Here you will find detailed analysis highlighted from the CricViz app.

Strategic & Tactical Analysis, Champions Trophy Final, India v Pakistan

Freddie Wilde analyses some of the key strategic decisions and tactical battles ahead of the 2017 ICC Champions Trophy Final between India and Pakistan at The Oval.

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Top bowling speeds in ICC Champions Trophy 2017

We’re keeping tabs on who’s bowling the quickest in the tournament, on what have been reasonably slippery pitches. Milne from New Zealand has marched into the lead; plenty more quicks to come who haven’t yet played, including a few in today’s India v Pakistan clash at Edgbaston. We’ll update this table as the tournament progresses, and feature more in our app.

Fast bowlers at the Champions Trophy

Strategy Analysis: England v Bangladesh

CricViz analyst Patrick Noone outlines how England should target Bangladesh’s key players. 

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Spin in or out? (Wicket Rates)

Freddie Wilde analyses whether the ball that spins into the batsman is more likely to take a wicket than the ball that spins away. 

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Spin in or out? (Scoring Rates)

Freddie Wilde analyses whether the ball that spins into the batsman is easier to score off than the ball that spins away. 

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Measuring boundary attempts in the IPL

Boundary-hitting is arguably the most important skill in Twenty20 cricket, and is therefore a critical factor in assessing performance.  Metrics such as boundary runs and boundary-balls-percentage don’t give us the full picture.  A batsman who scores 65% of their runs in boundaries might be below average at rotating the strike.  A batsman who hits half his deliveries to the fence could be swinging every other ball.

Ideally, we need to know how often a batsman attempts to hit a boundary and how successful they are when doing so.  A boundaries attempted metric will be quite subjective.  When a batsman has a big swing, it’s obvious what his intentions are regardless of the outcome.  On the other hand, a simple leg-glance for a single might turn into a boundary if timed well enough.  It’s also important to consider how the field is set and the situation of the game to give us extra clues about the batsman’s plans.  However, without someone sitting down and explicitly recording boundary attempts in every match (as is done with ESPNcricinfo’s control metric) we will have to infer it from ball-by-ball data.

Defining a boundary attempt

Our data records 22 types of shots a batsman could play including all manners of drives, sweeps, pulls and cuts.  Using data from over 2,000 T20 matches, we can analyse which types of shots are most likely to result in a boundary.

shot typeboundary %
Slog-sweep40
Upper Cut34
Scoop34
Switch Hit31
Pull29
Slog27
Hook27
Reverse Sweep26
Drive23
Sweep22
Late Cut18
Flick18
Cut17
Glance14
Steer8
Fended5
Worked2
Pushed1
Dropped1
Backward Defensive1
No Shot1
Forward Defensive1

Slog-sweeps result in a boundary 40% of the time followed by upper cuts and scoops on 34%.  The top performing shots on the list down to and including reverse sweeps we may reasonably assume are played with the intention to hit a boundary.  So let’s draw the line here and use these top 8 shots as our proxy for boundary attempts.  Drives and conventional sweeps do result in boundaries but we’re not confident enough that they are always boundary attempts.

We also have data on what connection the batsman makes with each shot.

batting connectionboundary %
Middled83
Strong60
Well Timed40
Outside Edge27
Top Edge25
Thick Edge24
Gloved13
Inside Edge10
False Shot10
Bottom Edge8
Leading Edge3
Mis-timed3
Neutral2
None2
Padded1
Bat-Pad1
Hit Helmet1
No Shot0
Missed (Leg Side)0
Hit Body0
Hit Pad0
Missed0
Left0
Play and Miss0
Shoulders Arms0
Play and Miss (Leg Side)0

Middling the ball or getting a strong or well-timed connection results in quite high boundary percentages.  We will take these three shots to add to our definition of a boundary attempt.  Finally we will assume all free hits are boundary attempts.

Boundary attempts in the IPL

In this season’s IPL, there have been 2,484 boundary attempts from the 32 matches so far.  That’s nearly 2 per over.  Our definition covers 92% of all boundaries scored i.e. about 8% of boundaries are unintentional.  The average boundary-success rate across the tournament is 49%.

The graph above shows the boundary success rate broken down by team.  This correlates quite well with the current standings; Mumbai Indians have the highest success rate and are one of the form teams at the moment.  Contrast this with RCB who have a success rate of 10 percentage points fewer, near the bottom of the table.

Gujarat Lions attempt by far the most boundaries per 120 balls faced.  Their batting lineup, which includes Raina, McCullum and Finch, are making a concerted effort to hit as many balls to the fence as possible.  However, their below-average success-rate suggests they’re not executing their plans.  Interestingly, Mumbai Indians attempt the fewest boundaries of all the teams despite having the highest success rate.  The likes of Nitish Rana, Jos Buttler and Pollard are choosing their boundary options with more care and it’s been working effectively so far.

Boundary attempts by player

Let’s take a look at boundary attempts on an individual batsman level.  The table below shows the 47 players to have attempted at least 20 boundary hits.

batsman nameboundary attemptsintentional boundariesboundary success %balls facedballs per boundary attempt
Manan Vohra3625691243.44
Sunil Narine382668822.16
Ajinkya Rahane3624671704.72
David Warner6945652543.68
Shaun Marsh412663952.32
Sanju Samson5333621963.70
Jos Buttler5031621563.12
Moises Henriques3622611444.00
Hashim Amla6640612103.18
Sam Billings2716591053.89
Chris Gayle3420591303.82
Hardik Pandya291759792.72
Robin Uthappa7745582002.60
Kedar Jadhav5029581342.68
Kane Williamson331958932.82
Gautam Gambhir8247572873.50
Yuvraj Singh301757732.43
Kieron Pollard4123561583.85
Nitish Rana5128552084.08
Manoj Tiwary351954862.46
Brendon McCullum7238531882.61
Glenn Maxwell5730531111.95
Steven Smith5931532093.54
Suresh Raina7438512212.99
Jason Roy201050412.05
Krunal Pandya261350803.08
Rahul Tripathi5728491442.53
Chris Morris381847852.24
Parthiv Patel5124471402.75
Shreyas Iyer3215471083.38
Chris Lynn321547742.31
AB de Villiers3717461173.16
Aaron Finch6228451121.81
Rohit Sharma291345973.34
Manish Pandey5826451943.34
Ishan Kishan251144532.12
Virat Kohli4118441333.24
Dwayne Smith321444732.28
Rishabh Pant371643952.57
Yusuf Pathan281243812.89
MS Dhoni3816421303.42
Shikhar Dhawan9037412502.78
Mandeep Singh22941823.73
Axar Patel2711411063.93
Dinesh Karthik4819401443.00
Ben Stokes301137963.20
Karun Nair25936903.60

Sunil Narine, in his role at the top of the KKR batting order, has the second highest boundary success rate of 68%.  He also has the fifth-lowest balls-per-boundary attempt figure – every other ball.  At the other end of the scale, Pune’s Ben Stokes has been struggling, producing a 37% success rate.  Meanwhile, Stokes’ teammate Ajinkya Rahane attempts a boundary almost every 5 balls – the highest in the list.  However he does have the 3rd highest boundary success rate suggesting he is quite picky over which balls to target.  This approach is perhaps not serving him so well as he has the lowest strike rate out of the top 20 run-scorers of the season so far.

This boundary-attempts metric has been fairly crudely formulated in this article. But there is clearly potential to lend insight into how teams and batsmen approach a T20 innings, and contribute to a more comprehensive analysis of a side’s performance.

Imran Khan, @cricketsavant

The top 10 T20 death bowlers: IPL 2017

Now that cricketers have dispensed with the tedium of a winter rest (“Sleep’s for wimps”; Derek Trotter, 1985), instead plying their trade on the global cricketing treadmill, we are better equipped to look at recent form. There are almost no significant gaps in the T20 calendar, so a bowler who rocks up for one tournament in April probably had a stint in another tournament a few months prior. Their form guides are more accurate now than, say, in Test matches twenty years ago when – sweet mercy – cricketers had months to themselves, or spent it playing cards on the boat back home from somewhere hot. Or in the case of Andrew Caddick, learning to fly helicopters while painting Taunton’s pavilion – disappointingly not at the same time – as a memorable issue of Cover Point once regaled.

 

For us, it’s great news. In years gone by we’d look at a small sample of a player’s stats, based on last season, and make little of it. Now, however, we have an almost continuous sample of live data coming in which gives us a much broader view of the game’s critical areas, while ensuring it’s recent and relevant.

 

All of which leads to the following list of bowlers, and how they perform at the death (any bowler who has bowled more than six overs since the start of 2016, in overs 16-20).

IPL economy rates at the death

PlayerTeamRunsBallsMatchesWicketsEconTypeYear
Shane WatsonRoyal Challengers Bangalore17411016129.49pace2016
Joe LeachWorcestershire15410512108.80pace2016
Mitchell McClenaghanMumbai Indians1301011497.72pace2016
Harry GurneyNottinghamshire124921198.09pace2016
Graham NapierEssex129851299.11pace2016
Jamie OvertonSomerset78721096.50pace2016
Mustafizur RahmanSunrisers Hyderabad1821521687.18pace2016
Matthew TaylorGloucestershire1291041287.44pace2016
Chris MorrisDelhi Daredevils64681275.65pace2016
Graham WaggGlamorgan (Wales)100661279.09NULL2016
Rumman RaeesIslamabad United4652775.31pace2017
Dwayne BravoGujarat Lions1951211579.67pace2016
Benny HowellGloucestershire85611478.36pace2016
Sunil NarineMelbourne Renegades8860678.80spin2017
Tymal MillsSussex89701167.63pace2016
Lasith MalingaSri Lanka6543469.07pace2017
James FullerMiddlesex86731167.07pace2016
Mitchell ClaydonKent145981168.88pace2016
Matt QuinnEssex126871468.69pace2016
George EdwardsLancashire87551069.49pace2016
Wahab RiazPeshawar Zalmi6372965.25pace2017
Chris JordanSussex6167965.46pace2016
Mohit SharmaKings XI Punjab143931469.23pace2016
Andrew TyeGloucestershire128801369.60pace2016
Umesh YadavKolkata Knight Riders8252969.46pace2016
Chris RushworthDurham100661269.09pace2016
Ben LaughlinAdelaide Strikers3736566.17pace2017
Paul CoughlinDurham61531166.91pace2016
David GriffithsKent139941368.87pace2016
Jade DernbachSurrey (England)6967656.18pace2016
Ashok DindaRising Pune Supergiants7752958.88pace2016
Rory KleinveldtNorthamptonshire96711058.11pace2016
Mohammed ShamiDelhi Daredevils6246858.09pace2016
Yuzvendra ChahalRoyal Challengers Bangalore69431349.63spin2016
Anwar AliQuetta Gladiators8256948.79pace2017
Sandeep SharmaKings XI Punjab100661449.09pace2016
Jasprit BumrahMumbai Indians136991448.24pace2016
Andrew CarterDerbyshire8455849.16pace2016
Michael HoganGlamorgan (Wales)45481345.63pace2016
Kesrick WilliamsWest Indies4336447.17pace2017
Ben DwarshuisSydney Sixers6043448.37pace2016
Tymal MillsQuetta Gladiators3240544.80pace2017
Usman ArshadDurham137921448.93pace2016
David WilleyYorkshire5244847.09pace2016
Bhuvneshwar KumarSunrisers Hyderabad1661111748.97pace2016
Mark SteketeeBrisbane Heat4242646.00pace2017
Ravi AshwinRising Pune Supergiants64601446.40spin2016
Ben HilfenhausMelbourne Stars6052836.92pace2017
Keaton JenningsDurham64431538.93pace2016
Jasprit BumrahIndia4143535.72pace2016
Murugan AshwinRising Pune Supergiants74521038.54spin2016
Shane WatsonSydney Thunder3937536.32pace2017
Zaheer KhanDelhi Daredevils110671239.85pace2016
Lewis GregorySomerset67641136.28pace2016
Sunil NarineLahore Qalandars5537838.92spin2017
Tom CurranSurrey (England)83661337.55pace2016
Imran TahirNottinghamshire6644639.00spin2016
Wahab RiazPakistan60364310.00pace2017
Oliver Hannon-DalbyWarwickshire120801339.00pace2016
Chris JordanRoyal Challengers Bangalore7246939.39pace2016
Dhawal KulkarniGujarat Lions88611438.66pace2016
Tymal MillsEngland4538337.11pace2017
Hasan AliPeshawar Zalmi86641038.06pace2017
Jake BallNottinghamshire83571038.74pace2016
Mohammad SamiIslamabad United7057937.37pace2017
Barinder SranSunrisers Hyderabad60471437.66pace2016
Tim BresnanYorkshire93641438.72pace2016
Sohail KhanKarachi Kings7546929.78pace2017
Mohammad NawazQuetta Gladiators76491029.31spin2017
Shiv ThakorDerbyshire69531127.81pace2016
Nuwan KulasekaraSri Lanka4237726.81pace2017
Clinton McKayLeicestershire50561225.36pace2016
Piyush ChawlaKolkata Knight Riders47381127.42spin2016
Jordan ClarkLancashire74491029.06pace2016
Tino BestHampshire5537928.92pace2016
Dwayne BravoSurrey (England)5641628.20pace2016
Andrew TyePerth Scorchers5939729.08pace2017
Richard GleesonNorthamptonshire61531026.91pace2016
Sunil NarineKolkata Knight Riders63471128.04spin2016
Tim SoutheeMumbai Indians57361129.50pace2016
Johan BothaSydney Sixers4636627.67spin2017
Matt HenryWorcestershire65461018.48pace2016
Ravi BoparaEssex99741518.03pace2016
Mohammad AamerKarachi Kings75511018.82pace2017
Jeetan PatelWarwickshire49361218.17spin2016
AzharullahNorthamptonshire72571207.58pace2016
Wahab RiazEssex5036508.33pace2016

 

Shane Watson, the interim captain for RCB in IPL 2017, was their go-to death bowler last season; his strike-rate was good, but economy was higher than the average. Jamie Overton, who has been on the cusp of an England career for a while, took death wickets for Somerset last year and kept his economy under 7/over, which isn’t approaching deadly-assassin status, but something close to it.

 

Chris Jordan, however, struggled in his the 2016 IPL (economy approaching 10/over) before following it up with a tighter performance for his home team Sussex a few months later. The green, green grass of home.

 

Try sorting the table and looking at who might pop up when we review the IPL mid-way through the tournament.

Say Elo to the IPL

The IPL, about to enter its 10th season, pits teams that are somewhat evenly matched and compete in more or less homogenous conditions.  This coupled with player drafts every three years should result in a competitive and exciting tournament every season.  Six different winners in nine seasons suggests this has largely been the case.  Imran Khan looks deeper into how certain teams have performed throughout their history by considering their Elo rating – a system that evaluates teams purely on their results.

Elo ratings introduction

A team’s Elo rating indicates its relative strength compared to other teams.  When a team wins a game it gets transferred a certain number of points from the other team i.e. the total points for both teams stays the same.  This ensures the average across all the teams stays roughly constant.  Additionally, a stronger team will gain fewer points when beating a weaker team than the other way around.  At the very beginning, every team is assumed to be of equal strength so start off with the same Elo rating (in our case it will be 1500).

Let’s say we have a match between Team A and Team B.  Team A is stronger and has an Elo rating of 1600 compared to 1400 for Team B.  The probabilities of the teams winning can be calculated to be 76% and 24% respectively (assuming there are no ties).  If Team A does indeed win, their Elo rating will go up to 1604 and Team B down to 1396.  However, if Team B causes a small upset and wins its Elo rating will go up 11 points to 1411 while Team A goes down to 1589.  The intuition is that we would like to reward underdogs more for winning than we reward favourites for winning.  Note that the total number of points is the same before and after the game.

A brief history of the IPL

Of the 584 scheduled IPL matches to date, there have been 568 outright winners, 6 ties and 10 no results.  I have counted the winners of the super over after a tie as the overall winner and given half a win each to the teams involved in no results.  The plot below shows the Elo ratings for each of the 13 IPL teams to have existed over the course of every season.

Although this looks quite cluttered at first, we can distinguish some general trends.  The league was fairly closely contested in the first four seasons with 2010 the most tightly packed.  No team reached an Elo rating of beyond 50 points from the average of 1500 in that season.  In fact, 4 points separated 6 teams in the final standings with even bottom-placed Kings XI Punjab taking points off both eventual finalists.  From 2012, things started to spread out a bit more as Chennai Super Kings dominated and some teams, in particular Delhi Daredevils, started to fall away.

Chennai Super Kings

The Super Kings, the most successful IPL team so far, won titles in 2010 and 2011, have a win rate of 61% and have made the playoffs in every season in which they participated.

During CSK’s first title win in 2010, the Elo ratings suggest they were far from the best team for much of the season.  They scraped into a playoff spot on net run rate.  They also won as many games as they lost in the group stages beating teams that eventually finished in the top 4 on only two occasions.  In seasons 2013-2015, the Elo ratings suggest that there was a significant gulf between them and most of the other teams.  The finished top of the group in 2013 and 2015 but ultimately stumbled at the playoff stage.  What the graph above illustrates quite well is their performance peaking in the middle of each of the 2013-2015 seasons as opposed to at the end in 2010/2011.

CSK enjoyed the highest Elo rating of all time when they beat Kolkata Knight Riders by 2 runs in the middle of the 2015 season to take their rating up to 1609.  We can conceivably say that this CSK team was the best of all time during that period.  However, it was not to continue having staggered through the latter half of the 2015 season and losing in the final to the Mumbai Indians.

Daredevils and Warriors

In contrast, the Daredevils have been in a steady decline since 2010 after finishing 4th, 3rd and 5th in the first three seasons.  Apart from in 2012 where they exhibited a brief resurgence, Delhi have finished at or near the bottom in every season since.

Their lowest point occurred at the beginning of the 2015 season.  However, this was not the lowest of all time; the dubious honour being claimed by the now defunct Pune Warriors India.

The Warriors finished in the bottom two in each season of their fleeting existence winning only 27% of their games.  A run of 9 consecutive defeats culminated in an Elo rating trough of 1378 points when they lost to the Mumbai Indians near the end of the 2013 season.

Biggest upsets

We can use the Elo ratings to compare the relative strengths of teams before each match.  Of the 570 matches in which there was a result, 55.4% of favourites, according to Elo, won the game.  If a team with a lower rating beat a team with a higher rating, we can quantify how much of an upset this was.

The table above shows the ten biggest upsets defined by the difference between the ratings of the two teams.  The top match on the list was a surprise in more ways than one.  Chennai were coming off the back of their peak Elo rating and Delhi were near the bottom of theirs.  In that match, Delhi restricted CSK to just a run a ball and knocked them off with ease.  The Daredevils also feature in a further six of the matches in that list highlighting how, in the last few seasons, any victory was a seen as a shock.

Biggest contests

We can also consider which games were of the highest quality defined by the sum of the Elo ratings for both teams.

According to the system, the final of the 2015 season between Mumbai and Chennai was the highest rated match of all.  Both teams had quite similar ratings.  But as the graph shows below, Mumbai were the form team going into the final while Chennai were riding on their early-season performances.  It’s probably no surprise that Mumbai won by a pretty hefty margin.

The Mumbai/Chennai rivalry takes up eight of the ten slots in the list.  Mumbai follow a similar pattern to Chennai in terms of the Elo ratings although they are slightly out of phase.  They tend to get off the mark slowly at the start of the season then surge to peak near the end.  This is most noticeable in seasons 2008 and 2013-2015.

Improvements to Elo

The Elo ratings are based on a simple concept – a team is credited for winning and penalised for losing, while underdogs are credited more for winning etc.  The ratings can be refined by considering home advantage, major team changes after auctions for example and the margin of victory.  We can also dynamically change the importance of certain matches. For example, playoff matches may offer greater payoffs in Elo points while a team’s matches from several seasons ago can be discounted in value.

Imran Khan, @cricketsavant

Tigers find their bite in Colombo

You may be familiar with the CLR James epigram which features in the preface of Beyond A Boundary: “What do they know of cricket who only cricket know?”

I often find a total break from the game – in my case a week covering horse racing’s Cheltenham Festival – gives me renewed energy to enjoy cricket, and a hunger to delve into the unique CricViz stats that underpin a particular match.

In this case, I am drawn inexorably to Bangladesh’s maiden Test victory over Sri Lanka. Put in the shade as it invariably will be by the titanic continuing struggle between India and Australia (1-1 heading to a Dharamsala decider, by the way) I certainly feel it deserves some extra attention.

First of all, there is the sheer landmark nature of this result. In their 100th Test, this was only Bangladesh’s ninth win and their first away from home against a team other than Zimbabwe or West Indies. It also comes five months after their Dhaka win against England which followed a winless year in 2015.

Then there was the less than ideal background to the win: a heavy defeat in the first Test, serious pressure and rumours of an impending axing for the captain Mushfiqur Rahim, an injury to the wicketkeeper Liton Das and three other players dropped after the Galle setback.

WinViz suggests it was the Shakib innings that turned Bangladesh from underdogs to favourites

And finally there was the troubling scorecard late on day two in Colombo: Bangladesh up against it at 198-5 on day two, some 140 runs behind. WinViz had a Sri Lanka win at 62% with Bangladesh at 27%: not a hopeless position for the tourists but an unencouraging one.

It was at this stage that Shakib Al Hasan crafted one of the most important centuries of his career. A naturally exuberant player (his strike rate exceeds that of all top current batsmen other than David Warner) he elected to curb his instincts to some degree but still scored at a healthy rate.

He watched the ball onto the bat well: only playing and missing five times from 159 balls faced while producing only two outside edges. When attacking, he timed the ball well – indeed our analysis shows he mistimed just two shots, the second of which finally brought his dismissal on 116, an innings which turned a probable Bangladesh deficit on first innings into a very valuable lead of 129.

WinViz suggests it was the Shakib innings, alongside valuable contributions from Mushfiqur and Mossadek Hossain, which turned Bangladesh from underdogs to favourites. But sometimes the hardest thing in a Test match is to reinforce a dominant position, or to get the job done when you hold all the aces – particularly if you’re a team without much experience of winning.

The next part of the job was carried out by Bangladesh’s bowlers, led by the hugely exciting fast bowler Mustafizur Rahman, backed up admirably by Shakib’s resourceful slow left-arm stuff.

There have been plenty of false dawns for Bangladesh fans in the past, but the rare successes are worth cheering

 

The key period came just after lunch on day four when these two bowlers operated in tandem and the draw had moved in excess of 50% probability on WinViz. Sri Lanka were 137-1, nudging into an overall lead – but suddenly Bangladesh found their bite.

The first breach came when Mustafizur had Kusal Mendis caught behind with a delightful delivery. It was the last ball of the over, and at 79.8mph it was the fastest too. Pitching on a fairly full, almost half-volley length – 5.9m from the stumps – it induced the drive.

All six balls in the over offered to swing away from the right-hander. But unlike two previous balls, which had carried on with the angle after hitting the wicket, this one straightened just enough (moving 0.9° degrees away from Mendis) to take the outside edge. Mushfiqur, the stand-in keeper as well as captain, gleefully accepted the chance.

Five overs went without a wicket before Mustafizur struck again. Continuing with a full length, he had Dinesh Chandimal fishing well wide of off-stump and nicking off. This was not per se a brilliant delivery, but an intelligent one, the sort with which Ian Botham used to take countless wickets. A tempting outswinger sometimes looks like it’s there to be hit. But Chandimal had not been at the crease long enough to play a relatively risky cover-drive and paid the price.

It was Shakib’s turn to get involved next: Asela Gunaratne lbw padding up for just seven. A misjudgement for sure, but again the bowler’s skills played their part: this ball drifted a fair bit, 2.5° into the right-hander who felt that on his initial observation he could afford to let this one bounce and turn away from him. The thing is the extra drift meant the ball was arrowing into the stumps and relatively modest turn away (2.6°, around half of the previous ball’s turn) meant it was straight enough to be hitting.

PlayViz recorded a -38 fielding score for the hosts

Shakib’s next wicket soon followed, a dismissal that reduced Sri Lanka to 190-5 (effectively 61-5). Bowling with a lovely rhythm, Shakib was getting some deliveries to turn really quite sharply, others to skid on without any turn at all. At times like these, batsmen often believe their best bet is to premeditate, and invariably out comes the sweep shot.

Niroshan Dickwella played one such sweep to a ball that turned a lot (6.6° in fact, putting it in the top dozen of Shakib turners for the innings). Also, the length was short of ideal length for sweeping, so the ball had time to turn and bounce before Dickwella’s bat made contact with the ball. Mushfiqur, who had a fine game behind the stumps, anticipated everything smartly to move across to complete the catch.

Sri Lanka fought on. The ninth wicket put on 80 to leave Bangladesh some kind of challenge, namely a target of 191. However the Tigers would not be denied and man-of-the-match Tamim Iqbal hit 82 (Shakib arguably had stronger claims to that individual gong). A memorable victory was achieved with four wickets in hand.

A final footnote: Sri Lanka have been poor in the field for much of the past six months and PlayViz recorded a -38 score for the hosts against a +52 aggregate for Bangladesh. The differential in batting was even more stark at +141 for the winning side (who had the clear disadvantage of batting second) against Sri Lanka’s -21. These indicators are very welcome for Bangladesh going forward while Sri Lanka’s side, still in transition following the retirements of so many key players of late, could find more roadblocks in their path.