1. Introduction
1.1 About Cricket
Cricket is a bat-and-ball game played between two teams of 11 players on a field at the centre of which is a rectangular 22-yard long pitch. Each team takes it in turn to bat, attempting to score runs, while the other team fields. Each turn is known as an innings.
The bowler delivers the ball to the batsman who attempts to hit the ball with his bat far enough for him to run to the other end of the pitch and score a run. Each batsman continues batting until he is out. The batting team continues batting until ten batsmen are out, at which point the teams switch roles and the fielding team comes in to bat.
In professional cricket the length of a game ranges from 20 overs of six bowling deliveries per side to Test cricket played over five days. The Laws of Cricket are maintained by the International Cricket Council (ICC) and the Marylebone Cricket Club (MCC) with additional Standard Playing Conditions for Test matches and One Day Internationals.
Cricket was first played in southern England in the 16th century. By the end of the 18th century, it had developed into the national sport of England. The expansion of the British Empire led to cricket being played overseas and by the mid-19th century the first international matches were being held. The ICC, the game's governing body, has 10 full members. The game is most popular in Australasia, England, the Indian subcontinent, the West Indies and Southern Africa.
1.2 Ratings in Cricket
Cricket is a team game, but the players are also individually ranked based on their performances. The ICC Player Rankings are a widely followed system of rankings for international cricketers based on their recent performances. The ratings were developed at the suggestion of Ted Dexter in 1987. The intention was to produce a better indication of players' current standing in the sport than is provided by comparing their averages. Career averages are based on a player's entire career and do not make any allowance for match conditions or the strength of the opposition, whereas the ratings are biased towards recent form and account for match conditions and the quality of the opponent using statistical measures.
Initially the rankings were for Test cricket only, but separate One Day International rankings were introduced in 1998. Both sets of rankings have now been calculated back to the start of those forms of the game. From 2003 onwards, David Kendix has been responsible for calculating and maintaining the rankings, with the t20 world cups even ranking for t20 is included. The ranking includes the top 10 test and ODI and even t20 batsmen, bowlers and alrounders based on the rating of each player. But there is no rating system by ICC that ranks players on their all time performance.
There are many aspects of Cricket, like batting, bowling, fielding, wicket keeping etc. A player can both bat and bowl as well. So mainly there are three kinds of ratings. First kind of ratings is for the batsman and it is mainly based on the number of runs they score. Second kind of ratings is for the bowlers and it is mainly based on the number of wickets they take. The third and last kind of rating is for the alrounders, i.e. for the players who are good at both batting and bowling and it considers overall performance of a player in both the areas.
1.3 Objective of the Project
The objective of this Project is to develop a rating system that can rank the players of all-time, that is not based on their recent performance. It should be based on the performance of the players over their entire cricket period. Their records should be seen in bulk and not in a particular phase. The ranking should be purely based on the records of a cricketer and not on their reputation of cricketing style.
In this project, a rating system will be developed for batsmen, bowlers and alrounders in all the three formats of Cricket, which are one day internationals, test matches and T20 matches.
1.4 Data for Records
All the required data for the players performance is taken from www.espncricinfo.com. The Players of only 10 countries will be considered. These countries are:
i) Australia
ii) Bangladesh
iii) England
iv) India
v) New Zealand
vi) Pakistan
vii) South Africa
viii) Sri Lanka
ix) West Indies
x) Zimbabwe
The data is up to 4th may 2014.
2. Normalization of Records
2.1 Need of Normalization
The Game of Cricket has evolved a lot from time to time. To make the game more interesting, the rules have been modified in such a way that the game becomes more favourable for batsman. The introduction of fielding restrictions and power plays has made the game easier for batsman. So it is necessary to normalize the performance of players so that the effect of change of rules can be minimized.
2.2 Normalization in ODI Cricket
2.2.1 Method for Normalization
For normalization, it is assumed that the overall average (the average no. of runs the batsman scores before he gets dismissed) and overall strike rate (the average no. of runs the batsman scores in every 100 balls) of batsmen in every year should be same.
To calculate this, the data of a player is divided equally to every year in which he has played. For example, Sachin Tendulkar has played in a span of 1989-2012, i.e. for 23 years in one day international cricket. In this period he has scored 18426 runs, faced 21367 balls and has been dismissed 411 times. So it will be assumed that he has scored 801.31 runs (18426/23), faced 929 balls (21367/23) and has been dismissed 17.86 times (411/23) every year. After calculating this for all the players, the batting averages and strike rate for each year will be calculated. Then, the averages and strike rate of all the players will be modified so that the performance of players in every year becomes same. Similarly for bowlers, the balls they have delivered, runs conceded from them and wickets they took will be considered and their records will be modified too.
The method used is as follows:
Let us suppose that Cricket is played for three years 2011, 2012 and 2013. The averages of batsman in these years are 28.00, 29.00 and 30.00 respectively. We have a player who has played for 3 years from 2011 to 2013 and has scored 3000 runs and has been dismissed 60 times.
So, current average of player = 3000/60 = 50.00
His modified average will be = (3000/3) * ( (30/28) + (30/29) + (30/30) ) / 60
= 1000 * 3.105911 / 60
= 51.77
This will be done for all the players.
2.2.2 Observation in One Day International (ODI) Cricket
The data of all the 1682 players who have played cricket for these 10 countries were collected and a program was made to do all the necessary calculations. The results obtained were very interesting.
This graph shows that how the batting averages of players have increased so much in the recent years with a range of 25.23 to 30.27. The major reason for this is fielding restrictions and introduction of new power play rules in cricket. But still the variation is small. We see an extreme variation in strike rates of cricketers. Here is a graph for that:
This graph shows how the speed of making runs has increased rapidly ever since 1980. The range of batting strike rate is 64.01 to 82.12.
Similarly, we have the graphs for bowling averages (No. of average runs given to take one wicket), bowling Strike Rate (No. of balls delivered to take one wicket) and Economy Rate (Average no. of runs given in one over).
2.2.3 Modified records in ODIs
Using this information the batting average and batting Strike rates of players is modified. Here is a list of 20 batsmen in Cricket who have scored maximum runs with their real batting average, modified batting average, real batting strike rate and modified batting strike rate.
Name
|
Runs
|
Ave
|
Mod Ave
|
SR
|
Mod SR
|
SR Tendulkar
|
18426
|
44.83
|
46.87
|
86.23
|
96.00
|
RT Ponting
|
13589
|
41.81
|
43.52
|
80.19
|
87.36
|
ST Jayasuriya
|
13364
|
32.51
|
34.06
|
91.25
|
101.93
|
KC Sangakkara
|
12241
|
40.39
|
41.38
|
77.32
|
82.08
|
Inzamam-ul-Haq
|
11701
|
39.53
|
41.60
|
74.20
|
83.49
|
JH Kallis
|
11545
|
45.63
|
47.32
|
73.19
|
79.12
|
DPMD Jayawardene
|
11243
|
32.77
|
33.75
|
78.07
|
83.48
|
SC Ganguly
|
11221
|
40.95
|
43.11
|
73.65
|
82.60
|
R Dravid
|
10768
|
39.15
|
40.78
|
71.18
|
77.47
|
BC Lara
|
10348
|
40.90
|
43.11
|
79.62
|
89.89
|
AC Gilchrist
|
9595
|
35.93
|
37.67
|
96.89
|
106.44
|
Mohammad Yousuf
|
9554
|
42.08
|
43.74
|
74.91
|
81.18
|
M Azharuddin
|
9378
|
36.92
|
39.78
|
74.02
|
87.36
|
PA de Silva
|
9284
|
34.90
|
37.60
|
81.13
|
95.10
|
Saeed Anwar
|
8824
|
39.21
|
41.64
|
80.67
|
92.63
|
S Chanderpaul
|
8778
|
41.60
|
43.43
|
70.74
|
77.65
|
CH Gayle
|
8688
|
37.77
|
38.92
|
84.17
|
90.06
|
DL Haynes
|
8648
|
41.37
|
45.62
|
63.09
|
77.71
|
MS Atapattu
|
8529
|
37.57
|
39.61
|
67.72
|
76.45
|
ME Waugh
|
8500
|
39.35
|
41.91
|
76.90
|
88.97
|
Comparison of real and modified batting averages and strike rates of players
Same modification is applied on the bowling average, bowling strike rate and bowling economy rate of all the players. Here is a list of 20 bowlers who have taken maximum wickets in Cricket. The six columns on the right shows their real bowling average, modified bowling average, real bowling strike rate, modified bowling strike rate, real economy rate and modified economy rate respectively.
Name
|
Wkts
|
Ave
|
Mod Ave
|
Econ
|
Mod Econ
|
SR
|
Mod SR
|
M Muralitharan
|
523
|
23.07
|
22.45
|
3.92
|
4.22
|
35.20
|
31.95
|
Wasim Akram
|
502
|
23.52
|
22.86
|
3.89
|
4.44
|
36.20
|
30.95
|
Waqar Younis
|
416
|
23.84
|
23.16
|
4.68
|
5.26
|
30.50
|
26.46
|
WPUJC Vaas
|
399
|
27.45
|
26.71
|
4.18
|
4.53
|
39.40
|
35.44
|
SM Pollock
|
387
|
24.31
|
23.64
|
3.65
|
3.93
|
39.80
|
36.12
|
B Lee
|
380
|
23.36
|
22.76
|
4.76
|
4.97
|
29.40
|
27.49
|
GD McGrath
|
380
|
21.98
|
21.38
|
3.87
|
4.23
|
34.00
|
30.37
|
Shahid Afridi
|
376
|
33.91
|
33.01
|
4.62
|
4.87
|
43.90
|
40.72
|
A Kumble
|
334
|
30.83
|
29.94
|
4.29
|
4.74
|
43.00
|
37.98
|
ST Jayasuriya
|
320
|
36.67
|
35.64
|
4.77
|
5.21
|
46.00
|
41.16
|
J Srinath
|
315
|
28.08
|
27.30
|
4.44
|
4.96
|
37.80
|
33.08
|
SK Warne
|
291
|
25.82
|
25.13
|
4.25
|
4.71
|
36.40
|
32.06
|
AB Agarkar
|
288
|
27.85
|
27.06
|
5.07
|
5.43
|
32.90
|
29.95
|
Saqlain Mushtaq
|
288
|
21.78
|
21.21
|
4.29
|
4.72
|
30.40
|
27.00
|
DL Vettori
|
276
|
31.76
|
30.86
|
4.11
|
4.33
|
46.30
|
42.85
|
AA Donald
|
272
|
21.78
|
21.18
|
4.15
|
4.63
|
31.40
|
27.48
|
Z Khan
|
269
|
30.11
|
29.34
|
4.95
|
5.17
|
36.40
|
34.07
|
JH Kallis
|
269
|
31.85
|
30.95
|
4.83
|
5.10
|
39.50
|
36.44
|
Abdul Razzaq
|
268
|
31.53
|
30.69
|
4.67
|
4.97
|
40.40
|
37.10
|
M Ntini
|
265
|
24.53
|
23.86
|
4.51
|
4.79
|
32.60
|
29.90
|
Comparison of real and modified bowling averages, bowling strike rates and economy rates of players
2.2.4 Program used to Normalize the data in ODIs
The program used takes its data from a file that contains 7 columns, that are the time span a player played, no. of the times the player was dismissed, runs he scored, balls he faced, no. of balls he delivered, wickets he took and the no. of runs given by him respectively. After this it calculates the averages, strike rates and economy rates for each year and outputs that data to a file. Using this information the above shown 5 graphs have been drawn. After this it modifies the statistics of the players and writes them to another file.
The source code of the program is given in the end.
2.3 Normalization in Test Cricket
2.3.1 Observations in Test Cricket
Similar method is used for normalization of all the records in Test Cricket. There were no test matches played in the years 1915-1919 and 1940-1945. So we have done modification to exclude these years.
So the graphs obtained for the normalization over a period of 138 years of Test Cricket are as follows:
A graph showing batting average of players in each year in Tests
A graph showing bowling average of players in each year in Tests
2.3.2. Modified Records in Tests
Using this information the batting average and batting Strike rates of players is modified. Here is a list of 20 batsmen in Cricket who have scored maximum runs with their real batting average and modified batting average.
Name
|
Runs
|
Ave
|
Mod Ave
|
SR Tendulkar
|
15921
|
53.78
|
55.82
|
RT Ponting
|
13378
|
51.85
|
53.90
|
R Dravid
|
13265
|
52.63
|
54.65
|
JH Kallis
|
13206
|
55.25
|
57.32
|
BC Lara
|
11912
|
53.17
|
55.87
|
DPMD Jayawardene
|
11319
|
50.30
|
51.73
|
S Chanderpaul
|
11219
|
51.93
|
53.92
|
AR Border
|
11174
|
50.56
|
53.52
|
KC Sangakkara
|
11151
|
58.07
|
59.15
|
SR Waugh
|
10927
|
51.06
|
54.03
|
SM Gavaskar
|
10122
|
51.12
|
54.21
|
GC Smith
|
9253
|
48.70
|
49.22
|
GA Gooch
|
8900
|
42.58
|
45.10
|
Javed Miandad
|
8832
|
52.57
|
55.75
|
Inzamam-ul-Haq
|
8829
|
50.16
|
52.74
|
VVS Laxman
|
8781
|
45.97
|
47.73
|
ML Hayden
|
8625
|
50.73
|
53.11
|
IVA Richards
|
8540
|
50.23
|
53.22
|
V Sehwag
|
8503
|
49.43
|
50.59
|
AJ Stewart
|
8463
|
39.54
|
41.87
|
2.4 Normalization in T20Is
2.4.1 Observations in T20Is
The following graph shows the batting average of players in the last 10 years in T20Is. This shows that it is not necessary to normalize the performance of players in T20Is as it is for a very short period of time as compared to other formats of the game.
A graph showing batting average of players in each year in T20Is
So we conclude that there is no need of normalization of records in T20I Cricket.
3 Ranking of batsmen
3.1 Minimum Criteria for Players
There are 1682 players who have played Cricket for the 10 countries that we are considered. But we cannot consider all of them for the rankings. The reason is that it will give weird results. As an example, a batsman from England named KJ Barnett played only one ODI match in 1988 and scored 84 runs in it. So his career batting average is 84.00 which is highest ODI batting average by any player. But we cannot judge his performance from a single match. So we need a player to satisfy some minimum criteria so that he can participate in the rankings. Other such examples are CT Radley of England who played only 4 ODI matches and has an batting average of 83.33 and BE Congdon from New Zealand who played 11 matches, made 338 runs but was dismissed only 6 times, so his career batting average is 56.33.
The same problem is with batting Strike rates of players. As an example, AF Milne of New Zealand played only 5 ODIs, but faced only 3 balls and scored 12 runs in that, so his career batting strike rate is 400.00 that is way more than any other player. J Louw of South Africa played only 3 ODIs, got one innings and scored 23 runs off 7 balls in it. So his career batting strike rate is 328.57. It is interesting to know that, the player who is considered as the biggest hitter of cricket ball, Shahid Afridi of Pakistan has a career batting strike rate of 114.57 and there are 34 batsmen who have better strike rate than him, but all of them except AD Russell of West Indies have played less than 20 ODI matches.
So to remove such data, we make the following minimum criteria for a player to be eligible for rankings:
i) ODIs
Criteria
|
Minimum Requirement
|
Innings
|
25
|
Balls Faced
|
500
|
ii) Test
Criteria
|
Minimum Requirement
|
Innings
|
20
|
Runs Scored
|
500
|
iii) T20Is
Criteria
|
Minimum Requirement
|
Innings
|
20
|
Balls Faced
|
300
|
So by these criteria, the numbers of players we are considering are 750 for test cricket, 461 for ODI cricket and 71 for T20Is.
3.2 Ranking in ODI
3.2.1 Parameters Considered for Batsmen Rankings in ODI
This is the most important part of ranking. We have to identify the parameters that are important to rank a batsman. Some of the obvious choices are runs a batsman scored, 100s scored by him, 50s scored by him, his batting average, his batting strike rate etc.
So, the parameters considered here will be:
i) Runs: The no. of runs a batsman has scored is a very important parameter as it shows his dominance in cricket.
ii) Modified batting average: It is the most important parameter.
iii) Modified batting strike rate: It shows how fast a player can score runs. It is very important in ODI matches.
iv) 100s: It shows the temperament of a player to play long innings
v) 50s: Shows the consistency of batsman.
vi) 50+ Scores/Inn: Shows the no. of times a batsman scores more than 50 runs per inning
vii) Span of Years Played: More no. of years shows consistency
Each of these parameters should have a particular weight-age.
3.2.2 Normalization of Parameters in ODI
To apply the weight age easily we have to normalize all the parameters. For each parameter we will give points out of 100 to a batsman. These points should be relative. It is done as follows:
Relative Points for Runs:
Sachin Tendulkar has scored maximum 18,426 runs in ODI Cricket. So he will receive 100.00 points for it.
Any batsman who has scored “x” runs in ODI Cricket will get = (x*100)/18426 points
Similar process is followed for 100s, 50s and 50+Scores/Inn.
But the process for relative points for modified batting average and modified batting strike rate is different.
Relative points for Modified batting average:
Any batsman whose modified batting average is below 10.00 will be given 0 points.
MG Bevan has maximum modified batting average of 56.48. So he will get 100.00 points for it.
Any other batsman with a modified batting average of “x” will get = ((x-10)*100)/46.58 points
Relative points for Modified strike rate:
Any batsman whose modified batting strike rate is below 60.00 will be given 0 points.
BL Cairns has the highest modified batting strike rate of 128.83. So he will get 100.00 points for it.
Any other batsman who has a modified batting strike rate of “x” will get = ((x-60)*100)/68.83 points
So here is a list of given points for each of these parameters to 20 Indian batsman who has maximum runs in ODI matches.
Name
|
Runs
|
100s
|
50s
|
50+/Inn
|
Ave
|
SR
|
SR Tendulkar
|
100.00
|
100.00
|
100.00
|
75.16
|
78.86
|
50.19
|
SC Ganguly
|
60.90
|
44.90
|
73.96
|
73.36
|
70.81
|
31.50
|
R Dravid
|
58.44
|
24.49
|
85.42
|
70.14
|
65.84
|
24.35
|
M Azharuddin
|
50.90
|
14.29
|
60.42
|
49.44
|
63.68
|
38.14
|
Yuvraj Singh
|
44.70
|
26.53
|
53.13
|
56.58
|
58.37
|
45.62
|
V Sehwag
|
43.39
|
30.61
|
38.54
|
51.84
|
56.57
|
72.14
|
MS Dhoni
|
42.72
|
16.33
|
56.25
|
68.84
|
93.10
|
45.76
|
V Kohli
|
30.58
|
38.78
|
31.25
|
91.11
|
90.63
|
45.61
|
A Jadeja
|
29.08
|
12.24
|
31.25
|
47.12
|
63.74
|
28.28
|
G Gambhir
|
28.43
|
22.45
|
35.42
|
73.73
|
65.17
|
41.85
|
SK Raina
|
24.94
|
6.12
|
30.21
|
46.28
|
55.10
|
49.22
|
NS Sidhu
|
23.95
|
12.24
|
34.38
|
71.95
|
63.54
|
31.06
|
K Srikkanth
|
22.20
|
8.16
|
28.13
|
50.09
|
47.47
|
39.42
|
N Kapil Dev
|
20.53
|
2.04
|
14.58
|
17.75
|
34.71
|
79.61
|
DB Vengsarkar
|
19.04
|
2.04
|
23.96
|
46.86
|
61.09
|
33.69
|
RG Sharma
|
18.60
|
8.16
|
22.92
|
52.06
|
55.53
|
29.86
|
RJ Shastri
|
16.87
|
8.16
|
18.75
|
40.27
|
47.54
|
21.11
|
SM Gavaskar
|
16.78
|
2.04
|
28.13
|
64.31
|
62.78
|
25.22
|
M Kaif
|
14.94
|
4.08
|
17.71
|
40.47
|
49.66
|
25.33
|
VG Kambli
|
13.44
|
4.08
|
14.58
|
38.65
|
52.54
|
32.05
|
3.2.3 Weightage for Parameters
Now as we have seven parameters that we will use to decide the all time greatest ODI batsman, we need to give some weightage to these parameters. Weightage should be according to the importance of the parameters. More important parameters should have a higher weightage than the others. It is a very difficult task to assign such weightage that will give the desired results as it can be biased and it can vary the results significantly. To do so we give a range of weightage to each of the parameters and after that we will use Monte Carlo simulation to get the desired results. The range decided for each parameter is as follows:
Parameter
|
Range
|
Parameter
|
Range
|
Runs
|
80 – 180
|
100s
|
8 – 35
|
Mod Bat Average
|
60 – 150
|
50s
|
5 – 20
|
Mod Bat Strike Rate
|
40 – 110
|
50+Score/Inn
|
20 – 40
|
3.2.3 Monte Carlo Simulation
To use Monte Carlo Simulation to find the rank of batsmen, we take a random weightage for each parameter in the above mentioned ranges. After that we give point to each player according to this formula:
Points = (weightage of runs * points for runs) + (weightage of average * points for average) + (weightage of strike rate * points for strike rate) + (weightage of 100s * points for 100s) + (weightage of 50s * points for 50s) + (weightage of 50+/Inn * points for 50+/Inn)
After calculating points for each player, we rank them according to the number of points they have. We carry on this process for 50,000 times and in the end the average rank of a player determines his final rank.
3.2.4 Final Rank List of all time Batsmen in ODI Cricket
After carrying out the Monte Carlo simulation using the program given at the end, the results that we got for the top 50 ODI batsmen of all time are as follows:
Rank
|
Name
|
Team
|
Avg Rank
|
Mat
|
Runs
|
Ave
|
SR
|
100
|
50
|
1
|
SR Tendulkar
|
Ind
|
1.00
|
463
|
18426
|
44.83
|
86.23
|
49
|
96
|
2
|
RT Ponting
|
Aus
|
2.19
|
374
|
13589
|
41.81
|
80.19
|
29
|
82
|
3
|
IVA Richards
|
WI
|
4.20
|
187
|
6721
|
47.00
|
90.20
|
11
|
45
|
4
|
JH Kallis
|
SA
|
4.49
|
320
|
11545
|
45.63
|
73.19
|
17
|
86
|
5
|
ST Jayasuriya
|
SL
|
4.98
|
441
|
13364
|
32.51
|
91.25
|
28
|
68
|
6
|
MS Dhoni
|
Ind
|
7.45
|
240
|
7872
|
52.83
|
88.67
|
8
|
54
|
7
|
KC Sangakkara
|
SL
|
7.67
|
362
|
12241
|
40.39
|
77.32
|
18
|
82
|
8
|
SC Ganguly
|
Ind
|
8.12
|
308
|
11221
|
40.95
|
73.65
|
22
|
71
|
9
|
BC Lara
|
WI
|
9.74
|
295
|
10348
|
40.90
|
79.62
|
19
|
62
|
10
|
AB de Villiers
|
SA
|
10.50
|
154
|
6181
|
50.25
|
93.92
|
16
|
35
|
11
|
Inzamam-ul-Haq
|
Pak
|
11.56
|
375
|
11701
|
39.53
|
74.20
|
10
|
83
|
12
|
AC Gilchrist
|
Aus
|
11.74
|
286
|
9595
|
35.93
|
96.89
|
16
|
55
|
13
|
V Kohli
|
Ind
|
12.58
|
134
|
5634
|
52.16
|
89.87
|
19
|
30
|
14
|
MG Bevan
|
Aus
|
14.75
|
232
|
6912
|
53.58
|
74.16
|
6
|
46
|
15
|
M Yousuf
|
Pak
|
17.03
|
281
|
9554
|
42.08
|
74.91
|
15
|
62
|
16
|
Saeed Anwar
|
Pak
|
17.14
|
247
|
8824
|
39.21
|
80.67
|
20
|
43
|
17
|
R Dravid
|
Ind
|
17.36
|
340
|
10768
|
39.15
|
71.18
|
12
|
82
|
18
|
HM Amla
|
SA
|
18.09
|
85
|
4054
|
53.34
|
90.14
|
12
|
23
|
19
|
ME Waugh
|
Aus
|
20.10
|
244
|
8500
|
39.35
|
76.90
|
18
|
50
|
20
|
V Sehwag
|
Ind
|
20.33
|
241
|
7995
|
35.37
|
104.44
|
15
|
37
|
21
|
DL Haynes
|
WI
|
20.49
|
238
|
8648
|
41.37
|
63.09
|
17
|
57
|
22
|
PA de Silva
|
SL
|
21.25
|
308
|
9284
|
34.90
|
81.13
|
11
|
64
|
23
|
Zaheer Abbas
|
Pak
|
24.31
|
62
|
2572
|
47.62
|
84.80
|
7
|
13
|
24
|
DM Jones
|
Aus
|
24.55
|
164
|
6068
|
44.61
|
72.56
|
7
|
46
|
25
|
CH Gayle
|
WI
|
24.86
|
252
|
8688
|
37.77
|
84.17
|
21
|
44
|
26
|
MJ Clarke
|
Aus
|
26.48
|
236
|
7683
|
44.66
|
78.76
|
8
|
55
|
27
|
DPMD Jayawardene
|
SL
|
27.82
|
407
|
11243
|
32.77
|
78.07
|
15
|
69
|
28
|
S Chanderpaul
|
WI
|
28.06
|
268
|
8778
|
41.60
|
70.74
|
11
|
59
|
29
|
M Azharuddin
|
Ind
|
28.55
|
334
|
9378
|
36.92
|
74.02
|
7
|
58
|
30
|
MEK Hussey
|
Aus
|
30.48
|
185
|
5442
|
48.15
|
87.16
|
3
|
39
|
31
|
Javed Miandad
|
Pak
|
31.23
|
233
|
7381
|
41.70
|
67.01
|
8
|
50
|
32
|
ML Hayden
|
Aus
|
31.25
|
160
|
6131
|
44.10
|
78.98
|
10
|
36
|
33
|
Yuvraj Singh
|
Ind
|
32.18
|
290
|
8237
|
36.28
|
86.98
|
13
|
51
|
34
|
HH Gibbs
|
SA
|
32.54
|
248
|
8094
|
36.13
|
83.26
|
21
|
37
|
35
|
CG Greenidge
|
WI
|
35.33
|
128
|
5134
|
45.03
|
64.92
|
11
|
31
|
36
|
TM Dilshan
|
SL
|
35.68
|
277
|
8025
|
37.67
|
85.70
|
17
|
34
|
37
|
G Kirsten
|
SA
|
36.06
|
185
|
6798
|
40.95
|
72.04
|
13
|
45
|
38
|
A Ranatunga
|
SL
|
39.23
|
269
|
7456
|
35.84
|
77.90
|
4
|
49
|
39
|
MS Atapattu
|
SL
|
39.71
|
268
|
8529
|
37.57
|
67.72
|
11
|
59
|
40
|
GC Smith
|
SA
|
41.48
|
196
|
6989
|
38.19
|
80.86
|
10
|
47
|
41
|
SR Watson
|
Aus
|
41.97
|
173
|
5256
|
41.06
|
90.20
|
9
|
30
|
42
|
A Symonds
|
Aus
|
42.30
|
198
|
5088
|
39.75
|
92.44
|
6
|
30
|
43
|
GJ Bailey
|
Aus
|
42.69
|
39
|
1647
|
53.12
|
91.39
|
2
|
12
|
44
|
G Gambhir
|
Ind
|
44.91
|
147
|
5238
|
39.68
|
85.25
|
11
|
34
|
45
|
Shahid Afridi
|
Pak
|
46.17
|
373
|
7582
|
23.69
|
115.54
|
6
|
36
|
46
|
IJL Trott
|
Eng
|
47.06
|
68
|
2819
|
51.25
|
77.06
|
4
|
22
|
47
|
A Flower
|
Zim
|
49.00
|
213
|
6786
|
35.34
|
74.59
|
4
|
55
|
48
|
DR Martyn
|
Aus
|
49.38
|
208
|
5346
|
40.80
|
77.73
|
5
|
37
|
49
|
Saleem Malik
|
Pak
|
49.56
|
283
|
7170
|
32.88
|
76.41
|
5
|
47
|
50
|
RR Sarwan
|
WI
|
50.09
|
181
|
5804
|
42.67
|
75.74
|
5
|
38
|
3.3 Ranking in Test Cricket
3.2.1 Parameters Considered for Batsmen Rankings in Test Cricket
Now we identify the parameters that are important to rank a batsman. Some of the obvious choices are runs a batsman scored, 100s scored by him, 50s scored by him, his batting average and 50+/Inn.
So, the parameters considered here will be:
i) Runs: The no. of runs a batsman has scored is a very important parameter as it shows his dominance in cricket.
ii) Modified batting average: It is the most important parameter.
iii) 100s: It shows the temperament of a player to play long innings
iv) 50s: Shows the consistency of batsman.
v) 50+ Scores/Inn: Shows the no. of times a batsman scores more than 50 runs per inning
Each of these parameters should have a particular weight-age.
3.2.2 Normalization of Parameters in Test
To apply the weight age easily we have to normalize all the parameters. For each parameter we will give points out of 100 to a batsman. These points should be relative. It is done as follows:
Relative Points for Runs:
Sachin Tendulkar has scored maximum 15,921 runs in Test Cricket. So he will receive 100.00 points for it.
Any batsman who has scored “x” runs in Test Cricket will get = (x*100)/15921 points
Similar process is followed for 100s, 50s and 50+Scores/Inn.
But the process for relative points for modified batting average.
Relative points for Modified batting average:
Any batsman whose modified batting average is below 20.00 will be given 0 points.
DG Bradman has maximum modified batting average of 101.869. So he will get 100.00 points for it.
Any other batsman with a modified batting average of “x” will get = ((x-20)*100)/81.869 points
3.2.5 Weightage for Parameters
Now as we have five parameters that we will use to decide the all time greatest Test batsman, we need to give some weightage to these parameters. Weightage should be according to the importance of the parameters. More important parameters should have a higher weightage than the others. It is a very difficult task to assign such weightage that will give the desired results as it can be biased and it can vary the results significantly. To do so we give a range of weightage to each of the parameters and after that we will use Monte Carlo simulation to get the desired results. The range decided for each parameter is as follows:
Parameter
|
Range
|
Parameter
|
Range
|
Runs
|
50 – 100
|
100s
|
10 – 40
|
Mod Bat Average
|
200 – 400
|
50s
|
10 – 25
|
50+Score/Inn
|
100 – 200
|
3.2.3 Monte Carlo Simulation
To use Monte Carlo Simulation to find the rank of batsmen, we take a random weight-age for each parameter in the above mentioned ranges. After that we give point to each player according to this formula:
Points = (weightage of runs * points for runs) + (weightage of average * points for average) + (weightage of 100s * points for 100s) + (weightage of 50s * points for 50s) + (weightage of 50+/Inn * points for 50+/Inn)
After calculating points for each player, we rank them according to the number of points they have. We carry on this process for 50,000 times and in the end the average rank of a player determines his final rank.
3.2.6 Final Rank List of all time Batsmen in Test Cricket
After carrying out the Monte Carlo simulation using the program given at the end, the results that we got for the top 20 Test batsmen of all time are as follows:
Rank
|
Name
|
Team
|
Avg Rank
|
Match
|
Ave
|
100
|
50
|
1
|
DG Bradman
|
Aus
|
1.00
|
52
|
99.94
|
29
|
13
|
2
|
SR Tendulkar
|
Ind
|
2.09
|
200
|
53.78
|
51
|
68
|
3
|
JH Kallis
|
SA
|
3.26
|
165
|
55.25
|
45
|
58
|
4
|
KC Sangakkara
|
SL
|
4.61
|
122
|
58.07
|
35
|
45
|
5
|
KF Barrington
|
Eng
|
5.11
|
82
|
58.67
|
20
|
35
|
6
|
RT Ponting
|
Aus
|
6.92
|
168
|
51.85
|
41
|
62
|
7
|
H Sutcliffe
|
Eng
|
7.20
|
54
|
60.73
|
16
|
23
|
8
|
R Dravid
|
Ind
|
8.38
|
163
|
52.63
|
36
|
63
|
9
|
JB Hobbs
|
Eng
|
9.08
|
61
|
56.94
|
15
|
28
|
10
|
ED Weekes
|
WI
|
10.02
|
48
|
58.61
|
15
|
19
|
11
|
BC Lara
|
WI
|
10.10
|
130
|
53.17
|
34
|
48
|
12
|
GS Sobers
|
WI
|
11.87
|
93
|
57.78
|
26
|
30
|
13
|
S Chanderpaul
|
WI
|
13.78
|
153
|
51.93
|
29
|
62
|
14
|
SM Gavaskar
|
Ind
|
13.93
|
125
|
51.12
|
34
|
45
|
15
|
RG Pollock
|
SA
|
16.16
|
23
|
60.97
|
7
|
11
|
16
|
AR Border
|
Aus
|
16.32
|
156
|
50.56
|
27
|
63
|
17
|
L Hutton
|
Eng
|
16.43
|
79
|
56.67
|
19
|
33
|
18
|
CL Walcott
|
WI
|
18.32
|
44
|
56.68
|
15
|
14
|
19
|
Javed Miandad
|
Pak
|
19.97
|
124
|
52.57
|
23
|
43
|
20
|
SR Waugh
|
Aus
|
19.99
|
168
|
51.06
|
32
|
50
|
3.3 Ranking in T20I Cricket
3.2.1 Parameters Considered for Batsmen Rankings in T20I Cricket
The parameters considered will be same as considered for ODI Cricket. But the weigthage for the parameters will be different as it is a fast game and requires a better batting strike rate.
So, the parameters considered here will be:
i) Runs
ii) Modified batting average
iii) Modified batting strike rate
iv) 100s
v) 50s
vi) 50+ Scores/Inn
Each of these parameters should have a particular weight-age.
3.2.2 Normalization of Parameters in T20I Cricket
To apply the weight age easily we have to normalize all the parameters. For each parameter we will give points out of 100 to a batsman. These points should be relative. It is done as follows:
Relative Points for Runs:
BB McCullum has scored maximum 2044 runs in T20I Cricket. So he will receive 100.00 points for it.
Any batsman who has scored “x” runs in Test Cricket will get = (x*100)/2044 points
Similar process is followed for 100s, 50s and 50+Scores/Inn.
But the process for relative points for batting average and strike rate is different.
Relative points for batting average:
Any batsman whose batting average is below 10.00 will be given 0 points. Virat Kohli has maximum batting average of 48.14. So he will get 100.00 points for it. Any other batsman with a modified batting average of “x” will get = ((x-10)*100)/38.14 points
Relative points for batting average:
Any batsman whose strike rate is below 100.00 will be given 0 points. KA Pollard has maximum batting strike rate of 149.73. So he will get 100.00 points for it.
Any other batsman with a strike rate of “x” will get = ((x-100)*100)/49.73 points
3.3.3 Weightage for Parameters
Now as we have six parameters that we will use to decide the all time greatest ODI batsman, we need to give some weightage to these parameters. Weightage should be according to the importance of the parameters. More important parameters should have a higher weightage than the others. It is a very difficult task to assign such weightage that will give the desired results as it can be biased and it can vary the results significantly. To do so we give a range of weightage to each of the parameters and after that we will use Monte Carlo simulation to get the desired results. The range decided for each parameter is as follows:
Parameter
|
Range
|
Parameter
|
Range
|
Runs
|
80 – 180
|
100s
|
5 – 10
|
Bat Average
|
80 – 180
|
50s
|
5 – 10
|
50+Score/Inn
|
10 – 20
|
Bat Strike Rate
|
80-180
|
3.2.3 Monte Carlo Simulation
To use Monte Carlo Simulation to find the rank of batsmen, we take a random weight-age for each parameter in the above mentioned ranges. After that we give point to each player according to this formula:
Points = (weightage of runs * points for runs) + (weightage of average * points for average) + (weightage of 100s * points for 100s) + (weightage of 50s * points for 50s) + (weightage of 50+/Inn * points for 50+/Inn)
After calculating points for each player, we rank them according to the number of points they have. We carry on this process for 50,000 times and in the end the average rank of a player determines his final rank.
3.3.4 Final Rank List of all time Batsmen in Test Cricket
After carrying out the Monte Carlo simulation using the program given at the end, the results that we got for the top 20 Test batsmen of all time are as follows:
Rank
|
Name
|
Team
|
Ave Rank
|
Mat
|
Runs
|
Ave
|
SR
|
1
|
BB McCullum
|
NZ
|
1.00
|
68
|
2044
|
35.85
|
135.81
|
2
|
KP Pietersen
|
Eng
|
2.10
|
37
|
1176
|
37.93
|
141.51
|
3
|
V Kohli
|
Ind
|
4.23
|
27
|
906
|
45.30
|
129.98
|
4
|
CH Gayle
|
WI
|
4.73
|
42
|
1239
|
32.60
|
135.55
|
5
|
DPMD Jayawardene
|
SL
|
4.84
|
55
|
1493
|
31.76
|
133.18
|
6
|
DA Warner
|
Aus
|
6.87
|
51
|
1391
|
28.38
|
138.96
|
7
|
AD Hales
|
Eng
|
7.02
|
31
|
956
|
36.76
|
136.37
|
8
|
Yuvraj Singh
|
Ind
|
7.07
|
40
|
968
|
31.22
|
144.69
|
9
|
SR Watson
|
Aus
|
8.14
|
45
|
1074
|
26.85
|
146.32
|
10
|
JP Duminy
|
SA
|
9.56
|
55
|
1342
|
37.27
|
124.48
|
11
|
MEK Hussey
|
Aus
|
11.01
|
38
|
721
|
37.94
|
136.29
|
12
|
SK Raina
|
Ind
|
11.95
|
43
|
922
|
32.92
|
135.78
|
13
|
CL White
|
Aus
|
14.23
|
44
|
918
|
31.65
|
133.81
|
14
|
MJ Guptill
|
NZ
|
15.63
|
47
|
1241
|
31.82
|
122.02
|
15
|
TM Dilshan
|
SL
|
15.84
|
61
|
1452
|
29.04
|
119.3
|
16
|
KC Sangakkara
|
SL
|
15.92
|
56
|
1382
|
31.40
|
119.55
|
17
|
Shahid Afridi
|
Pak
|
17.37
|
74
|
1112
|
19.17
|
144.04
|
18
|
GC Smith
|
SA
|
17.54
|
33
|
982
|
31.67
|
127.53
|
19
|
EJG Morgan
|
Eng
|
18.13
|
48
|
1071
|
28.94
|
129.5
|
20
|
KA Pollard
|
WI
|
19.63
|
37
|
569
|
22.76
|
149.73
|
4 Ranking of Bowlers
3.2 Minimum Criteria for Players
As described for the batsmen, similarly for bowlers we cannot consider all the players while we are developing the rankings for best bowlers in the history of cricket. There are many instances when a single bowler has taken just one wicket giving very less runs, making his bowling average the best in the world. As an example, South African wicket keeper Mark Boucher has taken only one wicket in his ODI career giving only 6 runs. So his career bowling average is 6.00. Similarly many bowlers have economy rate of 0.00. So it is important to set minimum criteria for the removal of such players.
So to remove such data, we make the following minimum criteria for a player to be eligible for rankings:
iv) ODIs
Criteria
|
Minimum Requirement
|
Overs Bowled
|
250
|
v) Test
Criteria
|
Minimum Requirement
|
Overs Bowled
|
500
|
vi) T20Is
Criteria
|
Minimum Requirement
|
Innings
|
20
|
Balls Faced
|
300
|
So by these criteria, the numbers of players we are considering are 750 for test cricket, 461 for ODI cricket and 71 for T20Is.
3.2 Ranking in ODI
3.2.1 Parameters Considered for Batsmen Rankings in ODI
This is the most important part of ranking. We have to identify the parameters that are important to rank a batsman. Some of the obvious choices are runs a batsman scored, 100s scored by him, 50s scored by him, his batting average, his batting strike rate etc.
So, the parameters considered here will be:
viii) Runs: The no. of runs a batsman has scored is a very important parameter as it shows his dominance in cricket.
ix) Modified batting average: It is the most important parameter.
x) Modified batting strike rate: It shows how fast a player can score runs. It is very important in ODI matches.
xi) 100s: It shows the temperament of a player to play long innings
xii) 50s: Shows the consistency of batsman.
xiii) 50+ Scores/Inn: Shows the no. of times a batsman scores more than 50 runs per inning
xiv) Span of Years Played: More no. of years shows consistency
Each of these parameters should have a particular weight-age.
3.2.2 Normalization of Parameters in ODI
To apply the weight age easily we have to normalize all the parameters. For each parameter we will give points out of 100 to a batsman. These points should be relative. It is done as follows:
Relative Points for Runs:
Sachin Tendulkar has scored maximum 18,426 runs in ODI Cricket. So he will receive 100.00 points for it.
Any batsman who has scored “x” runs in ODI Cricket will get = (x*100)/18426 points
Similar process is followed for 100s, 50s and 50+Scores/Inn.
But the process for relative points for modified batting average and modified batting strike rate is different.
Relative points for Modified batting average:
Any batsman whose modified batting average is below 10.00 will be given 0 points.
MG Bevan has maximum modified batting average of 56.48. So he will get 100.00 points for it.
Any other batsman with a modified batting average of “x” will get = ((x-10)*100)/46.58 points
Relative points for Modified strike rate:
Any batsman whose modified batting strike rate is below 60.00 will be given 0 points.
BL Cairns has the highest modified batting strike rate of 128.83. So he will get 100.00 points for it.
Any other batsman who has a modified batting strike rate of “x” will get = ((x-60)*100)/68.83 points
So here is a list of given points for each of these parameters to 20 Indian batsman who has maximum runs in ODI matches.
Name
|
Runs
|
100s
|
50s
|
50+/Inn
|
Ave
|
SR
|
SR Tendulkar
|
100.00
|
100.00
|
100.00
|
75.16
|
78.86
|
50.19
|
SC Ganguly
|
60.90
|
44.90
|
73.96
|
73.36
|
70.81
|
31.50
|
R Dravid
|
58.44
|
24.49
|
85.42
|
70.14
|
65.84
|
24.35
|
M Azharuddin
|
50.90
|
14.29
|
60.42
|
49.44
|
63.68
|
38.14
|
Yuvraj Singh
|
44.70
|
26.53
|
53.13
|
56.58
|
58.37
|
45.62
|
V Sehwag
|
43.39
|
30.61
|
38.54
|
51.84
|
56.57
|
72.14
|
MS Dhoni
|
42.72
|
16.33
|
56.25
|
68.84
|
93.10
|
45.76
|
V Kohli
|
30.58
|
38.78
|
31.25
|
91.11
|
90.63
|
45.61
|
A Jadeja
|
29.08
|
12.24
|
31.25
|
47.12
|
63.74
|
28.28
|
G Gambhir
|
28.43
|
22.45
|
35.42
|
73.73
|
65.17
|
41.85
|
SK Raina
|
24.94
|
6.12
|
30.21
|
46.28
|
55.10
|
49.22
|
NS Sidhu
|
23.95
|
12.24
|
34.38
|
71.95
|
63.54
|
31.06
|
K Srikkanth
|
22.20
|
8.16
|
28.13
|
50.09
|
47.47
|
39.42
|
N Kapil Dev
|
20.53
|
2.04
|
14.58
|
17.75
|
34.71
|
79.61
|
DB Vengsarkar
|
19.04
|
2.04
|
23.96
|
46.86
|
61.09
|
33.69
|
RG Sharma
|
18.60
|
8.16
|
22.92
|
52.06
|
55.53
|
29.86
|
RJ Shastri
|
16.87
|
8.16
|
18.75
|
40.27
|
47.54
|
21.11
|
SM Gavaskar
|
16.78
|
2.04
|
28.13
|
64.31
|
62.78
|
25.22
|
M Kaif
|
14.94
|
4.08
|
17.71
|
40.47
|
49.66
|
25.33
|
VG Kambli
|
13.44
|
4.08
|
14.58
|
38.65
|
52.54
|
32.05
|
4.2.3 Weightage for Parameters
Now as we have seven parameters that we will use to decide the all time greatest ODI batsman, we need to give some weightage to these parameters. Weightage should be according to the importance of the parameters. More important parameters should have a higher weightage than the others. It is a very difficult task to assign such weightage that will give the desired results as it can be biased and it can vary the results significantly. To do so we give a range of weightage to each of the parameters and after that we will use Monte Carlo simulation to get the desired results. The range decided for each parameter is as follows:
Parameter
|
Range
|
Parameter
|
Range
|
Runs
|
80 – 180
|
100s
|
8 – 35
|
Mod Bat Average
|
60 – 150
|
50s
|
5 – 20
|
Mod Bat Strike Rate
|
40 – 110
|
50+Score/Inn
|
21 – 40
|
3.2.3 Monte Carlo Simulation
To use Monte Carlo Simulation to find the rank of batsmen, we take a random weightage for each parameter in the above mentioned ranges. After that we give point to each player according to this formula:
Points = (weightage of runs * points for runs) + (weightage of average * points for average) + (weightage of strike rate * points for strike rate) + (weightage of 100s * points for 100s) + (weightage of 50s * points for 50s) + (weightage of 50+/Inn * points for 50+/Inn)
After calculating points for each player, we rank them according to the number of points they have. We carry on this process for 50,000 times and in the end the average rank of a player determines his final rank.
4.2.4 Final Rank List of all time Batsmen in ODI Cricket
After carrying out the Monte Carlo simulation using the program given at the end, the results that we got for the top 50 ODI batsmen of all time are as follows:
Rank
|
Name
|
Team
|
Avg Rank
|
Mat
|
Runs
|
Ave
|
SR
|
100
|
50
|
1
|
SR Tendulkar
|
Ind
|
1.00
|
463
|
18426
|
44.83
|
86.23
|
49
|
96
|
2
|
RT Ponting
|
Aus
|
2.19
|
374
|
13589
|
41.81
|
80.19
|
29
|
82
|
3
|
IVA Richards
|
WI
|
4.20
|
187
|
6721
|
47.00
|
90.20
|
11
|
45
|
4
|
JH Kallis
|
SA
|
4.49
|
320
|
11545
|
45.63
|
73.19
|
17
|
86
|
5
|
ST Jayasuriya
|
SL
|
4.98
|
441
|
13364
|
32.51
|
91.25
|
28
|
68
|
6
|
MS Dhoni
|
Ind
|
7.45
|
240
|
7872
|
52.83
|
88.67
|
8
|
54
|
7
|
KC Sangakkara
|
SL
|
7.67
|
362
|
12241
|
40.39
|
77.32
|
18
|
82
|
8
|
SC Ganguly
|
Ind
|
8.12
|
308
|
11221
|
40.95
|
73.65
|
22
|
71
|
9
|
BC Lara
|
WI
|
9.74
|
295
|
10348
|
40.90
|
79.62
|
19
|
62
|
10
|
AB de Villiers
|
SA
|
10.50
|
154
|
6181
|
50.25
|
93.92
|
16
|
35
|
11
|
Inzamam-ul-Haq
|
Pak
|
11.56
|
375
|
11701
|
39.53
|
74.20
|
10
|
83
|
12
|
AC Gilchrist
|
Aus
|
11.74
|
286
|
9595
|
35.93
|
96.89
|
16
|
55
|
13
|
V Kohli
|
Ind
|
12.58
|
134
|
5634
|
52.16
|
89.87
|
19
|
30
|
14
|
MG Bevan
|
Aus
|
14.75
|
232
|
6912
|
53.58
|
74.16
|
6
|
46
|
15
|
M Yousuf
|
Pak
|
17.03
|
281
|
9554
|
42.08
|
74.91
|
15
|
62
|
16
|
Saeed Anwar
|
Pak
|
17.14
|
247
|
8824
|
39.21
|
80.67
|
20
|
43
|
17
|
R Dravid
|
Ind
|
17.36
|
340
|
10768
|
39.15
|
71.18
|
12
|
82
|
18
|
HM Amla
|
SA
|
18.09
|
85
|
4054
|
53.34
|
90.14
|
12
|
23
|
19
|
ME Waugh
|
Aus
|
20.10
|
244
|
8500
|
39.35
|
76.90
|
18
|
50
|
20
|
V Sehwag
|
Ind
|
20.33
|
241
|
7995
|
35.37
|
104.44
|
15
|
37
|
21
|
DL Haynes
|
WI
|
20.49
|
238
|
8648
|
41.37
|
63.09
|
17
|
57
|
22
|
PA de Silva
|
SL
|
21.25
|
308
|
9284
|
34.90
|
81.13
|
11
|
64
|
23
|
Zaheer Abbas
|
Pak
|
24.31
|
62
|
2572
|
47.62
|
84.80
|
7
|
13
|
24
|
DM Jones
|
Aus
|
24.55
|
164
|
6068
|
44.61
|
72.56
|
7
|
46
|
25
|
CH Gayle
|
WI
|
24.86
|
252
|
8688
|
37.77
|
84.17
|
21
|
44
|
26
|
MJ Clarke
|
Aus
|
26.48
|
236
|
7683
|
44.66
|
78.76
|
8
|
55
|
27
|
DPMD Jayawardene
|
SL
|
27.82
|
407
|
11243
|
32.77
|
78.07
|
15
|
69
|
28
|
S Chanderpaul
|
WI
|
28.06
|
268
|
8778
|
41.60
|
70.74
|
11
|
59
|
29
|
M Azharuddin
|
Ind
|
28.55
|
334
|
9378
|
36.92
|
74.02
|
7
|
58
|
30
|
MEK Hussey
|
Aus
|
30.48
|
185
|
5442
|
48.15
|
87.16
|
3
|
39
|
31
|
Javed Miandad
|
Pak
|
31.23
|
233
|
7381
|
41.70
|
67.01
|
8
|
50
|
32
|
ML Hayden
|
Aus
|
31.25
|
160
|
6131
|
44.10
|
78.98
|
10
|
36
|
33
|
Yuvraj Singh
|
Ind
|
32.18
|
290
|
8237
|
36.28
|
86.98
|
13
|
51
|
34
|
HH Gibbs
|
SA
|
32.54
|
248
|
8094
|
36.13
|
83.26
|
21
|
37
|
35
|
CG Greenidge
|
WI
|
35.33
|
128
|
5134
|
45.03
|
64.92
|
11
|
31
|
36
|
TM Dilshan
|
SL
|
35.68
|
277
|
8025
|
37.67
|
85.70
|
17
|
34
|
37
|
G Kirsten
|
SA
|
36.06
|
185
|
6798
|
40.95
|
72.04
|
13
|
45
|
38
|
A Ranatunga
|
SL
|
39.23
|
269
|
7456
|
35.84
|
77.90
|
4
|
49
|
39
|
MS Atapattu
|
SL
|
39.71
|
268
|
8529
|
37.57
|
67.72
|
11
|
59
|
40
|
GC Smith
|
SA
|
41.48
|
196
|
6989
|
38.19
|
80.86
|
10
|
47
|
41
|
SR Watson
|
Aus
|
41.97
|
173
|
5256
|
41.06
|
90.20
|
9
|
30
|
42
|
A Symonds
|
Aus
|
42.30
|
198
|
5088
|
39.75
|
92.44
|
6
|
30
|
43
|
GJ Bailey
|
Aus
|
42.69
|
39
|
1647
|
53.12
|
91.39
|
2
|
12
|
44
|
G Gambhir
|
Ind
|
44.91
|
147
|
5238
|
39.68
|
85.25
|
11
|
34
|
45
|
Shahid Afridi
|
Pak
|
46.17
|
373
|
7582
|
23.69
|
115.54
|
6
|
36
|
46
|
IJL Trott
|
Eng
|
47.06
|
68
|
2819
|
51.25
|
77.06
|
4
|
22
|
47
|
A Flower
|
Zim
|
49.00
|
213
|
6786
|
35.34
|
74.59
|
4
|
55
|
48
|
DR Martyn
|
Aus
|
49.38
|
208
|
5346
|
40.80
|
77.73
|
5
|
37
|
49
|
Saleem Malik
|
Pak
|
49.56
|
283
|
7170
|
32.88
|
76.41
|
5
|
47
|
50
|
RR Sarwan
|
WI
|
50.09
|
181
|
5804
|
42.67
|
75.74
|
5
|
38
|
how much free time do we have!!
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