The document analyzes different parameters in cricket through statistical analysis using Microsoft Excel and SPSS. It contains the introduction, objectives, literature review, and four hypotheses to compare different cricket parameters. The first hypothesis analyzes the relationship between a player's average strike rate in ODI and Test matches. Statistical tests like t-test and F-test are applied on data of two players, Virender Sehwag and Sachin Tendulkar, to compare their strike rates in the two formats. The results show Sehwag's average strike rate is higher but more varied in ODIs, while Tendulkar's data is still being analyzed.

1. Akshat Aggarwal (105011)
Ronit Arora (105016)
Shrawan Arya (105044)
Kunwar Preet Singh (105046)
P Ganesh (105054)
Nikhil Bansal (105067)
Anmol Jain (105076)
Deepak Kataria (105080)
ANALYSIS OF DIFFERENT PARAMETER
IN THE GAME OF CRICKET

2. `
DECLARATION
This is to certify that the material embodied in this present project is based on our original
research work. Our indebtedness to other works, studies and publications have been duly
acknowledge at the relevant places. This project work has not been submitted in part or in full
for any other diploma or degree in this or any other university.
Project Supervisor : Mrs Manisha Rao
Group Members
Akshat Aggarwal (105011)
Ronit Arora (105016)
Shrawan Arya (105044)
KunwarPreet Singh (105046)
P Ganesh (105054)
Nikhil Bansal (105067)
Anmol Jain (105076)
Deepak Kataria (105080)

3. `
INDEX
SR. NO TOPIC PAGE NO
1. Acknowledgement 1
2. Introduction –
Aim 2
Objective 2
3. Review Of Literature 3
4. Hypothesis 1 5
5. Hypothesis 2 23
6. Hypothesis 3 36
7. Hypothesis 4 45
8. Conclusion 56
9. Limitations and Further Scope 57
10. Bibliography 58

4. 1
ACKNOWLEDGEMENT
The satisfaction and euphoria that accompany the successful completion of any task would be
incomplete without mentioning the people who made it possible, whose consistent guidance
and encouragement crowned the effort with success.
First of all, we are thankful to our Computer Teacher – Mr Hitesh Sachdeva, under whose
guidance we are able to complete our project. We are wholeheartedly thankful to him for
giving us his valuable time & attention & for providing us a systematic way for completing our
project in time.
We must also make special mention of Mrs Manisha Rao, our Project Supervisor for her co-
operation and assistance in making of the project. We would thank all lab maintenance staff for
providing us assistance in various problem encountered during the course of the project.
Also, we will like to thank our collegues, friend and everyone who has helped us in completing
this project.
Thank You

5. `
2
INTRODUCTION
AIM
To understand and compare the different parameters in the field of cricket with the help of
Statistical Software like Microsoft Excel and SPSS, and check whether there is any relation
between them.
OBJECTIVE
For carrying out the project, the following objectives have been formulated:
 To do a literature review in order to learn from the past studies already done on the
topic.
 To learn the use of software for doing statistical analysis.
 To prepare various hypothesis in order to compare different parameters in the field of
cricket to check:
o A relation between the strike rate of the player in One day International (ODI)
matches & Test matches,
o A relation between mean economy rate of a fast bowler & a spinner in ODI’s,
o A relation between the team winning the Toss, and the team eventually wining
the match,
o A relation between the no. of wickets taken and 3 things bowling average, strike
rate and economy rate.

6. 3
REVIEW OF LITERATURE
An attempt was made in order to learn from the similar studies done in the past, the following
sub-section summarises the major findings of the project.
LITERATURE 1
In the study done by Silva B.M. and Swartz T.B. (1994), statistical analysis of 427 one-day
international cricket matches playe during the 1990s was done. Two general conclusion were
obtained (1) Contrary to widespread opinion, winning the coin toss at the outset of a match
provides no competitive advantage ( 2 ) the advantage of playing on one’s home field increases
the log-odds of the probability of winning by approximately 0.5
LITERATURE 2
In the study done by Staden P.J (2012), comparison of cricketer’s batting and bowling abilities
was done with very basic performance measures. More sophisticated measures
were been proposed, but were generally not used due to a variety of reasons, including
the statistical illiteracy of those involved in cricket, the way cricket data is captured
and presented for bowlers and for batsmen and the different rules applicable for the
various formats of the game. Graphical displays for comparisons have not featured
prominently. In this paper a graph, originally proposed for comparing bowlers, was
presented and adapted for comparing batsmen and all-rounders. The construction
and interpretation of the graphs was illustrated with cricket records from the recent
Indian Premier League (IPL)

7. 4
LITERATURE 3
In a study done by Gill P.S, Beaudoin D, a test was conducted in search for optimal or nearly
optimal batting orders in one-day cricket. . A search was conducted over the space of
permutations of batting orders where simulated annealing was used to explore the space.
A non-standard aspect of the optimization was that the objective function (which is the mean
number of runs per innings) was unavailable and was approximated via simulation. The
simulation component generates runs ball by ball during an innings taking into account the
state of the match and estimated characteristics of individual batsmen.

8. `
5

9. `
6
 Null Hypothesis: Average strike rate of a player in ODI matches is
equal to average strike rate in TEST matches.
Alternative Hypothesis: Average strike rate of a player in ODI
matches is not equal to average strike rate in TEST matches.
 Null Hypothesis: Variation in strikes rate of a player in equal in
TEST and ODI matches.
Alternative Hypothesis: Variation in strike rates of a player is
different in TEST and ODI matches.
We will test the above Hypothesis using the Stats of two players,
that are – Virendre Sehwag (India ), and Sachin Tendulkar ( India )

10. `
7
First, we will apply different test on the Stats of Virendr Sehwag.
Score of Virendre Sehwag(Ind) in 50 random innings of his test career and their Strike rate.
Virendra Sehwag
test matches
INNINGS RUNS
SCORED
NO. OF BALLS
FACED
STRIKE
RATES
1 84 96 87.5
2 61 65 93.84
3 195 233 83.69
4 309 375 82.4
5 76 82 92.68
6 254 247 102.83
7 201 262 76.71
8 76 89 85.39
9 180 190 94.73
10 65 75 86.66
11 151 236 63.98
12 319 304 104.93
13 201 231 87.01
14 90 122 73.77
15 66 69 95.65
16 92 107 85.98
17 83 68 122.05
18 131 122 107.37
19 293 254 115.35
20 52 51 101.96
21 109 139 78.41
22 165 174 94.82
23 109 118 92.37
24 99 101 98.01
25 109 105 103.8
26 59 54 109.25
27 173 119 86.93
28 96 120 80
29 54 54 100

11. `
8
30 74 73 101.36
31 55 46 119.56
32 55 55 100
33 60 65 92.3
34 67 83 80.72
35 62 53 116.98
36 105 173 60.69
37 147 206 71.35
38 47 50 94
39 173 244 70.13
40 44 48 70.9
41 50 52 91.66
42 56 63 96.15
43 47 41 88.8
44 38 33 114.63
45 45 51 115.15
46 135 221 88.23
47 88 118 70.13
48 44 44 100
49 36 28 128.57
50 63 90 74.57
0
20
40
60
80
100
120
140
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
INNINGS
STRIKERATE
Inning Wise Strike Rate Of Virendre Sehwag in
50 Innings of his Test Career.

12. `
9
Score of Virendre Sehwag(Ind) in 50 random innings of his ODI career and their Strike rate.
odi matches
INNINGS RUNS
SCORED
NO. OF BALLS
FACED
STRIKE
RATES
1 58 54 107.41
2 100 70 142.86
3 55 43 127.91
4 51 58 87.93
5 82 62 132.26
6 71 65 109.23
7 42 36 116.67
8 126 104 121.15
9 59 58 101.72
10 114 82 139.02
11 108 119 90.76
12 112 139 80.58
13 66 76 86.84
14 82 81 101.23
15 43 44 97.73
16 130 134 97.01
17 90 102 88.24
18 99 101 98.02
19 109 105 103.81
20 59 54 109.26
21 173 119 145.38
22 96 120 80
23 54 54 100
24 74 73 101.37
25 55 46 119.57
26 55 55 100
27 60 65 92.31
28 67 83 80.72
29 62 53 116.98
30 105 173 60.69
31 147 206 71.36
32 47 50 94
33 173 244 70.9

13. `
10
34 44 48 91.67
35 50 52 96.15
36 56 63 88.89
37 47 41 114.63
38 38 33 115.15
39 45 51 88.24
40 155 221 70.14
41 70 52 134.62
42 74 40 185
43 48 22 218.18
44 75 65 115.38
45 77 62 124.19
46 78 44 177.27
47 119 95 125.26
48 49 33 148.48
49 60 36 166.67
50 114 87 131.03
0
50
100
150
200
250
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
INNINGS
STRIKERATE
Inning Wise Strike Rate Of Virendre Sehwag in
50 Innings of his ODI Career.

14. `
11
This is how we get the above results. Using
Formulas in Excel.
TEST MATCHES ( Strike Rate ) ODI MATCHES ( Strike Rate )
AVERAGE 92.679 AVERAGE 111.2774
VARIANCE 242.9632622 VARIANCE 955.0886931
CORRELATION 0.040662259 CORRELATION 0.040662259
CV 16.81% CV 27.70%

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12
t-Test: Two-Sample Assuming Unequal Variances
Variable 1 Variable 2
Mean 92.679 111.2774
Variance 242.9632622449 955.08869310204
Observations 50 50
Hypothesized Mean Difference 0
Df 72
t Stat -3.79946772730192
t Critical two-tail 1.99346353904453
F-Test Two-Sample for Variances
Variable 1 Variable 2
Mean 92.679 111.2774
Variance 242.9632622449 955.08869310204
Observations 50 50
Df 49 49
F 0.254388167297612
F Critical two-tail 0.622165466996477

16. `
13
VIRENDER SEHWAG
1. THE AVERAGE STRIKE RATE IS 92.679 IN TEST AND 111.2774 IN ODI. THIS
SUGGEST THAT THIS IS NOT AS SIGNIFICANT AS COMPARED TO SACHIN AND
OTHER PLAYERS. THIS IS DUE TO HIS BATTING STYLE/
2. THE CV IN TEST IS 16.81 IN TEST AND 27.7 IN ODI . IT SUGGEST THAT THE
VARIATION IS HIGH IN BOTH THE CASES.
3. THE CV IS LESS IN TEST THAN ODI . IT SUGGEST THAT SEHWAG IS MORE
CONSISTENT IN TEST THAN ODI.
4. THE CORRELATION COFFICIENT IS 0.04. IT IMPLIES THAT THER IS VERY LOW
DEGREE OF ASSOCIATION (ALMOST NO) BETWEEN STRIKE RATES IN BOTH THE
FORMATS.

17. `
14
HYPOTHESIS TESTING
LET U1 AND U2 THE AVERAGE STRIKE RATES OF A PLAYER IN TEST AND ODI
RESPECTIVELY
NULL (H0):U1=U2
ALTERNATIVE (H1):U1 ≠ U2
The test statistic value -3.79 is less than critical value -1.99. Therefore we have
sufficient evidence to reject null at 5% significance level.
CONCLUSION
SO WE CONCLUDE THAT THERE IS SIGNIFICANT DIFFERNCE BETWEEN AVERAGE
STRIKE RATES OF SEHWAG IN BOTH THE FORMATS.
Let σ1
2
and σ2
2
be the variance in strike rates of a player in TEST and ODI
matches respectively.
Null (Ho): σ1
2
= σ2
2
Alternative (H1): σ1
2
≠ σ2
2
CONCLUSION:
The F-Test shows that “Test statistic value” (0.254) is less than “Critical value”
(0.622), So we do not reject Null at 5% significance level.
So we conclude that Variation in strike rates of Sehwag is same in both the
formats.

18. `
15
Now, we will apply different test on the Stats of Sachin Tendulkar.
Score of Sachin Tendulkar(Ind) in 50 random innings of his test career and their Strike rate.
Sachin Tendulkar
test matches
INNINGS RUNS
SCORED
NO. OF BALLS
FACED
STRIKE
RATES
1 88 266 33.08
2 119 189 62.96
3 148 213 69.48
4 114 161 70.8
5 111 270 41.11
6 73 208 35.09
7 165 296 55.74
8 104 161 64.59
9 96 140 68.57
10 179 322 55.59
11 122 177 68.92
12 177 360 49.16
13 74 97 76.28
14 169 254 66.55
15 92 147 62.58
16 139 266 52.25
17 155 191 81.15
18 113 151 74.83
19 136 273 49.81
20 44 39 112.82
21 217 344 63.08
22 116 191 60.73
23 97 163 59.5
24 74 128 57.89
25 155 184 84.23
26 88 144 61.11
27 103 197 52.28
28 176 316 55.69
29 117 260 45
30 92 113 81.41

19. `
16
31 193 330 58.48
32 176 298 59.06
33 241 436 55.27
34 194 348 55.74
35 248 379 65.43
36 94 202 46.53
37 109 196 55.61
38 64 130 49.23
39 101 159 59.76
40 122 226 53.98
41 154 243 63.37
42 153 205 74.63
43 109 188 59.97
44 103 196 52.55
45 160 260 61.53
46 100 211 47.59
47 105 166 63.25
48 143 182 78.57
49 100 179 55.86
50 106 206 51.45
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
INNINGS
STRIKERATE
Inning Wise Strike Rate Of Sachin Tendulkar in
50 Innings of his Test Career.

20. `
17
Score of Sachin Tendulkar(Ind) in 50 random innings of his ODI career and their Strike rate.
ODI matches
INNINGS RUNS
SCORED
NO. OF BALLS
FACED
STRIKE
RATES
1 84 107 78.5
2 110 130 84.61
3 115 136 84.55
4 105 134 78.35
5 112 137 104.67
6 127 138 92.02
7 137 137 100
8 100 11 90.09
9 118 140 84.28
10 110 138 79.71
11 114 126 90.47
12 104 97 107.21
13 117 137 85.4
14 91 87 104.59
15 100 89 112.35
16 143 131 109.16
17 134 131 102.29
18 100 103 97.08
19 128 131 97.7
20 127 130 97.69
21 141 128 110.15
22 118 112 105.35
23 124 92 134.78
24 140 101 138.61
25 120 140 85.1
26 186 150 124
27 122 138 88.4
28 146 153 95.42
29 139 125 111.2
30 122 131 93.12
31 105 108 97.22
32 113 102 110.78

21. `
18
33 152 151 100.66
34 98 75 130.66
35 97 120 80.83
36 141 135 104.44
37 123 130 94.61
38 141 148 95.27
39 99 143 69.23
40 99 112 88.39
41 117 120 97.5
42 163 133 122.25
43 200 147 136.05
44 120 115 104.34
45 111 101 109.9
46 85 115 73.91
47 96 104 92.3
48 91 121 75.2
49 99 91 108.79
50 84 80 105
0
20
40
60
80
100
120
140
160
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
INNINGS
STRIKERATE
Inning Wise Strike Rate Of Sachin Tendulkar in
50 Innings of his ODI Career.

22. `
19
This is how we get the above results. Using
Formulas in Excel.
TEST MATCHES ODI MATCHES
AVERAGE 60.8028 AVERAGE 99.2836
VARIANCE 180.0747471 VARIANCE 260.8006725
CORRELATION - 0.094141578 CORRELATION -0.094141578
SKEWNESS 1.10005858 SKEWNESS 0.5789129
CV 22.07% CV 16.26%

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20
t-Test: Two-Sample Assuming Unequal Variances
Variable 1
Variable
2
Mean 60.8028 99.2836
Variance 180.07475 260.8007
Observations 50 50
Hypothesized Mean Difference 0
df 95
t Stat -12.95899
t Critical two-tail 1.985251
F-Test Two-Sample for Variances
Variable 1 Variable 2
Mean 60.8028 99.2836
Variance 180.0747471 260.80067
Observations 50 50
df 49 49
F 0.690468876
F Critical one-tail 0.622165467

24. `
21
1. THE AVERAGE STRIKE RATE IN TEST MATCHES AND ODI MATCHES DIFFERS
SIGNIFICANTLY. IT IS HIGH IN ODI AND LOW IN TEST.
2. THE COFFICIENT OF VARIATION IS 22.07% IN TEST AND 16.26% IN ODI. IT SUGGEST
THAT VARIATION IN STRIKE RATES IS HIGH IN BOTH THE CASES.
3. CV IS HIGH IN TEST THAN IN ODI. IT SUGGEST THAT THERE IS MORE VARIATION IN
TEST MATCHES IN STRIKE RATES. IT MAY BE BECAUSE DURATION OF TEST MATCHES
IS MORE THAN OF ODI AND DUE TO BATTING STYLE
4. THE CORRELATION COFFICIENT IS -0.09, IT IMPLIES THAT THERE IS ALMOST NO
CORRELATION (OR LOW DEGREE OF NEGATIVE CORRELATION) BETWEEN THE STRIKE
RATES IN 2 FORMATS.
5. THE COFFICIENT OF SKEWNESS IN TEST MATCHES IS 1.1 OF THE SAMPLE TAKEN .
THIS IMPLIES THAT THE DATA IS HIGHLY POSITIVELY SKEWED. IT IS 0.57 IN ODI
MATCHES , THIS IS ALSO POSITIVELY SKEWED BUT APPROXIMATELY NORMAL.

25. `
22
HYPOTHESIS TESTING
 Let u1 and u2 the average strike rates of a player in test and odi respectively
Null (ho): u1=u2
alternative (h1): u1 ≠ u2
Level of signicance=5%
The test done shows that test statistic value (-12.95) is less than crtical value(-
1.985). Therefore we reject null at 5% significance level.
CONCLUSION
We conclude that there is significant differnce between average strike rates of
sachin in ODI and test. The average strike rate is more in ODI than test.
 Let σ1
2
and σ2
2
be the variance in strike rates of a player in TEST and ODI
matches respectively.
Null (Ho): σ1
2
= σ2
2
Alternative (H1): σ1
2
≠ σ2
2
CONCLUSION
The F- Test shows that “Test statistic value” (0.699) is greater than “Critical
value” (0.622), So we reject the null at 5 % significance level.
So we conclude that variation in strike rates of “Sachin” is different in both the
formats.

26. `
23

27. `
24
The statistical test presented here is to analyze the relationship between economy rates in odi’s
of fast bowlers and slow bowlers in their respective last 10 matches.
The sample is taken from five most active countries playing odi’s i.e. India, Australia, Sri lanka,
Pakistan and South Africa. From each team we have selected one main fast bowler and one
slow bowler in order to test whether or not there is any significant difference between their
economy rates.
The sample size is taken of 50 for each in order to cater to the normality assumption
 Null Hypothesis: Average Economy Rate of the Fast Bowler and
the Slow Bowler is equal.
Alternative Hypothesis: Average Economy Rate of the Fast Bowler
and the Slow Bowler is not equal.
 Null Hypothesis: Variance in Economy Rate of the Fast Bowler and
the Slow Bowler is equal.
Alternative Hypothesis: Variance in Economy Rate of the Fast
Bowler and the Slow Bowler is not equal.

28. `
25
DATA
Economy Rate of Top 2 Bowler’s from Different countries in 10
random ODI matches
South Africa
Dale Styne ( Fast Bowler )
Overs Mdns Runs Wkts Econ Opposition
9 2 24 2 2.66 v England
9.4 0 47 2 4.86 v England
7 0 32 1 4.57 v England
7 2 28 0 4 v New Zealand
10 1 37 1 3.7 v New Zealand
9 0 37 1 4.11 v New Zealand
10 0 55 1 5.5 v Sri Lanka
9 1 54 1 6 v Sri Lanka
3 0 7 1 2.33 v Sri Lanka
10 0 44 1 4.4 v Australia
RE van der Marwe ( Spinner )
Overs Mdns Runs Wkts Econ Opposition
10 1 27 1 2.7 v West Indies
10 0 47 2 4.7 v India
10 0 62 1 6.2 v India
6 0 50 0 8.33 v England
9 0 55 0 6.11 v England
8.3 0 27 3 3.17 v Zimbabwe
9 0 67 0 7.44 v England
10 1 35 2 3.5 v New Zealand
10 0 42 0 4.2 v Sri Lanka
10 0 44 2 4.4 v Australia

29. `
26
Australia
bret lee ( Fast Bowler )
Overs Mdns Runs Wkts Econ Opposition
2.2 1 12 0 5.14 v England
10 0 58 0 5.8 v England
10 1 57 1 5.7 v England
3 1 10 2 3.33 v Ireland
9 3 42 3 4.66 v West Indies
10 0 72 2 7.2 v West Indies
9.4 1 52 1 5.37 v West Indies
8 1 37 1 4.62 v West Indies
7 1 25 1 3.57 v West Indies
8 0 59 3 7.37 v Sri Lanka
Brad Hogg ( Spinner )
Overs Mdns Runs Wkts Econ Opposition
7 0 38 1 5.42 v India
10 1 33 1 3.3 v Sri Lanka
9 0 62 1 6.88 v India
4.3 1 15 0 3.33 v Sri Lanka
8 1 30 2 3.75 v India
10 1 41 2 4.1 v Sri Lanka
6 1 17 1 2.83 v Sri Lanka
6 1 49 3 8.16 v New Zealand
10 1 49 1 4.9 v New Zealand
8 0 40 0 5 v India

30. `
27
India
Zaheer Khan ( Fast Bowler )
Overs Mdns Runs Wkts Econ Opposition
9 1 53 1 5.88 v Sri Lanka
6 0 36 0 6 v Sri Lanka
10 0 39 2 3.9 v Sri Lanka
6 0 39 0 6.5 v Sri Lanka
10 0 63 1 6.3 v Sri Lanka
9 0 61 1 6.77 v Sri Lanka
10 0 46 1 4.6 v Australia
10 0 46 1 4.6 v Australia
10 1 44 2 4.4 v Sri Lanka
10 3 60 2 6 v Sri Lanka
R Ashwin ( Spinner )
Overs Mdns Runs Wkts Econ Opposition
9 0 37 0 4.11 v Sri Lanka
10 1 46 2 4.6 v Sri Lanka
10 0 50 0 5 v Sri Lanka
5 1 18 1 3.6 v Sri Lanka
10 1 46 2 4.6 v Sri Lanka
10 0 56 1 5.6 v Pakistan
10 0 56 1 5.6 v Bangladesh
9 0 39 3 4.33 v Sri Lanka
10 0 52 0 5.2 v Sri Lanka
10 0 45 0 4.5 v Australia

31. `
28
Pakistan
Umar Gul ( Fast Bowler )
Overs Mdns Runs Wkts Econ Opposition
10 1 43 0 4.3 v Sri Lanka
8 1 51 1 6.37 v Sri Lanka
9 0 58 0 6.44 v Sri Lanka
9 2 24 3 2.66 v Sri Lanka
10 2 65 2 6.5 v Bangladesh
8.5 0 65 2 7.35 v India
8 1 20 2 2.5 v Sri Lanka
9.1 0 58 3 6.32 v Bangladesh
7 0 59 0 8.42 v England
7 1 43 0 6.14 v England
Saeed Ajmal ( Spinner )
Overs Mdns Runs Wkts Econ Opposition
9 1 37 3 4.11 v Australia
10 0 32 4 3.2 v Australia
10 0 30 3 3 v Australia
10 1 50 2 5 v Sri Lanka
10 0 49 1 4.9 v Sri Lanka
10 2 40 2 4 v Bangladesh
9 0 49 1 5.44 v India
8.4 1 27 3 3.11 v Sri Lanka
10 0 45 2 4.5 v Bangladesh
10 0 62 3 6.2 v England

32. `
29
Sri Lanka
Lasith Malinga ( Fast Bowler )
Overs Mdns Runs Wkts Econ Opposition
10 0 39 2 3.9 v New Zealand
10 0 64 3 6.4 v India
8 1 41 1 5.12 v India
10 0 60 2 6 v India
7.3 0 36 2 4.8 v India
10 0 83 0 8.3 v India
10 1 52 1 5.2 v Pakistan
7 0 30 2 4.28 v Pakistan
3 0 9 1 3 v Pakistan
8 1 40 2 5 v Pakistan
Ajantha Mendis ( Spinner )
Overs Mdns Runs Wkts Econ Opposition
8 0 54 1 6.75 v South Africa
9.1 0 49 3 5.34 v Australia
7 0 31 0 4.42 v Australia
8 0 38 1 4.75 v Australia
6 0 23 0 3.83 v Australia
6 0 32 1 5.33 v Australia
8 2 24 1 3 v Scotland
9.5 0 35 3 3.55 v New Zealand
10 0 34 1 3.4 v England
6 0 24 2 4 v New Zealand

33. `
30
FAST BOWLERS ECONOMY RATE IN ODIs
MATCHES SA AUS IND PAK SL
1 2.66 5.14 5.88 4.3 3.9
2 4.86 5.8 6 6.37 6.4
3 4.57 5.7 3.9 6.44 5.12
4 4 3.33 6.5 2.66 6
5 3.7 4.66 6.3 6.5 4.8
6 4.11 7.2 6.77 7.35 8.3
7 5.5 5.37 4.6 2.5 5.2
8 6 4.62 4.6 6.32 4.28
9 2.33 3.57 4.4 8.42 3
10 4.4 7.37 6 6.14 5
VARIANCE
2.107397714
MEAN ECONOMY RATE
5.1768
COEFFICIENT OF VARIATION
0.28042186
SKEWNESS
0.043348757

34. `
31
SLOW BOWLERS ECONOMY RATE IN ODIs
MATCHES SA AUS IND PAK SL
1 2.7 5.42 4.11 4.11 6.75
2 4.7 3.3 4.6 3.2 5.34
3 6.2 6.88 5 3 4.42
4 8.33 3.33 3.6 5 4.75
5 6.11 3.75 4.6 4.9 3.83
6 3.17 4.1 5.6 4 5.33
7 7.44 2.83 5.6 5.44 3
8 3.5 8.16 4.33 3.11 3.55
9 4.2 4.9 5.2 4.5 3.4
10 4.4 5 4.5 6.2 4
VARIANCE
1.763029755
MEAN ECONOMY RATE
4.6678
COEFFICIENT OF VARIATION
0.284457626
SKEWNESS
0.900545694

35. `
32
HYPOTHESIS TESTING
Testing for equality of average ECONOMY RATES
t-Test: Two-Sample Assuming Equal Variances
Variable 1 Variable 2
Mean 5.1768 4.6678
Variance 2.107397714 1.763029755
Observations 50 50
Pooled Variance 1.935213735
Hypothesized Mean Difference 0
df 98
t Stat 1.829461683
t Critical two-tail 1.984467455
TESTING FOR EQUALITY OF VARIANCES IN ECONOMY RATES
F-Test Two-Sample for Variances
Variable 1 Variable 2
Mean 5.1768 4.6678
Variance 2.107397714 1.763029755
Observations 50 50
df 49 49
F 1.195327367
F Critical two-tail 1.607289463

36. `
33
1. The average economy rate of fast bowlers and slow bowlers in ODI’s are
calculated as 5.18 and 4.67 respectively. It shows that there is almost no
significant difference between economy rates of fast bowlers and slow bowlers.
2. The COEFFICIENT of VARIATION is 28.04% in economy rates of fast bowlers and
28.44% in economy rates of slow bowlers in ODI’s. It implies that the variation is
high in both the economy rates of fast bowlers and slow bowlers and more
importantly they are almost equal which shows that the variation in economy
rates is almost similar.
3. The coefficient of skewness in economy rates of fast bowlers and slow bowlers is
0.04 and 0.90 respectively. The values show that the economy rates are positively
skewed for both the cases and they are approximately normal as well.
4. The COEFFICIENT of CORRELATION between the economy rates is -0.051.It
implies that there is low degree of negative linear correlation between the
economy rates.

37. `
34
HYPOTHESIS TESTING
(1) LET µ1 and µ2 be the average economy rates of fast bowlers and slow bowlers
in ODI’s respectively. Our purpose is to check whether there is any significant
difference between the average economy rates of the fast bowlers and slow bowlers
in ODI’s or not.
Null Hypothesis (H0): average economy rates are equal i.e. µ1 = µ2
Alternative Hypothesis (H1): average economy rates are not equal i.e.
µ1≠ µ2
LEVEL OF SIGNIFICANCE = 5%
INFERENCE:
The T-test here done shows that the TEST STATISTIC VALUE of 1.829 is less than
the 5% critical value of 1.984.
Therefore we have insufficient evidence to reject null hypothesis at this
level of significance and conclude that the average economy rates of fast
bowlers and slow bowlers is equal in ODI’s.
(2) Let σ1
2
and σ2
2
be the variance of economy rates in ODI’s of fast bowlers and
slow bowlers respectively. Our purpose is to check whether there is any significant
difference between the variance of economy rates of the fast bowlers and slow
bowlers in ODI’s or not.
Null Hypothesis (H0): Variances are equal i.e. σ1
2
= σ2
2
Alternative (H1):
Alternative Hypothesis (H1): Variances are not equal i.e. σ1
2
≠ σ2
2
LEVEL OF SIGNIFICANCE = 5%

38. `
35
INFERENCE:
The F-test here done shows that the TEST STATISTIC VALUE of 1.195 is less than
the 5% critical value of 1.607.
Therefore we have insufficient evidence to reject null hypothesis at this
level of significance and conclude that the variance of economy rates, in
ODI’s, of fast bowlers and slow bowlers is equal.

39. `
36

40. `
37
We will test the above Hypothesis using the Stats of 18
Countries that have played 294 matches starting from 1975
ODI World Cup to 2007 ODI World Cup
 Null Hypothesis: In long run, Number of times a team won the
toss is equal to number of wins, when a team wons the toss.
Alternative Hypothesis: In long run, Number of times a team won
the toss is not equal to number of wins, when a team won the
toss.

41. `
38
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
AUSTRALIA
BANGLADESH
BERMUDA
CANADA
ENGLAND
INDIA
IRELAND
KENYA
NETHERLAND
NAMIBIA
NEWZEALAND
PAKISTAN
SOUTHAFRICA
SCOTLAND
SRILANKA
UNITEDARAB…
WESTINDIES
ZIMBAWE
p(winning match)
p(winning match)
COUNTRIES
TOTAL NUMBER OF MATCHES
PLAYED
TOTAL NUMBER OF
MATCHES WON P(WINNING MATCH)
AUSTRALIA 69 52 0.754
BANGLADESH 19 5 0.263
BERMUDA 3 0 0.000
CANADA 12 1 0.083
ENGLAND 58 36 0.621
INDIA 56 32 0.571
IRELAND 8 2 0.250
KENYA 25 6 0.240
NETHERLAND 14 2 0.143
NAMIBIA 6 0 0.000
NEWZEALAND 61 34 0.557
PAKISTAN 54 30 0.556
SOUTH AFRICA 39 25 0.641
SCOTLAND 8 0 0.000
SRI LANKA 54 24 0.444
UNITED ARAB
EMIRATES 5 1 0.200
WEST INDIES 56 36 0.643
ZIMBAWE 41 8 0.195
588
COUNTRIES
PROBABILITY

42. `
39
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
AUSTRALIA
BANGLADESH
BERMUDA
CANADA
ENGLAND
INDIA
IRELAND
KENYA
NETHERLAND
NAMIBIA
NEWZEALAND
PAKISTAN
SOUTHAFRICA
SCOTLAND
SRILANKA
UNITEDARAB…
WESTINDIES
ZIMBAWE
p(winning match when toss won)
p(winning matchwhen toss
won/winning toss)
COUNTRIES
TOTAL NUMBER OF
TOSS WON P(WINNING TOSS)
TOTAL NUMBER OF
TIME TEAM WON
MATCH WINNING THE
TOSS
P(WINNING
MATCHWHEN TOSS
WON)
AUSTRALIA 37 0.536 26 0.703
BANGLADESH 5 0.263 1 0.200
BERMUDA 1 0.333 0 0.000
CANADA 7 0.583 1 0.143
ENGLAND 34 0.586 20 0.588
INDIA 26 0.464 13 0.500
IRELAND 3 0.375 2 0.667
KENYA 12 0.480 2 0.167
NETHERLAND 7 0.500 2 0.286
NAMIBIA 3 0.500 0 0.000
NEWZEALAND 34 0.557 18 0.529
PAKISTAN 25 0.463 11 0.440
SOUTH AFRICA 17 0.436 9 0.529
SCOTLAND 4 0.500 0 0.000
SRI LANKA 28 0.519 13 0.464
UNITED ARAB
EMIRATES 4 0.800 1 0.250
WEST INDIES 26 0.464 15 0.577
ZIMBAWE 21 0.512 3 0.143
COUNTRIES
PROBABILITY

43. `
40
Countries total number of toss
won
total number of time team
won match winning the toss
AUSTRALIA 37 26
BANGLADESH 5 1
BERMUDA 1 0
CANADA 7 1
ENGLAND 34 20
INDIA 26 13
IRELAND 3 2
KENYA 12 2
NETHERLAND 7 2
NAMIBIA 3 0
NEWZEALAND 34 18
PAKISTAN 25 11
SOUTH AFRICA 17 9
SCOTLAND 4 0
SRI LANKA 28 13
UNITED ARAB
EMIRATES 4 1
WEST INDIES 26 15
ZIMBAWE 21 3
0
5
10
15
20
25
30
35
40
AUSTRALIA
BANGLADESH
BERMUDA
CANADA
ENGLAND
INDIA
IRELAND
KENYA
NETHERLAND
NAMIBIA
NEWZEALAND
PAKISTAN
SOUTHAFRICA
SCOTLAND
SRILANKA
UNITEDARABEMIRATES
WESTINDIES
ZIMBAWE
total number of toss won
total number of time team won
match winning the toss
COUNTRIES
Resp.Figures

44. `
41
SUMMARY
OUTPUT
Regression
Statistics
Multiple R 0.94530
R Square 0.89361
Adjusted R
Square
0.88696
Standard
Error
4.22770
Observations 18
ANOVA
df SS MS F Significance
F
Regression 1 2402.02 2402.02 134.3904161 3.38759E-09
Residual 16 285.97 17.8734
Total 17 2688
Coefficients Standard
Error
t Stat P-value Lower 95% Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 5.31561 1.37703 3.86017 0.001385289 2.39641864 8.2348 2.3964 8.23480
X Variable 1 1.44758 0.12487 11.5926 3.38759E-09 1.182870478 1.7122 1.1828 1.71229

45. `
42
We analyse the relationship between the winning of toss and winning of a match . herein we
take regression analysis taking:
Y: DEPENDENT VARIABLE: total number of time team won match winning the toss
X: INDEPENDENT VARIABLE: total number of toss won
Regression
Statistics
Multiple R 0.945309601
R Square 0.893610242
Adjusted R
Square
0.886960882
Standard Error 4.227703795
Observations 18
Multiple R: The correlation between the two variables is 94.53%. A high level of correlation
which approaching +1 denotes that the two variables are positively linearly
correlated
R Square: it means that 89.36% of the variation in the Y(total number of matches won when
toss is won), is explained by the independent variable X( total number of tosses won)
Adjusted R Square = 1 - (Total df / Residual df)(Residual SS / Total SS)
Used to test if an additional independent variable improves the model.
Standard Error: The Standard Error is the error you would expect between the predicted and
actual dependent variable.
Thus, 4.22 mean that the expected error for a team winning the match after
winning the toss prediction is off by 4.22.
Observations: The number of observations we have taken is 18 as the number of countries are
18 .

46. `
43
ANOVA
df SS MS F Significance
F
Regression 1 2402.02433 2402.02433 134.3904161 3.38759E-
09
Residual 16 285.97567 17.87347938
Total 17 2688
The ANOVA (analysis of variance) table splits the sum of squares into its components.
Total sums of squares
= Residual (or error) sum of squares + Regression (or explained) sum of squares.
REGRESSION:
DF: Degrees Of Freedom=Number of Independent Variable =1
SS: Regeression Sum of Squares= 2402.02
MS:Regression SS/ Regression Df=2402.02
F= Regression MS / Residual MS=134.39
SIGNIFICANCE F = Probability that independent variable does NOT explain the variation in y, i.e.
that any fit is purely by chance. This is based on the F probability
distribution. If the Significance F is not less than 0.1 (10%) you do not have a
meaningful correlation. Since we have significance F which is nearly
approaching zero it means we have a meaningful correlation, there exists a
valid relation between the two variables.
RESIDUAL DF = residual degrees of freedom = Total df - Regression df = n - 1 - number of
independent variables =16
RESIDUAL SS = sum of squares of the differences between the values of y predicted by analysis
and the actual values of y. If the data exactly fit equation 1, then Residual SS
would be 0 and R2
would be 1 which is equal to 285.97
RESIDUAL MS = mean square error = Residual SS / Residual df which is equal to 17.87
TOTAL DF = total degrees of freedom = n – 1=18-1=17
TOTAL SS = the sum of the squares of the differences between values of y and the average y

47. `
44
= (n-1)*(standard deviation of y)2
=17* (12.57)2
=2688
Coefficients Standard
Error
t Stat P-value
Intercept 5.315610915 1.37703901 3.860174531 0.001385289
X Variable 1 1.447583967 0.124870432 11.59268804 3.38759E-09
COEFFICIENTS = Values which minimize the Residual SS (maximize R2
).
The Intercept Coefficient is 5.31
And independent variable coefficient is 1.44
STANDARD ERROR= intercept: 1.37
Independent variable=0.12
T stat = = Coefficient for that variable / Standard error for that variable
P-value = 3.38759E-09
Consider test H0: β1 = 0 against Ha: β1 ≠ 0 at significance level α = .05
P value (β1 )= 3.38759E-09
Reject the null hypothesis at level .05 since the p-value is < 0.05.
59.11
124.0
0447.1ˆ
1
11
ˆ
1





SE
BB
tB
)ˆ(
ˆ
j
j
j
S
t




48. `
45

49. `
46
 Null Hypothesis: There is a relationship exists between the no. of
wickets taken and 3 things bowling average, strike rate and
economy rate.
Alternative hypothesis: there is no relationship exists between
them.

50. `
47
PLAYER NO.OF
MATCHES
PLAYED
RUNS
CONCEDED
BOWLING
AVERAGE
STRIKE
RATE
WICKETS
TAKEN
ECONOMY
RATE
Irfan pathan 24 618 22.07 16.5 28 8.62
Harbhajan singh 25 573 26.04 24.5 22 6.36
Shane watson 36 715 20.42 17 35 7.19
Mitchell johnson 28 724 20.11 16.8 36 7.14
Umar gul 49 1153 18.59 16 62 6.95
Saed ajmal 48 1092 15.82 15.4 69 6.13
Dwayne bravo 32 617 25.7 18 24 8.56
Tim southee 31 654 25.47 18.1 36 8.41
Struat broad 43 1113 23.18 18.9 48 7.34
Albie morkel 42 734 33.36 25 22 7.99
Johan botha 40 823 22.24 20.9 37 6.37
Dale steyn 28 636 17.18 16.2 37 6.36
Nuwan kulusekra 25 637 25.48 20.8 25 7.33
Lasith malinga 40 1025 21.35 17.1 48 7.48
Shahid afridi 56 1312 21.16 20.4 62 6.22

51. `
48

52. `
49

53. `
50

54. `
51
Model Summaryb
Model R R Square Adjuste
d R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .749
a
.561 .441 11.446 .561 4.678 3 11 .024
a. Predictors: (Constant), Economy Rate, Bowling Srike Rate, Bowling Average
b. Dependent Variable: Wickets
Taken
ANOVA
b
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 1838.574 3 612.858 4.678 .024a
Residual 1441.026 11 131.002
Total 3279.600 14
a. Predictors: (Constant), Economy Rate, Bowling Srike Rate, Bowling Average
b. Dependent Variable: Wickets Taken
a. Dependent Variable = Wicket Taken
Coefficients
a
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 228.628 102.607 2.228 .048
Bowling Average 3.280 4.632 .924 .708 .494
Bowling Srike Rate -6.689 5.697 -1.304 -1.174 .265
Economy Rate -19.032 13.793 -1.068 -1.380 .195

55. `
52
Descriptive Statistics
Mean Std. Deviation N
Wickets Taken 39.40 15.305 15
Bowling Average 22.5447 4.31333 15
Bowling Srike Rate 18.7733 2.98340 15
Economy Rate 7.2300 .85887 15
Correlations
Wickets Taken Bowling
Average
Bowling Srike
Rate
Economy Rate
Wickets Taken Pearson Correlation 1.000 -.690
**
-.501
*
-.510
*
Sig. (1-tailed) .002 .028 .026
N 15.000 15 15 15
Bowling Average Pearson Correlation -.690
**
1.000 .806
**
.528
*
Sig. (1-tailed) .002 .000 .022
N 15 15.000 15 15
Bowling Srike Rate Pearson Correlation -.501
*
.806
**
1.000 -.054
Sig. (1-tailed) .028 .000 .424
N 15 15 15.000 15
Economy Rate Pearson Correlation -.510
*
.528
*
-.054 1.000
Sig. (1-tailed) .026 .022 .424
N 15 15 15 15.000
**. Correlation is significant at the 0.01 level (1-tailed).
*. Correlation is significant at the 0.05 level (1-tailed).

56. `
53
Correlation analysis
High degree of Positive correlation: There is high degree of positive correlation between strike
rate and bowling average, which is easily justified. As if strike rate of a bowler is high, his
bowling average is also high.
Moderate degree of positive correlation: This is between bowling average and economy rate.
Low degree of Negative correlation: this is between strike rate and economy rate.
Moderate degree of negative correlation: this is between wickets taken and strike rate, wickets
taken and bowling average, and economy rate and wickets taken, which are all true in all
formats.
Regression Analysis
The regression analysis is done to analyze the relation between total no. of wickets taken with
each of the 3 things namely bowling average, economy rate and strike rate of a bowler.
Dependent variable(Y): wickets taken
Independent variables: Bowling average(X1), Strike rate(X2) and economy rate(X3)
Multivariate linear regression model
Intercept coefficient: A1=228.628
Slope coefficients :
B1 (represent change in Y due to change in X1) =3.280
B2 (represent change in Y due to change in X2) =(-6.689)
B3 (represent change in Y due to change in X2) = (-19.032)
Regression equation:
Y = 228.628+3.280X1-6.289X2-19.032X3

57. `
54
Actual Values Estimated Values Residual
28 26.6 1.4
22 29.11 -7.11
35 45.05 -10.05
36 46.32 -10.32
62 50.3 11.7
69 60.85 8.15
24 29.6 -5.6
36 31.03 4.97
48 38.54 9.46
22 18.75 3.25
37 40.54 -3.54
37 55.57 -18.57
25 33.56 -8.56
48 41.91 6.09
62 43.2 18.8
-25
-20
-15
-10
-5
0
5
10
15
20
25
0 5 10 15 20
Series1

58. `
55
Appropriateness of regression model
1. R2
(Coefficient of determination) : It means the proportion of total variation in
dependent variable which is explained by independent variables( regression model).
R2
=0.561
It means 56% of total variation in no. of wickets taken is explained by variation in
Bowling average, strike rate and economy rate.
It is not high but it is good.
2. Scatterplot of residuals : from the diagram we can see that residuals are randomly
scaterred with mean approximately 0.
The above results show that our regression model is good fit.
Anova analysis
Null hypothesis (H0): B1=B2=B3=0
Alternative Hypothesis (H1): the slope coefficients are not equal to 0.
Explanation: the test statistical value (4.678) is lot higher than critical value ( values from
F- tables), so we reject the null at 5% significance level.
Conclusion: so se conclude that slope coefficients are not equal to 0. They may be
greater or less than 0.
OVERALL CONCLUSION
From all the above results we conclude that there is a good relationship exists between
the no. of wickets taken a 3 things bowling average, strike rate and economy rate and
also between each of the 3 things

59. `
56
CONCLUSION
After going through a lots of data and applying different statistical tools in different parameters of the
game of cricket. The following was concluded from the study –
 The Strike rate of player in a ODI and Test differs from each other and there is very less
correlation between them, which is evident from the fact that there are only 50 overs to play in
ODI, but no limit in Test matches.
 There do exist a relation between the economy rate of a Fast Bowler and a Spinner of same
country in a ODI match.
 Contrary to widespread opinion, there is no competitive advantage of winning a toss on the
result of the match, this was proved using all the data of world cup matches starting from 1975.
 There exist a relation between No. of wickets taken and Economy rate, Strike Rate and Bowling
average of a bowler.

60. `
57
LIMITATIONS AND FURTHER STUDY
Following limitations were encountered during the making of this report-
 Data regarding results of toss of past matches was not available easily.
 Accurate predictions cannot be made just by evaluating a handful of random data.
 Comparing data of different time span may not be giving us a correct interpretation of result
 In hypothesis of a toss eventually leading to win, one more big factor is the home advantage,
which we donot consider here, since a team playing at its home ground do have more chances
of winning, irrespective of who wins the toss.
 In hypothesis of equal strike rate in ODI and Test, only Indian players were considered for
formulation of analysis, the result may change with players of other countries.
Scope of further study/research-
 An analysis can be made on the relation between winning of toss and winning of match after
that in “Test” matches, at toss play more important role in Test match rather in ODI.
 An hypothesis testing can be made on the relationship between the strike rate of player an ODI
and T-20, and this may come to be true.
 A detailed analysis should be done on why contrary to widespread opinion, there was no
relation between winning of toss and the team eventually winning the match.
 An analysis should also be done on whether there is an advantage for a team playing on its
home ground.

61. `
58
BIBLIOGRAPHY
The following internet sites were used for the collection of data
 www.cricinfo.com
 www.stats.com/cricket.asp
 www.howstat.com.au/
 www.icc-cricket.com/
 www.thatscricket.com/statistics/
Silva B.M and Swartz T.B ( 1994 ) “A statistical look at cricket data”, in Mathematics and Computers in
Sports, Bond University, Australia pp 89-104
Stalen P.J (2012) “Comparison of bowlers, batsman and all-rounders in cricket using graphical displays”,
at Department of Statistics, University of Pretoria, South Africa
Gill P.S and Beaudoin D (2004 ) “Dynamic Programming in one-day cricket”, in Journal of Operational
Reasearch Society, RMIT University, GPO Box 2476V, Melbourne, Australia.