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Brian Lara’s significant rise in alternative look at batting averages

Lara was probably a greater batsman that we think he is © Getty Images
Lara was probably a greater batsman that we think he is © Getty Images

The conventional average is dependent a lot on batsmen’s performances where he has remained unbeaten. There have been attempts to use a new measure to rank batsmen Abhishek Mukherjee delves deeper in the matter.

Barry Richards (no less) has recently shown his dissatisfaction on Facebook on the existing method to calculate batting averages. In his opinion the ideal ratio should be the ratio between runs scored and innings batted. Let us consider the batsmen with over 2,000 Test runs and consider their batting averages:

Player M I NO R Ave 100 Index 1(Runs/Innings) Drop
Brian Lara 131 232 6 11,953 52.88 34 51.52 2.60%
Everton Weekes 48 81 5 4,455 58.61 15 55 6.20%
Jack Hobbs 61 102 7 5,410 56.94 15 53.04 6.90%
Mohammad Yousuf 90 156 12 7,530 52.29 24 48.27 7.70%
Kumar Sangakkara 124 213 17 11,493 58.63 36 53.96 8.00%
Clyde Walcott 44 74 7 3,798 56.68 15 51.32 9.40%
Graeme Pollock 23 41 4 2,256 60.97 7 55.02 9.80%
George Headley 22 40 4 2,190 60.83 10 54.75 10.00%
Sachin Tendulkar 200 329 33 15,921 53.78 51 48.39 10.00%
Ricky Ponting 168 287 29 13,378 51.85 41 46.61 10.10%
AB de Villiers 92 154 16 7,168 51.94 19 46.55 10.40%
Herbert Sutcliffe 54 84 9 4,555 60.73 16 54.23 10.70%
Len Hutton 79 138 15 6,971 56.67 19 50.51 10.90%
Javed Miandad 124 189 21 8,832 52.57 23 46.73 11.10%
Rahul Dravid 164 286 32 13,288 52.31 36 46.46 11.20%
Dudley Nourse 34 62 7 2,960 53.81 9 47.74 11.30%
Wally Hammond 85 140 16 7,249 58.45 22 51.78 11.40%
Ken Barrington 82 131 15 6,806 58.67 20 51.95 11.40%
Michael Hussey 79 137 16 6,235 51.52 19 45.51 11.70%
Don Bradman 52 80 10 6,996 99.94 29 87.45 12.50%
Greg Chappell 87 151 19 7,110 53.86 24 47.09 12.60%
Garry Sobers 93 160 21 8,032 57.78 26 50.2 13.10%
Jacques Kallis 166 280 40 13,289 55.37 45 47.46 14.30%
Andy Flower 63 112 19 4,794 51.54 12 42.8 17.00%
Shivnarine Chanderpaul 156 266 46 11,414 51.88 29 42.91 17.30%

As expected, Lara, Weekes, and Hobbs (in other words, the men with low not outs) have very little deviation. Bradman’s average drops significantly, but he still remains way clear of the next man on the list, Pollock (who is marginally ahead of Weekes). Along with Hobbs, the other usual suspects —Sutcliffe and Sangakkara — rank high as well.

What is wrong with this measure?

Consider the following batsmen:

Batsman A scores 50 and a duck.

Batsman B scores 49 and zero not out.

The usual average gives the batting averages as 25 for A and 49 for B. By the new index we get 25 for A and 24.50 for B, which is somewhat flawed, since remaining unbeaten on zero cannot be B’s fault.

What, then, is a better measure?

Let us try to find out the root cause of the problem: the usual complaint is about batsmen getting a boost in batting averages based on not outs. The usual argument is that a single 50 should not be treated equal to 50 runs from ten innings after getting out once. A perfect example of this is Australia’s batting charts from the Ashes tour of 1953, where Bill Johnston managed to top the charts simply by remaining not out:

What, then, is the way out?

Arunabha Sengupta has suggested the application of Kaplan-Meier method used commonly in survival analysis. That is obviously an excellent tried-and-tested method, but is unfortunately not the easiest of concepts to explain to the world.

This article tries to come up with another method. Let us ask ourselves this question: how many runs do we expect from a batsman with a significantly long career when he walks out to bat?

Let us leave Don Bradman out of this question. Consider Tendulkar, the man with the largest data size. While an entire nation expected him to score a hundred every time he had walked out to bat, the fact remains that he has scored 51 Test hundreds in 329 innings; in other words, a hundred every 6.45 innings.

Does this mean that Tendulkar has performed below expectations? No. We need to set our expectations right.

How much should we expect? The average, is it not? One may argue that one should expect our new Index (runs/innings) when a batsman goes out to bat, but should we expect Tendulkar to score 54 when India requires, say, 30, or are likely to declare in the next couple of overs?

Let us fall back on average, then. For each of these 25 champions, let us consider their batting average. Let us not allow the innings where they have crossed their batting average (61 onwards for Pollock, to give an example) to tamper with the batting average. Since he has already crossed more than his batting average, let us consider it as “out”. What does this new number suggest?

Player M I NO R Ave 100 Index 1(Runs/Innings) Drop I2* Index2(Runs/(Innings-Innings2)) Drop
Don Bradman 52 80 10 6,996 99.94 29 87.45 12.50% 4 92.05 7.90%
Herbert Sutcliffe 54 84 9 4,555 60.73 16 54.23 10.70% 5 57.66 5.10%
Everton Weekes 48 81 5 4,455 58.61 15 55 6.20% 3 57.12 2.60%
Graeme Pollock 23 41 4 2,256 60.97 7 55.02 9.80% 1 56.4 7.50%
Ken Barrington 82 131 15 6,806 58.67 20 51.95 11.40% 10 56.25 4.10%
George Headley 22 40 4 2,190 60.83 10 54.75 10.00% 1 56.15 7.70%
Kumar Sangakkara 124 213 17 11,493 58.63 36 53.96 8.00% 8 56.06 4.40%
Jack Hobbs 61 102 7 5,410 56.94 15 53.04 6.90% 4 55.2 3.00%
Clyde Walcott 44 74 7 3,798 56.68 15 51.32 9.40% 5 55.04 2.90%
Wally Hammond 85 140 16 7,249 58.45 22 51.78 11.40% 5 53.7 8.10%
Garry Sobers 93 160 21 8,032 57.78 26 50.2 13.10% 10 53.55 7.30%
Len Hutton 79 138 15 6,971 56.67 19 50.51 10.90% 7 53.21 6.10%
Brian Lara 131 232 6 11,953 52.88 34 51.52 2.60% 3 52.2 1.30%
Dudley Nourse 34 62 7 2,960 53.81 9 47.74 11.30% 5 51.93 3.50%
Greg Chappell 87 151 19 7,110 53.86 24 47.09 12.60% 11 50.79 5.70%
Sachin Tendulkar 200 329 33 15,921 53.78 51 48.39 10.00% 15 50.7 5.70%
Jacques Kallis 166 280 40 13,289 55.37 45 47.46 14.30% 15 50.15 9.40%
Mohammad Yousuf 90 156 12 7,530 52.29 24 48.27 7.70% 5 49.87 4.60%
Rahul Dravid 164 286 32 13,288 52.31 36 46.46 11.20% 18 49.58 5.20%
Javed Miandad 124 189 21 8,832 52.57 23 46.73 11.10% 9 49.07 6.70%
Ricky Ponting 168 287 29 13,378 51.85 41 46.61 10.10% 14 49 5.50%
AB de Villiers 92 154 16 7,168 51.94 19 46.55 10.40% 7 48.76 6.10%
Michael Hussey 79 137 16 6,235 51.52 19 45.51 11.70% 8 48.33 6.20%
Andy Flower 63 112 19 4,794 51.54 12 42.8 17.00% 7 45.66 11.40%
Shivnarine Chanderpaul 156 266 46 11,414 51.88 29 42.91 17.30% 13 45.11 13.00%
Number of innings where a batsman has scored less than his batting average and has remained not out

This changes the rankings, but not significantly. Do note the rise for Lara and the drop for Chanderpaul. That should keep the anti-not outs happy.

Moral of the story:

Whatever you do, Bradman remains beyond the reach of all mortals.

(Abhishek Mukherjee is the Deputy Editor and Cricket Historian at CricketCountry. He blogs here and can be followed on Twitter here.)

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