THE CORDON HOME

BLOGS ARCHIVES
SELECT BLOG
November 3, 2010

ODIs

ODI wins: with oodles of resources to spare

Anantha Narayanan
Sanath Jayasuriya: set up a 245 run win over India  © Getty Images
Enlarge

First, something of interest to the readers. In one of my responses I had mentioned that I had just finished reading Lynne Truss's classic "Eats, shoots and leaves" and hoped that my apostrophes were correctly placed. This was like a fleeting peep into the unknown. Couple of readers mailed and asked me about the book. A nice diversion for me, I thought. Here we go.

Lynne Truss's book is an all-time classic on English punctuation and should be read by anyone who does anything in English. It is now availeble in a low-cost edition in India. Okay, what about the title.

A panda walks into a cafe. He orders a sandwich, eats it, then draws a gun and fires shots in the air. He then walks towards the exit.
The waiter is baffled and asks the panda "why ?". The panda produces a wildlife manual and asks the waiter to read the desription of himself, the panda.
Sure enough, there is an explanation.
"Panda: Large black-and-white bear-like animal, native to China. Eats, shoots and leaves."
The waiter understands.
To those of you who have not got it, the single comma, inserted unnecessarily between eats and shoots, changes the whole sentence on the panda's eating habits. It should have been "Eats shoots and leaves.", "shoots", of course, referring to bamboo shoots. No wonder, he protested.
Do not miss this classic on correct punctuation. It is a constant bed-side companion book for me.

At the beginning of last year I had looked at huge ODI wins, using scoring rates as the base. This represented the relative usage of resources in an indirect manner. In this article I have looked at wins achieved with least deployment of resources looking at the same directly.

Let us first look at the chasing wins which offer the clearest look at deployment of resources. Everything is cut and dried at the beginning of the chasing innings and we would be able to determine the availability of resources in a very accurate manner.

Let me say that a team is restricted to 99 in the first innings, it does not matter whether they were dismissed in the 31st over or scored 99 for 8 (as Bangladesh did way back) in 45 overs. The target in front of the chasing team is 100. They have two resources at their disposal, viz., 10 wickets and x balls (normally 300). This is fixed and set in stone, ignoring the matches where rains come in and the duo of dons from England take over. In most cases the resource situation remain clear.

In the first part of this article I am going to look at the wins in which the winning teams expended the minimum of these resources and won by a few miles, so to speak.

First, the balls available. There may be Power play options, fielding restrictions in force, ball changes et al. However it is safe to conclude that the balls resource can be measured in a linear manner. With the advent of two optional Power Plays, no one can say that the middle overs are low-scoring overs. The batting team can take the PP at any time to compensate for a low-scoring period, although the captains rarely do so. Hence I will take the balls resource in a linear manner. If the maximum balls is 300 and 210 balls are available, 30% of this resource has been used. If 120 balls are available, 60% has been used and so on.

The wickets resource is slightly tricky. Not all wickets are equal. Certainly the top order wickets are more valuable than the low order wickets. There are two ways of doing this. The first is a simpler but very good method. I assign the following weights for the wickets. Not subjectively done, but by an informed allocation.

Wickets 1 to 6: 12.5% - 75%
Wicket  7:      10.0% - 85%
Wicket  8:       7.5% - 92.5%
Wicket  9:       5.0% - 97.5%
Wicket 10:       2.5% - 100%


If 4 wickets are lost, 50% of wicket resource has been used, if 7 wickets are lost, 85% of the wicket resource has been used and so on. It works very well. The only point of contention might be the argument that at the fall of 6th wicket we might still have a pair of very good batsman batting. But the weight is a fair 10% for the seventh wicket and Gilchrists and Dhonis, batting at 7, do not abound in plenty.

The second is to do a more accurate weighting of wicket values. I add the ODI Index (Batting average x Strike rate) values for all 11 batsmen. This represents the total available resource. I then determine the sum of ODI Index for the dismissed batsmen. This represents the already deployed resource. The ratio between the two gives us the required value. If Tendulkar and Vijay open, and one of them is out in a 9-wkt win, the resource available will vary significantly depending on which one is out. Of course for 10-wkt wins, the two methods lead to the same result.

I have done the calculations on both methods. My personal take is that the simpler method is sufficient and works well in 95% of the situations. The two resources carry equal weights. There might be situations where one might be more valuable than the other. Bot there is no clear way in which this sort of variable weight can be determined.

Let us now look at the tables. First, the one based on a simple wicket weight.

Wicket resource calculation: Method 2 - Simple wkt weight based

MtId Year FBt Score SBt Score Result % Res left

2660 2007 Bng: 93/10 Nzl: 95/ 0 ( 6.0) won by 10 wkts 94.0 1776 2001 Zim: 38/10 Slk: 40/ 1 ( 4.2) won by 9 wkts 89.4 1958 2003 Can: 36/10 Slk: 37/ 1 ( 4.4) won by 9 wkts 89.1 1758 2001 Ken: 90/10 Ind: 91/ 0 (11.3) won by 10 wkts 88.5 1961 2003 Bng:108/10 Saf:109/ 0 (12.0) won by 10 wkts 88.0 1940 2003 Eng:117/10 Aus:118/ 0 (12.2) won by 10 wkts 87.7 2063 2003 Eng: 88/10 Slk: 89/ 0 (13.5) won by 10 wkts 86.2 2521 2007 Pak:107/10 Saf:113/ 0 (14.0) won by 10 wkts 86.0 2172 2004 USA: 65/10 Aus: 66/ 1 ( 7.5) won by 9 wkts 85.9 2754 2008 Saf: 83/10 Eng: 85/ 0 (14.1) won by 10 wkts 85.8 2599 2007 Hol: 80/10 Win: 82/ 0 (14.3) won by 10 wkts 85.5 2122 2004 Zim: 35/10 Slk: 40/ 1 ( 9.2) won by 9 wkts 84.4 2489 2007 Ber:133/10 Ken:137/ 0 (18.1) won by 10 wkts 81.8 2570 2007 Ire: 91/10 Aus: 92/ 1 (12.2) won by 9 wkts 81.4 2254 2005 Bng:139/10 Aus:140/ 0 (19.0) won by 10 wkts 81.0 2428 2006 Win: 80/10 Slk: 83/ 1 (13.2) won by 9 wkts 80.4 2733 2008 Bng:115/10 Pak:116/ 0 (19.4) won by 10 wkts 80.3 1891 2002 Bng:154/ 9 Saf:155/ 0 (20.2) won by 10 wkts 79.7 2424 2006 Zim: 85/10 Win: 90/ 1 (14.2) won by 9 wkts 79.4 1950 2003 Bng:124/10 Slk:126/ 0 (21.1) won by 10 wkts 78.8


It is obvious that a 10-wicket win would lead the table. However it is also essential that no balls were wasted and the win was reached post-haste. That is what New Zealand did against Bangladesh. They chased a target of 94 runs in 6 overs, at a scoring rate exceeding 15 and did this without losing a wicket. I have already talked about this match in my article on McCullum. This is the most devastating chasing win in ODI history. The next two blitzkriegs were by Sri Lanka who dismissed their opponents for sub-40 totals and then proceeded to win in below 5 overs, both times for the loss of one wicket. If either of these wins had been for no loss of a wicket, the resources available would have been 95%+. Then come India 10-wkt win over Kenya and South Africa's 10-wicket win over Bangladesh.

However, from point of significant win over a good opposition, the next two losses by England are the ones to turn to. In the first case England were dismissed for 117 and then Australia blasted to 118 for no loss in a mere 12 overs. England were dismissed for 88 and Sri Lanka overhauled this for no loss in fewer than 14 overs. Pakistan also lost by 10 wickets in 14 overs to South Africa.

To view/down-load the complete table, containing wins with reserves exceeding 50%, please click/right-click here.

Wicket resource calculation: Method 1 - ODI Index based

MtId Year FBt Score SBt Score Result % Res left

2660 2007 Bng: 93/10 Nzl: 95/ 0 ( 6.0) won by 10 wkts 94.0 1776 2001 Zim: 38/10 Slk: 40/ 1 ( 4.2) won by 9 wkts 90.8 1758 2001 Ken: 90/10 Ind: 91/ 0 (11.3) won by 10 wkts 88.5 1958 2003 Can: 36/10 Slk: 37/ 1 ( 4.4) won by 9 wkts 88.0 1961 2003 Bng:108/10 Saf:109/ 0 (12.0) won by 10 wkts 88.0 1940 2003 Eng:117/10 Aus:118/ 0 (12.2) won by 10 wkts 87.7 2063 2003 Eng: 88/10 Slk: 89/ 0 (13.5) won by 10 wkts 86.2 2521 2007 Pak:107/10 Saf:113/ 0 (14.0) won by 10 wkts 86.0 2172 2004 USA: 65/10 Aus: 66/ 1 ( 7.5) won by 9 wkts 85.9 2754 2008 Saf: 83/10 Eng: 85/ 0 (14.1) won by 10 wkts 85.8 2599 2007 Hol: 80/10 Win: 82/ 0 (14.3) won by 10 wkts 85.5 2122 2004 Zim: 35/10 Slk: 40/ 1 ( 9.2) won by 9 wkts 84.8 2489 2007 Ber:133/10 Ken:137/ 0 (18.1) won by 10 wkts 81.8 2570 2007 Ire: 91/10 Aus: 92/ 1 (12.2) won by 9 wkts 81.6 2254 2005 Bng:139/10 Aus:140/ 0 (19.0) won by 10 wkts 81.0 2875 2009 Ken:113/10 Can:117/ 1 (16.2) won by 9 wkts 80.9 2428 2006 Win: 80/10 Slk: 83/ 1 (13.2) won by 9 wkts 80.8 2733 2008 Bng:115/10 Pak:116/ 0 (19.4) won by 10 wkts 80.3 1891 2002 Bng:154/ 9 Saf:155/ 0 (20.2) won by 10 wkts 79.7 1883 2002 Hol:136/10 Pak:142/ 1 (16.2) won by 9 wkts 79.2


This is based on the ODI Index and has a slight variation to the previous table. The 10-wicket wins retain the same values as in the first table. The resource-left values for other wins depends on the quality of wicket(s) lost. Note the difference in the two Sri Lankan 9-wicket wins. In one case it was Avishka Gunawardene whose wicket was lost. In the other case, the more valuable wicket of Jayasuriya which was lost.

To view/down-load the complete table, containing wins with reserves exceeding 50%, please click/right-click here.

To complete the exercise, I have also presented here the table of huge wins, this time by teams batting first. Before any reader rushes in, let me say that these two are apples and oranges or more aptly mangos and kiwi fruits. The resources are used in totally different ways. The first batting team uses all available balls resource (again rain-d-l situations excluded) and a significant proportion of the wickets resource. The second batting team is dismissed for a low total and the win is by runs and the win margin reflects the size of win. I have ordered these wins by the ratio of second innings score to the first innings score. This clearly reflects the dominance, rather than the run margins. Let us now look at the tables.

MtId Year FBt Score  SBt Score         Result          % score

1970 2003 Aus:301/ 6 Nam: 45/10 (14.0) lost by 256 runs 85.0 1652 2000 Slk:299/ 5 Ind: 54/10 (26.3) lost by 245 runs 81.9 2088 2004 Saf:263/ 4 Win: 54/10 (23.2) lost by 209 runs 79.5 0405 1986 Win:248/ 5 Slk: 55/10 (28.3) lost by 193 runs 77.8 2803 2009 Slk:309/ 5 Pak: 75/10 (22.5) lost by 234 runs 75.7 2534 2007 Slk:321/ 6 Ber: 78/10 (24.4) lost by 243 runs 75.7 0358 1986 Nzl:276/ 7 Aus: 70/10 (26.3) lost by 206 runs 74.6 1832 2002 Pak:295/ 6 Slk: 78/10 (16.5) lost by 217 runs 73.6 1599 2000 Pak:320/ 3 Bng: 87/10 (34.2) lost by 233 runs 72.8 2001 2003 Ind:276/10 Bng: 76/10 (27.3) lost by 200 runs 72.5 2471 2007 Slk:262/ 6 Nzl: 73/10 (26.3) lost by 189 runs 72.1 2727 2008 Nzl:402/ 2 Ire:112/10 (28.4) lost by 290 runs 72.1 0297 1985 Aus:323/ 2 Slk: 91/10 (35.5) lost by 232 runs 71.8 2274 2005 Ind:226/ 6 Zim: 65/10 (24.3) lost by 161 runs 71.2 2758 2008 Aus:254/ 8 Bng: 74/10 (27.4) lost by 180 runs 70.9 1878 2002 Slk:292/ 6 Hol: 86/10 (29.3) lost by 206 runs 70.5 3030 2010 Nzl:288/10 Ind: 88/10 (29.3) lost by 200 runs 69.4 2716 2008 Ind:374/ 4 Hkg:118/10 (36.5) lost by 256 runs 68.4 1885 2002 Nzl:244/ 9 Bng: 77/10 (19.3) lost by 167 runs 68.4 3061 2010 Saf:399/ 6 Zim:127/10 (29.0) lost by 272 runs 68.2 2345 2006 Saf:289/ 7 Aus: 93/10 (34.3) lost by 196 runs 67.8 1242 1997 Zim:284/10 Bng: 92/10 (32.3) lost by 192 runs 67.6


Australia's 256-run demolition of Namibia (who ???) leads this table since Namibia scored only 15% of the Australian total. However this match must be discounted since Namibia could have been beaten by half of the state sides of South Africa, England, Australia or India. The miracle was how they restricted Australia to only 301.

The real match of significance, in this regard, was Sri Lanka's 245-run blitz of a near-full-strength Indian team. Jayasuriya, Vaas and Muralitharan made India look like a collection of novices. India scored less than Russell Arnold. In fact I have done some detailed performance analyses for a company and this Sri Lankan performance has come out to be the greatest team performance ever. Everything has to click, the bowling and batting, that too against a top side.

The next three wins by South Africa, West Indies, Sri Lanka (recently) were all against good opponents and rank only slightly below the Sri Lankan win. A feature of this table is the proliferation of top teams at the top of the tables which have lost. This indicates that it is more likely that a good team has a bad day chasing a big total. In the first collection, it was mostly the minnows who were at the receiving end.

I am amazed at the frequency with which Sri Lanka has inflicted heavy defeats. Maybe the Murali factor.

To view/down-load the complete table, containing wins with margins exceeding 50%, please click/right-click here.

Let me re-iterate to the readers that I have not compared the numbers in these two different types of wins. Each stands by itself.

Anantha Narayanan has written for ESPNcricinfo and CastrolCricket and worked with a number of companies on their cricket performance ratings-related systems

RSS Feeds: Anantha Narayanan

© ESPN Sports Media Ltd.

Posted by Meety on (November 8, 2010, 9:48 GMT)

Just a quick comment on the book, in Oz there is a variation on this play on words. In Oz some blokes who have a reputation end up with the nickname Wombat, the reason being they "Eat, Roots, Shoots & Leaves". The 2nd word has a more adult meaning in OZ, the 4th word is a descriptive inevitably functional result of the second word!!!!!!

Posted by Faisal Afsar on (November 7, 2010, 13:54 GMT)

thanx for the reply....

Posted by Abhi on (November 7, 2010, 9:22 GMT)

Ha, upto to VVS, Raina and Dhoni now. BTW , just checked VVS and SRT have a 4th inn. avg of 39.

Posted by Abhi on (November 7, 2010, 8:59 GMT)

HA,The 2nd Inn score is 2/3, India lead by 30! My comment was the equivalent of "commentator's curse" I guess. [[ Abhi Time for two guys who have served India wonderfully over the past decade to come to the rescue. One with a great overall but average fourth innings record and the other one a good overall but great fourth innings record. But if one of them gets out before close of play, I fear for the Indians. I think there are two New Zealand teams playing now. You have to admire the skill and tenacity of the Kiwi batsman in general and Williamson in particlular. Ananth: ]]

Posted by Faisal Afsar on (November 5, 2010, 20:11 GMT)

sir i asked a question about batsmen(top order) who have hit minimum 6's (max is obviously of afridi) in ODIs having played certain amount of balls(or matches)..? specifically fawad alam. can u plz reply... [[ Faisal I could not answer your question since I do not have ball-by-ball data. Also I do not have the data on 6s and 4s for a number of early matches. Only thing I can conbfirm is that fawad Alam has hit 1 six in 657 balls.When he hit it, after how many balls, I am not able to answer. Ananth: ]]

Posted by Upamanyu on (November 5, 2010, 7:03 GMT)

Hey ananth, good analysis. Quite sound, I must say. I also find it quite interesting that many of the matches have been minnows vs minnows, or biggies vs biggies. It would be interesting to do a chart of player contributions (by runs in %) in the match aggregate. If I remember correctly, someone from the cricinfo stats dept did an anylisis for contributions of players in their innings...

Posted by Anush on (November 5, 2010, 6:22 GMT)

One other thing to note is that SL have inflicted those heavy defeats away from home. Against PAK in PAK, against NZ in NZ and against IND at Sharjah. [[ Anush That is correct. And it is heart-warming to see them not to take their foot off the pedal today. Ananth: ]]

Posted by Abhi on (November 5, 2010, 4:05 GMT)

Glancing through the tables shows up Bangladesh as the poor whipping boys several times. However, a similar table 5 years hence would probably have some New Zealand on it several times.

With the exception of one world class player in Vettori, NZ is probably the worst team in cricket now. [[ Abhi Their biggest loss has been Shane Bond, due to various reasons. Let us see tomorrow. They certainly survived some anxious overs today. Ananth: ]]

Posted by Faisal Afsar on (November 4, 2010, 13:12 GMT)

good work sir.. i have a question for you. Did fawad alam hit a six in his career so far? And also can you give a list of batsmen(top-order) who played most deliveries (or matches) before hitting a six? [i think dravid in tests]. thanx.. {i was watching PAK vs SA 3rd ODI, and my friend told me that i will give you 1000 rupees if fawad alam hit a six or a boundary (four) on one bounce. thats why i have asked this. I will be very thankful if you give a reply}

Posted by Abhishek Mukherjee on (November 4, 2010, 7:31 GMT)

Anantha:

Quoting my comment first: "The SL 299 Ind 54 match has a (possibly) a unique record: the match aggregate was 353 out of which Jayasuriya scored 189 - possibly the only instance of a >50%."

Then your response: "That is great. Let me see what can be done with this gold nugget. And this can happen only in such wins. Even McCullum has only 42% (80/188)."

This almost CANNOT happen unless the side batting first wins (there can be exceptions, Team A 90 all out, Team B 93/0, with a single player scoring all 93 runs or something like that).

I'd really love to see a list on this: the ratio between a player's score and the MATCH aggregate. [[ Yes, Abhishek, I was aware of that. Just wanted to put the dominance of McCullum in perspective. Will do this exercise one of those days. Ananth: ]]

Comments have now been closed for this article

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.

All articles by this writer