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!!

You can download the latest PDF version of the book at ‘Cricket analytics with cricketr and cricpy: Analytics harmony with R and Python-6th edition‘

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")

batsmanPerfBoxHist("./dravid.csv","Rahul Dravid")

batsmanPerfBoxHist("./ganguly.csv","Sourav Ganguly")

batsmanPerfBoxHist("./gavaskar.csv","Sunil Gavaskar")

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)

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)

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")

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")

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")

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

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")

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")

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")

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")

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")

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")

dev.off()

## null device 
##           1

Key findings

Here are some key conclusions

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

Introducing cricketr! : An R package to analyze performances of cricketers

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!!

You can download the latest PDF version of the book at ‘Cricket analytics with cricketr and cricpy: Analytics harmony with R and Python-6th edition‘

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 CRAN and use many of the functions available in the package. Please be mindful of ESPN Cricinfo Terms of Use

(Note: This page is also hosted as a GitHub page at cricketr and also at RPubs as cricketr: A R package for analyzing performances of cricketers

You can download this analysis as a PDF file from Introducing cricketr

You can clone the cricketr code from Github at cricketr

(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

Please look at my recent post, which includes updates to this post, and 8 new functions added to the cricketr package “Re-introducing cricketr: An R package to analyze the performances of cricketers”

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”

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

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

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

Frequency percentage of runs in each run range over the whole career
Mean Strike Rate for runs scored in the given range
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")

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")

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")

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")

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")

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")

## 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

Sachin Tendulkar :Average:53.78,100’s – 51, 50’s – 68
Jacques Kallis : Average: 55.47, 100’s – 45, 50’s – 58
Ricky Ponting : Average: 51.85, 100’s – 41 , 50’s – 62
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")

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

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

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

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")

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()

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)

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)

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")

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")

dev.off()

## null device 
##           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")
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./ponting.csv","Ponting")
battingPerf3d("./sangakkara.csv","Sangakkara")
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. 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")

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")

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")

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")

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)

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)

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")

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")

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")

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")

dev.off()

## null device 
##           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: 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

Sangakkara has the highest average, followed by Tendulkar, Kallis and then Ponting.
Ponting has the highest strike rate followed by Tendulkar,Sangakkara and then Kallis
The predicted runs for a given Balls faced and Minutes at crease is highest for Ponting, followed by Tendulkar, Sangakkara and Kallis
The moving average for Tendulkar and Ponting shows a downward trend while Kallis and Sangakkara retired too soon
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.
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

Muralitharan has the highest wickets and best economy rate followed by Warne and Kumble
Muralitharan has higher wickets frequency percentage between 3 to 8 wickets
Muralitharan has the best Economy Rate for wickets between 2 to 7
The moving average plot shows that the time was up for Kumble and Muralitharan but Warne had a few years ahead
The check for form status shows that Muralitharan and Kumble time was over while Warne still in great form
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

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
Ponting takes the 2nd spot with the 2nd highest number of centuries, 1st in Strike Rate and 2nd in home and away performance.
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
Kallis has the 2nd highest number of centuries but his performance overseas and strike rate are behind others
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

Bowling

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

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
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
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.
Also while Kumble and Muralitharan’s career was on the decline , Warne was going great and had a couple of years ahead.

You can download this analysis at Introducing cricketr

Hope you have fun using the cricketr package as I had in developing it. Do take a look at my follow up post Taking cricketr for a spin – Part 1

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

Do take a look at my 2nd package “The making of cricket package yorkr – Part 1”

Also see
1. My book “Deep Learning from first principles” now on Amazon
2. My book ‘Practical Machine Learning with R and Python’ on Amazon
3. Taking cricketr for a spin – Part 1
4. cricketr plays the ODIs
5. cricketr adapts to the Twenty20 International
6. Analyzing cricket’s batting legends – Through the mirage with R
7. Masters of spin: Unraveling the web with R
8. Mirror,mirror …best batsman of them all

The common alphabet of programming languages

“All animals are equal, but some animals are more equal than other.” “Four legs good, two legs bad.”

from Animal Farm by George Orwell

Note: This post is largely intended for those who are embarking on their journey into the world of programming. The article below highlights a set of constructs that recur in many imperative, dynamic and object-oriented languages. While these constructs cannot be applied directly to functional programming languages like Lisp,Haskell or Clojure, it may help. To some extent the programming language domain has been intentionally oversimplified to show that languages are not as daunting as they seem. Clearly there are a lot more subtle and complex differences among languages. Hope you have fun programming!

Introduction: Anybody who is about to venture into the deep waters of programming will be bewildered and awed by the almost limitless number of programming languages and the associated paradigms on which they are based on. It is easy to feel apprehensive of programming, when faced with this this array of languages, not to mention the seemingly quirky syntax of each language. Many opinions abound, about what is the best programming language. In my opinion each language is best suited for a particular class of problems and is usually clunky if used outside of this. As an aside here is an interesting link provided by reader AKS to Rosetta Code, which is stated to be a a programming chrestomathy (present solutions to the same task in as many different languages as possible, to demonstrate how languages are similar and different, and to aid a person with a grounding in one approach to a problem in learning another. Rosetta Code currently has 772 tasks, 165 draft tasks, and is aware of 582 languages)

You are likely to hear “All programming languages are equal, but some languages are more equal than others” from seasoned programmers who have their own pet language. There may also be others who swear that “procedural languages good, object oriented languages bad” or maybe “object oriented languages good, aspect oriented languages bad”. Unity in diversity Regardless of the language this post discusses a thread that is common to all programming languages. In fact any programming language can be expressed as

Lx = C + Sx

Where Lx is any programming language ‘x’. All programming languages have a set of core, common constructs which I have denoted as ‘C’ and a set of Specialized constructs, unique to each language ‘x’ which I have denoted as Sx. I would like to look at these constructs that are common to most programming languages like C,C++,Perl, Python, Ruby, C#, R, Octave etc. In my opinion knowing these core, common constructs and a few of the more specialized constructs should allow you to get started off in the language of your choice. You can pick up the more unique constructs as you go along. Here are the common constructs (C mentioned above) that you must familiarize yourself with when embarking on a new language

Reading user input and printing to screen
Reading and writing from a file
Conditional statement if-then-else if-else
Loops – For, while, repeat, do while etc.

Knowing these constructs and some of the basic concepts unique to each language for e.g.
– Structure, Pointers in C,
– Classes, inheritance in C++
– Subsetting in Octave, R
– car, cdr in Lisp will enable you to get started off in your chosen language.
I show the examples of these core constructs in many languages. Note the similarity between these constructs
1. C
Read from and write to console
scanf(x,”%d); printf(“The value of x is %d”, x);
Read from and write to filefread(buffer, strlen(c)+1, 1, fp); fwrite(c, strlen(c) + 1, 1, fp);

Conditional
if(x > 5) { printf(“x is greater than 5”); } else if (x < 5) { printf(“x is less than 5”); } else{ printf(“x is equal to 5”); }
Loops I will only consider for loops, though one could use while, repeat etC.
for(i =0; i <100; i++) { money = money++) }
2. C++
Read from and write to console
cin >> age; Cout << “The value is “ << value

Read from and write to a file // open a file in read mode.
ifstream infile; infile.open("afile.dat"); cout << "Reading from the file" << endl; infile >> data; ofstream outfile; outfile.open("afile.dat"); // write inputted data into the file. outfile << data << endl;
Conditional same as C
if(x > 5) { printf(“x is greater than 5”); }
else if (x < 5) { printf(“x is less than 5”); } else{ printf(“x is equal to 5”); }
Loops
for(i =0; i <100; i++) { money = money++)

}

2. C++ Read from and write to console
cin >> age;
Cout << “The value is “ << value
Read from and write to a file // open a file in read mode.
ifstream infile;
infile.open("afile.dat");
cout << "Reading from the file" << endl;
infile >> data; ofstream outfile;
outfile.open("afile.dat");
// write inputted data into the file.
outfile << data << endl;
Conditional same as C
if(x > 5) {
printf(“x is greater than 5”);
}
else if (x < 5) {
printf(“x is less than 5”);
}
else{ printf(“x is equal to 5”);
}
Loops
for(i =0; i <100; i++){
money = money++)
}
3. Java
Reading from and writing to standard input
Console c = System.console();
int val = c.readLine("Enter a value: ");
System.out.println("Value is "+ val);
Reading and writing from file
try {
in = new FileInputStream("input.txt");
out = new FileOutputStream("output.txt");
int c;
while ((c = in.read()) != -1) {
out.write(c); } } ...
Conditional (same as C)
if(x > 5) {
printf(“x is greater than 5”);
}
else if (x < 5) {
printf(“x is less than 5”);
}
else{ printf(“x is equal to 5”); }
Loops (same as C)
for(i =0; i <100; i++){
money = money++)
}

4. Perl Read from console
#!/usr/bin/perl
$userinput = ;
chomp ($userinput);
Write to console
print "User typed $userinput\n";
Reading and write to a file
open(IN,"infile") || die "cannot open input file";
open(OUT,"outfile") || die "cannot open output file";
while() {
print OUT $_;
# echo line read
}
close(IN);
close(OUT)
Conditional
if( $a == 20 ){
# if condition is true then print the following
printf "a has a value which is 20\n";
}
elsif( $a == 30 ){
# if condition is true then print the following
printf "a has a value which is 30\n";
}else{
# if none of the above conditions is true
printf "a has a value which is $a\n";
}
Loops
for (my $i=0; $i <= 9; $i++) {
print "$i\n";
}

5. Lisp
The syntax for Lisp will be different from the others as it is a functional language. You need to familiarize yourself with these constructs to move ahead
Read and write to console
To read from standard input use
(let ((temp 0))
(print ‘(Enter temp))
(setf temp (read))
(print (append ‘(the temp is) (list temp))))
Read from and write to file
(with-open-file (stream “C:\\acl82express\\lisp\\count.cl”)
(do ((line (read-line stream nil) (read-line stream nil)))
(with-open-file (stream “C:\\acl82express\\lisp\\test.txt” :direction :output :if-exists :supersede)
(write-line “test” stream) nil)
Conditional
$ (cond ((< x 5)
(setf x (+ x 8))
(setf y (* 2 y)))
((= x 10) (setf x (* x 2)))
(t (setf x 8)))
Loops
$ (setf x 5)
$ (let ((i 0))
(loop (setf y (* x i))
(when (> i 10) (return))
(print i) (prin1 y) (incf i )))

6. Python
Reading and writing from console
var = raw_input("Please enter something: ")
print “You entered: ” value
Reading and writing from files
f = open(filename, 'r')
a = f.readline().strip()
target = open(filename, 'w')
target.write(line1)
Conditionals
if x > 5:
print "x is greater than 5”
elif
x < 5:
print "x is less than 5"
else:
print "x is equal to 5"
Loops
for i in range(0, 6):
print "Value is :" % i 7.

R
x=5
paste('The value of x is =',x)
Reading and writing to a file
infile = read.csv(“file”)
write(x, file = "data", sep = " ")
Conditional
if(x > 5){
print(“x is greater than 5”)
}else if(x < 5){
print(“x is less than 5”)
}else {
print(“x is equal to 5”)
}
Loops
for (i in 1:10) print(i)

Conclusion
As can be seen the core constructs are very similar in different languages save for some minor variations. It is generally useful to get started with just knowing these constructs and few other important other features of the language that you are trying to learn. It is possible to code most programs with these Core constructs and a few of the Specialized constructs in the language. These Core constructs are the glue that hold your code together.

You can learn more compact and more powerful features of the language as you go along The above core constructs are like the letters of the programming language alphabet. You need to construct words by stringing together these constructs and form sensible sentences which will be your program. Good luck with your adventure in your next new programming language!!!

Also see
1.Programming languages in layman’s language
2. The mind of the programmer
3. How to program – Some essential tips
4. Programming Zen and now – Some essential tips -2

Mirror, mirror … the best batsman of them all?

“Full many a gem of purest serene
The dark oceans of cave bear.”
Thomas Gray – Elegy in country churchyard

In this post I do a fine grained analysis of the batting performances of cricketing icons from India and also from the international scene to determine how they stack up against each other. I perform 2 separate analyses 1) Between Indian legends (Sunil Gavaskar, Sachin Tendulkar & Rahul Dravid) and another 2) Between contemporary cricketing stars (Brian Lara, Sachin Tendulkar, Ricky Ponting and A B De Villiers)

In the world and more so in India, Tendulkar is probably placed on a higher pedestal than all other cricketers. I was curious to know how much of this adulation is justified. In “Zen and the art of motorcycle maintenance” Robert Pirsig mentions that while we cannot define Quality (in a book, music or painting) we usually know it when we see it. So do the people see an ineffable quality in Tendulkar or are they intuiting his greatness based on overall averages?

In this context, we need to keep in mind the warning that Daniel Kahnemann highlights in his book, ‘Thinking fast and slow’. Kahnemann suggests that we should regard “statistical intuition with proper suspicion and replace impression formation by computation wherever possible”. This is because our minds usually detects patterns and associations even when none actually exist.

So this analysis tries to look deeper into these aspects by performing a detailed statistical analysis.

The data for all the batsman has been taken from ESPN Cricinfo. The data is then cleaned to remove ‘DNB’ (did not bat), ‘TDNB’ (Team did not bat) etc before generating the graphs.

The code, data and the plots can be cloned,forked from Github at the following link bestBatsman. You should be able to use the code as-is for any other batsman you choose to.

Feel free to agree, disagree, dispute or argue with my analysis.

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

The batting performances of the each of the cricketers is described in 3 plots a) Combined boxplot & histogram b) Runs frequency vs Runs plot c) Mean Strike Rate vs Runs plot

A) Batting performance of Sachin Tendulkar

a) Combined Boxplot and histogram of runs scored

The above graph is combined boxplot and a histogram. The boxplot at the top shows the 1st quantile (25th percentile) which is the left side of the green rectangle, the 3rd quantile( 75% percentile) right side of the green rectangle and the mean and the median. These values are also shown in the histogram below. The histogram gives the frequency of Runs scored in the given range for e.g (0-10, 11-20, 21-30 etc) for Tendulkar

b) Batting performance – Runs frequency vs Runs

The graph above plots the best fitting curve for Runs scored in the frequency ranges.

c) Mean Strike Rate vs Runs

This plot computes the Mean Strike Rate for each interval for e.g if between the ranges 11-21 the Strike Rates were 40.5, 48.5, 32.7, 56.8 then the average of these values is computed for the range 11-21 = (40.5 + 48.5 + 32.7 + 56.8)/4. This is done for all ranges and the Mean Strike Rate in each range is plotted and the loess curve is fitted for this data.

B) Batting performance of Rahul Dravid
a) Combined Boxplot and histogram of runs scored

The mean, median, the 25th and 75 th percentiles for the runs scored by Rahul Dravid are shown above

b) Batting performance – Runs frequency vs Runs

c) Mean Strike Rate vs Runs

C) Batting performance of Sunil Gavaskar
a) Combined Boxplot and histogram of runs scored

The mean, median, the 25th and 75 th percentiles for the runs scored by Sunil Gavaskar are shown above
b) Batting performance – Runs frequency vs Runs

c) Mean Strike Rate vs Runs

D) Relative performances of Tendulkar, Dravid and Gavaskar

The above plot computes the percentage of the total career runs scored in a given range for each of the batsman.
For e.g if Dravid scored the runs 23, 22, 28, 21, 25 in the range 21-30 then the
Range 21 – 20 => percentageRuns = ( 23 + 22 + 28 + 21 + 25)/ Total runs in career * 100
The above plot shows that Rahul Dravid’s has a higher contribution in the range 20-70 while Tendulkar has a larger percentahe in the range 150-230

E) Relative Strike Rates of Tendulkar, Dravid and Gavaskar

With respect to the Mean Strike Rate Tendulkar is clearly superior to both Gavaskar & Dravid

F) Analysis of Tendulkar, Dravid and Gavaskar

The above table captures the the career details of each of the batsman
The following points can be noted
1) The ‘number of innings’ is the data you get after removing rows with DNB, TDNB etc
2) Tendulkar has the higher average 48.39 > Gavaskar (47.3) > Dravid (46.46)
3) The skew of Dravid (1.67) is greater which implies that there the runs scored are more skewed to right (greater runs) in comparison to mean

G) Batting performance of Brian Lara
a) Combined Boxplot and histogram of runs scored

The mean, median, 1st and 3rd quartile are shown above

b) Batting performance – Runs frequency vs Runs

c) Mean Strike Rate vs Runs

H) Batting performance of Ricky Ponting
a) Combined Boxplot and histogram of runs scored

b) Batting performance – Runs frequency vs Runs

c) Mean Strike Rate vs Runs

I) Batting performance of AB De Villiers
a) Combined Boxplot and histogram of runs scored

b) Batting performance – Runs frequency vs Runs

c) Mean Strike Rate vs Runs

J) Relative performances of Tendulkar, Lara, Ponting and De Villiers

Clearly De Villiers is ahead in the percentage Runs scores in the range 30-80. Tendulkar is better in the range between 80-120. Lara’s career has a long tail.

K) Relative Strike Rates of Tendulkar, Lara, Ponting and De Villiers

The Mean Strike Rate of Lara is ahead of the lot, followed by De Villiers, Ponting and then Tendulkar
L) Analysis of Tendulkar, Lara, Ponting and De Villiers

The following can be observed from the above table
1) Brian Lara has the highest average (51.52) > Sachin Tendulkar (48.39 > Ricky Ponting (46.61) > AB De Villiers (46.55)
2) Brian Lara also the highest skew which means that the data is more skewed to the right of the mean than the others

You can clone the code rom Github at the following link bestBatsman. You should be able to use the code as-is for any other batsman you choose to.

Also see
1. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
2. Informed choices through Machine Learning-2: Pitting together Kumble, Kapil, Chandra
3. Analyzing cricket’s batting legends – Through the mirage with R
4. Masters of spin – Unraveling the web with R

Masters of Spin: Unraveling the web with R

Here is a look at some of the masters of spin bowling in cricket. Specifically this post analyzes 3 giants of spin bowling in recent times, namely Shane Warne of Australia, Muthiah Muralitharan of Sri Lanka and our very own Anil Kumble of India. As to “who is the best leggie” has been a hot topic in cricket in recent years. As in my earlier post “Analyzing cricket’s batting legends: Through the mirage with R”, I was not interested in gross statistics like most wickets taken.

In this post I try to analyze how each bowler has performed over his entire test career. All bowlers have bowled around ~240 innings. All other things being equal, it does take a sense to look a little deeper into what their performance numbers reveal about them. As in my earlier posts the data has been taken from ESPN CricInfo’s Statguru

acks), and $4.99/Rs 320 and $6.99/Rs448 respectively

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

I have chosen these 3 spinners for the following reasons

Shane Warne : Clearly a deadly spinner who can turn the ball at absurd angles
Muthiah Muralitharan : While controversy dogged Muralitharan he was virtually unplayable on many cricketing venues
Anil Kumble: A master spinner whose chess like strategy usually outwitted the best of batsmen.

The King of Spin according to my analysis below is clearly Muthiah Muralitharan. This is clearly shown in the final charts where the performances of bowlers are plotted on a single graph. Muralitharan is clearly a much more lethal bowler and has a higher strike rate. In addition Muralitharan has the lowest mean economy rate amongst the 3 for wickets in the range 3 to 7. Feel free to add your own thoughts, comments and dissent.

The code for this implementation is available at GitHub at mastersOfSpin. Feel free to clone,fork or hack the code to your own needs. You should be able to use the code as-is on other bowlers with little or no modification

So here goes

Wickets frequency percentage vs Wickets plot
For this plot I determine how frequently the bowler takes ‘n’ wickets in his career and calculate the percentage over his entire career. In other words this is done as follows in R

# Create a table of Wickets vs the frequency of the wickts colnames(wktsDF) # Calculate wickets percentage wktsDF$freqPercent
and plot this as a graph.

This is shown for Warne below
1) Shane Warne – Wickets Frequency percentage vs Wickets plot

Wickets – Mean Economy rate chart
This chart plots the mean economy rate for ‘n’ wickets for the bowler. As an example to do this for 3 wickets for Shane Warne, a list is created of economy rates when Warne has taken 3 wickets in his entire career. The average of this list is then computed and stored against Warne’s 3 wickets. This is done for all wickets taken in Warne’s career. The R snippet for this implementation is shown below

econRate for (i in 0: max(as.numeric(as.character(bowler$Wkts)))) { # Create a vector of Economy rate for number of wickets 'i' a b # Compute the mean economy rate by using lapply on the list econRate[i+1] print(econRate[i]) }

Shane Warne – Wickets vs Mean Economy rate
This plot for Shane Warne is shown below

The plots for M Muralithan and Anil Kumble are included below

2) M Muralitharan – Wickets Frequency percentage vs Wickets plot

M Muralitharan – Wickets vs Mean Economy rate

3) Anil Kumble – Wickets Frequency percentage vs Wickets plot

Anil Kumble – Wickets vs Mean Economy rate

Finally the relative performance of the bowlers is generated by creating a single chart where the wicket frequencies and the mean economy rate vs wickets is plotted.

This is shown below

Relative wicket percentages

Relative mean economy rate

As can be seen in the above 2 charts M Muralidharan not only has a higher strike rate as far as wickets in 3 to 7 range, he also has a much lower mean economy rate

You can clone/fork the R code from GitHub at mastersOfSpin

Conclusion: The performance of Muthiah Muralitharan is clearly superior to both Shane Warne and Kumble. In my opinion the king of spin is M Muralitharan, followed by Shane Warne and finally Anil Kumble

Feel free to dispute my claims. Comments, suggestions are more than welcome

Also see

1. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
2. Informed choices through Machine Learning-2: Pitting together Kumble, Kapil, Chandra
3. Analyzing cricket’s batting legends – Through the mirage with R

Analyzing cricket’s batting legends – Through the mirage with R

In this post I do a deep dive into the records of the all-time batting legends of cricket to identify interesting information about their achievements. In my opinion, the usual currency for batsman’s performance like most number of centuries or highest batting average are too gross in their significance. I wanted something finer where we can pin-point specific strengths of different players

This post will answer the following questions.
– How many times has a batsman scored runs in a specific range say 20-40 or 80-100 and so on?
– How do different batsmen compare against each other?
– Which of the batsmen stayed well beyond their sell-by date?
– Which of the batsmen retired too soon?
– What is the propensity for a batsman to get caught, bowled run out etc?

For this analysis I have chosen the batsmen below for the following reasons
Sir Don Bradman : With a batting average of 99.94 Bradman was an obvious choice
Sunil Gavaskar is one of India’s batting icons who amassed 774 runs in his debut against the formidable West Indies in West Indies
Brian Lara : A West Indian batting hero who has double, triple and quadruple centuries under his belt
Sachin Tendulkar: A prolific run getter, India’s idol, who holds the record for most test centuries by any batsman (51 centuries)
Ricky Ponting:A dangerous batsman against any bowling attack and who can demolish any bowler on his day
Rahul Dravid: He was India’s most dependable batsman who could weather any storm in a match single-handedly
AB De Villiers : The destructive South African batsman who can pulverize any attack when he gets going

The analysis has been performed on these batsmen on various parameters. Clearly different batsmen have shone in different batting aspects. The analysis focuses on each of these to see how the different players stack up against each other.

The data for the above batsmen has been taken from ESPN Cricinfo. Only the batting statistics of the above batsmen in Test cricket has been taken. The implementation for this analysis has been done using the R language. The R implementation, datasets and the plots can be accessed at GitHub at analyze-batting-legends. Feel free to fork or clone the code. You should be able to use the code with minor modifications on other players. Also go ahead make your own modifications and hack away!

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

Key insights from my analysis below
a) Sir Don Bradman’s unmatchable record of 99.94 test average with several centuries, double and triple centuries makes him the gold standard of test batting as seen in the ‘All-time best batsman below’
b) Sunil Gavaskar is the king of batting in India, followed by Rahul Dravid and finally Sachin Tendulkar. See the charts below for details
c) Sunil Gavaskar and Rahul Dravid had at least 2 more years of good test cricket in them. Their retirement was premature. This is based on the individual batsmen’s career graph (moving average below)
d) Brian Lara, Sachin Tendulkar, Ricky Ponting, Vivian Richards retired at a time when their batting was clearly declining. The writing on the wall was clear and they had to go (see moving average below)
e) The biggest hitter of 4’s was Vivian Richards. In the 2nd place is Brian Lara. Tendulkar & Dravid follow behind. Dravid is a surprise as he has the image of a defender.
e) While Sir Don Bradman made huge scores, the number of 4’s in his innings was significantly less. This could be because the ground in those days did not carry the ball far enough
f) With respect to dismissals Richards was able to keep his wicket intact (11%) of the times , followed by Ponting Tendulkar, De Villiers, Dravid (10%) who carried the bat, and Gavaskar & Bradman (7%)

A) Runs frequency table and charts
These plots normalize the batting performance of different batsman, since the number of innings played ranges from 89 (Bradman) to 348 (Tendulkar), by calculating the percentage frequency the batsman scores runs in a particular range. For e.g. Sunil Gavaskar made scores between 60-80 10% of his total innings

This is shown in a tabular form below

The individual charts for each of the players are shwon belowThe top performers after removing ranges 0-20 & 20-40 are
Between 40-60 runs – 1) Ricky Ponting (16.4%) 2) Brian lara (15.8%) 3) AB De Villiers (14.6%)
Between 60-80 runs – 1) Vivian Richards (18%) 2) AB De Villiers (10.2%) 3) Sunil Gavaskar (10%)
Between 80-100 runs – 1) Rahul Dravid (7.6%) 2) Brian Lara (7.4%) 3) AB De Villiers (6.4%)
Between 100 -120 runs – 1) Sunil Gavaskar (7.5%) 2) Sir Don Bradman (6.8%) 3) Vivian Richards (5.8%)
Between 120-140 runs – 1) Sir Don Bradman (6.8%) 2) Sachin Tendulkar (2.5%) 3) Vivian Richards (2.3%)

The percentage frequency for Brian Lara is included below
1) Brian Lara

The above chart shows out of the total number of innings played by Brian Lara he scored runs in the range (40-60) 16% percent of the time. The chart also shows that Lara scored between 0-20, 40% while also scoring in the ranges 360-380 & 380-400 around 1%.
The same chart is displayed as continuous graph below

The run frequency charts for other batsman are
2) Sir Don Bradman
a) Run frequency

Note: Notice the significant contributions by Sir Don Bradman in the ranges 120-140,140-160,220-240,all the way up to 340
b) Performance

3) Sunil Gavaskar
a) Runs frequency chart

b) Performance chart

4) Sachin Tendulkar
a) Runs frequency chart

b) Performance chart

5) Ricky Ponting
a) Runs frequency

b) Performance

6) Rahul Dravid
a) Runs frequency chart

b) Performance chart

7) Vivian Richards
a) Runs frequency chart

b) Performance chart

8) AB De Villiers
a) Runs frequency chart

b) Performance chart

B) Relative performance of the players
In this section I try to measure the relative performance of the players by superimposing the performance graphs obtained above. You may say that “comparisons are odious!”. But equally odious are myths that are based on gross facts like highest runs, average or most number of centuries.
a) All-time best batsman
(Sir Don Bradman, Sunil Gavaskar, Vivian Richards, Sachin Tendulkar, Ricky Ponting, Brian Lara, Rahul Dravid, AB De Villiers)

From the above chart it is clear that Sir Don Bradman is the ‘gold’ standard in batting. He is well above others for run ranges above 100 – 350
b) Best Indian batsman (Sunil Gavaskar, Sachin Tendulkar, Rahul Dravid)

The above chart shows that Gavaskar is ahead of the other two for key ranges between 100 – 130 with almost 8% contribution of total runs. This followed by Dravid who is ahead of Tendulkar in the range 80-120. According to me the all time best Indian batsman is 1) Sunil Gavaskar 2) Rahul Dravid 3) Sachin Tendulkar

c) Best batsman -( Brian Lara, Ricky Ponting, Sachin Tendulkar, AB De Villiers)
This chart was prepared since this comparison was often made in recent times

This chart shows the following ranking 1) AB De Villiers 2) Sachin Tendulkar 3) Brian Lara/Ricky Ponting
C) Chart of 4’s

This chart is plotted with a 2nd order curve of the number of 4’s versus the total runs in the innings
1) Brian Lara

2) Sir Don Bradman

3) Sunil Gavaskar

4) Sachin Tendulkar

5) Ricky Ponting

6) Rahul Dravid

7) Vivian Richards

8) AB De Villiers

D) Proclivity for type of dismissal
The below charts show how often the batsman was out bowled, caught, run out etc
1) Brian Lara

2) Sir Don Bradman

3) Sunil Gavaskar

4) Sachin Tendulkar

5) Ricky Ponting

6) Rahul Dravid

7) Vivian Richard

8) AB De Villiers

E) Moving Average
The plots below provide the performance of the batsman as a time series (chronological) and is displayed as the continuous gray lines. A moving average is computed using ‘loess regression’ and is shown as the dark line. This dark line represents the players performance improvement or decline. The moving average plots are shown below
1) Brian Lara

2) Sir Don Bradman

Sir Don Bradman’s moving average shows a remarkably consistent performance over the years. He probably could have a continued for a couple more years
3)Sunil Gavaskar

Gavaskar moving average does show a good improvement from a dip around 1983. Gavaskar retired bowing to public pressure on a mistaken belief that he was under performing. Gavaskar could have a continued for a couple of more years
4) Sachin Tendulkar

Tendulkar’s performance is clearly on the decline from 2011. He could have announced his retirement at least 2 years prior
5) Ricky Ponting

Ponting peak performance was around 2005 and does go steeply downward from then on. Ponting could have also retired around 2012
6) Rahul Dravid

Dravid seems to have recovered very effectively from his poor for around 2009. His overall performance shows steady improvement. Dravid’s announcement appeared impulsive. Dravid had another 2 good years of test cricket in him
7) Vivian Richards

Richard’s performance seems to have dropped around 1984 and seems to remain that way.
8) AB De Villiers

AB De Villiers moving average shows a steady upward swing from 2009 onwards. De Villiers has at least 3-4 years of great test cricket ahead of him.

Finally as mentioned above the dataset, the R implementation and all the charts are available at GitHub at analyze-batting-legends. Feel free to fork and clone the code. The code should work for other batsman as-is. Also go ahead and make any modifications for obtaining further insights.

Conclusion: The batting legends have been analyzed from various angles namely i) What is the frequency of runs scored in a particular range ii) How each batsman compares with others for relative runs in a specified range iii) How does the batsman get out? iv) What were the peak and lean period of the batsman and whether they recovered or slumped from these periods. While the batsman themselves have played in different time periods I think in an overall sense the performance under the conditions of the time will be similar.
Anyway feel free to let me know your thoughts. If you see other patterns in the data also do drop in your comment.

Also see
– A crime map of India in R – Crimes against women
– What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
– Bend it like Bluemix, MongoDB with autoscaling – Part 1

R incantations for the uninitiated

Here are some basic R incantations that will get you started with R

A) Scalars & Vectors:
Chant 1 – Now repeat after me, with your right hand forward at shoulder height “In R there are no scalars. There are only vectors of length 1”.
Just kidding:-)

To create an integer variable x with a value 5 we write
x <- 5 or x = 5
While the former notation may seem odd, it is actually more logical considering that the RHS is assigned to LHS. Anyway both seem to work
Vectors can be created as follows
a <- c( 2:10) b <- c("This", "is", 'R","language")

B) Sequences:
There are several ways of creating sequences of numbers which you intend to use for your computation
<- seq(5:25) # Sequence from 5 to 25

Other ways to create sequences
Increment by 2
> seq(5, 25, by=2) [1] 5 7 9 11 13 15 17 19 21 23 25

>seq(5,25,length=18) # Create sequence from 5 to 25 with a total length of 18 [1] 5.000000 6.176471 7.352941 8.529412 9.705882 10.882353 12.058824 13.235294 [9] 14.411765 15.588235 16.764706 17.941176 19.117647 20.294118 21.470588 22.647059 [17] 23.823529 25.000000

C) Conditions and loops
An if-else if-else construct goes like this
if(condition) { do something } else if (condition) { do something } else { do something }

Note: Make sure the statements appear as above with the else if and else appearing on the same line as the closing braces, otherwise R complains about ‘unexpected else’ in else statement

D) Loops
I would like to mention 2 ways of doing ‘for’ loops in R.
a) for (i in 1:10) { statement }
> a <- seq(5,25,length=10) > a [1] 5.000000 7.222222 9.444444 11.666667 13.888889 16.111111 18.333333 [8] 20.555556 22.777778 25.000000

b) Sequence along the vector sequence. Note: This is useful as we don’t have to know the length of the vector/sequence
for (i in seq_along(a)){ + print(a[i]) + }

[1] 5
[1] 7.222222
[1] 9.444444
[1] 11.66667
…

There are others ways of looping with ‘while’ and ‘repeat’ which I have not included in this post.

R makes manipulation of matrices and data frames really easy. All the elements in a matrix are numeric while data frames can have different types for each of the element

E) Matrix
> rnorm(12,5,2) [1] 2.699961 3.160208 5.087478 3.969129 3.317840 4.551565 2.585758 2.397780 [9] 5.297535 6.574757 7.468268 2.440835

a) Create a vector of 12 random numbers with a mean of 5 and SD of 2
> a <-rnorm(12,5,2) b) Convert the vector to a matrix with 4 rows and 3 columns > mat <- matrix(a,4,3) > mat[,1] [,2] [,3] [1,] 5.197010 3.839281 9.022818 [2,] 4.053590 5.321399 5.587495 [3,] 4.225763 4.873768 6.648151 [4,] 4.709784 4.129093 2.575523

c) Subset rows 1 & 2 from the matrix
> mat[1:2,] [,1]     [,2]     [,3] [1,] 5.19701 3.839281 9.022818 [2,] 4.05359 5.321399 5.587495
d) Subset matrix a rows 1& 2 and with columns 2 & 3
> mat[1:2,2:3] [,1]     [,2] [1,] 3.839281 9.022818 [2,] 5.321399 5.587495
e) Subset matrix a for all row elements for the column 3
> mat[,3] [1] 9.022818 5.587495 6.648151 2.575523
e) Add row names and column names for the matrix as follows
> names <- c(“tim”,”pat”,”joe”,”jim”)
> v <- data.frame(names,mat)
> v names       X1       X2       X3 1   tim 5.197010 3.839281 9.022818 2   pat 4.053590 5.321399 5.587495 3   joe 4.225763 4.873768 6.648151 4   jim 4.709784 4.129093 2.575523

> colnames(v) <- c("names","a","b","c") > v names a b c 1 tim 5.197010 3.839281 9.022818 2 pat 4.053590 5.321399 5.587495 3 joe 4.225763 4.873768 6.648151 4 jim 4.709784 4.129093 2.575523

F) Data Frames
In R data frames are the most important method to manipulate large amounts of data. One can read data in .csv format into data frame using
df <- read.csv(“mydata.csv”)
To get a feel of data frames it is useful to play around with the numerous data sets that are available with the installation of R
To check the available dataframes do
>data() AirPassengers Monthly Airline Passenger Numbers 1949-1960 BJsales Sales Data with Leading Indicator BJsales.lead (BJsales) Sales Data with Leading Indicator BOD Biochemical Oxygen Demand CO2 Carbon Dioxide Uptake in Grass Plants ChickWeight Weight versus age of chicks on different diets ...
…

I will be using the mtcars data frame. Here are some of the most important commands on data frames
a) load data from mtcars
data(mtcars)
b) > head(mtcars,3) # Display the top 3 rows of the data frame mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1

c) > tail(mtcars,4) # Display the boittom 4 rows of the data frame mpg cyl disp hp drat wt qsec vs am gear carb Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2

d) > names(mtcars) # Display the names of the columns of the data frame [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" "carb"

e) > summary(mtcars) # Display the summary of the data frame mpg cyl disp hp drat wt Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0 Min. :2.760 Min. :1.513 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080 1st Qu.:2.581 Median :19.20 Median :6.000 Median :196.3 Median :123.0 Median :3.695 Median :3.325 Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7 Mean :3.597 Mean :3.217 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920 3rd Qu.:3.610 Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 Max. :4.930 Max. :5.424 qsec vs am gear carb Min. :14.50 Min. :0.0000 Min. :0.0000 Min. :3.000 Min. :1.000 1st Qu.:16.89 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000 Median :17.71 Median :0.0000 Median :0.0000 Median :4.000 Median :2.000 Mean :17.85 Mean :0.4375 Mean :0.4062 Mean :3.688 Mean :2.812 3rd Qu.:18.90 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000 Max. :22.90 Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :8.000

f) > str(mtcars) # Generate a concise description of the data frame - values in each column, factors 'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
g) > mtcars[mtcars$mpg == 10.4,] #Select all rows in mtcars where the mpg column has a value 10.4 mpg cyl disp hp drat wt qsec vs am gear carb Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4

h) > mtcars[(mtcars$mpg >20) & (mtcars$mpg <24),] # Select all rows in mtcars where the mpg > 20 and mpg < 24 mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

i) > myset <- mtcars[(mtcars$cyl == 6) | (mtcars$cyl == 4),] # Get all calls which are either 4 or 6 cylinder > myset mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2… … …

j) > mean(myset$mpg) # Determine the mean of the set created above [1] 23.97222
k) > table(mtcars$cyl) #Create a table of cars which have 4,6, or 8 cylinders

4 6 8
11 7 14

G) lapply,sapply,tapply
I use the iris data set for these commands
a) > data(iris) #Load iris data set

b)> names(iris) #Show the column names of the data set [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" c) > lapply(iris,class) #Show the class of all the columns in iris $Sepal.Length [1] "numeric" $Sepal.Width [1] "numeric" $Petal.Length [1] "numeric" $Petal.Width [1] "numeric" $Species [1] "factor"

d) > sapply(iris,class) # Display a summary of the class of the iris data set Sepal.Length Sepal.Width Petal.Length Petal.Width Species "numeric" "numeric" "numeric" "numeric" "factor"

e) tapply: Instead of getting the mean for each of the species as below we can use tapply > a <-iris[iris$Species == "setosa",] > mean(a$Sepal.Length) [1] 5.006 > b <-iris[iris$Species == "versicolor",] > mean(b$Sepal.Length) [1] 5.936 > c <-iris[iris$Species == "virginica",] > mean(c$Sepal.Length) [1] 6.588

> tapply(iris$Sepal.Length,iris$Species,mean)
setosa versicolor virginica
5.006 5.936 6.588

Hopefully this highly condensed version of R will set you on a R-oll.

A crime map of India in R – Crimes against women

In this post I take a look at the gory crime scene across India to determine which states are the heavy weights in crimes. Who is the undisputed champion of rapes in a year? Which state excels in cruelty by husbands and the relatives to wives? Which state leads in dowry deaths? To get the answers to these questions I perform analysis of the state-wise crime data against women with the data from Open Government Data (OGD) Platform India. The dataset for this analysis was taken for the Crime against Women from OGD.

(Do see my post Revisiting crimes against women in India which includes an interactive Shiny app)

The data in OGD is available for crimes against women in different states under different ‘crime heads’ like rape, dowry deaths, kidnapping & abduction etc. The data is available for years from 2001 to 2012. This data is plotted as a scatter plot and a linear regression line is then fit on the available data. Based on this linear model, the projected incidence of crimes likes rapes, dowry deaths, abduction & kidnapping is performed for each of the states. This is then used to build a table of different crime heads for all the states predicting the number of crimes till the year 2018. Fortunately, R crunches through the data sets quite easily. The overall projections of crimes against as women is shown below based on the linear regression for each of these states

Projections over the next couple of years
The tables below are based on the projected incidence of crimes under various categories assuming that these states maintain their torrid crime rate. A cursory look at the tables below clearly indicate the Uttar Pradesh is the undisputed heavy weight champion in 4 of 5 categories shown. Maharashtra and Andhra Pradesh take 2nd and 3rd ranks in the total crimes against women and are significant contenders in other categories too.

A) Projected rapes in India
The top 3 heavy weights in projected rapes over the next 5 years are 1) Madhya Pradesh 2) Uttar Pradesh 3) Maharashtra

Full table: Rape.csv
B) Projected Dowry deaths in India

Full table: Dowry Deaths.csv
C) Kidnapping & Abduction

Full table: Kidnapping&Abduction.csv
D) Cruelty by husband & relatives

Full table: Cruelty by husbands_relatives.csv
E) Total crimes against women

Full table: Total crimes.csv
Here is a visualization of ‘Total crimes against women’ created as a choropleth map

The implementation for this analysis was done using the R language. The R code, dataset, output and the crime charts can be accessed at GitHub at crime-against-women

Directory structure
– R code
– dataset used
– output
– statewise-crime-charts

The analysis has been completely parametrized. A quick look at the implementation is shown below. A function state crime was created as given below

statecrime.R
This function (statecrime.R) does the following
a) Creates a scatter plot for the state for the crime head
b) Computes a best linear regression fir and draws this line
c) Uses the model parameters (coefficients) to compute the projected crime in the years to come
d) Writes the projected values to a text file
c) Creates a directory with the name of the state if it does not exist and stores the jpeg of the plot there.

statecrime <- function(indiacrime, row, state,crime) { year <- c(2001:2012) # Make seperate folders for each state if(!file.exists(state)) { dir.create(state) } setwd(state) crimeplot <- paste(crime,".jpg") jpeg(crimeplot)

# Plot the details of the crime
plot(year,thecrime ,pch= 15, col="red", xlab = "Year", ylab= crime, main = atitle, ,xlim=c(2001,2018),ylim=c(ymin,ymax), axes=FALSE)
A linear regression line is fit using ‘lm’

# Fit a linear regression model lmfit <-lm(thecrime~year) # Draw the lmfit line abline(lmfit)

The model parameters are then used to draw the line and also project for the next 5 years from 2013 to 2018

nyears <-c(2013:2018) nthecrime <- rep(0,length(nyears)) # Projected crime incidents from 2013 to 2018 using a linear regression model for (i in seq_along(nyears)) { nthecrime[i] <- lmfit$coefficients[2] * nyears[i] + lmfit$coefficients[1] }

The projected data for each state is appended into an appropriate file which is then used to display the tables at the top of this post

# Write the projected crime rate in a file
nthecrime <- round(nthecrime,2) nthecrime <- c(state, nthecrime, "\n") print(nthecrime) #write(nthecrime,file=fileconn, ncolumns=9, append=TRUE,sep="\t") filename <- paste(crime,".txt") # Write the output in the ./output directory setwd("./output") cat(nthecrime, file=filename, sep=",",append=TRUE)

The above function is then repeatedly called for each state for the different crime heads. (Note: It is possible to check the read both the states and crime heads with R and perform the computation repeatedly. However, I have done this the manual way!)

crimereport.R
# 1. Andhra Pradesh i <- 1 statecrime(indiacrime, i, "Andhra Pradesh","Rape") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Kidnapping& Abduction") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Dowry Deaths") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Assault on Women") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Insult to modesty") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Cruelty by husband_relatives") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Imporation of girls from foreign country") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Immoral traffic act") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Dowry prohibition act") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Indecent representation of Women Act") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Commission of Sati Act") i <- i+38 statecrime(indiacrime, i, "Andhra Pradesh","Total crimes against women") ... ...

and so on for all the states

Charts for different crimes against women

1) Uttar Pradesh

The plots for Uttar Pradesh are shown below

Rapes in UP

Dowry deaths in UP

Cruelty by husband/relative

Total crimes against women in Uttar Pradesh

You can find more charts in GitHub by clicking Uttar Pradesh

2) Maharashtra : Some of the charts for Maharashtra

Rape

Kidnapping & Abduction

Total crimes against women in Maharashtra

More crime charts for Maharashtra

Crime charts can be accessed for the following states from GitHub ( in alphabetical order)

3) Andhra Pradesh
4) Arunachal Pradesh
5) Assam
6) Bihar
7) Chattisgarh
8) Delhi (Added as an exception based on its notoriety)
9) Goa
10) Gujarat
11) Haryana
12) Himachal Pradesh
13) Jammu & Kashmir
14) Jharkhand
15) Karnataka
16) Kerala
17) Madhya Pradesh
18) Manipur
19) Meghalaya
20) Mizoram
21) Nagaland
22) Odisha
23) Punjab
24) Rajasthan
25) Sikkim
26) Tamil Nadu
27) Tripura
28) Uttarkhand
29) West Bengal

The code, dataset and the charts can be cloned/forked from GitHub at crime-against-women

Let me know if you find any interesting patterns in the data.
Thoughts, comments welcome!

A peek into literacy in India: Statistical Learning with R

In this post I take a peek into the literacy landscape across India as a whole using R language. The dataset from Open Government Data (OGD) platform India was used for this purpose. This data is based on the 2011 census. The XL sheets for the states were downloaded for data for each state. The Union Territories were not included in the analysis.

A thin slice of the data from each data set was taken from the data for each individual state (Note: This could also have been done from the consolidated india.xls XL sheet which I came to know of, much later).

I calculate the following for age group

Males (%) attending education institutions = (Males attending educational institutions * 100)/ Total males
Females (%) attending education institutions = (Females attending educational institutions * 100)/ Total Females

This is then plotted as a bar chart with the age distribution. I then overlay the national average for each state over the barchart to check whether the literacy in the state is above or below the national average. The implementation in R is included below

The code and data can be forked/cloned from GitHub at india-literacy

The results based on the analysis is given below.

Kerala is clearly the top ranker with the literacy rates for both males and females well above the average
The states with above average literacy are – Kerala, Himachal Pradesh, Uttarakhand, Tamil Nadu, Haryana, Himachal Pradesh, Karnataka, Maharashtra, Punjab, Uttarakhand
The states with just about average literacy – Karnataka, Andhra Pradesh, Chattisgarh, Gujarat, Madhya Pradesh, Odisha, West Bengal
The states with below average literacy – Uttar Pradesh, Bihar, Jharkhand, Arunachal Pradesh, Assam, Jammu and Kashmir, Jharkhand, Rajasthan

A brief implementation of the basic code in R is shown bwelow

# Read the Arunachal Pradhesh literacy related data arunachal = read.csv("arunachal.csv") # Create as a matrix arunachalmat = as.matrix(arunachal) arunachalTotal = arunachalmat[2:19,7:28] # Take transpose as this is necessary for plotting bar charts arunachalmat = t(arunachalTotal) # Set the scipen option to format the y axis (otherwise prints as e^05 etc.) getOption("scipen") opt <- options("scipen" = 20) getOption("scipen") #Create a vector of total Males & Females arunachalTotalM = arunachalmat[3,] arunachalTotalF = arunachalmat[4,] #Create a vector of males & females attending education institution arunachalM = arunachalmat[6,] arunachalF = arunachalmat[7,] #Calculate percent of males attending education of total arunachalpercentM = round(as.numeric(arunachalM) *100/as.numeric(arunachalTotalM),1) barplot(arunachalpercentM,names.arg=arunachalmat[1,],main ="Percentage males attending educational institutions in Arunachal Pradesh", xlab = "Age", ylab= "Percentage",ylim = c(0,100), col ="lightblue", legend= c("Males")) points(age,indiapercentM,pch=15) lines(age,indiapercentM,col="red",pch=20,lty=2,lwd=3) legend( x="bottomright", legend=c("National average"), col=c("red"), bty="n" , lwd=1, lty=c(2), pch=c(15) ) #Calculate percent of females attending education of total arunachalpercentF = round(as.numeric(arunachalF) *100/as.numeric(arunachalTotalF),1) barplot(arunachalpercentF,names.arg=arunachalmat[1,],main ="Percentage females attending educational institutions in Arunachal Pradesh ", xlab = "Age", ylab= "Percentage", ylim = c(0,100), col ="lightblue", legend= c("Females")) points(age,indiapercentF,pch=15) lines(age,indiapercentF,col="red",pch=20,lty=2,lwd=3) legend( x="bottomright", legend=c("National average"), col=c("red"), bty="n" , lwd=1, lty=c(2), pch=c(15) )

A) Overall plot for India

a) India – Males

b) India – females

The plots for each individual state is given below

1) Literacy in Tamil Nadu

Tamil Nadu is slightly over the national average. The women seem to do marginally better than the males

a) Tamil Nadu – males

b) Tamil Nadu – females

2) Literacy in Uttar Pradesh

UP is slightly below the national average. Women are comparatively below men here

a) Uttar Pradesh – males

b) Uttar Pradesh – females

3) Literacy in Bihar

Bihar is well below the national average for both men and women

a) Bihar – males

b) Bihar – females

4. Literacy in Kerala

Kerala is the winner all the way in literacy with almost 100% literacy across all age groups

a) Kerala – males

b) Kerala -females

5. Literacy in Andhra Pradesh

AP just meets the national average for literacy.

a) Andhra Pradesh – males

b) Andhra Pradesh – females

6. Literacy in Arunachal Pradesh

Arunachal Pradesh is below average for most of the age groups

a) Arunachal Pradesh – males

b) Arunachal Pradesh – females

7. Literacy in Assam

Assam is below national average

a) Assam – males

b) Assam – females

8. Literacy in Chattisgarh

Chattisgarh is on par with the national average for both men and women

a) Chattisgarh – males

b) Chattisgarh – females

9. Literacy in Gujarat

Gujarat is just about average

a) Gujarat – males

b) Gujarat – females

10. Literacy in Haryana

Haryana is slightly above average

a) Haryana – males

b) Haryana – females

11. Literacy in Himachal Pradesh

Himachal Pradesh is cool and above average.

a) Himachal Pradesh – males

b) Himachal Pradesh – females

12. Literacy in Jammu and Kashmir

J & K is marginally below average

a) Jammu and Kashmir – males

b) Jammu and Kashmir – females

13. Literacy in Jharkhand

Jharkhand is some ways below average

a) Jharkhand – males

b) Jharkhand – females

14. Literacy in Karnataka

Karnataka is on average for men. Womem seem to do better than men here

a) Karnataka – males

b) Karnataka – females

15. Literacy in Madhya Pradesh

Madhya Pradesh meets the national average

a) Madhya Pradesh – males

b) Madhya Pradesh – females

16. Literacy in Maharashtra

Maharashtra is front-runner in literacy

a) Maharashtra – females

b) Maharastra – females

17. Literacy in Odisha

Odisha meets national average

a) Odisha – males

b) Odisha – females

18. Literacy in Punjab

Punjab is marginally above average with women doing even better

a) Punjab – males

b) Punjab – females

19. Literacy in Rajasthan

Rajasthan is average for males and below average for females

a) Rajasthan – males

b) Rajasthan – females

20. Literacy in Uttarakhand

Uttarakhand rocks and is above average

a) Uttarakhand – males

b) Uttarakhand – females

21. Literacy in West Bengal

West Bengal just about meets the national average.

a) West Bengal – males

b) West Bengal – females

The code can be cloned/forked from GitHub india-literacy. I have done my analysis on the overall data. The data is further sub-divided across districts in each state and further into urban and rural. Many different ways of analysing are possible. One method is shown here

Conclusion

Kerala is clearly head and shoulders above all states when it comes to literacy
Many states are above average. They are Kerala, Himachal Pradesh, Uttarakhand, Tamil Nadu, Haryana, Himachal Pradesh, Karnataka, Maharashtra, Punjab, Uttarakhand
States with average literacy are – Karnataka, Andhra Pradesh, Chattisgarh, Gujarat, Madhya Pradesh, Odisha, West Bengal
States which fall below the national average are – Uttar Pradesh, Bihar, Jharkhand, Arunachal Pradesh, Assam, Jammu and Kashmir, Jharkhand, Rajasthan

Statistical learning with R: A look at literacy in Tamil Nadu

In this post I make my first foray into data mining using the R language. As a start, I picked up the data from the Open Government Data (OGD Platform of India from the Ministry of Human Resources. There are many data sets under Education. To get started I picked the data set on Tamil Nadu which deals with the population attending educational institutions by age, sex and institution type. Similar data is available for all states.

I wanted to start off on a small scale, primarily to checkout some of the features of the R language. R is clearly the language of choice for processing large amounts of data. R has close to 4000+ packages that can do various things like statistical, regression analysis etc. However I found this is no easy task. There are a zillion ways in which you can take cross-sections of a large dataset. Some of them will provide useful insights while others will lead you nowhere.

Also see my post Literacy in India – A deepR dive!

Data science, which is predicted to be the technology of the future based on with the mountains of data being generated daily, will in my opinion, will be more of an art and less of a science. There will be wizards who will be able to spot remarkable truths in the mundane data while others will not be that successful.

Anyway back to my attempt to divine intelligence in the Tamil Nadu(TN) literacy data. The data downloaded was an Excel sheet with 1767 rows and 28 columns. The first 60 rows deal with the overall statistics of literacy in Tamil Nadu state as a whole. Further below are the statistics on the individual districts of Tamil nadu.
Each of this is further divided into urban and rural parts. The data covers persons from the age of 4 upto the age of 60 and whether they attended school, college, vocational institute etc. To make my initial attempt manageable I have just focused on the data for Tamil Nadu state as a whole including the breakup of the urban and rural data.

My analysis is included below. The code and the dataset for this implementation is in R language and can be cloned from GitHub at tamilnadu-literacy-analysis

Analysis of Tamil Nadu (total)
The total population of Tamil Nadu based on an age breakup is shown below

1) Total population Tamil Nadu

2) Males & Females attending education institutions in TN

There are marginally more males attending educational institutions. Also the number of persons attending educational institutions seems to drop from 11 years of age. There is a spike around 20-24 years and people go to school and college at this age. See pie chart 8) below

3) Percentage of males attending educational institution of the total males

4) Percentage females attending educational institutions in TN

There is a very similar trend between males and females. The attendance peaks between 9 – 11 years of age and then falls to roughly 50% around 15-19 years and rapidly falls off

5) Boys and girls attending school in TN

For some reason there is a marked increase for boys and girls around 20-24. Possibly people repeat classes around this age

6) Persons attending college in TN

7) Educational institutions attended by persons between 15- 19 years

8) Educational institutions attended by persons between 20-24

As can be seen there is a large percentage (30%) of people in the 20-24 age group who are in school. This is probably the reason for the spike in “Boys and girls attending school in TN” see 2) for the 20-24 years of age

Education in rural Tamil Nadu

1) Total rural population Tamil Nadu

2) Males & Females attending education institutions in rural TN3) Percentage of rural males attending educational institution

4) Percentage females attending educational institutions in rural TN of total females

The persons attending education drops rapidly to 40% between 15-19 years of age for both males and females

5) Boys and girls attending school in rural TN

6) Persons attending college in rural TN

7) Educational institutions attended by persons between 15- 19 years in rural TN

8) Educational institutions attended by persons between 20-24 in rural TN

As can be seen there is a large percentage (39%) of rural people in the 20-24 age group who are in school

Education in urban Tamil Nadu

1) Total population in urban Tamil Nadu

2) Males & Females attending education institutions in urban TN3) Percentage of males attending educational institution of the total males in urban TN4) Percentage females attending educational institutions in urban TN

5) Boys and girls attending school in urban TN

6) Persons attending college in urban TN

7) Educational institutions attended by persons between 15- 19 years in urban TN

8) Educational institutions attended by persons between 20-24 in urban TN

As can be seen there is a large percentage (25%) of rural people in the 20-24 age group who are in school

The R implementation and the Tamil Nadu dataset can be cloned from my repository in GitHub at tamilnadu-literacy-analysis

The above analysis is just one of a million possible ways the data can be analyzed and visually represented. I hope to hone my skill as progress along in similar analysis.

Hasta la vista! I’ll be back.

Watch this space!

Introduction

Box Histogram Plot

Relative Mean Strike Rate

Relative Runs Frequency Percentage

Moving Average of runs over career

Runs forecast

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

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

Predicting Runs given Balls Faced and Minutes at Crease

Contribution to matches won and lost

Home and overseas performance

Average runs at ground and against opposition

Key findings

Final thoughts

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Introduction

The cricketr package

Sachin Tendulkar’s performance – Basic Analyses

More analyses

3D scatter plot and prediction plane

Average runs at different venues

Average runs against different opposing teams

Highest Runs Likelihood

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

Box Histogram Plot

Contribution to won and lost matches

Performance at home and overseas

Relative Mean Strike Rate plot

Relative Runs Frequency plot

Moving Average of runs in career

Future Runs forecast

Check Batsman In-Form or Out-of-Form

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

Predicting Runs given Balls Faced and Minutes at Crease

Analysis of Top 3 wicket takers

Wicket Frequency Plot

Wickets Runs plot

Average wickets at different venues

Average wickets against different opposition

Relative Wickets Frequency Percentage

Relative Economy Rate against wickets taken

Wickets taken moving average

Future Wickets forecast

Contribution to matches won and lost

Performance home and overseas

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

Key Findings

Analysis of Top 4 batsman

Analysis of Top 3 legs spinners

Final thoughts

Batting

Bowling

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