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January 3, 2012

Pitch quality analysis across all Tests

Anantha Narayanan
Shane Bond in the Hamilton Test match in 2002 when 36 wickets fell for just 507 runs  © Photosport
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This is the second of three very important and significant articles on batting performances against differing conditions and players. The first did a revised take on the Bowling Quality Index. This one covers the Pitch Quality and the third one would combine both and do an analysis of runs scored by batsmen.

Before I get into the article I have to report a very significant move forward in my database contents. Most readers would know that I had the ball data for only around 740 matches, a meagre 37%. In order to redress this situation, I had approached a few readers and five of them, Raghav, Boll, Rameshkumar, Ranga and Anshu, responded magnificently.

Over the past two weeks, the six of us have shared the work and downloaded over 600 scorecards. I have incorporated the balls played information for all these and also took the opportunity to post the 4s/6s information also. Now my database is looking wonderful with 1374 scorecards (68% - a far cry from 37%) containing balls played and 4s/6s data. From match no 1070 (1987), I have an unbroken sequence of 957 scorecards with complete data. This opens up many new avenues of analysis, especially in the analysis of boundaries hit.

Once again my heartfelt thanks to Raghav, Boll, Rameshkumar, Ranga and Anshu, who spent hours during the holiday season. May their tribe flourish.

There is nothing to be gained by looking at history to determine the quality of pitch. The following example will convince anyone on the futility of such a view. Let us look at happenings in the same ground, Hamilton, in two matches played within 12 months of each other.

Tale of two Tests at Hamilton

2003: Ind 99 ao & 154 ao. Nzl 94 ao and 160 for 6. 36 wkts at 14.1.
2003: Nzl 563 ao & 96/8. Pak 463 ao. 28 wkts at 40.1, despite the last innings.

Let us move north for a few thousand kilometres from the Waikato river to Sabarmathi river and into dusty Ahmedabad. Again two matches within an year of each other.

India at Ahmedabad within a 18-month period

2008: India 76 ao against South Africa. RpW: 33.2.
2009: India 760/7 against Sri Lanka. RpW: 76.1.

If I averaged these two figures and come out with 400 or so, I would be correctly laughed off the pitch. That is the type of mistake non-informed and superficial analysts would do. So I am not going to look at history. There are hundreds of such examples. There is nothing worse than averaging such widely varying values.

Instead I am going to look only at the specific match. I am not also going to make the mistake of going down to innings. The recent match from "The Twilight Zone" at Cape Town is enough reason to stay away from this. 284 ao, 96 ao, 47 ao, 236/2 neither indicates a horror pitch of RpW (Runs per Wicket) of 7.2 considering the middle two innings nor a very comfortable pitch with an RpW of 43.3 looking at the first and fourth innings. This is a match with a RpW value of 20.7. A difficult pitch but not an impossible one to bat on.

Thus it is clear that the overall match RpW seems to be closer to the actual pitch condition. This will take care of many a match in which the innings scores change significantly. Four innings and five days present us with a chance to come very close to determining the way the match went.

I will only consider the first 7 batsmen. The last four batsmen will distort the picture. Thus I am going to look at a maximum of 28 innings and determine the average. This is called T7-RpW.

Now we come to a different problem. The RpW works well in most cases. However there are some matches in which the extremely low rate of scoring means that very few runs were scored but quite a few balls were faced. This indicates not a diabolical pitch but only a difficult pitch. To a considerable extent the ultra-defensive approach of the batsmen would have contributed to this situation.

First let us see two examples, at either end, where the RpW and BpW (Balls per wicket) are in sync.

Match  216: Scores 36 ao, 153 ao & 45 ao. RpW: 8.5. BpW: 22.3.
Match 1374: Scores 537/8 & 952/7.         RpW: 113. BpW: 215.

These represent the two extremes. Whatever measures are taken the pitches remain where they are: diabolical to the nth degree in either direction. Hence in these and hundreds of other matches the RpW values are sufficient.

Now let us take a look at three matches, taken as two comparisons.

Match 1037: Saf 109 & 101,  Aus 230.      RpW: 14.1. BpW: 33.7.  RpO: 2.50
Match 0438: Saf 164 & 134, Eng 110 & 130. RpW: 14.3. BpW: 60.8. RpO: 1.40

These two matches have similar RpWs of around 14. If we consider only the RpW, we may conclude that batsmen of both matches had the same level of difficulty. However that is far from the truth. In the second match the BpW is nearly twice that in the first match. If it took the bowlers nearly 61 balls to claim a wicket, that is below the career strike rates of Walsh, Swann, Caddick, Zaheer Khan and Snow, how can the pitch be that difficult. It is clear that the pitch is far from diabolical but the batsmen have made a meal and half of it. The scoring rate has been abysmal. The RpW value has to be adjusted slightly upwards.

Match 0438: Saf 164 & 134, Eng 110 & 130.    RpW: 14.3. BpW: 60.8. RpO: 1.40
Match 0782: Pak 417 & 105/4, Nzl 157 & 360.  RpW: 33.8. BpW: 60.7. RpO: 3.23

In this pair of matches, one being repeated, the RpW values show nearly 150% variation. However the BpW values are similar. In the second match the batsmen have been positive and achieved very respectable RpW value. The first team has gone on the defensive and got only a sub-15 RpW value.

I am sure the readers are going to say that the period, viz., the 1950s, when Test no 438 was played, was a defensive one and teams did not think twice about scoring very slowly, irrespective of the situation. The slowest Test innings ever is New Zealand scoring 69 for 6, during 1955, against Pakistan in 90 overs: Yes, it is correct, not a misprint. In fact it is this very trend which has to be taken care of. New Zealand lost only 6 wickets in 90 overs, leading to an innings RpW of 90. Was it a 11.5-RpW quagmire of pitch. No, certainly not. It was probably a 30-RpW wicket. It is this adjustment I am referring to.

After trying out a few scenarios I have hit upon a simple method. One which would be clearly understood and accepted by all. I have realized that both RpW and BpW are important. Hence I have determined the Pitch Quality Index by adding the two values together. However I feel that the RpW value is more important. Hence the RpW gets three-fourths weight and the BpW value, one-fourth weight.

This is not as arbitrary as it looks. With an overall RpO of 3.0 across all matches, the BpW value is around twice that of the RpW value and 75-25 looks perfect. 67-33 will make this skewed too much towards the BpW value. As an example, take RpW of 30 and BpW of 60. 67-33 takes PQI to 40 (20 + 20). 75-25 comes out with a PQI of 37.5 (22.5 + 15). The final PQI values do not matter. The almost equal weight, as shown in the first case, negates my requirement that RpW should have a higher impact.

Again readers should realize that the seemingly arbitrary nature of this does not matter since it is only a derived interim index value. It is also applied across all matches. I will call this composite value PQI (Pitch Quality Index).

I can anticipate a question why the BpW has been taken and not BpI since that seems to make more sense. However since I am adding two disparate figures, it is not correct to have one based on wickets and the other based on innings. If a top-7 batsman could not be dismissed, then to that extent the pitch has to be related to that. If Lara's 582 balls are not included in the computation of the BpW (as against the BpI) figure at Antigua, that would correctly increase the BpW value by about 35 (since 16 top order wickets fell in that match), bringing to light the true dead nature of the pitch.

It can be seen that the period in which the match is played does not have any relevance since only what happened in the match is taken into account. For instance take two Tests in 2001: Test# 2021 (Aus vs Nzl) has a PQI of 27.2 (PG-5), Test# 2016 (Saf-Aus) has a PQI of 27.7 (PG-5). Now look at Test# 2008 (Slk vs Aus) with a PQI of 77.6 (PG-1) and Test# 2009 (Pak-Slk) with a PQI of 77.6 (PG-1). Such examples abound even during the notorious batting-friendly periods. Readers should never forget that this is a post-match actual measure.

Given below are the matches with extreme PQI values. Teams are all out if not mentioned otherwise.

The top-10 and bottom-10 Tests in PQI table


MtId Year Hme Awy< ------Top-7 Batsmen------ > Ins No Runs BpW RpW PQI Grp Innings scores

0028 1888 Eng Aus 28 1 190 7.0 19.1 10.1 5 (116, 53, 60, 62) 0216 1932 Aus Saf 21 0 178 8.5 22.3 11.9 5 (36, 153, 45) 0238 1935 Win Eng 28 3 248 9.9 20.6 12.6 5 (102, 81/7, 51/6, 75/6) 0030 1888 Eng Aus 21 0 199 9.5 23.1 12.9 5 (172, 81, 70) 0027 1888 Aus Eng 28 0 229 8.2 27.5 13.0 5 (113, 42, 137, 82) ... ... ... 0878 1980 Pak Aus 11 2 931 103.4 216.9 131.8 0 (612, 382/2) 1374 1997 Slk Ind 14 2 1366 113.8 215.4 139.2 0 (537/8, 952/6) 0696 1972 Win Nzl 14 5 903 100.3 270.1 142.8 0 (365/7, 543/3, 86/0) 0418 1955 Ind Nzl 14 5 1012 112.4 292.3 157.4 0 (450/2, 531/7, 112/1). 1781 2006 Pak Ind 10 3 1025 146.4 190.7 157.5 0 (679/7 and 410/1)


Three of the low PQI matches were played during before WW1 and two during the 1930s. On the other hand the high PQI Tests have been distributed over the years. The PQI runs from 10.1 for the 1888 Test through 11.9 during 1932 at MCG and through 157.4 at New Delhi during 1955 and finally 157.5 during the run-deluge at Lahore during 2006.

A brief referral back to the three matches we had considered earlier.

1037: 14.1 & 33.7 lead to 20.6 (Group 5 - but lower)
0438: 14.3 & 60.8 lead to 29.8 (Group 5)
0782: 33.8 & 60.7 lead to 42.8 (Group 3).

It can be seen that the differing BpW figures has certainly separated these three matches in a clear manner.

The distribution is quite skewed. This is confirmed by the statistical measures. The distribution has a mean of 48.5 and a Standard Deviation of 16.6 which means the Coefficient of Variation is a rather high 0.34.

The range of the PQI is so wide that all calculations go awry. Remember that this is post-match measure, unlike the BQI which is a pre-match expectation. Hence it was possible to put limits on BQI. Here nothing like that can be done. The distribution is also not a Normal one like the BQI. There are only 80 matches between 86 (half of 172) and 172. So the PQI cannot be allocated blindly or by making standard assumptions. Hence I have done the following. The 27 is a starting point determined by looking at low RpW and BpW values. Then reasonable gaps are allowed for subsequent groups. It is possible that some fine-tuning will be done when I do the Batsmen analysis, especially in the formation of groups.

Summary of PQI Grouping


Below 27.0: Group 5 - 101 ( 5.0%) A nightmare for the batsmen 27.0 - 37.0: Group 4 - 388 (19.2%) Difficult to bat on 37.0 - 47.0: Group 3 - 563 (27.8%) Good pitch - slightly favouring bowlers 47.0 - 60.0: Group 2 - 576 (28.4%) Good pitch - slightly favouring batsmen 62.0 - 80.0: Group 1 - 313 (15.4%) A belter (I have had too much of Shastri!!!) Above 80.0 : Group 0 - 85 ( 4.2%) Where "open season" is declared on bowlers.
Finally while the memory is fresh, let me show the relevant values for the two Tests which finished a few days back.

Boxing Day Tests

Match 2025: Scores 333, 282, 240 & 169. RpW: 25.6. BpW: 52.9. PQI: 32.5
Match 2026: Scores 338, 168, 279 & 241. RpW: 29.0. BpW: 57.6. PQI: 36.1

Note how similar the matches have been. If the sequence is ignored, the innings are virtually identical. The total runs scored in the two matches are 1024 and 1026 respectively. The only significant difference is that the Top-7 in the Melbourne Test have performed poorly. This, and the slightly higher BpW value has pushed the PQI for the Kingsmead Test slightly higher. However both are in Pitch Group 4, the second toughest one. I think very few will disagree with this. It is of interest to note that the 8-11 Batting average for the Melbourne Test is 21.3.

To download/view the document containing the complete PQI tables please click/right-click here.

In the next article I will form a composite of BQI and PQI for each innings and arrive at a Bowling-Pitch Group. This would be a true indicator of the conditions the batsmen played in and the attack he faced. I will do a analysis of the runs scored by batsmen against different combination groups.

Revision of PQI calculation based on Arjun's alternative method.


Given below is the revised Pitch Group allocation based on MT10 (Top 10 innings of match) values. This was suggested by Arjun Hemnani. This has a lot of pluses going for it, mainly the inclusion of all performances irrespective of the batting position.

Match# 684 is a perfect example of why we should not take Average but Runs per Innings.

Win 363 ao. Ind 376 ao. Win 307/3. Ind 123/0.

The third and fourth innings had two high score not outs. The first two innings had a high score not out each. Net result is 754 runs in 4 (10-6) innings leading to a MT10-Avge of 188.5, the third best. A total farce. This is the only Test with 6 MT10 not outs. 5 not outs, there are two matches. One seems okay. The other not. Then come the matches with 4 not outs, led by two matches with the highest MT10-Avge of 191.0 and 190.3. Only the later, Match# 1426 truly deserves this number. This is the Taylor-334 match.

So taking average is truly out. The current match, with 3 top innings already as not outs, would also go that way. One possibility to is to limit the number of not outs to 3. Works well but rather artificial.

Finally the simplest and most elegant solution is to take the MT10-RpI. After all these are the top-10 innings of the match. So remaining unbeaten does not mean that much of a difference. What does it matter whether we take 400*/329* or 400/329. The RpI has worked out very well.

For the T7-PQI I used the BpW as an additional measure. However here there is no need to do that for reason given below.

The 28 innings used to determine the T7-RpW had a number of small innings, with varying balls played associations. Hence I used the BpW measure to smoothen these wide variations within a match and across matches. However in this case we would select only the top-10 innings of the match. As such I have found that the MT10 Runs have a strong correlation to the MT10 Balls played. Hence there is no need to incorporate the Balls played information, which anyhow gets determined on a pro-rata basis for a third of the matches.

Summary of PQI Grouping - Based on Top-10 innings in match


Below 40.0: Group 5 - 112 ( 5.5%) A nightmare for the batsmen 40.0 - 52.5: Group 4 - 358 (17.7%) Difficult to bat on 52.5 - 65.0: Group 3 - 490 (24.2%) Good pitch - slightly favouring bowlers 65.0 - 80.0: Group 2 - 599 (29.5%) Good pitch - slightly favouring batsmen 80.0 - 95.0: Group 1 - 342 (16.9%) A belter (I have had too much of Shastri!!!) Above 95.0 : Group 0 - 125 ( 6.2%) Where "open season" is declared on bowlers.

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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 Surya on (January 20, 2012, 5:05 GMT)

Brilliant! I read your Jan 17th piece first (batsmen numbers) and I was extremely curious about the PQI metric. Needless to say, there were lot of debates on this metric in various circles. This is a very objective way of assigning a numerical value to a metric which often remains the realm of specialized knowledge of the match.

Posted by Ramesh Kumar on (January 11, 2012, 7:53 GMT)

Ananth,

I still feel that the methodolgy favours batsmen in strong bowling teams. If the team has only 1/2 good batsmen and many good bowlers, all the more better.

The pich difficulty is also a function of bowlers being available to exploit the pitches. If you take 1974 India tour of Eng, right thru the series, England scored runs and India could not. This type has happened many times.You may argue that the pitch was not devilish as it was made out to be and not as placid as England scores would show. But if you start extrapolating batsmen individual performance based on averaging match scores and then going further saying these are stand out performances or otherwise, it might be a twist to the evaluation. My humble submission is that this methodology is good for pitch valuation from overall match point of view and cannot be drilled into individual performance evaluation. [[ Ramesh, This is not an Innings Ratings analysis. It is to look at what proportion of runs are scored in which circumstances based on two key components, bowling quality and pitch type. Combining these into a single factor lets us get a handle of what the batsman was against.I suggest you wait for the article. Ananth: ]]

Posted by Gerry_the_Merry on (January 10, 2012, 16:19 GMT)

The team I and team II numbers given in your comments on Jan 7, 8:09 AM match exactly with cricinfo, not that i ever doubted.

One last question remains before you pull the trigger...in your previous article, i had mentioned that weighting the home and away BQI by taking average of BQI yielded a delta between home and away of ~4.3%, whereas this delta, using sigma runs / sigma wickets from cricinfo yielded a delta of ~10%.

The difference in the sigma-runs/sigma-wickets and the average of BQI was shown by you to be around 3.9% (i think) but this impact would have been similar for home and away.

Hence my query remains: When raw cricinfo data suggests 10% delta for home/away, your adjustments not being home/away specific compress this delta to around 4.3%. Does that not bother you? [[ No, not at all. But what bothers me more is your sticking to a single inflexible doctrine and not letting go of that. I explained, using Philander's career, how the macro calculations differe widely with micro calculations. One is done at match level. The balls are used to do the weighting. The balls can vary between 1 and 2000+. These values are then added for all matches. You have derived a mean.. The other is two totals across 70000 innings and then a final division. How can you expect these to be in sync especially when the average values are multiplied by a factor between 1 and 2012. A great bowling team could dismiss the opponents in 75 balls while a poor bowling team might bowl 250 overs or, theoretivally, vice versa. Ananth: ]]

Posted by Gerry_the_Merry on (January 10, 2012, 9:06 GMT)

Ananth, is the current XL file on the MT-10 method? Also the group 5 cutoffs seem very harsh if one takes "modern cricket" starting from Don Bradman's time. Very few tests make it. I suspect pre-1930 tests drag the cutoff down. If the idea is relative ranking, then the toughest conditions that apply to modern cricket should be classified group 5 i feel. [[ I have worked on the basis that the 5 and 0 groups are the real extremes and should not happen often. The 4 and 1 groups are the ones strongly fabouring bowlers and batsmen. The 3 and 2 are the middle groups tilting slightly either side. From that point of view the 5 and 0 values of 5.5% and 6.2% seem to be okay. The key is what do 5/4/3 combine to. That is 47.4% and 3/2/1 come to 52.6%. I can bring these two closer to 50.0. That is a welcome tweak. Also please understand that these are post match values and relate to actual figures. When I combine the RSI and BQI I get good groupings. Although let me say there are going to be fireworks. Ananth: ]]

Posted by Prashant on (January 10, 2012, 3:23 GMT)

It seems to me that there may be a certain amount of double counting when taking into account both bowling and pitch qualities combined. (Though RSI : Run scoring index is probably the better term)

For eg.: 1)Good bowling attacks will reduce the total amount of runs scored. [[ Not necessarily. The great batsmen will bide his time and score runs. Maybe that is what we are looking for here. Double counting is in a measure like ODI Batting index which is Runs * S/R which works to Runs*Runs/Balls. Here one may lead to other but not necessarily double counting. The idea is to recognize batting performances in difficult situations, (i-e) let us say both indixes 3 and above. Ananth: ]]

This in turn will make it appear that the pitch is worse than it may be. But ,then again, credit is separately already given for the good bowling attack. Chicken and egg.

2)Similarly, good batsmen may make a pitch appear better than relatively poor batsmen.

So,seems to me that a combined (bowling/pitch) quality index would be accurate only when also factoring in the quality of batting involved (batting quality index).

As a rough eg. In general 50+ avg batsmen would be expected to put up a better score than 40+ avg batsmen. [[ Don't forget that this is not a Innings Rating analysis. As such many match-contextual factors do not come in. The BQI is a pre-match prediction of how good the bowling attck is or expected to be. The RSI is a post-match determination of how the pitch turned out to be. The combination of the two should put things in place. Almost all the 300s are in Group 0 or 1. Only one, # 226, Hammond's 336 match is in Group 3. That too because after 336 and 83 comes 24. But then Hammond is put in place because he faced college students and part-timers. Bradman, Inzamam and Edrich have their 300s in Group 2. Looking at the bowling side, three performances likely to make anybody's top-10, Hadlee's 9-wkt haul and Murali's 9-wkyt haul were in matches with RSI of 1. Holding's is the only 8-wkt haul in RSI group 0. Calcultta 2001 is in Group 0 (RSI-98.9). However Laxman gets some credit because the expected bowling was good. Ananth: ]]

Posted by Gerry_the_Merry on (January 7, 2012, 12:56 GMT)

Your numbers are very intriguing. The overall 1.14 is outside the other two numbers 1.17 and 1.19? Is 1.14 bowling and the other two batting? Even then, for batting the overall should work out to something like 1.185 (closer to 1.19 than 1.17). Still quite different from 1.14 for bowling. [[ These are Batting averages extracted from Cricinfo. The first line is 1-11 batsmen. "All" was a bit confusing. The 1.14 is the ratio between 1/2 and 3/4. It only shows that there is a greater delta between the "first" and "second" innings so far as the top-order is concerned. Probably the late order does not really care. As shown by the relative freedom with which the late order batsmen have added runs at both SCG and MCG in the "second" innings. I would suggest that you validate these numbers. Ananth: ]]

Agree that peer-group burning deck can be only for innings rating, not career.

Posted by Gerry_the_Merry on (January 7, 2012, 8:09 GMT)

BQI takes care of the quality of opposition. PQI takes care of scoring runs in teh context of RPI of the match. What about peer-group outperformance from within own team. Let us take Lara's example again. Examples of 2005-2006 matches. Folks scored truckloads of runs against them. Lara scored alone against some good quality opposition. But in BQI, the own team peer outperformance will not come in. In PQI, the opposite team scoring against weak WI bowling dilutes Lara's achievement, and in any case it is meant to be PQI. But what is the metric to assign value to the "burning deck" metric. This was a valuable feature of your Wisden 100. There are several methods one can propose, but I am sure you already have an adequate inventory. I am not talking in an innings rating context at all (in this an in all future comments). I am saying that if you ever did a second version of "Batsmen Across Bowler Groups" such items also should ideally feature. [[ The minute Peer comparisons come in these relate not to a specific match/innings but to the entire career. How will this be relevant in an analysis which has as its base a match/team-innings. i aceept your point about 1/2 and 3/4 innings but this does not seem relevant. Ananth: ]] Also the team Ist team IInd inn delta... [[ Since this analysis has the match/team-innings as the basis and the 1/2 and 3/4 inning separation occurs strictly within the match, you want a delta applied. This is validated by the following summaries

Class  1-2   3-4
All:  31.71  27.68 1.14
1-7:  38.30  32.29 1.19
8-11: 15.98  13.62 1.17
I can do that but probably only at the weighted average determination level. The problem is that I combine the PQI and RSI together and form a composite index. This index is used to determine a BRI. I cannot apply the delta at this stage.It will confuse everyone. However I am also working on the overall Sum (Runs x BRI) / Sum(Runs). I will apply this delta at the summing time. Ananth: ]]

Posted by Arjun on (January 6, 2012, 11:46 GMT)

Ananth,

Now pqi looks simple and easy to understand. As you have mentioned earlier bqi is a pre-match calculation and pqi is post-match calculation so instead of 'pitch quality', the better term could be 'Level of Run Scoring' in the match. [[ I agree. Also Pitch Quality indicates that at one level there is lack of quality. In reality quality exists at the middle levels with assistance to both batsmen and bowlers. I would probably combine both ideas and call it RSI: Run Scoring Index Ananth: ]]

Posted by Ananth on (January 6, 2012, 10:09 GMT)

Arjun, the revised PQI table has been uploaded. The SCG Test came under PQI 0 (MT10-RpI=103.7). As per the Top-7 BpW it was 1. However considering the way it played for most of the match, it was a 0 or 1 level. So either seems okay. As I have mentioned in Ranga's comment, 622 for 1 certainly points to the flattest of pitches.

Posted by Ranga on (January 6, 2012, 8:29 GMT)

An interesting observation of the summary of 2026 Tests ( both methods throw a sort of normal distribution), is that Arjun's method threw a more normalized distribution, ie., more identical numbers of 0&5, 1&4, 2&3, than Ananth's method. Whether any population, whatever be the data points is something that I dont know. Ananth's PQI supports batsmen (ie., the number of PQI >3 is greater than the PQI<3) - Whereas Arjun's is a mirror image of that!!! Ananth: 1052/974 vs Arjun 960/1086). . . . This is a significant difference. While one method shows that batting is slightly tougher, the other says batting is slightly easier. Is this because of MT10, which would include some aberrations like No.8 and 9s scoring 50s more often than we think they would? Or is it not that simple as I think? [[ Ranga, it must be recognized that my earlier method considered only 64% of the population. As such Arjun's method is more correct. Even today, Ashwin's 62 and Zaheer Khan's 35 come into the Top-10 adding credence to the easy paced nature of the pitch. In the T7 method these two innings would have been ignored and the top order failures highlighted. Even then you will see that the Pitch Group is either 1 or 0. That will necessarily and correctly lower the value of all innings, including Clarke's. If 622 for 1 is not an absolutely flat track I don't know what is. Ananth: ]]

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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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