Re-introducing cricketr! : An R package to analyze performances of cricketers

In this post I re-introduce R package cricketr. I have added 8 new functions to my R package cricketr, available from version cricketr_0.0.13 namely

  1. batsmanCumulativeAverageRuns
  2. batsmanCumulativeStrikeRate
  3. bowlerCumulativeAvgEconRate
  4. bowlerCumulativeAvgWicketRate
  5. relativeBatsmanCumulativeAvgRuns
  6. relativeBatsmanCumulativeStrikeRate
  7. relativeBowlerCumulativeAvgWickets
  8. relativeBowlerCumulativeAvgEconRate

This post updates my earlier post Introducing cricketr:An R package for analyzing performances of cricketrs

Yet all experience is an arch wherethro’
Gleams that untravell’d world whose margin fades
For ever and forever when I move.
How dull it is to pause, to make an end,
To rust unburnish’d, not to shine in use!

Ulysses by Alfred Tennyson

 Introduction

This is an initial post in which I introduce a cricketing package ‘cricketr’ which I have created. This package was a natural culmination to my earlier posts on cricket and my finishing 10 modules of Data Science Specialization, from John Hopkins University at Coursera. The thought of creating this package struck me some time back, and I have finally been able to bring this to fruition.

So here it is. My R package ‘cricketr!!!’

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. The current version of this package can handle all formats of the game including Test, ODI and Twenty20 cricket.

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

Important note 1: The latest release of ‘cricketr’ now includes the ability to analyze performances of teams now!!  See Cricketr adds team analytics to its repertoire!!!

Important note 2 : Cricketr can now do a more fine-grained analysis of players, see Cricketr learns new tricks : Performs fine-grained analysis of players

Important note 3: Do check out the python avatar of cricketr, ‘cricpy’ in my post ‘Introducing cricpy:A python package to analyze performances of cricketers

Note: This page is also hosted as a GitHub page at cricketr

This post is also hosted on Rpubs at Reintroducing cricketr. You can also down the pdf version of this post at reintroducing_cricketr.pdf

(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

Also see my 2nd book “Beaten by sheer pace”  based on my R package yorkr which is now available in paperback and kindle versions at Amazon

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.

 The cricketr package

The cricketr 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, forecast and a function to check whether the batsman/bowler is in in-form or out-of-form.

The data for a particular player can be obtained with the getPlayerData() function from the package. To do this you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Ricky Ponting, Sachin Tendulkar etc. This will bring up a page which have the profile number for the player e.g. for Sachin Tendulkar this would be http://www.espncricinfo.com/india/content/player/35320.html. Hence, Sachin’s profile is 35320. This can be used to get the data for Tendulkar as shown below

The cricketr package is now available from  CRAN!!!.  You should be able to install directly with

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

Getting help from cricketr

?getPlayerData

## 
## 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 vector with either 1,2 or both. 1 is for home 2 is for away
##     result      
##     This is a vector 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 <tvganesh.85@gmail.com>
##     
##     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=c(1,2),result=c(1,2,4))
##     
##     # Only away. Get data only for won and lost innings
##     tendulkar = getPlayerData(35320,dir=".", file="tendulkar2.csv",
##     type="batting",homeOrAway=c(2),result=c(1,2))
##     
##     # Get bowling data and store in file for future
##     kumble = getPlayerData(30176,dir=".",file="kumble1.csv",
##     type="bowling",homeOrAway=c(1),result=c(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 cricketr package includes some pre-packaged sample (.csv) files. You can use these sample to test functions  as shown below

# Retrieve the file path of a data file installed with cricketr
pathToFile ,"Sachin Tendulkar")

unnamed-chunk-2-1

Alternatively, the cricketr package can be installed from GitHub with

if (!require("cricketr")){ 
    library(devtools) 
    install_github("tvganesh/cricketr") 
}
library(cricketr)

The pre-packaged files can be accessed as shown above.
To get the data of any player use the function getPlayerData()

tendulkar <- getPlayerData(35320,dir="..",file="tendulkar.csv",type="batting",homeOrAway=c(1,2),
                           result=c(1,2,4))

Important Note This needs to be done only once for a player. This function stores the player’s data in a CSV file (for e.g. tendulkar.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

Sachin Tendulkar’s performance – Basic Analyses

The 3 plots below provide the following for Tendulkar

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

tendulkar-batting-1

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

More analyses

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

tendulkar-4s6sout-1

 

3D scatter plot and prediction plane

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

battingPerf3d("./tendulkar.csv","Sachin Tendulkar")

tendulkar-3d-1

Average runs at different venues

The plot below gives the average runs scored by Tendulkar at different grounds. The plot also displays the number of innings at each ground as a label at x-axis. It can be seen Tendulkar did great in Colombo (SSC), Melbourne ifor matches overseas and Mumbai, Mohali and Bangalore at home

batsmanAvgRunsGround("./tendulkar.csv","Sachin Tendulkar")
tendulkar-avggrd-1

Average runs against different opposing teams

This plot computes the average runs scored by Tendulkar against different countries. The x-axis also gives the number of innings against each team

batsmanAvgRunsOpposition("./tendulkar.csv","Tendulkar")
tendulkar-avgopn-1

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 using. K-Means. The centroids of 3 clusters are computed and plotted. In this plot. Sachin Tendulkar’s highest tendencies are computed and plotted using K-Means

batsmanRunsLikelihood("./tendulkar.csv","Sachin Tendulkar")

tendulkar-kmeans-1

## Summary of  Sachin Tendulkar 's runs scoring likelihood
## **************************************************
## 
## There is a 16.51 % likelihood that Sachin Tendulkar  will make  139 Runs in  251 balls over 353  Minutes 
## There is a 58.41 % likelihood that Sachin Tendulkar  will make  16 Runs in  31 balls over  44  Minutes 
## There is a 25.08 % likelihood that Sachin Tendulkar  will make  66 Runs in  122 balls over 167  Minutes

A look at the Top 4 batsman – Tendulkar, Kallis, Ponting and Sangakkara

The batsmen with the most hundreds in test cricket are

  1. Sachin Tendulkar :Average:53.78,100’s – 51, 50’s – 68
  2. Jacques Kallis : Average: 55.47, 100’s – 45, 50’s – 58
  3. Ricky Ponting : Average: 51.85, 100’s – 41 , 50’s – 62
  4. Kumara Sangakarra: Average: 58.04 ,100’s – 38 , 50’s – 52

in that order.

The following plots take a closer at their performances. The box plots show the mean (red line) and median (blue line). The two ends of the boxplot display the 25th and 75th percentile.

Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency. The calculated Mean differ from the stated means possibly because of data cleaning. Also not sure how the means were arrived at ESPN Cricinfo for e.g. when considering not out..

batsmanPerfBoxHist("./tendulkar.csv","Sachin Tendulkar")

tkps-boxhist-1

batsmanPerfBoxHist("./kallis.csv","Jacques Kallis")

tkps-boxhist-2

batsmanPerfBoxHist("./ponting.csv","Ricky Ponting")

tkps-boxhist-3

batsmanPerfBoxHist("./sangakkara.csv","K Sangakkara")

tkps-boxhist-4

Contribution to won and lost matches

The plot below shows the contribution of Tendulkar, Kallis, Ponting and Sangakarra in matches won and lost. The plots show the range of runs scored as a boxplot (25th & 75th percentile) and the mean scored. The total matches won and lost are also printed in the plot.

All the players have scored more in the matches they won than the matches they lost. Ricky Ponting is the only batsman who seems to have more matches won to his credit than others. This could also be because he was a member of strong Australian team

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

tendulkarsp 
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanContributionWonLost("tendulkarsp.csv","Tendulkar")
batsmanContributionWonLost("kallissp.csv","Kallis")
batsmanContributionWonLost("pontingsp.csv","Ponting")
batsmanContributionWonLost("sangakkarasp.csv","Sangakarra")

tkps-wonlost-1

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

Performance at home and overseas

From the plot below it can be seen
Tendulkar has more matches overseas than at home and his performance is consistent in all venues at home or abroad. Ponting has lesser innings than Tendulkar and has an equally good performance at home and overseas.Kallis and Sangakkara’s performance abroad is lower than the performance at home.

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

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfHomeAway("tendulkarsp.csv","Tendulkar")
batsmanPerfHomeAway("kallissp.csv","Kallis")
batsmanPerfHomeAway("pontingsp.csv","Ponting")
batsmanPerfHomeAway("sangakkarasp.csv","Sangakarra")
dev.off()
tkps-homeaway-1
dev.off()
## null device 
##           1
 

Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4. Clearly . Kallis and Sangakkara have a few more years of great batting ahead. They seem to average on 50. . Tendulkar and Ponting definitely show a slump in the later years

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("./tendulkar.csv","Sachin Tendulkar")
batsmanMovingAverage("./kallis.csv","Jacques Kallis")
batsmanMovingAverage("./ponting.csv","Ricky Ponting")
batsmanMovingAverage("./sangakkara.csv","K Sangakkara")

tkps-ma-1

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

Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Tendulkar averages around 50, while Sangakkarra touches 55 towards the end of his career

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

tkps-car-1

batsmanCumulativeAverageRuns("./kallis.csv","Kallis")

tkps-car-2

batsmanCumulativeAverageRuns("./ponting.csv","Ponting")

tkps-car-3

batsmanCumulativeAverageRuns("./sangakkara.csv","Sangakkara")

tkps-car-4

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

Cumulative Average strike rate of batsman in career

This function gives the cumulative strike rate of the batsman over the career

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

tkps-casr-1

batsmanCumulativeStrikeRate("./kallis.csv","Kallis")

tkps-casr-2

batsmanCumulativeStrikeRate("./ponting.csv","Ponting")

tkps-casr-3

batsmanCumulativeStrikeRate("./sangakkara.csv","Sangakkara")

tkps-casr-4

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

Future Runs forecast

Here are plots that forecast how the batsman will perform in future. In this case 90% of the career runs trend is uses as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated runs trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

Take a look at the runs forecasted for the batsman below.

  • Tendulkar’s forecasted performance seems to tally with his actual performance with an average of 50
  • Kallis the forecasted runs are higher than the actual runs he scored
  • Ponting seems to have a good run in the future
  • Sangakkara has a decent run in the future averaging 50 runs
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfForecast("./tendulkar.csv","Sachin Tendulkar")
batsmanPerfForecast("./kallis.csv","Jacques Kallis")
batsmanPerfForecast("./ponting.csv","Ricky Ponting")
batsmanPerfForecast("./sangakkara.csv","K Sangakkara")

tkps-perffcst-1

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

Relative Mean Strike Rate plot

The plot below compares the Mean Strike Rate of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following Range 0 – 50 Runs – Ponting leads followed by Tendulkar Range 50 -100 Runs – Ponting followed by Sangakkara Range 100 – 150 – Ponting and then Tendulkar

frames <- list("./tendulkar.csv","./kallis.csv","ponting.csv","sangakkara.csv")
names <- list("Tendulkar","Kallis","Ponting","Sangakkara")
relativeBatsmanSR(frames,names)

tkps-relSR-1

Relative Runs Frequency plot

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

Sangakkara leads followed by Ponting

frames <- list("./tendulkar.csv","./kallis.csv","ponting.csv","sangakkara.csv")
names <- list("Tendulkar","Kallis","Ponting","Sangakkara")
relativeRunsFreqPerf(frames,names)

tkps-relRunFreq-1

Relative cumulative average runs in career

The plot below compares the relative cumulative runs of the batsmen over the career. While Tendulkar seems to lead over the others with a cumulative average of 50, we can see that Sangakkara goes over everybody else between 180-220th inning. It is likely that Sangakkarra may have overtaken Tendulkar if he had played more

frames <- list("./tendulkar.csv","./kallis.csv","ponting.csv","sangakkara.csv")
names <- list("Tendulkar","Kallis","Ponting","Sangakkara")
relativeBatsmanCumulativeAvgRuns(frames,names)

tkps-relcar-11

Relative cumulative average strike rate in career

As seen in earlier charts Ponting has the best overall strike rate, followed by Sangakkara and then Tendulkar

frames <- list("./tendulkar.csv","./kallis.csv","ponting.csv","sangakkara.csv")
names <- list("Tendulkar","Kallis","Ponting","Sangakkara")
relativeBatsmanCumulativeStrikeRate(frames,names)

tkps-relcsr-1

Check Batsman In-Form or Out-of-Form

The below computation uses Null Hypothesis testing and p-value to determine if the batsman is in-form or out-of-form. For this 90% of the career runs is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the batsman continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the batsman is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

Note Ideally the p-value should be done for a population that follows the Normal Distribution. But the runs population is usually left skewed. So some correction may be needed. I will revisit this later

This is done for the Top 4 batsman

checkBatsmanInForm("./tendulkar.csv","Sachin Tendulkar")
## *******************************************************************************************
## 
## Population size: 294  Mean of population: 50.48 
## Sample size: 33  Mean of sample: 32.42 SD of sample: 29.8 
## 
## Null hypothesis H0 : Sachin Tendulkar 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Sachin Tendulkar 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Sachin Tendulkar 's Form Status: Out-of-Form because the p value: 0.000713  is less than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./kallis.csv","Jacques Kallis")
## *******************************************************************************************
## 
## Population size: 240  Mean of population: 47.5 
## Sample size: 27  Mean of sample: 47.11 SD of sample: 59.19 
## 
## Null hypothesis H0 : Jacques Kallis 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Jacques Kallis 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Jacques Kallis 's Form Status: In-Form because the p value: 0.48647  is greater than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./ponting.csv","Ricky Ponting")
## *******************************************************************************************
## 
## Population size: 251  Mean of population: 47.5 
## Sample size: 28  Mean of sample: 36.25 SD of sample: 48.11 
## 
## Null hypothesis H0 : Ricky Ponting 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Ricky Ponting 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Ricky Ponting 's Form Status: In-Form because the p value: 0.113115  is greater than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./sangakkara.csv","K Sangakkara")
## *******************************************************************************************
## 
## Population size: 193  Mean of population: 51.92 
## Sample size: 22  Mean of sample: 71.73 SD of sample: 82.87 
## 
## Null hypothesis H0 : K Sangakkara 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : K Sangakkara 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "K Sangakkara 's Form Status: In-Form because the p value: 0.862862  is greater than alpha=  0.05"
## *******************************************************************************************

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("./tendulkar.csv","Tendulkar")
battingPerf3d("./kallis.csv","Kallis")
plot-3-1par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./ponting.csv","Ponting")
battingPerf3d("./sangakkara.csv","Sangakkara")
plot-4-1dev.off()
## null device 
##           1

Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease. A sample sequence of Balls Faced(BF) and Minutes at crease (Mins) is setup as shown below. The fitted model is used to predict the runs for these values

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
tendulkar <- batsmanRunsPredict("./tendulkar.csv","Tendulkar",newdataframe=newDF)
kallis <- batsmanRunsPredict("./kallis.csv","Kallis",newdataframe=newDF)
ponting <- batsmanRunsPredict("./ponting.csv","Ponting",newdataframe=newDF)
sangakkara <- batsmanRunsPredict("./sangakkara.csv","Sangakkara",newdataframe=newDF)

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease. It can be seen Ponting has the will score the highest for a given Balls Faced and Minutes at crease.

Ponting is followed by Tendulkar who has Sangakkara close on his heels and finally we have Kallis. This is intuitive as we have already seen that Ponting has a highest strike rate.

batsmen <-cbind(round(tendulkar$Runs),round(kallis$Runs),round(ponting$Runs),round(sangakkara$Runs))
colnames(batsmen) <- c("Tendulkar","Kallis","Ponting","Sangakkara")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns
##    BallsFaced MinsAtCrease Tendulkar Kallis Ponting Sangakkara
## 1          10           30         7      6       9          2
## 2          38           71        23     20      25         18
## 3          66          111        39     34      42         34
## 4          94          152        54     48      59         50
## 5         121          193        70     62      76         66
## 6         149          234        86     76      93         82
## 7         177          274       102     90     110         98
## 8         205          315       118    104     127        114
## 9         233          356       134    118     144        130
## 10        261          396       150    132     161        146
## 11        289          437       165    146     178        162
## 12        316          478       181    159     194        178
## 13        344          519       197    173     211        194
## 14        372          559       213    187     228        210
## 15        400          600       229    201     245        226

Checkout my book ‘Deep Learning from first principles Second Edition- In vectorized Python, R and Octave’.  My book is available on Amazon  as paperback ($18.99) and in kindle version($9.99/Rs449).

You may also like my companion book “Practical Machine Learning with R and Python:Second Edition- Machine Learning in stereo” available in Amazon in paperback($12.99) and Kindle($9.99/Rs449) versions.

Analysis of Top 3 wicket takers

The top 3 wicket takes in test history are
1. M Muralitharan:Wickets: 800, Average = 22.72, Economy Rate – 2.47
2. Shane Warne: Wickets: 708, Average = 25.41, Economy Rate – 2.65
3. Anil Kumble: Wickets: 619, Average = 29.65, Economy Rate – 2.69

How do Anil Kumble, Shane Warne and M Muralitharan compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

Wicket Frequency Plot

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

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerWktsFreqPercent("./kumble.csv","Anil Kumble")
bowlerWktsFreqPercent("./warne.csv","Shane Warne")
bowlerWktsFreqPercent("./murali.csv","M Muralitharan")

relBowlFP-1

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

Wickets Runs plot

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerWktsRunsPlot("./kumble.csv","Kumble")
bowlerWktsRunsPlot("./warne.csv","Warne")
bowlerWktsRunsPlot("./murali.csv","Muralitharan")
wktsrun-1
dev.off()
## null device 
##           1

Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues. Muralitharan has taken an average of 8 and 6 wickets at Oval & Wellington respectively in 2 different innings. His best performances are at Kandy and Colombo (SSC)

bowlerAvgWktsGround("./murali.csv","Muralitharan")
avgWktshrg-1

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

bowlerAvgWktsOpposition("./murali.csv","Muralitharan")
avgWktoppn-1

Wickets taken moving average

From th eplot below it can be see 1. Shane Warne’s performance at the time of his retirement was still at a peak of 3 wickets 2. M Muralitharan seems to have become ineffective over time with his peak years being 2004-2006 3. Anil Kumble also seems to slump down and become less effective.

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerMovingAverage("./kumble.csv","Anil Kumble")
bowlerMovingAverage("./warne.csv","Shane Warne")
bowlerMovingAverage("./murali.csv","M Muralitharan")

tkps-bowlma-1

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

Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerCumulativeAvgWickets("./kumble.csv","Kumble")

kwm-bowlcaw-1

bowlerCumulativeAvgWickets("./warne.csv","Warne")

kwm-bowlcaw-2

bowlerCumulativeAvgWickets("./murali.csv","Muralitharan")

kwm-bowlcaw-3

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

Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerCumulativeAvgEconRate("./kumble.csv","Kumble")

kwm-bowlcer-1

bowlerCumulativeAvgEconRate("./warne.csv","Warne")

kwm-bowlcer-2

bowlerCumulativeAvgEconRate("./murali.csv","Muralitharan")

kwm-bowlcer-3

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

Future Wickets forecast

Here are plots that forecast how the bowler will perform in future. In this case 90% of the career wickets trend is used as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated wickets trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

Take a look at the wickets forecasted for the bowlers below. – Shane Warne and Muralitharan have a fairly consistent forecast – Kumble forecast shows a small dip

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerPerfForecast("./kumble.csv","Anil Kumble")
bowlerPerfForecast("./warne.csv","Shane Warne")
bowlerPerfForecast("./murali.csv","M Muralitharan")

kwm-perffcst-1

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

Contribution to matches won and lost

The plot below is extremely interesting
1. Kumble wickets range from 2 to 4 wickets in matches wons with a mean of 3
2. Warne wickets in won matches range from 1 to 4 with more matches won. Clearly there are other bowlers contributing to the wins, possibly the pacers
3. Muralitharan wickets range in winning matches is more than the other 2 and ranges ranges 3 to 5 and clearly had a hand (pun unintended) in Sri Lanka’s wins

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

kumblesp 
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerContributionWonLost("kumblesp.csv","Kumble")
bowlerContributionWonLost("warnesp.csv","Warne")
bowlerContributionWonLost("muralisp.csv","Murali")

kwm-wl-1

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

Performance home and overseas

From the plot below it can be seen that Kumble & Warne have played more matches overseas than Muralitharan. Both Kumble and Warne show an average of 2 wickers overseas,  Murali on the other hand has an average of 2.5 wickets overseas but a slightly less number of matches than Kumble & Warne

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerPerfHomeAway("kumblesp.csv","Kumble")
bowlerPerfHomeAway("warnesp.csv","Warne")
bowlerPerfHomeAway("muralisp.csv","Murali")

kwm-ha-1
dev.off()
## null device 
##           1
 

Relative Wickets Frequency Percentage

The Relative Wickets Percentage plot shows that M Muralitharan has a large percentage of wickets in the 3-8 wicket range

frames <- list("./kumble.csv","./murali.csv","warne.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")
relativeBowlingPerf(frames,names)

relBowlPerf-1

Relative Economy Rate against wickets taken

Clearly from the plot below it can be seen that Muralitharan has the best Economy Rate among the three

frames <- list("./kumble.csv","./murali.csv","warne.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")
relativeBowlingER(frames,names)

relBowlER-1

Relative cumulative average wickets of bowlers in career

The plot below shows that Murali has the best cumulative average wickets taken followed by Kumble and then Warne

frames <- list("./kumble.csv","./murali.csv","warne.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")
relativeBowlerCumulativeAvgWickets(frames,names)

rbcaw-1

Relative cumulative average economy rate of bowlers

Muralitharan has the best economy rate followed by Warne and then Kumble

frames <- list("./kumble.csv","./murali.csv","warne.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")
relativeBowlerCumulativeAvgEconRate(frames,names)

rbcer-1

Check for bowler in-form/out-of-form

The below computation uses Null Hypothesis testing and p-value to determine if the bowler is in-form or out-of-form. For this 90% of the career wickets is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the bowler continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the bowler is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

Note Ideally the p-value should be done for a population that follows the Normal Distribution. But the runs population is usually left skewed. So some correction may be needed. I will revisit this later

Note: The check for the form status of the bowlers indicate 1. That both Kumble and Muralitharan were out of form. This also shows in the moving average plot 2. Warne is still in great form and could have continued for a few more years. Too bad we didn’t see the magic later

checkBowlerInForm("./kumble.csv","Anil Kumble")
## *******************************************************************************************
## 
## Population size: 212  Mean of population: 2.69 
## Sample size: 24  Mean of sample: 2.04 SD of sample: 1.55 
## 
## Null hypothesis H0 : Anil Kumble 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Anil Kumble 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Anil Kumble 's Form Status: Out-of-Form because the p value: 0.02549  is less than alpha=  0.05"
## *******************************************************************************************
checkBowlerInForm("./warne.csv","Shane Warne")
## *******************************************************************************************
## 
## Population size: 240  Mean of population: 2.55 
## Sample size: 27  Mean of sample: 2.56 SD of sample: 1.8 
## 
## Null hypothesis H0 : Shane Warne 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Shane Warne 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Shane Warne 's Form Status: In-Form because the p value: 0.511409  is greater than alpha=  0.05"
## *******************************************************************************************
checkBowlerInForm("./murali.csv","M Muralitharan")
## *******************************************************************************************
## 
## Population size: 207  Mean of population: 3.55 
## Sample size: 23  Mean of sample: 2.87 SD of sample: 1.74 
## 
## Null hypothesis H0 : M Muralitharan 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : M Muralitharan 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "M Muralitharan 's Form Status: Out-of-Form because the p value: 0.036828  is less than alpha=  0.05"
## *******************************************************************************************
dev.off()
## null device 
##           1

Key Findings

The plots above capture some of the capabilities and features of my cricketr 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

Analysis of Top 4 batsman

The analysis of the Top 4 test batsman Tendulkar, Kallis, Ponting and Sangakkara show the folliwing

  1. Sangakkara has the highest average, followed by Tendulkar, Kallis and then Ponting.
  2. Ponting has the highest strike rate followed by Tendulkar,Sangakkara and then Kallis
  3. The predicted runs for a given Balls faced and Minutes at crease is highest for Ponting, followed by Tendulkar, Sangakkara and Kallis
  4. The moving average for Tendulkar and Ponting shows a downward trend while Kallis and Sangakkara retired too soon
  5. Tendulkar was out of form about the time of retirement while the rest were in-form. But this result has to be taken along with the moving average plot. Ponting was clearly on the way out.
  6. The home and overseas performance indicate that Tendulkar is the clear leader. He has the highest number of matches played overseas and his performance has been consistent. He is followed by Ponting, Kallis and finally Sangakkara

Analysis of Top 3 legs spinners

The analysis of Anil Kumble, Shane Warne and M Muralitharan show the following

  1. Muralitharan has the highest wickets and best economy rate followed by Warne and Kumble
  2. Muralitharan has higher wickets frequency percentage between 3 to 8 wickets
  3. Muralitharan has the best Economy Rate for wickets between 2 to 7
  4. The moving average plot shows that the time was up for Kumble and Muralitharan but Warne had a few years ahead
  5. The check for form status shows that Muralitharan and Kumble time was over while Warne still in great form
  6. Kumble’s has more matches abroad than the other 2, yet Kumble averages of 3 wickets at home and 2 wickets overseas liek Warne . Murali has played few matches but has an average of 4 wickets at home and 3 wickets overseas.

Final thoughts

Here are my final thoughts

Batting

Among the 4 batsman Tendulkar, Kallis, Ponting and Sangakkara the clear leader is Tendulkar for the following reasons

  1. Tendulkar has the highest test centuries and runs of all time.Tendulkar’s average is 2nd to Sangakkara, Tendulkar’s predicted runs for a given Balls faced and Minutes at Crease is 2nd and is behind Ponting. Also Tendulkar’s performance at home and overseas are consistent throughtout despite the fact that he has a highest number of overseas matches
  2. Ponting takes the 2nd spot with the 2nd highest number of centuries, 1st in Strike Rate and 2nd in home and away performance.
  3. The 3rd spot goes to Sangakkara, with the highest average, 3rd highest number of centuries, reasonable run frequency percentage in different run ranges. However he has a fewer number of matches overseas and his performance overseas is significantly lower than at home
  4. Kallis has the 2nd highest number of centuries but his performance overseas and strike rate are behind others
  5. Finally Kallis and Sangakkara had a few good years of batting still left in them (pity they retired!) while Tendulkar and Ponting’s time was up
  6. While Tendulkars cumulative average stays around 50 runs, Sangakkara briefly overtakes Tendulkar towards the end of his career. Sangakkara may have finished with a better average if he had played for a few more years
  7. Ponting has the best overall strike rate followed by Sangakkara

Bowling

Muralitharan leads the way followed closely by Warne and finally Kumble. The reasons are

  1. Muralitharan has the highest number of test wickets with the best Wickets percentage and the best Economy Rate. Murali on average gas taken 4 wickets at home and 3 wickets overseas
  2. Warne follows Murali in the highest wickets taken, however Warne has less matches overseas than Murali and average 3 wickets home and 2 wickets overseas
  3. Kumble has the 3rd highest wickets, with 3 wickets on an average at home and 2 wickets overseas. However Kumble has played more matches overseas than the other two. In that respect his performance is great. Also Kumble has played less matches at home otherwise his numbers would have looked even better.
  4. Also while Kumble and Muralitharan’s career was on the decline , Warne was going great and had a couple of years ahead.
  5. Muralitharan has the best cumulative wicket rate and economy rate. Kumble has a better wicket rate than Warne but is more expensive than Warne

You can download this analysis at Introducing cricketrYou can download this analysis at Re-Introducing cricketr

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

Also see

1.Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
2.yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
3.yorkr crashes the IPL party !Part 1
4.Introducing cricketr! : An R package to analyze performances of cricketers
5.Beaten by sheer pace! Cricket analytics with yorkr in paperback and Kindle versions
6. Cricket analytics with cricketr in paperback and Kindle versions

You may also like
1. A crime map of India in R: Crimes against women
2.  What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
3.  Bend it like Bluemix, MongoDB with autoscaling – Part 2
4. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
5. Thinking Web Scale (TWS-3): Map-Reduce – Bring compute to data
6. Deblurring with OpenCV:Weiner filter reloaded
7. Fun simulation of a Chain in Androidhttp://www.r-bloggers.com/introducing-cricketr-an-r-package-to-analyze-performances-of-cricketers/

yorkr ranks ODI batsmen and bowlers

This is the last and final post in which yorkr ranks ODI batsmen and bowlers. These are based on match data from Cricsheet. The ranking is done on

  1. average runs and average strike rate for batsmen and
  2. average wickets and average economy rate for bowlers.

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!!

1

nd $4.99/Rs 320 and $6.99/Rs448 respectively

 

This post has also been published in RPubs RankODIPlayers. You can download this as a pdf file at RankODIPlayers.pdf.

Checkout my interactive Shiny apps GooglyPlus (plots & tables) and Googly (only plots) which can be used to analyze IPL players, teams and matches.

You can take a look at the code at rankODIPlayers (available in yorkr_0.0.5)

rm(list=ls())
library(yorkr)
library(dplyr)
source("rankODIBatsmen.R")
source("rankODIBowlers.R")

Rank ODI batsmen

The top 3 ODI batsmen are hashim Amla (SA), Matther Hayden(Aus) & Virat Kohli (Ind) . Note: For ODI a a cutoff of at least 50 matches played was chosen.

ODIBatsmanRank <- rankODIBatsmen()
as.data.frame(ODIBatsmanRank[1:30,])
##            batsman matches meanRuns    meanSR
## 1          HM Amla     185 51.96216  84.15508
## 2        ML Hayden      79 50.08861  81.20646
## 3          V Kohli     279 48.51971  78.55197
## 4   AB de Villiers     253 47.93676  95.05561
## 5     SR Tendulkar     151 45.82119  79.62311
## 6         S Dhawan     116 45.03448  81.54043
## 7         V Sehwag     167 44.49102 106.27563
## 8          JE Root     111 43.64865  81.66054
## 9        Q de Kock      85 43.61176  82.55235
## 10       IJL Trott     113 43.36283  70.69761
## 11   KC Sangakkara     293 42.81911  75.10420
## 12      TM Dilshan     283 41.76678  89.70360
## 13   KS Williamson     146 41.24658  73.49267
## 14   S Chanderpaul      93 40.07527  70.59613
## 15        HH Gibbs      75 40.00000  79.03813
## 16     Salman Butt      57 39.85965  59.29807
## 17    Anamul Haque      58 39.72414  56.45224
## 18      RT Ponting     238 38.88235  71.94294
## 19       JH Kallis     136 38.77941  67.17794
## 20        MS Dhoni     328 38.57927  90.30555
## 21      MJ Guptill     199 38.54774  73.88090
## 22       DA Warner     138 38.52174  87.24978
## 23 Mohammad Yousuf      94 38.44681  72.69851
## 24        JD Ryder      66 38.40909  91.29667
## 25       GJ Bailey     133 38.38346  75.74519
## 26       G Gambhir     209 37.83254  75.15483
## 27      AJ Strauss     122 37.80328  71.54844
## 28       MJ Clarke     301 37.67442  69.78415
## 29       SR Watson     274 37.08029  83.46489
## 30        AJ Finch     103 36.36893  79.49845

Rank ODI bowlers

The top 3 ODI bowlers are R J Harris (Aus), MJ Henry(NZ) and MA Starc(Aus). Mohammed Shami is 4th and Amit Mishra is 8th A cutoff of 20 matches was considered for bowlers

ODIBowlersRank <- rankODIBowlers()
## [1] 35072     3
## [1] "C:/software/cricket-package/york-test/yorkrData/ODI/ODI-matches"
as.data.frame(ODIBowlersRank[1:30,])
##               bowler matches meanWickets   meanER
## 1  Mustafizur Rahman      56    4.000000 4.293214
## 2           JH Davey      53    3.528302 4.455094
## 3          RJ Harris      94    3.276596 4.361489
## 4           MA Starc     208    3.144231 4.425865
## 5           MJ Henry      88    3.125000 4.961250
## 6         A Flintoff     139    2.956835 4.283022
## 7           A Mishra     106    2.886792 4.365849
## 8     Mohammed Shami     144    2.777778 5.609306
## 9     MJ McClenaghan     165    2.751515 5.640424
## 10          CJ McKay     230    2.704348       NA
## 11       MF Maharoof     114    2.701754 4.427018
## 12       Imran Tahir     156    2.660256 4.461923
## 13        BAW Mendis     234    2.641026 4.532308
## 14     RK Kleinveldt      54    2.629630 4.306667
## 15      Arafat Sunny      62    2.612903 4.103226
## 16         JE Taylor     156    2.602564 5.115192
## 17           AJ Hall      55    2.600000 3.879091
## 18        WD Parnell     129    2.596899 5.477597
## 19         CR Woakes     129    2.596899 5.340620
## 20      DE Bollinger     152    2.592105 4.282763
## 21        Wahab Riaz     206    2.567961 5.431748
## 22        PJ Cummins     148    2.567568 5.715405
## 23         R Rampaul     173    2.549133 4.726590
## 24      Taskin Ahmed      56    2.535714 5.325357
## 25          DW Steyn     292    2.534247 4.534007
## 26      JR Hazlewood      64    2.531250 4.392500
## 27        Abdur Rauf      84    2.523810 4.786667
## 28           SW Tait     141    2.517730 5.173191
## 29      Hamid Hassan     106    2.509434 4.686038
## 30        SL Malinga     419    2.498807 4.968974

Hope you have fun with my yorkr package.!

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

yorkr ranks T20 batsmen and bowlers

Here is another short post which ranks T20 batsmen and bowlers. These are based on match data from Cricsheet. The ranking is done on

  1. average runs and average strike rate for batsmen and
  2. average wickets and average economy rate for bowlers.

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!!

1

.99/Rs 320 and $6.99/Rs448 respectively

 

This post has also been published in RPubs RankT20Players. You can download this as a pdf file at RankT20Players.pdf.

Checkout my interactive Shiny apps GooglyPlus (plots & tables) and Googly (only plots) which can be used to analyze IPL players, teams and matches.

You can take a look at the code at rankT20Players (available in yorkr_0.0.5)

rm(list=ls())
library(yorkr)
library(dplyr)
source("rankT20Batsmen.R")
source("rankT20Bowlers.R")

Rank T20 batsmen

Virat Kohli (Ind), Chris Gayle (WI) and Kevin Pietersen (Eng) top the T20 rankings. Virat Kohli stands tall among the batsmen with a average of 39.1935, followed by Chris Gayle who has an average of 32.69 and finally Kevin Pietersen.

Note: For T20 a cutoff of at least 30 matches played was chosen.

T20BatsmanRank <- rankT20Batsmen()
as.data.frame(T20BatsmanRank[1:30,])
##             batsman matches meanRuns   meanSR
## 1           V Kohli      31 39.19355 128.8371
## 2          CH Gayle      43 32.69767 119.6467
## 3      KP Pietersen      37 32.43243 138.6732
## 4     KS Williamson      31 32.25806 130.1255
## 5  Mohammad Shahzad      33 31.66667 115.4582
## 6       BB McCullum      69 30.98551 126.0610
## 7        MJ Guptill      54 30.83333 120.0669
## 8          AD Hales      37 30.75676 115.3511
## 9       H Masakadza      38 29.26316 109.6182
## 10         GC Smith      32 27.59375 114.1831
## 11        DA Warner      56 27.53571 123.2209
## 12        JP Duminy      58 26.84483 117.3952
## 13 DPMD Jayawardene      51 26.47059 112.4257
## 14        SR Watson      50 26.30000 118.9464
## 15    KC Sangakkara      52 26.23077 112.4665
## 16       TM Dilshan      66 26.18182 102.5683
## 17         SK Raina      43 25.90698 124.3044
## 18        RG Sharma      41 25.68293 123.3983
## 19        G Gambhir      36 25.66667 117.5764
## 20     Yuvraj Singh      41 25.12195 119.5846
## 21    Misbah-ul-Haq      32 25.09375 106.6762
## 22       EJG Morgan      52 24.71154 121.1462
## 23       MN Samuels      40 24.35000 105.8547
## 24       MEK Hussey      30 24.03333 129.1250
## 25    Ahmed Shehzad      41 23.82927 100.8805
## 26  Shakib Al Hasan      40 23.35000 109.3798
## 27          HM Amla      30 23.33333 111.2513
## 28         CL White      45 22.73333       NA
## 29      LMP Simmons      33 22.54545       NA
## 30       Umar Akmal      69 22.20290 108.3590

Rank T20 bowlers

The top 3 T20 bowlers are BAW Mendis (SL) Umar Gul (Pak) and Steyn(SA). R Ashwin is 13th. As with batsmen, a minimum of 30 matches played was taken into consideration.

T20BowlersRank <- rankT20Bowlers()
as.data.frame(T20BowlersRank[1:30,])
##             bowler matches meanWickets   meanER
## 1       BAW Mendis      36   1.6944444 6.581111
## 2         Umar Gul      57   1.5964912 7.306842
## 3         DW Steyn      38   1.5526316 6.407632
## 4      Saeed Ajmal      63   1.4920635 6.316190
## 5       SL Malinga      59   1.4576271 7.163898
## 6       TG Southee      37   1.4054054 8.840000
## 7       MG Johnson      30   1.4000000 7.080667
## 8         GP Swann      38   1.3947368 6.576842
## 9      JW Dernbach      33   1.3636364 8.550303
## 10        M Morkel      39   1.3333333 7.384872
## 11 Shakib Al Hasan      37   1.2972973 6.648649
## 12       SP Narine      32   1.2500000 5.757812
## 13        R Ashwin      33   1.2424242 7.247273
## 14 KMDN Kulasekara      42   1.2380952 6.938095
## 15       SCJ Broad      55   1.2363636 7.832182
## 16      WD Parnell      34   1.2058824 8.227941
## 17        KD Mills      41   1.1951220 8.077317
## 18      DL Vettori      34   1.1470588 5.708235
## 19   Shahid Afridi      85   1.1294118 6.748000
## 20       SR Watson      44   1.1136364 8.015227
## 21   Sohail Tanvir      48   1.1041667 7.354167
## 22   Sohail Tanvir      48   1.1041667 7.354167
## 23     NL McCullum      56   1.0535714 7.246964
## 24     NLTC Perera      34   1.0294118 8.916471
## 25         J Botha      39   1.0256410 6.647436
## 26        DJ Bravo      45   1.0222222 8.630000
## 27   Mohammad Nabi      32   0.9687500 7.208437
## 28       DJG Sammy      55   0.8909091 7.899818
## 29 Mohammad Hafeez      56   0.8392857 6.996964
## 30      AD Mathews      44   0.7954545 6.827727

Conclusion

Conclusion

As expected Virat Kohli stands head and shoulders above the rest. Hamid Hasan and Mohammed Shami figuring the top T20 bowlers was a bit of a surprise to me.

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

Watch this space!

  1. Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
  2. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
  3. yorkr crashes the IPL party !Part 1
  4. Introducing cricketr! : An R package to analyze performances of cricketers
  5. Cricket analytics with cricketr in paperback and Kindle versions

yorkr ranks IPL batsmen and bowlers

Here is a short post which ranks IPL batsmen and bowlers. These are based on match data from Cricsheet. Ranking batsmen and bowlers in IPL is more challenging as the players can belong to different teams in different years. Hence I create a combined data frame of the batsmen and bowlers regardless of their IPL teams and calculate a) average runs and average strike rate for batsmen and c) average wickets and d) average economy rate for bowlers.

I will be doing this ranking for T20 and ODI batting and bowling performances shortly.

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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This post has also been published in RPubs RankIPLPlayers. You can download this as a pdf file at RankIPLPlayers.pdf.

You can take a look at the code at rankIPLPlayers (should be available in yorkr_0.0.5)

Checkout my interactive Shiny apps GooglyPlus (plots & tables) and Googly (only plots) which can be used to analyze IPL players, teams and matches.

The results are slightly surprising

rm(list=ls())
library(yorkr)
library(dplyr)
setwd("C:/software/cricket-package/cricsheet/cleanup/IPL/rank")
source("rankIPLBatsmen.R")
source("rankIPLBowlers.R")

Rank IPL batsmen

Chris Gayle, MEK Hussey and Shane Watson are top 3 IPL batsmen. Gayle towers over the others in mean runs and mean strike rate. Surprisingly Ajinkya Rahane is the top Indian T20 batsman, if we leave out Sachin Tendulkar (who tops India yet again!). The other top IPL T20 batsmen are Raina, Gambhir, Rohit Sharma in that order. Virat Kohli comes a distant 14th.

iplBatsmanRank <- rankIPLBatsmen()
as.data.frame(iplBatsmanRank[1:30,])
##             batsman matches meanRuns    meanSR
## 1          CH Gayle     128 40.00781 144.92188
## 2        MEK Hussey      64 33.57812 107.23500
## 3         SR Watson      75 31.46667 129.97733
## 4      SR Tendulkar     127 29.74803 108.86622
## 5         AM Rahane      77 29.14286 101.40065
## 6         DA Warner     134 29.10448 118.38313
## 7         JP Duminy      94 28.77660 124.61702
## 8          SK Raina     128 28.62500 122.12656
## 9         G Gambhir     210 28.13810 108.78090
## 10        RG Sharma     181 28.07182 118.57801
## 11         DR Smith      78 27.82051 119.64462
## 12      BB McCullum      98 27.81633 114.91255
## 13         S Dhawan     109 27.74312 112.21000
## 14          V Kohli     188 27.56915 113.81261
## 15   AB de Villiers     150 27.46000 136.70860
## 16         R Dravid     104 27.02885 107.78923
## 17        JH Kallis     167 26.54491  94.65641
## 18         V Sehwag     174 26.39655 140.29011
## 19       RV Uthappa     166 26.27711 120.48506
## 20       SC Ganguly      86 25.98837  96.39849
## 21     AC Gilchrist      81 25.77778 122.69074
## 22    KC Sangakkara      70 25.67143 112.97529
## 23         MS Dhoni     119 25.29412 130.99832
## 24       TM Dilshan      82 24.13415 101.12634
## 25          M Vijay      96 23.92708 102.01771
## 26        AT Rayudu     146 23.63014 117.91000
## 27 DPMD Jayawardene     109 22.95413 110.73862
## 28        MK Pandey     105 22.71429        NA
## 29     Yuvraj Singh     112 22.48214 114.51018
## 30      S Badrinath      66 22.22727 114.97061

Rank IPL bowlers

The top 3 IPL T20 bowlers are SL Malinga,SP Narine and DJ Bravo.

Don’t get hung up on the decimals in the average wickets for the bowlers. All it implies is that if 2 bowlers have average wickets of 1.0 and 1.5, it implies that in 2 matches the 1st bowler will take 2 wickets and the 2nd bowler will take 3 wickets.

iplBowlersRank <- rankIPLBowlers()
as.data.frame(iplBowlersRank[1:30,])
##             bowler matches meanWickets   meanER
## 1       SL Malinga      96    1.645833 6.545208
## 2        SP Narine      54    1.555556 5.967593
## 3         DJ Bravo      58    1.517241 7.929310
## 4         M Morkel      37    1.405405 7.626216
## 5        IK Pathan      40    1.400000 7.579250
## 6         RP Singh      42    1.357143 7.966429
## 7         MM Patel      31    1.354839 7.282581
## 8  Shakib Al Hasan      32    1.343750 6.911250
## 9    R Vinay Kumar      63    1.317460 8.342540
## 10       MM Sharma      46    1.304348 7.740652
## 11         P Awana      33    1.303030 8.325758
## 12        MM Patel      30    1.300000 7.569667
## 13          Z Khan      41    1.292683 7.735854
## 14        A Mishra      43    1.255814 7.226512
## 15         PP Ojha      53    1.245283 7.268679
## 16     JP Faulkner      40    1.225000 8.502250
## 17     DS Kulkarni      32    1.156250 8.372188
## 18        UT Yadav      46    1.152174 8.394783
## 19        A Kumble      41    1.146341 6.567073
## 20       JA Morkel      73    1.136986 8.131370
## 21        SK Warne      53    1.132075 7.277170
## 22 Harbhajan Singh     107    1.102804 7.014953
## 23        L Balaji      34    1.088235 7.186176
## 24        R Ashwin      92    1.065217 6.812391
## 25        AR Patel      31    1.064516 7.137097
## 26  M Muralitharan      39    1.051282 6.470256
## 27         P Kumar      36    1.027778 8.148056
## 28       PP Chawla      85    1.023529 8.017765
## 29       SR Watson      67    1.014925 7.695224
## 30        DJ Bravo      30    1.000000 7.966333

Conclusion: The results are somewhat surprising. The ranking was based on data from Cricsheet. The data in this site are available from 2008-2015. I hope to do this ranking for T20 and ODIs shortly

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

Watch this space!

  1. Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
  2. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
  3. yorkr crashes the IPL party !Part 1
  4. Introducing cricketr! : An R package to analyze performances of cricketers
  5. Cricket analytics with cricketr in paperback and Kindle versions

Introducing cricket package yorkr:Part 4-In the block hole!

Introduction

“The nitrogen in our DNA, the calcium in our teeth, the iron in our blood, the carbon in our apple pies were made in the interiors of collapsing stars. We are made of starstuff.”

“If you wish to make an apple pie from scratch, you must first invent the universe.”

“We are like butterflies who flutter for a day and think it is forever.”

“The absence of evidence is not the evidence of absence.”

“We are star stuff which has taken its destiny into its own hands.”

                              Cosmos - Carl Sagan

This post is the 4th and possibly, the last part of my introduction, to my latest cricket package yorkr. This is the 4th part of the introduction, the 3 earlier ones were

  1. Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
  2. Introducing cricket package yorkr: Part 2-Trapped leg before wicket!
  3. Introducing cricket package yorkr: Part 3-Foxed by flight!

The 1st part included functions dealing with a specific match, the 2nd part dealt with functions between 2 opposing teams. The 3rd part dealt with functions between a team and all matches with all oppositions. This 4th part includes individual batting and bowling performances in ODI matches and deals with Class 4 functions.

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!!

1

d $4.99/Rs 320 and $6.99/Rs448 respectively

 

This post has also been published at RPubs yorkr-Part4 and can also be downloaded as a PDF document from yorkr-Part4.pdf.

You can clone/fork the code for the package yorkr from Github at yorkr-package

Checkout my interactive Shiny apps GooglyPlus (plots & tables) and Googly (only plots) which can be used to analyze IPL players, teams and matches.

Important note 1: Do check out all the posts on the python avatar of yorkr, namely ‘yorkpy’ in my post ‘Pitching yorkpy … short of good length to IPL – Part 1

Batsman functions

  1. batsmanRunsVsDeliveries
  2. batsmanFoursSixes
  3. batsmanDismissals
  4. batsmanRunsVsStrikeRate
  5. batsmanMovingAverage
  6. batsmanCumulativeAverageRuns
  7. batsmanCumulativeStrikeRate
  8. batsmanRunsAgainstOpposition
  9. batsmanRunsVenue
  10. batsmanRunsPredict

Bowler functions

  1. bowlerMeanEconomyRate
  2. bowlerMeanRunsConceded
  3. bowlerMovingAverage
  4. bowlerCumulativeAvgWickets
  5. bowlerCumulativeAvgEconRate
  6. bowlerWicketPlot
  7. bowlerWicketsAgainstOpposition
  8. bowlerWicketsVenue
  9. bowlerWktsPredict

Note: The yorkr package in its current avatar only supports ODI, T20 and IPL T20 matches.

library(yorkr)
library(gridExtra)
library(rpart.plot)
library(dplyr)
library(ggplot2)
rm(list=ls())

A. Batsman functions

1. Get Team Batting details

The function below gets the overall team batting details based on the RData file available in ODI matches. This is currently also available in Github at (https://github.com/tvganesh/yorkrData/tree/master/ODI/ODI-matches).  However you may have to do this as future matches are added! The batting details of the team in each match is created and a huge data frame is created by rbinding the individual dataframes. This can be saved as a RData file

setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-matches")
india_details <- getTeamBattingDetails("India",dir=".", save=TRUE)
dim(india_details)
## [1] 11085    15
sa_details <- getTeamBattingDetails("South Africa",dir=".",save=TRUE)
dim(sa_details)
## [1] 6375   15
nz_details <- getTeamBattingDetails("New Zealand",dir=".",save=TRUE)
dim(nz_details)
## [1] 6262   15
eng_details <- getTeamBattingDetails("England",dir=".",save=TRUE)
dim(eng_details)
## [1] 9001   15

2. Get batsman details

This function is used to get the individual batting record for a the specified batsmen of the country as in the functions below. For analyzing the batting performances the following cricketers have been chosen

  1. Virat Kohli (Ind)
  2. M S Dhoni (Ind)
  3. AB De Villiers (SA)
  4. Q De Kock (SA)
  5. J Root (Eng)
  6. M J Guptill (NZ)
setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-matches")
kohli <- getBatsmanDetails(team="India",name="Kohli",dir=".")
## [1] "./India-BattingDetails.RData"
dhoni <- getBatsmanDetails(team="India",name="Dhoni")
## [1] "./India-BattingDetails.RData"
devilliers <-  getBatsmanDetails(team="South Africa",name="Villiers",dir=".")
## [1] "./South Africa-BattingDetails.RData"
deKock <-  getBatsmanDetails(team="South Africa",name="Kock",dir=".")
## [1] "./South Africa-BattingDetails.RData"
root <-  getBatsmanDetails(team="England",name="Root",dir=".")
## [1] "./England-BattingDetails.RData"
guptill <-  getBatsmanDetails(team="New Zealand",name="Guptill",dir=".")
## [1] "./New Zealand-BattingDetails.RData"

3. Runs versus deliveries

Kohli, De Villiers and Guptill have a good cluster of points that head towards 150 runs at 150 deliveries.

p1 <-batsmanRunsVsDeliveries(kohli,"Kohli")
p2 <- batsmanRunsVsDeliveries(dhoni, "Dhoni")
p3 <- batsmanRunsVsDeliveries(devilliers,"De Villiers")
p4 <- batsmanRunsVsDeliveries(deKock,"Q de Kock")
p5 <- batsmanRunsVsDeliveries(root,"JE Root")
p6 <- batsmanRunsVsDeliveries(guptill,"MJ Guptill")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

runsVsDeliveries-1

4. Batsman Total runs, Fours and Sixes

The plots below show the total runs, fours and sixes by the batsmen

kohli46 <- select(kohli,batsman,ballsPlayed,fours,sixes,runs)
p1 <- batsmanFoursSixes(kohli46,"Kohli")
dhoni46 <- select(dhoni,batsman,ballsPlayed,fours,sixes,runs)
p2 <- batsmanFoursSixes(dhoni46,"Dhoni")
devilliers46 <- select(devilliers,batsman,ballsPlayed,fours,sixes,runs)
p3 <- batsmanFoursSixes(devilliers46, "De Villiers")
deKock46 <- select(deKock,batsman,ballsPlayed,fours,sixes,runs)
p4 <- batsmanFoursSixes(deKock46,"Q de Kock")
root46 <- select(root,batsman,ballsPlayed,fours,sixes,runs)
p5 <- batsmanFoursSixes(root46,"JE Root")
guptill46 <- select(guptill,batsman,ballsPlayed,fours,sixes,runs)
p6 <- batsmanFoursSixes(guptill46,"MJ Guptill")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

foursSixes-1

5. Batsman dismissals

The type of dismissal for each batsman is shown below

p1 <-batsmanDismissals(kohli,"Kohli")
p2 <- batsmanDismissals(dhoni, "Dhoni")
p3 <- batsmanDismissals(devilliers, "De Villiers")
p4 <- batsmanDismissals(deKock,"Q de Kock")
p5 <- batsmanDismissals(root,"JE Root")
p6 <- batsmanDismissals(guptill,"MJ Guptill")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

dismissal-1

6. Runs versus Strike Rate

De villiers has the best strike rate among all as there are more points to the right side of the plot for the same runs. Kohli and Dhoni do well too. Q De Kock and Joe Root also have a very good spread of points though they have fewer innings.

p1 <-batsmanRunsVsStrikeRate(kohli,"Kohli")
p2 <- batsmanRunsVsStrikeRate(dhoni, "Dhoni")
p3 <- batsmanRunsVsStrikeRate(devilliers, "De Villiers")
p4 <- batsmanRunsVsStrikeRate(deKock,"Q de Kock")
p5 <- batsmanRunsVsStrikeRate(root,"JE Root")
p6 <- batsmanRunsVsStrikeRate(guptill,"MJ Guptill")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

runsSR-1

7. Batsman moving average

Kohli’s average is on a gentle increase from below 50 to around 60’s. Joe Root performance is impressive with his moving average of late tending towards the 70’s. Q De Kock seemed to have a slump around 2015 but his performance is on the increase. Devilliers consistently averages around 50. Dhoni also has been having a stable run in the last several years.

p1 <-batsmanMovingAverage(kohli,"Kohli")
p2 <- batsmanMovingAverage(dhoni, "Dhoni")
p3 <- batsmanMovingAverage(devilliers, "De Villiers")
p4 <- batsmanMovingAverage(deKock,"Q de Kock")
p5 <- batsmanMovingAverage(root,"JE Root")
p6 <- batsmanMovingAverage(guptill,"MJ Guptill")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

ma-1

8. Batsman cumulative average

The functions below provide the cumulative average of runs scored. As can be seen Kohli and Devilliers have a cumulative runs rate that averages around 48-50. Q De Kock seems to have had a rocky career with several highs and lows as the cumulative average oscillates between 45-40. Root steadily improves to a cumulative average of around 42-43 from his 50th innings

p1 <-batsmanCumulativeAverageRuns(kohli,"Kohli")
p2 <- batsmanCumulativeAverageRuns(dhoni, "Dhoni")
p3 <- batsmanCumulativeAverageRuns(devilliers, "De Villiers")
p4 <- batsmanCumulativeAverageRuns(deKock,"Q de Kock")
p5 <- batsmanCumulativeAverageRuns(root,"JE Root")
p6 <- batsmanCumulativeAverageRuns(guptill,"MJ Guptill")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cAvg-1

9. Cumulative Average Strike Rate

The plots below show the cumulative average strike rate of the batsmen. Dhoni and Devilliers have the best cumulative average strike rate of 90%. The rest average around 80% strike rate. Guptill shows a slump towards the latter part of his career.

p1 <-batsmanCumulativeStrikeRate(kohli,"Kohli")
p2 <- batsmanCumulativeStrikeRate(dhoni, "Dhoni")
p3 <- batsmanCumulativeStrikeRate(devilliers, "De Villiers")
p4 <- batsmanCumulativeStrikeRate(deKock,"Q de Kock")
p5 <- batsmanCumulativeStrikeRate(root,"JE Root")
p6 <- batsmanCumulativeStrikeRate(guptill,"MJ Guptill")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cSR-1

10. Batsman runs against opposition

Kohli’s best performances are against Australia, West Indies and Sri Lanka

batsmanRunsAgainstOpposition(kohli,"Kohli")

runsOppn1-1

batsmanRunsAgainstOpposition(dhoni, "Dhoni")

runsOppn2-1

Kohli’s best performances are against Australia, Pakistan and West Indies

batsmanRunsAgainstOpposition(devilliers, "De Villiers")

runsOppn3-1

Quentin de Kock average almost 100 runs against India and 75 runs against England

batsmanRunsAgainstOpposition(deKock, "Q de Kock")

runsOppn4-1

Root’s best performances are against South Africa, Sri Lanka and West Indies

batsmanRunsAgainstOpposition(root, "JE Root")

runsOppn5-1

batsmanRunsAgainstOpposition(guptill, "MJ Guptill")

runsOppn6-1

11. Runs at different venues

The plots below give the performances of the batsmen at different grounds.

batsmanRunsVenue(kohli,"Kohli")

runsVenue1-1

batsmanRunsVenue(dhoni, "Dhoni")

runsVenue2-1

batsmanRunsVenue(devilliers, "De Villiers")

runsVenue3-1

batsmanRunsVenue(deKock, "Q de Kock")

runsVenue4-1

batsmanRunsVenue(root, "JE Root")

runsVenue5-1

batsmanRunsVenue(guptill, "MJ Guptill")

runsVenue6-1

12. Predict number of runs to deliveries

The plots below use rpart classification tree to predict the number of deliveries required to score the runs in the leaf node. For e.g. Kohli takes 66 deliveries to score 64 runs and for higher number of deliveries scores around 115 runs. Devilliers needs

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(kohli,"Kohli")
batsmanRunsPredict(dhoni, "Dhoni")
batsmanRunsPredict(devilliers, "De Villiers")

runsPredict1,runsVenue1-1

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(deKock,"Q de Kock")
batsmanRunsPredict(root,"JE Root")
batsmanRunsPredict(guptill,"MJ Guptill")

runsPredict2,runsVenue1-1

B. Bowler functions

13. Get bowling details

The function below gets the overall team bowling details based on the RData file available in ODI matches. This is currently also available in Github at (https://github.com/tvganesh/yorkrData/tree/master/ODI/ODI-matches). The bowling details of the team in each match is created and a huge data frame is created by rbinding the individual dataframes. This can be saved as a RData file

setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-matches")
ind_bowling <- getTeamBowlingDetails("India",dir=".",save=TRUE)
dim(ind_bowling)
## [1] 7816   12
aus_bowling <- getTeamBowlingDetails("Australia",dir=".",save=TRUE)
dim(aus_bowling)
## [1] 9191   12
ban_bowling <- getTeamBowlingDetails("Bangladesh",dir=".",save=TRUE)
dim(ban_bowling)
## [1] 5665   12
sa_bowling <- getTeamBowlingDetails("South Africa",dir=".",save=TRUE)
dim(sa_bowling)
## [1] 3806   12
sl_bowling <- getTeamBowlingDetails("Sri Lanka",dir=".",save=TRUE)
dim(sl_bowling)
## [1] 3964   12

14. Get bowling details of the individual bowlers

This function is used to get the individual bowling record for a specified bowler of the country as in the functions below. For analyzing the bowling performances the following cricketers have been chosen

  1. R A Jadeja (Ind)
  2. Ravichander Ashwin (Ind)
  3. Mitchell Starc (Aus)
  4. Shakib Al Hasan (Ban)
  5. Ajantha Mendis (SL)
  6. Dale Steyn (SA)
jadeja <- getBowlerWicketDetails(team="India",name="Jadeja",dir=".")
ashwin <- getBowlerWicketDetails(team="India",name="Ashwin",dir=".")
starc <-  getBowlerWicketDetails(team="Australia",name="Starc",dir=".")
shakib <-  getBowlerWicketDetails(team="Bangladesh",name="Shakib",dir=".")
mendis <-  getBowlerWicketDetails(team="Sri Lanka",name="Mendis",dir=".")
steyn <-  getBowlerWicketDetails(team="South Africa",name="Steyn",dir=".")

15. Bowler Mean Economy Rate

Shakib Al Hassan is expensive in the 1st 3 overs after which he is very economical with a economy rate of 3-4. Starc, Steyn average around a ER of 4.0

p1<-bowlerMeanEconomyRate(jadeja,"RA Jadeja")
p2<-bowlerMeanEconomyRate(ashwin, "R Ashwin")
p3<-bowlerMeanEconomyRate(starc, "MA Starc")
p4<-bowlerMeanEconomyRate(shakib, "Shakib Al Hasan")
p5<-bowlerMeanEconomyRate(mendis, "A Mendis")
p6<-bowlerMeanEconomyRate(steyn, "D Steyn")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

meanER-1

16. Bowler Mean Runs conceded

Ashwin is expensive around 6 & 7 overs

p1<-bowlerMeanRunsConceded(jadeja,"RA Jadeja")
p2<-bowlerMeanRunsConceded(ashwin, "R Ashwin")
p3<-bowlerMeanRunsConceded(starc, "M A Starc")
p4<-bowlerMeanRunsConceded(shakib, "Shakib Al Hasan")
p5<-bowlerMeanRunsConceded(mendis, "A Mendis")
p6<-bowlerMeanRunsConceded(steyn, "D Steyn")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

meanRunsConceded-1

17. Bowler Moving average

RA jadeja and Mendis’ performance has dipped considerably, while Ashwin and Shakib have improving performances. Starc average around 4 wickets

p1<-bowlerMovingAverage(jadeja,"RA Jadeja")
p2<-bowlerMovingAverage(ashwin, "Ashwin")
p3<-bowlerMovingAverage(starc, "M A Starc")
p4<-bowlerMovingAverage(shakib, "Shakib Al Hasan")
p5<-bowlerMovingAverage(mendis, "Ajantha Mendis")
p6<-bowlerMovingAverage(steyn, "Dale Steyn")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

bowlerMA-1

17. Bowler cumulative average wickets

Starc is clearly the most consistent performer with 3 wickets on an average over his career, while Jadeja averages around 2.0. Ashwin seems to have dropped from 2.4-2.0 wickets, while Mendis drops from high 3.5 to 2.2 wickets. The fractional wickets only show a tendency to take another wicket.

p1<-bowlerCumulativeAvgWickets(jadeja,"RA Jadeja")
p2<-bowlerCumulativeAvgWickets(ashwin, "Ashwin")
p3<-bowlerCumulativeAvgWickets(starc, "M A Starc")
p4<-bowlerCumulativeAvgWickets(shakib, "Shakib Al Hasan")
p5<-bowlerCumulativeAvgWickets(mendis, "Ajantha Mendis")
p6<-bowlerCumulativeAvgWickets(steyn, "Dale Steyn")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cumWkts-1

18. Bowler cumulative Economy Rate (ER)

The plots below are interesting. All of the bowlers seem to average around 4.5 runs/over. RA Jadeja’s ER improves and heads to 4.5, Mendis is seen to getting more expensive as his career progresses. From a ER of 3.0 he increases towards 4.5

p1<-bowlerCumulativeAvgEconRate(jadeja,"RA Jadeja")
p2<-bowlerCumulativeAvgEconRate(ashwin, "Ashwin")
p3<-bowlerCumulativeAvgEconRate(starc, "M A Starc")
p4<-bowlerCumulativeAvgEconRate(shakib, "Shakib Al Hasan")
p5<-bowlerCumulativeAvgEconRate(mendis, "Ajantha Mendis")
p6<-bowlerCumulativeAvgEconRate(steyn, "Dale Steyn")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cumER-1

19. Bowler wicket plot

The plot below gives the average wickets versus number of overs

p1<-bowlerWicketPlot(jadeja,"RA Jadeja")
p2<-bowlerWicketPlot(ashwin, "Ashwin")
p3<-bowlerWicketPlot(starc, "M A Starc")
p4<-bowlerWicketPlot(shakib, "Shakib Al Hasan")
p5<-bowlerWicketPlot(mendis, "Ajantha Mendis")
p6<-bowlerWicketPlot(steyn, "Dale Steyn")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

wktPlot-1

20. Bowler wicket against opposition

#Jadeja's' best pertformance are against England, Pakistan and West Indies
bowlerWicketsAgainstOpposition(jadeja,"RA Jadeja")

wktsOppn1-1

#Ashwin's bets pertformance are against England, Pakistan and South Africa
bowlerWicketsAgainstOpposition(ashwin, "Ashwin")

wktsOppn2-1

#Starc has good performances against India, New Zealand, Pakistan, West Indies
bowlerWicketsAgainstOpposition(starc, "M A Starc")

wktsOppn3-1

bowlerWicketsAgainstOpposition(shakib,"Shakib Al Hasan")

wktsOppn4-1

bowlerWicketsAgainstOpposition(mendis, "Ajantha Mendis")

wktsOppn5-1

#Steyn has good performances against India, Sri Lanka, Pakistan, West Indies
bowlerWicketsAgainstOpposition(steyn, "Dale Steyn")

wktsOppn6-1

21. Bowler wicket at cricket grounds

bowlerWicketsVenue(jadeja,"RA Jadeja")

wktsAve1-1

bowlerWicketsVenue(ashwin, "Ashwin")

wktsAve2-1

bowlerWicketsVenue(starc, "M A Starc")
## Warning: Removed 2 rows containing missing values (geom_bar).

wktsAve3-1

bowlerWicketsVenue(shakib,"Shakib Al Hasan")

wktsAve4-1

bowlerWicketsVenue(mendis, "Ajantha Mendis")

wktsAve5-1

bowlerWicketsVenue(steyn, "Dale Steyn")

wktsAve6-1

22. Get Delivery wickets for bowlers

Thsi function creates a dataframe of deliveries and the wickets taken

setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-matches")
jadeja1 <- getDeliveryWickets(team="India",dir=".",name="Jadeja",save=FALSE)
ashwin1 <- getDeliveryWickets(team="India",dir=".",name="Ashwin",save=FALSE)
starc1 <- getDeliveryWickets(team="Australia",dir=".",name="MA Starc",save=FALSE)
shakib1 <- getDeliveryWickets(team="Bangladesh",dir=".",name="Shakib",save=FALSE)
mendis1 <- getDeliveryWickets(team="Sri Lanka",dir=".",name="Mendis",save=FALSE)
steyn1 <- getDeliveryWickets(team="South Africa",dir=".",name="Steyn",save=FALSE)

23. Predict number of deliveries to wickets

#Jadeja and Ashwin need around 22 to 28 deliveries to make a break through
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(jadeja1,"RA Jadeja")
bowlerWktsPredict(ashwin1,"RAshwin")

wktsPred1-1

#Starc and Shakib provide an early breakthrough producing a wicket in around 16 balls. Starc's 2nd wicket comed around the 30th delivery
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(starc1,"MA Starc")
bowlerWktsPredict(shakib1,"Shakib Al Hasan")

wktsPred2-1

#Steyn and Mendis take 20 deliveries to get their 1st wicket
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(mendis1,"A Mendis")
bowlerWktsPredict(steyn1,"DSteyn")

wktsPred3-1

Conclusion

This concludes the 4 part introduction to my new R cricket package yorkr for ODIs. I will be enhancing the package to handle Twenty20 and IPL matches soon. You can fork/clone the code from Github at yorkr.

The yaml data from Cricsheet have already beeen converted into R consumable dataframes. The converted data can be downloaded from Github at yorkrData. There are 3 folders – ODI matches, ODI matches between 2 teams (oppnAllMatches), ODI matches between a team and the rest of the world (all matches,all oppositions).

As I have already mentioned I have around 67 functions for analysis, however I am certain that the data has a lot more secrets waiting to be tapped. So please do go ahead and run any machine learning or statistical learning algorithms on them. If you do come up with interesting insights, I would appreciate if attribute the source to Cricsheet(http://cricsheet.org), and my package yorkr and my blog Giga thoughts*, besides dropping me a note.

Hope you have a great time with my yorkr package!

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

Also see

  1. Introducing cricketr! : An R package to analyze performances of cricketers
  2. Cricket analytics with cricketr in paperback and Kindle versions
  3. My TEDx talk on the “Internet of Things”
  4. Bend it like Bluemix,MongoDB with autoscaling – Part 1
  5. The mind of a programmer
  6. Fun simulation of a chain in Android
  7. Taking cricketr for a spin-Part 1
  8. Latency,throughput implications for the cloud
  9. Hand detection through haar-training: A hands-on approach
  10. Cricket analytics with cricketr

Taking cricketr for a spin – Part 1

“Curiouser and curiouser!” cried Alice
“The time has come,” the walrus said, “to talk of many things: Of shoes and ships – and sealing wax – of cabbages and kings”
“Begin at the beginning,”the King said, very gravely,“and go on till you come to the end: then stop.”
“And what is the use of a book,” thought Alice, “without pictures or conversation?”

            Excerpts from Alice in Wonderland by Lewis Carroll

Introduction

This post is a continuation of my previous post “Introducing cricketr! A R package to analyze the performances of cricketers.” In this post I take my package cricketr for a spin. For this analysis I focus on the Indian batting legends

– Sachin Tendulkar (Master Blaster)
– Rahul Dravid (The Will)
– Sourav Ganguly ( The Dada Prince)
– Sunil Gavaskar (Little Master)

This post is also hosted on RPubs – cricketr-1

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!!

1

d $4.99/Rs 320 and $6.99/Rs448 respectively

Important note 1: The latest release of ‘cricketr’ now includes the ability to analyze performances of teams now!!  See Cricketr adds team analytics to its repertoire!!!

Important note 2 : Cricketr can now do a more fine-grained analysis of players, see Cricketr learns new tricks : Performs fine-grained analysis of players

Important note 3: 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)

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.

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)

Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency The plot below indicate the Tendulkar’s average is the highest. He is followed by Dravid, Gavaskar and then Ganguly

batsmanPerfBoxHist("./tendulkar.csv","Sachin Tendulkar")
tkps-boxhist-1
batsmanPerfBoxHist("./dravid.csv","Rahul Dravid")
tkps-boxhist-2
batsmanPerfBoxHist("./ganguly.csv","Sourav Ganguly")
tkps-boxhist-3
batsmanPerfBoxHist("./gavaskar.csv","Sunil Gavaskar")
tkps-boxhist-4

Relative Mean Strike Rate

In this first plot I plot the Mean Strike Rate of the batsmen. Tendulkar leads in the Mean Strike Rate for each runs in the range 100- 180. Ganguly has a very good Mean Strike Rate for runs range 40 -80

frames <- list("./tendulkar.csv","./dravid.csv","ganguly.csv","gavaskar.csv")
names <- list("Tendulkar","Dravid","Ganguly","Gavaskar")
relativeBatsmanSR(frames,names)

plot-1-1

Relative Runs Frequency Percentage

The plot below show the percentage contribution in each 10 runs bucket over the entire career.The percentage Runs Frequency is fairly close but Gavaskar seems to lead most of the way

frames <- list("./tendulkar.csv","./dravid.csv","ganguly.csv","gavaskar.csv")
names <- list("Tendulkar","Dravid","Ganguly","Gavaskar")
relativeRunsFreqPerf(frames,names)

plot-2-1

Moving Average of runs over career

The moving average for the 4 batsmen indicate the following – Tendulkar and Ganguly’s career has a downward trend and their retirement didn’t come too soon – Dravid and Gavaskar’s career definitely shows an upswing. They probably had a year or two left.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("./tendulkar.csv","Tendulkar")
batsmanMovingAverage("./dravid.csv","Dravid")
batsmanMovingAverage("./ganguly.csv","Ganguly")
batsmanMovingAverage("./gavaskar.csv","Gavaskar")

tdsg-ma-1

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

Runs forecast

The forecast for the batsman is shown below. The plots indicate that only Tendulkar seemed to maintain a consistency over the period while the rest seem to score less than their forecasted runs in the last 10% of the career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfForecast("./tendulkar.csv","Sachin Tendulkar")
batsmanPerfForecast("./dravid.csv","Rahul Dravid")
batsmanPerfForecast("./ganguly.csv","Sourav Ganguly")
batsmanPerfForecast("./gavaskar.csv","Sunil Gavaskar")

tdsg-perf-1

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

Check for batsman in-form/out-of-form

The following snippet checks whether the batsman is in-inform or ouyt-of-form during the last 10% innings of the career. This is done by choosing the null hypothesis (h0) to indicate that the batsmen are in-form. Ha is the alternative hypothesis that they are not-in-form. The population is based on the 1st 90% of career runs. The last 10% is taken as the sample and a check is made on the lower tail to see if the sample mean is less than 95% confidence interval. If this difference is >0.05 then the batsman is considered out-of-form.

The computation show that Tendulkar was out-of-form while the other’s weren’t. While Dravid and Gavaskar’s moving average do show an upward trend the surprise is Ganguly. This could be that Ganguly was able to keep his average in the last 10% to with the 95$ confidence interval. It has to be noted that Ganguly’s average was much lower than Tendulkar

checkBatsmanInForm("./tendulkar.csv","Tendulkar")
## *******************************************************************************************
## 
## Population size: 294  Mean of population: 50.48 
## Sample size: 33  Mean of sample: 32.42 SD of sample: 29.8 
## 
## Null hypothesis H0 : Tendulkar 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Tendulkar 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Tendulkar 's Form Status: Out-of-Form because the p value: 0.000713  is less than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./dravid.csv","Dravid")
## *******************************************************************************************
## 
## Population size: 256  Mean of population: 46.98 
## Sample size: 29  Mean of sample: 43.48 SD of sample: 40.89 
## 
## Null hypothesis H0 : Dravid 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Dravid 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Dravid 's Form Status: In-Form because the p value: 0.324138  is greater than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./ganguly.csv","Ganguly")
## *******************************************************************************************
## 
## Population size: 169  Mean of population: 38.94 
## Sample size: 19  Mean of sample: 33.21 SD of sample: 32.97 
## 
## Null hypothesis H0 : Ganguly 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Ganguly 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Ganguly 's Form Status: In-Form because the p value: 0.229006  is greater than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./gavaskar.csv","Gavaskar")
## *******************************************************************************************
## 
## Population size: 125  Mean of population: 44.67 
## Sample size: 14  Mean of sample: 57.86 SD of sample: 58.55 
## 
## Null hypothesis H0 : Gavaskar 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Gavaskar 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Gavaskar 's Form Status: In-Form because the p value: 0.793276  is greater than alpha=  0.05"
## *******************************************************************************************
dev.off()
## null device 
##           1

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("./tendulkar.csv","Tendulkar")
battingPerf3d("./dravid.csv","Dravid")

plot-3-1

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./ganguly.csv","Ganguly")
battingPerf3d("./gavaskar.csv","Gavaskar")

plot-4-1

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

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, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
tendulkar <- batsmanRunsPredict("./tendulkar.csv","Tendulkar",newdataframe=newDF)
dravid <- batsmanRunsPredict("./dravid.csv","Dravid",newdataframe=newDF)
ganguly <- batsmanRunsPredict("./ganguly.csv","Ganguly",newdataframe=newDF)
gavaskar <- batsmanRunsPredict("./gavaskar.csv","Gavaskar",newdataframe=newDF)

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease. It can be seen Tendulkar has a much higher Runs scored than all of the others.

Tendulkar is followed by Ganguly who we saw earlier had a very good strike rate. However it must be noted that Dravid and Gavaskar have a better average.

batsmen <-cbind(round(tendulkar$Runs),round(dravid$Runs),round(ganguly$Runs),round(gavaskar$Runs))
colnames(batsmen) <- c("Tendulkar","Dravid","Ganguly","Gavaskar")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns
##    BallsFaced MinsAtCrease Tendulkar Dravid Ganguly Gavaskar
## 1          10           30         7      1       7        4
## 2          38           71        23     14      21       17
## 3          66          111        39     27      35       30
## 4          94          152        54     40      50       43
## 5         121          193        70     54      64       56
## 6         149          234        86     67      78       69
## 7         177          274       102     80      93       82
## 8         205          315       118     94     107       95
## 9         233          356       134    107     121      108
## 10        261          396       150    120     136      121
## 11        289          437       165    134     150      134
## 12        316          478       181    147     165      147
## 13        344          519       197    160     179      160
## 14        372          559       213    173     193      173
## 15        400          600       229    187     208      186

Contribution to matches won and lost

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanContributionWonLost(35320,"Tendulkar")
batsmanContributionWonLost(28114,"Dravid")
batsmanContributionWonLost(28779,"Ganguly")
batsmanContributionWonLost(28794,"Gavaskar")

tdgg-1

Home and overseas performance

From the plot below Tendulkar and Dravid have a lot more matches both home and abroad and their performance has good both at home and overseas. Tendulkar has the best performance home and abroad and is consistent all across. Dravid is also cossistent at all venues. Gavaskar played fewer matches than Tendulkar & Dravid. The range of runs at home is higher than overseas, however the average is consistent both at home and abroad. Finally we have Ganguly.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfHomeAway(35320,"Tendulkar")
batsmanPerfHomeAway(28114,"Dravid")
batsmanPerfHomeAway(28779,"Ganguly")
batsmanPerfHomeAway(28794,"Gavaskar")
tdgg-ha-1

Average runs at ground and against opposition

Tendulkar has above 50 runs average against Sri Lanka, Bangladesh, West Indies and Zimbabwe. The performance against Australia and England average very close to 50. Sydney, Port Elizabeth, Bloemfontein, Collombo are great huntings grounds for Tendulkar

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./tendulkar.csv","Tendulkar")
batsmanAvgRunsOpposition("./tendulkar.csv","Tendulkar")
avgrg-1-1
dev.off()
## null device 
##           1

Dravid plundered runs at Adelaide, Georgetown, Oval, Hamiltom etc. Dravid has above average against England, Bangaldesh, New Zealand, Pakistan, West Indies and Zimbabwe

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./dravid.csv","Dravid")
batsmanAvgRunsOpposition("./dravid.csv","Dravid")
avgrg-2-1
dev.off()
## null device 
##           1

Ganguly has good performance at the Oval, Rawalpindi, Johannesburg and Kandy. Ganguly averages 50 runs against England and Bangladesh.

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./ganguly.csv","Ganguly")
batsmanAvgRunsOpposition("./ganguly.csv","Ganguly")
avgrg-3-1
dev.off()
## null device 
##           1

The Oval, Sydney, Perth, Melbourne, Brisbane, Manchester are happy hunting grounds for Gavaskar. Gavaskar averages around 50 runs Australia, Pakistan, Sri Lanka, West Indies.

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./gavaskar.csv","Gavaskar")
batsmanAvgRunsOpposition("./gavaskar.csv","Gavaskar")
avgrg-4-1
dev.off()
## null device 
##           1

Key findings

Here are some key conclusions

  1. Tendulkar has the highest average among the 4. He is followed by Dravid, Gavaskar and Ganguly.
  2. Tendulkar’s predicted performance for a given number of Balls Faced and Minutes at Crease is superior to the rest
  3. Dravid averages above 50 against 6 countries
  4. West Indies and Australia are Gavaskar’s favorite batting grounds
  5. Ganguly has a very good Mean Strike Rate for the range 40-80 and Tendulkar from 100-180
  6. In home and overseas performance, Tendulkar is the best. Dravid and Gavaskar also have good performance overseas.
  7. Dravid and Gavaskar probably retired a year or two earlier while Tendulkar and Ganguly’s time was clearly up

Final thoughts

Tendulkar is clearly the greatest batsman India has produced as he leads in almost all aspects of batting – number of centuries, strike rate, predicted runs and home and overseas performance. Dravid follows Tendulkar with 48 centuries, consistent performance home and overseas and a career that was still green. Gavaskar has fewer matches than rest but his performance overseas is very good in those helmetless times. Finally we have Ganguly.

Dravid and Gavaskar had a few more years of great batting while Tendulkar and Ganguly’s career was on a decline.

Note:It is really not fair to include Gavaskar in the analysis as he played in a different era when helmets were not used, even against the fiery pace of Thomson, Lillee, Roberts, Holding etc. In addition Gavaskar did not play against some of the newer countries like Bangladesh and Zimbabwe where he could have amassed runs. Yet I wanted to include him and his performance is clearly excellent

Also see my other posts in R

  1. A peek into literacy in India: Statistical Learning with R
  2. A crime map of India in R – Crimes against women
  3. Analyzing cricket’s batting legends – Through the mirage with R
  4. Masters of Spin: Unraveling the web with R
  5. Mirror, mirror . the best batsman of them all?

You may also like

  1. A crime map of India in R: Crimes against women
  2. What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
  3. Bend it like Bluemix, MongoDB with autoscaling – Part 2
  4. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
  5. Thinking Web Scale (TWS-3): Map-Reduce – Bring compute to data
  6. Deblurring with OpenCV:Weiner filter reloaded