the games, and a quarter will be draws.

We can take the same approach with Test matches, by creating a probability space based on how well each team scores during the match. Let’s take a look at the probability space of a Test match between two well-matched teams with no weather interruptions.

Here the batting performances of each team form our axes. So the left-hand side are low Team A scores, the right-hand side are high scores. The top half of the chart shows good Team B batting performances, the bottom half are poor performances. As you can see, when both sides score highly (top, right-hand corner) we get a draw. When one side or the other scores poorly, they tend to lose.

Probability space for two well-matched teams:


So, if Team A’s scores are in the 70th percentile then we are looking at the column above the number 70.  And we can see by looking up this column that they won’t lose if they bat this well.  The result now depends on Team B’s batting.  If they perform better than their 40th percentile then they will save the match, otherwise Team A will win.

One thing you will notice is that the chart is not exactly symmetrical.  The team batting first has a slight advantage in terms of scoring; the pitch tends to be more batsman friendly in innings 1 and 3 than in 2 and 4.  So in low-scoring matches, where there will be a result, Team A has a slight edge.  If you look at the 100 squares (from 5th to 50th percentile for each team) in the bottom left-hand section of the chart, you will see that 53 of them are blue Team A wins, and 47 are red Team B wins.  The blue squares include the 50th percentile match, our median match, which is what PredictViz shows.  If both teams produce their average performance, then the result will be a Team A win by a very small margin.  This is true even though the balance of power lies with Team B who will win 7% more matches than Team A.

So, if Team A scores more runs, why does Team B win more matches?  Well, the key is what happens in relatively high-scoring games.  It is far easier for Team B to force a result in matches where they bat well.  Look at the top, right-hand corner of the chart where both sides have batted better than average.  You can see that the red squares extend into this space where as the blue squares don’t.  There are no wins to the team batting first in matches where both sides score relatively highly.

We can see what this means in reality by thinking about the effect of a first innings lead.  If Team A gets a first innings lead, then to win the match they will generally bat on until they can make the game safe, declare and then bowl out the opposition, who know just what they have to do to save the game.  On the other hand, if Team B get a first innings lead, they just have to bowl Team A out and chase down the resulting total.  They are able to use the time left in the match far more efficiently to force a result.

PredictViz shows what will happen if both teams perform exactly as expected, how their innings will fall across the days of the match and what the average shape of the match from here will look like.  It is obviously showing just one of the infinite ways that the match can evolve from here.  How many of those fall to each team is represented by WinViz.


After ending the Lord’s Test with a batting collapse, England inflicted one of their own on the opening day of the third Test. Conditions were rather different at a damp Edgbaston to those which Australia prospered in at Lord’s and James Anderson duly delivered a seam bowling masterclass.

The Lancastrian’s 6/47 helped bowl out Australia for 136 inside 37 overs, a collapse which saw the tourists’ starting win probability of 31.9% reduce to 11.7% at the change of innings. Anderson’s lateral movement proved too difficult for Australia to deal with, but their shot selection played a major part in their slump.

The analysis of Hawkeye data for each delivery reveals how the Australian top order got caught in two minds when dealing with Anderson’s movement. As well as producing the average wicket probability and run total for each ball based on similar deliveries in the CricViz database, BatViz can analyse the type of shots played (see below table).

Anderson delivered 88 balls in Australia’s first innings. Of these, 10 had an attacking shot percentage between 40% and 49% – based on the similar delivery evaluation, these type of balls are typically attacked somewhere between 40% to 49% of the time.

All four of Anderson’s top order wickets – David Warner, Adam Voges, Mitchell Marsh and Peter Nevill – fell in this range. Warner (playing defensively too late), Voges (withdrawing his bat to leave too late) and Nevill (no shot) paid the price for tentativeness; Marsh (flat-footed drive away from body) ill-advisedly took the attacking option.

BatsmanShot typeDismissalLeave %Attacking %
VogesNo shotCaught31.344.9
NevillNo shotBowled3.441.1

The type of deliveries Warner and Nevill received were clearly ones to play at. BatViz takes into account the speed, line, length and deviation when picking out similar deliveries and these two balls that were on the stumps were left alone just 3.7% and 3.4% of the time respectively.

This highlights the seeds of doubts that Anderson can plant in a batsman’s mind, despite the lack of extreme pace. Warner’s wicket was 82mph, Nevill’s 83.6mph. His day one wicket burst against a confused batting line-up was the crucial factor in England’s victory, a template that was followed emphatically at Trent Bridge by Stuart Broad’s opening salvo.


One recurring childhood memory of a typical summer’s day involves my father and I watching together, in silence, the first session of a Test match. It would be some time in the 1980s. In these memories it’s usually a beautiful summer’s day too, but in order to be able to see the screen at all – this being in the days of giant cathode ray tube televisions – all sunlight is firmly blocked behind shutters and curtain.

Eventually, approaching the lunch interval, one of my sisters would breeze in from a game of tennis or something and the first question was always the same: “Who’s winning?” My dad’s answer was always the same too. After letting out a resigned sigh, and pausing for effect, he would reply: “You can’t possibly know the answer to that question on the first morning of a Test match.”

Many things have changed since those days. Live Test cricket on the BBC disappeared long ago. TVs are rather less allergic to sunlight. England have not yet found a batsman quite so glorious to watch in full flow as David Gower (and imagine how good he would have looked in HD). But I digress. One thing, largely, has remained as it always was: we still have no real way of judging who is winning a match, especially in its early stages, other than through subjective opinion or through listening to what the pundits in the commentary box make of it all.

Many, perhaps most other popular sports play out in a way that makes secondary interpretation of events largely redundant. A scoreline which directly reflects the balance of power is the means by which football, rugby, golf, tennis, hockey, volleyball and so on are both conducted and eventually decided. In horse racing, cycling, athletics and rowing there is an extra degree of subtlety in that the eventual winner may not actually be in front until late in the race, and may not even want to be in front until its climax.

The same is partially true of cricket, although there are more layers of complexity to deal with. Without having certain facts to hand – for example, the quality of the bowling and the movement available in the air and off the pitch – it is extremely hard to know if 75-3 at lunch on the first day of a Test match is a good score or a sub-standard score. If you only have 20 seconds to check the score in a busy day of meetings, and don’t have time to absorb and interpret other factors, you are left yearning for something a little bit more.

Cricket is ready to advance from this point. Instead of having to read through a 400-word bulletin and then try to interpret from the raw facts presented whether your team has gained or squandered a slim advantage – and, let’s face it, some reporters posting snapshot copy online lack the experience and specialised knowledge to be able to offer even cursory analysis – the time has come to be given a scientific view on the match situation. And this is where CricViz has the opportunity to blaze a trail. Go back to that 20-second window I was describing earlier. If, instead of checking your phone for the score on cricinfo or BBC Sport you did the same on CricViz, then you would immediately be given extra information (in addition to the score) that gives you so much more. Win probability for each team, most likely final outcome, performance indicators and the degree of batting difficulty – are the four additional pieces of data which say so much more than 75-3 at lunch on day one.

Where the app has the scope to really come into its own is when a match ebbs and flows. As we know, the perceived strength of a team’s position, when batting, can be radically eroded by a flurry of wickets or boosted magnificently by one huge partnership. The fourth one-day international between England and Australia at Headingley last Saturday experienced some wild fluctuations. But the fascinating thing is that most of these pronounced swings of fortune were confined not to England’s chase, but to Australia’s own innings, after Steve Smith had won the toss and opted to bat first (a little surprisingly given an early start in September when bowling conditions are often very favourable).

That the Aussies managed to set a target of 300 was remarkable from an initial platform of 30-3 in the ninth over. Glen Maxwell was then dropped twice before producing a thrilling counter-punch of 85 from just 64 balls and suddenly a really huge score seemed possible. Back came England with a burst of momentum-checking wickets to leave it 215-7 in the 42nd over. Now, surely, Australia had to be set for a modest total. Not a bit of it. Australia’s number nine John Hastings provided muscular support for the free-wheeling Matthew Wade and there we had it – a final total of 299-7.

If this innings had been a historical stock-market index it would have lived through the Wall Street Crash, the dotcom bubble, Black Monday, you name it. Shrewd in-play bettors would have had great opportunities to establish strong positions on the hugely popular exchanges, and certainly CricViz would have enjoyed charting the violent pendulum swings in play and relaying its own interpretation of individual situations.

The potential is there for something radical and exciting. Stay tuned.



With two well-matched sides, each batting and bowling well, to a large degree the deciding factor in the series opener was the quality of their fielding in the first innings. In a game where both sides got a number of half-chances, England were sharp and clung onto theirs, Australia spilt a few and suffered as a result.   The difference between the impact of the two sides’ fielding in the first innings was 113 runs, almost the entire 1st innings lead that gave England control of the match.

Eng Fielding ScoresAus Fielding Scores
1st Innings51-62
2nd Innings3336

At the end of Australia’s first innings WinViz had England at nearly 70% to win the match.

Take away the 113 runs between the teams’ fielding and the situation would have been different. England would still have had a small edge – Australia still had to bat last on a wearing wicket – but it would have been far more evenly poised contest.



Australia pursued a policy of aggression against the English spinners, but in doing so lost 7 wickets for 158, including 4 key top order wickets to Moeen Ali. Australia’s record against spin overseas has been poor in recent years, and Ali was the bowler against whom they underperformed most in this match.

Test Avg Overseas – since 2010

From the Hawkeye data, BatViz predicts that an average Test batsman would have attacked 39% of the balls bowled by English spinners in Cardiff. The Australians attacked almost exactly half. On this occasion, the strategy hurt them considerably.   With long periods when there was little assistance for the spinners from the pitch, BatViz estimates that an average Test side should have averaged 45 against spin in this match, but instead Australia lost their wickets at 22.6.

Australians v Spin in Cardiff
BatViz PredictionActual
Batting Avg45.122.6
Attacking %39%50%


CricViz’s analysis of the two pace attacks shows that while both sides bowled well in Cardiff, they did so in slightly different ways. Australia bowled slightly quicker, and swung the ball more in the air.

England in contrast, were able to get more movement off the wicket (often through the use of cutters) and were far more accurate. Australia were able to induce slightly more mistakes from the batsman, England did so in more dangerous areas.

[visualizer id=”3952″]


With little pace or life in the surface, the pitch became more of a new ball wicket as the match went on.

BatViz Predicted Average by phase of innings
BallsInn 1Inn 2Inn 3Inn 4

[visualizer id=”3956″]

On the first day, under cloudy skies, the ball swung for most of the day, and batting although slightly easier after the first two hours, remained difficult all day. As you can see from the graph, England’s new ball spells were more potent, but as the ball stopped swinging they were unable to sustain the threat to the batsman that Australia had in more helpful conditions on Day 1.


This was a high quality encounter. An excellent Australian side buoyed by recent successes, and a good, young England side playing in their home conditions. As we can see from the PlayViz output, the general standard of play was very high.


Over the course of the match, England’s batting was 79 runs better than an average Test side’s under the same conditions, their bowling 81 runs better and the quality of their fielding was worth another 84 runs.

Australia’s bowlers were outstanding, 150 runs better than a typical attack, but they were let down by their fielding, particularly in the first innings. The Australian batting, whilst 17 runs better than a par Test side, was also down on their usual performance levels.