Graham Gooch: one of the most prolific batting streaks © Getty Images

The idea for this article came when I was discussing Lara's 400 with a friend, arguing that that was not even his fifth best innings. He countered by saying that since this was the maximum runs scored in a test it should be considered great. I had to correct him saying that there were two other batsmen who have scored more runs than Lara in a test. He was quite surprised since he could not think of someone scoring more than 400. He was only thinking of one innings. Then I explained to him about Gooch and Mark Taylor.

It made me think that there may be many cricket followers who might be in the dark about this and the maximum runs scored in one or more tests. I was also sure we would be in for some major surprises if we looked deeply into it. I myself did not have the answers ready. Would Lara's 688 be the maximum in three consecutive tests (no, it is not) or would Bradman's 974 runs during the 1930 5-test series be the maximum scored in a 5-test sequence (no, it is not) or would Tendulkar's recent streak of 1323 in 10 tests (before the Hyderabad Test) would be amongst the top 10-match sequences (no, it is not) and so on.

A very fascinating set of questions. I decided I would do a complete article on this. I am glad that I did it since it has thrown up quite a few great insights into Test batting. So much so I would do a similar article on Test bowling also.

Indeed this turned out to be a tough task since I also wanted to utilize this opportunity to build a Player-performance Database. This is essential since I needed to get the best 1-10 test performances for each player and then get the all-time best performances. I also wanted to provide the information on the top players' 1-10 tests best performances so that the readers could do their own comparisons. And I was sure that there would be queries on the best performances by specific players after the article was published. I wanted to be able to provide the information quickly. In fact I have also provided the huge table of all players' for downloading.

First let me emphasize that this is only a run aggregate. I myself will clarify that this aggregating of runs in specific sequences of 1-10 tests is irrespective of opposing team, home or away, match conditions, period lapsed between matches, not outs et al. That is not the purpose of this article. Readers should appreciate this and not come in with a comment such as "opposition bowling quality is not considered". But that is wishful thinking !!! Also readers who worry bout batting average should understand that when someone scores over 1500 runs in 10 tests, it does not matter about averages. It is going to be quite high.

Let us now look at the tables.

Maximum runs scored in a single test

Batsman         Runs  StartTest

Gooch G.A        456 (1148-1990)  
  333+123

Taylor M.A       426 (1426-1998)
  334+92

Lara B.C         400 (1696-2004)
  400

This table refers to the discussion which led into this analysis and a start of dominance by an extremely under-rated player, Graham Gooch. His triple and single centuries in the 1990 Lord's test add upto 456 runs and leads this table. This is followed by Mark Taylor's 426 against Pakistan. He followed a 334 (declared since he wanted to be at par with Bradman, not wanting to go past it !!!) with 92. Then follows Lara's single innings score of 400.

The cricketing story behind the Test is that Gooch declared late on the fourth day, leaving India with just over 8 hours to get runs. When asked why he did not declare earlier, he replied that he wanted to be able to attack right through the Indian innings. On slightly helpful tracks, Gooch's reason is the one which makes more sense rather than the often repeated "we must give the batting team a chance" maxim.

There is also a personal story behind the Gooch Test. I was in England at that time on my company work and was scheduled to leave London for Bombay, via Kuwait on 1 August. I wanted to see the last day of the Lord's Test on 31 July, hoping for a great fight back. Unfortunately India, starting at 57 for 2, collapsed in less than 3 hours. I decided to save 100 pounds in expenses, advanced my flight to 31 July and returned a day earlier.

Some readers might ask, so what. The horrifying truth was that the flight which left on August 1, landed at Kuwait, not knowing that Iraq had invaded and then could not take off again. In fact the plane was torched. The passengers had a harrowing time for 30 days and finally had to travel overland through Jordan to return to India. There, but for the grace of God and the ineptness of the Indian batting, I, a confirmed grass-eater, would have been in occupied-Kuwait. Lucky it was not the Harbhajan-led tail of today which might have batted on till evening.

Maximum runs scored in 2 consecutive tests

Gooch G.A        640 (1147-1990)
  154+30, 333+123
  184, 456

Bradman D.G      625 (0236-1934)
  304, 244+77
  304, 321

Smith G.C        621 (1651-2003)
  277+85, 259
  362, 259

Since Gooch preceded his Lord's test with another great one, he leads in the 2-test table with 640 runs. How can you keep Bradman out. He is next with 625 runs. Then there is a surprise with Graeme Smith with 621 runs, mainly with two huge double centuries.

Maximum runs scored in 3 consecutive tests

Hammond W.R      779 (0177-1928)
  251, 200+32, 119+177
  251, 232, 296

Gooch G.A        763 (1147-1990)
  154+30, 333+123, 116+7
  184, 456, 123

Sobers G.St.A    731 (0448-1958)
  52+80, 365, 125+109
  132, 365, 234

The 3-test sequence is headed by Hammond, with two double hundreds and two centuries in 3 tests, aggregating to 779 runs. Gooch is just behind, with 763 runs since he had an excellent test after the humongous Lord's one. For once Bradman is kept out. Sobers, book-ending his 365 with two good tests has aggregated 731 runs.

Maximum runs scored in 4 consecutive tests

Gooch G.A        936 (1147-1990)
  154+30, 333+123, 116+7, 85+88
  184, 456, 123, 173

Sangakkara K.C   915 (1838-2007)
  200, 222, 57+192, 92+152
  200, 222, 249, 244

Bradman D.G      888 (0180-1929)
  123+37, 8+131, 254+1, 334
  160, 139, 255, 334

Gooch continues to lead the tables. In 4 consecutive tests he scored 936 runs. Now there is a modern presence. Sangakkara's golden run during 2007 comes in second with 915 runs, supported by two double and two big centuries. He is ahead of Bradman whose quartet of tests aggregated 888 runs.

Maximum runs scored in 5 consecutive tests

Gooch G.A       1058 (1146-1990)
  85+37, 154+30, 333+123, 116+7, 85+88
  122, 184, 456, 123, 173

Bradman D.G     1028 (0236-1934)
  304, 244+77, 38+0, 0+82, 13+270
  304, 321, 38, 82, 283

Sobers G.St.A   1009 (0450-1958)
  365, 125+109, 14+27, 25+142, 4+198
  365, 234, 41, 167, 202

We are now back to the trusted trio of Gooch, Bradman and Sobers. Note that these three have exceeded 1000 runs in 5 tests. These are the only three to do so.

Maximum runs scored in 6 consecutive tests

Bradman D.G     1266 (0236-1934)
  304, 244+77, 38+0, 0+82, 13+270, 26+212
  304, 321, 38, 82, 283, 238

Gooch G.A       1147 (1148-1990)
  333+123, 116+7, 85+88, 20+58, 59+54, 87+117
  456, 123, 173, 78, 113, 204

Sobers G.St.A   1141 (0448-1958)
  52+80, 365, 125+109, 14+27, 25+142, 4+198
  132, 365, 234, 41, 167, 202

The same three batsmen lead the table for the 6-test aggregates. However the sequence is different, with Bradman displacing Gooch. Sobers stays in third place. 11 batsmen have crossed 1000 runs in 6 tests.

Maximum runs scored in 7 consecutive tests

Bradman D.G     1435 (0236-1934)
  304, 244+77, 38+0, 0+82, 13+270, 26+212, 169
  304, 321, 38, 82, 283, 238, 169

Gooch G.A       1331 (1147-1990)
  154+30, 333+123, 116+7, 85+88, 20+58, 59+54, 87+117
  184, 456, 123, 173, 78, 113, 204

Mohammad Yousuf 1296 (1809-2006)
  202+48, 38+15, 192+8, 128, 192, 56+191, 102+124
  250, 53, 200, 128, 192, 247, 226

Now for the 7-test aggregate table. Bradman has aggregated 1435 runs, over 200 runs per test. Gooch has aggregated 1331 runs. Now the current generation comes in, represented by the top class Pakistani batsman, Mohammad Yousuf who had a wonderful year during 2006. He aggregated 1296 runs in 7 tests. What Pakistan would do to have Yousuf playing half as well now. 20 batsmen have exceeded 1000 runs in 7 tests.

Maximum runs scored in 8 consecutive tests

Bradman D.G     1630 (0236-1934)
  304, 244+77, 38+0, 0+82, 13+270, 26+212, 169, 51+144
  304, 321, 38, 82, 283, 238, 169, 195

Gooch G.A       1453 (1146-1990)
  85+37, 154+30, 333+123, 116+7, 85+88, 20+58, 59+54, 87+117
  122, 184, 456, 123, 173, 78, 113, 204

Richards I.V.A  1385 (0773-1976)
  142, 130+20, 177+23, 64, 232+63, 4+135, 66+38, 291
  142, 150, 200, 64, 295, 139, 104, 291

These two giants, Bradman and Gooch have monopolized the top two positions in the 8-test tables. Bradman still maintains his 200+ runs per test and is way ahead of Gooch. Then comes the incomparable Richards who had one of the greatest of batsman-years during 1976. With a finale of the wonderful Oval innings of 291, he had aggregated 1385 runs. No fewer than 44 batsmen have exceeded 1000 runs in eight tests, Bradman being the only 1500+ run gatherer.

Maximum runs scored in 9 consecutive tests

Bradman D.G     1750 (0236-1934)
  304, 244+77, 38+0, 0+82, 13+270, 26+212, 169, 51+144, 18+102
  304, 321, 38, 82, 283, 238, 169, 195, 120

Gooch G.A       1550 (1147-1990)
  154+30, 333+123, 116+7, 85+88, 20+58, 59+54, 87+117, 13+18, 34+154
  184, 456, 123, 173, 78, 113, 204, 31, 188

Richards I.V.A  1533 (0770-1976)
  50+98, 142, 130+20, 177+23, 64, 232+63, 4+135, 66+38, 291
  148, 142, 150, 200, 64, 295, 139, 104, 291

Same three batsmen occupy the top three places in the 9-test table. Bradman's total of 1750 means that the average runs per test falls below 200. Gooch totals 1550 runs and Richards 1533. Gooch's sequence ends with the all-time classic of 154 against West Indies which must rank amongst the five best ever Test innings in anyone's reckoning. 75 batsmen have crossed 1000 runs in 9 Tests and 5 of these have crossed 1500 runs.

Maximum runs scored in 10 consecutive tests

Bradman D.G     1869 (0236-1934)
  304, 244+77, 38+0, 0+82, 13+270, 26+212, 169, 51+144, 18+102, 103+16
  304, 321, 38, 82, 283, 238, 169, 195, 120, 119

Gooch G.A       1672 (1146-1990)
  85+37, 154+30, 333+123, 116+7, 85+88, 20+58, 59+54, 87+117, 13+18, 34+154
  122, 184, 456, 123, 173, 78, 113, 204, 31, 188

Richards I.V.A  1664 (0768-1976)
  30+101, 50+98, 142, 130+20, 177+23, 64, 132+63, 4+135, 66+38, 291
  131, 148, 142, 150, 200, 64, 195, 139, 104, 291

Finally the 10-test table. Again the same three batsmen. Bradman has aggregated 1869 runs in a 10-test sequence. The irony is that there is a zero embedded in this sequence. Gooch and Richards only suffer when compared to Bradman. 114 batsmen have crossed 1000 runs in 9 Tests and 8 of these have crossed 1500 runs.

The surprise in these 10x3 efforts is the complete absence of a single Indian batsman. I am wary of giving a possible reason. Only thing I can think of is the overall strong batting lineup of India, not allowing one batsman to dominate for a series of Tests. That might very well have been the case for Australia a few years back. Incidentally Gambhir has a 10-test aggregate of 1640 runs and is just behind Richards.

Readers would have noted that Gooch is the only batsman to have featured in the top-3 positions in all these 10 tables. Bradman is missing in the 1-test and 3-tests tables. I agree that one swallow does not make a summer and these 10 tests are not representative of the batsman's career. However we have to recognize Gooch's 10 golden tests.

I am sure readers would like to see the best 1-10 test sequence aggregates of their favourite batsmen. Instead of cluttering up the main article I have uploaded the file and readers can view/download the complete player file.

This has been added as a postscript. This is the 10-innings sequence, rather than the 10-test sequence, as asked for by some readers. The table is presented with no comments.

Lara B.C             1   400 (1696-2004)
Hayden M.L           1   380 (1661-2003)
Jayawardene D.P.M.D  1   374 (1810-2006) (Lara's 375 is in between)

Hammond W.R          2   563 (0225-1933)
Bradman D.G          2   548 (0236-1934)
Sobers G.St.A        2   490 (0450-1958)

Hammond W.R          3   638 (0224-1933)
Bradman D.G          3   625 (0236-1934)
Smith G.C            3   621 (1651-2003)

Hammond W.R          4   739 (0224-1933)
Bradman D.G          4   720 (0194-1930)
Sobers G.St.A        4   679 (0448-1958)

Bradman D.G          5   835 (0195-1930)
Hammond W.R          5   779 (0177-1928)
Sangakkara K.C       5   763 (1838-2007)

Gooch G.A            5   756 (1147-1990)
Zaheer Abbas         5   747 (0936-1982)
Sobers G.St.A        5   731 (0448-1958)

Bradman D.G          6   966 (0194-1930)
Sangakkara K.C       6   915 (1838-2007)
Zaheer Abbas         6   838 (0935-1982)

Bradman D.G          7   984 (0196-1930)
Sangakkara K.C       7   921 (1837-2007)
Gooch G.A            7   878 (1146-1990)

Bradman D.G          8  1087 (0195-1930)
Mohammad Yousuf      8   993 (1813-2006)
Sangakkara K.C       8   962 (1838-2007)

Bradman D.G          9  1239 (0195-1930)
Sangakkara K.C       9  1085 (1822-2006)
Mohammad Yousuf      9  1025 (1813-2006)

Bradman D.G         10  1370 (0194-1930)
Sangakkara K.C      10  1185 (1820-2006)
Sobers G.St.A       10  1115 (0450-1958)

R.V.Subbu has asked one of the most intriguing and exciting questions on this blog. He wanted to know who has the best 52-test streak, second to Bradman. Thanking him for a wonderful question I set to work, the process already having been set, and the results are given below. The funny thing is that the first time I did this I did not set the Bradman exclusion filter and got the following information.

Bradman D.G. 52 6996 99.94

Laughing at my own idiocy, I set the filter and got the results.

Ricky Ponting, in a 52-test span between Test # 1595 (Saf vs Aus 15/03/2002) and Test # 1819 (Aus vs Eng 1/12/2006) accumulated 5853 runs at an average of 74.09 (90-11-5853-74.09-23 hundreds). His average improved from 45.09 to 59.97..

This is a logical extension of the current article and I must thank R.V.Subbu again for setting the spark.

The second is, surprise, Lara, who, starting with Test # 1542 and ending at one test before the end of his career, aggregated 5573 runs in 52 tests. Supports my contention that he retired couple of years too soon, or was forced to retire.

The third is, surprise again, Sobers, who scored 5468 runs in 52 tests starting Test # 443 (just before his record-breaking 365).

To view/down-load the complete 1-10 tests table, please click/right-click here.

To view/down-load the complete player table, please click/right-click here. The batsmen who have scored 2000 runs or more are included.

To view/down-load the 52-Test sequence table, please click/right-click here.

To view/down-load the 80-innings sequence table, please click/right-click here. Readers should note that Logie does not figure in this table since he played in 52 tests but had only 78 innings.

Sanath Jayasuriya: surpassed aggregate of team and opposition © Getty Images

Little would Abhishek have realized what he unleashed when he made the comment on Jayasuriya's 189 being more than 50% of the combined team scores. A simple statement. However it opened up a chain reaction of multiple analysis of outlying performances.

I decided to first do the work related to what Abhishek suggested. This is really a player's performance compared with the other 21 players. Then I did some analysis of the player against the 11 players of the other team. Finally there is one analysis of the player compared to the other 10 members of his team. A few very interesting facts have come to light.

First the share of a batsman's score in the aggregate score of the two teams. For this share to be higher than 50%, quite a few factors have to come through. Barring an outrageous scoreline of "Team1: 75 a.o., Team 2: 80 for x (Player1 78)" this can only happen in matches won by the teams batting first. Even there the player has to outscore his own team-mates by a mile, enough to offset the other team score.

So much so, there is only one case of a player scoring over 50% of the combined match aggregate. That is Jayasuriya, whose 189 formed 53.5% of the total of 353 (299 + 54). Jayasuriya scored 63.2% of his team score and the very low score of India made sure the overall figure remained above 50%. As I have already said, there is necessity for a specific pattern of scores in this analysis. A batsman dominating his own team's innings AND a very low score by the opposing team.

I kept the cut-off at 33.33% and created the table. The table is given below.

SNo MtId Year For Batsman           Runs   Vs  Total  Score   Share%

  1.1652 2000 Slk Jayasuriya S.T     189 vs Ind 353 (299+ 54) 53.54%
  2.0264 1984 Win Richards I.V.A     189 vs Eng 440 (272+168) 42.95%
  3.2660@2007 Nzl McCullum B.B        80 vs Bng 188 ( 95+ 93) 42.55%
  4.1049 1996 Saf Kirsten G          188 vs Uae 473 (321+152) 39.75%
  5.0020 1975 Nzl Turner G.M         171 vs Eaf 437 (309+128) 39.13%
  6.1943 2003 Zim Wishart C.B        172 vs Nam 444 (340+104) 38.74%
  7.0322 1985 Win Haynes D.L         145 vs Nzl 388 (259+129) 37.37%
  8.0323@1985 Win Haynes D.L          85 vs Nzl 233 (117+116) 36.48%
  9.0405 1986 Win Richardson R.B     109 vs Slk 303 (248+ 55) 35.97%
 10.0747@1992 Pak Rameez Raja        119 vs Nzl 333 (167+166) 35.74%
 11.2547 2007 Pak Imran Nazir        160 vs Zim 448 (349+ 99) 35.71%
 12.2803 2009 Slk Dilshan T.M        137 vs Pak 384 (309+ 75) 35.68%
 13.2299@2005 Saf Smith G.C          134 vs Ind 377 (189+188) 35.54%
 14.1890 2002 Saf Gibbs H.H          153 vs Bng 434 (301+133) 35.25%
 15.2447 2006 Saf Kallis J.H         119 vs Ind 339 (248+ 91) 35.10%
 16.0216 1983 Ind Kapil Dev N        175 vs Zim 501 (266+235) 34.93%
 17.0441@1987 Win Greenidge C.G      133 vs Nzl 383 (192+191) 34.73%
 18.1736 2001 Nzl Astle N.J          117 vs Ind 338 (211+127) 34.62%
 19.1832 2002 Pak Mohammad Yousuf    129 vs Slk 373 (295+ 78) 34.58%
 20.0015@1974 Pak Zaheer Abbas        57 vs Eng 165 ( 84+ 81) 34.55%
 21.2912 2009 Zim Masakadza H        178 vs Ken 516 (329+187) 34.50%
 22.1964 2003 Ind Tendulkar S.R      152 vs Nam 441 (311+130) 34.47%
 23.2088 2004 Saf Kallis J.H         109 vs Win 317 (263+ 54) 34.38%
 24.0457 1987 Win Richards I.V.A     181 vs Slk 529 (360+169) 34.22%
 25.2828@2009 Win Gayle C.H           80 vs Eng 234 (117+117) 34.19%
 26.0549 1989 Aus Marsh G.R          125 vs Pak 366 (258+108) 34.15%
 27.2427 2006 Bng Shahriar Nafees    123 vs Zim 361 (231+130) 34.07%
 28.1981 2003 Win Gayle C.H          119 vs Ken 350 (246+104) 34.00%
 29.1837@2002 Win Gayle C.H           84 vs Ind 247 (124+123) 34.01%
 30.0638 1990 Pak Rameez Raja        114 vs Nzl 341 (223+118) 33.43%
Note: @ indicates second innings.

Next to Jayasuriya's 189 is the other 189, almost certainly the greatest ODI innings played. By Viv Richards, whose 189 (out of 272+168) formed 42.95% of the aggregate. However the most breath-taking of the chasing innings of all, McCullum's 80 (out of 93+95) formed 42.55% of the aggregate. This bizarre (real-life) scoreline is almost close to the outrageous scoreline I had earlier talked about. For a near-100 target to be chased by a team and one batsman scoring over 84% is unreal. No other innings exceeds 40% of the aggregate. The next chasing innings is Haynes's 85 (out of 116+117) working out to 36.5%.

It is not surprising that 9 West Indian batsmen figure in this list. Gayle is the leading batsman with 3 such dominating performances. In general the top three batsmen dominate the table. It is surprising that there is a single Australian entry (that too from Geoff Marsh) and nothing from England.

The next set of outlier innings are the ones where a batsman has outscored the other team by a wide margin. Note the clear distinction. In the first one we looked at the share of the batsman out of the aggregate. Here we look at the factor by which he outscored the opponents. Let us look at the table.

SNo MtId Year For Batsman            Runs  Vs    Score Ratio

  1.1652 2000 Slk Jayasuriya S.T      189 vs Ind  54/10 3.50
  2.2088 2004 Saf Kallis J.H          109 vs Win  54/10 2.02
  3.0405 1986 Win Richardson R.B      109 vs Slk  55/10 1.98
  4.1970 2003 Aus Hayden M.L           88 vs Nam  45/10 1.96
  5.2803 2009 Slk Dilshan T.M         137 vs Pak  75/10 1.83
  6.1943 2003 Zim Wishart C.B         172 vs Nam 104/ 5 1.65
  7.1832 2002 Pak Mohammad Yousuf     129 vs Slk  78/10 1.65
  8.2547 2007 Pak Imran Nazir         160 vs Zim  99/10 1.62
  9.2727 2008 Nzl McCullum B.B        166 vs Ire 112/10 1.48
 10.2727 2008 Nzl Marshall J.A.H      161 vs Ire 112/10 1.44
 11.1868 2002 Aus Hayden M.L          146 vs Pak 108/10 1.35
 12.2001 2003 Ind Yuvraj Singh        102 vs Bng  76/10 1.34
 13.0020 1975 Nzl Turner G.M          171 vs Eaf 128/ 8 1.34
 14.1970 2003 Aus Symonds A            59 vs Nam  45/10 1.31
 15.2447 2006 Saf Kallis J.H          119 vs Ind  91/10 1.31
 16.0297 1985 Aus Border A.R          118 vs Slk  91/10 1.30
 17.1049 1996 Saf Kirsten G           188 vs Uae 152/ 8 1.24
 18.0405 1986 Win Greenidge C.G        67 vs Slk  55/10 1.22
 19.0777 1992 Win Haynes D.L           96 vs Pak  81/10 1.19
 20.1964 2003 Ind Tendulkar S.R       152 vs Nam 130/10 1.17

Here also Jayasuriya's 189 rules the roost. This innings was 3.5 times the Indian score of 54. Then, after a mile or two, comes Kallis's 109 against the West Indian score of 54, a factor of 2.02. These are the only two innings with a factor above 2.0. That puts the Jayasuriya innings in perspective. Readers can note how quickly the factor drops off. Needless to say that all these innings are when the team batting first wins.

In general the weaker teams have been at the receiving end. It is of interest that Sri Lanka has figured prominently at either end. For the few matches Namibia has played (6), they have been the victims 5 times (by two batsmen in a single match).

Now for the third analysis. This time against the batsman's own team mates. Taking the runs only would be quite silly since many a batsman has out-scored his team mates. Hence I have taken the scoring rate as the basis. I determined the following ratio and then ranked the players on that ratio.

                          Batsman's own scoring rate
Batsman outlier ratio = -------------------------------
                        Rest of the team's scoring rate

In order to avoid getting in a batsman scoring 10 in 2 balls and "outscoring" the rest of his team by a wide margin, I selected only innings of 50 and above. Also I have excluded the team extras from the rest of the team runs to be completely correct. Now for the table.

No MtId Year For Batsman            Vs  Score   S/R   Others  S/R Ratio

 1.1093 1996 Slk Jayasuriya S.T    Pak  76( 28) 2.71  81(169) 0.48 5.66
 2.0182 1983 Nzl Cairns B.L        Aus  52( 25) 2.08  92(214) 0.43 4.84
 3.0657 1990 Slk Ranatunga A       Ind  58( 27) 2.15 135(267) 0.51 4.25
 4.0152 1982 Ind Kapil Dev N       Eng  60( 37) 1.62 115(293) 0.39 4.13
 5.3039 2010 Nzl Mills K.D         Ind  52( 35) 1.49  53(146) 0.36 4.09
 6.0225 1983 Ind Patil S.M         Pak  51( 28) 1.82 106(216) 0.49 3.71
 7.0053 1978 Nzl Cairns B.L        Eng  60( 43) 1.40  79(205) 0.39 3.62
 8.0216 1983 Ind Kapil Dev N       Zim 175(138) 1.27  79(222) 0.36 3.56
 9.0576 1989 Aus Border A.R        Eng  84( 44) 1.91 140(256) 0.55 3.49
10.2365 2006 Aus Gilchrist A.C     Bng  76( 46) 1.65 103(218) 0.47 3.50
11.1901 2002 Zim Ervine S.M        Pak  61( 41) 1.49  67(157) 0.43 3.49
12.0608 1990 Pak Wasim Akram       Aus  86( 76) 1.13  69(211) 0.33 3.46
13.1660 2000 Ind Agarkar A.B       Zim  67( 25) 2.68 213(275) 0.77 3.46
14.2239 2005 Pak Shahid Afridi     Ind 102( 46) 2.22 134(207) 0.65 3.43
15.2687 2008 Aus Gilchrist A.C     Slk  83( 50) 1.66 116(239) 0.49 3.42
16.2252 2005 Bng Mohammad Ashraful Eng  94( 52) 1.81 117(220) 0.53 3.40
17.0015 1974 Pak Zaheer Abbas      Eng  57( 61) 0.93  13( 47) 0.28 3.38
18.1963 2003 Can Davison J.M       Win 111( 76) 1.46  79(181) 0.44 3.35
19.2638 2007 Ber Cann L.O.B        Ken  52( 32) 1.62 106(216) 0.49 3.31
20.0245 1984 Win Holding M.A       Aus  64( 39) 1.64 110(222) 0.50 3.31
21.2660 2007 Nzl McCullum B.B      Bng  80( 28) 2.86   7(  8) 0.88 3.27
22.2875 2009 Can Rizwan Cheema     Ken  76( 38) 2.00  37( 60) 0.62 3.24
23.0547 1989 Pak Imran Khan        Win  67( 41) 1.63 132(259) 0.51 3.21
24.2752 2008 Can Rizwan Cheema     Win  61( 45) 1.36 102(236) 0.43 3.14
25.0481 1987 Ind Kapil Dev N       Win  87( 64) 1.36  89(204) 0.44 3.12
26.1864 2002 Pak Shahid Afridi     Saf  62( 40) 1.55 125(251) 0.50 3.11
27.2059 2003 Eng Flintoff A        Bng  70( 47) 1.49  57(119) 0.48 3.11
28.0221 1983 Ind Patil S.M         Eng  51( 32) 1.59 152(296) 0.51 3.11
29.2828 2009 Win Gayle C.H         Eng  80( 43) 1.86  27( 45) 0.60 3.11
30.0622 1990 Aus Jones D.M         Nzl 102( 91) 1.12  52(144) 0.36 3.11
31.2980 2010 Win Sammy D.J.G       Saf  58( 24) 2.42 206(265) 0.78 3.11
32.0344 1985 Win Richards I.V.A    Pak  66( 39) 1.69 111(201) 0.55 3.06
33.1814 2002 Zim Marillier D.A     Ind  56( 24) 2.33 209(274) 0.76 3.06
34.2735 2008 Ind Sehwag V          Slk  60( 36) 1.67 110(201) 0.55 3.05
35.2584 2007 Pak Shahid Afridi     Slk  73( 34) 2.15 156(218) 0.72 3.00

Ah! how can you get this guy off the top. This is the third time, out of three, in this article that the "Matara marauder" has been at the top. At least it is for a different innings. His 76 in 28 balls against Pakistan had a scoring rate of 2.71 and the rest of his team scored at 0.48, giving this innings the stupendous ratio of 5.66. Then comes "father" Cairns whose 52 in 25 balls was scored 4.84 times faster than his team mates. Ranatunga's 58 in 27 balls gives this innings a ratio of 4.25. The century with the highest ratio is for Kapil Dev's 175 which scores 3.56. Anything said about this gem is an under-statement Afridi's 102 comes in next, clocking in at 3.43.

Holding deserves a special mention. His 64 off 39 balls compared to his team total of 197 in 43.3 overs is one of only two instances of a bowler in this exalted company. It is all the more commendable since the other teams included the greats and the bowling was Lawson/Alderman/Hogg/Rackemann. Kyle's 52 also deserves equal credit. Also spare a thought for L'O.B Cann, playing for unfancied Bermuda.

Overall it can be said that Sri Lanka and West Indies have often been at either end of the spectrum. English batsmen have rarely figured in such dominating innings. Also, with Gilchrist at the top, Australia has not appeared that frequently as expected.

This only shows the influence Jayasuriya had on the game. He outscored 21 other players, outscored 11 of his opposite team players by a factor of 3.50 and scored at a rate 5.66 times that of his 10 team-mates. He comes through on top on all three measures. Also note the daylight which exists between Jayasuriya and the second placed innings in the first two tables. When we talk of the greatest ODI players we almost always talk of Tendulkar and Richards. I think Jayasuriya needs to be included in this company.

It should be noted that these differ from the "PIF" based on the "Alex Factor" quite a lot although the third one comes near that analysis.

My thanks to Abhishek for providing the spark.

Sanath Jayasuriya: set up a 245 run win over India © Getty Images

First, something of interest to the readers. In one of my responses I had mentioned that I had just finished reading Lynne Truss's classic "Eats, shoots and leaves" and hoped that my apostrophes were correctly placed. This was like a fleeting peep into the unknown. Couple of readers mailed and asked me about the book. A nice diversion for me, I thought. Here we go.

Lynne Truss's book is an all-time classic on English punctuation and should be read by anyone who does anything in English. It is now availeble in a low-cost edition in India. Okay, what about the title.

A panda walks into a cafe. He orders a sandwich, eats it, then draws a gun and fires shots in the air. He then walks towards the exit.
The waiter is baffled and asks the panda "why ?". The panda produces a wildlife manual and asks the waiter to read the desription of himself, the panda.
Sure enough, there is an explanation.
"Panda: Large black-and-white bear-like animal, native to China. Eats, shoots and leaves."
The waiter understands.
To those of you who have not got it, the single comma, inserted unnecessarily between eats and shoots, changes the whole sentence on the panda's eating habits. It should have been "Eats shoots and leaves.", "shoots", of course, referring to bamboo shoots. No wonder, he protested.
Do not miss this classic on correct punctuation. It is a constant bed-side companion book for me.

At the beginning of last year I had looked at huge ODI wins, using scoring rates as the base. This represented the relative usage of resources in an indirect manner. In this article I have looked at wins achieved with least deployment of resources looking at the same directly.

Let us first look at the chasing wins which offer the clearest look at deployment of resources. Everything is cut and dried at the beginning of the chasing innings and we would be able to determine the availability of resources in a very accurate manner.

Let me say that a team is restricted to 99 in the first innings, it does not matter whether they were dismissed in the 31st over or scored 99 for 8 (as Bangladesh did way back) in 45 overs. The target in front of the chasing team is 100. They have two resources at their disposal, viz., 10 wickets and x balls (normally 300). This is fixed and set in stone, ignoring the matches where rains come in and the duo of dons from England take over. In most cases the resource situation remain clear.

In the first part of this article I am going to look at the wins in which the winning teams expended the minimum of these resources and won by a few miles, so to speak.

First, the balls available. There may be Power play options, fielding restrictions in force, ball changes et al. However it is safe to conclude that the balls resource can be measured in a linear manner. With the advent of two optional Power Plays, no one can say that the middle overs are low-scoring overs. The batting team can take the PP at any time to compensate for a low-scoring period, although the captains rarely do so. Hence I will take the balls resource in a linear manner. If the maximum balls is 300 and 210 balls are available, 30% of this resource has been used. If 120 balls are available, 60% has been used and so on.

The wickets resource is slightly tricky. Not all wickets are equal. Certainly the top order wickets are more valuable than the low order wickets. There are two ways of doing this. The first is a simpler but very good method. I assign the following weights for the wickets. Not subjectively done, but by an informed allocation.

Wickets 1 to 6: 12.5% - 75%
Wicket  7:      10.0% - 85%
Wicket  8:       7.5% - 92.5%
Wicket  9:       5.0% - 97.5%
Wicket 10:       2.5% - 100%

If 4 wickets are lost, 50% of wicket resource has been used, if 7 wickets are lost, 85% of the wicket resource has been used and so on. It works very well. The only point of contention might be the argument that at the fall of 6th wicket we might still have a pair of very good batsman batting. But the weight is a fair 10% for the seventh wicket and Gilchrists and Dhonis, batting at 7, do not abound in plenty.

The second is to do a more accurate weighting of wicket values. I add the ODI Index (Batting average x Strike rate) values for all 11 batsmen. This represents the total available resource. I then determine the sum of ODI Index for the dismissed batsmen. This represents the already deployed resource. The ratio between the two gives us the required value. If Tendulkar and Vijay open, and one of them is out in a 9-wkt win, the resource available will vary significantly depending on which one is out. Of course for 10-wkt wins, the two methods lead to the same result.

I have done the calculations on both methods. My personal take is that the simpler method is sufficient and works well in 95% of the situations. The two resources carry equal weights. There might be situations where one might be more valuable than the other. Bot there is no clear way in which this sort of variable weight can be determined.

Let us now look at the tables. First, the one based on a simple wicket weight.

Wicket resource calculation: Method 2 - Simple wkt weight based

MtId Year FBt Score  SBt Score        Result       % Res left

2660 2007 Bng: 93/10 Nzl: 95/ 0 ( 6.0) won by 10 wkts  94.0
1776 2001 Zim: 38/10 Slk: 40/ 1 ( 4.2) won by  9 wkts  89.4
1958 2003 Can: 36/10 Slk: 37/ 1 ( 4.4) won by  9 wkts  89.1
1758 2001 Ken: 90/10 Ind: 91/ 0 (11.3) won by 10 wkts  88.5
1961 2003 Bng:108/10 Saf:109/ 0 (12.0) won by 10 wkts  88.0
1940 2003 Eng:117/10 Aus:118/ 0 (12.2) won by 10 wkts  87.7
2063 2003 Eng: 88/10 Slk: 89/ 0 (13.5) won by 10 wkts  86.2
2521 2007 Pak:107/10 Saf:113/ 0 (14.0) won by 10 wkts  86.0
2172 2004 USA: 65/10 Aus: 66/ 1 ( 7.5) won by  9 wkts  85.9
2754 2008 Saf: 83/10 Eng: 85/ 0 (14.1) won by 10 wkts  85.8
2599 2007 Hol: 80/10 Win: 82/ 0 (14.3) won by 10 wkts  85.5
2122 2004 Zim: 35/10 Slk: 40/ 1 ( 9.2) won by  9 wkts  84.4
2489 2007 Ber:133/10 Ken:137/ 0 (18.1) won by 10 wkts  81.8
2570 2007 Ire: 91/10 Aus: 92/ 1 (12.2) won by  9 wkts  81.4
2254 2005 Bng:139/10 Aus:140/ 0 (19.0) won by 10 wkts  81.0
2428 2006 Win: 80/10 Slk: 83/ 1 (13.2) won by  9 wkts  80.4
2733 2008 Bng:115/10 Pak:116/ 0 (19.4) won by 10 wkts  80.3
1891 2002 Bng:154/ 9 Saf:155/ 0 (20.2) won by 10 wkts  79.7
2424 2006 Zim: 85/10 Win: 90/ 1 (14.2) won by  9 wkts  79.4
1950 2003 Bng:124/10 Slk:126/ 0 (21.1) won by 10 wkts  78.8

It is obvious that a 10-wicket win would lead the table. However it is also essential that no balls were wasted and the win was reached post-haste. That is what New Zealand did against Bangladesh. They chased a target of 94 runs in 6 overs, at a scoring rate exceeding 15 and did this without losing a wicket. I have already talked about this match in my article on McCullum. This is the most devastating chasing win in ODI history. The next two blitzkriegs were by Sri Lanka who dismissed their opponents for sub-40 totals and then proceeded to win in below 5 overs, both times for the loss of one wicket. If either of these wins had been for no loss of a wicket, the resources available would have been 95%+. Then come India 10-wkt win over Kenya and South Africa's 10-wicket win over Bangladesh.

However, from point of significant win over a good opposition, the next two losses by England are the ones to turn to. In the first case England were dismissed for 117 and then Australia blasted to 118 for no loss in a mere 12 overs. England were dismissed for 88 and Sri Lanka overhauled this for no loss in fewer than 14 overs. Pakistan also lost by 10 wickets in 14 overs to South Africa.

To view/down-load the complete table, containing wins with reserves exceeding 50%, please click/right-click here.

Wicket resource calculation: Method 1 - ODI Index based

MtId Year FBt Score  SBt Score        Result       % Res left

2660 2007 Bng: 93/10 Nzl: 95/ 0 ( 6.0) won by 10 wkts  94.0
1776 2001 Zim: 38/10 Slk: 40/ 1 ( 4.2) won by  9 wkts  90.8
1758 2001 Ken: 90/10 Ind: 91/ 0 (11.3) won by 10 wkts  88.5
1958 2003 Can: 36/10 Slk: 37/ 1 ( 4.4) won by  9 wkts  88.0
1961 2003 Bng:108/10 Saf:109/ 0 (12.0) won by 10 wkts  88.0
1940 2003 Eng:117/10 Aus:118/ 0 (12.2) won by 10 wkts  87.7
2063 2003 Eng: 88/10 Slk: 89/ 0 (13.5) won by 10 wkts  86.2
2521 2007 Pak:107/10 Saf:113/ 0 (14.0) won by 10 wkts  86.0
2172 2004 USA: 65/10 Aus: 66/ 1 ( 7.5) won by  9 wkts  85.9
2754 2008 Saf: 83/10 Eng: 85/ 0 (14.1) won by 10 wkts  85.8
2599 2007 Hol: 80/10 Win: 82/ 0 (14.3) won by 10 wkts  85.5
2122 2004 Zim: 35/10 Slk: 40/ 1 ( 9.2) won by  9 wkts  84.8
2489 2007 Ber:133/10 Ken:137/ 0 (18.1) won by 10 wkts  81.8
2570 2007 Ire: 91/10 Aus: 92/ 1 (12.2) won by  9 wkts  81.6
2254 2005 Bng:139/10 Aus:140/ 0 (19.0) won by 10 wkts  81.0
2875 2009 Ken:113/10 Can:117/ 1 (16.2) won by  9 wkts  80.9
2428 2006 Win: 80/10 Slk: 83/ 1 (13.2) won by  9 wkts  80.8
2733 2008 Bng:115/10 Pak:116/ 0 (19.4) won by 10 wkts  80.3
1891 2002 Bng:154/ 9 Saf:155/ 0 (20.2) won by 10 wkts  79.7
1883 2002 Hol:136/10 Pak:142/ 1 (16.2) won by  9 wkts  79.2

This is based on the ODI Index and has a slight variation to the previous table. The 10-wicket wins retain the same values as in the first table. The resource-left values for other wins depends on the quality of wicket(s) lost. Note the difference in the two Sri Lankan 9-wicket wins. In one case it was Avishka Gunawardene whose wicket was lost. In the other case, the more valuable wicket of Jayasuriya which was lost.

To view/down-load the complete table, containing wins with reserves exceeding 50%, please click/right-click here.

To complete the exercise, I have also presented here the table of huge wins, this time by teams batting first. Before any reader rushes in, let me say that these two are apples and oranges or more aptly mangos and kiwi fruits. The resources are used in totally different ways. The first batting team uses all available balls resource (again rain-d-l situations excluded) and a significant proportion of the wickets resource. The second batting team is dismissed for a low total and the win is by runs and the win margin reflects the size of win. I have ordered these wins by the ratio of second innings score to the first innings score. This clearly reflects the dominance, rather than the run margins. Let us now look at the tables.

MtId Year FBt Score  SBt Score         Result          % score

1970 2003 Aus:301/ 6 Nam: 45/10 (14.0) lost by 256 runs 85.0
1652 2000 Slk:299/ 5 Ind: 54/10 (26.3) lost by 245 runs 81.9
2088 2004 Saf:263/ 4 Win: 54/10 (23.2) lost by 209 runs 79.5
0405 1986 Win:248/ 5 Slk: 55/10 (28.3) lost by 193 runs 77.8
2803 2009 Slk:309/ 5 Pak: 75/10 (22.5) lost by 234 runs 75.7
2534 2007 Slk:321/ 6 Ber: 78/10 (24.4) lost by 243 runs 75.7
0358 1986 Nzl:276/ 7 Aus: 70/10 (26.3) lost by 206 runs 74.6
1832 2002 Pak:295/ 6 Slk: 78/10 (16.5) lost by 217 runs 73.6
1599 2000 Pak:320/ 3 Bng: 87/10 (34.2) lost by 233 runs 72.8
2001 2003 Ind:276/10 Bng: 76/10 (27.3) lost by 200 runs 72.5
2471 2007 Slk:262/ 6 Nzl: 73/10 (26.3) lost by 189 runs 72.1
2727 2008 Nzl:402/ 2 Ire:112/10 (28.4) lost by 290 runs 72.1
0297 1985 Aus:323/ 2 Slk: 91/10 (35.5) lost by 232 runs 71.8
2274 2005 Ind:226/ 6 Zim: 65/10 (24.3) lost by 161 runs 71.2
2758 2008 Aus:254/ 8 Bng: 74/10 (27.4) lost by 180 runs 70.9
1878 2002 Slk:292/ 6 Hol: 86/10 (29.3) lost by 206 runs 70.5
3030 2010 Nzl:288/10 Ind: 88/10 (29.3) lost by 200 runs 69.4
2716 2008 Ind:374/ 4 Hkg:118/10 (36.5) lost by 256 runs 68.4
1885 2002 Nzl:244/ 9 Bng: 77/10 (19.3) lost by 167 runs 68.4
3061 2010 Saf:399/ 6 Zim:127/10 (29.0) lost by 272 runs 68.2
2345 2006 Saf:289/ 7 Aus: 93/10 (34.3) lost by 196 runs 67.8
1242 1997 Zim:284/10 Bng: 92/10 (32.3) lost by 192 runs 67.6

Australia's 256-run demolition of Namibia (who ???) leads this table since Namibia scored only 15% of the Australian total. However this match must be discounted since Namibia could have been beaten by half of the state sides of South Africa, England, Australia or India. The miracle was how they restricted Australia to only 301.

The real match of significance, in this regard, was Sri Lanka's 245-run blitz of a near-full-strength Indian team. Jayasuriya, Vaas and Muralitharan made India look like a collection of novices. India scored less than Russell Arnold. In fact I have done some detailed performance analyses for a company and this Sri Lankan performance has come out to be the greatest team performance ever. Everything has to click, the bowling and batting, that too against a top side.

The next three wins by South Africa, West Indies, Sri Lanka (recently) were all against good opponents and rank only slightly below the Sri Lankan win. A feature of this table is the proliferation of top teams at the top of the tables which have lost. This indicates that it is more likely that a good team has a bad day chasing a big total. In the first collection, it was mostly the minnows who were at the receiving end.

I am amazed at the frequency with which Sri Lanka has inflicted heavy defeats. Maybe the Murali factor.

To view/down-load the complete table, containing wins with margins exceeding 50%, please click/right-click here.

Let me re-iterate to the readers that I have not compared the numbers in these two different types of wins. Each stands by itself.