Anantha Narayanan
Recently I received a trigger mail that the first four Indian batsmen during the recently concluded Napier Test had a batting average above 50
First a few points on the qualifying criteria.
Initially I thought of using the career batting average. However I discarded that in favour of using a career-to-date batting average because of the following reasons.
- This is the more accurate and correct option and a very interesting one to incorporate.
- Using the career average will move blocks of Tests in and out of the table as a batsman's career average moves either side of 50. For instance, if Gambhir's average moves back to below 50 quite a few Tests will go out of the table. That is not correct.
- Using the batting average is a simple task and can be done by any reader using Cricinfo's Statsguru. However the career-to-date batting average requires the special database I have.
- It allows me to include many a good batsmen such as Inzamam, Gilchrist, Walters, Worrell (in the last innings) et al who have had a fair proportion of their careers at above 50 and finished with a career batting average below 50.
I have only incorporated the following caveats.
During the first 10 Tests of the batsman, if the average exceeds 50, this will be considered only if his career batting average is above 50. This is to take care of the Azharuddin/Phil Hughes/Walters situation. Also if a batsman finished his career with fewer than 1000 runs, a batting average of above 50 will be ignored. This is to take care of batsmen such as Taslim Arif or CF Walters who scored fewer than 1000 runs but finished with averages exceeding 50.
Now let us look at the table.
Tests in which teams had four or more batsmen with 50 plus batting averages
1768 2005 Icc 6 Smith(55.50), Sehwag(55.81), Dravid(58.30), Lara(54.09), Kallis(56.88), Inzamam-ul-Haq(50.80)
Finally I have come around to my first T20 analysis
Let me add that my database, current upto the West Indies - England game, is limited to T20 International matches and as of now I have no intention of building a Database of other club-based T20 matches.
First some facts about T20 matches. Let me say that I have completely ignored team strengths, pitch conditions et al since there is not enough data and in this short version of the game, there is lot more evening out between the two teams.
1. A total of 84 matches have been played and completed. Out of these, 4 have been tied. 2 matches outside these 84 have been washed out.
2. 34 (out of 80) matches have been won by the team batting first. This represents 42.5% of the completed matches. One of these wins has been through D/L method.
3. 46 (out of 80) matches have been won by the team chasing. This represents 57.5% of the completed matches. One of these wins has been through D/L method.
4. Out of these 46 matches, the top 4 run chases have been against scores of 165 and above. These four succesful run chases are detailed below. In other words, any team scoring 165 and above has a 90+ % chance of winning the game. This seems to be true irrespective of the relative team strengths. It is also possible that the weaker team might bat first more often than not.
020 2007 Win 205/ 6 (20.0) Lost to Saf 208/ 2 (17.4) at Wanderer's, Jo'burg 082 2009 Slk 171/ 4 (20.0) Lost to Ind 174/ 7 (19.2) at Premadasa, Colombo 016 2007 Win 169/ 7 (20.0) Lost to Eng 173/ 5 (19.3) at Oval, London 047 2007 Aus 166/ 5 (20.0) Lost to Ind 167/ 3 (18.1) at Brabourne, Mumbai
5. It is a reasonable assumption to make that the team batting first should set themselves a Target score of 165 runs to have a 90+ % chance of winning. Anything more would obviously further increase the chance of winning. However we are not looking at a Target score with 100% chance, which, at the current moment is 206.
6. If we drop the number from 165 to 160, the number of losses is more than doubled since 5 more matches are won by batting teams chasing 164, 164, 164, 162 and 162 successfully. The win % drops to 80% so there is a need to retain the Target score at 165.
It is possible that in the next 5 matches, 170+ scores would have been chased. However that does not make the idea of working on a Target score invalid and as things stand, 165 seems to be a very good number for a captain to write on the team sheet.
The reason this score is very relevant is because of what happened in the two T20 matches between New Zealand and India. Each time India had an explosive start, looked good to score 200, tried to score 200 and finished with 162 and 149. Both scores were chased down with ease, although New Zealand were too cautious in the middle overs in the scond T20 and almost threw the match away. They should have won more comfortably with the explosive start set by the openers.
The importance of not aiming for too much cannot be over-emphasized especially in T20 matches. In T20 it is paramount for the captains to understand the nuances of the game. It is possible that Dhoni is aware of this. However his batsmen, Gambhir, Sehwag, Yuvraj, Sharma et al tried to attack without a clear understanding of the par score.
In ODIs, nowadays even scores of 300+ are chased quite comfortably. However even there a reasonable target score should be aimed at. The 100% winning score is 435. However the par Target score might very well be 285. But it must be remembered that data is available for 2822 matches for us to make a facts-based determination of a par Target score for a venue.
Just to sum up the first batting wins. Out of the 34 wins, 8 teams have won by putting up a total of 200 and above, 11 by posting wins of between 180 and 200, 10 by posting between 150 and 180 and 4 have been bowling wins with sub-150 totals. One has been an amazing defence by Ireland of a total of 43 for 7 in a D/L match.
It is impossible to infuse the other Test/ODI parameters such as Ground/Pitch conditions, Team strengths, Average scores et al because of the low number of matches, the absence of any meaningful statistics and the very nature of the game.
Out of the 86 T20 matches, a whopping 34 have been played in South Africa, mainly because of the 2007 WC, in addition to one washed out match. 11 matches have been played in Ireland, in addition to one washed out match. 10 matches have been played in New Zealand. 8 matches have been played in Canada, all in one centre.
Wanderer's has staged the maximum of T20 matches, 16 in all. Just to give the readers an idea of the analyst's nightmare of determining a target score at Wanderer's, I have given below the 16 first innings scores. These seem to move like a yo-yo although there seems to be a recent trend for lower scores.
133, 201, 126, 129, 205, 164, 260, 164, 190, 189, 164, 147, 157, 129, 131 and 118.
Note: I stayed up to watch the interesting T20 match between Australia and South Africa. Australia scored 166 (just passing the par Target score mentioned) and lost a very close match. Strike 1 against me, I suppose.
Important footnote:
This refers to the points raised by Aneesh and Kieran. They have correctly questioned my 90+% figure.
First let me say that the 90+% is based on all instances of chasing team winning, which is 46. Out of these 46, only 4 chases have been of scores of 165 and above. Thus the figure of 90% came in.
However stricly speaking, both Aneesh and Kieran are correct. My sample should be the teams which crossed 165 and not the successful chases. Let me work out that figure below.
26 teams crossed 165 (barring the last Saf-Aus match, which has been excluded for sake of consistency). Out of these, 4 teams lost and the other 22 won. So the winning % is 84 and not 90.
Hence I am going to change my Target score to 170, which would lead to 24 wins and 2 losses (win % of 92).
My thanks to Aneesh and Kieran.
T20 Batsman Strike Rates (Min 200 runs) - (Gokul)
Some readers have suggested that I should look at the worst bowlers in Test cricket the same way I have looked at the worst specialist batsmen
For the batsmen I had a very effective measure, the Batting Position Average, which could be used to identify a specialist batsman, in addition to other measures. We do not have such a measure for bowlers and we have to improvise.
Let me list down the criteria for selection.
1. The bowler must have played in a minimum of 15 Tests.
2. The bowler should have bowled, on an average, a minimum of 150 balls per Test. This excludes casual bowlers.
3. The bowling average should be above 40.00. Fair enough condition.
5. I have also excluded bowlers such as Mohammad Rafique, who have a bowling average between 40 and 50 and a difference in average values (bowling average minus batting average) less than 30. There is no way a quality player such as Rafique should get in this collection of incompetents.
This gets us a list of 18 bowlers.
It would be very simple to rank these based on the Bowling Average and that table would as well be enough. However I have done a simple additional analysis of the constituent measures to bring out the level of bowling. The following measures are used.
1. The Bowling Strike rate.
2. The Bowling RpO.
3. The number of wickets captured per Test.
It must be remembered that the Bowling Strike rate and RpO are the two components which form the Bowling Average and I have separated these to let the readers judge the lack of effectiveness.
The formula is given below.
Index=StrikeRate x 0.25 + RpO x 5 + (3.0 - W/T) x 10.The final table is given below.
Cty Bowler Mat Balls B/M Wkts Avge B/W RpO W/T Index
Then we have a couple of average Bangladeshi bowlers and a trio of average New Zealand bowlers.
The interesting entry then is Agarkar. How he could have played 26 Tests as an all-rounder is one of the mysteries of Indian cricket. I can understand his being selected for 191 ODI matches because he had one of the best strike rates as a bowler in ODIs (288 wickets in 191 matches). But 26 Tests, even conceding the Adelaide contribution, is inexplicable.
I am equally amazed that Mohammad Sami was selected for 33 Tests and captured fewer than 2.5 wickets per Test at a 50+ average. I will not make any comments except that Pakistan has had very competent and effective pace bowlers during this period and it is a surprise that Sami was on for such a long time.
Just to demonstrate the point that the Bowling Average is the most effective of all cricketing measures I have given below the table in decreasing order of Bowling average. Readers will note that there are very few significant changes.
Cty Bowler Mat Wkts BowAvge BatAvge
A number of remarks raised in response to my last article on the worst Test batsmen suggested that I should also look at the specialist batsmen to determine who was the worst ever
As usual I have set some criteria for selection. Let me outline these first.
1. These should be specialist batsmen. Bowlers (even those who might only have averaged 1-2 wickets per Test) and wicketkeepers have been excluded.
2. A minimum of 25 Test innings should have been played.
3. The Batting Average should be below 20.00 for those who played their entire career before 1925 and below 25.00 for those who played afterwards.
4. The Batting Position Average for the batsman (already presented and discussed by me in these columns) should not be below 6.5. This is to make sure that only specialist batsmen are included. Otherwise bowlers like Kumble, Warne, Vaas et al would come in. The number 6.5 ensures a tilt towards no.6 position than no.7 position.
These entry constraints let 41 batsmen walk under the bar.
Now for the analysis.
I have considered the following three measures for analysis. These are all logical and make sense.
1. The Batting Average, the truest of all measures. The highest weight is given for this measure.
2. The % of single digit scores. This is an improvement on the number of Zeroes I considered earlier and was suggested by Karthik. The lower this % is, the greater credit to the batsman. The range is from 26.7% to 70.0%.
3. The quality of bowling faced. Just in case the less-performing specialist batsmen faced top quality bowling, they have to be given credit. I have also used the weighted bowling average faced, in other words, the exact quality of bowling faced. If Parker faced a Pakistani bowling attack sans Imran, playing, but only as a batsman, this is taken care of. The lower this Average Bowling Quality figure is, the greater credit to the batsman. The range is from 26.6 to 41.5.
The formula is given below.
Index = (100.0 - Single digit inns %) (60 - Avge Bowling Quality) Batting Average + --------------------------- + ------------------------- 10 5
Let us look at the tables.
Cty Batsman Mats Inns NO Runs HS BPA Batting Scores<10 Bow Index Avge No % Qty
A lot of analysis has been done on the best batsmen in Test cricket
Let us leave that topic aside. I have always felt that the other end of batting table presents a fascinating possibility. Who is the worst batsman who ever carried a bat and walked in. Is it Chris Martin, is it one of the Indian spinners, is it a West Indian fast bowler or an unexpected batsman out side this lot? Without further ado, let us delve in.
First a few criteria to be fixed.
The first is that the batsman (okay, I know I am stretching the point) has to have played 25 Test innings, which, for a tail-ender, represents nearly 20 Tests. The next is that the career batting average should be below 10.00. These twin criteria have enabled 70 tail-end batsmen to be selected.
I have considered three measures for analysis. These are explained below.
1. Batting Average. This is the simplest and most acceptable of all batting measures. Readers can easily identify with this measure and it reflects the batting ability very realistically, notwithstanding the "not outs" conundrum. In this particular analysis even the "not outs" do not matter since most of these batsmen remain not out on quite a few occasions. This measure will carry a weight of 20 points.
2. Dismissed Zeroes. The emphasis here is on both the words. An innings which ends at 0 means that, barring a few exceptional circumstances, very little has been contributed and another batsman, almost always a better one, has been left in the limbo. I have determined the number of dismissed zeros and determined a frequency of innings in which this has occured. The lower this figure is, the worse the batsman is. This measure will carry a weight of 15 points.
3. Average partnership runs added. This is a useful measure since it tests another facet of the tail-end batsman's skills, which is the support he provides to the senior batsmen. Basically I have computed the number of runs added while the tail end batsman was at the crease, mostly at no.10 or no.11, and determined the measure of average partnership runs per innings. This measure will carry a weight of 15 points.
I have considered (and ignored) the batsman's highest score since that does not convey any additional information. I have also not considered the "Balls played" information since that is available only for about a third of Tests. And extrapolating based on team scoring rate will not work since these batsmen are likely to take a lot more balls to score the runs.
Let us take a look at tables, first the support table.
Cty Batsman Ins No Runs Avge HS Dis Runs Avge 0s Added Bpa
Cty Batsman Batting Avge Dis 0s Freq Avge Ptship Total (20) (15) (15) (50) Zim Mbangwa M 4.00 (2.00) 2.08 ( 2.78) 5.13 ( 6.84) 11.21 Nzl Martin C.S 4.34 (2.17) 1.95 ( 2.60) 7.65 (10.20) 13.94 Win King R.D 6.95 (3.47) 2.89 ( 3.86) 7.64 (10.19) 17.48 Bng Manjural Islam(Sr) 7.36 (3.68) 2.48 ( 3.30) 7.98 (10.64) 17.82 Ind Chandrasekhar B.S 8.15 (4.07) 2.61 ( 3.48) 7.12 ( 9.50) 17.88 Ind Maninder Singh 7.62 (3.81) 2.59 ( 3.45) 7.82 (10.42) 18.02 Ind Doshi D.R 9.21 (4.61) 2.04 ( 2.71) 7.58 (10.11) 18.83 Aus Reid B.A 9.30 (4.65) 4.25 ( 5.67) 5.78 ( 7.71) 19.33 Ind Nehra A 11.00 (5.50) 1.88 ( 2.50) 6.63 ( 8.84) 19.50 Win Valentine A.L 9.40 (4.70) 3.19 ( 4.25) 7.38 ( 9.84) 19.97As foreseen, a dark horse has emerged. Who would have thought of a batsman who could come ahead of Chris Martin. (Mpumelelo) Pommy Mbangwa's batting is for the Gods to view. 25 innings, 8 not outs and 34 runs gives him an unbelievable average of 2.00. He has been dismissed at 0 for nearly 40% of his crease visits. He has a highest score of 8, the only one in this elite group not to have crossed 9 runs. He has always batted at no.11. His average partnership is an unbelievably low 6.8. What more do you want. I would have paid money to see Pommy bat. Note his batting sequence: 0, 2, 0, 4, 0*, 0, 0, 0, 0, 2*, 3, 2, 0, 1*, 2, 0*, 0*, 1*, 3, 0, 0, 1*, 8, 0*, 5. One fascinating string of scores.
I can see the New Zealand readers having mixed feelings. They would dearly love to have Chris Martin head this table because they love his batting. I can only suggest that if you increase the number of innings to 30, Chris Martin will be at the top. Let us see Martin's exploits. 65 innings, 30 not outs, 76 runs giving Martin a slightly higher average of 2.17 as compared to Pommy. He has crossed single figures once in his career, an unbeaten 12 against Bangladesh when he outscored O'Brien. He has 25 dismissed zeroes, the most frequent amonst all these batsmen. But his partnership average is a healthy 10+. Only twice has Martin batted at no.10 when Shane Bond and Cummings could not bat. Let me add, I would also pay money to see Chris Martin bat.
Reon King is next. Not as great a fast bowler as some of the other greats such as Walsh or Ambrose, but equally inept a batsman.
Then comes Manjual Islam, followed by three Indian spinners. Reid of Australia separates these three from Ashish Nehra, another rabbit of a batsman. Alf Valentine is last in this table.
Fidel Edwards, who is 11th in the table is the only other batsman wiith a sub-5.00 batting average. However he has recently batted very well, saving West Indies twice at Antigua and Napier.
To view the complete list please click here.
During the last few Tests of 2008 I got the feeling that late order batsmen were playing rear-guard innings far more effectively than they normally do
This analysis covers 19 premier Test grounds across 9 countries
The most comical situation in an ODI telecast are the pitch specialist's comments. They are as reliable as a weather forecaster's. When Ravi Shastri pontificates "it is a belter", one can be rest assured that one in two innings would have floundered to 201 for 7 in 50 overs. Alternately when David Lloyd says with his "Roses" twang that "250 should be a winning score", I alwasys look for the situation 7 hours later when the batting team has successfully chased a 300+ total. I wish the broadcasters show a split image of the pitch specialist's comments and the innings scores.
Test matches are different. Normally the specialists comment on the first session and make overall comments. One thing I am sure. No pitch specialist, no analyst or for that matter no curator can, with confidence, forecast how the pitch would behave.
This analysis covers 19 premier Test grounds across 9 countries. MCG, SCG, WACA, Lord's, Oval and Headingley lead the field. These are the major Test playing grounds, with most of these grounds clocking in at over 100 Tests. Then I have taken two grounds from each of the other six major Test playing countries. One ground from Bangladesh completes the selection. This brings up the 19 grounds.
I have taken matches played in these grounds during the last 19 years (from 1.1.1990 onwards) for consideration. Barring Calcutta and Chennai where only 9 Tests have been played during these 19 years (because of BCCI's rotation policies), the other grounds have completed 10 or more Test matches, with 32 Tests at Lord's, London leading the field. A total of 338 Tests are analysed.
Anticipating the readers' comments, I looked at excluding the Test matches played against Bangladesh and Zimbabwe. However that is fundamentally wrong since this is a statistical analysis and I cannot take casual liberties with my selection methodology. Also one of the grounds is in Bangladesh. One should also not forget the fact that a strong team like India was dismissed for 75 on the opening day by South Africa in India and the same team, a few months back, scored 705 against a strong Australia at Sydney. So all the Tests are considered.
In order to have uniform conditions I have taken the completed (all out or delaration) first innings. This is to avoid a Test abandoned with the first innings standing at 24 for 3 or 150 for 5. Later innings vary a lot and will distort the figures considerably.
Readers should remember that this is a departure from my usual analysis insofar as it is a purely statistical analysis. I have tried to make the analysis simple and understandable and explained the statistical terms. With this background, let us look at the tables.
The first is a simple table listed in order of the Mean. The mean is an alternate term for Average. It is worked out by the following formula.
Sum of all values Mean = ----------------- No. of valuesMean is a very useful value for analysis. One can make a generalised observation on a possible score at the ground. However Mean is strongly affected by very high and very low values. As such, a pinch of salt should be available nearby. I have also got the mean of the most recent 5 Tests played on the ground and presented this and compared with the mean. That shows a recent trend.
Table of Mean scores (in order of Mean)
Ground Num Total Mean Last Ratio Tests Runs 5 mat
At the other end, Eden Gardens, WACA and Oval have had a fairly high Mean values. It is surprising that there is almost a 75% difference between the low and high Mean values.
Asgiriya Stadium, Kandy has shown an alarming dip in the first innings scores recently. The ratio is 0.68. Basin Reserve, Wellington has seen its Mean value dip by 20%. At the other end, there is a marked increase in first innings scores at Lord's.
The Mean does not reflect the data distribution truly. A simple example. A batsman scoring 100 and 0 in the two innings of a test has a Mean value of 50, which is the same value of another batsman who has scored 50 and 50. However the two values of the first batsman have a much higher degree of variance. This is determined by the measure Standard Deviation which is probably the most used of all statistical measures.
Table of Standard Deviation and CoV (in order of CoV)
Ground Mean StdDevn CoV
How do today's great opening batsmen like Hayden, Sehwag and Smith compare with those of the past like Hobbs, Gavaskar and Sutcliffe
The study of opening batsmen is a complicated task. Over the years the role of opening batsmen has changed. From defensive, stay-at-wicket-at-all-costs batsmen they have become match-winners who have been primarily responsible for the attacking attitudes which captains employ now. The study has to recognise this evolution and be fair to all types of opening batsmen.
The first task is to fix a minimum limit criteria. I have fixed this as 3000 runs, scored in the opening position (not complete career). This lets in most great openers. The only top-drawer opener left out is Hanif Mohammad (2638 runs). Unfortunately nothing can be done. I apologise to my Pakistani friends for this. I have also given at the end Hanif Mohammad's values. The other great opener left out, Victor Trumper, has scored only 1650 runs in the opening position. I wanted to avoid any longevity-based weighting and the only way is to keep a high entrance bar. The number of qualifying batsmen has also to be kept at a reasonable number, 35 in this case.
In order to cater to the different playing times, tactics, grounds et al, I have used the following 7 criteria. Each is explained in full later.
1. Home Batting Average. 2. Away batting Average. 3. Average Runs scored - weighted by the quality of bowling attack. 4. Scoring Rate. 5. Average opening partnerships participated in. 6. Quality of the top 3 pace bowlers faced. 7. Quality of batting support - Other opener and next 3 batsmen.
The principle I have followed is that the three direct measures, Home average, Away average and Average weighted runs, will carry a total weight of 50%. The other four secondary measures will have equal weight.
1. Home Batting Average (15 points).
This is the most basic of all measures. It is a straight forward computation of the home batting average. Since the minimum number of home runs scored by a batsman in the group of 35 is 1246 (by Michael Vaughan), any average figure will be valid.
The highest home average is that of Herbert Sutcliffe who has an outanding 64.60 average while playing as an opener in England. Mike Atherton of England is at the bottom with an average of 39.14.
2. Away batting Average (20 points).
This is the other basic measure. It is a straight forward computation of the away batting average. It carries a higher weighting than the home batting average for obvious reasons. Since the minimum number of away runs scored by a batsman in the group of 35 is 916 (by John Edrich), any average figure will be reasonably valid.
Away from home, the other great opener Hobbs averages 59.17. Mudassar Nazar travels very poorly with an average of 25.75.
3. Average Runs scored - weighted by the quality of bowling attack (15 points).
The first two were basic measures. However there is need to value the runs scored against better bowling attacks higher. Greame Smith should get much more credit for his knock of 154 against England as compared to his innings of 232 against Bangladesh even though both were match-winning innings and the second is 50% higher. This is done by weighting the runs scored by the bowling strength of the opposing team and averaging the same.
Hobbs' run tally comes down to 90% while Andrew Strauss' tally moves up to 109%.
4. Scoring Rate (12.5 points).
This is a new measure. The openers have changed the way the Tests are played now. First Hayden and then Greame Smith, Sehwag and Gayle et al have scored consistently at well above 3 runs per over and this has resulted in many more decisive games. This factor has to be recognized and has been.
We have accurate balls played information for the past 15 years and this can be used. For the early Tests I have assigned to the opening batsmen the team's strike rate for the innings. This might vary slightly from actual balls played information, which is, unfortunately, available nowhere. However this will even out over a career. It is also true that the olden day openers, barring a very few attacking players, played quite slowly and most of them would in reality be benefited by this methodology. For openers such as Jayasuriya, Greenidge, Haynes et al, wherever available, actual balls faced information is utilised.
The highest scoring rate for an opener has been achieved by Sehwag who has scored at an incredible 4.75 runs per over.
5. Average opening partnerships participated in (12.5 points).
This is a very good measure since it provides an indication of the effectiveness of the opener. Herbert Sutcliffe has averaged opening stands around 73 runs. The lowest figure is for Alec Stewart, around 36 runs.
6. Quality of the top 3 pace bowlers faced (12.5 points).
When the openers walk in at 0 for 0, they have a daunting task. If they reach lunch at xyz for 0, they would have done their job. Everything afterwards is a bonus. During these two hours or so, the opening batsmen are likely to face the three best pace bowlers of the other team. If these three happen to be Marshall, Holding and Garner as a few opening pairs faced during the 80s, as against the openers who faced Madan Lal, Amarnath and Solkar, they have to be given due credit.
The best three pace bowlers' averages are summed and averaged over the number of times the batsman opened.
Alec Stewart has faced the toughest pace bowlers with a low average of 27.75. A number of recent English opening batsmen have somewhat low figures since they have faced strong Australian attacks in frequent Ashes series. At the other end Hobbs, surprisingly, has had the easiest of opening stints at 37.09. Understandable since the non-English bowling between 1908 and 1930 was quite ordinary.
7. Quality of batting support - Other opener and next 3 batsmen (12.5 points).
Imagine Greenidge walking in with Haynes, with Richards, Kallicharan and Lloyd to follow. Or Langer walking in with Hayden with Ponting, Clarke and Hussey to follow. Contrast this with Gavaskar walking with the happy-go-lucky Srikkanth and P Sharma, Viswanath and BP Patil to follow. These are the extremes. This measure takes into account the supporting batsmen. The other opener gets highest weighting, followed by the no.3, no.4 and no.5 batsmen with progressive lower weightings. These proportionate averages are added and averaged. Higher credit is given for lower support averages.
It is clear that a strong bowler in a weak team has the benefit that he can take a greater share of wickets than a strong bowler in a strong team (Hadlee/Muralitharan against McGrath/Warne). Contrast this with batting where good support is always a boost to the batsmen.
As can be expected, Justin Langer has the best supporting batting with a figure of around 50. Don't forget that Langer had Mathew Hayden as the other opener. The one who had the least support is Chris Gayle with 33.63, despite the presence of Lara at no.4.
Table of top opening batsmen of all time
No Cty Batsman HmAvg AwAvg AdjRpt ScRate OpPshp PaceBow BatSup
This article has been in the pipeline for long
This article has been in the pipeline for long. An analysis of Test Captains is not an easy task and will lead to many arguments and comments. However that cannot deter us from making an honest attempt. As long as the comments are positive in nature, it does not matter.
What are the requirements of a good Test captain? The measurable factors are on-field performance as a player, leading from the front, achieving good match and series results, both home and away. The non-measurable factors are man management, identification of talent, getting players to do their best and support of team members with selection entities. I will only concentrate on the measurable factors and stay away from the non-measurable ones. I am confident that this will be fair even to great captains whose on-field performance might be below par.
Until now we have only looked at wins by huge number of runs or by 10 wickets as comprehensive wins
While I was perusing a table I found that there was an innings scoring rate of 15.83. I went back to the scorecard and saw what could be termed as the most devastating win in ODI history. I started thinking about such matches. Until now we have only looked at wins by huge number of runs or by 10 wickets as comprehensive wins. Now there is a different angle in terms of scoring rates.
This also enables us to look across both types of matches, whether teams win batting first or second. In both these matches the RpO differential is a clear indicator of the extent of domination. We should remember that a 10-wkt win need not be that dominating a victory. Imagine a team bats first and scores 200 in 40 overs. The chasing team bats very carefully and wins, say, in 45 overs by 10 wickets. This is certainly not a very comprehensive a win.
There are no qualifying conditions for this analysis. It is a very simple one of finding the RpO differential and ranking by this measure. I have separated the two tables so that we can have a clearer understanding of the win margins.
Let us look at the tables.
Big wins in ODI matches : Batting second