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November 2, 2011

ODI batsmen against bowler groups: across ages

Anantha Narayanan
Viv Richards: the best average against the top bowling group  © AllSport UK Ltd
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A few months back I had come out with an article on Test batsmen by bowling quality, in groups. This was one of the best received of all my articles since the analysis took Test batting into hitherto unchartered seas. Many new insights were drawn from the analysis. I think it is time I do a similar analysis for ODI batsmen also since the bowling quality varies considerably across teams and years. The average runs scored by batsmen in their careers is also quite high and an analysis like this will let us look at the batsmen with a new perspective.

This analysis has come out partly because a single number indicating the weighted average bowling quality faced by a batsman across the career hides many truths. This is based on the Arjun Hemnani's suggestion. This is a quasi-rating work based on the most important of parameters, viz., the Bowling quality.

I have summarized below all relevant facts related to this analysis. First let me emphasize that this is not a ODI innings Ratings analysis. There are many other relevant factors which would have to be considered in such an analysis. I have not done so in this analysis which is centred on Bowler quality. I would appreciate if the readers do not keep on repeating again and again that other relevant factors such as Pitch type, Innings status at entry, Result, Match importance, Bowler recent form, Innings target et al, have not been included. That would be counter-productive.

1. The Bowling quality index (BQI) is based on Career-to-date values. This is the most dependable and accurate of the bowling measures. There is no situation where the Ctd figure is not the appropriate one. Coupled with the fine-tuned handling of established bowlers described later, this works very well. This takes into account the way a bowler's career shaped up.

2. The BQI is based on the Bowling average. In Test matches the bowling strike rate has greater relevance. However in ODIs, both strike rate and bowling accuracy (RpO) have equal importance and the Bowling Average is a perfect representation of this. Very good averages of say, 25.0, can be reached by a combination of 60 and 0.41 or 50 and 0.5 or 40 and 0.62. All these, patently different, bowlers are considered similar in this analysis. Individual match circumstances might require bowlers with varying attacking and accuracy-related skills, but, in general the average takes care of all conditions.

3. The BQI is based on the actual bowlers who bowled in the particular innings. This is very important. If Imran Khan played as a batsman, to that extent, the bowling attack would be less strong.

4. The BQI is determined using the modified reciprocal method suggested by Arjun Hemnani which irons out the imbalance created by weak fifth bowlers.

5. I have taken care of top bowlers during their initial Initial figures for bowlers with career haul of 100+ wickets. Whatever be the Ctd figures for these qualifying bowlers, their Ctd bowling average will be fixed at their career bowling average levels. This takes care of both situations: Walsh capturing 10 wickets at 50+, nearly 20 more than his career average and Mendis, at one point capturing 25 wickets at 9.83. Of course once any bowler crosses 50 wickets, their Ctd figures will apply.

For the bowlers who have not captured 100 career wickets, their Ctd bowling averages below 50 wickets is pegged at a minimum of 40.0. Makes eminent sense.

6. The computed BQI values will be used only for innings of 10 overs or more. For shorter innings the minimum BQI value is pegged at a minimum of 30.0. This is to prevent situations like Wasim Akram and Waqar Younis bowling 6 overs between them. The BQI would be a very low number.

7. The BQI is reduced by 5% for Home games and increased by 5% for away games. Reader should remember that the lower the BQI, the more potent the attack is. 5% either way is ample and provides some compensation for batsmen playing away. In general this concept is fine and works well in most cases.

It is possible that the visiting team has the right bowlers and can exploit the "away" bowling conditions. However there is no denying that, in most cases, the home bowlers would have the advantage of familiarity with and knowledge of local conditions. Great examples are the recent whitewashes in England and India and the way West Indies are struggling in Bangladesh.

8. No period-based adjustment is done. Such adjustment is relevant only for determining team strength values. If the period was a great one for the bowlers, as the 1971-84 was, it was a tough one for the batsmen and this is taken care of by leaving the relatively lower BQI values as they are. It is obvious that the runs scored during 1971-1984 were more valuable than the runs scored in more batting-friendly conditions later.

Finally the bowling attacks are classified into 5 groups, as described below. The fifth group was necessary to separate the really weak bowling attacks.

There have been 6302 qualifying innings until the fifth ODI between India and England which was played on October 25. The underlying idea is that the middle group should have about a third and the other groups symmetrically lower. In view of the profusion of weak bowling attacks, the first and the last would not necessarily have similar % shares. There may be a subjective element in this part of the exercise but that cannot be avoided. Around 28% for the first two groups means that at any time there are 2-3 really good bowling attacks makes eminent sense. The other cut-offs follow logically. The group cut-off details are given below.

Group     B Q I   # of Inns   %

1 21.30-27.99: 709 11.25 % Very good bowling attack. 2 28.00-30.99: 1070 16.98 % Good bowling attack. 3 32.00-35.99: 2104 33.38 % Average bowling attack. 4 36.00-39.99: 1203 19.09 % Passable bowling attack. 5 40.00-57.98: 1216 19.30 % Poor bowling attack.

The best bowling attack ever, BQI of 21.32, was fielded by Pakistan against New Zealand. All 5 bowlers who shared the 30 overs between them, Akram, Younis, Akhtar, Saqlain and Razzaq had Ctd bowling averages of below 25.

Pakistan has a few bowling attacks around the 23 mark, as also West Indies of the 1980s and Australia of the 2000s.

The average BQI for this huge sample is 34.4 and the median is at 33.6. This indicates a fairly balanced distribution of values. The Standard Deviation is 5.87. I have explained the whole concept of determining the BQI with the following examples.

First is Match 1833 between Pakistan and New Zealand, played at Karachi during 2002. In the New Zealand innings, Wasim Akram (Ctd 456 @ 23.86) bowled 7.0 overs, Waqar Younis (Ctd 372 @ 23.54) bowled 6.0 overs, Abdul Razzaq (Ctd 136 @ 24.68 (but career 31.84!)) bowled 4.0 overs, Shoaib Akhtar (Ctd 99 @ 20.68) bowled 9.0 overs and Saqlain Mushtaq (Ctd 270 @ 20.90) bowled 4.0 overs. Through the reciprocal method, the the weighted BQI starts life at 22.44. This is multiplied by 0.95 (this being a home game for Pakistan). The final BQI value is 21.32 which places this attack as the best ever one. Any runs scored in this particular innings will get into the highest classification. Astle's 25 (out of 122) might not figure in anyone's list of the best ODI innings. However it was made against the best ever bowling attack which took the field.

The second is Match 132 between West Indies and Pakistan, played at Sydney. Holding (Ctd 41 @ 18.44, taken as career 21.37), Roberts (Ctd 55 @ 18.96), Marshall (14 @ 24.14, taken as career 26.96), Garner (Ctd 35 @ 25.31, taken as career 18.85) and Richards (Ctd 21 @ 37.57, taken as career 35.83) all bowled 10 overs each. The base BQI is 22.98. This is multiplied by 1.00 (this being a neutral ODI). The final BQI value is 22.98 which puts this attack into the top drawer. Any runs scored in this particular innings, say Imran's 62 will get into the top classification.

I have got into details here so as to give the readers a clear idea of the calculations. I have selected two of the best ever bowling combinations put on the field. I have also selected one in which all five bowlers had crossed 50 wickets and their Ctd values were impeccable and another attack in which four bowlers (three greats amongst them) had just started their careers. This will show that the great bowlers have always been given their due credit.

There is so much data available that even the organization of the article is getting into trouble. I can only present in the article a certain amount of data. The serious reader should download the complete files and read the same. I have given below what I would be presenting within the article.

1. Top 20 batsmen for group 1, the top one. Ordered by batting average.
2. Top 20 batsmen for group 2, the second best one. Ordered by batting average.
3. Top 20 batsmen for groups 1/2, the groups which matter. Ordered by batting average.
4. Top 20 batsmen for group 3, the middle and most-populated. Ordered by batting average.
5. Top 10 batsmen for groups 4. Ordered by batting average.
6. Top 10 batsmen for groups 5, the weakest one. Ordered by % of career runs scored.
7. For selected batsman, their group-wise distribution of runs scored and % of career.

For all the above, complete files are available for downloading/viewing.

Let us look at the tables. First the Group tables based on Batting average. The batsman should have scored a minimum of 750 for Group 1, 1000 for Group 2, 2000 for Group 3, 1000 for Group 4 and 1000 runs for Group 5 to be considered. I cannot use the same cut-offs across bowler groups since the population sizes vary considerably. For instance, taking 1000 as cut-off for the group 1 will let us have only 13 entries. It should also be noted that Runs scored should not be a criteria for ordering since that is a measure of longevity.

This analysis covers all matches upto ODI # 3210, the fifth ODI between India and England. While a few days have passed since the third ODI between Saf-Aus was played, it was too much of an effort to re-do all tables and article.

Batsman             Team BG CRuns  Inns Nos Runs   %     Avge

Richards I.V.A Win 1 6721 19 3 870 12.9 54.38 Waugh S.R Aus 1 7569 37 12 1330 17.6 53.20 Kirsten G Saf 1 6798 30 7 1219 17.9 53.00 Pietersen K.P Eng 1 3903 22 4 938 24.0 52.11 Ponting R.T Aus 1 13675 34 3 1535 11.2 49.52 Bevan M.G Aus 1 6912 25 7 891 12.9 49.50 Dhoni M.S Ind 1 6497 23 6 799 12.3 47.00 Richardson R.B Win 1 6248 33 7 1156 18.5 44.46 Imran Khan Pak 1 3709 26 6 881 23.8 44.05 Rhodes J.N Saf 1 5935 35 10 1094 18.4 43.76 Haynes D.L Win 1 8648 28 4 1043 12.1 43.46 Cronje W.J Saf 1 5565 25 5 868 15.6 43.40 Dravid R Ind 1 10889 52 5 1992 18.3 42.38 Atapattu M.S Slk 1 8529 50 5 1837 21.5 40.82 Ganguly S.C Ind 1 11363 41 4 1502 13.2 40.59 Border A.R Aus 1 6524 33 6 1053 16.1 39.00 McMillan C.D Nzl 1 4707 39 4 1271 27.0 36.31 de Silva P.A Slk 1 9284 52 6 1661 17.9 36.11 Hooper C.L Win 1 5761 31 7 846 14.7 35.25 Tendulkar S.R Ind 1 18111 71 7 2250 12.4 35.16

Richards suffers a little bit since the best bowling attacks during his time were from his part of the woods. He still has done very well and averaged 54.38 against the top group. The runs are low but that is an indication of the number of matches played. However it should be seen that he has scored 12.9% of his runs against the top group. Steve Waugh and Gary Kirsten have averaged over 50 and have also scored more than a sixth of their career runs against the top group. It helped that the other respective bowling attacks were very good.

Pietersen is a revelation. Nearly a quarter of his runs have been against the top attacks at an average of 52.11. This single fact is enough ammunition to show the futility of using Batting average as an omnipotent analysis factor. Pietersen has a batting average barely reaching 50 but his runs seem to have a much higher value. Ponting has a lower % but a near-50 average.

Imran Khan's 23.8% of his runs against the top group is nearly as much as that of Pietersen and that too at an average of 44.1. This deserves a special mention especially as he was not the leading batsman of Pakistan.

Dhoni has not scored many runs but he has scored 12.3% of his runs at a high average of 47 against the top bowlers. He is no doubt helped by a slew of not outs. Dravid clocks in with a very respectable 18.3% and average of 42.38. Ganguly has a similar average but lower %. The surprise is that Tendulkar has just about crossed the datum % of 11.25% but a reasonably low average of 35.16. This is possibly because of his opening the batting. However it must be remembered that Ganguly was also in a similar position.

Batsman             Team BG CRuns  Inns Nos Runs   %     Avge

Symonds A Aus 2 5088 28 5 1188 23.3 51.65 Sangakkara K.C Slk 2 9540 51 11 2001 21.0 50.02 Dhoni M.S Ind 2 6497 35 10 1235 19.0 49.40 Bevan M.G Aus 2 6912 38 12 1270 18.4 48.85 Hayden M.L Aus 2 6133 33 4 1358 22.1 46.83 Tendulkar S.R Ind 2 18111 91 5 3961 21.9 46.06 Sarwan R.R Win 2 5644 33 4 1317 23.3 45.41 Marsh G.R Aus 2 4357 31 3 1233 28.3 44.04 Kallis J.H Saf 2 11318 54 11 1831 16.2 42.58 Chanderpaul S Win 2 8778 46 5 1689 19.2 41.20 Jones D.M Aus 2 6068 38 5 1348 22.2 40.85 Trescothick M.E Eng 2 4335 28 2 1053 24.3 40.50 Ponting R.T Aus 2 13675 65 5 2421 17.7 40.35 Lamb A.J Eng 2 4010 33 4 1153 28.8 39.76 Haynes D.L Win 2 8648 48 5 1701 19.7 39.56 Gayle C.H Win 2 8087 49 3 1769 21.9 38.46 Inzamam-ul-Haq Pak 2 11739 62 5 2178 18.6 38.21 Lara B.C Win 2 10405 65 3 2351 22.6 37.92 Javed Miandad Pak 2 7381 34 5 1095 14.8 37.76 Hooper C.L Win 2 5761 47 9 1431 24.8 37.66

Symonds has scored 23.3% of his runs at a very high average, a late-order batting benefit, of 51.65. Sangakkara has done very well, scoring over 2000 runs, 21.0% of his runs, at a very creditable 50+ average. Dhoni also has a near-50 average, slightly below his career average. as does Bevan. Tendulkar has asserted his class against this strong bowling group, scoring nearly 4000 runs, 21.9% of his career runs at an average of 46.83, better than his career average.

Batsman             Team BG CRuns  Inns Nos Runs   %     Avge

Bevan M.G Aus 1/2 6912 63 19 2161 31.3 49.11 Dhoni M.S Ind 1/2 6497 58 16 2034 31.3 48.43 Ponting R.T Aus 1/2 13675 99 8 3956 28.9 43.47 Kirsten G Saf 1/2 6798 62 7 2286 33.6 41.56 Tendulkar S.R Ind 1/2 18111 162 12 6211 34.3 41.41 Haynes D.L Win 1/2 8648 76 9 2744 31.7 40.96 Rhodes J.N Saf 1/2 5935 79 17 2401 40.5 38.73 Sangakkara K.C Slk 1/2 9540 92 13 3024 31.7 38.28 Kallis J.H Saf 1/2 11318 82 13 2592 22.9 37.57 Chanderpaul S Win 1/2 8778 82 7 2790 31.8 37.20 Hooper C.L Win 1/2 5761 78 16 2277 39.5 36.73 Dravid R Ind 1/2 10889 135 15 4283 39.3 35.69 Lara B.C Win 1/2 10405 114 8 3762 36.2 35.49 Atapattu M.S Slk 1/2 8529 94 9 3016 35.4 35.48 Richardson R.B Win 1/2 6248 78 11 2324 37.2 34.69 Gilchrist A.C Aus 1/2 9619 92 5 3009 31.3 34.59 Fleming S.P Nzl 1/2 8037 113 12 3459 43.0 34.25 Waugh S.R Aus 1/2 7569 100 18 2803 37.0 34.18 Inzamam-ul-Haq Pak 1/2 11739 103 9 3201 27.3 34.05 de Silva P.A Slk 1/2 9284 111 12 3371 36.3 34.05

Now for a special table, the elite group table. In this I have considered the top two bowling groups and selected players who have crossed 2000 runs against the two groups together. This table is ordered by the batting average. As such it represents a table of quality batsmen against quality bowlers.

Bevan and Dhoni are in the top two positions. But they have been helped by a high number of not outs. Hence we should take Ponting as the real top batsman. He has scored near;y 4000 runs, which is 29% of his career runs at an average of 43.47. Truly outstanding batting. Gary Kirsten has averaged 41.56 and scored nearly a third of his career runs against this double group. Tendulkar makes up for his group 1 under-performance and clocks in with a creditable 41.41, while scoring over 6000 runs and just above a third of his career runs. This indicates that both Ponting and Tendulkar have done very creditably against top quality bowling. Haynes is the only other batsman to cross 40. Readers may wonder where Richards, who topped Group 1 is. The fact is that he does not meet the higher cut-off point of 2000 runs for Groups 1 & 2 combined.

Batsman             Team BG CRuns  Inns Nos Runs   %     Avge

Hussey M.E.K Aus 3 4817 58 21 2183 45.3 59.00 Richards I.V.A Win 3 6721 63 15 2720 40.5 56.67 Bevan M.G Aus 3 6912 80 26 2979 43.1 55.17 Clarke M.J Aus 3 6596 74 16 2926 44.4 50.45 Kallis J.H Saf 3 11318 132 24 5243 46.3 48.55 Tendulkar S.R Ind 3 18111 149 14 6292 34.7 46.61 Mohammad Yousuf Pak 3 9720 94 16 3623 37.3 46.45 Dhoni M.S Ind 3 6497 70 15 2525 38.9 45.91 Gayle C.H Win 3 8087 67 3 2845 35.2 44.45 Kirsten G Saf 3 6798 79 7 3031 44.6 42.10 Lara B.C Win 3 10405 92 13 3308 31.8 41.87 Symonds A Aus 3 5088 74 17 2358 46.3 41.37 Javed Miandad Pak 3 7381 71 11 2467 33.4 41.12 Saeed Anwar Pak 3 8824 85 6 3173 36.0 40.16 Chanderpaul S Win 3 8778 86 13 2932 33.4 40.16 Shoaib Malik Pak 3 5204 68 10 2315 44.5 39.91 Dilshan T.M Slk 3 5616 62 9 2115 37.7 39.91 Gibbs H.H Saf 3 8094 96 7 3471 42.9 39.00 Boon D.C Aus 3 5964 62 5 2218 37.2 38.91 Hayden M.L Aus 3 6133 55 3 2016 32.9 38.77

Hussey and Bevan, no doubt aided by a high number of not outs, are in the top three positions in this staple group. Richards averages 55+. Michael Clarke is the only other batsmen with a 50+ average. Note the very high % of career runs for all these players. Tendulkar's group 3 performance is almost identical to his groups 1/2 performances, at a higher average.

Batsman             Team BG CRuns  Inns Nos Runs   %     Avge

Dhoni M.S Ind 4 6497 31 12 1382 21.3 72.74 de Villiers A.B Saf 4 4523 29 6 1453 32.1 63.17 Ganguly S.C Ind 4 11363 42 7 2138 18.8 61.09 Astle N.J Nzl 4 7090 29 3 1541 21.7 59.27 Javed Miandad Pak 4 7381 31 7 1267 17.2 52.79 Dravid R Ind 4 10889 40 6 1791 16.4 52.68 Shakib Al Hasan Bng 4 3340 30 6 1235 37.0 51.46 Clarke M.J Aus 4 6596 49 12 1882 28.5 50.86 Hayden M.L Aus 4 6133 33 4 1455 23.7 50.17 Tendulkar S.R Ind 4 18111 68 9 2907 16.1 49.27 Lara B.C Win 4 10405 39 6 1586 15.2 48.06 Chanderpaul S Win 4 8778 40 7 1516 17.3 45.94 Kallis J.H Saf 4 11318 61 10 2339 20.7 45.86 Mohammad Yousuf Pak 4 9720 53 11 1921 19.8 45.74 Waugh M.E Aus 4 8500 41 3 1727 20.3 45.45 Cronje W.J Saf 4 5565 29 5 1076 19.3 44.83 Tharanga W.U Slk 4 4064 36 2 1516 37.3 44.59 Richardson R.B Win 4 6248 31 5 1146 18.3 44.08 Gambhir G Ind 4 4286 26 3 1010 23.6 43.91 Ponting R.T Aus 4 13675 74 6 2976 21.8 43.76

Now we get into the weaker bowling groups. Note the number of 50+ averages. Many modern batsmen have feasted on these below-average bowling attacks.

Batsman             Team BG CRuns  Inns Nos Runs   %     Avge

Otieno K.O Ken 5 2016 33 1 1094 54.3 34.19 Shahriar Nafees Bng 5 2162 29 4 1129 52.2 45.16 Tikolo S.O Ken 5 3421 60 8 1722 50.3 33.12 Odoyo T.M Ken 5 2418 50 9 1133 46.9 27.63 Zaheer Abbas Pak 5 2572 22 2 1098 42.7 54.90 Tamim Iqbal Bng 5 3111 43 1 1300 41.8 30.95 Shakib Al Hasan Bng 5 3340 46 9 1362 40.8 36.81 Wright J.G Nzl 5 3891 47 0 1536 39.5 32.68 Jones A.H Nzl 5 2784 27 2 1085 39.0 43.40 Mohammad Ashraful Bng 5 3397 58 7 1306 38.4 25.61 Srikkanth K Ind 5 4091 40 1 1368 33.4 35.08 Taylor B.R.M Zim 5 3985 28 4 1316 33.0 54.83 Crowe M.D Nzl 5 4704 42 5 1528 32.5 41.30 Sidhu N.S Ind 5 4413 32 3 1234 28.0 42.55 Ijaz Ahmed Pak 5 6564 45 13 1678 25.6 52.44 Jones D.M Aus 5 6068 28 10 1500 24.7 83.33 ... Dravid R Ind 5 10889 44 11 1549 14.2 46.94 Jayasuriya S.T Slk 5 13430 51 3 1799 13.4 37.48 Atapattu M.S Slk 5 8529 29 7 1135 13.3 51.59 de Silva P.A Slk 5 9284 23 3 1104 11.9 55.20 Kallis J.H Saf 5 11318 27 6 1145 10.1 54.52

The last group is the buffet-lunch group. I have ordered this in a different sequence, the % of career runs. This figure is essential to see how much the batsmen got against the really weak bowling attacks.

As could be expected the top of the table is dominated by players from weaker countries who almost always play against weaker attacks. The top three players have got more than 50% of their runs against very weak attacks. The real surprise is Zaheer Abbas, whose high batting average is now on shaky ground, he having scored 42% of his runs against the lowest group. Same with Srikkanth, whose bubble is blown a little, with over a third of his runs against the buffet-lunch bowlers. And Sidhu and Martin Crowe and Ijaz and Dean Jones.

At the other end, raise your hat for Dravid who has scored only 14% of his runs in this group. The three Sri Lankan stalwarts have got sub-14%. But let us all raise the hat and toast Kallis whose % here is the lowest amongst all established batsmen, a mere 10%. This should put to bed all theories on his scoring against minnows.

In terms of averages, Dean Jones has really feasted with an average of 80+. The average table is led by three Australians of the previous generation. Ganguly has not done his averages any damage by clocking in 60+ here. Tendulkar, with an average of 47 does not seem to have benefited much against these weaker bowling attacks. Lara does not even appear in the top-20 of the averages table.

Now for the group-wise runs and % of career runs for selected 25+ batsmen. The complete file is available for downloading.

Batsman    Team CRuns G1-Runs-%  G2-Runs-%  G3-Runs-%  G4-Runs-%  G5-Runs-%

Tendulkar Ind 18111 2250(12.4) 3961(21.9) 6292(34.7) 2907(16.1) 2701(14.9)*** Ponting Aus 13675 1535(11.2) 2421(17.7) 4706(34.4) 2976(21.8) 2036(14.9) Jayasuriya Slk 13430 1759(13.1) 1944(14.5) 4493(33.5) 3435(25.6) 1799(13.4) Inzamam Pak 11739 1023( 8.7) 2178(18.6) 4211(35.9) 2265(19.3) 2062(17.6) Ganguly Ind 11363 1502(13.2) 1875(16.5) 3289(28.9) 2138(18.8) 2559(22.5) Kallis Saf 11318 761( 6.7) 1831(16.2) 5243(46.3) 2339(20.7) 1145(10.1) Dravid Ind 10889 1992(18.3) 2291(21.0) 3266(30.0) 1791(16.4) 1549(14.2) Lara Win 10405 1411(13.6) 2351(22.6) 3308(31.8) 1586(15.2) 1750(16.8) Jayawardene Slk 9913 1307(13.2) 1535(15.5) 3084(31.1) 2456(24.8) 1531(15.4) Mohd Yousuf Pak 9720 818( 8.4) 1608(16.5) 3623(37.3) 1921(19.8) 1751(18.0) Gilchrist Aus 9619 1289(13.4) 1720(17.9) 3837(39.9) 1375(14.3) 1398(14.5) Sangakkara Slk 9540 1023(10.7) 2001(21.0) 2950(30.9) 2141(22.4) 1425(14.9) Azharuddin Ind 9378 928( 9.9) 1818(19.4) 3462(36.9) 1573(16.8) 1597(17.0) de Silva Slk 9284 1661(17.9) 1710(18.4) 2858(30.8) 1951(21.0) 1104(11.9) Saeed Anwar Pak 8824 512( 5.8) 1231(14.0) 3567(40.4) 1939(22.0) 1574(17.8) Waugh M.E Aus 8500 891(10.5) 876(10.3) 3555(41.8) 1727(20.3) 1451(17.1) Sehwag Ind 7760 876(11.3) 1393(18.0) 3209(41.4) 1151(14.8) 1131(14.6) Waugh S.R Aus 7569 1330(17.6) 1473(19.5) 2761(36.5) 1257(16.6) 748( 9.9) J Miandad Pak 7381 840(11.4) 1095(14.8) 2467(33.4) 1267(17.2) 1712(23.2) Bevan Aus 6912 891(12.9) 1270(18.4) 2979(43.1) 894(12.9) 880(12.7) Flower A Zim 6786 565( 8.3) 1183(17.4) 2086(30.7) 1577(23.2) 1374(20.2) Richards Win 6721 870(12.9) 861(12.8) 2720(40.5) 1334(19.8) 936(13.9) Dhoni Ind 6497 799(12.3) 1235(19.0) 2525(38.9) 1382(21.3) 556( 8.6) Hayden Aus 6133 588( 9.6) 1358(22.1) 2016(32.9) 1455(23.7) 715(11.7) Hussey Aus 4817 215( 4.5) 947(19.7) 2183(45.3) 1129(23.4) 343( 7.1) Crowe M.D Nzl 4704 219( 4.7) 775(16.5) 1554(33.0) 628(13.4) 1528(32.5) Gooch G.A Eng 4290 502(11.7) 827(19.3) 1796(41.9) 727(16.9) 438(10.2) Shakb AlHsn Bng 3340 206( 6.2) 115( 3.4) 422(12.6) 1235(37.0) 1362(40.8)

Tendulkar seems to have mirrored the overall % pattern, as has Ponting. Note Inzamam's figures. Possibly because the best bowling attack in the world was his team's, he has a lop-sided bottom-heavy distribution. Kallis has scored lower % both against the best and worst attacks and he has a centre-heavy distribution. Dravid has scored fair bit against top attacks while Lara's follows Tendulkar's pattern. Surprisingly, as has Richards. Intriguingly, Hussey's figures against top bowling attacks has been quite below-average. Flower and Shakib-Al-Hasan have low numbers against top attacks since they, Shakib especially, play quite often against weak sides.

This is not an analysis from which the analyst could make finite conclusions. The readers should read and understand the methodology and tables and then come with their views. To view/down-load the complete Team Strength related tables, please click on links given below.

Group tables - by Batting average: please click/right-click here.
Group tables - by Runs scored: please click/right-click here.
Batsman table - by Group (for all 2000+ batsmen): please click/right-click here.
BQI table - ordered by Group/BQI (for all 6302 innings): please click/right-click here.
Batsman-Bqi average across career: as required by Arjun (for all 2000+ batsmen): please click/right-click here. These are in fact the Test tables.
This time the ODI tables. Batsman-Bqi average across career: as required by Arjun/Mahendran (for all 2500+ batsmen): please click/right-click here.

RELATED LINKS

Anantha Narayanan has written for ESPNcricinfo and CastrolCricket and worked with a number of companies on their cricket performance ratings-related systems

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Posted by anshu_n_jain on (December 9, 2011, 7:13 GMT)

Ananth,

just going through the detailed batsmen tables including career BQI faced, i couldnt help notice that Dravid has, on average, faced better bowling attacks, in both Tests and ODIs, than Tendulkar has. Dravid has played 142 of his 159 Tests, and 245 of his 344 ODIs alongside Tendulkar. Of course, in the period 2001-06 (and post 2009), Tendulkar has played far fewer ODIs than team India has because of injury and choiceful rest.

This is quite surprising because: 1. it seems to me Tendulkar would have faced better quality bowling attacks before Dravid made his debut in 1996 (Waqar, Wasim, Imran; Walsh, Ambrose; Donald, Pollock; McGrath, McDermott, Warne etal), in both Tests and ODIs. 2. this seems to indicate that Tendulkar has missed matches when the Bowling quality has been better than his average BQI

unless Tendulkar played relatively weak bowling units quite often before Dravid debuted (SL before 1996, Zim, Eng, NZ).

Could be interesting to look at in detail.

Posted by Shahir on (November 29, 2011, 14:51 GMT)

Cheers, thanks!

Posted by Shahir on (November 29, 2011, 6:03 GMT)

Hi Anantha,

Could you explain how the BQI is calculated? For example, using the numbers from ODI #1833 between Pakistan and New Zealand, I take a weighted average of the bowlers' cumulative average times overs bowled. As a result I get 22.56 as opposed to your 22.44. I guess I don't follow what you mean by 'reciprocal method'. Can you help me out? Thanks. [[ Shabir, You have done the following. (42*23.86+36*23.54+24*24.68+54*20.68+24*20.90)/180 This works to 22.56. You should do 180/(42/23.86+36/23.54+24/24.68+54/20.68+24/20.90) You will get 22.43. Ananth: ]]

Posted by Steve Ferrier on (November 27, 2011, 23:55 GMT)

Thanks Anantha,

I think I will just stick to Excel for now ;-)

-Steve [[ Anytime you want some special table or other, pl ask for it. If I can, I can crete a Text file and send you. Ananth: ]]

Posted by Steve Ferrier on (November 27, 2011, 11:17 GMT)

Thanks Anantha,

How would someone like me start a database like that? What software would I need?

Cheers. [[ Steve, very tough task. Mine is the result of 20 years' work. The Cricinfo scorecard can be downloaded as a text file. That is in public domain. However to convert that to a database is next to impossible. You have to write complex translation program. Simple names like Khan or Singh will create problems. Ananth: ]]

Posted by Steve Ferrier on (November 26, 2011, 7:31 GMT)

Anantha, I enjoy reading your articles and those of the other contributors for IT figures.

I also do some ratings and tables for test batsmen and bowlers myself, including one on the greatest test innings of all time.

I was wondering what program you are using to do your analysis on?

I only use Excel but I was wondering if there are other programs which can quickly access and sort out scorecards and statsguru results?

Thank you, Steve Ferrier [[ Steve, I have my own very exhaustive and complex proprietary database which is virtually updated minutes after the match-end by me ( I have already updated the Test database with the exciting Mumbai draw). I do all extraction through hundreds of 'C' programs. Virtually every day I write a program or two. Ananth: ]]

Posted by Rangarajan on (November 14, 2011, 9:29 GMT)

@ Srhikanthk: It also has to do with the home team winning. Eng is on a roll now and they bat, bowl and win in England like they havent done before. Yes, they were always strong, but they never had so many batsmen and bowlers coming to party together as consistently and as frequently as they are doing now. Till some time back., they alwyas use to win the last ashes test (after being routed in the previous 5 . . . They always gave hope of revival, but never managed to until recently). The best way to bring crowds - Play quality cricket CONSISTENTLY. One good match followed by one boring match takes the crowd away. COnsistent good matches would produce quality crowd as well

Posted by shrikanthk on (November 13, 2011, 15:26 GMT)

If it is not popular in its home territory, it is bad management, administration, marketing.It will be unlikely that such poorly packaged sports catch on elsewhere. With teh right packaging, any sport can be sold

Cricket has to stick to its core strengths in order to survive. There's no better example than how Test cricket has managed to revive itself in England!!!

The Crowds in England for Test matches these days is actually better than what they were some 20-30 years ago. How did they do it? I'd love to hear from some English voices on this thread.

I think it has got to do with the marketing effort. The ability to sell cricket as a niche upper-class sport. The branding of Lord's as the this sacred "Home of cricket" - an aura that didn't exist 2-3 decades ago. No wonder grounds that weren't getting filled even at very low prices back in the 70s/80s are now attracting full houses at prices as high as 100 quids if I'm not mistaken.

Englishmen - correct me if I'm wrong...

Posted by Gerry_the_Merry on (November 13, 2011, 7:40 GMT)

Shrikantk, Ramesh

May i remind you that we are struggling to get ourselves interested in One Days. Forget about popularising the game in other countries.

If it is not popular in its home territory, it is bad management, administration, marketing.

It will be unlikely that such poorly packaged sports catch on elsewhere. With teh right packaging, any sport can be sold. [[ Let us sow the acorn here and see whether an Oak tree comes out of it. Ananth: ]]

Posted by shrikanthk on (November 13, 2011, 7:28 GMT)

But Golf is like that and is very popular.

Nevertheless Golf is a niche sport. Played in most countries yes. But not a mass obsession like Football or even Cricket!

ODIs brought two important aspects to the game-it brought finality to the contest and it brought speed and fitness to the game.

Speed and fitness were a natural consequence of greater professionalism. Moreover, let's not talk up fitness too much. Even as early as the 20s and 30s, bowling 1000 overs a season was quite common in England. One can't do that without being fit. By the way, Test cricket was quite riveting in the mid/late 70s, before the rising popularity of the ODIs. Not sure if ODIs massively improved either run-rates or the tempo of cricket in general.

Cricket is the only sport in the world that lacks confidence in itself. The only sport where the powers that be change rules in order to appease people who don't follow the game. This is a disturbing trend. And I've no doubt it will kill the game

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ABOUT THE AUTHOR

Anantha Narayanan
Anantha spent the first half of his four-decade working career with corporates like IBM, Shaw Wallace, NCR, Sime Darby and the Spinneys group in IT-related positions. In the second half, he has worked on cricket simulation, ratings, data mining, analysis and writing, amongst other things. He was the creator of the Wisden 100 lists, released in 2001. He has written for ESPNcricinfo and CastrolCricket, and worked extensively with Maruti Motors, Idea Cellular and Castrol on their performance ratings-related systems. He is an armchair connoisseur of most sports. His other passion is tennis, and he thinks Roger Federer is the greatest sportsman to have walked on earth.

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