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The original article received nearly 200 responses. Unfortunately not all could be posted, mainly because quite a few responses contained readers' own selection of their all-time best ODI teams. This was outside the theme of the article and I can assure the readers that they will have a chance later to come out with their views on this topic as well. Some posts were also rejected because they contained offensive language and/or referred to other responders in negative terms.
I must thank the readers for the interest they have shown. I must confess that I keep learning new things because of the interaction. There are new perspectives which had escaped me the first time around.
I have gone through all the responses. I have adopted the following three significant improvements. There were a few other valid suggestions which have not been implemented. These are summarised at the end, with my reasons for not implementing them.
1. The most important and often-repeated comment was that the game has changed considerably over the years and the analysis should make allowances for such changes. Most of these readers' observations are subjective in nature (Difficult to score runs in the 80s; Scoring rates nowadays are higher; Easier to chase targets nowadays; et al). However since these have been made with a deep understanding of the game, there is no way I can refuse to accept these, especially as I myself share these observations. It is my responsibility, as a computer analyst to translate such subjective inferences into objective, verifiable and acceptable algorithms. I have done this adjustment in my Test analyses, weighting down/up pre-WW1 bowling/batting figures respectively. It is high time the ODI analyses is also done this way. This has been explained in depth later.
2. The second concerns the late order batsmen. I had given equal weighting of 0.25 to each of these 4 batsmen. Most readers have accepted this. However I myself felt that it is wrong to treat Akram at the same level, as a batsman, say, as Sikander Bakht. The weightings, explained later, have been graded now.
3. The third change concerns home advantage. Barring the great teams, most other teams struggle outside their home country and do well in their own backyard. The advantage of 50000 (give or take a few thousand) fans at Kolkatta or Lahore or MCG or Kingston rooting for the home team can never be ignored. Though some might say that India enjoy home advantage wherever they play.
1. Decade-level adjustments
To do this I have split the matches into four decades, the (swinging) 1970s, the (exciting) 1980s, the (nervous) 1990s and the (Twenty20-driven) 2000s. Please see the following table, first for batting and then for bowling. Incidentally this concept itself deserves of an independent post.
In both tables I have used the base factor as the All match numbers, which is presented in the first column. I concede that this is heavily weighted towards the later years. However there is no other way. If I take the median match (no.1354) as a cut-off point, that match itself was played as recently as 1998. So whatever one does, this problem will remain.
|Runs per match||414||369||393||414||426|
|Runs per innings||23.83||21.36||22.96||23.76||24.44|
|% of all-matches avge||100.0%||89.6%||96.3%||99.7%||102.5%|
|Runs per ball||0.774||0.656||0.731||0.764||0.811|
|% of all-matches avge||100.0%||84.7%||94.4%||98.7%||104.7%|
a. There is a clear increase in the Runs per match, which has been done mainly to show the trend.
b. Runs per innings, which is used to avoid the not outs impact, has clearly shown a move up, from 21.36 during the 1970s to 24.44 for the current decade matches.
c. Similarly, the scoring rate (runs per ball) has shown a clear move upward, from 0.656 (Rpo of 3.94) during the 1970s to 0.811 (Rpo of 4.86) now.
The adjustment is done in the following manner.
The Batting Index figures are adjusted by the Decade adjustment values. In other words, the Batting Average Index is divided by 0.896 for the 1970s teams, by 0.963 for the 1980s teams, by 0.997 for the 1990s teams and by 1.025 for the current teams. Similarly the Batting Strike Rate Index is divided by 0.847 for the 1970s teams, by 0.944 for the 1980s teams, by 0.987 for the 1990s teams and by 1.047 for the current teams.
|Team Runs conceded||1119374||30292||202884||386508||499690|
|Wkts per match||14.10||14.10||13.75||14.16||14.21|
|% of all-matches avge||100.0%||89.2%||97.4%||99.6%||102.2%|
|Balls per wkt||37.9||40.0||39.1||38.3||37.0|
a. It is surprising, maybe not so, that the average number of wickets captured per match has remained fairly constant over these 30-odd years.
b. The bowling averages have shown a clear move upwards from 26.20 during the 1970s to 30.01 for the current decade. A minor concession, likely to have little impact on the final numbers, is made in that the bowling average for this purpose is calculated based on the team runs and team wickets.
c. The balls per wkt figures show a slight reduction as time has gone by, with the difference being only around 7.5%. It's given here only for information.
The adjustment is done in the following manner.
The Bowling Index figures are adjusted by the Decade adjustment values. In other words, the Bowling Index is multiplied by 0.892 for the 1970s teams, by 0.974 for the 1980s teams, by 0.996 for the 1990s teams and by 1.022 for the current teams.
Maybe it's not perfect, but this significant tweak has gone a long way in redressing the imbalance, as the results show.
2. Changing the weightings given to late order batsmen
Jeff Grimshaw has demonstrated that the higher average batsmen would, most probably, be able to bat through their 50 (or whatever) overs without even approaching the late-order batsmen. On the other hand, the lower-average, quicker-scoring batsmen might need the late-order batsmen often. It is, however, essential that we recognize the quality of late-order batsmen. After all, Vettori and Martin are poles apart, when it comes to batting. Hence the weightage is changed, as follows.
No. 8 Batsman: 0.40
No. 9 Batsman: 0.30
No.10 Batsman: 0.20
No.11 Batsman: 0.10
3. Home Advantage
I have effected a 5% increase for all the Index values for home teams for reasons already explained. This value is not applied for the hundreds of matches played in neutral venues. The only question is, why 5%, why not 2.5% or why not 10%. I have no answer other than my gut feel that the additional weighting cannot exceed the value assigned for Fielding.
The revised tables are summarized below.
1. 2004 2196 1 AUS (vs Nzl) 19.95 20.68 40.63; Gilchrist A.C, Hayden M.L, Ponting R.T, Lehmann D.S, Martyn D.R, Symonds A, Clarke M.J. (after 21 other Australian teams (as compared to 107 Australian teams earlier)) 23. 1999 1390 2 SAF (vs Win) 18.80 20.63 39.43 Kirsten G, Gibbs H.H, Kallis J.H, Cullinan D.J, Cronje W.J, Rhodes J.N, Pollock S.M. (after 44 other teams) 68. 2005 2237 2 IND (vs Pak) 18.35 20.27 38.62 (Match lost) Sehwag V, Tendulkar S.R, Dhoni M.S, Ganguly S.C, Dravid R, Yuvraj Singh, Kaif M.
1. 1981 0116 2 WIN (vs Eng) 1.62 39.53 41.15 Roberts A.M.E, Holding M.A, Garner J, Croft C.E.H + Richards/Gomes. 2. 2001 1670 2 AUS (vs Win) 2.55 38.57 41.12 Warne S.K, Lee B, Bracken N.W, McGrath G.D, Symonds A. 3. 1981 0115 1 WIN (vs Eng) 1.37 39.53 40.90 Roberts A.M.E, Garner J, Holding M.A, Croft C.E.H + Lloyd/Gomes. 4. 2000 1552 2 AUS (vs Ind) 2.58 38.22 40.80 Warne S.K, Lee B, Fleming D.W, McGrath G.D, S.R.Waugh. 5. 2000 1622 2 AUS (vs Saf) 2.56 38.21 40.77 Warne S.K, Lee S, Gillespie J.N, Lee B, McGrath G.D.
It is in Bowling that these changes are felt a lot. The top 5 teams are now composed of West Indian and Australian teams since the Australian bowlers have got their Indices adjusted accordingly.
1. 2001 1670 2 AUS (vs Win) 39.47 38.57 2.55 80.59 Gilchrist A.C, Waugh M.E, Ponting R.T, Bevan M.G, Lehmann D.S, Martyn D.R, Symonds A, Warne S.K, Lee B, Bracken N.W, McGrath G.D. (after 24 other Australian teams (as compared to 144 Australian teams earlier)) 26. 1983 0189 1 WIN (vs Ind) 37.28 38.24 2.15 77.67 Greenidge C.G, Haynes D.L, Richards I.V.A, Logie A.L, Lloyd C.H, Gomes H.A, Dujon P.J.L, Marshall M.D, Roberts A.M.E, Holding M.A, Garner J. (after 19 other Australian/West Indian teams) 46. 2002 1918 1 SAF (vs Pak) 37.48 37.17 2.45 77.10 Smith G.C, Gibbs H.H, Dippenaar H.H, Kallis J.H, Rhodes J.N, Boucher M.V, Pollock S.M, Klusener L, Hall A.J, Donald A.A, Ntini M.
You can note the significant change. The 1983 West Indian team moves up considerably. The top 100 now has teams from Australia, West Indies and South Africa.
The best teams for all the 10 Test-playing countries can be viewed by clicking here.
1. Career-to-date average or recent form adjusted values instead of career average
I evaluated this option but decided not to do the change. The reasons are many. Richards is an outstanding 47.00(Avge) / .887(Strt) batsman. If his mid-career figures were lower or his recent form was not good, that does not make him any lesser, at any time in his career. Similarly for other great players such as Tendulkar, Lara, Wasim Akram, McGrath et al. The other reason is that between the 11 players these numbers would get evened out. The last reason is that this will involve too much work, for very little improvemet.
2. RPI instead of Batting Average
This was also considered seriously. I did not do this because that meant I would be going away from the widely accepted Batting Average. It is true that a Hussey or Bevan might gain in view of the high number of Not outs. However this is more than compensated by the fact that they would have had very little time to settle down, they would have to throw the bat around and in general play for the team score. The early batsmen, on the other hand, may be hampered by the high number of dismissals. However they would have time to settle down, play themselves in and in general play longer innings.
3. Consider the two Bowling parameters separately
This was also a good suggestion. However, I could not get away from the fact that the bowling average is a composite value of the two components (Bowling Average = Strike Rate x Accuracy). I also did some trial calculations. These showed that the impact of splitting the two components would be minimal. Hence I retained the Bowling Average.
4. Finally the Fielding
Everyone knows that Jonty Rhodes was a great fielder. But then how great a fielder was he? Was he greater than Colin Bland, Roger Harper, Ricky Ponting et al or not? Is there a quantifiable and verifiable measure available? Even run-outs started getting attributed to specific fielders only recently. Possibly the greatest fielding display of all time was effected by Richards during the 1975 World Cup final against Australia. His three run-outs do not find a place on the scorecard.
We do not have a measure for fielding. Until we get that (even then what about the earlier matches) it will be impossible to quantify fielding. I am not going to do a subjective error-prone Fielding Index. Instead I have done a low weighting of 5% for Fielding, done using the available Catches/Stumpings values.
I have also resisted the temptation to come out with an all-time best world team. That is outside the scope of this team-oriented analysis and I want to avoid making the mistake I made in my previous post. Surely there will be another time when such an analysis will be done.
Anantha Narayanan has written for ESPNcricinfo and CastrolCricket and worked with a number of companies on their cricket performance ratings-related systemsFeeds: Anantha Narayanan
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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.