Anantha Narayanan

Sri Lanka: 257 all out. England: 191/1 and 278/2. What are we looking at? The consensus is that England would get a lead of around 400-plus runs and win the match comfortably. We all know what happened. Despite England's fightback on the last day and last hour, Sri Lanka essayed one of their greatest wins. Where does this match stand in the echelons of coming-from-behind wins? Is it better than Australia's Kingsmead win (1950) or England's win at Headingley (1981) or India's Calcutta miracle (2001) or Australia's win at the SCG (2010) et al?

Suddenly I notice an opportunity to pen an anecdotal article, a nice change from the recent heavy analyses. But this cannot be done by memory and recall. I, for that matter anyone, would certainly forget some important match or other. I have to back up my selections with some sound analysis. Hence this easy-to-read article on the greatest coming-from-behind wins in Test matches.

When I started doing this, I understood that Test cricket is so nuanced that each innings, from second onwards, has its own set of parameters to be considered. The dynamics vary so much that each innings must be evaluated independently.

Let us first get the first innings out of the way. Nothing can be deduced during and after the first innings. A dismissal for 45 does not mean the end of the world, nor does a score of 445 indicate everything is hunky dory. Incidentally, the former match was won and the latter was lost. After all there have been 28 Tests in which sub-150 first innings have been matched by sub-150 scores and 33 matches in which 500-plus scores have been matched by 500-plus scores.

The third innings is radically different to the second innings. The situation faced by New Zealand during the Wellington Test this year is quite different to the situation faced by Sri Lanka against England at Headingley recently and the situation faced by Australia against South Africa at Kingsmead during 1950. So the third and fourth innings will be covered in a later article.

The second innings is very relevant. At the end of the second innings the match is at or past the halfway stage and the match situation becomes clear. At any time during the second innings, the situation for either team could vary significantly and we should look at the match status in an objective form. Hence I developed the MSI (Match Situation Index) before or after the fall of a wicket and see whether the poorly placed teams go on to win.

There are two types of situations in the second-innings analysis. The first is similar to the Headingley Test. The first batting team gets out for a low-to-middling score and the second batting team is looking at a massive first-innings lead. Miracles happen and the first batting team, through the combined efforts of its bowlers and batsmen, wins. The other is similar to the 1950 Kingsmead Test. The second batting team is batting poorly and is looking at a huge deficit. This deficit might even be conceded but the second batting team goes on to win. These have to be looked at separately. First let us look at wins by teams batting first.

Wins by teams batting first

When we look at the MSI in these cases, there is a subtle but important point to be taken care of. At Headingley, England lost the second wicket at 191. But the situation was much worse for Sri Lanka when England was at 191 for 1 (just before the wicket-ball was bowled) than at the fall of the second wicket. Hence here the situation is evaluated before the delivery of the ball which resulted in the wicket.

The other practical point that is to be taken care of is the projection. I use the "resource utilised" at the fall of each wicket, a measure jointly developed by Milind and me by studying the fall of wickets in all 2100 matches. At the fall of the first wicket this stands at 0.121. Thus a score of 125 for 1 will lead to a projection of over 1000 runs, which is capped to get practical scores. In these cases of highly unlikely projections, I tweak the projection by guesstimating a declaration scenario with a maximum lead of 500 runs, duly adjusted by wickets available. This is common-sense-based approach.

The MSI determination is simple. It is determined by the following formula.

                First_Inns_Score
100 x -----------------------------------------
      First_Inns_Score + Second_Inns_Projection

The reason why I have used the team scores, rather than the deficit, to determine the MSI is to differentiate between the two cases presented below. A score of 250 is responded with 50 in the first match. The second is one in which a score of 450 is countered with 250. The lead is 200 in both cases. The 200 deficit in the former Test is a much bigger mountain to climb, because of the team's own low score of 50, than the 200 deficit in the latter Test. In the current method, the trailing team in the former Test will have an MSI of 16.7% and the team in the latter Test, an MSI of 28.6%: a fairly accurate depiction of the match situations.

Given below are two examples from the Headingley Test, one with the possible declaration scenario and the other with an all-out scenario.

Facing the Sri Lankan total of 257, England lost the third wicket at 278. Just before this delivery was bowled, England were at 278 for 2. This translates to a theoretical projection of over 1000(278/0.248). Hence it is projected that there would be a declaration with a lead of 400 (based on a maximum lead, adjusted by the wickets available). So the MSI for Sri Lanka is 257/(257+657), which works out to 28.1%. Quite strongly in favour of England, but not as desperate a situation as one with 20% MSI.

When England were at 344 for 7, about to lose the eighth wicket, the situation had changed considerably. The projection is only 344/0.833, which works to 412. This is retained since it is a very realistic projection and the MSI works to 38.4%, which is a considerable improvement over earlier MSI values. The lowest MSI value for Sri Lanka was 26.7% when England was lording it over at 191 for 1. At the end of the innings, the MSI had improved to 41.3%.

But I can now reveal that this was nowhere near the desperate situation I initially thought it would be. There are many other match situations with much lower first-innings scores that have MSI values well below 20%. The SCG classic win, inspired by Michael Hussey, was resurrected by Australia from a much worse situation. It must be remembered that a really low first-innings score also indicates a sub-par pitch and any 100-plus lead would be almost insurmountable. Hence scores of around 250 in the first innings, on normal pitches, are not bad.

Let us now look at the table and then move on to the summarised potted scores of the Tests. Needless to say, in one match, the team could have low MSI values more than once but the worst situation is selected.

* indicates that an adjusted projection has been done based on an estimated lead and possible declaration.

The first Test featured here was a low-scoring one in which the very low MSI of 14.9% is primarily due to the very low first innings of 45. The next one is still vividly in memory. It is known for the Mike Hussey masterpiece. Barring this one, almost all are from 1990 and earlier. Of special interest to the subcontinent is the one in the sixth place. That is Sunil Gavaskar's last Test, featuring the famous all-time classic innings of 96. Pakistan won a similar Test against West Indies a few days later.

Let us now see the potted scores of the featured. Since the MSI situation is clearly depicted, I will only give a brief summary of the match.

Test #25. Australia vs England.

On 28,29,31 January 1887 at Sydney Cricket Ground.
Eng:  45 all out (won by 13 runs)
Aus: 119 all out  (at 64/2, MSI of Eng was 14.9%)
Eng: 184 all out    Aus:  97 all out

There was no notable batting performance in the low-scoring Test. Billy Barnes secured England's narrow win with last-innings figures of 6 for 28.

Test #1945. Australia vs Pakistan.

On 3,4,5,6 January 2010 at Sydney Cricket Ground.
Aus: 127 all out (won by 36 runs)
Pak: 333 all out (at 109/0, MSI of Aus was 16.8%)
Aus: 381 all out    Pak: 139 all out

Who can forget this recent classic fightback by Australia? Facing a poor Australian total of 127, Pakistan were comfortably placed right through their innings and finished with a lead of 206. Then Australia, inspired by one of the best innings played at the SCG, by Hussey, set Pakistan 176 to win. Nathan Hauritz and Mitchell Johnson spearheaded a memorable victory for Australia.

Test #169. South Africa vs England.

On 31 Dec 1927,2,3,4 Jan 1928 at Newlands, Cape Town.
Eng: 133 all out (won by 87 runs)
Saf: 250 all out (at 72/1, MSI of Eng was 18.6%)
Eng: 428 all out    Saf: 224 all out

After conceding a sizable lead of 117, England batted purposefully with the top three batsmen exceeding 85 runs. Bob Wyatt chipped in with 91 and the task of over 300 proved too much for the home team. The wickets were shared.

Test #100. Australia vs England.

On 21,22,24,25,26,27 Feb 1908 at Sydney Cricket Ground.
Aus: 137 all out (won by 49 runs)
Eng: 281 all out (at 135/1, MSI of Aus was 18.9%)
Aus: 422 all out   Eng: 229 all out

This match is an almost perfect replica of the previously referred one. The same description would hold good. The batting hero was the great Victor Trumper, with the famous 166, and the bowling kingpin was Jack Saunders, with 5 for 82.

Test #36. Australia vs England.

On 29,30 Jan, 1,2,3 Feb 1892 at Sydney Cricket Ground.
Aus: 144 all out (won by 72 runs)
Eng: 307 all out (at 79/1, MSI of Aus was 19.5%)
Aus: 391 all out    Eng: 156 all out

This match also followed a similar pattern. A big first-innings lead of over 150 was nullified by Alec Bannerman's 91 and John Lyons' 134. George Giffen and Charlie Turner were unplayable on the last day.

Test #1073. India vs Pakistan.

On 13,14,15,17 Mar 1987 at Chinnaswamy Stadium, Bangalore.
Pak: 116 all out (won by 16 runs)
Ind: 145 all out (at 56/1, MSI of Pak was 20.1%)
Pak: 249 all out    Ind: 204 all out

We came back to recent times with this match. Maninder Singh destroyed Pakistan with a spell of 7 for 27. Then India collapsed from 56 for 1 to 145. Pakistan set India a fair target with almost all batsmen contributing. Then came the greatest swan song of all times. Gavaskar's farewell innings of 96 was not enough to prevent Pakistan winning narrowly. Shades of the Chennai Test a few years later. Iqbal Qasim and Tauseef Ahmed shared almost all the Indian wickets.

Test #483. India vs Australia.

On 19,20,21,23,24 December 1959 at Green Park, Kanpur.
Ind: 152 all out (won by 119 runs)
Aus: 219 all out (at 149/2, MSI of Ind was 20.2%)
Ind: 291 all out
Aus: 105 all out

A very famous Indian win, orchestrated by Jasubhai Patel's 14 wickets in the match. Nari Contractor, Ramnath Kenny and Bapu Nadkarni helped India reach a good total in the third innings. Then Patel and Polly Umrigar destroyed Australia for 105. One could say that with this win Indian cricket came of age.

Test #840. Australia vs England.

On 6,7,8,10,11 January 1979 at Sydney Cricket Ground.
Eng: 152 all out (won by 93 runs)
Aus: 294 all out (at 126/1, MSI of Eng was 20.2%)
Eng: 346 all out    Aus: 111 all out

England conceded a first-innings lead of nearly 150 runs. Then Derek Randall played one of his two great innings against Australia. His 150 helped England set a fair target. John Emburey and Geoff Miller administered the last rites.

Test #659. India vs New Zealand.

On 25,26,27,28,30 Sep 1969 at Brabourne Stadium, Mumbai.
Ind: 156 all out (won by 60 runs)
Nzl: 229 all out (at 78/1, MSI of Ind was 20.5%)
Ind: 260 all out    Nzl: 127 all out

This match has a lot of similarities with the Kanpur match. India conceded a lead of 70 runs. MAK Pataudi's polished 67 and a clutch of useful scores set New Zealand an easy target, which proved too much for them thanks to the wonderful pair of Bishan Bedi and Erapalli Prasanna.

Test #1055. Pakistan vs West Indies.

On 24,26,27,28,29 Oct 1986 at Iqbal Stadium, Faisalabad.
Pak: 159 all out (won by 186 runs)
Win: 248 all out (at 103/1, MSI of Pak was 20.7%)
Pak: 328 all out    Win:  53 all out

This match followed the, by now routine, scenario of good leads and a substantial third innings. Then the script changed. West Indies were dismissed for 53 by Imran Khan and Abdul Qadir. The unlikely batting line-up for this score: Gordon Greenidge, Desmond Haynes, Richie Richardson, Larry Gomes, Viv Richards and Jeff Dujon.

Wins by teams batting second

The analysis for wins achieved from desperate situations by teams batting second is more clear-cut. The first batting team has posted a good score and the second batting team is in a desperate situation with fall of many wickets. The win is achieved from this situation. It could very well be that this team concedes a huge first-innings lead and wins, or there is a batting recovery in this innings itself.

The calculations in this case are simpler. The projection works straightaway and there is no estimate of leads and declarations. The MSI formula is the same. Also the situation is analysed in this segment, immediately after the fall of the wicket. The MSI determination is simple. It is determined by the following formula.

                Second_Inns_Projection
100 x -----------------------------------------
      First_Inns_Score + Second_Inns_Projection

For example I will take a recent famous coming-from-behind win by England at Edgbaston against New Zealand in 1999. New Zealand posted a below-par score of 226 in the first innings. England went to pieces and slumped to 45 for 7. The projection at this point was a very low 53 (45/0.833). The MSI was an equally low 19.0% (53/(53+226)). They recovered a little and despite a first-innings deficit of 100 went on to win the Test easily.

Now a look at the table, followed by the potted scores.

As expected, the Test #320 leads by a country mile. Australia's MSI was a measly 15% at 47 for 6. It is of interest to note that barring the first two wickets, Australia's MSI was always below 20%. The next two Tests are of recent vintage. The famous Kolkata 2001 Test and the Bridgetown classic are featured next. There is even a match featuring Australia and Bangladesh. Interestingly it was Australia who were in dire straits. The recent Newlands masterpiece (remember Australia 21 for 9?) rounds off the ten exhilarating Tests.

Since one very famous win is missing from this top-ten featured list, let me give the numbers for that. This was the 1981 Headingley classic. Leaving the third innings out, England's worst situation was when they were 87 for 5, facing 401. This leads to a projection of 136. The MSI works out to 25.3%, clearly outside the top ten. This also illustrates that MSI values below 20% are exceptional.

Test #320. South Africa vs Australia.

On 20,21,23,24 January 1950 at Kingsmead, Durban.
Saf: 311 all out
Aus:  75 all out (won by 5 wickets)
  (at 46/7, Aus had a low MSI of 15.0%)
Saf:  99 all out    Aus: 336 for 5 wkt(s)

Most people only talk of Kolkata and Headingley. This Test, played over 60 years earlier, should be an eye-opener for all. Look at the score. Saf:311, Aus:46/7. The MSI was at an all-time low 15%. Australia kept sliding and were eventually dismissed for 75. A deficit of 235. The situation must have looked like climbing the Table Mountain blind-folded, for Australia. Then the Johnson-Johnston pair went to town and dismissed South Africa for 99. It was still very much South Africa's game. Neil Harvey essayed one of the finest fourth-innings efforts ever and his 151* took Australia to a memorable win. Pushed against the wall, I would put this as the greatest comeback win ever.

Test #1455. England vs New Zealand.

On 1,2,3 July 1999 at Edgbaston, Birmingham.
Nzl: 226 all out
Eng: 126 all out (won by 7 wickets)
  (at 40/6, Eng had a low MSI of 19.0%)
Nzl: 107 all out    Eng: 211 for 3 wkt(s)

Forty-five for 7 against 226 must have looked like curtains for England. They limped to 126. Then Andy Caddick and Alan Mullaly dismissed New Zealand for 107. England won quite comfortably thanks to an amazing contribution from their nightwatchman, Alex Tudor. He added an unbeaten 99 to his first innings effort of 32*.

Test #1453. West Indies vs Australia.

On 26,27,28,29,30 Mar 1999 at Kensington Oval, Bridgetown.
Aus: 490 all out
Win: 329 all out (won by 1 wicket)
  (at 64/4, Win had a low MSI of 20.1%)
Aus: 146 all out    Win: 311 for 9 wkt(s)

This was similar to the Kingsmead Test, although the scores were higher. But the talk here is not on Brian Lara's fourth-innings masterpiece. We should not forget that West Indies were struggling at 64 for 4 in response to 490. The MSI was a low 20.1%. They recovered to post a good total and then Courtney Walsh, with the ball and Lara, with the bat, rewrote history.

Test #1535. India vs Australia.

On 11,12,13,14,15 March 2001 at Eden Gardens, Kolkata.
Aus: 445 all out
India       : 171 all out (won by 171 runs)
  (at 97/7, Ind had a low MSI of 20.7%)
Ind: 657 for 7 wkt(s)    Aus: 212 all out

Everyone talks about VVS Laxman's 281, Rahul Dravid's 180 and Harbhajan Singh's bowling. They tend to forget that India were deep down in the dumps at 97 for 7, 113 for 8 and 129 for 9 in the first innings, in response to 445. They recovered mainly through Laxman's wonderful 59. Steve Waugh enforced follow-on and the rest, as everyone knows, was history.

Test #1503. England vs West Indies.

On 29,30 June, 1 July 2000 at Lord's, London.
Win: 267 all out
Eng: 134 all out (won by 2 wickets)
  (at 37/4, Eng had a low MSI of 21.0%)
Win:  54 all out    Eng: 191 for 8 wkt(s)

West Indies posted a good first-innings total and England were staring at the abyss at 37 for 4. They recovered to reach at least the halfway mark of 134. Then West Indies had one of their inexplicable meltdowns and were all out for 54. They were destroyed by Caddick's 5 for 16. England struggled to reach the target and this was achieved mainly through Dominic Cork's innovative 33. It was an extremely competitive Test.

Test #1797. Bangladesh vs Australia.

On 9,10,11,12,13 Apr 2006 at Narayanganj Stadium, Fatullah.
Bng: 427 all out
Aus: 269 all out (won by 3 wickets)
  (at 61/4, Aus had a low MSI of 21.5%)
Bng: 148 all out    Aus: 307 for 7 wkt(s)

Yes, these scores are true. It was not Bangladesh, but Australia who were in the direst of straits. 61 for 4, 79 for 5 and 93 for 6, in response to 427 must have raised visions of a disastrous loss. Then Adam Gilchrist played arguably his most valuable Test innings and took Australia to a decent score of 269. He scored the 144 out of 208 added while at crease. Then Shane Warne and Jason Gillespie dismissed Bangladesh for 148. Still 307 was a tough target. This time it was Ricky Ponting's turn to take Australia to an unlikely win.

Test #88. South Africa vs England.

On 2,3,4 January 1906 at Old Wanderers, Johannesburg.
Eng: 184 all out
Saf:  91 all out (won by 1 wicket)
  (at 44/7, Saf had a low MSI of 22.0%)
Eng: 190 all out    Saf: 287 for 9 wkt(s)

Forty-four for 7, even against 184, was a very poor situation. South Africa reached only 91. Then dismissed England for a low score. Still 287 looked insurmountable. White scored 81. But the innings of the match was Dave Nourse's unbeaten 93, coming in at No. 8. He added 48 for the last wicket and South Africa won a close match. This innings was very highly ranked in the Wisden 100 tables.

Test #68. Australia vs England.

On 14,15,17,18 February 1902 at Sydney Cricket Ground.
Eng: 317 all out
Aus: 299 all out (won by 7 wickets)
  (at 48/4, Aus had a low MSI of 22.5%)
Eng:  99 all out    Aus: 121 for 3 wkt(s)

The key in this match was the second innings, played by Australia. From 48 for 4, they recovered to 299, thanks to contributions from all six batsmen batting at positions 5 to 10. They conceded a lead of only 18 and the rest of the match followed the usual script. Saunders and Monty Noble bowled unchanged for 48 overs in England's second innings.

Test #1673. Australia vs India.

On 12,13,14,15,16 December 2003 at Adelaide Oval.
Aus: 556 all out
Ind: 523 all out (won by 4 wickets)
  (at 85/4, Ind had a low MSI of 22.8%)
Aus: 196 all out    Ind: 233 for 6 wkt(s)

This is a recent Test match fresh in our memory. Ponting's 242 led Australia to a massive 556, destined to end as the highest losing first-innings ever. India were struggling at 85 for 4 and the MSI was only 22.8%. Then Dravid and Laxman, the Kolkata pair, this time with their roles reversed, added over 300 runs and got the deficit down to 33. Afterwards Ajit Agarkar had his day in the sun and Dravid anchored the Indian fourth innings for a memorable win.

Test #2016. South Africa vs Australia.

On 9,10,11 November 2011 at Newlands, Cape Town.
Aus: 284 all out
Saf:  96 all out (won by 8 wickets)
  (at 83/9, Saf had a low MSI of 23.2%)
Aus:  47 all out    Saf: 236 for 2 wkt(s)

This recent vintage is still plastered in everybody's memory. Eighty-three for 9, in response to 284, was even a follow-on possibility. South Africa was dismissed for 96. Then Vernon Philander bowled like Sydney Barnes and Australia, struggling at 21 for 9, reached 47. Graeme Smith and Hashim Amla helped themselves to a century each to lead South Africa to a comfortable win.

An interesting insight. The worst situation any team faced at the end of the second innings was in match #320. In reply to South Africa's 311, Australia were dismissed for 75. The MSI was a very low 19.4%. That means that the winning chances were lower than one-fifth. Of course this is only for teams that went on to win. In match #153, when England scored 438 and South Africa was dismissed for 30, their MSI was an abysmal 6.4%, the lowest ever, and they went on to lose the match by an innings. As recently as 2012, Zimbabwe's response to New Zealand's 495 was 51 and the MSI was 9.3% and they lost by about four innings and a few runs.

For information and use by interested readers, the resource utilised at the fall of each wicket is 0.121, 0.248, 0.384, 0.517, 0.637, 0.747, 0.833, 0.902, 0.957 and 1.0 respectively. The resource available is "1.0 - resource utilised". It is possible for readers to determine the MSI at any time during the second innings using these values.

We can safely conclude that the greatest comeback win of all time was achieved well over 60 years back. It was orchestrated by an elegant left-hander, one of the best ever. Like Rohan Kanhai in the consistency analysis, Harvey is a batsman who does not enter into any "great" discussions. But on that day at Kingsmead, he played one of the greatest fourth innings ever and took Australia to an extremely unlikely victory.

Let us move forward a few months and on to an imaginary scoreline.

SCG, March 26, 2015. World Cup semi-final. The scores - *********: 300. Australia: 55. Alternately, to have a shorter match, Australia: 54. *********: 55 for 1. This is what happened at Belo Horizonte last evening. In tennis parlance it was like a 0-6, 0-6, 1-6 scoreline in the 2015 Wimbledon semi-final between Andy Murray and another top-ten player. How else can one describe this astonishing match. In the history of the game has there been a better 30 minutes for a team like what Germany had at the beginning of the match. Nine shots on goal and five clinical finishes. And to think that this was the first match for which I stayed up almost all night. It is not often that we see trailing teams praying for the final whistle. What a contrast to the next semi-final?

There are a few cricket matches that come to mind. Only matches involving the top-eight Test teams have been considered.

The first was played during 1938 at The Oval. Eng: 903/7, Aus: 201 & 123.
Sharjah 2002 saw a scoreline of Pak: 59, Aus: 310 & Pak: 53.
Then Paarl during 2012. Saf: 301/8 & Slk: 43.
No less devastating was the 2000 ODI in Sharjah. Slk: 299/8 & Ind: 54.
Finally there was the Newlands T20 match. Slk: 101 & Aus: 102/0/10.2.

In my previous article I had analysed the consistency of Test batsmen, from the innings point of view. I received a number of good comments and a few excellent ideas were sent by the readers. The one idea which appealed to me most was by Santosh Sequeira who suggested that the Consistency analysis will have much better value if done using a single Test as the basis. Once I got out of my self-created mental block that 100 and 0 in a single Test represented inconsistency, this made a lot of sense.

I could see the following benefits accruing if Test Consistency was measured by Test, and not by innings.

- A Test is the logical unit of delivery for a player since the result is driven by a Test.
- There was a well-justified concern that many batsmen, even the very best, were short-changed in the innings-based analysis. A 100 and 0 represented two innings out of the consistency zone. This will disappear if these two innings have been played within a single Test.
- Top batsmen rarely have double failures in a Test. They make up for failures in one innings with a good innings in the other. The Test-based analysis recognises this characteristic.
- The results bear out this improvement since many top batsmen who were languishing in the lower half of the table of the select group of batsmen, have moved up considerably.
- The upper limit of the consistency zone was fairly low and this meant that many a good innings went out of the consistency zone. This is partly alleviated in the Test-based analysis.
- The impact of not-outs is fairly negligible. Since there are two innings to combine, I could adopt a different approach.

Let me reassure the readers that the following ideas that went into my to-do list are still active candidates for inclusion in future articles.

- Consistency analysis using the Median (Q2) value. This will be an assumption-free analysis.
- Batsman low-score analysis.
- Consistency value at batsman peak.
- Analysis of batsman troughs.

At the end of this article I will check whether there is good correlation between the innings-based analysis and Test-based analysis. If there is good correlation, we could work with either of the methods. If there are many variations, we have to peg our hat on either of the analysis methods. The criteria could be many. Why cross a bridge which is a few kilometres away?

I have devised a simple concept of "Batsman-active Tests". If a batsmen batted in either innings, I consider that Test as an active one for him. Else I do not include it. Don Bradman batted in 50 Tests only, and Sachin Tendulkar in 197 Tests. Just to give a specific example, when South Africa scored 637 for 2 at The Oval and won, AB de Villiers did not bat at all. So this Test is excluded from this analysis. On the other hand Alviro Peterson scored a duck and this is certainly an "active Test" for him. This is eminently fair, simple to understand and easy to work out.

If a batsman is not out at 10 in the only innings he played in a Test, well, these would even out across a career. It is the same policy for all batsmen. I briefly considered, and discarded, the method of taking a fraction of a Test, derived from the scores, in the denominator. Quite confusing, and just not worth it. An analysis of 39.42 Tests? No way.

The top position in the Test-based Consistency table is taken by Saeed Ahmed, the attacking Pakistani batsmen whose Test average of 40.41 belied his value to his team. Out of the 40 Tests he batted in, he was in the ConZone an amazing 25 times, leading to an outstanding index value of 62.5%: That is 5 out of 8 Tests. The other telling statistic is the fact that he failed to reach the ConZone in only ten Tests out of these 40. That is a very low failure rate of 25%. In 1965, he made an unforgettable 172, out of a score of 307 for 8, against New Zealand, saving Pakistan from possible defeat. That was his highest Test score.

Then come four more attacking batsmen: Rohan Kanhai, Roy Fredericks, Clive Lloyd and Doug Walters. There are three West Indians and one Australian. This re-emphasises my belief that the attacking batsmen are as likely to be as consistent as the staid batsmen. With their more aggressive attitude, they are more likely to be able to make up for failures in one innings with good showings in the other. All these batsmen have Consistency indices above 56%.

There are a number of lovely batsmen in the top-20. Norm O'Neill is in the top-10. He might not have been the "next Bradman" but was a terrific batsman. The under-rated Misbah-ul-Haq rounds off the top-10. We will look at Jonathan Trott later on. I am very happy to see John Edrich in 18th place. Frank Woolley was a classical left-hander who is deservedly in 13th position. They are both favourites of mine. The 20th ranked batsman is Len Hutton, possibly the best in this lot. He himself clocks in at 51.9%. There are 36 batsmen who have index values of 50% or higher. The last batsman featured is Dudley Nourse who was placed in first position in the other table.

Let us look at the table proppers. Marvan Atapattu takes possession of the 200th position. What makes Atapattu so inconsistent? He batted in 88 Tests. He reached the ConZone mark of 31-94 only 26 times. That is a meagre 29.5%: not even a third of his Tests. This index is less than a half of Saeed Ahmed, the table topper. And Atapattu's inconsistency is emphasised by the 40 Tests below the ConZone. A look at his series of scores indicates that there were 30 Tests in which he scored 20 runs or less, and 11 Tests in which he scored 150 or higher.

Ijaz Ahmad has been a revelation. He was 200th in the innings-based table and 198th in this one: a very firm indicator that he was the embodiment of inconsistency. His index is a very low 31.0%. The inscrutable Graeme Hick is in the last-5 with a low index value of 32.3%. Dennis Amiss also confirms that his inconsistency moves on from the innings level to Test level, with an index of 34%. He was 193rd in the earlier table and he has moved one place below to 194th in this one. Michael Clarke has moved away from 192nd to 167th, still way down the table, but at least some distance away from the bottom.

A couple of years back I did a two-part analysis on Test player consistency. You can access the batsmen-specific article here. You have to move to the top of the page to view the article. Overall, it was well received. The analysis was based on a "slice concept". I split the careers of Test batsmen into slices of ten innings and looked at consistency across these slices. As many readers had expressed therein, this went past the unit of innings, which is the most important measurable contribution of a batsman. It also allowed a batsman to be very inconsistent within a slice but come out with acceptable numbers for the slice.

I realised that I have to do the batsmen consistency work with innings as the base, not even a Test. Based on Tests, a batsman could come out roses in the consistency stakes by scoring a 100 and 0. Perfect for the Test but way off as far as innings are concerned.

Let me remind the readers that I will not do any article which is not understood by 90% of the readers. These articles may not come through the statistical validations test but have to be based on common sense and understood by most of the readers. So there will not be any Z-factors or skewness coefficients, or whatever else it is that statisticians look for. Do not look for these in this article and complain about the absence of the same.

First, let me say that the score distribution for almost all batsmen is skewed (note only a verb is used) to the left. An established batsman's lowest score is 0 and the highest score could be anything from, say, 200 to 400. His mean score is around 50. This means that he would have more scores below the mean than above. This is what I meant by being skewed to the left. For the selected population of 200 batsmen, the average percentage of scores above the mean is only 35%. The highest is for Bruce Mitchell with 44.9% and the lowest is for Marvan Atapattu with 29.1%. So this is way away from a normal distribution and we have to adopt special methods to analyse the scores.

What is consistency? OED says: The quality of achieving a level of performance that does not vary greatly in quality over time. DicCom says: Agreement or accordance with facts, form, or characteristics previously shown or stated. FreeDic says: Reliability or uniformity of successive results or events. So what we are looking at is uniformity of performance, absence of surprises, reduction in number of outliers and probably clustering of performances towards the central positions.

Taking a pair of scores, it is clear and obvious that a 100 and 0 is woefully inconsistent, an 85 and 15, quite inconsistent, a 70 and 30, reasonably consistent, a 60 and 40 quite consistent and a 50 and 50 the pinnacle of consistency. For this analysis it does not matter if the 100 was scored master-minding a successful 150 for 9 chase or part of a 700 for 3 score in Faisalabad. Let us see how we can move forward on this premise.

Let us assume that this is a three-Test series and the eight batsmen below have played five innings each. All these batsmen have scored 250 runs in the series and are averaging 50. Let us get a handle on their consistency by perusing the scores, rather than through any mathematical methods.

A;  25@  45@  50@   60@   70@  (5)
B:  10   45@  55@   65@   75@  (4)
C:  25@  30@  40@   55@  100   (4)
D:   5   30@  45@   75@   95   (3)
E:   0   10   40@   60@  140   (2)
F:   0   30@  40@   80   100   (2)
G:   5   10   20    50@  165   (1)
H:   0    0   10   110   130   (0)

A is the epitome of consistency and can be called Mr Consistent (with apologies to Michael Hussey, the original Mr C). No really low or high score.
B and C can be called very consistent. B has got one low score and C, one high score. The other four are in the consistency zone.
D is consistent. There are two outliers: one on each side. Three are in the zone.
E and F can be called somewhat inconsistent. Only two of the five scores are in the consistency zone, i.e. in the middle.
G is quite unpredictable. Four of his scores are outliers. Tough to expect what his next score would be.
H is so inconsistent that we have no clue what he will do. A duck or 100 might come off his bat next.

Even though I used only a visual inspection while determining the consistency levels of these batsmen, we are beginning to get a handle on what analytical method can be used to determine consistency of a batsman. The key phrase is "consistency zone", which I used couple of times in these sentences.

Let me make a brace of somewhat sweeping statements and justify these later.

Define a consistency zone for each batsman and check how many of his innings are within this zone. The higher the percentage of innings within the consistency zone, the more consistent the batsman was.

There is nothing intrinsically wrong with this statement. There is no attempt to define a consistency zone across batsmen. This postulate accepts that the basis for consistency determination for Don Bradman would be totally different to the same for Habibul Bashar. It is dynamic and will accommodate significant changes across the career of batsmen. It could be applicable to selected parts of a batsman's career. So we seem to be on a very nice wicket.

The only problem seems to be to define a valid consistency zone, hereafter called Con_Zone. There is no mathematical solution. If one exists, I would not understand it myself and cannot explain the same in simple words to the readers. So I have to use common sense and the cricketing knowledge acquired over the years.

The one point I am certain is that for this exercise, the batting average cannot be used as the basis. Especially when I am going to say that 400* or 257* are two of the greatest outliers ever, what is the point of adding these runs but not the innings played? I have to use a Runs per innings (RpI), but a slightly modified one, RpxI, after taking care of the next bone of contention, the not-outs. I will come to this later, after explaining the basis for Con_Zone.

After days of trials and evaluating aggregates of various measures, I have defined Con_Zone as the range of scores that falls between 50% of RpxI to 150% of RpxI. It is dynamic and varies according to the batsman's career performance. It gives me an exact RpxI width of scores, enough to give very high confidence level while proclaiming a batsman's consistency or lack of.

Three examples - Bradman's Con_Zone ranges between 44.4 and 133.3. Ken Barrington's Con_Zone ranges between 26.7 and 80.1. Habibul Bashar's between 15.3 to 45.8. While looking at these examples, do not forget that a 365 or 293 is as much of an outlier as a 0 or 1.

Now for the not-outs. My first article in the Cordon was called "The vexed question of not outs in Test cricket". Unfortunately, I could not view the comments and respond to those because of certain technical issues. But I knew that there were arguments for and against my suggestion of extending the not-out innings by his recent-form runs. A revolutionary idea it was but some of the respondents felt that there was really no problem and I was trying to solve a non-existent problem. They were probably correct. Some felt that the RpFI, described below, was an arbitrary number.

It is clear that the not-outs have to be addressed properly. Let us take Garry Sobers with a basic RpI value of around 50. His 178* or 365* are clear outliers and have to be considered as valid innings. His 50* has to be considered, as a perfect innings, along with his 50. His 33* is considered since this is within the Con_Zone. His 5 or 8 are clear outliers and cannot be ignored. But what about the 16*? It is not fair to Sobers if we take this innings as one falling outside the Con_Zone (25.6 to 75.7). He could have scored 34 more runs or 134 more. On the other hand we cannot certify that this falls within the Con_Zone. He could have been out next ball.

In the article I have referred to, I also developed an alternate and simpler concept of considering only fulfilled innings(FI). These are the not-outs above 50% of the RpI and all dismissals. It was an elegant and simple method.

Incidentally Milind has tackled the question of not-outs in his excellent blog, which takes cricket analysis to a higher level. He has tweaked the RpFI, which I had created for the said article and created a further adjusted RpI, called µ, by mapping all not-out innings based on their values. It is a lovely idea and the reader could get the complete information on this tweak and other fascinating analyses. Once you are there his earlier articles on Geometric Mean, Bradman's innings and the like can be viewed.

However, I have decided to stick to my RpFI concept since it is simpler and this is only a Batsman Consistency analysis. Like a perfect Lego block fitting, the beginning of the Con_Zone is pegged at 50% of the RpI value. So I have come to a (hopefully Solomonic and not Tughlaqian) decision for this analysis. I will ignore all not-outs that are below the low-end of the Con_Zone (50% of RpI). These will be excluded from the innings count, RpI determination and consistency determination.

I can hear those knives being sharpened. Before you take those off the scabbard, look at it carefully. No batsman loses out. Sobers' 16* would be outside the consistency calculations, that is all. He will neither benefit nor be hampered. No assumption of any sort has been made regarding his innings. There are no magic numbers. The RpI, if anything, will only be slightly boosted. So any reader who is offended by this, if he takes a minute to think laterally, will see the soundness behind this tweak. And let us not forget, it is uniform but customised and dynamic treatment for all batsmen.

The final justification. For the 200 batsman considered, there are 26,172 innings and of these the excluded special not-outs are just 642, a mere 2.4%. So there is a negligible impact on the numbers but a considerable improvement in the soundness of calculations.

The cut-off is 2684 runs. What? Such an odd number! Before anyone says that I have done this to exclude or include any specific player, let me say that my initial cut-off was 3000 Test runs. Two-thousand, I felt, was too low since only around 30-40 innings would have been played. Three-thousand meant that a reasonable number of innings, well over 50, would have been played.

However, when I did a run with 2500, I suddenly found out that a new batsman started dominating the tables. That was Dudley Nourse. His numbers were way out and I felt that his inclusion would set a benchmark for other batsmen and would validate the approach taken very effectively. But he had scored only 2960 runs. Hence I lowered the cut-off to 2950 Test runs. After all it is my analysis. Finally I decided that instead of having runs as cut-off, I would select the top 200 run scorers. So the population size determined the cut-off. Hence the number 2684. Mark Burgess was the last batsman to get in. In the bargain, Glenn Turner, MAK Pataudi, Norman O'Neill, Stan McCabe and Keith Miller got in. Not a bad lot to look at.

Let us move on to the tables. I have also plotted the graph for five interesting batsman to get a visual idea of how the Consistency Index works.

Most consistent batsmen: When readers peruse the tables they will realise why I was so enthused about Dudley Nourse. Let me present his career numbers. 62 innings. The mean score was 48.7 allowing the Con_Zone range of 24.4 to 73.1. This entire range is indicative of acceptable scores. Two scores, 17* and 19*, are ignored. Nourse has 31 scores in the Con_Zone. He is the only batsman to have more scores inside the Con_Zone than outside it. If this is not consistency, that too across 16 years, I am not sure what is. He has two double-hundreds but the next highest score is 149. That explains his excellent Con_Index.

Herbert Sutcliffe and Jack Hobbs are almost inseparable even in this analysis, as they were on the field. For Sutcliffe, two unbeaten innings, viz., 1* and 13*, are excluded. For Hobbs, four innings, viz., 9*, 11*, 19* and 23*, are removed. Otherwise, look at how close their numbers are. Very similar Con_Zone ranges (~20 to ~80). Con_Index coming at well above 42%. These are their individual numbers. How well they would have performed together. Right at the top, as far opening pairs are concerned.

Wally Hammond, who followed Hobbs and Sutcliffe, has similar figures. His Consistency Index is also well above 42%. The top 20 of the table features batsmen who have Consistency Index values above 40%. This includes some unlikely batsman. Who would have expected the flamboyant Kanhai to have a fairly high value of 40.6%. David Gower is another surprise 40+% batsman featured here. Sobers and Barrington are two top-level batsmen standing at just below 40%.

Contemporary batsmen: For all the problems he has faced recently, Trott is the most consistent of the contemporary batsmen. Thirty-six of his 86 qualifying innings are within the Con_Zone, giving him an index value of 41.9%. Watson might not have scored many hundreds but he is certainly high on the Consistency Index value table, with 41.2%. His Con_Zone range is, of course, lower at 18-53. He is expected to deliver at lower levels.

Since Watson and Trott have played fewer matches, AB de Villiers' lays claim to be the most consistent current batsman. This is borne out by his recent record-breaking form. His exclusions are 4*, 4*, 8*, 19* and 19*. He has 61 innings within the Con_Zone range of 25-79, out of 149 qualifying innings. This gives him a high Consistency Index of 40.9%. Any number above 35% is very good and anything above 40% is outstanding.

Strauss with 39.4% and Langer, with 38.2% are in the top-40.

Summary of a few top batsmen: Many top batsmen are not even in the top 50 of the table. Hence I have summarised the Consistency Index of a few top batsmen. Bradman is way down the table with a barely acceptable index of 30.8%. This is understandable since 15% of his innings are above 200 and there have to be compensating low scores.

Sachin Tendulkar's index value is a fairly low 31.2%, Brian Lara's is slightly better at 33%, Rahul Dravid at a relatively high 37.2%, Kumar Sangakkara is similarly placed at 36.8%, Ricky Ponting at a low index value of 32.9%, Jacques Kallis at a moderate 34.4%, and finally Sunil Gavaskar, at a very low 30.5%. To those who are surprised at the last figure, let me remind readers that Gavaskar was a poor starter and had 55 single-digit dismissals. And these have been balanced by 12 150-plus scores.

Now for the other end. The most interesting in this lot is Michael Clarke, with a really low index value of 26.7%. That means that just about one in four fulfilled innings have been within the Con_Zone range of 23 to 70. His exclusions are 6*, 14*, 17* and 21*. The fact that there are 16 other not-outs has also contributed to this. He has had 47 single-digit dismissals and ten 150-plus scores do not help.

Dennis Amiss, Clem Hill and Mansur Ali Khan are two prominent batsmen in this group. Let us look at the most inconsistent batsman amongst the selected 200 - Ijaz Ahmed. Look at the Consistency Index. It is a very low 20%, which means one in five innings are within the Cons_Zone of 18-54. He has only 18 innings in this group, out of a total of 90. Not surprising considering the fact that 33 innings, out of 90, a whopping 37%, are single-digit dismissals. No doubt compensated by 12 hundreds.

Now for a few graphs. The graphs are plotted in increasing order of scores. Only the fulfilled innings are plotted. Also the Con_Zone and mean are shown.

Like Tests, and unlike T20s, we do not have complete ball-by-ball data for all ODIs that have been played. The starting point is match #1443, the first match of the 1999 World Cup, played in England. We have the data available for all the World Cup matches and then there is a vacuum. For over 200 matches there is no data available. Then we have data available from match #1719 (2001). After that only a few matches are missing. At the final count we have the data available for 1745 matches out of 3489 played to date (20 May 2014). This works to a second decimal point above 50%.

Now we come to the players. Since only part data is available from 1999, many modern batsmen have incomplete data. However it is good that we have reasonable data for many great batsmen. The table below gives a complete idea of the data availability pattern for the top batsmen. This table is relevant because I decided to feature 13 batsmen in this article. In bold letters, let me proclaim that the data for all batsmen, barring none, is available in the huge Excel file, which can be downloaded. In fact that table is more complete than the featured tables since the cut-offs are much lower and even for these featured batsmen you will get additional data in that.

BBB data availability for top batsmen

No L Batsman         Team  Runs Balls BBD-Bls  & %  Feature

I would like readers to recall my first article on T20, published a few weeks back. In that article I had worked on the same over-group concept. I had mentioned that one-third of the total ball resource is normally utilised in each of the three over-groups. At that time I did not have the ball-by-ball data and this was a reasonable assumption. I was projecting that the final score would be around three times the score at the end of sixth over and 150% of the score at the end of the 15th over. In this analysis let us see whether that postulate can be verified and how far off I was. I am quite sure that my estimates that the scoring rate would drop off drastically during the overs 7 and 15 will take a big hit.

A bowler should earn his maiden today. Already we have amendments to the law that do not allow wides and no-balls to be exempt while looking at dot balls and maiden overs. I have simply extended this concept to byes and leg byes. When Dale Steyn bowled six balls to Virender Sehwag and Gautam Gambhir in the league game in the 2012 World T20 in Colombo and conceded five leg byes, he, wonderful bowler though he is, did not deserve a maiden. There is no denying that five runs were accrued to the Indian total, which is all that matters. Interesting sidebar is that India won by a single run. Similarly, Shaun Tait bowled a wonderful first over to Imrul Keyes in a league game in the 2010 World T20. It is classified as a maiden. But he conceded four byes and that was not a dot ball.

One last point to support my definition. A bowler agrees with the wicketkeeper that the third ball will be a googly. The keeper moves down leg side anticipating this. The bowler forgets and bowls a vicious legbreak. There's a good chance it will go for four byes. Whose fault is this? Just a simple example. The bottom line, as far as I am concerned, is that it is the runs conceded to the other team that matter, not the runs conceded to the batsman. This is to emphasise the team game concept.

I have talked about this in depth since I know that the point will be raised by readers. I will accept and post all such comments but will not change my interpretation. The bowling analysis has already been changed to reflect this. I have 151 maidens and the other scorecards, including ESPNcricinfo, have 192. Of these, 41 overs have had one to five byes/leg byes conceded and thus have been classified by me as non-maiden overs. Good change: makes the maiden that much more treasured.

A look at the most extraordinary overs: Batsman-dominant

Stuart Broad to Yuvraj Singh, 2007: Everyone, and their neighbour's dog, knows about these five minutes of madness. Broad bowled six balls, mostly of good length, and was sent over the fence six times by Yuvraj Singh, then at the peak of his form. Four of these were on the on side and two over the off-side ropes. I have always felt that Broad could have bowled a deliberate wide, if nothing else, at least to break the rhythm of Yuvraj. But then history would not have been made in a very symmetric fashion.

Wayne Parnell to Jos Buttler, 2012: The sequence of events, fairly self-explanatory, ran thus: 6, 6, 2, nb+0, nb+4, 4, 6 and 2. A disjointed sequence, but England moved from 74 to 106 in this 11-over match, and won comfortably.

Izatullah Dawlatzai vs England, 2012: The sequence was Buttler 4, Buttler lbw, Jonny Bairstow nb+6, nb+1, Luke Wright 6, 6, 6 and 1. England, not exactly moving the world at 155 for 3 in 18, suddenly propelled to 187 for 5 at the end of the 19th over.

Daryl Tuffey to Ricky Ponting, 2005: This was the very first T20I match played, and Ponting took Tuffey to the cleaners to the tune of 6, 2, 6, 6, 4 and 6. Tuffey never really recovered from this mauling.

Bilawal Bhatti vs Australia, 2014: This was in the recent World T20. Aaron Finch scored 4 and 1 and then Glenn Maxwell took over with 4, 6, 6, nb+4 and 4. The irony was that this was in the eighth over. Australia moved from 72 for 2 to 102 for 2 in eight overs. They needed 90 runs in 12 overs but messed up the chase and lost. Bhatti redeemed himself when he was entrusted with the last over. He conceded only six runs and captured two wickets.

A look at the most extraordinary overs: Bowler-dominant - through wickets

Mohammad Amir vs Australia, 2010: I consider this to be the most incredible over in T20I history. Do I hear Yuvraj's 36 against Broad? Excellent credentials indeed. But I feel a move from 191 for 5 to 191 all out during the course of a single over gets the biscuit. I will always take the side of the bowlers anyhow. And the opponents were Australia and established batsmen were at the crease. The sequence was Brad Haddin caught, Mitchell Johnson bowled, Michael Hussey run out, Steven Smith run out, Tait dot ball and Tait bowled. Five dismissals, three wickets to the bowler, six dot balls, this was the Twilight Zone.

RP Singh vs New Zealand, 2007: The sequence was Daniel Vettori bowled, Shane Bond 4, Bond run out, Craig McMillan 1, McMillan run out and Jeetan Patel run out. New Zealand moved from 185 for 6 to 190 all out. But still won the match.

Doug Bracewell vs Zimbabwe, 2011: Forster Mutizwa 1, Mutizwa run out, Ray Price run out, Kyle Jarvis caught and Chris Mpofu caught (5 balls). Bracewell hastened the end of Zimbabwe innings getting rid of four batsmen in five balls.

Haseeb Amjad vs Nepal, 2014: Malla 6, Malla 2, Malla c&b, Vesawkar run out, 1 bye and Budayair run out.

Al-Amin Hossain vs West Indies, 2014: Marlon Samuels caught, Andre Russell caught, 1 bye, Dwayne Bravo caught, 1 bye, Denesh Ramdin run out.

Now let us have a look at the table containing key indices for the 20 overs. This is a massive table containing around ten key indices for each over. Detailed interpretation of this table could run to pages. So a brief commentary is provided.

First, an explanation of the number of matches covered and the base numbers. Out of 400 matches played so far, two matches (26 and 68) were abandoned after the toss. Two matches (9 and 335) do not have ball-by-ball data available. In two matches (273 & 318), no second innings was played. So we have ball-by-ball data for 396 matches and 790 innings. Out of these 790, two innings (119 and 318) lasted four and two balls respectively. So the number of first overs is 789. The rest follow based on this.

The number of overs has already been explained. One additional point: 19.4 and 18.4 overs will add to 38.2, not 39. In other words the overs are converted to balls, added and then converted back. This is done to get an accurate measure when doing the percentage calculations and per over values.

Only two of the first overs went for above 20 runs while as many as 17 of the last overs went for above 20 runs. As expected, the 20th over leads the table in this regard. The total number of such overs is 194. In other words, an average of one 20-plus over every two matches. The pattern is a slow increase, with one exception. Look at the drop in the ninth over. From seven occurrences in the eighth over, the figure drops to two in the ninth over and then jumps to six in the 10th over. And look at the jump in the 13th over and abrupt drop in the 14th over.

The ten-plus runs numbers follow a similar increasing pattern to the 20-plus-run overs except that the kinks in the curve as seen in the ninth, 13th and 14th overs do not exist. The abrupt drop in the seventh over is clearly a result of the removal of field restrictions. Look at the last few overs. Nearly half the overs are ten-plus run overs. Some people might even argue that they expect more.

The four-and-below run overs represent the tight overs. As expected the first over leads the field. Three out of eight first overs have had only four or fewer runs taken off. Let me remind the readers that these refer to team runs: that means including all extras. That the seventh over comes close to the first one should not surprise anyone. But there is a surprise in the 17th over. Why is there a big drop from the 16th? Are the teams which have lost around five wickets or so trying to play safe to ensure that they can go on an all-out attack mode in the last three overs.

I will cover fascinating topic of maidens in depth in the next part of the article. Here I will refer to the overall percentage figures. As expected, maidens occur most frequently in the first over: just short of one every three innings. This drops very drastically to 2.5% in the second over and then to 1% in the third over. Goes upto 2.4% in the sixth over and then a huge drop to 0.77% in the seventh over. The figures keeps on dropping to the lowest at 0.17% in the 19th over and then to 0.38% in the last over. The last figure is misleading. It only indicates two maidens, as against one, the previous over. The overall percentage value is 1%, meaning one every five innings. But it is a rather steady decline, overall.

Now we come to Dot Balls per over. The overall figure is 2.32. But this starts with a fairly high 3.43 in the first over through to 1.73 in the last over. It is interesting to note that there is no great variation in this measure. It is possible that in the early stages the non-dot-balls are ones and twos while in the later stages these are boundaries.

We now come to the most important measure here: the average Runs per Over. The average across 400 matches is 7.55. The first over is a fairly low 6.1 and quickly reaches the average in the third over. Then this value drops off drastically in the seventh over and takes a further seven overs to pick up. The last two overs are above nine and the last over is fast approaching ten. However this figure is only over 500 overs as against the 6.1 over 789 overs. There are no major surprises here.

The number of wickets, which fell in the specific overs, is less relevant than the balls per wicket figure since the number of overs figure varies a lot. This figure, surprisingly, has a choppy ride. It starts off with an understandable 24-plus (once in four first overs) for first over to 20 for the sixth over. Then there is a sharp increase in the seventh over as caution takes over and the fielders are banished to the outfields. Even within the first group, see the sudden increase in over No. 5. Afterwards the value drops steadily from over No. 7 to 10. Then there is a completely inexplicable sudden drop in over No. 11 and a sudden increase in over No. 12. From over 15 onwards the number drops abruptly to a value of around 6.8 for over No. 20: a wicket in the last over of every match. Quite a performance indeed.

The final column lists maximum runs conceded. I have already written about the 36-run over and the 30-plus run overs. The 32-run over was the 11th of the innings and the 36-run over was the 19th. Most other overs have the 20s as their highest run count. For reasons not clear the 14th over has the lowest high score: a mere 21. Possibly the lull before the storm in the 16-20 overs.

The graph below is self-explanatory.

At the end of the Indian innings in the 2014 World Twenty20 final, I did my usual strike-rate comparison between the best and worst batsmen in the team. I can do this calculation in my mind and I worked out that Virat Kohli was striking at around 2.5 times that of Yuvraj Singh. I remembered that Dwayne Bravo was also at a similar level against Marlon Samuels in their semi-final match against Sri Lanka. I was certain that Kohli and Bravo, having scored more runs at 2.5 times the strike rate of the other batsman, would be seething within, despite contrary public utterances, because the matches were lost. On a whim, I looked up the 2012 World Twenty20 final. Samuels' strike rate clocked in at 7.4 times that of Gayle. But all sins are forgotten in a Gangnam dance if the team wins. Then I suddenly realised that I had the material for a nice and easy-to-understand article. I also remembered Mohan's comment on my last article that he loved the simpler ones which he could understand clearly. I reminded myself that I should do these simple anecdotal and low-key analytical articles more often.

I took a major decision. I decided that I would go with the ODI analysis first. I wanted to wet my feet initially on the longer limited-overs format since the T20 format is a sharp and unforgiving format: both on the playing and analytical arenas. The time is so short that one performs or perishes. So my cut-offs as well as metrics in T20s have to be radically different from those of the ODI game. So I have covered the ODI game here. I promise you that the T20 analysis, which will be a far sharper one, will follow, in due course.

The idea is simple. Set reasonable cut-offs for the (batting) innings and (bowling) innspells. Find the best and worst and ratio between both as the HL_Index. Order these on the basis of the Index values. The great thing about the analysis is that anyone can determine the Index value in two minutes flat by perusing any scorecard. The USP of this article is its essential simplicity.

The pleasing aspect of this analysis is that it is a true peer comparison. I agree that there could be early-morning support for the pace bowlers, the drying of pitch and consequent help to spinners, the dew factor in the evening games, the post-rain skid factor and so on. But everything is capsuled into the 200 minutes of an ODI innings. Conditions might change by about 10% but not more. If the opening batsmen need some time to settle down, they also have attacking fields to help in scoring runs. If they take 20 balls to break their duck, they are expected to convert this to a 70-ball-50, not a 30-ball-1. And let us not forget that all comparisons are within a single innings.

BATTING

But cut-offs have to be set. I cannot let Sunil Narine's (yes, you got the name right) cameo of 6-ball-24 or James Franklin's 8-ball-31 or Brendon McCullum's 9-ball-32 et al disturb the balance of the idea. These are freakish cameos. So I decided on a cut-off of 20 balls. This is essential since I wanted to cross the 20-ball mark to give the batsman some more time to get his innings back on track. We are not looking for low-scoring in 20 balls or so but getting out soon after. On the faster side, there is only one innings below 20 that merits inclusion: Afridi's 18-ball-55. I have checked this innings and made sure that the index for this team innings is below the selection mark.

Twenty balls represents more than 50% of the average innings size of 31 balls. It allows the batsman to settle down and score reasonably quickly to finish with a 100+ strike rate. On the slower side, 20 balls represents sufficiently long batting stint to settle down and score at least at around 33.3%.

The table represents the top entries while the potted scores below are for ten matches involving the major teams. While it is of interest to know that Samarasekera of UAE scored 25 times faster than Mehra, it is unlikely to be of further interest to anyone, including the UAE cricket followers.

At the turn of the century West Indies tried many opening batsmen. No one was really good enough to hold their place for a long time. This is reflected in a couple of entries at the top. Wavell Hinds was one and Runako Morton was another. Hinds laboured to 1 in 28 and Morton to 0 in 31 (taken as 1 in 32, for calculation purposes). Their scoring rates were abysmally low 3 or so. Carl Hooper scored at 131 and outscored Hinds by over 36 times. Smith scored at 100 and outscored by a mere 31 times. There is a personal take on the Hinds-Hooper match, chronicled later.

Robin Singh outscored his more illustrious team-mate Sachin Tendulkar by 30 times. Imagine Wasim Akram outscoring the normally quick-scoring Imran Khan by 27 times. And in another match, Tendulkar, despite opening the batting, outscored his second-wicket partner, Rahul Dravid, by 24 times. But it can be seen that many of the slow scoring batsmen have opened. Allan Lamb's performance is praiseworthy since he has scored a hundred at better than run-a-ball and outscored Graeme Fowler, who did not open his account in 21 balls, by a margin of 21 times.

It can be seen that more matches are lost than won in these matches. It is clear that the quicker scoring batsman has not always been able to undo damage done by the slow-scoring batsmen. The top teams seem to find ways of undoing the damage more than the teams in the lower rung.

Potted Scores

ODI # 1898. India vs West Indies. India won by 3 wickets.
Played on 21 November 2002 at Jodhpur. Mom: Agarkar A.B.
West Indies: 201 all out in 46.3 overs
   WW Hinds             1  in  28 (  3.6)
   CL Hooper           38  in  29 (131.0)
India: 202 for 7 wkt(s) in 46.2 overs

The Jodhpur match, the top one in the table, was one of the matches in which I did television work with Doordarshan. Just out of interest I looked back at my notes, still preserved carefully, for this match. I have written there "Hooper 36 times faster than Hinds. Discuss with Greenidge". I remember talking to Greenidge about it and Greenidge's discomfiture at a West Indian opening batsman dawdling. He also mentioned that Javagal Srinath was good but Ajit Agarkar and Sanjay Bangar were only medium pacers. His grouse was at Hinds getting out at 1. Little did I realise that one day this match would be the leading entry in a list.

What followed was even more painful to watch. Samuels scored 3 in 28 balls. So the 2 and 3 batsmen accumulated an incredible tally of 4 in 56 balls: less than half a run per over. West Indies lost the match and it is certain that Hinds and Samuels have to be considered responsible. Not to forget Ramnaresh Sarwan's contribution: 14 in 38 balls, making a total of 18 in 84 balls.

ODI # 2422. Australia vs West Indies. Australia won by 127 runs. 
Played on 24 September 2006 at Kuala Lumpur. Mom: Clarke.
Australia: 240 for 6 wkt(s) in 50.0 overs
West Indies: 113 all out in 34.2 overs
   RS Morton            0  in  31 (  3.1)
   DR Smith            30  in  30 (100.0)

What does one say of Morton? 31 balls to score 0 and then getting out. Granted, he walked in at 0 for 1 with Gayle getting out first ball. Granted, the bowling was top class: Glenn McGrath, Brett Lee, Nathan Bracken and Shane Watson. But surely a forgettable performance, resulting in a big loss. It is possible that even if Morton had scored a few more runs, West Indies would have lost.

ODI # 1524. India vs New Zealand.  India won by 14 runs.
Played on 11 November 1999 at Gwalior. Mom: Ganguly S.C.
India: 261 for 5 wkt(s) in 50.0 overs
   SR Tendulkar         1  in  23 (  4.3)
   Robin Singh         45* in  34 (132.4)
New Zealand: 247 for 8 wkt(s) in 50.0 overs

Tendulkar, opening, scored 1 in 23 and Robin Singh, coming in at 7, scored at 132. A factor of 30. However Sourav Ganguly scored a majestic run-a-ball 150-plus and India won well.

ODI # 3401. Sri Lanka vs South Africa. Sri Lanka won by 128 runs. 
Played on 31 July 2013 at Colombo. Mom: TM Dilshan.
Sri Lanka: 307 for 4 wkt(s) in 50.0 overs
South Africa: 179 all out in 43.5 overs
   Q de Kock           27  in  21 (128.6)
   F Behardien          1  in  22 (  4.5)

This was a recent match. Quinton de Kock, free and flowing in the opening position, scored at 128. Farhaan Behardien, coming in at 6, scored at below 5. Surely South Africa could do better than Behardien. The match was lost anyhow.

ODI # 610. Australia vs Pakistan. Australia won by 7 wickets. 
Played on 23 February 1990 at M.C.G. Mom: Not awarded.
Pakistan: 162 all out in 47.5 overs
   Imran Khan           1  in  24 (  4.2)
   Wasim Akram         86  in  76 (113.2)
Australia: 163 for 3 wkt(s) in 45.5 overs

The mercurial Imran Khan scored at just over 4 and Akram at 113. Pakistan lost comfortably. This was a peculiar match. Ijaz Ahmed scored a 34-ball-7 and Javed Miandad a 26-ball-2. So no one was comfortable, barring Wasim.

ODI # 274. Sri Lanka vs New Zealand. Sri Lanka won by 4 wickets. 
Played on 3 November 1984 at Colombo. Mom: de Silva P.A.
New Zealand: 171 for 6 wkt(s) in 45.0 overs
Sri Lanka: 174 for 6 wkt(s) in 39.4 overs
   S Wettimuny          1  in  27 (  3.7)
   PA de Silva         50* in  54 ( 92.6)

Sidath Wettimuny's dawdle at the beginning was offset by Aravinda de Silva's brilliant innings resulting in a win. Maybe the low target prompted Wettimuny to see through the fast bowlers. So we cannot really blame him.

ODI # 182. Australia vs New Zealand.  Australia won by 149 runs.
Played on 13 February 1983 at M.C.G. Mom: Hughes K.J.
Australia: 302 for 8 wkt(s) in 50.0 overs
New Zealand: 153 all out in 39.5 overs
   JV Coney             2  in  23 (  8.7)
   BL Cairns           52  in  25 (208.0)

This is chalk with a vengeance and cheese likewise. One batsman scores at 8 and the other at 200+. At 44 for 6, Lance Cairns threw his bat around, and connected. It did not matter. Neither innings could save New Zealand. Jeff Crowe scored a 48-ball-27 and Chatfield, at the end, a 36-ball-10. The bowling was awesome: Dennis Lillee, Rodney Hogg and Geoff Lawson.

ODI # 1335. India vs Bangladesh.
Played on 25 May 1998 at Mumbai. India won by 5 wickets. Mom: Kumble A.
Bangladesh: 115 all out in 36.3 overs
India: 116 for 5 wkt(s) in 29.2 overs
   SR Tendulkar        33  in  29 (113.8)
   R Dravid             1  in  21 (  4.8)

Now Tendulkar is on the other side. He blazes away and Dravid goes into his shell. The scoring rates speak for themselves. But let us not forget that the target was only 116. And Ajay Jadeja's 47-ball-17 led to a laboured win.

ODI # 1973. Sri Lanka vs West Indies. Sri Lanka won by 6 runs. 
Played on 28 February 2003 at Cape Town. Mom: Vaas WPUJC.
Sri Lanka: 228 for 6 wkt(s) in 50.0 overs
West Indies: 222 for 9 wkt(s) in 50.0 overs
   BC Lara              1  in  22 (  4.5)
   RR Sarwan           47* in  44 (106.8)

No one seems to be exempt from this malaise. One can never accuse Brian Lara of scoring slowly. But in this match against Sri Lanka, he dawdled and might even have caused West Indies the match. He scored a single in 22 balls as against Sarwan's fluent 44-ball-47. That too in the World Cup with possibly a place in the next round at stake.

ODI # 1807. Pakistan vs West Indies. Pakistan won by 4 wickets.
Played on 14 February 2002 at Sharjah. Mom: Abdul Razzaq.
West Indies: 190 all out in 48.3 overs
Pakistan: 193 for 6 wkt(s) in 46.1 overs
   Inzamam-ul-Haq       1  in  20 (  5.0)
   Abdul Razzaq        46* in  41 (112.2)

And the malaise runs wide. Inzamam-ul-Haq outscored 22 times by Abdul Razzaq. But the target was a low one and Inzamam's dawdle did not cost the match.

BOWLING

For the bowlers I decided that I would consider only the bowling accuracy for comparison. The strike rates do not mean much and bowlers who come at the end and capture couple of wickets in an over will cause havoc. The bowling accuracy, on the other hand, is a stable measure. The cut-offs here are dicey. After a lot of deliberation I have decided to go with two cut-offs. 5 overs for the accurate bowlers and 3 overs for the expensive bowlers. The reason is simple. I want the accurate bowlers to maintain their accuracy for an extra over or two. 5-0-10-0 is tougher to achieve than 4-0-8-0 or 3-0-5-0. On the other hand the expensive bowlers can easily concede tons of runs in 3 overs: (e-g) Ravi Rampaul 64 in 3, Sreesanth 48 in 3, Rangana Herath 43 in 3 and so on. Now on to the tables.

Now look at what Curtly Ambrose did. He completed his innings spell with an RpO value of 0.5. The part-time bowling of Sherwin Campbell had an RpO value of 6.50. It could as well have been Nixon McLean who conceded 5 runs per over. We now come to Wasim Akram's way-out innspell of 7.2-4-4-2 compared with Manzoor Elahi's 5-0-32-1. Then comes Lillee's five-over burst conceding 3 runs against Len Pascoe's 26 runs in 4 overs. The two bowling efforts of Lillee and Pascoe disguise the match-winning effort by Greg Chappell who had figures of 9.5-5-15-5.

The next entry is, arguably, the most incredible one. During the 1992 World Cup, Phil Simmons bowled, what I consider one of the most devastating spells ever, against Pakistan. He finished with the unbelievable figures of 10-8-3-4. All four were top-order dismissals. This is the only instance of eight maiden overs in a ten-over spell. Note Bishan Bedi's bowling figures, albeit against East Africa. He shares the record of eight maidens with Simmons, although in 12 overs. Kenny Benjamin's 9 over spell at 3.11 was over 10 times more expensive than Simmons'. Pakistan had no answer to the extraordinary bowling effort of Simmons and lost by a big margin. Incidentally Jimmy Adams had figures of 4-2-2-1.

Kapil Dev's RpO was nearly 10 times better that of Kris Srikkanth. But that was certainly in vain as West Indies won quite comfortably. This is the first match, involving major teams, which has been featured here which was lost.

There is a missing entry which deserves special mention. It did not qualify because Courtney Walsh bowled 4.3 overs. This was the 1986 match in Sharjah between West Indies and Sri Lanka. How can anyone ever understand Walsh's analysis: 4.3-3-1-5? The nearest to this piece of magic was Herath's spell in the World T20 against New Zealand. With the type of bowling accuracy of Walsh, a figure of 0.22, almost any other spell would form the other half of the HL-Index pair. It fell to Malcolm Marshall's fairly accurate spell of 5-1-16-1 to complete the pair. The index works out to 14.4.

It can be seen that most of the matches have been won by the bowling teams. Hence I will not be presenting any potted scores.

The bowling side of this analysis leaves very little room for further nuanced looks. First the index values have a much narrower range than the batting index values. Look at the highest index values: 36+ and 14+. Possibly because the bowling performances are limited to 5/6 bowlers in an innings. Also most of the matches involving the A-teams have resulted in wins for the considered bowling combinations.

The reason could very well be that the bowling stint of the top bowler normally represents a fifth of the team effort and if that is outstanding, say like Simmons' 10-8-3-4, that effort straightaway puts the team on the ascendancy. And this spell is also indicative of a track which is somewhat helpful to the bowlers. The combination of these factors normally leads to wins. Of course, in a combination like Harbhajan Singh's 10-2-14-2 and Agarkar's 6-0-60-1, Agarkar's nightmare effort effectively cancelled Harbhajan's spell and the team lost. But the instances are far and few in between.

Incidentally for the 6874 innings, the average Batting HT-Index value is 2.59. The average Bowling HT-Index value is 2.14. These are the average spreads between the best and worst performances. There seems to be a more balanced distribution amongst the Bowling index values. There are 2638 Bowling index values above the average while there are only 2127 Batting index values above the average, even though the high Batting index values are much higher.

To download/view the part list of the HT_Index tables, please CLICK HERE.

5. The point I had already made regarding the limiting factor of ball-resource as against wicket-resource can be best demonstrated using one stunning fact. Out of the 188 matches that were won by teams batting second, there is only one match in which the wicket-resources available at the end of the match was lower than the ball-resources. This is the match between UAE and Zimbabwe in the recently concluded World T20. The scores were UAE: 116 for 9 in 20. Zimbabwe: 118 for 5 in 13.4. The wicket-resource available was 31.2% and the ball-resource available was 38.3%. In the other 187 matches, the wicket-resource available was higher than the ball-resource. I do not think there has been a more emphatic statistic to decide a point of view.

6. Eight teams, batting first, won by one run. The highest victory margin was Sri Lanka's 172-run win over Kenya in the 2007 World T20.

7. Teams that batted second won on the last ball of the match on 19 occasions. Since no ball was left in the match to determine a win resource available value for these teams, the margin of victory has been taken as one ball. The wicket resource remaining does not mean anything, as we have already seen.

T. The biggest unutilised ball resource was during Sri Lanka's recent demolition of Netherlands in the World T20 - they still had 90 balls left: a whopping 72.2% of resources were still available.

Resource determination

The methodology for the win margin percentage is summarized below.

- First batting team wins: The formula 100.0*run margin/target is used. This applies to the D/L matches also.

- All wins through a tied match and Super Over are treated as x-run wins where x is the single over difference in runs. This information is available for two matches. For the other two matches the run margin is taken as two.

- Second batting team wins: The values of the wicket resource and the ball resource available at the end of the match are determined and the lower of these two values is taken as the win margin. Enough explanation has already been given on this. Whichever is the limiting resource is used.

- For D/L wins by the second batting team by runs, the formula 100.0*run-margin/second-innings-score is used.

I have given below the win margin percentage for the last-three matches of the World T20, matches which are still fresh in our memory.

- SF: SL won by 27 runs (D/L). SL - 25.2% (27/107)
- SF: Ind won by 6 wkts and 5 balls. Ind - 6.5% (100.0*(1.0-0.986578^5))
- F : SL won by 6 wkts and 13 balls. SL - 16.1% (100.0*(1.0-0.986578^13))

Let me put these numbers in another way. What could Sri Lanka have chased? This is one occasion when it is necessary to consider the number of wicketa in hand. They had enough. My projection for them is 160 (134/(1.00-.161). So it is clear that Sri Lanka could easily have chased a target up to 155, it would have been a toss-up for targets between 155 and 165 and anything above 165 would have made India favourites. This is one nice fall out of this analysis.

This is possibly the longest preamble I have ever done. But I am certain this will not have put any reader to sleep. It took me three days just to write this. So do not expect to assimilate this in three minutes. Now let us go on to the tables.

Sri Lanka lead the Performance table, based on the tried and trusted 2-1-0 points allocation, with an additional tweak. The World T20 winners get an additional three points and the runners-up get one point. Sri Lanka have a very good 68.2% performance index value, above the outstanding two-thirds achievement mark. Pakistan are next, some distance behind. India follow closely behind in third position. These three teams, and South Africa, have a performance index exceeding 60%. Ireland are a welcome top-five entry. Despite their single World T20 win, England have been ordinary.

The last column is the average of the margin achieved in the matches won. This an indicator of the comfort with which wins were achieved. Australia are in the lead by a huge margin. Their average margin is a huge 24.9%. This indicates that when they win, they win well. If we ignore Zimbabwe, with their high average, albeit in six matches, Ireland are right at the top, with 19.1%. Then come West Indies and Sri Lanka. India are in the last position, with 14.5%. This means that they had more narrow wins than other teams.

This table splits the wins into first batting and second batting classifications. The table is ordered on the number of matches. Pakistan are a very strong defending team with 62% of their wins having been achieved batting first. West Indies have a still higher first batting win percentage. South Africa and Sri Lanka also have first batting wins of around 60%. These are the four teams which have excellent bowling combinations and this is borne out by these numbers.

Bangladesh and Ireland have had a lot more chasing wins. The other teams are around the middle. India have a 10% edge in chasing wins.

When Australia won, they win very well. Their average margin is 24.2%, Also look at their average run-margin: a whopping 45. England have similar numbers. And West Indies too. Pakistan have had a lot of first-batting wins and have average run-margin in excess of 35 and win margin exceeding 20%. South Africa is at the bottom of the table with 21 and 12.9%.

Australia dismissed teams on ten occasions and contained them eight times. England could dismiss teams only five times. West Indies are approximately even in this. Pakistan have contained more than dismissed teams. The same applies for the other teams. Come to think of it, only Australia have had more wins by dismissing the opposition batsmen than containing them.

Australia are again the leaders by a country mile, with an average margin of 25.7%. Their average win has been by 6.7 wickets and by 24 balls. Very impressive figures, indeed! India and South Africa have average wins by 6.6 wickets. Ireland are right at the top. If anyone says they did not face top teams, let us agree that they faced teams of matching strength, as all Test-playing teams did. Pakistan have had low average margin percentage. Interesting fact is the average win by only 11 balls for India and Pakistan.

T20 World Cup 2014

Two teams entered the World T20 with huge albatrosses around their necks. One succeeded in sending off the bird and the other did not.

South Africa had the big white bird emblazoned "semi-finalists" around their collective necks. At the end of the tournament they still had the bird firmly entrenched. Another semi-final and another different result await them. The Sri Lankan bird had "eternal bridesmaid" in big blue letters on it. In 200 minutes of faultless cricket they managed to send the bird off flying.

A much-loved team, at home and away, two great gentlemen cricketers playing their last game in this format, Sri Lanka's success was very well received and appreciated. As already mentioned, they were the fifth team to win the title, in five different World T20s. The match was won in the first four and last four overs of the Indian innings: 34 runs, two wickets and one four in eight overs bowled by Nuwan Kulasekara, Angelo Mathews, Sachithra Senanayake and Lasith Malinga tells the story.

It is sad that Yuvraj Singh is being blamed by all and sundry. He might have played poorly, but the others were not much better. How can one batsman get the blame when the well-set Virat Kohli and the master finisher MS Dhoni could not do anything? Give credit to the bowlers and stop at that. In the last 27 balls; the following is the story.

Kohli: 10 balls - 8 runs
Yuvraj: 10 balls - 5 runs
Dhoni: 7 balls - 4 runs

I hope that Australia or South Africa or New Zealand win the next World T20 to round-off a perfect half-dozen. I will also extend my coverage of a format that is an analyst's delight.

To download/view the complete list of the 398 T20-I matches, please CLICK HERE. My take is that many of the questions can be answered if you download this file and view the contents.

The Tendulkar brace (on Tests and on ODIs) of articles, which were written during late-2013, led to the formation and development of HSI (High Score Index), a very effective measure for batsmen. There were many insightful and valuable comments from the readers and some of these are excellent suggestions which could shape HSI into an invaluable tool for measuring batsmen contributions. More of that in a later article.

A few readers wanted a similar measure for the bowlers. Excellent idea, and I had already planned for that. Meanwhile I had done a nostalgic, evocative article on forgotten Test innings, which was very well received. For this article as well, the readers wanted a bowler reprise. I said I would pen that.

Two promises. Two articles. Which one to take up first? I decided on the HSI-related article as I felt that was more important. I spent some time and worked out an excellent bowling measure. While reviewing the tables for that measure, lo and behold, I also found my second article. The top ten in the main table had six unknown performances. So one article, two great ideas taken care of.

Batsman-Runs: What is the first and foremost requirement of a Test batsman? In one phrase, score runs. Playing out time and facing many deliveries is normally secondary. To win matches, one has to score runs. Let us take two recent South Africa Tests. South Africa faced daunting tasks in both Tests: score well over 400 runs to win the Test, or bat out 140 overs.

Granted that the Indian and Australian bowling attacks they had to face were chalk and cheese. At Newlands they played defensively, playing out time, and lost. At Wanderers they batted in an attacking manner and almost won. The point is that when South Africa scored runs, India were pushed into a defensive stance and was almost made to pay for it. At no point were Australia put in that position. They attacked right through the nine hours and won.

If the batsmen score enough runs (even a single run more is sufficient) the team wins. So the batsmen measure was based on runs scored. Of course, readers could come up with instances in which a slow 50 would work better than a quick 100, or where playing out 100 balls is more important than scoring 100 runs. But these are exceptions and are more relevant to saving rather than winning Tests.

Bowler-Wickets: What is the bowler's role? Not to churn over after over. Not to bowl 50 overs at 1.5 RpO. It is to take wickets, pure and simple. That is the only way to win a Test. Let me exclude two Tests from all further discussions. The "arranged" single innings non-Test in Centurion, and the Test conceded by Pakistan at The Oval, when they were well ahead of England. To win a Test, a team has to capture 20 wickets: give or take a wicket or two due to declarations or player's inability to bat.

There can be only one method to measure a bowler's non-contextual contribution in a Test. Use the wickets he captured. This is a reflection of the adage that almost any day 10-0-50-5 is more valuable than 20-10-20-1 in Tests. And from the last Test at Newlands on the fifth day, Smith's 13-3-41-1 was far more valuable to Australia than Lyon's 22-17-10-0.

There are two principles I have worked on. All wickets are valuable. Some wickets are more valuable than others. To dismiss Michael Clarke at 10 is more valuable than dismissing him at 30, which is more valuable than dismissing him at 100, which is more valuable than letting Clarke go unbeaten.

ISV (InnSpell Value): So I developed a bowling index which is based only on the wickets captured. We work on a simple principle. Who did the bowler dismiss AND at what score did the bowler dismiss the batsman? At any time in his innings the batsman has in his tank enough fuel to score his average ahead. This was my basis for the extended batting average also. Whether the batsman is at 0 or 100, he is capable of adding his average to the score. He could add a lot more or a lot less. But this is a simple assumption which lets us get a handle on the situation.

The calculations are minimal. Each wicket has a fixed component, which is the dismissed batsman's appropriate career-to-date home/away/all batting average. There is a variable component, which is the difference between the appropriate career-to-date home/away/all batting average and the batsman score, subject to a minimum value of zero. At a pinch readers can substitute the career-to-date average with the career average since no online information portal provides career-to-date averages, that too separated home and away. However, then it must be understood that the ISV figures for a bowling performance will keep on varying as the dismissed batsman's career moves on. The career-to-date home/away method brings into focus the extraordinary batting strength of teams like Pakistan at home during the mid-1980s.

When Virender Sehwag was dismissed for 293 by Muttiah Muralitharan, the latter gets credit for 56.18, which was Sehwag's career-to-date Home (CTH) average at that time. This is because Sehwag was eminently capable of adding 56 runs to his score. Murali gets nothing on the variable front. In the same match when Mahela Jayawardene was dismissed for 12 by Zaheer Khan, Zaheer gets 72.16 points: Jayawardene's career-to-date Away (CTA) average of 44.08 (he could very well have added 44 to his score) plus 28.08 being the runs gained by dismissing Jayawardene early.

The highest credit for a dismissal is given to Eric Hollies for his dismissal of Don Bradman for 0 at The Oval during 1948. Bradman's CTA average at this time was 102.85. He got 102.85 for dismissing Bradman and 102.85 for dismissing Bradman at 0, a well-deserved total of 205.70. This huge credit went some way in getting Hollies 401 ISV points for his 5 for 131.

Readers may raise a very pertinent query. Why credit the bowler with the batsman average when they dismiss him past his average? The fact is that ANY dismissal has to have a certain value. Let us say Bradman scored 304, 244 and 105 in three consecutive innings. There is no way I can say that the third dismissal should not get any points since Bradman has already gone past his average. By dismissing Bradman at 105, the bowler has served his team quite well since he probably prevented Bradman scoring another double-century. The same idea will apply to other batsmen, at lower levels. Look at a Ricky Ponting sequence of 50, 242 and 257. Or the Brian Lara sequence of 400, 53 and 120. Or the Tendulkar sequence of 83, 143 and 139. Aren't those dismissals at 50, 53 and 83 worth at least the batsman averages? These cannot be pegged at zero just because the average was breached. Most certainly a bigger score was prevented from being scored.

Dimension: It should be remembered that, with run values distributed equally in numerator and denominator, the HSI is a dimension-less entity. However the ISV is a run-value since two run-values are added. Let me add that I have referred to ISV points in the articles rather than ISV runs to avoid confusion.

I have looked at methods of converting this into a dimension-less value. The only way is to determine the team ISV and get a ratio. But that immediately brings with it its own set of problems. No problems with high number of wicket-captures in an innings. At a pinch we could even give Suraj Randiv a 1.00 for capturing all five wickets out of India's 258 for 5 at the P Sara Oval, Colombo. However, if we do that in Sri Lanka's innings of 137 for 1, with the sole wicket being captured by Saeed Ajmal, he gets 1.00, which seems totally wrong. So we have to exclude innings in which less than a certain number of wickets were captured and so on. It seems to me a good idea to give that a miss now and leave it to the readers to come out with their own suggestions.

So it can be seen that the current work has no exclusions whatsoever. Every single relevant "innspell" is included. More on that later. By the by, what is an "innspell"? Frequent visitors to my blog will already be aware of this. For others, let me explain. This is a term I coined a few years back to define the complete bowling performance in an innings. A spell normally denotes an unbroken continuous bowling effort. When Ajmal bowls 56 overs, this could easily have been 3/4 spells. How do we refer to the complete bowling effort? "Bowling analysis" is too prosaic. Hence the term "innspell", a very descriptive term.

What do we have here? Could anyone have imagined that in a table of top bowling performances, on a new logical measure, the top ten would have bowlers such as Ravi Ratnayeke, Ashantha de Mel, Kenny Benjamin, Tony Greig, Doug Wright and Stephen Boock. Anil Kumble and Jim Laker are here because they captured ten wickets and Murali and Kapil Dev because they captured nine. And not one of these "lesser" (only in a "not-so-famous" sense) bowlers has even captured nine wickets: Benjamin, only five. So it looks like I have my "forgotten" performances also. Now I am quite convinced that this is indeed a measure that transcends numbers and reflects very strongly the quality of bowling.

What did Ratnayeke do to top a table of 26,262 relevant bowling performances spread over 137 years? Simply, he met the Pakistani juggernaut at their batting peak, away from Sri Lanka, and ripped the strong batting apart. This Pakistan team is the third-strongest batting combination ever: only their own team couple of years back and West Indies during 1958 are above them. Ratnayeke captured the wickets of Qasim Umar for 1(CTH avge:37.9), Javed Miandad for 40(81.8), Zaheer Abbas for 4(58.2), Salim Malik for 22(53.1), Imran Khan for 6(39.7), Saleem Yousuf for 23(20.0), Abdul Qadir for 10(14.9) and Wasim Akram for 4(4.0). Look at the high averages and you will understand the value of these wickets. It was indeed very tough for the visiting bowlers during the 1980s. So deservedly, Ratnayeke stands at the top.

De Mel almost did a reprise in the next match. He dismissed the top six Pakistani batsmen for 16, 13, 8, 63, 5 and 52. This time Mudassar Nazar, with a CTA-avge of 65.3, and Mohsin Khan with 48.3, were in the dismissed group. Needless to say, both were in a losing cause.

Murali captured eight England wickets, away at Trent Bridge and Laker captured all ten Australian wickets at Old Trafford. But look at Benjamin's effort. Only five wickets, but what a collection! Benjamin dismissed Navjot Sidhu for 0 (48.7), Sachin Tendulkar for 10 (72.9), Mohammad Azharuddin for 5 (59.0), Vinod Kambli for 0 (50.0) and Anil Kumble for 1 (17.7). Kambli's CTH average was higher but lowered since he had played fewer than 15 innings at home. West Indies won this Test comfortably. I would venture to say that this is probably amongst the best five-wicket hauls in Test history.

When we go into the 11-20 entries, we see Neil Hawke, Monty Panesar, Sikander Bakht, Trevor Bailey, Chris Pringle and Devon Malcolm. That means 12 relatively less-known bowlers occupy the top 20. More vindication for the efficacy of this measure. It is heart-warming to see Panesar's truly outstanding match-winning Mumbai spell in the top 20.

By the by, where are Bob Willis' 8 for 43, Hugh Tayfield's 9 for 113 and de Villiers' 6 for 43. Tayfield is in the 41st place, Willis is in 84th place and de Villiers is in 197th position. But let us not forget that Fanie de Villiers had three late-order batsmen among his six wicket spell. Allan Donald captured three top-order wickets.