Sachin Tendulkar is very near the top among batsmen in the cricketing landscape spanning across time

Sachin Tendulkar (above) does not quite finish next to Don Bradman, but is among the clutch of batsmen who follow the Australian genius © Getty Images

Numbers may not tell the complete story, but they give a fair indication of the trail one has blazed during one’s playing days. Therefore, at the end of a magnificent career, it does make sense to look back and figure out how Sachin Tendulkar fared in comparison with the other greats across time and teams. Arunabha Sengupta uses statistical techniques to evaluate the position of Tendulkar among all-time batting legends.

Add Cricket Country as a Preferred Source

“One of the fascinations of cricket is that its statistics, if taken over a sufficiently long period, can give proof of a player’s ability, not only as an individual but also in comparison with players of other eras” – Don Bradman in the foreword to Hedley Verity’s excellent biography by Alan Hill.

It is important to insert the Bradman quote right at the beginning to negate the effect of the numerous math-averse pundits and followers of the game who hold that numbers don’t reveal much. They do. One just needs to look at them properly.

Almost two years earlier, CricketCountry had carried out a series of statistical articles that aimed to place cricketers across eras on the same scale.

With the great career of Sachin Tendulkar having come to an end, it makes sense to revisit our computations and look at where the great man ended up in the cricketing landscape across time.

I am fully aware that the very thought of comparing cricketers across eras makes many a brow knit up in disapproving frowns. After all, how can we compare someone who played in the 1930s with batsmen of the 2000s? The game has changed, the rules have undergone several upheavals, the tactics, the quality of bats, the fitness levels all have witnessed numerous major shifts. Besides, there are those often repeated factors of uncovered pitches, helmets and improved protective gear.

Why not allow greats to be greats and enjoy their contribution to the game instead of putting them through the soulless statistical exercise? Quite recently, an all-time great of the game asked me: Numbers can be manipulated to come to different conclusions, can’t they?

Motivation

Well, the answer is threefold.

Firstly, comparing cricketers may be taboo for a lot of adherents. But there exists a large number of cricket followers who consider comparative analysis as one of the many alluring dimensions of the game. And many of them can vouch for me when I say that appreciation of the aesthetics and excitement of the game can exist side by side with a fascination for the numbers.

Secondly, with slightly sophisticated methods, we can eliminate the need to directly compare a batsman of 2013 with someone from 1933 and still manage to place them on the same scale. We will see how.
Thirdly, statistics can be used as a brutal knife or a surgeon’s scalpel.Properly used, the numbers can be very revealing.

And not all the cricketers are against using numbers either. Ted Dexter was one of the main architects behind the ranking system for cricketers that now live under the name ICC [International Cricket Council] Cricket ratings. He considered it to be his most important contribution to cricket, “far more important than a couple of cover drives.”

In the foreword to Hedley Verity’s biography by Alan Hill, the great Don Bradman wrote the following which is also reproduced at the very beginning of the piece: “One of the fascinations of cricket is that its statistics, if taken over a sufficiently long period, can give proof of a player’s ability, not only as an individual but also in comparison with players of other eras.”

Obviously, someone averaging 27.62 in First-Class cricket can have an entirely different opinion than Bradman, but we do believe in numbers telling a fair amount of the story if not the entire tale.

Comparative Index

Comparison between eras cannot be carried out through simple analysis of batting average.

Different eras did have different conditions, and different benchmarks, and the same average scores had different implications.

However, throughout history, “the best batsmen of a particular period with a sufficient number of matches always ended up with the best averages”. Be it Bradman with 99.94, Sobers with 57.78 or Trumper with 39.04, all these averages were the best or at the very top of their respective eras.

Using the batting averages as yardstick, our method will be to try and compare how different batsmen across time stood in comparison against their exact contemporaries.

To do this, we first consider the playing days of a batsman — from the day he made his debut till his last Test.

Next, we find where he stood in terms of average among all the players during that exact period.

This does away with the problem of direct comparison of batsmen from different eras.

But, in one period one batsman may rank say 4th among 30 men, and in another era a different batsman may rank 9th among 126. How do we place the two of them on the same scale?

So the next step is to see where each batsman would have stood in his era if there had been exactly 100 other batsmen in his time.

We thus derive this comparative index for each batsman. If someone stood in front of 99 of the 100 contemporaries of his era, and another stood ahead of 87 of his era, we can conclude with a fair amount of certainty that the former’s stature as a great in his era was more pronounced than the latter’s.

Two problems may arise due to this approach.

1. Too many lower order batsmen during an era can skew the figures.

2. A player who has played very few Tests can end up with a very high average and distort the rankings.

To circumvent the problems, we use the following counter approaches respectively.

1. We look only at batsmen who scored at more than the global average runs scored during the period

2. We set a minimum criteria of 20 Tests for the modern (post 1950) era and 15 before that.

So, to repeat, our algorithm is the following:

1. Select a batsman.

2. Find his first and last days in Test cricket.

3. Compute the average of all players during that period (global average).

4. Consider all players satisfying minimum Test criterion who averaged higher than global average during that
period.

5. Find out the rank of the batsman among all the batsmen in (step 4).

6. Given the rank, find out how many batsmen he would be ahead of if the number of contemporary batsmen (step 4) had been exactly 100.

The positives of this calculation are that:
 

• A player is only compared with his exact contemporaries, so we do not compare batsmen of different eras. We simply find a modified percentile figure the batsman belonged to during his playing days
   
• We can compare two players of different eras by contrasting their respective comparative indices
   
• This eliminates the problems of fluctuations due to conditions, bowling quality etc.
   

For example,

i. Sachin Tendulkar has played between Nov 15, 1989 and Nov 18, 2013

ii. The average of all batsmen (global average) during this period was 31.17.

iii. Between the dates in (i), there were145 batsmen who played 20 or more Tests and scored above this average.

iv. Tendulkar’s average is 53.78, which ranks 3rd among the 145. Hence he was ahead of 142 of 144 contemporary batsmen of his era.

v. That gives him a comparative index figure of 99. For every 100 contemporary batsmen of his era, he would be ahead of 99.

For Sunil Gavaskar, the same computation yields 94. He was ahead of 94 among 100 contemporary batsmen who averaged more than the global average during his playing days.

Yes, Jacques Kallis and Kumar Sangakkara average higher than Tendulkar during his playing period. But, therein is the sophistication of this approach. We cannot automatically conclude that Kallis and Sangakkara have comparative indices higher than Tendulkar. One is staying clear of direct comparison of averages.
Indeed we find Sangakkara averages less than three other batsmen during his playing period till now (Kallis, Andy Flower and Mohammad Yousuf have higher averages between July 2000 and  March 2013 — Sangakkara’s playing career) and hence he has a comparative index just lower than Tendulkar. This is because he is behind three of his 90 contemporaries, which means his comparative index is 97.

The ranking of Indian batsmen

In the following table we have carried out the exercise for the all-time list of Indian batsmen.
The results provide validation of what we have known all along. Tendulkar leads the charts, with a 99 comparative index. Dravid and Gavaskar end up in the 90s as well, after which there is a large gap.

Indian batsmen across eras ranked on the same scale

*Criteria for minimum Tests played is 20 for all batsmen other than 15 for Hazare.

The only major surprises maybe to find Mohammad Azharuddin high up and Vijay Hazare quite low down in the ranking.

Hazare may have suffered from playing in an era of Don Bradman, Len Hutton, Denis Compton, the 3 Ws, Neil Harvey and a roll call of other great batsmen belonging to more established sides. Azharuddin’s ranking on the other hand is perhaps an indication that we often fail to recognise greatness when it is right in front of us.Given the global average of his period, it is also apparent that Azharuddin played in one of the most difficult eras for batsmen.

Note:

i. Vijay Merchant, the other great Indian batsman, played only 10 Tests and has to be omitted because of low sample size.

ii. Home/Away, first innings/second innings etc. have not been considered here. However, this method can be refined for those specific analyses as well

Now, let us continue this exercise and find out where exactly Tendulkar stands in the global context.

For this analysis we have only considered batsmen who have completed their careers or seem to be on the verge of doing so. Excellent contenders like Hashim Amla, Michael Clarke, AB de Villiers, Kevin Pietersen, Ian Bell, Alastair Cook, Graeme Smith and others have been omitted from the study. This is because we would like to believe they are at the prime of their abilities and will continue for a long time. Thus the numbers associated with them can vary considerably.

Also, for reasons given in the Appendix, we have considered batsmen who have not played full or part of their careers before the First World War.

Global batsmen across eras ranked on the same scale

According to the analysis Sachin Tendulkar does not quite finish next to Don Bradman, but is among the clutch of batsmen who follow the Australian genius.  He was not quite the best that ever was – not indisputably the best after Bradman. But, he was very, very close. 

Appendix:

Batsmen who played entire or part of their careers before 1920 are not considered.In an earlier article, we noted that the batting conditions became standardised only after 1920, from which time the overall average has remained more or less constant. This makes it meaningful to compare someone like Zaheer Abbas (1969 and 1984) with Allan Border (1978 to 1994) in the spans that their careers intersected, because conditions did not undergo statistically significant changes between 1969 and 1994. 

Before 1920s, the conditions were unstable. To compare Victor Trumper (1899 to 1912) with Jack Hobbs (1908 to 1930) would make little sense. Although their careers intersected, Trumper played a lot of his cricket in the era of bad pitches, and Hobbs got much better conditions. So, in comparison to Trumper’s total career, the period when Hobbs’ career intersected with the Australian would have a much higher average – although they may have been scoring equally between 1908 and 1914.This is because Trumper would be handicapped by the 1899 to 1908 played on bad tracks, and Hobbs would be in the happier position of having batted from 1914 to 1930 on much better pitches.

(Arunabha Sengupta is a cricket historian and Chief Cricket Writer at CricketCountry. He writes about the history and the romance of the game, punctuated often by opinions about modern day cricket, while his post-graduate degree in statistics peeps through in occasional analytical pieces. The author of three novels, he can be followed on Twitter at https://twitter.com/senantix)