The Clash of the Titans in Test and ODI cricket

Who looks outside, dreams; who looks inside, awakes.
Show me a sane man and I will cure him for you.

            Carl Jung 

 

We’re made of star stuff. We are a way for the cosmos to know itself.
If you want to make an apple pie from scratch, you must first create the universe.

            Carl Sagan

Introduction

The biggest nag in the collective psyche of cricketing fraternity these days, is whether Virat Kohli has surpassed Sachin Tendulkar. This question has been troubling cricket lovers the world over and particularly in India, for quite a while. This nagging question has only grown stronger with Kohli’s 41st ODI century and with Michael Vaughan bestowing the GOAT title to Virat Kohli for ODI cricket. Hence, I decided to do my bit in addressing this, by doing analysis of Kohli’s and Tendulkar’s performance in ODI cricket. I also wanted to address the the best among the cricketing idols of India in Test cricket, namely Sunil Gavaskar, Sachin Tendulkar and Virat Kohli. Hence this post has 2 parts

  1. Analysis of Tendulkar, Gavaskar and Kohli in Test cricket
  2. Analysis of Tendulkar and Kohli in ODIs

In this post, I analyze the performances of these titans in Test and ODI cricket using my R package cricketr. While some may feel that comparisons are not possible as these batsmen are from different eras. To some extent this is true. I would give some leeway to Gavaskar as he had to bat in a pre-helmet era. But with Tendulkar and Kohli a fair and objective comparison is possible. There were pre-eminient bowlers in the times of Tendulkar as there are now.

From the analysis below, it can be seen that Tendulkar is ahead  of everybody else in Test cricket. However it must be noted that Tendulkar’s performance deteriorated towards the end of his career. Such was not the case with Gavaskar. Kohli has some catching up to do and he still has a lot of Test cricket in him.

In ODI Kohli can be seen to pulling ahead of Tendulkar in several aspects.

My R package cricketr can be installed directly from CRAN and you can use it analyze cricketers.

This package uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package supports all formats of the game including Test, ODI and Twenty20 versions.

You should be able to install the package from GitHub and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

Take a look at my short video tutorial on my R package cricketr on Youtube – R package cricketr – A short tutorial

Do check out my interactive Shiny app implementation using the cricketr package – Sixer – R package cricketr’s new Shiny avatar

Note 1: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricketr templatefrom Github (which is the R Markdown file I have used for the analysis below).

Note 2: I sprinkle the charts with my observations. Feel free to look at them more closely and come to your conclusions.

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

Untitled

Important note: Do check out the python avatar of cricketr, ‘cricpy’ in my post Introducing cricpy:A python package to analyze performances of cricketers

1 Load the cricketr package

if (!require("cricketr")){
    install.packages("cricketr",lib = "c:/test")
}
library(cricketr)

A Test cricket  – Analysis of Gavaskar, Tendulkar and Kohli

2. Get player data

tendulkar <- getPlayerData(35320,dir=".",file="tendulkar.csv",type="batting")
kohli <- getPlayerData(253802,dir=".",file="kohli.csv",type="batting")
gavaskar <- getPlayerData(28794,dir=".",file="gavaskar.csv",type="batting")

3a. Basic analyses for Tendulkar

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("./tendulkar.csv","Tendulkar")
batsmanMeanStrikeRate("./tendulkar.csv","Tendulkar")
batsmanRunsRanges("./tendulkar.csv","Tendulkar")
dev.off()

3b Basic analyses for Kohli

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("./kohli.csv","Kohli")
batsmanMeanStrikeRate("./kohli.csv","Kohli")
batsmanRunsRanges("./kohli.csv","Kohli")
dev.off()

3c Basic analyses for Gavaskar

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("./gavaskar.csv","Gavaskar")
batsmanMeanStrikeRate("./gavaskar.csv","Gavaskar")
batsmanRunsRanges("./gavaskar.csv","Gavaskar")
dev.off()

4a.More analyses for Tendulkar

It can be seen that Tendulkar and Gavaskar has been bowled more often than Kohli. Also Kohli does not have as many sixes in Test cricket as Tendulkar and Gavaskar

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./tendulkar.csv","Tendulkar")
batsman6s("./tendulkar.csv","Tendulkar")
batsmanDismissals("./tendulkar.csv","Tendulkar")
dev.off()

4b. More analyses for Kohli

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./kohli.csv","Kohli")
batsman6s("./kohli.csv","Kohli")
batsmanDismissals("./kohli.csv","Kohli")
dev.off()

4c More analyses for Gavaskar

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./gavaskar.csv","Gavaskar")
batsman6s("./gavaskar.csv","Gavaskar")
batsmanDismissals("./gavaskar.csv","Gavaskar")
dev.off()

5 Performance of batsmen on different grounds

par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./tendulkar.csv","Tendulkar")
batsmanAvgRunsGround("./kohli.csv","Kohli")
batsmanAvgRunsGround("./gavaskar.csv","Gavaskar")

a

#dev.off()

6. Performance if batsmen against different Opposition

  1. Tendulkar averages 50 against the following countries – Australia, Bangladesh, England, Sri Lanka, West Indies and Zimbabwe
  2. Kohli average almost 50 against all the nations he has played – Australia, Bangladesh, England, New Zealand, Sri Lanka and West Indies
  3. Gavaskar averages 50 against Australia, Pakistan, West Indies, Sri Lanka
par(mar=c(4,4,2,2))
batsmanAvgRunsOpposition("./tendulkar.csv","Tendulkar")
batsmanAvgRunsOpposition("./kohli.csv","Kohli")
batsmanAvgRunsOpposition("./gavaskar.csv","Gavaskar")

7. Get player data special

This is required for the next 2 function calls

tendulkarsp <- getPlayerDataSp(35320,tdir=".",tfile="tendulkarsp.csv",ttype="batting")
kohlisp <- getPlayerDataSp(253802,tdir=".",tfile="kohlisp.csv",ttype="batting")
gavaskarsp <- getPlayerDataSp(28794,tdir=".",tfile="gavaskarsp.csv",ttype="batting")

#dev.off()

8 Get contribution of batsmen in matches won and lost

Kohli contribution has had an equal contribution in won and lost matches. Tendulkar’s runs seem to have not helped in winning as much as only 50% of matches he has played have been won

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanContributionWonLost("tendulkarsp.csv","Tendulkar")
batsmanContributionWonLost("./kohlisp.csv","Kohli")
batsmanContributionWonLost("./gavaskarsp.csv","Gavaskar")
  

a

9 Performance of batsmen at home and overseas

The boxplots show that Kohli performs better overseas than at home. The 3rd quartile is higher, though the median seems to lower overseas. For Tendulkar the performance is similar both ways. Gavaskar’s median runs scored overseas is higher.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))


batsmanPerfHomeAway("tendulkarsp.csv","Tendulkar")
batsmanPerfHomeAway("./kohlisp.csv","Kohli")
batsmanPerfHomeAway("./gavaskarsp.csv","Gavaskar")

10. Moving average of runs

Gavaskar’s moving average was very good at the time of his retirement. Kohli seems to be going very strong. Tendulkar’s performance shows signs of deterioration around the time of his retirement.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanMovingAverage("./tendulkar.csv","Tendulkar")
batsmanMovingAverage("./kohli.csv","Kohli")
batsmanMovingAverage("./gavaskar.csv","Gavaskar")

#dev.off()

11 Boxplot and histogram of runs

Kohli has a marginally higher average (50.69) than Tendulkar (48.65) while Gavaskar 46. The median runs are same for Tendulkar and Kohli at 32

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfBoxHist("./tendulkar.csv","Sachin Tendulkar")
batsmanPerfBoxHist("./kohli.csv","Kohli")
batsmanPerfBoxHist("./gavaskar.csv","Gavaskar")

12 Cumulative average Runs for batsmen

Looking at the cumulative average runs we can see a gradual drop in the cumulative average for Tendulkar while Kohli and Gavaskar’s performance seems to be getting better

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanCumulativeAverageRuns("./tendulkar.csv","Tendulkar")
batsmanCumulativeAverageRuns("./kohli.csv","Kohli")
batsmanCumulativeAverageRuns("./gavaskar.csv","Gavaskar")

13. Cumulative average strike rate of batsmen

Tendulkar’s strike rate is better than Kohli and Gavaskar

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanCumulativeStrikeRate("./tendulkar.csv","Tendulkar")
batsmanCumulativeStrikeRate("./kohli.csv","Kohli")
batsmanCumulativeStrikeRate("./gavaskar.csv","Gavaskar")

14 Performance forecast of batsmen

The forecasted performance for Kohli and Gavaskar is higher than that of Tendulkar

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfForecast("./tendulkar.csv","Sachin Tendulkar")
batsmanPerfForecast("./kohli.csv","Kohli")
batsmanPerfForecast("./gavaskar.csv","Gavaskar")

#dev.off()

15. Relative strike rate of batsmen

par(mar=c(4,4,2,2))

frames <- list("./tendulkar.csv","./kohli.csv","gavaskar.csv")
names <- list("Tendulkar","Kohli","Gavaskar")
relativeBatsmanSR(frames,names)
#dev.off()

16. Relative Runs frequency of batsmen

par(mar=c(4,4,2,2))
frames <- list("./tendulkar.csv","./kohli.csv","gavaskar.csv")
names <- list("Tendulkar","Kohli","Gavaskar")
relativeRunsFreqPerf(frames,names)
#dev.off()

17. Relative cumulative average runs of batsmen

Tendulkar leads the way here, but it can be seem Kohli catching up.

par(mar=c(4,4,2,2))
frames <- list("./tendulkar.csv","./kohli.csv","gavaskar.csv")
names <- list("Tendulkar","Kohli","Gavaskar")
relativeBatsmanCumulativeAvgRuns(frames,names)
#dev.off()

18. Relative cumulative average strike rate

Tendulkar has better strike rate than the other two.

par(mar=c(4,4,2,2))
frames <- list("./tendulkar.csv","./kohli.csv","gavaskar.csv")
names <- list("Tendulkar","Kohli","Gavaskar")
relativeBatsmanCumulativeStrikeRate(frames,names)
#dev.off()

19. Check batsman in form

As in the moving average and performance forecast and cumulative average runs, Kohli and Gavaskar are in-form while Tendulkar was out-of-form towards the end.

checkBatsmanInForm("./tendulkar.csv","Sachin Tendulkar")
## [1] "**************************** Form status of Sachin Tendulkar ****************************
\n\n Population size: 294  Mean of population: 50.48 \n Sample size: 33  Mean of sample: 32.42 SD of 
sample: 29.8 \n\n Null hypothesis H0 : Sachin Tendulkar 's sample average is within 95% confidence interval 
of population average\n Alternative hypothesis Ha : Sachin Tendulkar 's sample average is below 
the 95% confidence interval of population average\n\n 
Sachin Tendulkar 's Form Status: Out-of-Form because the p value: 0.000713  is less than alpha=  0.05 \n *******************************************************************************************\n\n"
checkBatsmanInForm("./kohli.csv","Kohli")
## [1] "**************************** Form status of Kohli ****************************\n\n Population size: 117
  Mean of population: 50.35 \n Sample size: 13  Mean of sample: 53.77 SD of sample: 46.15 \n\n Null 
hypothesis H0 : Kohli 's sample average is within 95% confidence interval of population average\n 
Alternative hypothesis Ha : Kohli 's sample average is below the 95% confidence interval of population
 average\n\n Kohli 's Form Status: In-Form because the p value: 0.603244  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"
checkBatsmanInForm("./gavaskar.csv","Gavaskar")
## [1] "**************************** Form status of Gavaskar ****************************\n\n 
Population size: 125  Mean of population: 44.67 \n Sample size: 14  Mean of sample: 57.86 SD of sample:
 58.55 \n\n Null hypothesis H0 : Gavaskar 's sample average is within 95% confidence interval of population
 average\n Alternative hypothesis Ha : Gavaskar 's sample average is below the 95% confidence interval of 
population average\n\n Gavaskar 's Form Status: In-Form because the p value: 0.793276  is greater 
than alpha=  0.05 \n *******************************************************************************************\n\n"
#dev.off()

20. Performance 3D

A 3D regression plane is fitted between the the Balls faced, Minutes at crease and Runs scored

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
battingPerf3d("./tendulkar.csv","Sachin Tendulkar")
battingPerf3d("./kohli.csv","Kohli")
battingPerf3d("./gavaskar.csv","Gavaskar")
#dev.off()

20. Runs likelihood

This functions computes the K-Means and determines the runs the batsmen are likely to score.

par(mar=c(4,4,2,2))
batsmanRunsLikelihood("./tendulkar.csv","Tendulkar")
## Summary of  Tendulkar 's runs scoring likelihood
## **************************************************
## 
## There is a 16.51 % likelihood that Tendulkar  will make  139 Runs in  251 balls over 353  Minutes 
## There is a 25.08 % likelihood that Tendulkar  will make  66 Runs in  122 balls over  167  Minutes 
## There is a 58.41 % likelihood that Tendulkar  will make  16 Runs in  31 balls over 44  Minutes
batsmanRunsLikelihood("./kohli.csv","Kohli")
## Summary of  Kohli 's runs scoring likelihood
## **************************************************
## 
## There is a 20 % likelihood that Kohli  will make  143 Runs in  232 balls over 330  Minutes 
## There is a 33.85 % likelihood that Kohli  will make  51 Runs in  92 balls over  127  Minutes 
## There is a 46.15 % likelihood that Kohli  will make  11 Runs in  24 balls over 31  Minutes
batsmanRunsLikelihood("./gavaskar.csv","Gavaskar")
## Summary of  Gavaskar 's runs scoring likelihood
## **************************************************
## 
## There is a 33.81 % likelihood that Gavaskar  will make  69 Runs in  159 balls over 214  Minutes 
## There is a 8.63 % likelihood that Gavaskar  will make  172 Runs in  364 balls over  506  Minutes 
## There is a 57.55 % likelihood that Gavaskar  will make  13 Runs in  35 balls over 48  Minutes

21. Predict runs for a random combination of Balls faced and runs scored

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
tendulkar <- batsmanRunsPredict("./tendulkar.csv","Tendulkar",newdataframe=newDF)
kohli <- batsmanRunsPredict("./kohli.csv","Kohli",newdataframe=newDF)
gavaskar <- batsmanRunsPredict("./gavaskar.csv","Gavaskar",newdataframe=newDF)
batsmen <-cbind(round(tendulkar$Runs),round(kohli$Runs),round(gavaskar$Runs))
colnames(batsmen) <- c("Tendulkar","Kohli","Gavaskar")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns
##    BallsFaced MinsAtCrease Tendulkar Kohli Gavaskar
## 1          10           30         7     6        4
## 2          38           71        23    24       17
## 3          66          111        39    42       30
## 4          94          152        54    60       43
## 5         121          193        70    78       56
## 6         149          234        86    96       69
## 7         177          274       102   114       82
## 8         205          315       118   132       95
## 9         233          356       134   150      108
## 10        261          396       150   168      121
## 11        289          437       165   186      134
## 12        316          478       181   204      147
## 13        344          519       197   222      160
## 14        372          559       213   240      173
## 15        400          600       229   258      186
#dev.off()

Key findings

  1. Kohli has a marginally higher average than Tendulkar
  2. Tendulkar has the best strike rate of all the 3.
  3. The cumulative average runs and the performance forecast for Kohli and Gavaskar show an improving trend, while Tendulkar’s numbers deteriorate towards the end of his career
  4. Kohli is fast catching up Tendulkar on cumulative average runs vs innings in career.

B ODI Cricket – Analysis of Tendulkar and Kohli

The functions below get the ODI data for Tendulkar and Kohli as CSV files so that the analyses can be done

22 Get player data for ODIs

tendulkarOD <- getPlayerDataOD(35320,dir=".",file="tendulkarOD.csv",type="batting")
kohliOD <- getPlayerDataOD(253802,dir=".",file="kohliOD.csv",type="batting")

#dev.off()

23a Basic performance of Tendulkar in ODI

par(mfrow=c(3,2))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("./tendulkarOD.csv","Tendulkar")
batsmanRunsRanges("./tendulkarOD.csv","Tendulkar")
batsman4s("./tendulkarOD.csv","Tendulkar")
batsman6s("./tendulkarOD.csv","Tendulkar")
batsmanScoringRateODTT("./tendulkarOD.csv","Tendulkar")
#dev.off()

23b. Basic performance of Kohli in ODI

par(mfrow=c(3,2))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("./kohliOD.csv","Kohli")
batsmanRunsRanges("./kohliOD.csv","Kohli")
batsman4s("./kohliOD.csv","Kohli")
batsman6s("./kohliOD.csv","Kohli")
batsmanScoringRateODTT("./kohliOD.csv","Kohli")
#dev.off()

24. Performance forecast in ODIs

Kohli’s forecasted runs are much higher than Tendulkar’s in ODIs

par(mar=c(4,4,2,2))
batsmanPerfForecast("./tendulkarOD.csv","Tendulkar")
batsmanPerfForecast("./kohliOD.csv","Kohli")

25. Batting performance

A 3D regression plane is fitted between Balls faced, Minutes at crease and Runs scored.

par(mar=c(4,4,2,2))
battingPerf3d("./tendulkarOD.csv","Tendulkar")
battingPerf3d("./kohliOD.csv","Kohli")

26. Predicting runs scored for the ODI batsmen

Kohli will score runs than Tendulkar for the same minutes at crease and balls faced.

BF <- seq( 10, 200,length=10)
Mins <- seq(30,220,length=10)
newDF <- data.frame(BF,Mins)
tendulkarDF <- batsmanRunsPredict("./tendulkarOD.csv","Tendulkar",newdataframe=newDF)
kohliDF <- batsmanRunsPredict("./kohliOD.csv","Kohli",newdataframe=newDF)
batsmen <-cbind(round(tendulkarDF$Runs),round(kohliDF$Runs))
colnames(batsmen) <- c("Tendulkar","Kohli")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns
##    BallsFaced MinsAtCrease Tendulkar Kohli
## 1          10           30         7     8
## 2          31           51        26    28
## 3          52           72        45    48
## 4          73           93        64    68
## 5          94          114        83    88
## 6         116          136       102   108
## 7         137          157       121   128
## 8         158          178       140   149
## 9         179          199       159   169
## 10        200          220       178   189

27. Runs likelihood for the ODI batsmen

Tendulkar has clusters around 13, 53 and 111 runs while Kohli has clusters around 13, 63,116. So it more likely that Kohli will tend to score higher

par(mar=c(4,4,2,2))
batsmanRunsLikelihood("./tendulkarOD.csv","Tendulkar")
## Summary of  Tendulkar 's runs scoring likelihood
## **************************************************
## 
## There is a 18.09 % likelihood that Tendulkar  will make  111 Runs in  118 balls over 172  Minutes 
## There is a 28.39 % likelihood that Tendulkar  will make  53 Runs in  63 balls over  95  Minutes 
## There is a 53.52 % likelihood that Tendulkar  will make  13 Runs in  18 balls over 27  Minutes
batsmanRunsLikelihood("./kohliOD.csv","Kohli")
## Summary of  Kohli 's runs scoring likelihood
## **************************************************
## 
## There is a 31.41 % likelihood that Kohli  will make  63 Runs in  69 balls over 97  Minutes 
## There is a 49.74 % likelihood that Kohli  will make  13 Runs in  18 balls over  24  Minutes 
## There is a 18.85 % likelihood that Kohli  will make  116 Runs in  113 balls over 163  Minutes

28. Runs in different venues for the ODI batsmen

par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./tendulkarOD.csv","Tendulkar")
batsmanAvgRunsGround("./kohliOD.csv","Kohli")

28. Runs against different opposition for the ODI batsmen

Tendulkar’s has 50+ average against Bermuda, Kenya and Namibia. While Kohli has a 50+ average against New Zealand, West Indies, South Africa, Zimbabwe and Bangladesh

par(mar=c(4,4,2,2))
batsmanAvgRunsOpposition("./tendulkarOD.csv","Tendulkar")
batsmanAvgRunsOpposition("./kohliOD.csv","Kohli")

29. Moving average of runs for the ODI batsmen

Tendulkar’s moving average shows an improvement (50+) towards the end of his career, but Kohli shows a marked increase 60+ currently

par(mar=c(4,4,2,2))
batsmanMovingAverage("./tendulkarOD.csv","Tendulkar")
batsmanMovingAverage("./kohliOD.csv","Kohli")

30. Cumulative average runs of ODI batsmen

Tendulkar plateaus at 40+ while Kohli’s cumulative average runs goes up and up!!!

par(mar=c(4,4,2,2))
batsmanCumulativeAverageRuns("./tendulkarOD.csv","Tendulkar")
batsmanCumulativeAverageRuns("./kohliOD.csv","Kohli")

31 Cumulative strike rate of ODI batsmen

par(mar=c(4,4,2,2))
batsmanCumulativeStrikeRate("./tendulkarOD.csv","Tendulkar")
batsmanCumulativeStrikeRate("./kohliOD.csv","Kohli")

32. Relative batsmen strike rate

par(mar=c(4,4,2,2))

frames <- list("./tendulkarOD.csv","./kohliOD.csv")
names <- list("Tendulkar","Kohli")
relativeBatsmanSRODTT(frames,names)
#dev.off()

33. Relative Run Frequency percentages

par(mar=c(4,4,2,2))

frames <- list("./tendulkarOD.csv","./kohliOD.csv")
names <- list("Tendulkar","Kohli")
relativeRunsFreqPerfODTT(frames,names)
#dev.off()

34. Relative cumulative average runs of ODI batsmen

Kohli breaks away from Tendulkar in cumulative average runs after 100 innings

par(mar=c(4,4,2,2))

frames <- list("./tendulkarOD.csv","./kohliOD.csv")
names <- list("Tendulkar","Kohli")
relativeBatsmanCumulativeAvgRuns(frames,names)
#dev.off()

35. Relative cumulative strike rate of ODI batsmen

This seems to be tussle with Kohli having an edge till about 40 innings and then from 40+ to 180 innings Tendulkar leads. Kohli just seems to be edging forward.

par(mar=c(4,4,2,2))

frames <- list("./tendulkarOD.csv","./kohliOD.csv")
names <- list("Tendulkar","Kohli")
relativeBatsmanCumulativeStrikeRate(frames,names)
#dev.off()

36. Batsmen 4s and 6s

par(mar=c(4,4,2,2))

frames <- list("./tendulkarOD.csv","./kohliOD.csv")
names <- list("Tendulkar","Kohli")
batsman4s6s(frames,names)
##                Tendulkar Kohli
## Runs(1s,2s,3s)     66.29 69.67
## 4s                 29.65 25.90
## 6s                  4.06  4.43
#dev.off()

37. Check ODI batsmen form

par(mar=c(4,4,2,2))

checkBatsmanInForm("./tendulkar.csv","Tendulkar")
## [1] "**************************** Form status of Tendulkar ********
********************\n\n Population size: 294  Mean of population: 50.48 \n
 Sample size: 33  Mean of sample: 32.42 SD of sample: 29.8 \n\n 
Null hypothesis H0 : Tendulkar 's sample average is within 95% confidence
 interval of population average\n Alternative hypothesis 
Ha : Tendulkar 's sample average is below the 95% confidence interval 
of population average\n\n Tendulkar 's Form Status: Out-of-Form because the p value: 0.000713  is less than alpha=  0.05 \n *******************************************************************************************\n\n"
checkBatsmanInForm("./kohli.csv","Kohli")
## [1] "**************************** Form status of Kohli ***********
*****************\n\n Population size: 117  Mean of population: 50.35 \n
 Sample size: 13  Mean of sample: 53.77 SD of sample: 46.15 \n\n 
Null hypothesis H0 : Kohli 's sample average is within 95% confidence 
interval of population average\n Alternative hypothesis 
Ha : Kohli 's sample average is below the 95% confidence interval 
of population average\n\n Kohli 's Form Status: In-Form because 
the p value: 0.603244  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"
#dev.off()

Key Findings

  1. Kohli has a better performance against oppositions like West Indies, South Africa and New Zealand
  2. Kohli breaks away from Tendulkar in cumulative average runs
  3. Tendulkar has been leading the strike rate rate but Kohli in recent times seems to be breaking loose.

Check out some other players with my R package cricketr

Important note: Do check out my other posts using cricketr at cricketr-posts

Also see

  1. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
  2. A primer on Qubits, Quantum gates and Quantum Operations
  3. De-blurring revisited with Wiener filter using OpenCV
  4. Deep Learning from first principles in Python, R and Octave – Part 4
  5. The Many Faces of Latency
  6. Fun simulation of a Chain in Android
  7. Presentation on Wireless Technologies – Part 1
  8. yorkr crashes the IPL party ! – Part 1

To see all posts click Index of posts

Introducing cricpy:A python package to analyze performances of cricketers

Full many a gem of purest ray serene,
The dark unfathomed caves of ocean bear;
Full many a flower is born to blush unseen,
And waste its sweetness on the desert air.

            Thomas Gray, An Elegy Written In A Country Churchyard
            

Introduction

It is finally here! cricpy, the python avatar , of my R package cricketr is now ready to rock-n-roll! My R package cricketr had its genesis about 3 and some years ago and went through a couple of enhancements. During this time I have always thought about creating an equivalent python package like cricketr. Now I have finally done it.

So here it is. My python package ‘cricpy!!!’

This package uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package supports only Test cricket

You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

This post is also hosted on Rpubs at Introducing cricpy. You can also download the pdf version of this post at cricpy.pdf

Do check out my post on R package cricketr at Re-introducing cricketr! : An R package to analyze performances of cricketers

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

Untitled

This package uses the statistics info available in ESPN Cricinfo Statsguru.

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricpy-template from Github (which is the R Markdown file I have used for the analysis below). You will only need to make appropriate changes for the players you are interested in. The functions can be executed in RStudio or in a IPython notebook.

The cricpy package

The cricpy package has several functions that perform several different analyses on both batsman and bowlers. The package has functions that plot percentage frequency runs or wickets, runs likelihood for a batsman, relative run/strike rates of batsman and relative performance/economy rate for bowlers are available.

Other interesting functions include batting performance moving average, forecasting, performance of a player against different oppositions, contribution to wins and losses etc.

The data for a particular player can be obtained with the getPlayerData() function. To do this you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Rahul Dravid, Virat Kohli, Alastair Cook etc. This will bring up a page which have the profile number for the player e.g. for Rahul Dravid this would be http://www.espncricinfo.com/india/content/player/28114.html. Hence, Dravid’s profile is 28114. This can be used to get the data for Rahul Dravid as shown below

The cricpy package is almost a clone of my R package cricketr. The signature of all the python functions are identical with that of its R avatar namely  ‘cricketr’, with only the necessary variations between Python and R. It may be useful to look at my post R vs Python: Different similarities and similar differences. In fact if you are familiar with one of the languages you can look up the package in the other and you will notice the parallel constructs.

You can fork/clone the cricpy package at Github cricpy

The following 2 examples show the similarity between cricketr and cricpy packages

1a.Importing cricketr – R

Importing cricketr in R

#install.packages("cricketr")
library(cricketr)

2a. Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy
# You could either do
#1.  
import cricpy.analytics as ca 
#ca.batsman4s("../dravid.csv","Rahul Dravid")
# Or
#2.
from cricpy.analytics import *
#batsman4s("../dravid.csv","Rahul Dravid")

I would recommend using option 1 namely ca.batsman4s() as I may add an advanced analytics module in the future to cricpy.

2 Invoking functions

You can seen how the 2 calls are identical for both the R package cricketr and the Python package cricpy

2a. Invoking functions with R package ‘cricketr’

library(cricketr)
batsman4s("../dravid.csv","Rahul Dravid")

2b. Invoking functions with Python package ‘cricpy’

import cricpy.analytics as ca 
ca.batsman4s("../dravid.csv","Rahul Dravid")

 

3a. Getting help from cricketr – R

#help("getPlayerData")

3b. Getting help from cricpy – Python

help(ca.getPlayerData)
## Help on function getPlayerData in module cricpy.analytics:
## 
## getPlayerData(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2], result=[1, 2, 4], create=True)
##     Get the player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory
##     
##     Description
##     
##     Get the player data given the profile of the batsman. The allowed inputs are home,away or both and won,lost or draw of matches. The data is stored in a .csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerData(profile,opposition="",host="",dir="./data",file="player001.csv",
##     type="batting", homeOrAway=c(1,2),result=c(1,2,4))
##     Arguments
##     
##     profile     
##     This is the profile number of the player to get data. This can be obtained from http://www.espncricinfo.com/ci/content/player/index.html. Type the name of the player and click search. This will display the details of the player. Make a note of the profile ID. For e.g For Sachin Tendulkar this turns out to be http://www.espncricinfo.com/india/content/player/35320.html. Hence the profile for Sachin is 35320
##     opposition  
##     The numerical value of the opposition country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,New Zealand:5,Pakistan:7,South Africa:3,Sri Lanka:8, West Indies:4, Zimbabwe:9
##     host        
##     The numerical value of the host country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,New Zealand:5,Pakistan:7,South Africa:3,Sri Lanka:8, West Indies:4, Zimbabwe:9
##     dir 
##     Name of the directory to store the player data into. If not specified the data is stored in a default directory "./data". Default="./data"
##     file        
##     Name of the file to store the data into for e.g. tendulkar.csv. This can be used for subsequent functions. Default="player001.csv"
##     type        
##     type of data required. This can be "batting" or "bowling"
##     homeOrAway  
##     This is a list with either 1,2 or both. 1 is for home 2 is for away
##     result      
##     This is a list that can take values 1,2,4. 1 - won match 2- lost match 4- draw
##     Details
##     
##     More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI
##     
##     Value
##     
##     Returns the player's dataframe
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh 
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://www.espncricinfo.com/ci/content/stats/index.html
##     https://gigadom.wordpress.com/
##     
##     See Also
##     
##     getPlayerDataSp
##     
##     Examples
##     
##     ## Not run: 
##     # Both home and away. Result = won,lost and drawn
##     tendulkar = getPlayerData(35320,dir=".", file="tendulkar1.csv",
##     type="batting", homeOrAway=[1,2],result=[1,2,4])
##     
##     # Only away. Get data only for won and lost innings
##     tendulkar = getPlayerData(35320,dir=".", file="tendulkar2.csv",
##     type="batting",homeOrAway=[2],result=[1,2])
##     
##     # Get bowling data and store in file for future
##     kumble = getPlayerData(30176,dir=".",file="kumble1.csv",
##     type="bowling",homeOrAway=[1],result=[1,2])
##     
##     #Get the Tendulkar's Performance against Australia in Australia
##     tendulkar = getPlayerData(35320, opposition = 2,host=2,dir=".", 
##     file="tendulkarVsAusInAus.csv",type="batting")

The details below will introduce the different functions that are available in cricpy.

3. Get the player data for a player using the function getPlayerData()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. dravid.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerData for all subsequent analyses

import cricpy.analytics as ca
#dravid =ca.getPlayerData(28114,dir="..",file="dravid.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])
#acook =ca.getPlayerData(11728,dir="..",file="acook.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])
import cricpy.analytics as ca
#lara =ca.getPlayerData(52337,dir="..",file="lara.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])253802
#kohli =ca.getPlayerData(253802,dir="..",file="kohli.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])

4 Rahul Dravid’s performance – Basic Analyses

The 3 plots below provide the following for Rahul Dravid

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("../dravid.csv","Rahul Dravid")

ca.batsmanMeanStrikeRate("../dravid.csv","Rahul Dravid")

ca.batsmanRunsRanges("../dravid.csv","Rahul Dravid") 

5. More analyses

import cricpy.analytics as ca
ca.batsman4s("../dravid.csv","Rahul Dravid")

ca.batsman6s("../dravid.csv","Rahul Dravid") 

ca.batsmanDismissals("../dravid.csv","Rahul Dravid")

6. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Dravid Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
ca.battingPerf3d("../dravid.csv","Rahul Dravid")

7. Average runs at different venues

The plot below gives the average runs scored by Dravid at different grounds. The plot also the number of innings at each ground as a label at x-axis. It can be seen Dravid did great in Rawalpindi, Leeds, Georgetown overseas and , Mohali and Bangalore at home

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("../dravid.csv","Rahul Dravid")

 

8. Average runs against different opposing teams

This plot computes the average runs scored by Dravid against different countries. Dravid has an average of 50+ in England, New Zealand, West Indies and Zimbabwe.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("../dravid.csv","Rahul Dravid")

9 . Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman. For this the performance of Sachin is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Dravid’s  highest tendencies are computed and plotted using K-Means

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("../dravid.csv","Rahul Dravid")

10. A look at the Top 4 batsman – Rahul Dravid, Alastair Cook, Brian Lara and Virat Kohli

The following batsmen have been very prolific in test cricket and will be used for teh analyses

  1. Rahul Dravid :Average:52.31,100’s – 36, 50’s – 63
  2. Alastair Cook : Average: 45.35, 100’s – 33, 50’s – 57
  3. Brian Lara : Average: 52.88, 100’s – 34 , 50’s – 48
  4. Virat Kohli: Average: 54.57 ,100’s – 24 , 50’s – 19

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

11. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("../dravid.csv","Rahul Dravid")

ca.batsmanPerfBoxHist("../acook.csv","Alastair Cook")

ca.batsmanPerfBoxHist("../lara.csv","Brian Lara")


ca.batsmanPerfBoxHist("../kohli.csv","Virat Kohli")


12. Contribution to won and lost matches

The plot below shows the contribution of Dravid, Cook, Lara and Kohli in matches won and lost. It can be seen that in matches where India has won Dravid and Kohli have scored more and must have been instrumental in the win

For the 2 functions below you will have to use the getPlayerDataSp() function as shown below. I have commented this as I already have these files

import cricpy.analytics as ca
#dravidsp = ca.getPlayerDataSp(28114,tdir=".",tfile="dravidsp.csv",ttype="batting")
#acooksp = ca.getPlayerDataSp(11728,tdir=".",tfile="acooksp.csv",ttype="batting")
#larasp = ca.getPlayerDataSp(52337,tdir=".",tfile="larasp.csv",ttype="batting")
#kohlisp = ca.getPlayerDataSp(253802,tdir=".",tfile="kohlisp.csv",ttype="batting")
import cricpy.analytics as ca
ca.batsmanContributionWonLost("../dravidsp.csv","Rahul Dravid")

ca.batsmanContributionWonLost("../acooksp.csv","Alastair Cook")

ca.batsmanContributionWonLost("../larasp.csv","Brian Lara")

ca.batsmanContributionWonLost("../kohlisp.csv","Virat Kohli")


13. Performance at home and overseas

From the plot below it can be seen

Dravid has a higher median overseas than at home.Cook, Lara and Kohli have a lower median of runs overseas than at home.

This function also requires the use of getPlayerDataSp() as shown above

import cricpy.analytics as ca
ca.batsmanPerfHomeAway("../dravidsp.csv","Rahul Dravid")

ca.batsmanPerfHomeAway("../acooksp.csv","Alastair Cook")

ca.batsmanPerfHomeAway("../larasp.csv","Brian Lara")

ca.batsmanPerfHomeAway("../kohlisp.csv","Virat Kohli")

14 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 (ignore the dip at the end of all plots. Need to check why this is so!). Lara’s performance seems to have been quite good before his retirement(wonder why retired so early!). Kohli’s performance has been steadily improving over the years

import cricpy.analytics as ca
ca.batsmanMovingAverage("../dravid.csv","Rahul Dravid")

ca.batsmanMovingAverage("../acook.csv","Alastair Cook")

ca.batsmanMovingAverage("../lara.csv","Brian Lara")

ca.batsmanMovingAverage("../kohli.csv","Virat Kohli")

15 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Dravid averages around 48, Cook around 44, Lara around 50 and Kohli shows a steady improvement in his cumulative average. Kohli seems to be getting better with time.

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("../dravid.csv","Rahul Dravid")

ca.batsmanCumulativeAverageRuns("../acook.csv","Alastair Cook")

ca.batsmanCumulativeAverageRuns("../lara.csv","Brian Lara")

ca.batsmanCumulativeAverageRuns("../kohli.csv","Virat Kohli")

16 Cumulative Average strike rate of batsman in career

Lara has a terrific strike rate of 52+. Cook has a better strike rate over Dravid. Kohli’s strike rate has improved over the years.

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("../dravid.csv","Rahul Dravid")

ca.batsmanCumulativeStrikeRate("../acook.csv","Alastair Cook")

ca.batsmanCumulativeStrikeRate("../lara.csv","Brian Lara")

ca.batsmanCumulativeStrikeRate("../kohli.csv","Virat Kohli")


17 Future Runs forecast

Here are plots that forecast how the batsman will perform in future. Currently ARIMA has been used for the forecast. (To do:  Perform Holt-Winters forecast!)

import cricpy.analytics as ca
ca.batsmanPerfForecast("../dravid.csv","Rahul Dravid")
##                              ARIMA Model Results                              
## ==============================================================================
## Dep. Variable:                 D.runs   No. Observations:                  284
## Model:                 ARIMA(5, 1, 0)   Log Likelihood               -1522.837
## Method:                       css-mle   S.D. of innovations             51.488
## Date:                Sun, 28 Oct 2018   AIC                           3059.673
## Time:                        09:47:39   BIC                           3085.216
## Sample:                    07-04-1996   HQIC                          3069.914
##                          - 01-24-2012                                         
## ================================================================================
##                    coef    std err          z      P>|z|      [0.025      0.975]
## --------------------------------------------------------------------------------
## const           -0.1336      0.884     -0.151      0.880      -1.867       1.599
## ar.L1.D.runs    -0.7729      0.058    -13.322      0.000      -0.887      -0.659
## ar.L2.D.runs    -0.6234      0.071     -8.753      0.000      -0.763      -0.484
## ar.L3.D.runs    -0.5199      0.074     -7.038      0.000      -0.665      -0.375
## ar.L4.D.runs    -0.3490      0.071     -4.927      0.000      -0.488      -0.210
## ar.L5.D.runs    -0.2116      0.058     -3.665      0.000      -0.325      -0.098
##                                     Roots                                    
## =============================================================================
##                  Real           Imaginary           Modulus         Frequency
## -----------------------------------------------------------------------------
## AR.1            0.5789           -1.1743j            1.3093           -0.1771
## AR.2            0.5789           +1.1743j            1.3093            0.1771
## AR.3           -1.3617           -0.0000j            1.3617           -0.5000
## AR.4           -0.7227           -1.2257j            1.4230           -0.3348
## AR.5           -0.7227           +1.2257j            1.4230            0.3348
## -----------------------------------------------------------------------------
##                 0
## count  284.000000
## mean    -0.306769
## std     51.632947
## min   -106.653589
## 25%    -33.835148
## 50%     -8.954253
## 75%     21.024763
## max    223.152901
## 
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
##   if issubdtype(paramsdtype, float):
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.
##   elif issubdtype(paramsdtype, complex):
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
##   if issubdtype(paramsdtype, float):

18 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following Range 30 – 100 innings – Lara leads followed by Dravid Range 100+ innings – Kohli races ahead of the rest

import cricpy.analytics as ca
frames = ["../dravid.csv","../acook.csv","../lara.csv","../kohli.csv"]
names = ["Dravid","A Cook","Brian Lara","V Kohli"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

19. Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show

Brian Lara towers over the Dravid, Cook and Kohli. However you will notice that Kohli’s strike rate is going up

import cricpy.analytics as ca
frames = ["../dravid.csv","../acook.csv","../lara.csv","../kohli.csv"]
names = ["Dravid","A Cook","Brian Lara","V Kohli"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

20. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("../dravid.csv","Rahul Dravid")

ca.battingPerf3d("../acook.csv","Alastair Cook")

ca.battingPerf3d("../lara.csv","Brian Lara")

ca.battingPerf3d("../kohli.csv","Virat Kohli")

21. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
dravid = ca.batsmanRunsPredict("../dravid.csv",newDF,"Dravid")
print(dravid)
##             BF        Mins        Runs
## 0    10.000000   30.000000    0.519667
## 1    37.857143   70.714286   13.821794
## 2    65.714286  111.428571   27.123920
## 3    93.571429  152.142857   40.426046
## 4   121.428571  192.857143   53.728173
## 5   149.285714  233.571429   67.030299
## 6   177.142857  274.285714   80.332425
## 7   205.000000  315.000000   93.634552
## 8   232.857143  355.714286  106.936678
## 9   260.714286  396.428571  120.238805
## 10  288.571429  437.142857  133.540931
## 11  316.428571  477.857143  146.843057
## 12  344.285714  518.571429  160.145184
## 13  372.142857  559.285714  173.447310
## 14  400.000000  600.000000  186.749436

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease.

22 Analysis of Top 3 wicket takers

The following 3 bowlers have had an excellent career and will be used for the analysis

  1. Glenn McGrath:Wickets: 563, Average = 21.64, Economy Rate – 2.49
  2. Kapil Dev : Wickets: 434, Average = 29.64, Economy Rate – 2.78
  3. James Anderson: Wickets: 564, Average = 28.64, Economy Rate – 2.88

How do Glenn McGrath, Kapil Dev and James Anderson compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

23. Get the bowler’s data

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca
#mcgrath =ca.getPlayerData(6565,dir=".",file="mcgrath.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])
#kapil =ca.getPlayerData(30028,dir=".",file="kapil.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])
#anderson =ca.getPlayerData(8608,dir=".",file="anderson.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])

24. Wicket Frequency Plot

This plot below plots the frequency of wickets taken for each of the bowlers

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("../mcgrath.csv","Glenn McGrath")

ca.bowlerWktsFreqPercent("../kapil.csv","Kapil Dev")

ca.bowlerWktsFreqPercent("../anderson.csv","James Anderson")

25. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("../mcgrath.csv","Glenn McGrath")

ca.bowlerWktsRunsPlot("../kapil.csv","Kapil Dev")

ca.bowlerWktsRunsPlot("../anderson.csv","James Anderson")

26 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues. McGrath best performances are at Centurion, Lord’s and Port of Spain averaging about 4 wickets. Kapil Dev’s does good at Kingston and Wellington. Anderson averages 4 wickets at Dunedin and Nagpur

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("../mcgrath.csv","Glenn McGrath")

ca.bowlerAvgWktsGround("../kapil.csv","Kapil Dev")

ca.bowlerAvgWktsGround("../anderson.csv","James Anderson")

27 Average wickets against different opposition

The plot gives the average wickets taken by Muralitharan against different countries. The x-axis also includes the number of innings against each team

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("../mcgrath.csv","Glenn McGrath")

ca.bowlerAvgWktsOpposition("../kapil.csv","Kapil Dev")

ca.bowlerAvgWktsOpposition("../anderson.csv","James Anderson")

28 Wickets taken moving average

From the plot below it can be see James Anderson has had a solid performance over the years averaging about wickets

import cricpy.analytics as ca
ca.bowlerMovingAverage("../mcgrath.csv","Glenn McGrath")

ca.bowlerMovingAverage("../kapil.csv","Kapil Dev")

ca.bowlerMovingAverage("../anderson.csv","James Anderson")

29 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. mcGrath plateaus around 2.4 wickets, Kapil Dev’s performance deteriorates over the years. Anderson holds on rock steady around 2 wickets

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("../mcgrath.csv","Glenn McGrath")

ca.bowlerCumulativeAvgWickets("../kapil.csv","Kapil Dev")

ca.bowlerCumulativeAvgWickets("../anderson.csv","James Anderson")

30 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. McGrath’s was very expensive early in his career conceding about 2.8 runs per over which drops to around 2.5 runs towards the end. Kapil Dev’s economy rate drops from 3.6 to 2.8. Anderson is probably more expensive than the other 2.

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("../mcgrath.csv","Glenn McGrath")

ca.bowlerCumulativeAvgEconRate("../kapil.csv","Kapil Dev")

ca.bowlerCumulativeAvgEconRate("../anderson.csv","James Anderson")

31 Future Wickets forecast

import cricpy.analytics as ca
ca.bowlerPerfForecast("../mcgrath.csv","Glenn McGrath")
##                              ARIMA Model Results                              
## ==============================================================================
## Dep. Variable:              D.Wickets   No. Observations:                  236
## Model:                 ARIMA(5, 1, 0)   Log Likelihood                -480.815
## Method:                       css-mle   S.D. of innovations              1.851
## Date:                Sun, 28 Oct 2018   AIC                            975.630
## Time:                        09:28:32   BIC                            999.877
## Sample:                    11-12-1993   HQIC                           985.404
##                          - 01-02-2007                                         
## ===================================================================================
##                       coef    std err          z      P>|z|      [0.025      0.975]
## -----------------------------------------------------------------------------------
## const               0.0037      0.033      0.113      0.910      -0.061       0.068
## ar.L1.D.Wickets    -0.9432      0.064    -14.708      0.000      -1.069      -0.818
## ar.L2.D.Wickets    -0.7254      0.086     -8.469      0.000      -0.893      -0.558
## ar.L3.D.Wickets    -0.4827      0.093     -5.217      0.000      -0.664      -0.301
## ar.L4.D.Wickets    -0.3690      0.085     -4.324      0.000      -0.536      -0.202
## ar.L5.D.Wickets    -0.1709      0.064     -2.678      0.008      -0.296      -0.046
##                                     Roots                                    
## =============================================================================
##                  Real           Imaginary           Modulus         Frequency
## -----------------------------------------------------------------------------
## AR.1            0.5630           -1.2761j            1.3948           -0.1839
## AR.2            0.5630           +1.2761j            1.3948            0.1839
## AR.3           -0.8433           -1.0820j            1.3718           -0.3554
## AR.4           -0.8433           +1.0820j            1.3718            0.3554
## AR.5           -1.5981           -0.0000j            1.5981           -0.5000
## -----------------------------------------------------------------------------
##                 0
## count  236.000000
## mean    -0.005142
## std      1.856961
## min     -3.457002
## 25%     -1.433391
## 50%     -0.080237
## 75%      1.446149
## max      5.840050

32 Get player data special

As discussed above the next 2 charts require the use of getPlayerDataSp()

import cricpy.analytics as ca
#mcgrathsp =ca.getPlayerDataSp(6565,tdir=".",tfile="mcgrathsp.csv",ttype="bowling")
#kapilsp =ca.getPlayerDataSp(30028,tdir=".",tfile="kapilsp.csv",ttype="bowling")
#andersonsp =ca.getPlayerDataSp(8608,tdir=".",tfile="andersonsp.csv",ttype="bowling")

33 Contribution to matches won and lost

The plot below is extremely interesting Glenn McGrath has been more instrumental in Australia winning than Kapil and Anderson as seems to have taken more wickets when Australia won.

import cricpy.analytics as ca
ca.bowlerContributionWonLost("../mcgrathsp.csv","Glenn McGrath")

ca.bowlerContributionWonLost("../kapilsp.csv","Kapil Dev")

ca.bowlerContributionWonLost("../andersonsp.csv","James Anderson")

34 Performance home and overseas

McGrath and Kapil Dev have performed better overseas than at home. Anderson has performed about the same home and overseas

import cricpy.analytics as ca
ca.bowlerPerfHomeAway("../mcgrathsp.csv","Glenn McGrath")

ca.bowlerPerfHomeAway("../kapilsp.csv","Kapil Dev")

ca.bowlerPerfHomeAway("../andersonsp.csv","James Anderson")

35 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate shows that McGrath has the best economy rate followed by Kapil Dev and then Anderson.

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

36 Relative Economy Rate against wickets taken

McGrath has been economical regardless of the number of wickets taken. Kapil Dev has been slightly more expensive when he takes more wickets

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlingER(frames,names)

37 Relative cumulative average wickets of bowlers in career

The plot below shows that McGrath has the best overall cumulative average wickets. Kapil’s leads Anderson till about 150 innings after which Anderson takes over

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

Key Findings

The plots above capture some of the capabilities and features of my cricpy package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.

Here are the main findings from the analysis above

Key insights

1. Brian Lara is head and shoulders above the rest in the overall strike rate
2. Kohli performance has been steadily improving over the years and with the way he is going he will shatter all records.
3. Kohli and Dravid have scored more in matches where India has won than the other two.
4. Dravid has performed very well overseas
5. The cumulative average runs has Kohli just edging out the other 3. Kohli is probably midway in his career but considering that his moving average is improving strongly, we can expect great things of him with the way he is going.
6. McGrath has had some great performances overseas
7. Mcgrath has the best economy rate and has contributed significantly to Australia’s wins.
8.In the cumulative average wickets race McGrath leads the pack. Kapil leads Anderson till about 150 matches after which Anderson takes over.

The code for cricpy can be accessed at Github at cricpy

Do let me know if you run into issues.

Conclusion

I have long wanted to make a python equivalent of cricketr and I have been able to make it. cricpy is still work in progress. I have add the necessary functions for ODI and Twenty20.  Go ahead give ‘cricpy’ a spin!!

Stay tuned!

Important note: Do check out my other posts using cricpy at cricpy-posts

cricketr flexes new muscles: The final analysis

Twas brillig, and the slithy toves
Did gyre and gimble in the wabe:
All mimsy were the borogoves,
And the mome raths outgrabe.

       Jabberwocky by Lewis Carroll
                   

No analysis of cricket is complete, without determining how players would perform in the host country. Playing Test cricket on foreign pitches, in the host country, is a ‘real test’ for both batsmen and bowlers. Players, who can perform consistently both on domestic and foreign pitches are the genuinely ‘class’ players. Player performance on foreign pitches lets us differentiate the paper tigers, and home ground bullies among batsmen. Similarly, spinners who perform well, only on rank turners in home ground or pace bowlers who can only swing and generate bounce on specially prepared pitches are neither  genuine spinners nor  real pace bowlers.

So this post, helps in identifying those with real strengths, and those who play good only when the conditions are in favor, in home grounds. This post brings a certain level of finality to the analysis of players with my R package ‘cricketr’

Besides, I also meant ‘final analysis’ in the literal sense, as I intend to take a long break from cricket analysis/analytics and focus on some other domains like Neural Networks, Deep Learning and Spark.

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

Untitled

 

As already mentioned, my R package ‘cricketr’ uses the statistics info available in ESPN Cricinfo Statsguru. You should be able to install the package from CRAN and use many of the functions available in the package. Please be mindful of ESPN Cricinfo Terms of Use

(Note: This page is also hosted at RPubs as cricketrFinalAnalysis. You can download the PDF file at cricketrFinalAnalysis.

Important note: Do check out my other posts using cricketr at cricketr-posts

For getting data of a player against a particular country for the match played in the host country, I just had to add 2 extra parameters to the getPlayerData() function. The cricketr package has been updated with the changed functions for getPlayerData() – Tests, getPlayerDataOD() – ODI and getPlayerDataTT() for the Twenty20s. The updated functions will be available in cricketr Version -0.0.14

The data for the following players have already been obtained with the new, changed getPlayerData() function and have been saved as *.csv files. I will be re-using these files, instead of getting them all over again. Hence the getPlayerData() lines have been commented below

library(cricketr)

1. Performance of a batsman against a host ountry in the host country

For e.g We can the get the data for Sachin Tendulkar for matches played against Australia and in Australia Here opposition=2 and host =2 indicate that the opposition is Australia and the host country is also Australia

#tendulkarAus=getPlayerData(35320,opposition=2,host=2,file="tendulkarVsAusInAus.csv",type="batting")

All cricketr functions can be used with this data frame, as before. All the charts show the performance of Tendulkar in Australia against Australia.

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))
batsman4s("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsman6s("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanRunsRanges("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanDismissals("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanAvgRunsGround("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanMovingAverage("./data/tendulkarVsAusInAus.csv","Tendulkar")

dev.off()
## null device 
##           1

2. Relative performances of international batsmen against England in England

While we can analyze the performance of a player against an opposition in some host country, I wanted to compare the relative performances of players, to see how players from different nations play in a host country which is not their home ground.

The following lines gets player’s data of matches played in England and against England.The Oval, Lord’s are famous for generating some dangerous swing and bounce. I chose the following players

  1. Sir Don Bradman (Australia)
  2. Steve Waugh (Australia)
  3. Rahul Dravid (India)
  4. Vivian Richards (West Indies)
  5. Sachin Tendulkar (India)
#tendulkarEng=getPlayerData(35320,opposition=1,host=1,file="tendulkarVsEngInEng.csv",type="batting")
#bradmanEng=getPlayerData(4188,opposition=1,host=1,file="bradmanVsEngInEng.csv",type="batting")
#srwaughEng=getPlayerData(8192,opposition=1,host=1,file="srwaughVsEngInEng.csv",type="batting")
#dravidEng=getPlayerData(28114,opposition=1,host=1,file="dravidVsEngInEng.csv",type="batting")
#vrichardEng=getPlayerData(52812,opposition=1,host=1,file="vrichardsEngInEng.csv",type="batting")
frames <- list("./data/tendulkarVsEngInEng.csv","./data/bradmanVsEngInEng.csv","./data/srwaughVsEngInEng.csv",
               "./data/dravidVsEngInEng.csv","./data/vrichardsEngInEng.csv")
names <- list("S Tendulkar","D Bradman","SR Waugh","R Dravid","Viv Richards")

The Lords and the Oval in England are some of the best pitches in the world. Scoring on these pitches and weather conditions, where there is both swing and bounce really requires excellent batting skills. It can be easily seen that Don Bradman stands heads and shoulders over everybody else, averaging close a cumulative average of 100+. He is followed by Viv Richards, who averages around ~60. Interestingly in English conditions, Rahul Dravid edges out Sachin Tendulkar.

relativeBatsmanCumulativeAvgRuns(frames,names)

# The other 2 plots on relative strike rate and cumulative average strike rate,
shows Viv Richards really  blasts the bowling. Viv Richards has a strike rate 
of 70, while Bradman 62+, followed by Tendulkar.
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

3. Relative performances of international batsmen against Australia in Australia

The following players from these countries were chosen

  1. Sachin Tendulkar (India)
  2. Viv Richard (West Indies)
  3. David Gower (England)
  4. Jacques Kallis (South Africa)
  5. Alastair Cook (Emgland)
frames <- list("./data/tendulkarVsAusInAus.csv","./data/vrichardsVAusInAus.csv","./data/dgowerVsAusInAus.csv",
               "./data/kallisVsAusInAus.csv","./data/ancookVsWIInWI.csv")
names <- list("S Tendulkar","Viv Richards","David Gower","J Kallis","AN Cook")

Alastair Cook of England has fantastic cumulative average of 55+ on the pitches of Australia. There is a dip towards the end, but we cannot predict whether it would have continued. AN Cook is followed by Tendulkar who has a steady average of 50+ runs, after which there is Viv Richards.

relativeBatsmanCumulativeAvgRuns(frames,names)

#With respect to cumulative or relative strike rate Viv Richards is a class apart.He seems to really
#tear into bowlers. David Gower has an excellent strike rate and is followed by Tendulkar
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

4. Relative performances of international batsmen against India in India

While England & Australia are famous for bouncy tracks with swing, Indian pitches are renowed for being extraordinary turners. Also India has always thrown up world class spinners, from the spin quartet of BS Chandraskehar, Bishen Singh Bedi, EAS Prasanna, S Venkatraghavan, to the times of dangerous Anil Kumble, and now to the more recent Ravichander Ashwon and Harbhajan Singh.

A batsmen who can score runs in India against Indian spinners has to be really adept in handling all kinds of spin.

While Clive Lloyd & Alvin Kallicharan had the best performance against India, they have not been included as ESPN Cricinfo had many of the columns missing.

So I chose the following international players for the analysis against India

  1. Hashim Amla (South Africa)
  2. Alastair Cook (England)
  3. Matthew Hayden (Australia)
  4. Viv Richards (West Indies)
frames <- list("./data/amlaVsIndInInd.csv","./data/ancookVsIndInInd.csv","./data/mhaydenVsIndInInd.csv",
               "./data/vrichardsVsIndInInd.csv")
names <- list("H Amla","AN Cook","M Hayden","Viv Riachards")

Excluding Clive Lloyd & Alvin Kallicharan the next best performer against India is Hashim Amla,followed by Alastair Cook, Viv Richards.

relativeBatsmanCumulativeAvgRuns(frames,names)

#With respect to strike rate, there is no contest when Viv Richards is around. He is clearly the best 
#striker of the ball regardless of whether it is the pacy wickets of 
#Australia/England or the spinning tracks of the subcontinent. After 
#Viv Richards, Hayden and Alastair Cook have good cumulative strike rates
#in India
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

5. All time greats of Indian batting

I couldn’t resist checking out how the top Indian batsmen perform when playing in host countries So here is a look at how the top Indian batsmen perform against different host countries

6. Top Indian batsmen against Australia in Australia

The following Indian batsmen were chosen

  1. Sunil Gavaskar
  2. Sachin Tendulkar
  3. Virat Kohli
  4. Virendar Sehwag
  5. VVS Laxman
frames <- list("./data/tendulkarVsAusInAus.csv","./data/gavaskarVsAusInAus.csv","./data/kohliVsAusInAus.csv",
               "./data/sehwagVsAusInAus.csv","./data/vvslaxmanVsAusInAus.csv")
names <- list("S Tendulkar","S Gavaskar","V Kohli","V Sehwag","VVS Laxman")

Virat Kohli has the best overall performance against Australia, with a current cumulative average of 60+ runs for the total number of innings played by him (15). With 15 matches the 2nd best is Virendar Sehwag, followed by VVS Laxman. Tendulkar maintains a cumulative average of 48+ runs for an excess of 30+ innings.

relativeBatsmanCumulativeAvgRuns(frames,names)

# Sehwag leads the strike rate against host Australia, followed by 
# Tendulkar in Australia and then Kohli
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

7. Top Indian batsmen against England in England

The top Indian batmen’s performances against England are shown below

  1. Rahul Dravid
  2. Dilip Vengsarkar
  3. Rahul Dravid
  4. Sourav Ganguly
  5. Virat Kohli
frames <- list("./data/tendulkarVsEngInEng.csv","./data/dravidVsEngInEng.csv","./data/vengsarkarVsEngInEng.csv",
               "./data/gangulyVsEngInEng.csv","./data/gavaskarVsEngInEng.csv","./data/kohliVsEngInEng.csv")
names <- list("S Tendulkar","R Dravid","D Vengsarkar","S Ganguly","S Gavaskar","V Kohli")

Rahul Dravid has the best performance against England and edges out Tendulkar. He is followed by Tendulkar and then Sourav Ganguly. Note:Incidentally Virat Kohli’s performance against England in England so far has been extremely poor and he averages around 13-15 runs per innings. However he has a long way to go and I hope he catches up. In any case it will be an uphill climb for Kohli in England.

relativeBatsmanCumulativeAvgRuns(frames,names)

#Tendulkar, Ganguly and Dravid have the best strike rate and in that order.
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

8. Top Indian batsmen against West Indies in West Indies

frames <- list("./data/tendulkarVsWInWI.csv","./data/dravidVsWInWI.csv","./data/vvslaxmanVsWIInWI.csv",
               "./data/gavaskarVsWIInWI.csv")
names <- list("S Tendulkar","R Dravid","VVS Laxman","S Gavaskar")

Against the West Indies Sunil Gavaskar is heads and shoulders above the rest. Gavaskar has a very impressive cumulative average against West Indies

relativeBatsmanCumulativeAvgRuns(frames,names)

# VVS Laxman followed by  Tendulkar & then Dravid have a very 
# good strike rate against the West Indies
relativeBatsmanCumulativeStrikeRate(frames,names)

9. World’s best spinners on tracks suited for pace & bounce

In this part I compare the performances of the top 3 spinners in recent years and check out how they perform on surfaces that are known for pace, and bounce. I have taken the following 3 spinners

  1. Anil Kumble (India)
  2. M Muralitharan (Sri Lanka)
  3. Shane Warne (Australia)
#kumbleEng=getPlayerData(30176  ,opposition=3,host=3,file="kumbleVsEngInEng.csv",type="bowling")
#muraliEng=getPlayerData(49636  ,opposition=3,host=3,file="muraliVsEngInEng.csv",type="bowling")
#warneEng=getPlayerData(8166  ,opposition=3,host=3,file="warneVsEngInEng.csv",type="bowling")

10. Top international spinners against England in England

frames <- list("./data/kumbleVsEngInEng.csv","./data/muraliVsEngInEng.csv","./data/warneVsEngInEng.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")

Against England and in England, Muralitharan shines with a cumulative average of nearly 5 wickets per match with a peak of almost 8 wickets. Shane Warne has a steady average at 5 wickets and then Anil Kumble.

relativeBowlerCumulativeAvgWickets(frames,names)

# The order relative cumulative Economy rate, Warne has the best figures,followed by Anil Kumble. Muralitharan
# is much more expensive.
relativeBowlerCumulativeAvgEconRate(frames,names)

11. Top international spinners against South Africa in South Africa

frames <- list("./data/kumbleVsSAInSA.csv","./data/muraliVsSAInSA.csv","./data/warneVsSAInSA.csv")
names <- list("Anil Kumble","M Muralitharan","Shane Warne")

In South Africa too, Muralitharan has the best wicket taking performance averaging about 4 wickets. Warne averages around 3 wickets and Kumble around 2 wickets

relativeBowlerCumulativeAvgWickets(frames,names)

# Muralitharan is expensive in South Africa too, while Kumble and Warne go neck-to-neck in the economy rate.
# Kumble edges out Warne and has a better cumulative average economy rate
relativeBowlerCumulativeAvgEconRate(frames,names)

11. Top international pacers against India in India

As a final analysis I check how the world’s pacers perform in India against India. India pitches are supposed to be flat devoid of bounce, while being terrific turners. Hence Indian pitches are more suited to spin bowling than pace bowling. This is changing these days.

The best performers against India in India are mostly the deadly pacemen of yesteryears

For this I have chosen the following bowlers

  1. Courtney Walsh (West Indies)
  2. Andy Roberts (West Indies)
  3. Malcolm Marshall
  4. Glenn McGrath
#cawalshInd=getPlayerData(53216  ,opposition=6,host=6,file="cawalshVsIndInInd.csv",type="bowling")
#arobertsInd=getPlayerData(52817  ,opposition=6,host=6,file="arobertsIndInInd.csv",type="bowling")
#mmarshallInd=getPlayerData(52419  ,opposition=6,host=6,file="mmarshallVsIndInInd.csv",type="bowling")
#gmccgrathInd=getPlayerData(6565  ,opposition=6,host=6,file="mccgrathVsIndInInd.csv",type="bowling")
frames <- list("./data/cawalshVsIndInInd.csv","./data/arobertsIndInInd.csv","./data/mmarshallVsIndInInd.csv",
               "./data/mccgrathVsIndInInd.csv")
names <- list("C Walsh","A Roberts","M Marshall","G McGrath")

Courtney Walsh has the best performance, followed by Andy Roberts followed by Andy Roberts and then Malcom Marshall who tips ahead of Glenn McGrath

relativeBowlerCumulativeAvgWickets(frames,names)

#On the other hand McGrath has the best economy rate, followed by A Roberts and then Courtney Walsh
relativeBowlerCumulativeAvgEconRate(frames,names)

12. ODI performance of a player against a specific country in the host country

This gets the data for MS Dhoni in ODI matches against Australia and in Australia

#dhoniAusODI=getPlayerDataOD(28081,opposition=2,host=2,file="dhoniVsAusInAusODI.csv",type="batting")

13. Twenty 20 performance of a player against a specific country in the host country

#dhoniAusTT=getPlayerDataOD(28081,opposition=2,host=2,file="dhoniVsAusInAusTT.csv",type="batting")

All the ODI and Twenty20 functions of cricketr can be used on the above dataframes of MS Dhoni.

Some key observations

Here are some key observations

  1. At the top of the batting spectrum is Don Bradman with a very impressive average 100-120 in matches played in England and Australia. Unfortunately there weren’t matches he played in other countries and different pitches. 2.Viv Richard has the best cumulative strike rate overall.
  2. Muralitharan strikes more often than Kumble or Warne even in pitches at ENgland, South Africa and West Indies. However Muralitharan is also the most expensive
  3. Warne and Kumble have a much better economy rate than Muralitharan.
  4. Sunil Gavaskar has an extremely impressive performance in West Indies.
  5. Rahul Dravid performs much better than Tendulkar in both England and West Indies.
  6. Virat Kohli has the best performance against Australia so far and hope he maintains his stellar performance followed by Sehwag. However Kohli’s performance in England has been very poor
  7. West Indies batsmen and bowlers seem to thrive on Indian pitches, with Clive Lloyd and Alvin Kalicharan at the top of the list.

You may like my Shiny apps on cricket

  1. Inswinger- Analyzing International. T20s
  2. GooglyPlus – An app for analyzing IPL
  3. Sixer – App based on R package cricketr

Also see

  1. Exploring Quantum Gate operations with QCSimulator
  2. Neural Networks: The mechanics of backpropagation
  3. Re-introducing cricketr! : An R package to analyze performances of cricketers
  4. yorkr crashes the IPL party ! – Part 1
  5. cricketr and yorkr books – Paperback now in Amazon
  6.  Hand detection through Haartraining: A hands-on approach

To see all my posts see Index of posts

 

Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr

Presenting ‘Googly’, a cool Shiny app that I developed over the last couple of days. This interactive Shiny app was on my mind for quite some time, and I finally got down to implementing it. The Googly Shiny app is based on my R package ‘yorkr’ which is now available in CRAN. The R package and hence this Shiny app is based on data from Cricsheet.

If you are passionate about cricket, and love analyzing cricket performances, then check out my 2 racy books on cricket! In my books, I perform detailed yet compact analysis of performances of both batsmen, bowlers besides evaluating team & match performances in Tests , ODIs, T20s & IPL. You can buy my books on cricket from Amazon at $12.99 for the paperback and $4.99/$6.99 respectively for the kindle versions. The books can be accessed at Cricket analytics with cricketr  and Beaten by sheer pace-Cricket analytics with yorkr  A must read for any cricket lover! Check it out!!

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Googly is based on R package yorkr, and uses the data of all IPL matches from 2008 up to 2016, available on Cricsheet.

Googly can do detailed analyses of a) Individual IPL batsman b) Individual IPL bowler c) Any IPL match d) Head to head confrontation between 2 IPL teams e) All matches of an IPL team against all other teams.

With respect to the individual IPL batsman and bowler performance, I was in a bit of a ‘bind’ literally (pun unintended), as any IPL player could have played in more than 1 IPL team. Fortunately ‘rbind’ came to my rescue. I just get all the batsman’s/bowler’s performance in each IPL team, and then consolidate it into a single large dataframe to do the analyses of.

The Shiny app can be accessed at Googly

The code for Googly is available at Github. Feel free to clone/download/fork  the code from Googly

Check out my 2 books on cricket, a) Cricket analytics with cricketr b) Beaten by sheer pace – Cricket analytics with yorkr, now available in both paperback & kindle versions on Amazon!!! Pick up your copies today!

Also see my post GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables

Based on the 5 detailed analysis domains there are 5 tabs

IPL Batsman: This tab can be used to perform analysis of all IPL batsman. If a batsman has played in more than 1 team, then the overall performance is considered. There are 10 functions for the IPL Batsman. They are shown below

  1. Batsman Runs vs. Deliveries
  2. Batsman’s Fours & Sixes
  3. Dismissals of batsman
  4. Batsman’s Runs vs Strike Rate
  5. Batsman’s Moving Average
  6. Batsman’s Cumulative Average Run
  7. Batsman’s Cumulative Strike Rate
  8. Batsman’s Runs against Opposition
  9. Batsman’s Runs at Venue
  10. Predict Runs of batsman

IPL Bowler: This tab can be used to analyze individual IPL bowlers. The functions handle IPL bowlers who have played in more than 1 IPL team.

  1. Mean Economy Rate of bowler
  2. Mean runs conceded by bowler
  3. Bowler’s Moving Average
  4. Bowler’s Cumulative Avg. Wickets
  5. Bowler’s Cumulative Avg. Economy Rate
  6. Bowler’s Wicket Plot
  7. Bowler’s Wickets against opposition
  8. Bowler’s Wickets at Venues
  9. Bowler’s wickets prediction

IPL match: This tab can be used for analyzing individual IPL matches. The available functions are

  1. Batting Partnerships
  2. Batsmen vs Bowlers
  3. Bowling Wicket Kind
  4. Bowling Wicket Runs
  5. Bowling Wicket Match
  6. Bowler vs Batsmen
  7. Match Worm Graph

Head to head : This tab can be used for analyzing head-to-head confrontations, between any 2 IPL teams for e.g. all matches between Chennai Super Kings vs. Deccan Chargers or Kolkata Knight Riders vs. Delhi Daredevils. The available functions are

  1. Team Batsmen Batting Partnerships All Matches
  2. Team Batsmen vs Bowlers all Matches
  3. Team Wickets Opposition All Matches
  4. Team Bowler vs Batsmen All Matches
  5. Team Bowlers Wicket Kind All Matches
  6. Team Bowler Wicket Runs All Matches
  7. Win Loss All Matches

Overall performance : this tab can be used analyze the overall performance of any IPL team. For this analysis all matches played by this team is considered. The available functions are

  1. Team Batsmen Partnerships Overall
  2. Team Batsmen vs Bowlers Overall
  3. Team Bowler vs Batsmen Overall
  4. Team Bowler Wicket Kind Overall

Below I include a random set of charts that are generated in each of the 5 tabs

A. IPL Batsman
a. A Symonds : Runs vs Deliveries
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b. AB Devilliers – Cumulative Strike Rate
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c.  Gautam Gambhir – Runs at venues
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d. CH Gayle – Predict runs 
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B. IPL Bowler
a. Ashish Nehra – Cumulative Average Wickets
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b.  DJ Bravo – Moving Average of wickets
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c. R Ashwin – Mean Economy rate vs Overs
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C.IPL Match
a. Chennai Super Kings vs Deccan Chargers   (2008 -05-06) – Batsmen Partnerships

Note: You can choose either team in the match from the drop down ‘Choose team’

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b. Kolkata Knight Riders vs Delhi Daredevils (2013-04-02) – Bowling wicket runs
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c. Mumbai Indians vs Kings XI Punjab (2010-03-30) – Match worm graph
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D. Head to head confrontation
a. Rising Pune Supergiants vs Mumbai Indians in all matches – Team batsmen partnerships

Note: You can choose the partnership of either team in the drop down ‘Choose team’
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b.  Gujarat Lions – Royal Challengers Bangalore all matches – Bowlers performance against batsmen
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E. Overall Performance
a.  Royal Challengers Bangalore overall performance – Batsman Partnership (Rank=1)
This is Virat Kohli for RCB. Try out other ranks
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b.  Rajashthan Royals overall Performance – Bowler vs batsman (Rank =2)
This is Vinay Kumar.
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The Shiny app Googly can be accessed at Googly. Feel free to clone/fork the code from Github at Googly

For details on my R package yorkr, please see my blog Giga thoughts. There are more than 15 posts detailing the functions and their usage.

Do bowl a Googly!!!

You may like my other Shiny apps

Also see my other posts

  1. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  2. Deblurring with OpenCV: Weiner filter reloaded
  3. Rock N’ Roll with Bluemix, Cloudant & NodeExpress
  4. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
  5. Fun simulation of a Chain in Android
  6. Beaten by sheer pace! Cricket analytics with yorkr in paperback and Kindle versions
  7. Introducing cricketr! : An R package to analyze performances of cricketers
  8. Cricket analytics with cricketr!!!

For more posts see Index of posts

cricketr plays the ODIs!

Published in R bloggers: cricketr plays the ODIs

Introduction

In this post my package ‘cricketr’ takes a swing at One Day Internationals(ODIs). Like test batsman who adapt to ODIs with some innovative strokes, the cricketr package has some additional functions and some modified functions to handle the high strike and economy rates in ODIs. As before I have chosen my top 4 ODI batsmen and top 4 ODI bowlers.

Unititled2

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

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Important note: Do check out the python avatar of cricketr, ‘cricpy’ in my post ‘Introducing cricpy:A python package to analyze performances of cricketers

Do check out my interactive Shiny app implementation using the cricketr package – Sixer – R package cricketr’s new Shiny avatar

You can also read this post at Rpubs as odi-cricketr. Dowload this report as a PDF file from odi-cricketr.pdf

Important note: Do check out my other posts using cricketr at cricketr-posts

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricketr template from Github (which is the R Markdown file I have used for the analysis below). You will only need to make appropriate changes for the players you are interested in. Just a familiarity with R and R Markdown only is needed.
Batsmen

  1. Virendar Sehwag (Ind)
  2. AB Devilliers (SA)
  3. Chris Gayle (WI)
  4. Glenn Maxwell (Aus)

Bowlers

  1. Mitchell Johnson (Aus)
  2. Lasith Malinga (SL)
  3. Dale Steyn (SA)
  4. Tim Southee (NZ)

I have sprinkled the plots with a few of my comments. Feel free to draw your conclusions! The analysis is included below

The profile for Virender Sehwag is 35263. This can be used to get the ODI data for Sehwag. For a batsman the type should be “batting” and for a bowler the type should be “bowling” and the function is getPlayerDataOD()

The package can be installed directly from CRAN

if (!require("cricketr")){ 
    install.packages("cricketr",lib = "c:/test") 
} 
library(cricketr)

or from Github

library(devtools)
install_github("tvganesh/cricketr")
library(cricketr)

The One day data for a particular player can be obtained with the getPlayerDataOD() function. To do you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Virendar Sehwag, etc. This will bring up a page which have the profile number for the player e.g. for Virendar Sehwag this would be http://www.espncricinfo.com/india/content/player/35263.html. Hence, Sehwag’s profile is 35263. This can be used to get the data for Virat Sehwag as shown below

sehwag <- getPlayerDataOD(35263,dir="..",file="sehwag.csv",type="batting")

Analyses of Batsmen

The following plots gives the analysis of the 4 ODI batsmen

  1. Virendar Sehwag (Ind) – Innings – 245, Runs = 8586, Average=35.05, Strike Rate= 104.33
  2. AB Devilliers (SA) – Innings – 179, Runs= 7941, Average=53.65, Strike Rate= 99.12
  3. Chris Gayle (WI) – Innings – 264, Runs= 9221, Average=37.65, Strike Rate= 85.11
  4. Glenn Maxwell (Aus) – Innings – 45, Runs= 1367, Average=35.02, Strike Rate= 126.69

Plot of 4s, 6s and the scoring rate in ODIs

The 3 charts below give the number of

  1. 4s vs Runs scored
  2. 6s vs Runs scored
  3. Balls faced vs Runs scored

A regression line is fitted in each of these plots for each of the ODI batsmen A. Virender Sehwag

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./sehwag.csv","Sehwag")
batsman6s("./sehwag.csv","Sehwag")
batsmanScoringRateODTT("./sehwag.csv","Sehwag")

sehwag-4s6sSR-1

dev.off()
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B. AB Devilliers

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./devilliers.csv","Devillier")
batsman6s("./devilliers.csv","Devillier")
batsmanScoringRateODTT("./devilliers.csv","Devillier")

devillier-4s6SR-1

dev.off()
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C. Chris Gayle

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./gayle.csv","Gayle")
batsman6s("./gayle.csv","Gayle")
batsmanScoringRateODTT("./gayle.csv","Gayle")

gayle-4s6sSR-1

dev.off()
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D. Glenn Maxwell

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./maxwell.csv","Maxwell")
batsman6s("./maxwell.csv","Maxwell")
batsmanScoringRateODTT("./maxwell.csv","Maxwell")

maxwell-4s6sout-1

dev.off()
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Relative Mean Strike Rate

In this first plot I plot the Mean Strike Rate of the batsmen. It can be seen that Maxwell has a awesome strike rate in ODIs. However we need to keep in mind that Maxwell has relatively much fewer (only 45 innings) innings. He is followed by Sehwag who(most innings- 245) also has an excellent strike rate till 100 runs and then we have Devilliers who roars ahead. This is also seen in the overall strike rate in above

par(mar=c(4,4,2,2))
frames <- list("./sehwag.csv","./devilliers.csv","gayle.csv","maxwell.csv")
names <- list("Sehwag","Devilliers","Gayle","Maxwell")
relativeBatsmanSRODTT(frames,names)

plot-1-1

Relative Runs Frequency Percentage

Sehwag leads in the percentage of runs in 10 run ranges upto 50 runs. Maxwell and Devilliers lead in 55-66 & 66-85 respectively.

frames <- list("./sehwag.csv","./devilliers.csv","gayle.csv","maxwell.csv")
names <- list("Sehwag","Devilliers","Gayle","Maxwell")
relativeRunsFreqPerfODTT(frames,names)

plot-2-1

Percentage of 4s,6s in the runs scored

The plot below shows the percentage of runs made by the batsmen by ways of 1s,2s,3s, 4s and 6s. It can be seen that Sehwag has the higheest percent of 4s (33.36%) in his overall runs in ODIs. Maxwell has the highest percentage of 6s (13.36%) in his ODI career. If we take the overall 4s+6s then Sehwag leads with (33.36 +5.95 = 39.31%),followed by Gayle (27.80+10.15=37.95%)

Percent 4’s,6’s in total runs scored

The plot below shows the contrib

frames <- list("./sehwag.csv","./devilliers.csv","gayle.csv","maxwell.csv")
names <- list("Sehwag","Devilliers","Gayle","Maxwell")
runs4s6s <-batsman4s6s(frames,names)

plot-46s-1

print(runs4s6s)
##                Sehwag Devilliers Gayle Maxwell
## Runs(1s,2s,3s)  60.69      67.39 62.05   62.11
## 4s              33.36      24.28 27.80   24.53
## 6s               5.95       8.32 10.15   13.36
 

Runs forecast

The forecast for the batsman is shown below.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfForecast("./sehwag.csv","Sehwag")
batsmanPerfForecast("./devilliers.csv","Devilliers")
batsmanPerfForecast("./gayle.csv","Gayle")
batsmanPerfForecast("./maxwell.csv","Maxwell")

swcr-perf-1

dev.off()
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3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A prediction plane is fitted

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./sehwag.csv","V Sehwag")
battingPerf3d("./devilliers.csv","AB Devilliers")

plot-3-1

dev.off()
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par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./gayle.csv","C Gayle")
battingPerf3d("./maxwell.csv","G Maxwell")

plot-4-1

dev.off()
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Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

BF <- seq( 10, 200,length=10)
Mins <- seq(30,220,length=10)
newDF <- data.frame(BF,Mins)

sehwag <- batsmanRunsPredict("./sehwag.csv","Sehwag",newdataframe=newDF)
devilliers <- batsmanRunsPredict("./devilliers.csv","Devilliers",newdataframe=newDF)
gayle <- batsmanRunsPredict("./gayle.csv","Gayle",newdataframe=newDF)
maxwell <- batsmanRunsPredict("./maxwell.csv","Maxwell",newdataframe=newDF)

The fitted model is then used to predict the runs that the batsmen will score for a hypotheticial Balls faced and Minutes at crease. It can be seen that Maxwell sets a searing pace in the predicted runs for a given Balls Faced and Minutes at crease followed by Sehwag. But we have to keep in mind that Maxwell has only around 1/5th of the innings of Sehwag (45 to Sehwag’s 245 innings). They are followed by Devilliers and then finally Gayle

batsmen <-cbind(round(sehwag$Runs),round(devilliers$Runs),round(gayle$Runs),round(maxwell$Runs))
colnames(batsmen) <- c("Sehwag","Devilliers","Gayle","Maxwell")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns
##    BallsFaced MinsAtCrease Sehwag Devilliers Gayle Maxwell
## 1          10           30     11         12    11      18
## 2          31           51     33         32    28      43
## 3          52           72     55         52    46      67
## 4          73           93     77         71    63      92
## 5          94          114    100         91    81     117
## 6         116          136    122        111    98     141
## 7         137          157    144        130   116     166
## 8         158          178    167        150   133     191
## 9         179          199    189        170   151     215
## 10        200          220    211        190   168     240

Highest runs likelihood

The plots below the runs likelihood of batsman. This uses K-Means It can be seen that Devilliers has almost 27.75% likelihood to make around 90+ runs. Gayle and Sehwag have 34% to make 40+ runs. A. Virender Sehwag

A. Virender Sehwag

batsmanRunsLikelihood("./sehwag.csv","Sehwag")

smith-1

## Summary of  Sehwag 's runs scoring likelihood
## **************************************************
## 
## There is a 35.22 % likelihood that Sehwag  will make  46 Runs in  44 balls over 67  Minutes 
## There is a 9.43 % likelihood that Sehwag  will make  119 Runs in  106 balls over  158  Minutes 
## There is a 55.35 % likelihood that Sehwag  will make  12 Runs in  13 balls over 18  Minutes

B. AB Devilliers

batsmanRunsLikelihood("./devilliers.csv","Devilliers")

warner-1

## Summary of  Devilliers 's runs scoring likelihood
## **************************************************
## 
## There is a 30.65 % likelihood that Devilliers  will make  44 Runs in  43 balls over 60  Minutes 
## There is a 29.84 % likelihood that Devilliers  will make  91 Runs in  88 balls over  124  Minutes 
## There is a 39.52 % likelihood that Devilliers  will make  11 Runs in  15 balls over 21  Minutes

C. Chris Gayle

batsmanRunsLikelihood("./gayle.csv","Gayle")

cook,cache-TRUE-1

## Summary of  Gayle 's runs scoring likelihood
## **************************************************
## 
## There is a 32.69 % likelihood that Gayle  will make  47 Runs in  51 balls over 72  Minutes 
## There is a 54.49 % likelihood that Gayle  will make  10 Runs in  15 balls over  20  Minutes 
## There is a 12.82 % likelihood that Gayle  will make  109 Runs in  119 balls over 172  Minutes

D. Glenn Maxwell

batsmanRunsLikelihood("./maxwell.csv","Maxwell")

oot-1

## Summary of  Maxwell 's runs scoring likelihood
## **************************************************
## 
## There is a 34.38 % likelihood that Maxwell  will make  39 Runs in  29 balls over 35  Minutes 
## There is a 15.62 % likelihood that Maxwell  will make  89 Runs in  55 balls over  69  Minutes 
## There is a 50 % likelihood that Maxwell  will make  6 Runs in  7 balls over 9  Minutes

Average runs at ground and against opposition

A. Virender Sehwag

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./sehwag.csv","Sehwag")
batsmanAvgRunsOpposition("./sehwag.csv","Sehwag")

avgrg-1-1

dev.off()
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B. AB Devilliers

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./devilliers.csv","Devilliers")
batsmanAvgRunsOpposition("./devilliers.csv","Devilliers")

avgrg-2-1

dev.off()
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C. Chris Gayle

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./gayle.csv","Gayle")
batsmanAvgRunsOpposition("./gayle.csv","Gayle")

avgrg-3-1

dev.off()
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D. Glenn Maxwell

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./maxwell.csv","Maxwell")
batsmanAvgRunsOpposition("./maxwell.csv","Maxwell")

avgrg-4-1

dev.off()
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Moving Average of runs over career

The moving average for the 4 batsmen indicate the following

1. The moving average of Devilliers and Maxwell is on the way up.
2. Sehwag shows a slight downward trend from his 2nd peak in 2011
3. Gayle maintains a consistent 45 runs for the last few years

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("./sehwag.csv","Sehwag")
batsmanMovingAverage("./devilliers.csv","Devilliers")
batsmanMovingAverage("./gayle.csv","Gayle")
batsmanMovingAverage("./maxwell.csv","Maxwell")

sdgm-ma-1

dev.off()
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Check batsmen in-form, out-of-form

  1. Maxwell, Devilliers, Sehwag are in-form. This is also evident from the moving average plot
  2. Gayle is out-of-form
checkBatsmanInForm("./sehwag.csv","Sehwag")
## *******************************************************************************************
## 
## Population size: 143  Mean of population: 33.76 
## Sample size: 16  Mean of sample: 37.44 SD of sample: 55.15 
## 
## Null hypothesis H0 : Sehwag 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Sehwag 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Sehwag 's Form Status: In-Form because the p value: 0.603525  is greater than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./devilliers.csv","Devilliers")
## *******************************************************************************************
## 
## Population size: 111  Mean of population: 43.5 
## Sample size: 13  Mean of sample: 57.62 SD of sample: 40.69 
## 
## Null hypothesis H0 : Devilliers 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Devilliers 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Devilliers 's Form Status: In-Form because the p value: 0.883541  is greater than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./gayle.csv","Gayle")
## *******************************************************************************************
## 
## Population size: 140  Mean of population: 37.1 
## Sample size: 16  Mean of sample: 17.25 SD of sample: 20.25 
## 
## Null hypothesis H0 : Gayle 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Gayle 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Gayle 's Form Status: Out-of-Form because the p value: 0.000609  is less than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./maxwell.csv","Maxwell")
## *******************************************************************************************
## 
## Population size: 28  Mean of population: 25.25 
## Sample size: 4  Mean of sample: 64.25 SD of sample: 36.97 
## 
## Null hypothesis H0 : Maxwell 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Maxwell 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Maxwell 's Form Status: In-Form because the p value: 0.948744  is greater than alpha=  0.05"
## *******************************************************************************************

Analysis of bowlers

  1. Mitchell Johnson (Aus) – Innings-150, Wickets – 239, Econ Rate : 4.83
  2. Lasith Malinga (SL)- Innings-182, Wickets – 287, Econ Rate : 5.26
  3. Dale Steyn (SA)- Innings-103, Wickets – 162, Econ Rate : 4.81
  4. Tim Southee (NZ)- Innings-96, Wickets – 135, Econ Rate : 5.33

Malinga has the highest number of innings and wickets followed closely by Mitchell. Steyn and Southee have relatively fewer innings.

To get the bowler’s data use

malinga <- getPlayerDataOD(49758,dir=".",file="malinga.csv",type="bowling")

Wicket Frequency percentage

This plot gives the percentage of wickets for each wickets (1,2,3…etc)

par(mfrow=c(1,4))
par(mar=c(4,4,2,2))
bowlerWktsFreqPercent("./mitchell.csv","J Mitchell")
bowlerWktsFreqPercent("./malinga.csv","Malinga")
bowlerWktsFreqPercent("./steyn.csv","Steyn")
bowlerWktsFreqPercent("./southee.csv","southee")

relBowlFP-1

dev.off()
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Wickets Runs plot

The plot below gives a boxplot of the runs ranges for each of the wickets taken by the bowlers. M Johnson and Steyn are more economical than Malinga and Southee corroborating the figures above

par(mfrow=c(1,4))
par(mar=c(4,4,2,2))

bowlerWktsRunsPlot("./mitchell.csv","J Mitchell")
bowlerWktsRunsPlot("./malinga.csv","Malinga")
bowlerWktsRunsPlot("./steyn.csv","Steyn")
bowlerWktsRunsPlot("./southee.csv","southee")

wktsrun-1

dev.off()
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Average wickets in different grounds and opposition

A. Mitchell Johnson

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./mitchell.csv","J Mitchell")
bowlerAvgWktsOpposition("./mitchell.csv","J Mitchell")

gr-1-1

dev.off()
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B. Lasith Malinga

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./malinga.csv","Malinga")
bowlerAvgWktsOpposition("./malinga.csv","Malinga")

gr-2-1

dev.off()
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C. Dale Steyn

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./steyn.csv","Steyn")
bowlerAvgWktsOpposition("./steyn.csv","Steyn")

gr-3-1

dev.off()
## null device 
##           1

D. Tim Southee

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./southee.csv","southee")
bowlerAvgWktsOpposition("./southee.csv","southee")

avgrg-4-1

dev.off()
## null device 
##           1

Relative bowling performance

The plot below shows that Mitchell Johnson and Southee have more wickets in 3-4 wickets range while Steyn and Malinga in 1-2 wicket range

frames <- list("./mitchell.csv","./malinga.csv","steyn.csv","southee.csv")
names <- list("M Johnson","Malinga","Steyn","Southee")
relativeBowlingPerf(frames,names)

relBowlPerf-1

Relative Economy Rate against wickets taken

Steyn had the best economy rate followed by M Johnson. Malinga and Southee have a poorer economy rate

frames <- list("./mitchell.csv","./malinga.csv","steyn.csv","southee.csv")
names <- list("M Johnson","Malinga","Steyn","Southee")
relativeBowlingERODTT(frames,names)

relBowlER-1

Moving average of wickets over career

Johnson and Steyn career vs wicket graph is on the up-swing. Southee is maintaining a reasonable record while Malinga shows a decline in ODI performance

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerMovingAverage("./mitchell.csv","M Johnson")
bowlerMovingAverage("./malinga.csv","Malinga")
bowlerMovingAverage("./steyn.csv","Steyn")
bowlerMovingAverage("./southee.csv","Southee")

jmss-bowlma-1

dev.off()
## null device 
##           1

Wickets forecast

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerPerfForecast("./mitchell.csv","M Johnson")
bowlerPerfForecast("./malinga.csv","Malinga")
bowlerPerfForecast("./steyn.csv","Steyn")
bowlerPerfForecast("./southee.csv","southee")

jsba-pfcst-1

dev.off()
## null device 
##           1

Check bowler in-form, out-of-form

All the bowlers are shown to be still in-form

checkBowlerInForm("./mitchell.csv","J Mitchell")
## *******************************************************************************************
## 
## Population size: 135  Mean of population: 1.55 
## Sample size: 15  Mean of sample: 2 SD of sample: 1.07 
## 
## Null hypothesis H0 : J Mitchell 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : J Mitchell 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "J Mitchell 's Form Status: In-Form because the p value: 0.937917  is greater than alpha=  0.05"
## *******************************************************************************************
checkBowlerInForm("./malinga.csv","Malinga")
## *******************************************************************************************
## 
## Population size: 163  Mean of population: 1.58 
## Sample size: 19  Mean of sample: 1.58 SD of sample: 1.22 
## 
## Null hypothesis H0 : Malinga 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Malinga 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Malinga 's Form Status: In-Form because the p value: 0.5  is greater than alpha=  0.05"
## *******************************************************************************************
checkBowlerInForm("./steyn.csv","Steyn")
## *******************************************************************************************
## 
## Population size: 93  Mean of population: 1.59 
## Sample size: 11  Mean of sample: 1.45 SD of sample: 0.69 
## 
## Null hypothesis H0 : Steyn 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Steyn 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Steyn 's Form Status: In-Form because the p value: 0.257438  is greater than alpha=  0.05"
## *******************************************************************************************
checkBowlerInForm("./southee.csv","southee")
## *******************************************************************************************
## 
## Population size: 86  Mean of population: 1.48 
## Sample size: 10  Mean of sample: 0.8 SD of sample: 1.14 
## 
## Null hypothesis H0 : southee 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : southee 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "southee 's Form Status: Out-of-Form because the p value: 0.044302  is less than alpha=  0.05"
## *******************************************************************************************

***************

Key findings

Here are some key conclusions ODI batsmen

  1. AB Devilliers has high frequency of runs in the 60-120 range and the highest average
  2. Sehwag has the most number of innings and good strike rate
  3. Maxwell has the best strike rate but it should be kept in mind that he has 1/5 of the innings of Sehwag. We need to see how he progress further
  4. Sehwag has the highest percentage of 4s in the runs scored, while Maxwell has the most 6s
  5. For a hypothetical Balls Faced and Minutes at creases Maxwell will score the most runs followed by Sehwag
  6. The moving average of indicates that the best is yet to come for Devilliers and Maxwell. Sehwag has a few more years in him while Gayle shows a decline in ODI performance and an out of form is indicated.

ODI bowlers

  1. Malinga has the highest played the highest innings and also has the highest wickets though he has poor economy rate
  2. M Johnson is the most effective in the 3-4 wicket range followed by Southee
  3. M Johnson and Steyn has the best overall economy rate followed by Malinga and Steyn 4 M Johnson and Steyn’s career is on the up-swing,Southee maintains a steady consistent performance, while Malinga shows a downward trend

Hasta la vista! I’ll be back!
Watch this space!

Also see my other posts in R

  1. Introducing cricketr! : An R package to analyze performances of cricketers
  2. cricketr digs the Ashes!
  3. A peek into literacy in India: Statistical Learning with R
  4. A crime map of India in R – Crimes against women
  5. Analyzing cricket’s batting legends – Through the mirage with R
  6. Mirror, mirror . the best batsman of them all?

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