Joel Garner averaged 18.85 in ODIs, the lowest among bowlers with at least 50 wickets © PA Sports

Post-Note:

"I urge readers to read and understand the reasoning behind the analysis. It is NOT to determine the best ODI team across years or teams. Rather it is to determine the best team that walked on to the field, as 11 players. Many comments have been made ignoring this fact. So much so, no comment which lists the readers' favourite team will be published. Let me add that over 50 comments have gone unpublished because of this."

For my next post, I wanted to stay away from Test cricket, on which most of the recent It Figures posts have been. At the other extreme we have Twenty20, which has had an all-pervading presence on almost all the channels on television, and the web and print media as well. That leaves the often-ridiculed form of cricket, one-day internationals. I never thought I would say this, but I have already started longing for ODI cricket.

This time I have taken for analysis a topic which I had looked at for Tests, and am now adapting to ODIs: how strong is an ODI team and how do the teams compare over the 37 years of ODI cricket? Where does the 2007 Australian team stand when compared to the West Indian teams of early 1980s, or for that matter the Australian teams of the late 1990s? It has turned out to be a fascinating study.

The one significant advantage we have when comparing ODI teams is that even the 1975 West Indies team had players most of us [barring those below 30, who would anyhow be familiar with them] have seen. It is not very difficult to identify with Viv Richards, Ian Chappell, Clive Lloyd, Michael Holding etc. unlike in Test cricket, where George Lohmann, with a Test bowliing average of 10.76, was born nearly 143 years ago. It isn't easy to relate to either fact.

A team is as strong as its batsmen, bowlers and fielders are. If we consider fielding as part of the bowling, these two main areas have to be given equal weightage. ODI laws might be tilted towards the batsmen, but the role of bowlers can never be underestimated. This happens even in the Twenty20 game.

Hence I have given a weightage of 50 for batting and 50 for bowling (further split as 45 for bowling and 5 for fielding). Because there is no quantified data for fielding per se, the weightage for fielding is in reality for catching/stumping. This also explains the low weightage.

The one thing I want to ensure is that this analysis will comprise only of measurable, objective parameters. The other areas such as captaincy, recent form, home advantage etc. are intangibles and subjective. A captain is only as good as his team is. Recent form has more relevance in Test matches. Home advantage is a mirage. The non-Australian strokemakers would love to play on the bouncy Australian pitches and the non-New Zealand seam/swing bowlers would love to bowl in Auckland or Hamilton.

Readers might be tempted to send the usual comments that these are obvious and why should there be a need to do analysis. Let me remind such readers that their conclusions would be based on error-prone subjective inferences and also not indicate how much a team is better than another. My results are based on objective analysis and indicate the quantitative differentials between teams.

Batting

ODI batting consists of two distinctly measurable and independent factors: how many runs are scored and how fast they are scored. In other words, the batting average and the strike-rate. No one can question the decision to treat these two parameters equally.

The average is taken rather than the lesser known and acceptable runs per innings or my own development, the extended batting average. The average is a widely accepted measure and presents the best method of measuring runs scored. Only two batsmen in ODI history, Michael Bevan and Michael Hussey, have averages higher than 50 (among those with a minimum of 20 innings), mainly because of their number of not-outs. However this is partly rectified by limiting the average to 50.0 for these two batsmen.

There is no problem with strike-rate. That is available as a straight computation of runs scored / balls faced. The averages and strike-rates of the top seven batsmen in the team's batting order are summed. The averages and strike-rates for batsmen nos. 8 to 11 are given a 25% weightage each. The arrived total is divided by eight and the Team Batting Average and Team Strike-Rate are arrived at.

The batting average index points are determined by dividing the team batting average by two. The maximum value for this is 25.0.

The strike-rate index points are determined by multiplying the team strike-rate (runs per ball) by 25.0. The maximum value for this is just over 25.0. Only one batsman in ODI history, Shahid Afridi, has a career strike rate of over 1.00.

Care is taken that these full values are applied only for career aggregates of 1000 runs and above. Otherwise Arvind Kandappah of Canada and Alex Obanda of Kenya will single-handedly make their team's batting averages huge. These two have Bradmanesque career batting averages of 97.0, although scoring only 97 and 194 runs respectively.

 SNo. Year MtNo I Team   vs     AvIdx  SRIdx  Bat


   1. 2005 2257 2 AUS (vs Bng) 19.89 20.99 40.87

      (Gilchrist, Hayden, Ponting, Martyn, Clarke M, 

       
Symonds, Hussey).   

   2. 2005 2261 2 AUS (vs Eng) 19.91 20.90 40.81

   3. 2005 2259 1 AUS (vs Eng) 19.67 20.78 40.45

   

Next 105 teams are Australian, followed by

 109. 2005 2282 2 ICC (vs Aus) 18.15 20.47 38.62

        (Sangakkara, Sehwag, Kallis, Lara, Dravid, 

         Pietersen, Flintoff, Afridi)

Next 16 teams are Australian, then

 126. 2004 2202 1 IND (vs Bng) 18.06 20.43 38.49

        (Sehwag, Tendulkar, Ganguly, Dravid, Kaif, 

         Yuvraj Singh, Dhoni). 

Then another 5273 teams

5400. 2004 2172 1 USA (vs Aus)  3.27  9.73 13.00

5401. 1979 0067 1 CAN (vs Eng)  5.05  7.85 12.91

5402. 1979 0070 1 CAN (vs Aus)  4.76  6.98 11.74

Note: Out of the 2703 matches considered, two matches were abandoned without even the team information being available.

The first 108 teams in the batting list are Australian. These 108 matches have come over a nine-year period, from 1999 to 2008, a period of total Australian domination, punctuated by three World Cup wins. The three batsmen who have been part of almost all these matches are Adam Gilchrist, Ricky Ponting and Andrew Symonds.

Bowling

Like batting, bowling also has two components, the bowling strike-rate and accuracy. However, unlike batting, the bowling average is a fantastic measure since it encompasses both these key measures in a single value. Consider the following.

                  Runs Conceded

Bowling Average = -------------

                  Wickets Taken


Rewriting this as

                  Runs Conceded     Balls Bowled

Bowling Average = -------------  x  -------------

                  Balls Bowled      Wickets Taken

This can be written as

Bowling Average = Bowling Accuracy x Bowling Strike-Rate.

There is no need to measure these two factors independently. It is sufficient to take the single composite measure, bowling average and work on it.

Unlike the batting computation, the bowling averages of the best five bowlers is taken and divided by five. This is because it is expected the team would use their best five bowlers. Even if Jacques Kallis bats at No. 3, he is likely to be used as a bowler if he is one of the best five. Whether he bowls in the concerned match or not is outside the scope of this analysis since this study only measures how strong a team potentially is, not how strong the team actually was.

Here also care is taken that bowlers with less than 50 wickets have their figures scaled down suiitably. Otherwise Gary Gilmour, with 16 career wickets at 10.31, will completely tilt the figures of the late-1970s Australian teams.

The bowling index is determined by subtracting the Team Bowling Average from 60.0. Since the best bowling average for qualifying bowlers [minimum 50 wickets] is 18.85 by Garner, the highest value will not exceed the maximum weightage given to bowlers, of 45.

For both batting and bowling, I have also taken the full career figures rather than the career-to-date figures in view of the complexity of calculation and the fact that we are averaging and the minor differences tend to get ironed out.

Fielding

Only catches and stumpings are considered. The values for all 11 players are added, divided by 11, and multiplied by two to get a team fielding average. The highest value is 1.95 and the maximum index value is 3.90. It is obvious that this figure will be strongly influenced by the wicketkeeper's figures. A per match average rather than catches/stumpings aggregate is taken to be fair to weaker teams.

 SNo. Year MtNo I Team   vs    Fld   Bow   Tot


   1. 1981 0116 2 WIN (vs Eng) 1.55 38.65 40.20

      (Roberts, Holding, Croft, Garner)

   2. 1982 0134 2 WIN (vs Pak) 2.25 37.79 40.03

   3. 1982 0135 2 WIN (vs Aus) 2.25 37.79 40.03

Next 21 teams are West Indian, then

  25. 2001 1670 2 AUS (vs Win) 2.44 35.95 38.38

Then another 5374 teams

5400. 1979 0070 1 CAN (vs Aus) 0.17 10.00 10.17

5401. 1979 0067 1 CAN (vs Eng) 0.17 10.00 10.17

5402. 1979 0064 1 CAN (vs Pak) 0.17 10.00 10.17

The first 24 teams in the batting list are West Indian teams. These 24 matches have come over a six-year period, from 1981 to 1987. The two bowlers who have been part of almost all these matches are Holding and Garner.

Final Team Strength

This arrived by adding the batting, bowling and fielding indices. The maximum is 100, making it easier to see things in perspective.

 SNo. Year MtNo I Team   vs     Bat   Bow  Fld  Team


   1. 2001 1670 2 AUS (vs Win) 38.95 35.95 2.44 77.34

      (Gilchrist, Waugh M, Ponting, Bevan, Lehmann, Symonds,

       Martyn, Warne, Lee, Bracken, McGrath).   

   2. 2004 2180 1 AUS (vs Eng) 39.28 35.39 2.56 77.23

      (Gilchrist, Hayden, Ponting, Martyn, Lehmann, Clarke M, 

       Symonds, Lee, Gillespie, Kasprowicz, McGrath).   

   3. 2004 2172 2 AUS (vs Usa) 39.28 35.39 2.56 77.23

       (Same as previous team)      

   4. 2004 2131 2 AUS (vs Zim) 39.10 35.30 2.69 77.09

   5. 2003 1951 2 AUS (vs Ind) 39.47 35.02 2.43 76.91

Next 140 teams are Australian, then

 146. 1982 0139 2 WIN (vs Aus) 33.82 37.79 2.25 73.86

      (Greenidge, Haynes, Richards, Gomes, Lloyd, 

       Bachhus, Dujon, Roberts, Holding, Clarke ST, Garner).

Next 19 teams are Australian/West Indian, then

 166. 2005 2282 2 ICC (vs Aus) 38.62 32.75 2.28 73.65

      (Sangakkara, Sehwag, Kallis, Lara, Dravid, 

       Pietersen, Flintoff, Shahid Afridi, Pollock S, 

       Vettori, Shoaib Akhtar, Muralitharan).

Then another 5233 teams

5400. 1975 0024 1 EAF (vs Ind) 13.06 10.00 0.52 23.58

5401. 1979 0067 1 CAN (vs Eng) 12.91 10.00 0.17 23.07

5402. 1979 0070 1 CAN (vs Aus) 11.74 10.00 0.17 21.91

The first 145 teams in the list are Australian. These 145 matches have come over a nine-year period, from 1999 to 2008, a period of total Australian domination, punctuated by three World Cup wins. The five players who have been part of almost all these matches are Gilchrist, Ponting, Symonds, Brett Lee and Glenn McGrath.

Finally I have done another "fourth dimension" formation. Australia have had the best batting teams ever and West Indies, the best bowling teams ever. Let us combine the two into one all-time great ODI team. Take the first seven players from the Australian 2005 team [Match no. 2257] and add to it the best four bowlers from the 1981 West Indies side [Match no. 116]. Given below is the final squad.

Just to round up the analysis, this all-time great team has an index value of 77.05, which is lower than the Australia 2005 figure. This has been caused no doubt by the loss of batting and fielding points of the Australian team (Watson/Lee/Gillespie/Kasprowicz are much better batsmen and fielders than the West Indian bowling quartet). However, the team listed below is an outstanding one with a superb bowling attack.

Adam Gilchrist

Matthew Hayden

Ricky Ponting

Damien Martyn

Michael Clarke

Andrew Symonds

Michael Hussey

Andy Roberts

Michael Holding

Colin Croft

Joel Garner.

Readers should not forget that this not necessarily the best ODI Team of all time, It has been formed by merely taking the first 7 players from the best ever Batting line-up and adding the 4 bowlers from the best ever Bowling line-up.

Theoretically this team can be further improved by taking in Tendulkar, Richards, Dhoni, Wasim Akram, Shane Warne et al. That is a different day and different motivation. For the present let us enjoy the combination of two different eras.

If we tamper with this team, the charm would be lost. The Australia-West Indies combination would be missing. After all, these two countries have dominated the ODI scene during these 37 years, West Indies during the first ten years and Australia, the last 20.

ODI Analysis - by decade

Batting	AllMats	1970s	1980s	1990s	2000s
Matches played	2703	82	516	933	1172
Runs scored	1119374	30292	202284	386508	499690
balls bowled	1445956	46208	277516	505727	616505
Batsmen innings	46968	1418	8838	16266	20446

	Brian Lara: the one batsman who managed to add another 100 after getting a triple hundred © Mid-day

When Virender Sehwag strode out on the fourth day of the recent Test against South Africa in Chennai, he already had 309 runs to his name. There would have been a great many fans wondering how far he could go: could he top Brian Lara’s 400?

Statistics, however, indicate the fans were very likely to be disappointed [as they were]. The truth is that while 309 and 400 sound like reasonably similar scores, they are not. In fact, it is harder for a batsman to add another 100 runs if he has already made 300, than it is at almost any other score.

There have now been 22 Test triple-centuries, enough for some statistics. Only one of those triples has gone on to produce the magic 400, while 17 others have been dismissed before reaching that mark. Only one out of 18: that is only a 5.6% conversion rate. (The other four innings finished not out between 300 and 399; it is better not to include them in this calculation.) It is interesting to compare this to the conversion rates at other scores:

Conversion rates in 100-run increments

Score range	No. of dismissals	No. of successes	Conversion rate
0-99*	33,822	2942	8%
100-199	2334	279	10.7%
200-299	192	22	10.3%
300-399	17	1	5.6%

*0-99 data involves only recognised batsmen (#1-6 in batting order). “Number of successes” refers to the number of innings that have passed through the specified range without dismissal, e.g., for 0-99 it refers to the number of centuries.

While interesting, this data is not very robust for the 300-399 range. If the next batsman to make a triple-century happens to go on to 400, the conversion rate will almost double [to a rate similar to the 300-400 conversion rate in first-class cricket of 11%]. However, the difficulty batsmen encounter above 300 can also be seen when we look more closely, at 20-run increments.

Conversion rates in 20-run increments

Score range	No. of dismissals	No. of successes	Conversion rate
100-119	1105	1791	62%
120-139	581	1087	65%
140-159	329	667	67%
160-179	209	414	66%
180-199	110	279	72%
200-219	96	142	60%
220-239	50	84	63%
240-259	22	55	71%
260-279	19	30	61%
280-299	5	22	81%
300-319	7	14	67%
320-339	5	7	58%

Note the similarity of the pattern at the 200-run mark and the 300-run mark. As batsmen approach 200, their conversion rate rises, only to fall suddenly after reaching the milestone; the same thing happens at 300. A dismissal between 280 and 299 is a rare thing.

It is also striking that a batsman’s ability to add runs once he has reached 300 [67% and 58% for 300-319 and 320-339] is, in effect, no better than for those who have just reached 100 [62% and 65%].

Further perspective can be gained by looking at the one batsman who did make it to 400, Brian Lara at St John’s in 2004. In that innings, Lara played with caution and great focus after reaching 300, taking 178 balls to go from 300 to 400 [56 runs per 100 balls]. This is probably the slowest progression from 300 to 400 in first-class cricket: in doing this under very benign conditions when quick runs were called for, Lara also sacrificed any chance his team had of winning the match.

Few triple-centurions take this approach. The surprisingly high rate of failures after reaching 300, when scoring should be easiest, is probably a combination of mental exhaustion and the need for quick runs in those circumstances. The typical scoring-rate for triple-centurions in their first 300 runs is about 63 runs per 100 balls, but for runs beyond 300 [apart from Lara], the rate is over 80 runs per 100 balls, in time-limited Tests.