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Analysis of Factors AffectingThe Winning Percentage of One Day International (ODI) Cricket Matches

MATH 240PROFESSOR LANCE ROBINS

SUMMER 2015

By

REDDY BHARGAVI KAPUGANTI (0579749)

SAI RAJU JAMPANA (0576385)

VENU GOPAL VEGI (0578423)

POORVA PATIL (578850)

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Table on Contents

Introduction – (Brief Background & Assumptions)………………………….3

Problem Statement……………………………………………………………..4

Test Design/Approach………………………………………………………….4

Results of Data Analysis/Testing………………………………………………5

Conclusion…………………….………………………………………………...9

References………………………………………………………………….......10

Appendix - (Data Formatted from Excel)……………………………………11

Descriptive Stats………………………………………………………...11

Linear Regression & Scatter Plots.........................................................13

Multiple Regression……………………………………………….……21

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Introduction

In this paper we endeavor to dissect the elements influencing the winning percentage in

One Day International (ODI) Cricket Matches considering the investigation of most recent 10

years. Separate relapse trials are distinguished for the examination of ODI among different

countries. This examination ought to give knowledge on exactly how vital every variable is, for

the execution of the teams in winning. Our group decided to break down the variables inside of

the one-day international cricket matches, on account of our interest in Cricket. We as students

of India had the delight of seeing the Indians win the latest series. Each of the four of us is from

India, so when India won match this year it was energizing, as everybody bolstered and

commended their win. In spite of the fact that we were not ready to exclusively concentrate on

the Indian cricket group as the information would be excessively restricted, we chose, making it

impossible to extend our center to the whole ODI. In this way we chose to check whether we

could figure out what variables inside ODI information would prompt a higher winning rate.

Our group gathered information from ESPN.com. The information gathered was of the 21

teams in one day global amid the normal season traversing from 10 years. We expressed the

needy variable as the winning rate, computed the winning percentage by dividing total matches

won by the total matches played. In addition we considered other important variables as Average

(Batting), Average (Bowling), Strike Rate (Bowling) and Highest Scores.

The independent variables that are considered are Average (Batting), Average (Bowling),

Strike Rate (Bowling) and Highest Scores. However, the dependent variable is Winning

Percentage. To score is to hit runs and expecting a team can score more runs, we must accept that

their batting attitudes are nice. In addition to this in bowling, the team is concerned on taking

wickets and maintaining proper average or run rate. Thus, we as a team have predicted that of all

the variables tested, the batting & bowling stats will yield the strongest linear relationship to the

winning percentage.

The free variables that group under bowling details are no ball, wide ball, run rate and

wickets taken. No ball is the point at which a bowler tosses over the height of the batsman or

bowling off the crease. Wide ball is the point at which the bowler bowls a ball which is too away

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from the wide crease. Run-rate is the runs given in a bowling over. Finally, wickets taken are

number of dismissals either by stump-outs; run-outs, lbw’s or catches. Bowling strike rate is

defined for a bowler as the average number of balls bowled per wicket taken. All these variables

are considered for the total average of bowling and strike rate.

Lastly, the free variables that group under batting details are runs and strike rate. Batting

strike rate is defined for a batsman as the average number of runs scored per 100 balls faced. The

higher the strike rate, the more effective a batsman is at scoring quickly. Runs are scored by a

batsman, and the aggregate of the scores of a team's batsmen (plus any extras) constitutes the

team's score. All these variables are considered for the total average of batting and strike rate.

Problem Statement

Our team chose to analyze the relationship of different details and the general winning

rate to better comprehend what constitutes a "winning group". In spite of the fact that we might

never be sure to what precisely constitutes a triumphant group, we might want to think our

outcomes will reveal some insight into what details to search for, in a team who has a higher

winning rate. We decided to break down the accompanying independent variables to see which

had the most influence on the winning rate: Average (Batting), Average (Bowling), Strike Rate

(Bowling) and Highest Score.

Test Design/Approach

This research paper is based on the data collected from the ESPN ODI official website

for the games played by 21 ODI teams within the last 10 years. The Excel data found in our

appendix contains data such as winning percentage, highest score, strike rate, average batting and

bowling.

Based on the acquired data, the first step was to perform the descriptive statistics. This

would give us a summary of the observations by analyzing the measures of central tendency

(mean, median, and mode), variation (rage, interquartile range, variance, standard deviation,

coefficient of variation), shape (skewness) etc.

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The second step was performing a linear regression analysis with each independent

variable in relationship to our dependent variable. The dependent variable chosen is Winning

percentage and the independent variables chosen are Average (Batting), Average (Bowling),

Strike Rate (Bowling) and Highest Score. This was performed to detect the strength of a

relationship between the two respected variables and by analyzing the scatter plot, measures of

variation, standard errors, intercept etc.

Next, we performed a numerous relapse examination, with the greater part of our

independent variables in relationship to our dependent variable (winning rate). This examination

spoke to the error between the real and anticipated worth served to analyze completely the

accompanying suppositions: linearity, autonomy and ordinariness, square with fluctuation, the

Durbin-Watson insights, leftover plot, t detail, p esteem and so forth. These outcomes would

permit us to investigate the relationship of one needy variable and more autonomous variables by

looking at the coefficient of numerous determinations (r2), balanced sr2 and the general F test.

The noteworthiness of the variables was dictated by the p-estimation of 0.05 or less:

If the p-worth is more noteworthy than or equivalent to α, don't dismiss the invalid

speculation;

If the p-worth is not as much as, reject the invalid speculation. As a final step, the

variable model was chosen as the most appropriate for our case and several tests were

conducted to check its assumptions including t-test, hypothesis test, regression analysis, p

value etc.

Results

We ran a multiple regression analysis as well as a simple linear regression on the data. The

simple linear regression results can be found in our Appendix. The multiple regression models

came out as significant. The variables batting average and bowling average affected the winning

percentage. We also noticed that both batting average and bowling average have an impact on

the winning percentage along with team performance. Therefore, for every change in the value of

batting average or blowing average will have an effect on winning percentage.

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Coefficients P-valueIntercept 0.1468 0.0344Ave(Batting) 0.0095 0.0000AVE(BOWLING) 0.0113 0.0000SR(BOWLING) -0.0057 0.0659HS 0.0003 0.0229

There was no need to re-run the multiple regression analysis again as the first model was

significant in itself, returning the p-values which are considerable for every relationship. The

multiple regressions were run on 95% confidence interval. The model has r square value

of .9646 and adjusted r square value of .9558, which represents a good number. The r square

number did not improve even if the least affecting independent variable was removed indicating

this to be the best option.

The effect of bowling average is interdependent on batting average score as well, since

they both contribute to winning percentage. Sure, teams might have a positive impact on their

home countries, but the absence of better bowling might change the game. Same as the batting

pitch has an impact on bowling, the blowing pitch would have impact the batting score. In this

case both terms have an impact on total score and winning percentage.

We have included the scatter plots for both of them below. The plots also go hand in hand

with this information and it is quite clear that the data for strike rate (bowling) and highest score

are quite scattered whereas average batting and bowling appears as a common collection of

performance numbers concentrated at one place.

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Scatter Plots – Team Batting Average & Team Bowling Average

Team Average Batting

Team Bowling Average

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The Multiple Regression Output

Regression StatisticsMultiple R 0.9821R Square 0.9646Adjusted R Square 0.9558Standard Error 0.0165Observations 21

ANOVA

df SS MS FSignificanc

e F

Regression 4 0.11850.029

6109.034

2 0.0000

Residual 16 0.00430.000

3Total 20 0.1228

Coefficients

Standard Error t Stat P-value Lower 95%

Upper 95%

Intercept 0.1468 0.06352.312

3 0.0344 0.0122 0.2814

Ave(Batting) 0.0095 0.00118.834

2 0.0000 0.0072 0.0118

AVE(BOWLING) 0.0113 0.00205.574

9 0.0000 0.0070 0.0156

SR(BOWLING) -0.0057 0.0029

-1.974

2 0.0659 -0.0118 0.0004

HS 0.0003 0.00012.516

2 0.0229 0.0000 0.0005

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Conclusion

The above illustration indicates that the coefficients are depicting correct relationships

with the dependent variable. For instance, the variables like Average Batting and Average

Bowling have positive impact on the winning percentage whereas Strike Rate and Highest

Scores do not better the winning percentage. The coefficient for Strike Rate is actually negative

proving the same.

As team batting average and bowling average have a higher impact on the winning

percentage and hence a team should be putting a higher emphasis on both bowling and batting.

Meaning more time should be spent with the bowler so that his stats in reference to dismissals

can be higher. This would return a positive impact on the overall winning percentage. Moreover,

the batsmen should work on increasing their strike rate by making more runs and using new

strategies on the field while playing. The team should also put more time in practicing on hitting

the ball. If the team as a whole can improve its batting average, it is more likely that number of

boundaries will increase as well.

When selecting players for the team, the captains should have an idea that which player

can play in a strategic way on the field to score more runs. They should also watch out for

bowlers who have a history of good run rate (Bowling) or a higher percentage in accomplishing

wickets. Although a player can lose abilities, or abilities can deplete over time, it is still better to

have a stronger history of bowling. When playing against other team, it could be beneficial for

the bowler and the captain to know which players are more likely to hit boundaries (sixes and

fours) more often.

We have taken into consideration that the higher the sample size, the more closely the

representation of actual numbers. Also, because of the range with respect to the time covered,

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these results can be trustworthy for not just the coming year but for many years to come. Hence,

our data covered 21 teams spanning a 10 year period.

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References

Data was retrieved primarily from ESPN.com varying by years:

ESPN cricketinfo. (2015). Statistics One- Day International. Retrieved from

http://stats.espncricinfo.com/ci/engine/stats/index.html?

class=2;filter=advanced;groupby=team;orderby=runs;result=1;template=results;type=ba

tting

ESPN cricketinfo. (2015).Statistics One- Day International. Retrieved from

http://stats.espncricinfo.com/ci/engine/stats/index.html?

class=2;filter=advanced;groupby=team;orderby=wickets;result=1;spanmax1=11+Aug+2

015;spanmin1=11+Aug+2005;spanval1=span;template=results;type=bowling

Levine, D., Stephan, D., &Szabat, K. (2014). Statistics for Manager: Using Microsoft Excel (p.

314). Upper Saddle River, NJ: Pearson Education.

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Appendix

Descriptive Summary: Average Batting

Ave(Batting)Mean 35.67714286Median 37.64Mode #N/AMinimum 17.4Maximum 42.77Range 25.37Variance 29.3790Standard Deviation 5.4202Coeff. of Variation 15.19%Skewness -2.0180Kurtosis 5.9253Count 21Standard Error 1.1828

Descriptive Summary: Average Bowling

AVE(BOWLING)

Mean 24.04619048Median 23.79Mode #N/AMinimum 19.4Maximum 33.17Range 13.77Variance 9.5197Standard Deviation 3.0854Coeff. of Variation 12.83%Skewness 1.3552Kurtosis 2.8792Count 21Standard Error 0.6733

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Descriptive Summary: Strike Rate (Bowling)

SR(BOWLING)Mean 32.23809524Median 31.8Mode 31.4Minimum 28.9Maximum 37.7Range 8.8Variance 4.3145Standard Deviation 2.0771Coeff. of Variation 6.44%Skewness 1.0017Kurtosis 1.1237Count 21Standard Error 0.4533

Descriptive Summary: Highest Scores

HSMean 161.8571429Median 158Mode #N/AMinimum 78Maximum 264Range 186Variance 2080.4286Standard Deviation 45.6117Coeff. of Variation 28.18%Skewness 0.3422Kurtosis 0.0602Count 21Standard Error 9.9533

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Descriptive Summary: Winning Percentage

Winning %Mean 0.620028571Median 0.6348Mode #N/AMinimum 0.394Maximum 0.7585Range 0.3645Variance 0.0061Standard Deviation 0.0784Coeff. of Variation 12.64%Skewness -1.0271Kurtosis 2.5563Count 21Standard Error 0.0171

Linear Regression: Average Batting

Regression StatisticsMultiple R 0.9334R Square 0.8713Adjusted R Square 0.8645Standard Error 0.0288Observations 21

ANOVA

df SS MS FSignificanc

e F

Regression 1 0.1070 0.1070128.594

0 0.0000Residual 19 0.0158 0.0008Total 20 0.1228

Coefficients

Standard Error t Stat P-value Lower 95%

Upper 95%

Intercept 0.1385 0.0429 3.2277 0.0044 0.0487 0.2284

Ave(Batting) 0.0135 0.001211.339

9 0.0000 0.0110 0.0160

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Scatter Plot: Average Batting

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Linear Regression: Average Bowling

Regression StatisticsMultiple R 0.7112R Square 0.5058Adjusted R Square 0.4798Standard Error 0.0565Observations 21

ANOVA

df SS MS FSignificanc

e F

Regression 1 0.06210.062

119.448

6 0.0003

Residual 19 0.06070.003

2Total 20 0.1228

Coefficients

Standard Error t Stat P-value Lower 95%

Upper 95%

Intercept 0.1856 0.09931.870

2 0.0769 -0.0221 0.3934

AVE(BOWLING) 0.0181 0.00414.410

1 0.0003 0.0095 0.0266

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Scatter Plot: Average Bowling

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Linear regression: Strike Rate (Bowling)

Regression StatisticsMultiple R 0.5266R Square 0.2773Adjusted R Square 0.2393Standard Error 0.0684Observations 21

ANOVA

df SS MS FSignificance

F

Regression 1 0.03410.034

17.291

0 0.0142

Residual 19 0.08880.004

7Total 20 0.1228

Coefficients

Standard Error t Stat

P-value Lower 95%

Upper 95%

Intercept -0.0205 0.2377

-0.086

20.932

2 -0.5180 0.4770

SR(BOWLING) 0.0199 0.00742.700

20.014

2 0.0045 0.0353

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Scatter Plot: Strike Rate (Bowling)

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Linear Regression: Highest Scores

Regression StatisticsMultiple R 0.6922R Square 0.4791Adjusted R Square 0.4517Standard Error 0.0580Observations 21

ANOVA

df SS MS FSignificanc

e F

Regression 1 0.05890.058

917.478

7 0.0005

Residual 19 0.06400.003

4Total 20 0.1228

Coefficients

Standard Error t Stat P-value Lower 95%

Upper 95%

Intercept 0.4275 0.04788.953

0 0.0000 0.3276 0.5275

HS 0.0012 0.00034.180

8 0.0005 0.0006 0.0018

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Scatter Plot: Highest Scores

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Multiple Regression Analysis:

Regression StatisticsMultiple R 0.9821R Square 0.9646Adjusted R Square 0.9558Standard Error 0.0165Observations 21

ANOVA

df SS MS FSignificanc

e F

Regression 4 0.11850.029

6109.034

2 0.0000

Residual 16 0.00430.000

3Total 20 0.1228

Coefficients

Standard Error t Stat P-value Lower 95%

Upper 95%

Intercept 0.1468 0.06352.312

3 0.0344 0.0122 0.2814

Ave(Batting) 0.0095 0.00118.834

2 0.0000 0.0072 0.0118

AVE(BOWLING) 0.0113 0.00205.574

9 0.0000 0.0070 0.0156

SR(BOWLING) -0.0057 0.0029

-1.974

2 0.0659 -0.0118 0.0004HS 0.0003 0.0001 2.516 0.0229 0.0000 0.0005

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