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At the outset let me apologise to all the readers for my inability to read and respond to the comments sent for my previous article. Even though the reasons were beyond my control, it is my corner of "the Cordon" and it was my responsibility to keep this small area working, accessible and free of trash. My thanks to Cricinfo technical team for addressing this sticky problem and I will have access to the comments for my articles, which will henceforth be published, interjected with my responses.
The year 2012 witnessed retirement of the two giants of ODI format. Two pillars of the game, neither of whom could ever be denied a place among the top four of the ODI game. They scored tons of runs and scored these in a magnificent manner, scored these when their team needed them and pulverised top-quality attacks. Sachin Tendulkar and Ricky Ponting played like kings and retired in style. Towards the end of the year another player retired from Test cricket, in the opinion of many, when he was still at the top. Unfortunately this also meant an exclusion from a farewell ODI series. However, this cannot deny the stellar contributions Michael Hussey made towards his team's successes. The retirement of these three giants has prompted me to do a very exhaustive analysis of the ODI batting giants. This is a two-part article due to the number of areas covered. At the end of this article I have outlined the second article so that reader requests can be incorporated wherever possible.
How many batsmen do I include in this analysis and how do I decide on the specific batsmen? A very difficult task indeed. I have used a combination of numbers, the team achievements, their contributions to the ODI format in general and their team in particular. I have tried to ensure as wide a representation across countries and as broad-based representation across the years as possible. Over the past 40 odd years, 10 World Cups and 6 Champions Trophy tournaments have been held. Australia has won 6, West Indies 3, India 2.5, Sri Lanka 1.5 and Pakistan, South Africa and New Zealand, one each. The selection of players reflects, to some extent, this level of success of their teams.
Taking all above into consideration, I have selected following 15 batsmen, listed in chronological order. There may be minor disagreements among readers but there is no denying that these 15 represent the very cream of ODI batting. They have contributed over 130,000 runs, just short of 10% of the total runs scored in ODI matches. Most of these batsmen select themselves.
Richards 1975 1991 Miandad 1975 1996 M Crowe 1982 1995 M Waugh 1988 2002 Tendulkar 1989 2012 Jayasuriya 1989 2011 Lara 1990 2007 Inzamam 1991 2007 A Flower 1992 2003 Bevan 1994 2004 Ponting 1995 2012 Kallis 1996 2012 Active Gilchrist 1996 2008 Pietersen 2004 2013 Active Dhoni 2004 2013 Active
I included Hussey in the beginning but towards the end felt that Mark Waugh could not be ignored. Since both are among my favourite batsmen, it was a wrench to exclude either of them. Martin Crowe played in tough times and just about edged out Fleming. Jayasuriya transformed the game itself. Miandad and Inzamam selected themselves. So did Richards and Lara. The Tendulkar lily need not be gilded. Dhoni's finishing exploits are legendary giving him the edge ahead of Ganguly. Pietersen and Andy Flower are, by far, their respective country's best batsmen.
My sincere apologies to Lloyd, Gayle, Haynes, Dean Jones, Hussey, Saeed Anwar, Mohammad Yousuf, de Silva, Sangakkara, Jayawardene, Ganguly, Azharuddin, Sehwag, Graeme Smith, Gibbs, Fleming et al, and their supporters. Any of them would have graced this list with distinction. Shakib Al Hasan will certainly qualify into an allrounder's list but has not done enough in batting.
In view of the number of tables and the huge amount of information, I have decided to do this mammoth analysis in two parts. The first part is based on available data and I would venture to say that most of this data could be extracted using statsguru. However, here it is available in a single place in easily understandable tables for the crème de la crème of ODI batsmen. The second part will be slightly different where I have done a lot of extraction and grouping for those tables. Many of the ideas in the second part are unique and will not be available anywhere. Readers would also have the opportunity to suggest any new tables which could be developed. I would be glad to create these if I can.
Here is one important factor about the presentation of these tables. I have decided not to order the tables on any data field. It will invite unnecessary discussions. These are all great players and this exercise is not to determine who the best is. Hence all tables will be presented in strictly chronological order. The highlighting would be done in the commentary after the tables.
| Batsman | Inngs | NOs | Runs | Balls | Avge | S/R | RpI | Index |
|---|---|---|---|---|---|---|---|---|
| Richards | 167 | 24 | 6721 | 7581 | 47.00 | 88.7 | 40.2 | 35.7 |
| Miandad | 218 | 41 | 7381 | 10979 | 41.70 | 67.2 | 33.9 | 22.8 |
| M Crowe | 141 | 19 | 4704 | 6464 | 38.56 | 72.8 | 33.4 | 24.3 |
| M Waugh | 236 | 20 | 8500 | 11063 | 39.35 | 76.8 | 36.0 | 27.7 |
| Tendulkar | 452 | 41 | 18426 | 21371 | 44.83 | 86.2 | 40.8 | 35.1 |
| Jayasuriya | 433 | 18 | 13430 | 14736 | 32.36 | 91.1 | 31.0 | 28.3 |
| Lara | 289 | 32 | 10406 | 13056 | 40.49 | 79.7 | 36.0 | 28.7 |
| Inzamam | 350 | 53 | 11739 | 15827 | 39.53 | 74.2 | 33.5 | 24.9 |
| Flower | 208 | 16 | 6785 | 9144 | 35.34 | 74.2 | 32.6 | 24.2 |
| Bevan | 196 | 67 | 6914 | 9299 | 53.60 | 74.4 | 35.3 | 26.2 |
| Ponting | 365 | 39 | 13703 | 17067 | 42.03 | 80.3 | 37.5 | 30.1 |
| Kallis | 307 | 53 | 11499 | 15756 | 45.27 | 73.0 | 37.5 | 27.3 |
| Gilchrist | 279 | 11 | 9619 | 9923 | 35.89 | 96.9 | 34.5 | 33.4 |
| Pietersen | 121 | 16 | 4369 | 5035 | 41.61 | 86.8 | 36.1 | 31.3 |
| Dhoni | 196 | 56 | 7259 | 8228 | 51.85 | 88.2 | 37.0 | 32.7 |
These are numbers which any cricket aficionado would reel off if woken up at 3am. The key columns here are RpI and the Index. The Batting average in ODIs is far more skewed than in Test matches because of the limited number of overs, non-completion of innings and high percentage of not-outs. The number of not-outs varies between 67 for the middle-order stalwart like Bevan to 18 for an attacking opener like Jayasuriya. Hence RpI (Runs per Innings) is a very important measure and reflects the relative positioning of batsmen far more accurately. Hence almost all these tables will have both Average and RpI.
Now for the Index. This has been a favourite combination measure of mine over the past 15 years. Realizing the importance of Average/RpI and the Strike Rate, I had multiplied the two measures, thus giving them equal importance. It allows players to compensate shortcomings in one measure with higher level performances in the other. Earlier I used this Index based on Batting average but now I always use RpI in all Index calculations.
The batting averages which stand out are Richards' 47.00, Bevan's 53.60 and Dhoni's 51.85. Jayasuriya's 90+ strike rate and the near-90 strike rate of Richards at 88.7 and Dhoni at 88.2 stand out. The 35+ Index values of Richards and Tendulkar sets them apart. Readers can note how Jayasuriya's low RpI value is partly compensated by the high strike rate. Similarly Kallis' average strike rate is offset by a good RpI value. The Index represents a clear value to the team: non-contextual of course.
| Batsman | Inns | NOs | Runs | Balls | Avge | S/R | RpI | Index | OPP Runs | OPP Avge |
|---|---|---|---|---|---|---|---|---|---|---|
| Richards | 0 | 0 | 0 | 0 | 0.00 | 0.0 | 0.0 | 0.0 | 0 | 0.0 |
| J Miandad | 4 | 0 | 85 | 86 | 21.25 | 98.8 | 21.2 | 21.0 | 102 | 25.5 |
| Crowe | 22 | 1 | 814 | 1173 | 38.76 | 69.4 | 37.0 | 25.7 | 1008 | 45.8 |
| M Waugh | 141 | 11 | 5729 | 7473 | 44.07 | 76.7 | 40.6 | 31.1 | 5959 | 42.3 |
| Tendulkar | 340 | 23 | 15310 | 17397 | 48.30 | 88.0 | 45.0 | 39.6 | 14154 | 41.6 |
| Jayasuriya | 383 | 15 | 12740 | 13788 | 34.62 | 92.4 | 33.3 | 30.7 | 12871 | 33.6 |
| Lara | 52 | 5 | 2166 | 2871 | 46.09 | 75.4 | 41.7 | 31.4 | 1597 | 30.7 |
| Inzamam | 12 | 0 | 516 | 722 | 43.00 | 71.5 | 43.0 | 30.7 | 493 | 41.1 |
| Flower | 44 | 2 | 1352 | 1928 | 32.19 | 70.1 | 30.7 | 21.5 | 1676 | 38.1 |
| Bevan | 1 | 1 | 40 | 62 | 0.00 | 64.5 | 40.0 | 25.8 | 17 | 17.0 |
| Ponting | 6 | 1 | 272 | 350 | 54.40 | 77.7 | 45.3 | 35.2 | 295 | 49.2 |
| Kallis | 7 | 0 | 135 | 206 | 19.29 | 65.5 | 19.3 | 12.6 | 243 | 34.7 |
| Gilchrist | 259 | 7 | 9200 | 9386 | 36.51 | 98.0 | 35.5 | 34.8 | 11092 | 42.8 |
| Pietersen | 8 | 1 | 412 | 469 | 58.86 | 87.8 | 51.5 | 45.2 | 563 | 70.4 |
| Dhoni | 2 | 0 | 98 | 113 | 49.00 | 86.7 | 49.0 | 42.5 | 21 | 10.5 |
The opening position in ODIs has undergone sea-changes across the years. Steady opening partnerships with both batsmen striking at around 60, with the objective of scoring a match-winning 250, were the order of the day around the 1980s. Mark Greatbatch was the first of the attacking openers who set the 1992 World Cup alight. This was followed by the Sri Lankan blitz with both openers, Jayasuriya and Kaluwitharana, blazing away, in the next edition. They won a World Cup with these attacking methods although the final two matches were won by conventional batsmen. Over the next 15 years the opening batting has changed in accordance with rule changes and power play implementations. Today it would be difficult to find a Greenidge-Haynes combination opening, both striking at below 65. This is a specialist position and various top batsmen have occupied it, with telling effect. Hence a special view of this anchor position is necessary.
The table contents are standard for most of these analyses. Richards never opened, Miandad once in three years and Martin Crowe, very rarely. Many of the others like Mark Waugh, Tendulkar, Jayasuriya and Gilchrist, spent most of their careers opening the batting. Mark Waugh, less than the other three. Gilchrist was the true opening-zone batsman, striking at 98. Jayasuriya was nearly as good, at 92 and Tendulkar did his scoring at 88. Mark Waugh was more sedate, probably content with watching Gilchrist's striking at the other end. Still a respectable 77. Tendulkar's RpI of 45 is mind-blowing. This also means that he has the highest Index value of nearly 40. It can be seen that all the four had Index values exceeding 30. Lara's opening stints were more effective than the rest of his batting efforts. Andy Flower, on the other hand, was less successful opening the batting.
I have also determined the average opening partnerships when the concerned batsman opened: mostly with some other batsman, other than Mark Waugh and Gilchrist who opened the innings regularly together. Gilchrist leads this quartet for an average opening partnership of 45.8 and is closely followed by Mark Waugh, with 45.3: a testament to the fact that they opened together often. Tendulkar, often with Ganguly, and the rest of the time with different batsmen, averages 41.6. Jayasuriya's average is only 33.6, a reflection of the fact that he and his partner were in attacking mood almost throughout their partnerships.
| Batsman | Inns | NOs | Runs | Balls | Avge | S/R | RpI | Index | TeamRuns | BatRuns % |
|---|---|---|---|---|---|---|---|---|---|---|
| Richards | 6 | 2 | 80 | 100 | 20.00 | 80.0 | 13.3 | 10.7 | 691 | 11.6 |
| Miandad | 9 | 1 | 175 | 267 | 21.88 | 65.5 | 19.4 | 12.7 | 400 | 43.8 |
| M Crowe | 3 | 1 | 138 | 176 | 69.00 | 78.4 | 46.0 | 36.1 | 507 | 27.2 |
| M Waugh | 4 | 2 | 51 | 60 | 25.50 | 85.0 | 12.8 | 10.8 | 153 | 33.3 |
| Tendulkar | 5 | 1 | 168 | 135 | 42.00 | 124.4 | 33.6 | 41.8 | 207 | 81.2 |
| Jayasuriya | 31 | 2 | 405 | 550 | 13.97 | 73.6 | 13.1 | 9.6 | 1720 | 23.5 |
| Lara | 8 | 3 | 263 | 274 | 52.60 | 96.0 | 32.9 | 31.6 | 515 | 51.1 |
| Inzamam | 25 | 3 | 542 | 754 | 24.64 | 71.9 | 21.7 | 15.6 | 1762 | 30.8 |
| Flower | 8 | 3 | 197 | 297 | 39.40 | 66.3 | 24.6 | 16.3 | 689 | 28.6 |
| Bevan | 106 | 45 | 3350 | 4286 | 54.92 | 78.2 | 31.6 | 24.7 | 8243 | 40.6 |
| Ponting | 6 | 3 | 75 | 90 | 25.00 | 83.3 | 12.5 | 10.4 | 424 | 17.7 |
| Kallis | 7 | 0 | 120 | 220 | 17.14 | 54.5 | 17.1 | 9.4 | 494 | 24.3 |
| Gilchrist | 19 | 4 | 390 | 493 | 26.00 | 79.1 | 20.5 | 16.2 | 1214 | 32.1 |
| Pietersen | 5 | 1 | 128 | 125 | 32.00 | 102.4 | 25.6 | 26.2 | 498 | 25.7 |
| Dhoni | 113 | 34 | 3375 | 4032 | 42.72 | 83.7 | 29.9 | 25.0 | 9479 | 35.6 |
Just as opening the batting is important, finishing an innings, whether setting a target or chasing is important. Unfortunately in this analysis the required data, which is the information on when a batsman was dismissed, is available only over the past 20 years, for two-thirds of the matches. So I have to take a view based on when the batsman entered to start his innings. So this may not be a complete analysis. But we can draw a few insights. For this analysis I have compiled the runs scored where a batsman came in at no.5/6 or afterwards. At no.6, only two of these batsmen, viz., Bevan and Dhoni have played enough innings and that is the reason why I have considered that the role of a finisher can be at No.5 or No.6. Those at 1 and 2 are openers, 3/4/5 are consolidators and 5/6 onwards are finishers. I know this is not perfect but it cannot be helped.
Examining the table reveals that only two batsmen have played enough innings at No.6 onwards. Bevan has played 106 very effective innings at an RpI of 31.6 and Index of 24.7. Dhoni has also excelled in this position with 113 innings at 29.9 and Index of 25.0. It should be noted that the RpI will necessarily be lower since these two batsmen have remained not out 45 and 34 times respectively. These values are very important for the team since these are scored at the end of the innings in crunch situations, whether the team was batting first or second.
Here I have looked for a different type of additional analysis. I compiled the sum of the runs added by the team after the entry of the concerned batsman and looked at what % was scored by the concerned batsman. I think I struck pay dirt since Bevan's 40.6% of all the runs scored by the team after his entry is a testament to the great impact on the Australian batting. Inclusive of Bevan's own failures this figure of 40+% is the essence of finishing a match. Dhoni is slightly below, at 35.6% possibly because he often had top-order batsmen batting with him.
| Batsman | Inns | NOs | Runs | Balls | Avge | S/R | RpI | Index | TeamRuns | BatRuns % |
|---|---|---|---|---|---|---|---|---|---|---|
| Richards | 35 | 3 | 930 | 1097 | 29.06 | 84.8 | 26.6 | 22.5 | 2828 | 32.9 |
| Miandad | 36 | 6 | 887 | 1321 | 29.57 | 67.1 | 24.6 | 16.5 | 3062 | 29.0 |
| M Crowe | 8 | 3 | 219 | 296 | 43.80 | 74.0 | 27.4 | 20.3 | 1257 | 17.4 |
| M Waugh | 41 | 5 | 985 | 1190 | 27.36 | 82.8 | 24.0 | 19.9 | 2813 | 35.0 |
| Tendulkar | 41 | 9 | 965 | 1118 | 30.16 | 86.3 | 23.5 | 20.3 | 3097 | 31.2 |
| Jayasuriya | 38 | 2 | 438 | 622 | 12.17 | 70.4 | 11.5 | 8.1 | 2184 | 20.1 |
| Lara | 47 | 9 | 1277 | 1665 | 33.61 | 76.7 | 27.2 | 20.8 | 4074 | 31.3 |
| Inzamam | 130 | 25 | 4015 | 5313 | 38.24 | 75.6 | 30.9 | 23.3 | 12820 | 31.3 |
| Flower | 56 | 7 | 1658 | 2384 | 33.84 | 69.5 | 29.6 | 20.6 | 4787 | 34.6 |
| Bevan | 139 | 50 | 4515 | 5848 | 50.73 | 77.2 | 32.5 | 25.1 | 11117 | 40.6 |
| Ponting | 12 | 4 | 124 | 164 | 15.50 | 75.6 | 10.3 | 7.8 | 1135 | 10.9 |
| Kallis | 30 | 6 | 950 | 1374 | 39.58 | 69.1 | 31.7 | 21.9 | 3563 | 26.7 |
| Gilchrist | 19 | 4 | 390 | 493 | 26.00 | 79.1 | 20.5 | 16.2 | 1214 | 32.1 |
| Pietersen | 17 | 7 | 826 | 830 | 82.60 | 99.5 | 48.6 | 48.4 | 1816 | 45.5 |
| Dhoni | 160 | 47 | 5258 | 6239 | 46.53 | 84.3 | 32.9 | 27.7 | 13831 | 38.0 |
This is a slight variation to the previous table. I have looked at batting efforts from No.5 onwards. A lot more batting efforts now find a place. Bevan improves slightly while Dhoni has a significant move upwards. Inzamam is the other batsman who has played many important innings at No.5 onwards. His Index value is a reasonable 23.3. Note how badly Jayasuriya and Ponting performed on the few occasions when they were moved out of their comfort zones of opening and No.3 respectively.
Bevan remains at 40.6% in the % team runs measure. Dhoni's figure improves to 38.0%. The new serious entrant to this table, Inzamam scored 34.6% of his team runs.
| Batsman | Team | Inns | NOs | Runs | Balls | Avge | S/R | RpI | Index |
|---|---|---|---|---|---|---|---|---|---|
| Richards | Aus | 50 | 7 | 2187 | 2582 | 50.86 | 84.7 | 43.7 | 37.0 |
| Miandad | Win | 64 | 7 | 1930 | 3005 | 33.86 | 64.2 | 30.2 | 19.4 |
| M Crowe | Aus | 34 | 3 | 1096 | 1581 | 35.35 | 69.3 | 32.2 | 22.3 |
| M Waugh | Win | 45 | 2 | 1708 | 2227 | 39.72 | 76.7 | 38.0 | 29.1 |
| Tendulkar | Slk | 80 | 9 | 3113 | 3558 | 43.85 | 87.5 | 38.9 | 34.0 |
| Jayasuriya | Ind | 85 | 5 | 2899 | 2989 | 36.24 | 97.0 | 34.1 | 33.1 |
| Lara | Aus | 50 | 3 | 1858 | 2429 | 39.53 | 76.5 | 37.2 | 28.4 |
| Inzamam | Ind | 64 | 9 | 2403 | 3060 | 43.69 | 78.5 | 37.5 | 29.5 |
| Flower | Ind | 35 | 3 | 1297 | 1724 | 40.53 | 75.2 | 37.1 | 27.9 |
| Bevan | Saf | 31 | 5 | 1163 | 1490 | 44.73 | 78.1 | 37.5 | 29.3 |
| Ponting | Ind | 59 | 5 | 2164 | 2658 | 40.07 | 81.4 | 36.7 | 29.9 |
| Kallis | Win | 40 | 7 | 1666 | 2144 | 50.48 | 77.7 | 41.6 | 32.4 |
| Gilchrist | Ind | 45 | 1 | 1622 | 1634 | 36.86 | 99.3 | 36.0 | 35.8 |
| Pietersen | Ind | 28 | 3 | 1138 | 1323 | 45.52 | 86.0 | 40.6 | 35.0 |
| Dhoni | Slk | 45 | 11 | 2041 | 2252 | 60.03 | 90.6 | 45.4 | 41.1 |
Who were the favourite opponents of these wonderful batsmen? I have gone on runs scored rather than an average since scoring 2000 at 40 is more significant than scoring 500 at 50. The table is self-explanatory. The numbers which stand out are Dhoni's outstanding Index value of 41 against Sri Lanka, Richards' 37 against Australia and Gilchrist's 35+ against India. Tendulkar's 3000+ runs against Sri Lanka is the only instance of a batsman scoring over 3000 runs against a single country. I must confess that this is an educated guess and I will be glad to be disproved.
| Batsman | Year | Inns | NOs | Runs | Balls | Avge | S/R | RpI | Index |
|---|---|---|---|---|---|---|---|---|---|
| Richards | 1985 | 25 | 5 | 1231 | 1332 | 61.55 | 92.4 | 49.2 | 45.5 |
| J Miandad | 1987 | 22 | 6 | 1084 | 1542 | 67.75 | 70.3 | 49.3 | 34.6 |
| Crowe | 1990 | 20 | 1 | 810 | 1177 | 42.63 | 68.8 | 40.5 | 27.9 |
| M Waugh | 1999 | 36 | 3 | 1468 | 1942 | 44.48 | 75.6 | 40.8 | 30.8 |
| Tendulkar | 1998 | 33 | 4 | 1894 | 1854 | 65.31 | 102.2 | 57.4 | 58.6 |
| Jayasuriya | 2001 | 33 | 1 | 1202 | 1444 | 37.56 | 83.2 | 36.4 | 30.3 |
| Lara | 1993 | 30 | 3 | 1349 | 1857 | 49.96 | 72.6 | 45.0 | 32.7 |
| Inzamam | 1999 | 28 | 4 | 1106 | 1571 | 46.08 | 70.4 | 39.5 | 27.8 |
| Flower | 2001 | 33 | 3 | 1060 | 1301 | 35.33 | 81.5 | 32.1 | 26.2 |
| Bevan | 1998 | 22 | 8 | 959 | 1174 | 68.50 | 81.7 | 43.6 | 35.6 |
| Ponting | 2007 | 24 | 6 | 1424 | 1553 | 79.11 | 91.7 | 59.3 | 54.4 |
| Kallis | 2000 | 38 | 9 | 1300 | 1952 | 44.83 | 66.6 | 34.2 | 22.8 |
| Gilchrist | 1999 | 37 | 0 | 1241 | 1393 | 33.54 | 89.1 | 33.5 | 29.9 |
| Pietersen | 2007 | 25 | 4 | 889 | 1130 | 42.33 | 78.7 | 35.6 | 28.0 |
| Dhoni | 2009 | 24 | 7 | 1198 | 1400 | 70.47 | 85.6 | 49.9 | 42.7 |
Measuring performance over a year is a good analysis since the year is a sufficiently long time to give due respect to high level of performances. It is possible that during the 1980s a player might have only played 20 matches while a player might have played 30 matches during the 2000s. However since this table uses runs scored as a basis the lean years do not figure in the same.
Tendulkar's 1998 must rank among the greatest achievement by a player across formats. Even though he played only 33 matches, he aggregated 1894 runs at better than run-a-ball. The Index value of 58 is certainly Bradmanesque. Ponting's 2007 performance, the year during which Australia won the World Cup, is second to Tendulkar's and the Index value of 54 defies description. Richards, during 1985, suffers only in comparison with these two giants. It is nice to note that, arguably the best three ODI batsmen of all time, feature in the top positions in this table.
| Batsman | Best Streak | Worst Streak | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| StODI | Year | Runs | Balls | RpI | S/R | Index | StODI | Year | Runs | Balls | RpI | S/R | Index | ||
| Richards | 1984 | 0253 | 994 | 1081 | 66.27 | 92.0 | 60.9 | 1988 | 0485 | 291 | 374 | 19.40 | 77.8 | 15.1 | |
| J Miandad | 1987 | 0417 | 906 | 1253 | 60.40 | 72.3 | 43.7 | 1977 | 0041 | 250 | 416 | 16.67 | 60.1 | 10.0 | |
| Crowe | 1992 | 0734 | 725 | 882 | 48.33 | 82.2 | 39.7 | 1985 | 0305 | 337 | 507 | 22.47 | 66.5 | 14.9 | |
| M Waugh | 2000 | 1620 | 846 | 1015 | 56.40 | 83.3 | 47.0 | 1989 | 0549 | 271 | 346 | 18.07 | 78.3 | 14.2 | |
| Tendulkar | 1998 | 1323 | 1105 | 1104 | 73.67 | 100.1 | 73.7 | 1993 | 0795 | 209 | 309 | 13.93 | 67.6 | 9.4 | |
| Jayasuriya | 1997 | 1207 | 922 | 745 | 61.47 | 123.8 | 76.1 | 1990 | 0623 | 112 | 163 | 7.47 | 68.7 | 5.1 | |
| Lara | 1994 | 0947 | 934 | 982 | 62.27 | 95.1 | 59.2 | 1998 | 1364 | 258 | 357 | 17.20 | 72.3 | 12.4 | |
| Inzamam | 2000 | 1580 | 744 | 1035 | 49.60 | 71.9 | 35.7 | 1996 | 1095 | 251 | 394 | 16.73 | 63.7 | 10.7 | |
| Flower | 2002 | 1814 | 719 | 854 | 47.93 | 84.2 | 40.4 | 1995 | 0982 | 248 | 380 | 16.53 | 65.3 | 10.8 | |
| Bevan | 1998 | 1300 | 740 | 929 | 49.33 | 79.7 | 39.3 | 2002 | 1802 | 309 | 434 | 20.60 | 71.2 | 14.7 | |
| Ponting | 2007 | 2473 | 925 | 1008 | 61.67 | 91.8 | 56.6 | 2008 | 2687 | 342 | 452 | 22.80 | 75.7 | 17.3 | |
| Kallis | 2003 | 2029 | 893 | 1097 | 59.53 | 81.4 | 48.5 | 2005 | 2244 | 315 | 555 | 21.00 | 56.8 | 11.9 | |
| Gilchrist | 2003 | 2052 | 860 | 746 | 57.33 | 115.3 | 66.1 | 2006 | 2434 | 306 | 342 | 20.40 | 89.5 | 18.3 | |
| Pietersen | 2004 | 2193 | 786 | 790 | 52.40 | 99.5 | 52.1 | 2009 | 2827 | 268 | 352 | 17.87 | 76.1 | 13.6 | |
| Dhoni | 2009 | 2815 | 773 | 858 | 51.53 | 90.1 | 46.4 | 2010 | 3030 | 311 | 501 | 20.73 | 62.1 | 12.9 |
This is an analysis based on 15 consecutive matches. 15 represents about 4-6 months of cricket and is a very clear indication of short-term form. And I have not even bothered with not-outs and average for this short period since three not-outs out of 15 will distort the figures considerably. So it is only RpI and Index based on RpI.
Tendulkar during 1998 features again. A golden run, rather platinum run, starting on 20 April, 1998 and ending on 28 October, 1998 produced an unforgettable sequence of 38, 143, 134, 33, 18, 100*, 65, 53, 17, 128*, 77, 127*, 29, 2 and 141: 1105 runs in 15 innings at a strike rate of almost exactly 100. The first two centuries were those famous Sharjah blitzes against Australia. Richards' golden run is equally noteworthy considering the run-scarcity during the 1980s. He scored 67, 189*(!!!), 3, 84*, 47, 98, 49, 103*, 30, 74, 51, 46, 68, 9 and 76. A total of 994 runs off 1081 balls, striking at 92. The 189, considered by many as the greatest ODI innings ever played, is the jewel in this crown. Ponting's sequence only suffers by comparison. His 2007 sequence was 82*, 10, 5, 51*, 111, 104, 75, 7, 113, 23, 91, 35, 86, 66* and 66. The aggregate was 925 runs at a strike rate exceeding 90. The later part of this golden run was during the 2007 World Cup.
Jayasuriya's worst streak during 1990, a run of 4, 1. 23, 5, 0, 4, 1, 26, 0, 0, 5, 3, 32, 5, 3 is something to behold. A mere 112 runs in 15 innings. Let me also add that he compiled 271 runs in 30 innings during this disastrous period. The next lowest is Tendulkar during the barren period of 1993. Note the consistency of Ponting. Even during his worst streak period, he aggregated 342 runs. Martin Crowe, against better bowlers, was nearly as consistent, aggregating 337 runs. Dhoni is in a surprise third position with 315 runs.
It is interesting to note that three other batsmen have exceeded 1000 runs in 15 innings. Hayden, with 1101 runs off 1108 balls during 2007, Amla, with 1083 runs off 1070 balls during 2009-10 and Kohli, with 1003 runs off 1054 balls during 2012. There are three instances of competent batsmen scoring below 112 runs in a 15-innings streak. Samuels had a nightmare run of 90 runs in 15 innings during 2006, Pollock compiled only 105 runs during 2002 and Boucher scored 109 runs during 1999. It may seem obvious but it is clear that when a batsman is in great form he scores much faster than when he is in miserable form.
A shot-in-the-dark measure, not based on any scientific reasoning, is the difference between the best 15-innings RpI and worst 15-innings RpI figures. Tendulkar leads in this measure with a difference of around 60, followed by Jayasuriya, with a near-55 figure. Richards has a value of around 46. Readers can make what they can of these numbers.
In Part-2 I will be covering the following areas of analyses. If any reader comes out with a good suggestion it can be incorporated. I request that the readers do not ask for changes in players. I have completed a part of this analysis with one set of players and cannot abruptly follow-up with another set. The next part covers mostly performance oriented measures.
8. World Cup SF-F Champions Trophy Finals / Significant / Early matches 9. Batting Position / Boundary analysis 10. First & Second Innings analysis 11. Home / Neutral / Away analysis 12. Won / Lost matches analysis 13. Impact Inns / High Scoring Index / MOM analysis 14. Team share of runs/balls
Since I see many new readers I have to make my usual pitch regarding this specific blogspace, a small corner of "The Cordon". Please bear with me for this, just once.
I am always welcome to criticism and negative comments. In fact I value these a lot since new ideas flow and I can correct any blinkered views I have. But there is a clear line of propriety drawn for this particular blogspace. I had established that with "It Figures" and I had the best collection of informed readers who I respected and whose respect was my reward. When you come into this blogspace you are expected to follow these ground rules since you are here by invitation. If anyone makes a rude or insulting comment it only reflects his own shortcomings, not mine.
You will not insult me, any fellow reader or any player. That is the single commandment here. I have no problems with the following observations:
- Your analysis is based on a wrong premise.
- Your computations are wrong.
- Your analysis is statistically weak.
- You have over-complicated a simple issue.
- You have over-simplified a complex issue.
- You are not being fair to *.
- You have tried to solve a non-existing problem.
- Reader * is wrong/has not understood.
- Player * should retire.
I have problems with the following statements (and the like) and your comment will be junked instantly.
- You are stupid. Even if you have a Ph.D. in Statistics, you have no right to say that. I could, but never will, counter with a retort in far more colourful language.
- You favour players from *. Because, to me all countries are same.
- You favour *. Because I do not, and always leave my personal preferences aside.
- Reader * is stupid. Because he is not.
- Player * is selfish/greedy. Not in this forum.
- You are biased. I may publish this if I feel I can provide additional insight by answering this comment.
Finally let me say this unequivocally. I write for the average and interested cricket enthusiast who may or may not possess any serious statistical/mathematical qualifications. I do not write for the statistical/mathematical experts. Although I possess graduate-level statistical knowledge, I limit myself to Mean, Median, Mode, Standard Deviation, Quartiles, Normal Distribution and the like. I know that I will lose 75% of my readers the minute I bring in p-Value or z-Score.
I have only one avowed objective. I want my article to be understood by 95% of the visiting readers. I am not writing to get my articles published in the conferences of RSC or ISI or ASA. I am certain they would not be. Common sense is the cornerstone of my articles and I am proud of that. If I do not meet your expectations, my apologies and if you feel very strongly about it, au revoir.
Let me close with a short story. I had an excellent reader during the early years of "It Figures". Sometime back he stopped following the blog for various reasons. But he was never rude or insulting to me. I thanked him and said that the doors of this blogspace would always remain open since I valued his insights. I see that he has made a comment for the first article - to the point and that too, a point well-made. I never have any problems with readers like him.
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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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.
Very good article. Thanks. I have one suggestion to make. When debating the effect that not-outs have in ODIs and how they skew averages, is it a good idea to include "percentage of team runs" in your index? i.e. index = (avg. * strike-rate * perc-team-runs). This will have an effect of dampening the not-out effect. It also serves another purpose. Usually openers and top-middle order batsman play more responsibly and hence have lower strike rates than a lower-order batsman who usually has the license to hit out in the slog overs. This may "artifically" inflate S/R because the same player may have batted slower if he was an opener. I know there are exceptions (pinch hitters as openers). Thoughts?
[[
Deepak, I cannot recollect straightaway. However I have seen a similar suggestion before. The idea is quite good. % Team runs is dimension-less. This also means the Average can be used since the effect of high not outs will be neutralized. I will keep this as a table for part 2. Anyhow there is a % of Tean runs/balls in Part 2.
Ananth
: ]]
Anantha,
Thank you for doing this analysis. I believe stastical analysis in cricket is way behind baseball. Baseball has seen a very successful movement towards statistical decision making lead by Oakland As and now Red Sox.
I want to make a couple of suggestions to improve your RPI methodology.
1. Calculate Average for each batsment excluding innings and runs with not out 2. Eliminate not out innings and runs from the analysis where the score is way below the batsmen average calculated in 1 above 3. For innings where the score is lower or close to the average mark the innings score as average calculated in 1 above 4. innings where the not out score is greater than calculated in step 1, mark the innings total as a fraction of the highest score by the batsman, ie further they are from average the closer their adjusted score should be to their highest
I believe Index is the ultimate measure, but not outs can really skew things. This will help us normalize the not out score
vikram
[[ At first sight these seem to make a lot of sense. I will look at in depth and revert. I can always [post a revised RpI/Index table in Part 2. Many thanks. Ananth: ]]
Anantha, I genuinely appreciate the time and effort it takes to put these kinds of statistical studies together. Often, they provide insights which pose more questions than answers. I do however, have one problem with reports of this nature ---- seldom is the *purpose* of the analysis explained at the beginning of the report in a clear and lucid summary. Perhaps you could summarize at the beginning of the report the purpose of the analysis? I feel this would help to engage more readers like me who aren't so statistically inclined. Some might consider it naive on my part, but I've always believed that the simplest questions are the most important and often difficult to answer. What is the purpose of your analysis? what is it attempting to find out? What is the *problem* to which this analysis is the solution? I pose those questions to you as a simple layman wishing to engage with your ideas from the outset. Once again, thanks for your time and effort.
[[ Many of these analyses have no definite objective, as is usually the case with most Cricinfo stories. How I have worked in "It Figures" is to treat these as the basis for interaction amongst readers and as a forum for such exchange of information. When I complete my second part you yourself might come back and tell me that my definition of impact innings is provocative and thought-provoking but I could have considered a different alternative. Insights are there to be drawn by the readers. Not for the wriiter to offer. And blinkered horses will not be able to appreciate the ride. Look at how many people are making comments only on player selection a la Ganguly and others. Other than one comment in passing has one reader bothered to look at the best/worst streaks analysis, an analysis which is not available anywhere. I am a completely atypical Cricinfo writer. My lifeblood is the reader and the reader-interaction. I genuinely feel I missed doing the table for the key positions of 3/4. And that has come about because of the reader reaction. A table of 1/2, 3/4, 5+ and 6+ might throw up many insights. I may very well extend that analysis to ALL the ODI batsmen. Finally my tables are farthest from statistical tables. They are analytical tables. Ananth: ]]
I see that this point has been made by some readers already but I just want to say this - you are using RPI. In the tables for openers, I can see that the index ranges between 24 and 40, but for lower order players drawn from the same set of players, the index drops significantly. You havent posted tables for positions #3, #4, but if you did, I suspect the numbers for the same 15 players will generally be significantly lower than the numbers for the same set of players in opening position. To test this perhaps you can choose batsmen who have played some minumum number of innings/runs at #1 / #2, and also some minimum innings/runs at #3. If you consistently find significant differences for the majority of batsmen as I suspect you will, then it will prove that 1) the opener's position is the most productive and the team's best batsman must play there and 2) openers stats cannot be compared with lower order bats on index measure.
[[ Gerry, welcome and thanks. I will do the 3/4 analysis. I suggest you wait for Part 2. This is an analysis of who I consider are amongst the top ODI batsmen. This is a general article. If you start looking at the micro-variations we will lose the benefit of an overall appreciation of ODI greats. Ananth: ]]
2 requests from my side : Against each of the above 15 players, can you include 3 columns with average of (batting average, strike rate, Index) for all batsman in that era (batting position 1 to 6).. I have a hypothesis that when comparing players across eras (& you are not doing that here, I know), the best way is to measure their performances against their contemporaries.. The columns that I am asking for will help in that. I have a feeling that Viv would be the best in his era, then Great Wall of China, & then the 2nd player.. For me, Viv was the greatest / most devastating batsman to have set foot on Cricket grounds ever!! Also, there is no table for position 3/4 (where Viv played), can you please include that also.. Sourabh
[[ Instead of doing a piecemeal work I will do a full-blown peer analysis. That will do justice to your valuable comemnt. BP 3/4 will complete the analysis. Yes, I will include it in Part 2. Ananth: ]]
Dear Sir,
Very good insight into the game as such, one common thing I want to bring about is the batsmen mentioned would have nothing other than the team in their mind while performing on the pitch, great guys. Can you please tell us also about Bridjesh Patel and Krish Sreekant, who basically triggered the one day habit for India. After 1979 world cup (India placed last) the Srilanka tour of India saw the emergence of Cheeka and Roger Binny into one dayers, and the comment of Roy Dias -"Srikant beat us not India".
Please.
Suresh Venkataraman
Posted by jainanshu00 on (April 12, 2013, 6:58 GMT)It is interesting to see that the two batsmen with the highest Index value in their best 15-match streaks have the lowest in their worst 15-match streak. While Jayasuriya could well be termed inconsistent, the same could hardly be said of Tendulkar. Yet, this is what the numbers throw up!
Posted by jainanshu00 on (April 12, 2013, 6:53 GMT)Hi Ananth,
On the vexed question of Not-outs, and by extension the measure of RpI: The problem, if it exists, clearly impacts ODIs more where over resources are limited. I have the following suggestion to make to deal with this:
1. Substitute each not out score with the mean/median of all dismissals at scores above the not out score (I prefer the median) 2. If the highest score is a not out score, take it as is. 3. Add all such scores to the total runs scored in dismissals. 4. Derive the Adj RpI using value in 3. above as the numerator, and all innings played in as denominator.
Moving on to reading part 2 of the article now :-)
Posted by Rohan1 on (April 8, 2013, 4:06 GMT)Contd... These 17 Grand Slams have been attained by dominance over a period of several years. Unlike cricket these years are forever "locked in"…Completely opposite to what you claim - Federer's "final average" simply does not matter - not one whit.
Federer's "final average" will not have the slightest impact whatsoever on his extended glory years - the same emphatically cannot be said about Tendulkar and other cricketers who's overall average simply doesn't tell the tale.
Ahmer Raza's point is very ,very valid and cannot simply be shrugged off.
Posted by Rohan1 on (April 8, 2013, 4:05 GMT)Ananth, I couldn't possibly disagree more. The reason the number of GS titles in tennis is the ultimate mark of greatness is because it effectively = "Number of years of extended dominance over peers".
Since, there are only 4 GS per year to collect many titles you have to be better than your peers for YEARS. Odd flashes of brilliance won't do.
THIS is effectively what Ahmer Raza is pointing out. Tendulkar had an extended period of dominance over his peers for an extended period of a decade.
This is something which very very few sportsmen can claim.
If Federer's "average" GS performance is now "Quarter final" - this is 100% sure to decline if he goes on since he is past his peak. If he goes for another say 9 years ala Connors he may end up with an average of "3 ½ or Fourth round"…Another player still at his peak ( assume Djokovic for now ) may at that point have a higher "average" …But ,unlike Federer, he doesn't have 17 grand slams. ...Contd...