It is the responsibility of the first 6 batsmen in a Test innings to score the required runs and the low order batsmen, normally the bowlers, to provide support. There are times when it happens the other way around. The low order batsmen score more runs than the top order. There is an inherent charm and excitement in these innings. Often these also turn out to be match-winning innings. More often than not one of the top order batsman stays on to shepherd the late order. It could also be that these are true cases of innings revival controlled by genuine late order batsmen. In this article I have taken a comprehensive look at such innings.

I may be wrong. However there is only one innings in test cricket in which, for strategic reasons, a captain sent his entire low order first on an a "gluepot" of a wicket, and then he himself came on to play one of the greatest Test innings ever. This match is discussed later. So this is the only innings in which the late order was expected to outscore the top order.

First some summary facts. These are current up to match no 1965, the second Pakistan-Australia match.

Number of innings played: 6187 (Maximum-7860)

Number of innings played in which the late order (wkts 6-10) has out-performed the top order (wkts 1-5).

All tests:  1431 (in 6187 innings - 0.23 times per innings).

Pre-WW2:     206 (in  883 innings - 0.23 times per innings).
  Pre-WW1:     120 (in  454 innings - 0.26 times per innings).
  WW1-WW2:      86 (in  429 innings - 0.20 times per innings).
1948-1969:   251 (in 1242 innings - 0.20 times per innings).
1970-1989:   340 (in 1426 innings - 0.24 times per innings).
1990-2010:   634 (in 2636 innings - 0.24 times per innings).
  1990-1999:   264 (in 1087 innings - 0.24 times per innings).
  2000-2010:   370 (in 1549 innings - 0.24 times per innings).

Overall there has been an average such occurrence of around 0.23 per innings (slightly below once a test). There have been distinct moves away from this mean value of 0.23. During the pre-WW1 period, at times of uncovered pitches, wide disparity in batsmen techniques, "gentlemen" teams playing etc., the low order had to come to the rescue of the top order more often, around 0.26 times per innings. Then came the batting era, with top batsmen playing, the move was in the other direction. Either side of the WW2, the number oscillated around 0.20 per innings. Bradman, Hammond, Headley before and Hutton, the three Ws, Sobers, after. During the period 1970-1989, the figure picked up to 0.24, just above the mean. Then from 1990 to 2010, the figure has oscillated at around the mean of 0.24. Even when I split up the period into two halves, the figure has not changed. It can thus be concluded that barring the Pre-WW1 and either side of WW2, the frequency has remained at around 0.24, during the past 40 years.

The complete table of 1431 innings is available for view, import and analysis for users. The table is in reverse chronological order. To view/down-load the complete table, please click/right-click here.

How do we view the information in a summary form in this analysis. I have created three summary tables for viewing. In all tables I have also shown the highest scorer and the batting position he batted in to give an idea of who coordinated the revival.

The first is a table ordered on the ratio between the second half runs and first half runs. For the selected innings this value is 1.00 or more. In this table I have selected only innings in which this ratio is 4.00 or more. There is no rocket science in this number. It is a high enough number to limit the number of table entries to a reasonable number. Also we are pushing up the bar. Anyhow this is only a cut-off for display. By definition these will be innings such as 25 for 5 recovering to 200 all out or 100 for 5 recovering to 400+ for 8 and so on. It is more likely we have low scoring innings in this table. Let us look at the table.

   Year MtId Bow Bat R 5Wkts Final <2nd Half> Highest Score
                     e Score Score Runs To1Hf Runs(BP) Batsman
                     s

 1.1952 0354 Eng Ind     6/5  98 ao  92 15.33  38 ( 4) Hazare V.S
 2.1995 1306 Slk Pak    15/5 212 ao 197 13.13 117 ( 7) Moin Khan
 3.2004 1683 Zim Bng    14/5 169 ao 155 11.07  61 ( 8) Khaled Mashud
 4.1935 0239 Win Eng    23/5 258 ao 235 10.22  85 ( 8) Holmes E.R.T
 5.2005 1765 Ind Zim    18/5 185 ao 167  9.28  52 ( 5) Taibu T
 6.1898 0056 Eng Aus W  32/5 323 ao 291  9.09 188 ( 3) Hill C
 7.1888 0030 Eng Aus     7/5  70 ao  63  9.00  32 ( 6) Lyons J.J
 8.1967 0623 Eng Pak    26/5 255 ao 229  8.81 146 ( 9) Asif Iqbal
 9.2008 1875 Win Aus W  18/5 167 ao 149  8.28  79 ( 7) Symonds A
10.2000 1520 Aus Win    22/5 196 ao 174  7.91  96 ( 7) Jacobs R.D

Before any reader comes in with his comment, let me confess that this is an odd table. The ratio depends on how quickly the first 5 wickets have fallen rather than on how many runs were scored by the last 5 wickets. However, having set out the base methodology, I did not want to exclude any innings based on an artificial lower limit for the innings size.

The highest ratio reached is 15.33 when India recovered from 6 for 5 to 98 all out, assisted by Hazare. Readers should remember that if the sixth wicket had fallen soon after, India might not have reached 26, the record low total of New Zealand. The bowling attack was a fearsome one, viz., Trueman, Bedser, Laker and Lock. Pakistan's recovery, controlled by Moin Khan, is lot more substantial, with a ratio of 13.13. However the innings which catches one's eye is the Australian recovery from 32 for 5 to 323 all out, orchestrated by Clem Hill's 188, which is in the top-10 of the Wisden-100 innings table. Recent recoveries have been led by the two keepers, Khaled Mashud and Taibu.

It can also be seen that very few of these tests are likely to be won, considering the low-score nature of recovery. Hill's innings was one of the successful ones and Symonds, which was in the second innings. 18 out of 76 have resulted in wins.

To view/down-load the complete table, please click/right-click here.

The second is a table ordered on the number of runs added by the last 5 wickets during the selected innings. In this table I have selected only innings in which the runs added are 300 or more. By definition these will be innings such as 150 for 5 recovering to 475 all out or 300 for 5 moving on to 700+ for 8 and so on. It is more likely we have high scoring innings in this table. Let us look at the table.

   Year MtId Bow Bat R 5Wkts Final <2nd Half> Highest Score
                     e Score Score Runs To1Hf Runs(BP) Batsman
                     s

 1.1955 0414 Nzl Pak W  87/5 561 ao 474  5.45 209 ( 8) Imtiaz Ahmed
 2.1937 0257 Eng Aus W  97/5 564 ao 467  4.81 270 ( 7) Bradman D.G
 3.1955 0406 Win Aus   233/5 668 ao 435  1.87 137 ( 5) Miller K.R
 4.2009 1911 Eng Win   334/5 749/9  415  1.24 291 ( 3) Sarwan R.R
 5.1966 0609 Win Eng W 130/5 527 ao 397  3.05 165 ( 4) Graveney T.W
 6.2010 1953 Bng Nzl W 158/5 553/7  395  2.50 189 ( 5) Guptill M.J
 7.1972 0695 Nzl Win   171/5 564/8  393  2.30 183 ( 5) Davis C.A
 8.2005 1774 Eng Pak W 247/5 636/8  389  1.57 223 ( 4) Mohammad Yousuf
 9.2009 1933 Ind Slk   375/5 760/7  385  1.03 275 ( 4) Jayawardene D.P.M.D
10.1996 1336 Zim Pak   176/5 553 ao 377  2.14 257 ( 8) Wasim Akram

This is a more interesting table since it is ordered on the number of runs added.

At the top of the table, Imtiaz Ahmad, batting at no.8, scored 205 and helped Pakistan recover from 87 for 5 to 561 all out.

The next match is an all-time classic. The innings by Bradman was determined to be the best ever Test innings in the Wisden-100 exercise. Australia's 200 for 9 was countered by England with 76 for 9, on a diabolical pitch. Then Bradman countered by sending his low order batsman, to let the pitch dry out. These batsmen promptly lost their wickets, but consumed valuable time. Bradman walked in and scored 270 to take Australia to 564 and a comfortable win. It was a tribute to Bradman the tactician as much as Bradman the batsman.

The most intriguing innings is by Wasim Akram who scored 257 at no.8 and took Pakistan from 176 for 5 to 553 all out in the company of Saqlain Mushtaq.

More tests in this table are won since the recovered innings score is almost always in excess of 400. 28 out of 59 have resulted in wins.

To view/down-load the complete table, please click/right-click here.

The third is a table ordered by the final score reached, but with a different criteria for selection. I have selected only innings in which the ratio is 2.50 or more and 200 or more runs are added by the last 5 wickets. This is done to ensure that we get a representative population of truly great late order batting performances. By definition these will be innings such as 150 for 5 recovering to 450 all out but not 7 for 5 to 70 all out nor 375 for 5 to 760 for 7. This table is likely to contain the really relevant innings. Let us look at the table.

   Year MtId Bow Bat R 5Wkts Final <2nd Half> Highest Score
                     e Score Score Runs To1Hf Runs(BP) Batsman
                     s

 1.1937 0257 Eng Aus W  97/5 564 ao 467  4.81 270 ( 7) Bradman D.G
 2.1955 0414 Nzl Pak W  87/5 561 ao 474  5.45 209 ( 8) Imtiaz Ahmed
 3.2010 1953 Bng Nzl W 158/5 553/7  395  2.50 189 ( 5) Guptill M.J
 4.1966 0609 Win Eng W 130/5 527 ao 397  3.05 165 ( 4) Graveney T.W
 5.1955 0406 Aus Win   143/5 510 ao 367  2.57 219 ( 7) Atkinson D.S.t.E
 6.1908 0098 Eng Aus W 135/5 506 ao 371  2.75 160*( 9) Hill C
 7.1925 0160 Eng Aus W 118/5 489 ao 371  3.14 201 ( 7) Ryder J
 8.2002 1594 Nzl Eng W 106/5 468/6  362  3.42 200 ( 5) Thorpe G.P
 9.1976 0784 Pak Nzl   104/5 468 ao 364  3.50 152 ( 7) Lees W.K
10.1984 0975 Nzl Eng   115/5 463 ao 348  3.03 164 ( 7) Randall D.W
11.2008 1857 Ind Aus W 121/5 463 ao 342  2.83 162 ( 6) Symonds A
12.1931 0209 Nzl Eng   129/5 454 ao 325  2.52 137 ( 7) Ames L.E.G
13.2005 1759 Zim Nzl W 113/5 452/9  339  3.00 127 ( 8) Vettori D.L
14.1983 0972 Win Ind    92/5 451/8  359  3.90 236*( 4) Gavaskar S.M
15.1994 1264 Eng Saf   105/5 447 ao 342  3.26 104 ( 6) Kirsten P.N
16.1970 0675 Eng Aus   107/5 440 ao 333  3.11 171 ( 5) Redpath I.R
17.2001 1566 Bng Zim    89/5 431 ao 342  3.84  94 ( 6) Wishart C.B
18.2006 1824 Eng Aus W  84/5 419 ao 335  3.99 156 ( 7) Symonds A
19.1984 0997 Aus Win W 104/5 416 ao 312  3.00 139 ( 7) Dujon P.J.L
20.1981 0907 Aus Eng W 104/5 404 ao 300  2.88 118 ( 7) Botham I.T

Truly a list of the greatest recoveries ever. We make our acquaintance with Bradman, Imtiaz and Hill again. We should admire the recent double hundred of Thorpe. Gavaskar's 236*, against Marshall, Roberts, Holding and Davis, at the unusual batting position of 4, must compete with his swan song classic of 97 for being considered his best innings. Symonds has played two such innings.

Since this test balances the ratio and runs added measures, the number of tests won in these matches is also likely to be on the higher side. 34 teams have won out of 87.

To view/down-load the complete table, please click/right-click here.

I would appreciate if readers download the master table, import into an Excel sheet and come out with nice nuggets of information including country-wise numbers. These would be published with due acknowledgement.

The one thing that strikes me at the outset is that there are very few such recoveries by India, barring the one led by Gavaskar. I am not sure whether this indicates a lack of quality of the Indian late order batsmen or the strength of top order batting or a combination of both. Surprisingly, West Indies and Pakistan have many such recoveries.

Winning a Test against Sri Lanka in Sri Lanka is one of the toughest tasks going around, but beating them in a home venue after losing the toss is perhaps the toughest task in international cricket. In April next year, the island will celebrate a decade of never having lost a home Test in which they’ve won the toss. An awesome stat for them, and a scary one for all opponents.

The table below lists the records of all teams after winning tosses in home games, and none is as imposing as the Sri Lankans. In 19 matches before the ongoing one in Colombo, they’d won 15 and drawn four. Their preferred method has been, as you’d expect, bat first and knock the stuffing out of the opposition – they’ve done that 11 times. And on six of the seven occasions when they’ve fielded, the opponents have been Bangladesh – so the move was probably to ensure an early finish to the match. None of the other sides have a record which is as dominant, though Pakistan haven’t lost any of ten Tests either. (To see how these teams perform when they lose the toss, click here.)

The last team to achieve the near-impossible feat of losing the toss and winning the match against Sri Lanka in Sri Lanka was England, in that acrimonious series in 2001, when they edged past the home team by four wickets at the SSC. On the basis of what has been witnessed in the first two sessions of the current match at the SSC, it can safely be said that MS Dhoni’s team won’t repeat that feat over the next four days.

Win-loss ratios of teams in Tests at home after winning toss since April 2001

Team	Matches	Win/loss	Draw	W/L ratio	Bat ave	Bowl ave
Sri Lanka	19	15/ 0	4	-	50.37	22.79
Pakistan	10	5/ 0	5	-	48.56	37.21
Australia	24	20/ 3	1	6.67	47.44	28.52
England	30	19/ 3	8	6.33	42.33	29.41
India	20	9/ 2	9	4.50	43.38	33.21
South Africa	24	14/ 7	3	2.00	37.20	29.87
New Zealand	21	9/ 5	7	1.80	31.69	30.12
West Indies	24	5/ 11	8	0.45	32.85	35.59
Zimbabwe	13	3/ 7	3	0.42	28.99	36.64
Bangladesh	20	1/ 18	1	0.05	23.87	46.30

This article is dedicated to Muralitharan, arguably the greatest but certainly one of the greatest of all Test bowlers. I will not be doing any comparisons with other bowlers, that will be done in a later article. I will probably select all the other top bowlers to do a comparison. In this article, as a mark of appreciation and admiration for this wonderful bowler and person, I will do comparisons only within his own career. I would appreciate if readers remember this view and no negative comments are made on one of the greatest ever. Let us leave that task to Mr Bedi and umpires whose sole claim to fame will be to act as nothing more than mere historical footnotes in his legendary career. I hope the reader will pardon this moment of strong feeling on my part. But it comes in disgust at the horrendous treatment to a great bowler, who took it in the most gentlemanly way and came through a stronger man. My own personal feelings apart, I hope to highlight Murali's achievements through numbers.

Muralitharan's career is analysed from many points of view. Some of these tables might be available elsewhere but a few are quite new and are being done for the first time. The Wikipedia entry on Muralitharan incidentally is full of very useful and nice-to-know facts. The summary file containing all these tables is available at the end for viewing/downloading. I have stayed away from tables on country performances since that is often shown on television screens and I have to keep this article to reasonable size. Anyhow Murali is the only bowler to have captured 50+ wickets against all Test-playing countries and three of these are 100+ wickets. Also this article covers only Murali's Test performances.

1. Career summary

Tests played:     133
Wickets captured: 800 
Wickets/Test:     6.02
Runs conceded:    18180
Overs bowled:     7340.0
Bowling average:  22.73
Strike rate:      55.0
Runs/over:        2.48
10 wkts in match: 22 (4 in consecutive tests, that too twice, and against all 9 
countries).
5 wkts in Inns:   67
Maidens bowled:   1792
Maidens %:        24.4
Best bowling:     40.0-19-51-9 (the first 9 wickets !!!). There is another 
9-wkt haul.
Fielder combination: 77 (Murali/Jayawardene - highest for non wicket-keeper).

This has been given just to provide a starting point. And what a starting point !!! What does one say.

- Let us not forget the 508 ODI wickets, again the leading ODI bowler of all time. He and Tendulkar lead both forms of cricket in terms of wickets and runs respectively.
- 800 wkts for Sri Lanka is followed by Vaas with 355 and Jayasuriya/Malinga with 98.
- 92 wickets ahead of the next bowler, Warne, and 181 wickets ahead of the third placed bowler, Kumble.
- A wickets/Test figure comparable to the best pre-war bowlers who bowled on uncovered wickets.
- A spinner with a bowling average that is normally expected of a fast bowler.
- A spinner with a fast bowler's strike rate.
- A tally of 10 wickets per match which is more than double that of the next placed bowler.
- A 5 wickets per innings count nearly double of the next.
- A quarter of overs bowled have been score-less.

Muralitharan is the nearest a bowler has come to Bradman, the batsman. It is safe to conclude that Bradman's batting average and Murali's tally of Test wickets are the two landmarks which are never likely to be broken. In terms of the overall impact Muralitharan has had on Sri Lankan cricket, I place him no less than Bradman as a cricketer.

2. Dismissals analysis

Batsmen early dismissals: 145 - 18.1% of Career wkts

Batsmen 50+ average :      60 -  7.5% of Career wkts
Batsmen 40+ average :     152 - 19.0% of Career wkts
Batsmen 30+ average :     157 - 19.6% of Career wkts
Batsmen 20+ average :     184 - 23.0% of Career wkts
Batsmen 20- average :     247 - 30.9% of Career wkts

Top order batsmen :       280 - 35.0% of Career wkts
Middle order batsmen :    260 - 32.5% of Career wkts
Late order batsmen :      260 - 32.5% of Career wkts

Unassisted dismissals :   352 - 44.0% of Career wkts
Assisted dismissals :     448 - 56.0% of Career wkts.

This is an analysis of the individual dismissals.

The first entry refers to the number of dismissals of batsmen well before they are set. This is a variable analysis in that I have selected only dismissals of batsmen at scores 25 or more runs below their batting average. Tendulkar at scores of below 31, Ponting at scores below 30, Lara at scores below 27, Langer at scores below 21, McCullum at scores below 10 and so on.

Next is an analysis of all dismissals from the point of view of dismissed batsman's batting average. Over a quarter of Muralitharan's dismissals have been of genuine batsmen with 40+ averages. Just over 30% of his dismissals have been of less talented batsmen, understandable in view of the inability of these batsmen to read Murali.

The third grouping refers to the batting position rather than batting average. This is especially relevant against the minnows many of whose top order batsmen would have batting averages of around 20-30.

The last grouping is a split by type of dismissal. Bowled, Lbw and Return catch fall into the first entry and the other dismissals, the next entry. For 44% of the dismissals, Murali did not depend on others, barring the umpires for Lbws. It was appropriate that the last and 800th wicket was a Muralitharan-Jayawardene combination.

3. Innspells analysis

Career :       133 800 6.02 18180 44040 22.73        2.48

Home :          73 493 6.75  9646 25062 19.57 116.1% 2.31 107.3%
Away :          60 307 5.12  8534 18978 27.80  81.8% 2.70 107.3%

First inns :   133 458 3.44 10968 26527 23.95  94.9% 2.48  99.8%
Second inns :  129 342 2.65  7212 17513 21.09 107.8% 2.47 100.2%

Top teams :    108 624 5.78 15523 36606 24.88  91.4% 2.54  97.3%
Minnows :       25 176 7.04  2657  7434 15.10 150.5% 2.14 115.5%

Career 1 half:  67 356 5.31  8804 21955 24.73  91.9% 2.41 102.9%
Career 2 half:  66 444 6.73  9376 22085 21.12 107.6% 2.55  97.2%

Team wins :     54 438 8.11  7088 18726 16.18 140.4% 2.27 109.1%
Team draws :    30 112 3.73  3500  9099 31.25  72.7% 2.31 107.3%
Team losses :   49 250 5.10  7592 16215 30.37  74.8% 2.81  88.2%

Innspells:     227  Productive innspells: 218  (96.0%)

This analysis has as the base, the complete innspell.

As with most bowlers, Murali's home performance is about 40% better than his away performance. However let us not forget that Murali's away performances fall short only by the high standards he himself has set. He has captured 5.1 wickets per Test, away, and has averaged 27.12, both higher than any other contemporary bowler.

Murali's second innings performances are about 16% better than his first innings. However his bowling accuracy has been almost the same in both innings.

Now we come to an important split. Against the minnows, Zimbabwe and Bangladesh, Murali has averaged 7+ wickets per Test (higher than the highest ever), averaged 15 (150% of his career average) and captured nearly 25% of his total tally of wickets. His performances against the minnows is over 60% better than the performance against the top teams.

Murali had two halves of his career as different as chalk and cheese. In almost every measure the second half was around 15-25% better than his first half. The only measure where he has performed better in the first half is in his accuracy.

Murali's contributions in Sri Lanka's wins are out of the world, nearly twice as good as the ones in drawn and losing matches. His wickets per winning Test was an amazing 8+ at an average of 16.

A single fact is sufficient to put Muralitharan's contribution in Sri Lankan wins in perspective. In the 38 Tests Sri Lanka played before Muralitharan's debut, they won 2 Tests. Subsequently in 133 Tests Muralitharan played in, Sri Lanka won 54 Tests. There has never been a more widely varying statistic. 5.3% before compared to 40.6% afterwards. Of course Ranatunga, Vaas, Aravinda De Silva, Jayawardene, Sangakkara et al have played their part. However the leading person in this revival is Muralitharan.

I have used my definition of consistent bowling to do a simple calculation. Any innspell in which Murali bowled more than 10 overs is considered as a considered innspell. Out of these I have considered any spell in which he has gone at least one wicket as relevant ones. His effectivity index was an astounding 96%. In only 9 spells, out of 227, has he gone wicketless.

4. Best & Worst periods

Best year :             90 (2006)

Worst year :            14 (1996)

Best 10-match streak :  89 (1802-1839)

Worst 10-match streak:  29 (1265-1319)

These figures are self-explanatory. 2006 was the golden year for Murali and a decade back, during 1996, he had the worst year, no doubt caused by the Australian accusations. Two ways of looking at what happened in 1996 and afterwards. He might have captured well over 800 wickets. Or, more likely, he steeled within because of the blatantly unfair accusations and performed much better.

5. Share of team wickets

Overall:       800  Team - 2065  Share - 38.7%

Home :         493  Career % - 61.6  Team - 1240  Share - 39.8%
Away :         307  Career % - 38.4  Team -  825  Share - 37.2%

First inns :   458  Career % - 57.2  Team - 1239  Share - 37.0%
Second inns :  342  Career % - 42.8  Team -  826  Share - 41.4%

Top teams :    624  Career % - 78.0  Team - 1598  Share - 39.0%
Minnows :      176  Career % - 22.0  Team -  467  Share - 37.7%

Career 1 half: 356  Career % - 44.5  Team -  967  Share - 36.8%
Career 2 half: 444  Career % - 55.5  Team - 1098  Share - 40.4%

Team Wins :    438  Team - 1070  Share - 40.9%
Team draws :   112  Team -  354  Share - 31.6%
Team losses :  250  Team -  641  Share - 39.0%

Muralitharan's overall share of team wickets is 38.7. This figure is exceeded slightly at home and below away. In the second innings his share moves up considerably to 41.5%. Against the minnows the others have also reaped the rewards. His career jump during the second half of his career is reflected in the share of team wickets also. Finally he has captured over 41% of the team wickets in won matches for Sri Lanka. A peculiar feature has emerged here. Muralitharan's share in drawn matches is way below his share in won or lost matches.

To view/down-load the complete table, please click/right-click here.

6. Into the crystal ball

Finally a note on whether Murali's tally of wickets would ever be surpassed. It was only Murali's innate goodness and hospitable nature which prompted him to say that Harbhajan is the only bowler capable of overhauling him. The truth is that there is probably less than 1% chance that Murali's record would be broken. I think Warne is right in saying that his record will stand forever. The relevant numbers, based on career performances and extrapolations on continuing their performances forever, are given below.

Harbhajan (12y/84t/355w) would take another 15 years and 105 Tests to go past Murali. He would be 45 by that time and would probably be enjoying a settled family life, not bowling doosras and teesras. 600 seems to be Harbhajan's limit.

Steyn (6y/41t/211w) would take another 16 years and 114 Tests to overtake Murali. Steyn, despite (or because of) his awesome strike rate, would have hung up his boots well before that time. For Steyn, 500 seems to be the pinnacle.

A new bowler making his debut next month would have to play 160 Tests over 20 years, in view of the ODIs, T20s and IPL-type jamborees, and maintain 5 wickets per Test. That is the 1% I have talked about earlier.

Other bowlers like Kallis and Vettori would probably require another 20 years and 200 Tests to reach 800. By that time, a son Vettori might very well be playing for New Zealand.

If there are any other analyses which could be done on Muralitharan's career, I invite readers to mail their suggestions. Let me also mention here that I have done this program as a general purpose one and could easily do that for all bowlers. That is what I would do in my follow-up analysis comparing the key measures of the top bowlers.

My only regret is that I wish Murali had chosen to play more Tests and cut down on ODIs and IPL. However the lure of IPL was probably too much of an attraction.

7. Muralitharan as a batsman

Muralitharan as a batsman was a very effective, entertaining and unorthodox no.11. His overall batting figures (1261 at 11.68) might not be very impressive. However he has played many a good potentially match-winning and match-saving innings, both in Tests and ODIS, as chronicled below. This is not necessarily a complete list.

Tests

- 26 against New Zealand in 1998.
- 22 against Pakistan in 2000.
- 43 against Australia in 2004.
- 36 against West Indies in 2005.
- 33 against England in 2006.

ODIs (both innings during 2009)

- 33 in 16 vs Bangladesh, who scored 152 and Sri Lanka were 114 for 8. 
           Murali took them to 153 for 8. 
- 32 in 15 vs Pakistan.

A final salute to the wonderful bowler and human being that Muralitharan is. There will never be a bowler like him. And the people of Sinigama, where he has helped build 1000 houses for the tsunami victims, will say that there has never been a human being like him.

In a recent Numbers game piece, the focus was on the match winning ability of South African spearhead Dale Steyn. Steyn has proven to be by far the best fast bowler in the last few years which have been predominantly in favour of batsmen. The earlier decades were more balanced with sporting pitches and presence of top quality fast bowlers in most teams. This prompted me to take a statistical look at match winning Test bowlers since 1970. Quite a few interesting numbers and names pop up during the course of this exercise.

The first table lists the bowlers with the best bowling averages in Test victories. Of all the bowlers, who have a minimum of 100 wickets in wins; Richard Hadlee has the best numbers. A stunning average of just over 13, with a strike rate of 33 further emphasises how important he was for New Zealand throughout his career. New Zealand did not win a single game when Hadlee wasn’t a part of the team. Imran Khan led Pakistan brilliantly throughout the 1980’s when they were the only team to compete with the West Indies, drawing three series against them. The presence of Dale Steyn at the top shows what an incredible match winner he has been for South Africa over the last few years.

Muttiah Muralitharan, who announced his retirement from Test cricket recently has been the key to Sri Lanka’s successes both home and away. His 16 wicket haul at the Oval enabled Sri Lanka to win their first series in England. Malcolm Marshall and Michael Holding were crucial to the success of the West Indies through the 70’s and 80’s. When both played together, the West Indies lost only a single match and won 19. Marshall was the best of the West Indian bowlers with excellent performances home and away and in all conditions. He averaged 23.05 in the subcontinent and an astounding 11.72 in subcontinent wins. The presence of Waqar Younis, Shoaib Akthar and Curtly Ambrose at the top clearly shows how vital they were to their team’s fortunes. Ambrose played quite a few matches in a team that was on its way down and together with Courtney Walsh, carried the hopes of success for the West Indies for much of the 90’s.

Bowlers with the best averages in Test wins (minimum of 100 wickets in wins)

Bowler	Team	Total Matches	Total Wickets	Matches won	Wickets in wins	Average in wins	Strike rate in wins	5	10
Sir Richard Hadlee	NZ	86	431	22	173	13.06	33.5	17	8
Imran Khan	Pak	88	362	26	155	14.5	38.3	11	6
Dale Steyn	SA	40	205	21	149	16	28.2	13	4
Muttiah Muralitharan	SL	132	792	53	430	16.03	42.6	40	18
Malcolm Marshall	WI	81	376	43	254	16.78	38.1	17	4
Allan Donald	SA	72	330	33	187	16.79	35.5	14	3
Curtly Ambrose	WI	98	405	44	229	16.86	44.4	13	3
Shoaib Akthar	Pak	46	178	20	104	17.36	33.4	7	2
Waqar Younis	Pak	87	373	39	222	18.2	35	14	4
Dennis Lillee	Aus	70	355	31	203	18.27	39	17	6
Shaun Pollock	SA	108	421	49	223	18.3	47.5	9	1
Michael Holding	WI	60	249	31	152	18.36	40.1	6	1
Wasim Akram	Pak	104	414	41	211	18.48	42.3	13	2
Anil Kumble	Ind	132	619	43	288	18.75	44.4	20	5
Glenn McGrath	Aus	124	563	84	414	19.19	47.7	18	3

The next table lists the bowlers with the best averages in Test wins at home. The presence of Hadlee, Imran and Marshall is not surprising. Dennis Lillee was Australia’s best fast bowlers throughout his career and this is vindicated by his presence. Anil Kumble and Harbhajan Singh have been crucial to almost every Indian win at home over the last fifteen years. Muralitharan has been nothing short of exceptional in home conditions, enabling Sri Lanka to be a very potent force in home games. His average though does go up a notch when the matches against Bangladesh and Zimbabwe are not considered. He averages close to 18 with a strike rate of about 47 in games not involving these two teams. Ian Botham bowled brilliantly in the 1981 Ashes and his remarkable performances kept England competitive, but his average of nearly 32 against West Indies, the best team of his era was rather poor.

Bowlers with the best averages in home Test wins (minimum of 100 wickets in home wins)

Bowler	Team	Home matches	Wickets	Home matches won	Wickets in wins	Average	Strike rate	5	10
Sir Richard Hadlee	NZ	43	201	15	109	13.95	34.9	9	3
Imran Khan	Pak	38	163	17	100	14	37	7	3
Waqar Younis	Pak	33	162	15	101	15.1	30.1	8	3
Muttiah Muralitharan	SL	72	485	36	297	15.21	41.9	29	13
Allan Donald	SA	38	177	21	127	16.36	33.5	11	2
Anil Kumble	Ind	63	350	28	208	17.38	44.8	16	5
Malcolm Marshall	WI	31	157	19	114	17.44	37.2	7	2
Curtly Ambrose	WI	52	203	26	131	17.5	46.2	7	2
Shaun Pollock	SA	59	235	35	155	18.5	47.1	7	1
Dennis Lillee	Aus	44	231	24	157	19.14	40.6	11	4
Harbhajan Singh	Ind	47	237	23	147	20.06	48.8	11	3
Ian Botham	Eng	59	226	22	120	20.13	43.6	11	1
Courtney Walsh	WI	58	229	27	127	20.4	49.3	3	1
Craig McDermott	Aus	43	193	20	107	20.53	44.2	7	2
Glenn McGrath	Aus	66	289	53	249	20.54	50.3	9	2

The list of bowlers with the best averages in away wins brings up some new names. Apart from the top bowlers like Marshall, Ambrose, Holding and McGrath, the presence of Zaheer Khan and Jason Gillespie is an excellent indicator of their superb away performances over the years. Gillespie, together with McGrath, formed the best opening bowling pair in the world for much of the first half of the 2000’s. Zaheer Khan’s bowling over the last few years has been instrumental in India’s improved away performances.

Bowlers with the best averages in away Test wins (minimum of 75 wickets in away wins)

Bowler	Team	Away matches	Wickets	Matches won	Wickets	Average	Strike rate	5	10
Curtly Ambrose	WI	46	202	18	98	16.02	41.9	6	1
Malcolm Marshall	WI	50	219	24	140	16.25	38.8	10	2
Glenn McGrath	Aus	58	274	31	165	17.15	43.8	9	1
Michael Holding	WI	37	163	17	95	17.27	38.3	6	1
Muttiah Muralitharan	SL	60	307	17	133	17.88	44.3	11	5
Wasim Akram	Pak	63	260	25	128	18.9	43.8	8	1
Courtney Walsh	WI	74	290	25	112	18.95	42.7	7	1
Waqar Younis	Pak	54	211	24	121	20.79	9	6	1
Shane Warne	Aus	76	389	43	260	21.25	47.8	15	4
Jason Gillespie	Aus	42	149	27	115	22.1	45.7	5	0
Anil Kumble	Ind	69	269	15	80	22.28	43.5	4	0
Zaheer Khan	Ind	43	164	17	77	24	42.9	3	1
Brett Lee	Aus	35	124	23	80	30.38	52.4	2	0

The tables below take a look at the bowlers with the highest percentage of their wickets in wins. The presence of bowlers from Australia and West Indies on top is a clear indicator that they were part of world class teams. Stuart MacGill and Jason Gillespie picked up more than 75% of their wickets in wins. Three more Australians McGrath, Lee and Warne make up the list at the top along with Dale Steyn. Marshall and Holding are also high on the list but the numbers are a little lower considering the higher number of draws then. Ambrose, Muralitharan and Akram despite being world class match winners were never part of a top team throughout and they have picked up only about half their wickets in wins.

Also listed in the table is the percentage contribution by a bowler to a team’s wickets in wins. Muralitharan and Richard Hadlee contributed over 40 % of the team wickets in wins which undoubtedly is an indicator of the team’s dependence on them. McGrath and Warne were part of a much more powerful bowling attack and the numbers are much more evenly distributed between them. Marshall, despite being a part of a quality attack, was easily the finest bowler and contributed over 30% of the team wickets in wins. Anil Kumble’s bowling was the single biggest reason why India were among the best at home through the 90’s and 2000’s and his contribution of almost 35% of the team’s wickets illustrates that.

Percentage contribution in wins(minimum 150 wickets in wins)

Bowler	Team	Matches	Wickets	Matches won	Wickets in wins	Average in wins	% of total wickets	Team Wickets	% of team wickets
Stuart MacGill	Aus	44	208	31	165	24.4	79.32	598	27.59
Jason Gillespie	Aus	71	259	47	197	21.68	76.06	909	21.67
Glenn McGrath	Aus	124	563	84	414	19.19	73.53	1607	25.76
Dale Steyn	SA	41	211	22	155	15.85	73.46	426	36.38
Brett Lee	Aus	76	310	54	225	27.52	72.58	1034	21.76
Shane Warne	Aus	145	708	92	510	22.47	72.03	1765	28.89
Malcolm Marshall	WI	81	376	43	254	16.78	67.55	828	30.67
Jacques Kallis	SA	140	266	68	167	23.41	62.78	1312	12.72
Michael Holding	WI	60	249	31	152	18.36	61.04	601	25.29
Makhaya Ntini	SA	101	390	50	233	22.21	59.74	966	24.12
Waqar Younis	Pak	87	373	39	222	18.2	59.51	761	29.17
Dennis Lillee	Aus	70	355	31	203	18.27	57.18	600	33.83
Allan Donald	SA	72	330	33	187	16.79	56.67	627	29.82
Curtly Ambrose	WI	98	405	44	229	16.86	56.54	839	27.29
Muttiah Muralitharan	SL	132	792	53	430	16.03	54.29	1018	42.23
Harbhajan Singh	Ind	83	355	35	192	21.67	54.08	675	28.44
Shaun Pollock	SA	108	421	49	223	18.3	52.96	940	23.72
Wasim Akram	Pak	104	414	41	211	18.48	50.96	804	26.24
Chaminda Vaas	SL	111	355	43	166	22.63	46.76	822	20.19
Anil Kumble	Ind	132	619	43	288	18.75	46.52	838	34.36
Courtney Walsh	WI	132	519	52	239	19.72	46.05	992	24.09
Ian Botham	Eng	102	383	33	172	20.09	44.90	631	27.25
Imran Khan	Pak	88	362	26	155	14.5	42.81	506	30.63
Sir Richard Hadlee	NZ	86	431	22	173	13.06	40.13	424	40.80

Australia, did not lose a single series at home for over 15 years until the loss to South Africa in 2009. This dominance can be seen in Glenn McGrath’s extraordinary figures of 87% of wickets in home wins. Steve Harmison is the surprise entry at the top, with 80% of his home wickets in wins. The other top bowlers in home wins include the Australians Warne and Lee and fast bowlers Malcolm Marshall and Allan Donald.

Muralitharan, as expected contributes 43% to the team wickets in home wins, while Anil Kumble and Hadlee are not far behind with about 38%. The Australian pairing of McGrath and Warne contributes a more even 25%.

Percentage contribution in home wins(minimum 100 wickets in wins)

Bowler	Team	Home matches	Wickets at home	Matches won	Wickets	Average	% of wickets	Team Wickets	% of team wickets
Glenn McGrath	Aus	66	289	53	249	20.54	86.15	1013	24.58
Steve Harmison	Eng	32	133	23	107	25.74	80.45	428	25
Shane Warne	Aus	69	319	49	250	23.73	78.37	934	26.76
Brett Lee	Aus	41	186	31	145	25.93	77.95	585	24.78
Stuart MacGill	Aus	27	135	20	105	24.20	77.77	386	27.20
Malcolm Marshall	WI	31	157	19	114	17.44	72.61	368	30.97
Allan Donald	SA	38	177	21	127	16.36	71.75	401	31.67
Jacques Kallis	SA	74	148	44	105	24.92	70.94	849	12.36
Dennis Lillee	Aus	44	231	24	157	19.14	67.96	463	33.91
Shaun Pollock	SA	59	235	35	155	18.50	65.95	675	22.96
Makhaya Ntini	SA	53	249	32	162	20.66	65.06	617	26.25
Curtly Ambrose	WI	52	203	26	131	17.50	64.53	496	26.41
Waqar Younis	Pak	33	162	15	101	15.10	62.34	290	34.82
Harbhajan Singh	Ind	47	237	23	147	20.06	62.02	446	32.95
Imran Khan	Pak	38	163	17	100	14.00	61.34	333	30.03
Muttiah Muralitharan	SL	72	485	36	297	15.21	61.23	692	42.91
Anil Kumble	Ind	63	350	28	208	17.38	59.42	545	38.16
Chaminda Vaas	SL	56	180	28	103	22.22	57.22	538	19.14
Courtney Walsh	WI	58	229	27	127	20.40	55.45	513	24.75
Craig McDermott	Aus	43	193	20	107	20.53	55.44	395	27.08
Sir Richard Hadlee	NZ	43	201	15	109	13.95	54.22	286	38.11
Ian Botham	Eng	59	226	22	120	20.13	53.09	422	28.43

The final table looks at the percentage of wickets in away wins and the contribution to team wickets in away wins. Jason Gillespie is on top here with almost 77% of his away wickets coming in wins. Shane Warne and Brett Lee, who were also a part of the top class Australian team make up the top three. Marshall and McGrath were consistent performers for their respective teams in away conditions and their presence is justified. Pakistan’s opening bowling pair of Wasim Akram and Waqar Younis pick up about 50% of their wickets in away wins. Zaheer Khan and Anil Kumble are the biggest contributors to Indian away wins in the last decade.

As expected, Muralitharan contributes 43% of the team’s wickets in away wins, despite the Sri Lankan team not being as dominant away as in home conditions. Shane Warne and Malcolm Marshall, with more than 30% contribution to team wickets definitely prove their worth in away matches.

Percentage contribution in away wins(minimum 75 wickets in wins)

Bowler	Team	Away matches	Wickets away	Matches won	Wickets	Average	% of wickets	Team Wickets	% of team wickets
Jason Gillespie	Aus	42	149	27	115	22.10	77.18	527	21.82
Shane Warne	Aus	76	389	43	260	21.25	66.83	831	31.28
Brett Lee	Aus	35	124	23	80	30.38	64.51	449	17.81
Malcolm Marshall	WI	50	219	24	140	16.25	63.92	460	30.43
Glenn McGrath	Aus	58	274	31	165	17.15	60.21	594	27.77
Michael Holding	WI	37	163	17	95	17.27	58.28	334	28.44
Waqar Younis	Pak	54	211	24	121	20.79	57.34	471	25.69
Wasim Akram	Pak	63	260	25	128	18.90	49.23	490	26.12
Curtly Ambrose	WI	46	202	18	98	16.02	48.51	343	28.57
Zaheer Khan	Ind	43	164	17	77	24.00	46.95	328	23.47
Muttiah Muralitharan	SL	60	307	17	133	17.88	43.32	326	40.79
Courtney Walsh	WI	74	290	25	112	18.95	38.62	479	23.38
Anil Kumble	Ind	69	269	15	80	22.28	29.73	293	27.30

* Team wickets are in matches involving player.

When one views T20 matches, there seems to be a feeling of continuous activity, not because there is a contest between bat and ball but because of the boundaries being hit, the stadium noise and the IPL hangover. At the end of the match Ravi Shastri, irrespective of how the match finished, would say that it was a "humdinger of a match". Alternately some other anchor would mouth similar "words of wisdom". But I have always felt that the matches are not as close as they are made out to be. The excitement seems to be a "manufactured" one. How does one prove or disprove this seemingly subjective observation? I propose to do that by delving into the scorecards and coming out with a suitable analysis.

First let us eliminate some of the matches. Needless to say that only T20 internationals will be considered. IPL matches are not true international matches. Also if the IPL is to be included, then all other club leagues should be included. All matches which finished through the D/L route are discarded. It is clear to most people that, Queen's honours notwithstanding, the learned duo, M/s Duckworth and Lewis, have made a pig's breakfast of the D/L calculations when it comes to T20 matches. More about it in a later article. Finally matches which finished in a tie and decided through the single-over-eliminator will be discarded. After all when the 40 overs were bowled, the teams have finished dead level.

That leaves us with 168 matches (out of the 185 we started with). Now we will separate the wins defending totals (first batting team wins) from the wins chasing the target totals since the two wins are as different as chalk and cheese. One is a bowler-driven defensive win and the other is a batsman-driven attacking win.

First let us take the matches won by teams batting first and defending their totals. There are 83 such matches, just below the 50% mark. There is only one objective in front of the defending team: restrict the opposite team to a total below their own total. Whether this is done by dismissing the other team or restricting them to a total below the total is immaterial. The win is stated as "by x runs" and this is the only measure necessary to measure the type of win. The only factor to be taken into consideration is that a match score of 200/190 is a less emphatic win than a match score of 100/90. This is achieved by dividing the run differential by the first innings total and the Win Index arrived at.

It is a fact that T20 wickets are cheaper to get than ODI wickets (a Balls-per-wicket value of 18.2 against 42.6). This makes the wickets valuation somewhat difficult. I tried adding the wickets captured component to the Win Index. It did not work out, especially for very close matches. Take a match such as 150/148 a.o. By all criteria this is a very close match and should have a very low Win Index value. However once I give credit to the winning team for capturing wickets, the Win Index moves way up and goes into a comfortable win zone (because of the 10 wickets), which is wrong.

A few statistical highlights of this group of matches.

1. The average Win Index is 20.5. This can be compared to the average for the other group later.
2. The average first innings score is 169 for 6.5 wickets.
3. The average second innings score is 133 for 8.5 wickets.
4. The average winning margin is 36 runs, which makes the wins quite comfortable.
5. Out of the 83 matches, the losing team has lost 8 or more wickets in 59 matches (71%).

Before we look at the tables, let me emphasise that absolute values cannot be used in these exercises. An over represents 5% of a team's balls-resource unlike ODIs in which an over represents 2% of the resource. There is less of a difference in terms of runs since T20 scoring rates are higher. Even then, 10 runs in T20 represents around 6% of the average T20 total while the same 10 runs represents around 4% in ODIs. What is normal in T20s is difficult in ODIs. Hence all comparisons are only in relative % values.

Now for the tables.

Matches won by teams defending totals

No  Win  MtId Cty  First Inns  Vs Second Inns  Vs Team Result
   Index          <--Score-->     <--Score-->

 1. 66.2 0027 Slk 260  6 20.0 Ken  88 10 19.3 lost by 172 runs
 2. 61.6 0094 Saf 211  5 20.0 Sco  81 10 15.4 lost by 130 runs
 3. 59.2 0075 Zim 184  5 20.0 Can  75 10 19.2 lost by 109 runs
 4. 55.9 0002 Eng 179  8 20.0 Aus  79 10 14.3 lost by 100 runs
 5. 50.7 0152 Win 138  9 20.0 Ire  68 10 16.4 lost by 70 runs
 6. 50.2 0055 Pak 203  5 20.0 Bng 101 10 16.0 lost by 102 runs
...
...
...
70.  4.7 0114 Pak 149  4 20.0 Saf 142  5 20.0 lost by 7 runs
71.  4.6 0123 Pak 153  5 20.0 Nzl 146  5 20.0 lost by 7 runs
72.  3.2 0046 Ind 157  5 20.0 Pak 152 10 19.3 lost by 5 runs
73.  3.0 0036 Nzl 164  9 20.0 Eng 159  8 20.0 lost by 5 runs
74.  2.3 0130 Can 176  3 20.0 Ire 172  8 20.0 lost by 4 runs
75.  2.1 0120 Nzl 141  8 20.0 Slk 138  9 20.0 lost by 3 runs
76.  2.0 0109 Eng 153  7 20.0 Ind 150  5 20.0 lost by 3 runs
77.  1.6 0134 Aus 127 10 18.4 Pak 125  9 20.0 lost by 2 runs
78.  1.2 0007 Slk 163 10 20.0 Eng 161  5 20.0 lost by 2 runs
79.  1.0 0006 Saf 201  4 20.0 Aus 199  7 20.0 lost by 2 runs
80.  0.8 0179 Saf 120  7 20.0 Win 119  7 20.0 lost by 1 run
81.  0.8 0167 Nzl 133  7 20.0 Pak 132  7 20.0 lost by 1 run
82.  0.8 0099 Saf 128  7 20.0 Nzl 127  5 20.0 lost by 1 run
83.  0.7 0083 Aus 150  7 20.0 Nzl 149  5 20.0 lost by 1 run

It can be seen that 5 of the 83 matches have been won with a very high Win Index of 50+. However more importantly, only 14 matches (around one in six matches) could be classified as close matches. The winning margin in the other matches has been 10 runs or more which is quite comfortably a full-over score. This puts paid, at least for these types of wins, to the general feeling that the T20 matches are close matches. Five out of 6 are not.

Now for wins by the second batting teams. There are 85 such matches, which is just over 50%. These are batsmen-driven chasing wins. The chasing team works with two clearly identified resources. The first, and the more important one, explained later, is the number of balls, normally 120. The other one is the number of wickets, 10. The win is normally stated in the lesser of the two resources, wickets. This is less of a resource restriction since the overall balls-per-wicket figure for all 185 matches is 18.2, meaning that the average number of wickets lost would be 6.4 in a 120-ball innings.

The balls left and the wickets left form the basis for determining the Win Index. The proportion of balls left to the maximum balls carries a 66.7% weight. The wickets remaining carries a 33.3% weight. This is not a linear scale since the top order wickets are more valuable. The wicket values are 0.12, 0.12, 0.12, 0.12, 0.12, 0.10, 0.10, 0.08, 0.06 and 0.06 for wickets 1-10. For instance if a team has lost only 1 wicket, their valuation for this component is 0.293 (0.333*0.88). On the other hand if they have lost 7 wickets the valuation for this components 0.067 (0.333*0.20) and so on.

A few statistical highlights of this group of matches.

1. The average Win Index is 27.5. This is a 25% increase over the first group of matches indicating that the chasing wins are a little more easy and the index values are higher.
2. The average first innings score is 129 for 8.0 wickets.
3. The average second innings score is 131.6 for 3.8 wickets. This confirms the view that the wins are relatively easier.
4. The average winning margin is 6.2 wickets and 18 balls. which makes the wins very comfortable.
5. Out of the 83 matches, the losing team has lost 5 or fewer wickets in 68 matches (80%).

Matches won by teams chasing totals

No  Win  MtId Cty  First Inns  Vs Second Inns  Vs Team Result
   Index          <--Score-->     <--Score-->

 1. 72.2 0131 Bng  78 10 17.3 Nzl  79  0  8.2 won by 10 wickets
 2. 70.4 0021 Ken  73 10 16.5 Nzl  74  1  7.4 won by 9 wickets
 3. 65.5 0041 Slk 101 10 19.3 Aus 102  0 10.2 won by 10 wickets
 4. 61.6 0014 Pak 129  8 20.0 Saf 132  0 11.3 won by 10 wickets
 5. 58.3 0129 Sco 109  9 20.0 Ken 110  0 12.3 won by 10 wickets
 6. 58.2 0052 Ind  74 10 17.3 Aus  75  1 11.2 won by 9 wickets
 7. 57.0 0067 Ber  70 10 20.0 Can  71  2 10.3 won by 8 wickets
...
...
...
80.  9.4 0172 Nzl 149  6 20.0 Eng 153  7 19.1 won by 3 wickets
81.  8.9 0082 Slk 171  4 20.0 Ind 174  7 19.2 won by 3 wickets
82.  7.2 0176 Pak 191  6 20.0 Aus 197  7 19.5 won by 3 wickets
83.  7.2 0072 Slk 137  9 20.0 Pak 141  7 19.5 won by 3 wickets
84.  7.2 0048 Nzl 129  7 20.0 Saf 131  7 19.5 won by 3 wickets
85.  4.6 0151 Slk 135  6 20.0 Nzl 139  8 19.5 won by 2 wickets

In line with our findings, the top 7 wins have a Win Index value exceeding 55. Also only 6 of the matches could be termed close. The cut-off for determining close matches varies between the two types of wins.

Adding the five tied matches to the 168, only 25 of the 173 matches (14%) can be termed as quite close. The other 86% of the matches are relatively easier wins. This confirms my feeling that the excitement is mostly artificially created.

Those of you who would like to raise a point on the relative strengths of the teams, let me point out that in T20s there are fewer contests between the top teams and the minnows. This normally happens only in the World Cups.

Also furthermore, the lesser number of overs actually reduces the relative strength-differential between teams because there is lesser room for error. Hence, it is quite intriguing that there have been such wide margins of victory.

To view/down-load the table of defending wins, please click/right-click here.

To view/down-load the table of chasing wins, please click/right-click here.

In my first column for It Figures, I took a look at innings-to-innings consistency among batsmen, and reached the conclusion that, on balance, it appears to be a good thing. This time around, I've performed an analysis looking at bowlers. My methods are identical, with particular reliance on the coefficient of variation (CoV) as an estimator of consistency; please see my previous post for full details.

At the outset, it should be noted that bowling stats present a small problem. Whereas our primary concern about batsmen is how many runs they score, we tend to be interested in two things with bowlers: how many wickets they take and how many runs they concede (and, of course, the standard measure by which we judge them – the bowling average – is a quotient of the two). The problem is that it is only straightforward to observe the innings-to-innings variability of one or other of these measures at a time. For the purposes of this analysis, then, I have just relied on wickets taken.

In a way, this is helpful: although it's not a stat on which we tend to focus much attention, wickets-per-innings (WPI) is the direct equivalent of runs-per-innings (or, give or take a little adjustment for not-outs, the batting average). It is also a good, sensible measure to use to think about bowling consistency: I hope most readers would agree that a bowler who takes 5/95, 5/176, and 5/23 in consecutive innings conforms more closely to our intuitive sense of bowling consistency than one who takes 1/30, 6/180, and 2/60, even though the latter took his wickets at an identical cost in each innings.

There are some fairly good reasons why WPI is a seldom-seen stat, however. The biggest problem is that it might be heavily influenced by factors over which the bowler has no control. You might be the finest bowler in your team but, unless your captain believes that, he won't ask you to bowl much and you won't take many wickets. Moreover, if the teammates with whom you share the ball are good bowlers, they are liable to take plenty of wickets, themselves, thereby depleting the finite number of scalps left for you to claim. (Pelham Barton has made the excellent point that batting in a team of good batsmen increases your opportunity to score runs, whereas bowling in a team of good bowlers reduces your opportunity to take wickets.) For these reasons, it might be argued that WPI tells us as much about the other players in a team as it reveals about the one in whom we're interested. This is fair enough: I have to acknowledge that a bowler might have a more or less consistent record for reasons for which he cannot, himself, take all the credit or blame, but that's a way to explain differences, rather than a rationale for assuming they don't exist.

Test consistency

There's a familiar name at the top of the most consistent bowlers list (Table 1). Unless something remarkable happens in his final game, Muttiah Muralitharan will retire not only as Test cricket's most prolific wicket-taker, but also as its most consistent. He has taken between 2 and 5 wickets in over two-thirds of the Test innings in which he has bowled, and his remaining analyses are fairly evenly divided between more and less successful returns. It is predictable that these characteristics would be reflected in an exceptionally low CoV.

Joel Garner may be an example of the type of bowler whose WPI is constrained by formidable competition for the scarce resource of opposition wickets. Seeing as he took at least 4 wickets in an innings 25 times, it's hard to imagine that he wouldn't have managed more than 7 fiver-fers if wickets hadn't invariably been tumbling at the other end, too.

In the upper reaches of a list that is dominated by some very high-class bowlers, Darren Gough's name may look a tiny bit out of place, but his low CoV is testament to his dependability at a time when his country's attack sorely needed it.

Table 1: Test bowlers sorted according to consistency (coefficient of variation) in wickets-per-innings

	Name	M	I	W	Ave	W/I	SD	CoV
1.	M Muralitharan	131	226	787	22.66	3.48	1.87	0.537
2.	CTB Turner	17	30	101	16.53	3.37	1.89	0.561
3.	DW Steyn	41	75	211	23.13	2.81	1.66	0.591
4.	WJ O'Reilly	27	48	144	22.60	3.00	1.78	0.593
5.	R Peel	20	35	101	16.98	2.89	1.75	0.607
6.	J Garner	58	111	259	20.98	2.33	1.44	0.615
7.	CV Grimmett	37	67	216	24.22	3.22	2.01	0.622
8.	D Gough	58	95	229	28.40	2.41	1.53	0.633
9.	SF Barnes	27	50	189	16.43	3.78	2.41	0.638
10.	AA Donald	72	129	330	22.25	2.56	1.65	0.644
	...
12.	DK Lillee	70	132	355	23.92	2.69	1.79	0.665
	...
15.	MD Marshall	81	151	376	20.95	2.49	1.71	0.687
16.	B Lee	75	148	308	30.71	2.08	1.44	0.690
	...
19.	A Kumble	132	236	619	29.65	2.62	1.84	0.700
20.	SK Warne	144	271	702	25.53	2.59	1.82	0.701
21.	RJ Hadlee	86	150	431	22.30	2.87	2.02	0.702
	...
26.	FS Trueman	67	127	307	21.58	2.42	1.78	0.737
	...
30.	GD McGrath	123	241	560	21.69	2.32	1.73	0.746
31.	SM Pollock	108	202	421	23.12	2.08	1.56	0.747
	...
33.	Wasim Akram	104	181	414	23.62	2.29	1.73	0.754
	...
38.	CEL Ambrose	98	179	405	20.99	2.26	1.72	0.758
	...
40.	Waqar Younis	87	154	373	23.56	2.42	1.84	0.761
41.	Imran Khan	88	142	362	22.81	2.55	1.94	0.763
42.	CA Walsh	132	242	519	24.45	2.14	1.64	0.765
	...
79.	IT Botham	102	168	383	28.40	2.28	1.92	0.844
	...
86.	GA Lohmann	18	36	112	10.76	3.11	2.66	0.856
	...
102.	JC Laker	46	86	193	21.25	2.24	2.00	0.891
	...
122.	Kapil Dev	131	227	434	29.65	1.91	1.81	0.946
123.	DL Underwood	86	151	297	25.84	1.97	1.86	0.946
	...
125.	GS Sobers	93	159	235	34.04	1.48	1.40	0.950
	...
140.	JG Bracewell	41	67	102	35.81	1.52	1.61	1.061
141.	JH Kallis	139	230	265	31.57	1.15	1.23	1.067
142.	AW Greig	58	93	141	32.21	1.52	1.62	1.071
143.	N Boje	43	72	100	42.65	1.39	1.52	1.097
144.	RJ Shastri	80	125	151	40.96	1.21	1.34	1.110
145.	MA Noble	42	71	121	25.00	1.70	1.93	1.133
146.	R Illingworth	61	100	122	31.20	1.22	1.43	1.168
147.	TE Bailey	61	95	132	29.21	1.39	1.68	1.210
148.	W Rhodes	58	90	127	26.97	1.41	1.81	1.285
149.	CL Hooper	102	145	114	49.43	0.79	1.15	1.457
qual. 100 Test wickets; complete list available here

I picked Derek Underwood out in the list because, of bowlers with any sort of reputation, he has one of the highest CoVs, indicating a less consistent innings-to-innings record. This comes about because there is a bit of a feast-or-famine profile to his Test wicket-taking. He took no more than one wicket in over half of the innings in which he bowled, but he also bagged 17 five-fers. Like the batsman who swings between cheap dismissals and big hundreds (remember Vinoo Mankad?), Deadly's record suggests that he could be inspirational or ineffectual in equal measure. His famous unplayability in particular conditions (above all, on drying wickets) might partly explain this finding.

In the main, the bowlers at the bottom of the list are allrounders and/or not especially penetrative spinners of the kind used to "tie up one end". This probably isn't a great surprise, since bowlers of these types are likely to take relatively few wickets in most of their innings (remember that, in these analyses, a bowler who takes 0, 0, 2, 1, & 0 wickets in consecutive innings is judged to be less consistent than one who takes 4, 4, 6, 5, & 4, even though the absolute variability within those hauls is identical). In addition, when these bowlers turn in a significant performance (as they all at least occasionally do), it stands out in much greater contrast from their typical level of achievement, and their SDs – and, consequently, CoVs – are increased. If a bowler with a WPI of 1 records a 5-wicket haul, that's 500% of his typical performance; for a bowler with a WPI of 2.5, the same feat would only be 200% of his norm.

The upshot is that there are a lot of good bowlers at the top of the list, and progressively fewer as consistency declines. As a result, it is no surprise to find a relatively pronounced correlation between CoV and bowling average (r ²=0.322; p<0.001). This relationship is illustrated in Figure 1; you can see that a substantial majority of bowlers who average under 30 have a CoV of less than 1.

Figure 2 shows the typical relationship between CoV and win-rate, with bowlers with the most consistent wicket-taking records apparently benefitting from a very slightly increased probability of victory, although the association is not an especially dramatic one (r ²=0.043; p<0.001).

There's an interesting twist in this story, though. You may recall that, when I looked at the same relationship for Test batsmen, I found that CoV was less strongly related to winning percentage than it was to not-losing percentage. This finding was explained by the fact that consistency is also associated with drawing rate (i.e. consistent batsmen are a bit more likely to win and a bit more likely to draw, with the net result that they're a fair bit less likely to lose). This phenomenon is not repeated amongst bowlers; in fact, something rather different is going on. The first thing I noticed was that, unlike their willow-wielding counterparts, more consistent bowlers are no less likely to lose matches (r ²<0.001; p=0.866). That, I thought, must mean there's something going on with drawn games that cancels out the benefit of consistency for winning. And that is exactly what proved to be the case: in a complete reversal of the situation for batsmen, the most consistent bowlers are less likely to draw Test matches (r ²=0.050; p<0.001). The only sensible way of explaining this, as far as I can see, is that consistent batsmen help their sides to draw matches they might otherwise have lost, whereas consistent bowlers help their sides to win matches they might otherwise have drawn.

ODI consistency

When I produced stats for the most consistent ODI wicket-takers, there were two unexpected names at the top of the list (Table 2).

I have to confess that I hadn't previously realised quite how good Chris Pringle's ODI bowling statistics are generally (for example, he amassed over 100 wickets at an average better than, say, Botham's or Marshall's or Imran's). What the current analysis emphasises is the way in which he achieved that record – namely, by chipping in dependably just about every time he played. His captains could invariably rely on him to contribute a dismissal or two (he went wicketless in just 10 of the 64 ODIs in which he bowled), though they couldn't really hope for many more (he only managed a four-fer or better on just three occasions). Similarly – in a career that has been anything but stable due to his terrible luck with injuries – Pringle's compatriot Kyle Mills has managed to piece together an admirably consistent wicket-taking record in ODIs. He has taken between 1 and 3 wickets in over three-quarters of his games. It is often said of New Zealand's ODI side of the last couple of decades that they have punched above their weight – that their players manage to play to something like their full ability in a reliable fashion, even if the fundamental level of that ability is perceived to be less than that seen in more glamorous teams. This analysis appears to provide a little support for that notion, if Pringle's and Mills's records are anything to go by (and Shane Bond isn't far behind).

Table 2: ODI bowlers sorted according to consistency (coefficient of variation) in wickets-per-innings

	Name	M	I	W	Ave	WPI	SD	CoV
1.	C Pringle	64	64	103	23.87	1.61	1.15	0.717
2.	KD Mills	108	109	162	26.46	1.49	1.11	0.744
3.	AA Donald	164	162	272	21.79	1.68	1.26	0.748
4.	B Lee	183	179	317	23.18	1.77	1.33	0.750
5.	CJ McDermott	138	138	203	24.72	1.47	1.13	0.769
6.	SK Warne	193	190	291	25.82	1.53	1.18	0.769
7.	MG Johnson	81	80	128	25.72	1.60	1.23	0.769
8.	Saqlain Mushtaq	169	165	288	21.79	1.75	1.36	0.779
9.	DW Fleming	88	88	134	25.39	1.52	1.19	0.779
10.	DK Lillee	63	63	103	20.83	1.63	1.28	0.781
	...
12.	SE Bond	78	80	147	20.88	1.84	1.45	0.791
	...
15.	SCJ Broad	65	65	109	25.76	1.68	1.34	0.797
	...
19.	IT Botham	116	115	145	28.54	1.26	1.01	0.804
20.	D Gough	158	155	234	26.30	1.51	1.21	0.805
21.	M Muralitharan	326	322	504	23.07	1.57	1.26	0.805
22.	GD McGrath	245	245	377	22.06	1.54	1.25	0.812
	...
26.	J Garner	98	98	146	18.85	1.49	1.23	0.826
27.	Shoaib Akhtar	142	141	221	23.64	1.57	1.31	0.833
	...
32.	A Kumble	269	263	334	30.84	1.27	1.09	0.855
33.	A Flintoff	138	116	168	23.62	1.45	1.24	0.857
	...
36.	Kapil Dev	225	221	253	27.45	1.14	1.00	0.870
	...
44.	Wasim Akram	356	351	502	23.53	1.43	1.27	0.885
	...
47.	Waqar Younis	262	258	416	23.84	1.61	1.43	0.890
	...
49.	RJ Hadlee	115	112	158	21.56	1.41	1.26	0.891
50.	SM Pollock	294	291	387	24.31	1.33	1.19	0.893
	...
52.	Harbhajan Singh	209	201	238	32.97	1.18	1.06	0.896
	...
56.	Imran Khan	175	153	182	26.62	1.19	1.11	0.936
57.	CEL Ambrose	176	175	225	24.13	1.29	1.21	0.940
58.	WPUJC Vaas	321	319	399	27.46	1.25	1.18	0.945
	...
60.	MD Marshall	136	134	157	26.96	1.17	1.11	0.947
	...
85.	JH Kallis	298	261	250	32.12	0.96	1.04	1.090
	...
93.	ST Jayasuriya	440	364	319	36.65	0.88	1.10	1.257
94.	IVA Richards	187	131	118	35.83	0.90	1.16	1.286
95.	SB Styris	165	144	125	34.95	0.87	1.12	1.290
96.	WJ Cronje	188	153	114	34.79	0.75	0.97	1.307
97.	Azhar Mahmood	142	139	123	39.13	0.88	1.17	1.322
98.	PA de Silva	308	156	106	39.41	0.68	0.90	1.323
99.	GW Flower	219	154	104	40.26	0.68	0.95	1.411
100.	PD Collingwood	182	139	103	38.86	0.74	1.05	1.414
101.	SR Tendulkar	442	267	154	44.27	0.58	0.95	1.641
102.	SC Ganguly	308	170	100	38.35	0.59	1.00	1.706
qual. 100 ODI wickets; complete list available here

Again, the lower reaches of the table are dominated by bits-and-pieces players, who might provide occasional match-altering spells (even Ganguly and Tendulkar each have two ODI five-fers under their belts), but are more commonly asked to use up overs (17 of the bottom 20 contribute fewer than one wicket per ODI).

Gough – along with three Australian heroes in the assorted shapes of Dennis Lillee, Shane Warne, and Brett Lee – makes the top 20s of both lists, but there's one player who ranks among the 10 most consistent for both Tests and ODIs, and that's Allan Donald. His skipper could reliably expect over 2½ wickets per test innings and 1⅔ scalps per ODI from him. Maybe that doesn't sound like much, but it's equivalent to saying that – match-in, match-out – Donald could be depended upon to contribute his share of opposition wickets, and probably somewhat more, whenever he took the ball (and regardless of whether that ball was red or white).

There's a very pronounced correlation between CoV and ODI bowling average (r ²=0.557; p<0.001), with increasing inconsistency obviously reflected in increasing averages (Figure 3). This association is a bit stronger than we saw in Test bowling figures. One possible explanation is that, because the amount of bowling available to any one bowler is constrained in ODIs (usually to 10 overs), WPI becomes a purer measure of a bowler's contribution, and consistency in this measure becomes a more direct index of out-and-out quality with the ball. One way or another, though, it's very clear that better ODI bowlers tend to be more consistent ODI bowlers.

The relationship between bowling consistency and ODI win-rate (Figure 4) is not quite as obvious, though it certainly appears that bowlers with lower CoVs tend to be more likely to win their games (r ²=0.034; p=0.007).

Conclusions

To at least as marked a degree as we saw with batsmen, consistent bowlers appear to be worth having on your team. And it can't be a surprise to learn that the bowlers who take wickets most dependably tend to be those with the lowest averages.

However, we have to be careful with our conclusions, this time. It may be that good bowlers with low averages are asked to bowl more, so they end up taking wickets more consistently. Perhaps less good bowlers would take wickets with equal consistency – though not as high frequency – given the opportunity. In this way, the fact that allrounders and certain types of spin bowlers have the least consistent WPI records may reflect the ways in which they are used as much as it reveals anything fundamental about the bowlers themselves (this takes us back to general concerns about the meaningfulness of WPI as a stat). For instance, we can assume that, if he batted like Chris Martin, Jacques Kallis would have been asked to take a greater share of South Africa's bowling, over the years. Had that been the case, he would surely have taken more wickets, and he would probably also have taken wickets more consistently. His record would not have been so dominated by 0- and 1-wicket innings (69% of his Tests and 72% of his ODIs fall into this category), and his significant wicket-taking bursts would have been much less occasional. As a result, we would expect him to climb the consistency table – perhaps markedly so. But, just because we think we can explain Kallis's relatively inconsistent record, that doesn't mean that his record is, in some way, actually more consistent than we're giving him credit for.

The relationship between consistency and winning is similar for bowlers as we saw for batsmen (although the finding that consistent bowlers draw fewer Test matches is an interesting one). Again, I tend to conclude that, in both forms of the game, teams benefit from dependable – though not necessarily dazzling – contributors at least as much as they do from hit-or-miss performers. However, the overall range of variation is somewhat narrower amongst bowlers: it appears that, while there really is such a thing as a ton-or-bust batsman, bowlers who are capable of significant hauls but commonly contribute little are altogether rarer beasts.

All stats calculated Jul 04, 2010 (i.e. all Tests up to West Indies v South Africa at Bridgetown, Jun 26-29, 2010 [Test # 1962] and all ODIs up to England v Australia at Lord's, Jul 3, 2010 [ODI # 3011]).

---

Technical appendix

(Some notes on my methods, for anyone who's almost as dull as me. Everyone else can stop reading now.)

Technical note #1. All regressions are limited to bowlers with at least 50 wickets. I'd have preferred to use fuller datasets, but they're just too noisy (probably due to the phenomenon of the truly occasional bowler).

Technical note #2. As last time, I performed a multivariate regression on these data. For both forms of the game, I regressed CoV against average, winning percentage, and an interaction term. In each instance, the only significant covariate was average (p<0.001). This suggests that the reason more consistent bowlers win more Test matches and ODIs is that they average less: there is no independent effect of consistency on winning.

Technical note #3.I said in my introduction that there is no straightforward way of measuring variability in wickets taken and runs conceded simultaneously. I can think of quite complicated ways, though. One approach would be to use a Poisson regression model, with wickets as events considered against an exposure variable of runs conceded. Such an analysis is a little beyond the scope of this kind of column, and I have some reservations about the strict applicability of the paradigm. Nevertheless, if anyone's remotely interested, I might try to find a moment to do explore this sort of approach.

The articles on significant innings in Tests received very good response from the readers with excellent suggestions for improvement which enabled me to come out with a follow-up article incorporating a number of suggestions. That has emboldened me to do the same for ODIs. Many of the lessons learnt from the earlier exercises have been applied in this effort.

The numbers have totally different implications in the two formats. The data availability, especially the scoring rates, is 100% in ODIs as compared to Tests. Balls faced has different implications in Tests and ODIs. Almost always cameos mean nothing in Tests, not so in ODIs. Hence the two processes are as different as chalk and cheese.

As I have already explained in the Test articles, the following measures have not been considered. All these have value in a Ratings exercise. Not in this effort. And if I consider one I have to consider all.

- Ground/Pitch conditions.
- Result.
- Quality of bowing attack.
- Team strength of opposition.
- Innings position at entry.
- Home/Away.
- Importance of match et al.

The only figures used are the runs scored and balls faced by the concerned batsman and the runs scored and balls faced by his team. No other data is used. It makes the exercise simpler and easier to understand.

The most important requirement of a batsman is to score runs. The next important requirement is that he scores these runs as quickly as possible. Nothing more. I have followed this important guideline in determining the significance of innings. "Significance" is a subjective word and this methodology converts this into an objective measurable process.

After a lot of experimentation, I have determined that I should adopt a different methodology for ODIs. Partly I have used my favourite weighting concept. I have weighted the innings on five measures, assigned these values based on a pre-determined formulae and summed these five values. That represents the significant innings index and the rest is routine. The measures are explained below.

1. Absolute runs scored: It took 26 years for the 175 to be overhauled by 189, the 189 to be equalized, the 189 to be overtaken by 194, the 194 to be equalized and (this time, quite quickly) the first 200 to be scored. Hence I have kept 200 as the pinnacle. Let me cross the 200+ bridge as and when it is crossed by a batsman. The little master's 200 gets 2 points and the other innings get proportionate values.

2. % of Team score: It is very important to differentiate innings such as the 189 which was scored out of 272 and the 175 out of 262 from the 200 which was out of 401 and a 101 out of 363. Hence this percentage is calculated and 2 points allocated for a % of team score value of 84.2% which is the highest % of team share achieved by McCullum (80 out of 95).

3. Score comparison with 'runs per batsman': This is somewhat similar to the Test significant innings work. This is necessary to distinguish between an innings of 150 out of 300 for 2 and an innings of 150 out of 300 all out. The first two values for these two innings will be identical. The Runs per batsman (the number of batsmen who took strike - as against the wickets) value is calculated and the ratio between the batsman runs and this value arrived at. The maximum value of 2 points are allotted for a ratio of 7.64. Richards' 189* has the highest ratio and gets allotted a point value of 2.00.

4. Absolute scoring rate: This is an important measure and gets recognized. Out of all qualifying innings, Chris Lewis' 20 in 6 has the highest scoring rate, viz., 333.3. This is taken as the base for fixing the 2.0 point mark. The other innings get proportionate allocations based on their innings strike rates. The highest innings included, Shahid Afridi's 55 off 18 balls, has a strike rate of 305.6. This innings gets a value for this index of 1.83 points.

5. Strike rate comparison with 'team strike rate': This is somewhat similar to the Test significant innings work. This is necessary to distinguish between an innings of 50 off 50 balls out of 150 in 50 overs and an innings of 50 off 50 balls out of 300 in 50 overs. The fourth measure value for these two innings will be identical. The Strike rate value for the team is calculated and the ratio between the batsman strike rate and this value arrived at. The maximum value of 2 points are allotted for a ratio of 4.67. Afridi's 29 off 10 (2.90) out of a Pakistani total of 142 off 37.5 (0.62) has the highest ratio of 4.67 and gets allotted a point value of 2.00. The others get allotted proportionate values.

Thus it can be seen that runs scored has a weight of 60% and strike rate 40%. It seems fair to me since ultimately runs matter more. However there is no overlap and each measure has a clear significance and separation.

A few other criteria to define the significant innings.

1. All not out scores of below 20 are excluded from consideration. Such scores happen often in ODIs and these should not distort the picture.

2. A SI total value of 2.50 has been considered to be the minimum required for being a significant innings. This represents 25% of the maximum and has worked out quite well.

3. The batsmen listed are those who have scored 3000 ODI runs and above. Readers should remember two facts. One is that all innings are considered and included in the analysis. It is only for display of the tables that the cut-off of 3000 runs is taken. There is nothing arbitrary about this. I also considered use of number of innings, say 100, for cut-off. However this leads to the inclusion of quite few bowlers. 3000 runs gives us 109 batsmen, a good population size. 2000 runs will increase this to 157 but will also bring in quite a few non-batsmen.

Now let us look at the tables.

First the list of top batsmen ordered by the % of SIs played out of the selected innings.

List of top batsmen ordered by the % of SIs played out of the selected innings

No.	Batsman	Country	Innings	Runs	Sel Inns	SI Inns	%SI	Avg SIIdx	Avg SI-RPI
1	Vivian Richards	WI	167	6721	163	64	39.26	3.434	75.0
2	Sachin Tendulkar	Ind	431	17598	424	155	36.56	3.413	84.2
3	Michael Hussey	Aus	120	4208	114	41	35.96	3.005	63.7
4	Chris Gayle	WI	215	7885	214	72	33.64	3.365	76.8
5	MS Dhoni	Ind	147	5593	139	46	33.09	3.111	73.7
6	Kevin Pietersen	Eng	93	3332	93	30	32.26	3.283	74.5
7	Marcus Trescothick	Eng	122	4335	121	39	32.23	3.374	80.4
8	Brian Lara	WI	289	10405	285	91	31.93	3.330	75.7
9	Gordon Greenidge	WI	127	5134	126	40	31.75	3.170	81.7
10	Saeed Anwar	Pak	244	8824	243	75	30.86	3.264	76.1
11	AB de Villiers	SA	97	3616	95	29	30.53	3.203	77.8
12	Andy Flower	Zim	208	6786	204	62	30.39	3.058	69.8
13	Jacques Kallis	SA	289	10838	280	85	30.36	3.119	79.4
14	Dean Jones	Aus	161	6068	157	47	29.94	3.232	76.5
15	Virender Sehwag	Ind	217	7112	215	64	29.77	3.298	71.7
16	Allan Lamb	Eng	118	4010	116	34	29.31	3.188	69.0
17	Martin Crowe	NZ	141	4704	137	40	29.20	3.312	72.2
18	Graeme Smith	SA	152	5732	152	44	28.95	3.211	76.2
19	Nick Knight	Eng	100	3637	98	28	28.57	3.300	81.1
20	Adam Gilchrist	Aus	279	9619	277	79	28.52	3.315	74.1

There should be no surprises in the top-3. Richards and Tendulkar are the two "First among equals". Nearly 40% of the innings Richards has played are significant indicating the impact he has had on the game. There is some daylight and then there is Tendulkar. The fact that more than a third of the 400+ innings he has played are significant is a stupendous achievement. His influence on the modern game and the Indian team's ODI performances is legendary. Now comes Hussey, whose place in the modern game as a finisher is substantiated by this high ranking. I do not understand why he should bat behind White in the current Australian team. He also comes in quite often in situations where he has no option but to take undue risks (as at Oval a few days back).

Next the list of top batsmen ordered by the number of SIs played.

List of top batsmen ordered by the num of SIs played

No.	Batsman	Country	Innings	Runs	Sel Inns	SI Inns	%SI	Avg SIIdx	Avg SI-RPI
1	Sachin Tendulkar	Ind	431	17598	424	155	36.56	3.413	84.2
2	Sanath Jayasuriya	SL	432	13428	428	113	26.40	3.410	75.5
3	Ricky Ponting	Aus	341	13057	331	92	27.79	3.135	82.2
4	Brian Lara	WI	289	10405	285	91	31.93	3.330	75.7
5	Inzamam-ul-Haq	Pak	350	11739	347	89	25.65	3.098	71.6
6	Jacques Kallis	SA	289	10838	280	85	30.36	3.119	79.4
7	Aravinda de Silva	SL	296	9284	292	81	27.74	3.235	72.5
8	Rahul Dravid	Ind	313	10765	303	81	26.73	3.031	74.1
9	Adam Gilchrist	Aus	279	9619	277	79	28.52	3.315	74.1
10	Saeed Anwar	Pak	244	8824	243	75	30.86	3.264	76.1
11	Saurav Ganguly	Ind	300	11363	297	75	25.25	3.333	89.2
12	Chris Gayle	WI	215	7885	214	72	33.64	3.365	76.8
13	Mohammad Yousuf	Pak	267	9624	265	72	27.17	3.199	76.9
14	Shahid Afridi	Pak	278	6222	269	70	26.02	3.237	54.1
15	Mahela Jayawardene	SL	302	8863	291	70	24.05	3.066	70.4
16	Mohammmed Azharuddin	Ind	308	9378	297	68	22.90	3.174	70.9
17	Shivnarine Chanderpaul	WI	245	8648	237	67	28.27	3.216	73.6
18	Vivian Richards	WI	167	6721	163	64	39.26	3.434	75.0
19	Virender Sehwag	Ind	217	7112	215	64	29.77	3.298	71.7
20	Herschelle Gibbs	SA	240	8094	238	64	26.89	3.328	82.7

This is the one time a longevity table has some meaning. Tendulkar has played over 150 significant innings. To do this day in and day out for over 21 years is the stuff only the all-time-greats can dream of. Note the 27% gap between Tendulkar and Jayasuriya, although both have played the same number of ODI games. Three wonderful ODI batsmen, Ponting, Lara and Inzamam follow next. The top-5 are all awe-inspiring ODI batsmen.

The last one is the list of top batsmen ordered by the average value of SI index.

List of top batsmen ordered by the average SI Index value

No.	Batsman	Country	Innings	Runs	Sel Inns	SI Inns	%SI	Avg SIIdx	Avg SI-RPI
1	David Gower	Eng	111	3170	109	20	18.35	3.505	80.8
2	Vivian Richards	WI	167	6721	163	64	39.26	3.434	75.0
3	Sachin Tendulkar	Ind	431	17598	424	155	36.56	3.413	84.2
4	Sanath Jayasuriya	SL	432	13428	428	113	26.40	3.410	75.5
5	Marcus Trescothick	Eng	122	4335	121	39	32.23	3.374	80.4
6	Graham Gooch	Eng	122	4290	121	25	20.66	3.372	82.6
7	Chris Gayle	WI	215	7885	214	72	33.64	3.365	76.8
8	Steve Tikolo	Ken	121	3304	120	31	25.83	3.342	68.5
9	Nathan Astle	NZ	217	7090	217	54	24.88	3.325	85.2
10	Saurav Ganguly	Ind	300	11363	297	75	25.25	3.333	89.2
11	Brian Lara	WI	289	10405	285	91	31.93	3.330	75.7
12	Herschelle Gibbs	SA	240	8094	238	64	26.89	3.328	82.7
13	Adam Gilchrist	Aus	279	9619	277	79	28.52	3.315	74.1
14	Desmond Haynes	WI	237	8648	236	60	25.42	3.312	84.1
15	Martin Crowe	NZ	141	4704	127	40	29.20	3.312	72.2
16	Shoaib Malik	Pak	172	5188	162	35	21.60	3.311	79.5
17	Geoff Marsh	Aus	115	4357	115	26	22.61	3.308	90.1
18	Nick Knight	Eng	100	3637	98	28	28.57	3.300	81.1
19	Virender Sehwag	Ind	217	7112	215	64	29.77	3.298	71.7
20	Kevin Pietersen	Eng	93	3332	93	30	32.26	3.283	74.5

There is a surprise at the top. I would have expected Richards and Tendulkar to be on top. However they have been leap-frogged by the stylist, David Gower. That indicates that when he performed, Gower went way past the line. Richards follows next close behind and is followed by Tendulkar. Very well-deserved positions indeed for these greats. Jayasuriya and Trescothick take the next two places. In this table Hussey is quite low with only around 3 points.

Readers will note that I have also given the average runs per innings for the SIs. Tendulkar's average RpI is very high at 84.2. Richards' RpI value is 80.0. Note the very high RpI value of Ganguly, 89.2. In fact Ganguly is second only to Geoff Marsh who has averaged 90.1 runs. Astle is in third place with 85.2 and Tendulkar is fourth. Hussey is way down with a value of 63.7 runs indicating the inability to play longer innings in late-order.

Finally the top four SI values and the related scorecards.

If some one does not know about Richards' all-time classic innings, he probably follows only T20 games or is not interested in Cricket. Over the past few years I have done quite a few ODI Innings rating lists, including the Wisden-100. The one constant factor in all these lists is the presence of Richards' 189* in the top place. Not once has this innings come second. It is my firm conviction that if 100 unbiased, objective and informed followers are asked the question of what was the best ever ODI innings, 90 would point to Richards' 189.

The points secured by Richards are 1.89, 1.65, 2.00, 0.67 and 0.58 totaling to 6.79 points.

The only reason why Kapil Dev's 175* is in second place is the presence of Richards' 189. Otherwise the same 90 would point to Kapil's classic as the best ever. To say that this innings was one of the major reasons for India winning the World Cup may not be hyperbole.

The points secured by Kapil Dev are 1.75, 1.56, 1.72, 0.76 and 0.74 totaling to 6.53 points.

Third is Coventry's monumental 194, albeit on the losing side. The points secured by Coventry are 1.94, 1.48, 1.62, 0.75 and 0.52 totaling to 6.31 points.

In fourth place is a surprise innings. Not even a hundred and not even in a winning cause. However its place is well-deserved mainly because of the way-out scoring rate. How Sri Lanka lost after this Jayasuriya classic is a mystery. The points secured by Jayasuriya are 0.76, 1.05, 1.27, 1.63 and 1.34 totaling to 6.05 points.

I have given the summary figures below. I have not done the %. I leave it for the readers.

TotInns:52235 TotSelInns: 46329 Tot SIs: 7952
100+runs: 1072 50+runs: 5054 <50runs:1826
BPos 1-7: 6967 BPos 8-11: 985
1Inns: 4284 2Inns: 3668
Wins: 4361 Losses: 3416 Ties/NRs: 175

I have also made available the complete list of significant performances for all the 109 qualifying batsmen. Let me warn that this is a huge file (660k). This is a formatted text file and could easily be imported into an Excel sheet.

To view/down-load the list of SIs , please click/right-click here.

To view/down-load the complete Player tables, all three in a single file, please click/right-click here.