Analyzing performances of cricketers using cricketr template

This post includes a template which you can use for analyzing the performances of cricketers, both batsmen and bowlers in Test, ODI and Twenty 20 cricket using my R package cricketr. To see actual usage of functions in the R package cricketr see Introducing cricketr! : An R package to analyze performances of cricketers.

This template can be downloaded from Github at cricketer-template

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

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

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

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

The cricketr package

The cricketr package has several functions that perform several different analyses on both batsman and bowlers. The package has function 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 batsmans in in-form or out-of-form.

The data for a particular player can be obtained with the getPlayerData() function. To do 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

1. Install the cricketr package

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

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 <- system.file("data", "tendulkar.csv", package = "cricketr")
#batsman4s(pathToFile, "Sachin Tendulkar")

# The general format is pkg-function(pathToFile,par1,...)
#batsman4s(<path-To-File>,"Sachin Tendulkar")

“` The pre-packaged files can be accessed as shown above. To get the data of any player use the function in Test, ODI and Twenty20 use the following

2. For Test cricket

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

2a. For ODI cricket

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

2b For Twenty 20 cricket

#tendulkarT20 <- getPlayerDataTT(35320,dir="..",file="tendulkarT20.csv",type="batting")

Analysis of batsmen

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 For example

3. Basic analyses

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

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Player 1
Player 2
Player 3
Player 4

4. More analyses

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#batsman4s("./player1.csv","Player1")
#batsman6s("./player1.csv","Player1")
#batsmanMeanStrikeRate("./player1.csv","Player1")

# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")
dev.off()

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par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#batsman4s("./player2.csv","Player2")
#batsman6s("./player2.csv","Player2")
#batsmanMeanStrikeRate("./player2.csv","Player2")
# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")
dev.off()

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par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#batsman4s("./player3.csv","Player3")
#batsman6s("./player3.csv","Player3")
#batsmanMeanStrikeRate("./player3.csv","Player3")
# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")

dev.off()

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par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#batsman4s("./player4.csv","Player4")
#batsman6s("./player4.csv","Player4")
#batsmanMeanStrikeRate("./player4.csv","Player4")
# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")
dev.off()

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Note: For mean strike rate in ODI and Twenty20 use the function batsmanScoringRateODTT()

5.Boxplot histogram plot

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

#batsmanPerfBoxHist("./player1.csv","Player1")
#batsmanPerfBoxHist("./player2.csv","Player2")
#batsmanPerfBoxHist("./player3.csv","Player3")
#batsmanPerfBoxHist("./player4.csv","Player4")

6. Contribution to won and lost matches

For the 2 functions below you will have to use the getPlayerDataSp() function. I have commented this as I already have these files. This function can only be used for Test matches

#player1sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player1sp.csv",ttype="batting")
#player2sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player2sp.csv",ttype="batting")
#player3sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player3sp.csv",ttype="batting")
#player4sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player4sp.csv",ttype="batting")

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanContributionWonLost("player1sp.csv","Player1")
#batsmanContributionWonLost("player2sp.csv","Player2")
#batsmanContributionWonLost("player3sp.csv","Player3")
#batsmanContributionWonLost("player4sp.csv","Player4")
dev.off()

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7, Performance at home and overseas

This function also requires the use of getPlayerDataSp() as shown above. This can only be used for Test matches

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanPerfHomeAway("player1sp.csv","Player1")
#batsmanPerfHomeAway("player2sp.csv","Player2")
#batsmanPerfHomeAway("player3sp.csv","Player3")
#batsmanPerfHomeAway("player4sp.csv","Player4")
dev.off()

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8. Batsman average at different venues

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanAvgRunsGround("./player1.csv","Player1")
#batsmanAvgRunsGround("./player2.csv","Player2")
#batsmanAvgRunsGround("./player3.csv","Ponting")
#batsmanAvgRunsGround("./player4.csv","Player4")
dev.off()

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9. Batsman average against different opposition

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanAvgRunsOpposition("./player1.csv","Player1")
#batsmanAvgRunsOpposition("./player2.csv","Player2")
#batsmanAvgRunsOpposition("./player3.csv","Ponting")
#batsmanAvgRunsOpposition("./player4.csv","Player4")
dev.off()

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10. Runs Likelihood of batsman

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanRunsLikelihood("./player1.csv","Player1")
#batsmanRunsLikelihood("./player2.csv","Player2")
#batsmanRunsLikelihood("./player3.csv","Ponting")
#batsmanRunsLikelihood("./player4.csv","Player4")
dev.off()

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11. Moving Average of runs in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanMovingAverage("./player1.csv","Player1")
#batsmanMovingAverage("./player2.csv","Player2")
#batsmanMovingAverage("./player3.csv","Ponting")
#batsmanMovingAverage("./player4.csv","Player4")
dev.off()

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12. Cumulative Average runs of batsman in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanCumulativeAverageRuns("./player1.csv","Player1")
#batsmanCumulativeAverageRuns("./player2.csv","Player2")
#batsmanCumulativeAverageRuns("./player3.csv","Ponting")
#batsmanCumulativeAverageRuns("./player4.csv","Player4")
dev.off()

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13. Cumulative Average strike rate of batsman in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanCumulativeStrikeRate("./player1.csv","Player1")
#batsmanCumulativeStrikeRate("./player2.csv","Player2")
#batsmanCumulativeStrikeRate("./player3.csv","Ponting")
#batsmanCumulativeStrikeRate("./player4.csv","Player4")
dev.off()

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14. 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.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanPerfForecast("./player1.csv","Player1")
#batsmanPerfForecast("./player2.csv","Player2")
#batsmanPerfForecast("./player3.csv","Player3")
#batsmanPerfForecast("./player4.csv","Player4")
dev.off()

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15. 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

frames <- list("./player1.csv","./player2.csv","player3.csv","player4.csv")
names <- list("Player1","Player2","Player3","Player4")
#relativeBatsmanSR(frames,names)

16. Relative Runs Frequency plot

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

frames <- list("./player1.csv","./player2.csv","player3.csv","player4.csv")
names <- list("Player1","Player2","Player3","Player4")
#relativeRunsFreqPerf(frames,names)

17. Relative cumulative average runs in career

frames <- list("./player1.csv","./player2.csv","player3.csv","player4.csv")
names <- list("Player1","Player2","Player3","Player4")
#relativeBatsmanCumulativeAvgRuns(frames,names)

18. Relative cumulative average strike rate in career

frames <- list("./player1.csv","./player2.csv","player3.csv","player4.csv")
names <- list("Player1","Player2","Player3","player4")
#relativeBatsmanCumulativeStrikeRate(frames,names)

19. 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("./player1.csv","Player1")
#checkBatsmanInForm("./player2.csv","Player2")
#checkBatsmanInForm("./player3.csv","Player3")
#checkBatsmanInForm("./player4.csv","Player4")

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

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

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
#battingPerf3d("./player1.csv","Player1")
#battingPerf3d("./player2.csv","Player2")

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
#battingPerf3d("./player3.csv","Player3")
#battingPerf3d("./player4.csv","player4")
dev.off()

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21. Predicting Runs given Balls Faced and Minutes at Crease

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

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
#Player1 <- batsmanRunsPredict("./player1.csv","Player1",newdataframe=newDF)
#Player2 <- batsmanRunsPredict("./player2.csv","Player2",newdataframe=newDF)
#ponting <- batsmanRunsPredict("./player3.csv","Player3",newdataframe=newDF)
#sangakkara <- batsmanRunsPredict("./player4.csv","Player4",newdataframe=newDF)

#batsmen <-cbind(round(Player1$Runs),round(Player2$Runs),round(Player3$Runs),round(Player4$Runs))
#colnames(batsmen) <- c("Player1","Player2","Player3","Player4")
#newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
#colnames(newDF) <- c("BallsFaced","MinsAtCrease")
#predictedRuns <- cbind(newDF,batsmen)
#predictedRuns

Analysis of bowlers

Bowler1
Bowler2
Bowler3
Bowler4

player1 <- getPlayerData(xxxx,dir=“..”,file=“player1.csv”,type=“bowling”) Note For One day you will have to use getPlayerDataOD() and for Twenty20 it is getPlayerDataTT()

21. 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("./bowler1.csv","Bowler1")
#bowlerWktsFreqPercent("./bowler2.csv","Bowler2")
#bowlerWktsFreqPercent("./bowler3.csv","Bowler3")
dev.off()

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22. Wickets Runs plot

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerWktsRunsPlot("./bowler1.csv","Bowler1")
#bowlerWktsRunsPlot("./bowler2.csv","Bowler2")
#bowlerWktsRunsPlot("./bowler3.csv","Bowler3")
dev.off()

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23. Average wickets at different venues

#bowlerAvgWktsGround("./bowler3.csv","Bowler3")

24. Average wickets against different opposition

#bowlerAvgWktsOpposition("./bowler3.csv","Bowler3")

25. Wickets taken moving average

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerMovingAverage("./bowler1.csv","Bowler1")
#bowlerMovingAverage("./bowler2.csv","Bowler2")
#bowlerMovingAverage("./bowler3.csv","Bowler3")

dev.off()

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26. Cumulative Wickets taken

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerCumulativeAvgWickets("./bowler1.csv","Bowler1")
#bowlerCumulativeAvgWickets("./bowler2.csv","Bowler2")
#bowlerCumulativeAvgWickets("./bowler3.csv","Bowler3")
dev.off()

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27. Cumulative Economy rate

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerCumulativeAvgEconRate("./bowler1.csv","Bowler1")
#bowlerCumulativeAvgEconRate("./bowler2.csv","Bowler2")
#bowlerCumulativeAvgEconRate("./bowler3.csv","Bowler3")
dev.off()

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28. 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

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerPerfForecast("./bowler1.csv","Bowler1")
#bowlerPerfForecast("./bowler2.csv","Bowler2")
#bowlerPerfForecast("./bowler3.csv","Bowler3")
dev.off()

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29. Contribution to matches won and lost

As discussed above the next 2 charts require the use of getPlayerDataSp(). This can only be done for Test matches

#bowler1sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler1sp.csv",ttype="bowling")
#bowler2sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler2sp.csv",ttype="bowling")
#bowler3sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler3sp.csv",ttype="bowling")

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerContributionWonLost("bowler1sp","Bowler1")
#bowlerContributionWonLost("bowler2sp","Bowler2")
#bowlerContributionWonLost("bowler3sp","Bowler3")
dev.off()

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30. Performance home and overseas.

This can only be done for Test matches

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerPerfHomeAway("bowler1sp","Bowler1")
#bowlerPerfHomeAway("bowler2sp","Bowler2")
#bowlerPerfHomeAway("bowler3sp","Bowler3")
dev.off()

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31 Relative Wickets Frequency Percentage

frames <- list("./bowler1.csv","./bowler3.csv","bowler2.csv")
names <- list("Bowler1","Bowler3","Bowler2")
#relativeBowlingPerf(frames,names)

32 Relative Economy Rate against wickets taken

frames <- list("./bowler1.csv","./bowler3.csv","bowler2.csv")
names <- list("Bowler1","Bowler3","Bowler2")
#relativeBowlingER(frames,names)

33 Relative cumulative average wickets of bowlers in career

frames <- list("./bowler1.csv","./bowler3.csv","bowler2.csv")
names <- list("Bowler1","Bowler3","Bowler2")
#relativeBowlerCumulativeAvgWickets(frames,names)

34 Relative cumulative average economy rate of bowlers

frames <- list("./bowler1.csv","./bowler3.csv","bowler2.csv")
names <- list("Bowler1","Bowler3","Bowler2")
#relativeBowlerCumulativeAvgEconRate(frames,names)

35 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

#checkBowlerInForm("./bowler1.csv","Bowler1")
#checkBowlerInForm("./bowler2.csv","Bowler2")
#checkBowlerInForm("./bowler3.csv","Bowler3")
dev.off()

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Key Findings

Analysis of batsman

Analysis of bowlers

Also see
1. Re-introducing cricketr! : An R package to analyze performances of cricketers
2. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
3. Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr
4. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
5. yorkpy takes a hat-trick, bowls out Intl. T20s, BBL and Natwest T20!!!
6. My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
7. Introducing cricpy:A python package to analyze performances of cricketers

The Clash of the Titans in Test and ODI cricket

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

            Carl Jung

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

            Carl Sagan

Introduction

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

Analysis of Tendulkar, Gavaskar and Kohli in Test cricket
Analysis of Tendulkar and Kohli in ODIs

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

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

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

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

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

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

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

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

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

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

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

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

This post is also available at Rpubs Clash of the Titans
You can download this in PDF format at Clash of the Titans
You can download this R Markdown file from Github at Clash of titans

1 Load the cricketr package

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

A Test cricket – Analysis of Gavaskar, Tendulkar and Kohli

2. Get player data

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

3a. Basic analyses for Tendulkar

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

dev.off()

3b Basic analyses for Kohli

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

dev.off()

3c Basic analyses for Gavaskar

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

dev.off()

4a.More analyses for Tendulkar

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

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

dev.off()

4b. More analyses for Kohli

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

dev.off()

4c More analyses for Gavaskar

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

dev.off()

5 Performance of batsmen on different grounds

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

batsmanAvgRunsGround("./kohli.csv","Kohli")

batsmanAvgRunsGround("./gavaskar.csv","Gavaskar")

#dev.off()

6. Performance if batsmen against different Opposition

Tendulkar averages 50 against the following countries – Australia, Bangladesh, England, Sri Lanka, West Indies and Zimbabwe
Kohli average almost 50 against all the nations he has played – Australia, Bangladesh, England, New Zealand, Sri Lanka and West Indies
Gavaskar averages 50 against Australia, Pakistan, West Indies, Sri Lanka

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

batsmanAvgRunsOpposition("./kohli.csv","Kohli")

batsmanAvgRunsOpposition("./gavaskar.csv","Gavaskar")

7. Get player data special

This is required for the next 2 function calls

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

#dev.off()

8 Get contribution of batsmen in matches won and lost

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

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

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

9 Performance of batsmen at home and overseas

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

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


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

10. Moving average of runs

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

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

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

#dev.off()

11 Boxplot and histogram of runs

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

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

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

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

12 Cumulative average Runs for batsmen

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

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

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

batsmanCumulativeAverageRuns("./gavaskar.csv","Gavaskar")

13. Cumulative average strike rate of batsmen

Tendulkar’s strike rate is better than Kohli and Gavaskar

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

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

batsmanCumulativeStrikeRate("./gavaskar.csv","Gavaskar")

14 Performance forecast of batsmen

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

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

#dev.off()

15. Relative strike rate of batsmen

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

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

#dev.off()

16. Relative Runs frequency of batsmen

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

#dev.off()

17. Relative cumulative average runs of batsmen

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

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

#dev.off()

18. Relative cumulative average strike rate

Tendulkar has better strike rate than the other two.

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

#dev.off()

19. Check batsman in form

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

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

## [1] "**************************** Form status of Sachin Tendulkar ****************************
\n\n Population size: 294  Mean of population: 50.48 \n Sample size: 33  Mean of sample: 32.42 SD of 
sample: 29.8 \n\n Null hypothesis H0 : Sachin Tendulkar 's sample average is within 95% confidence interval 
of population average\n Alternative hypothesis Ha : Sachin Tendulkar 's sample average is below 
the 95% confidence interval of population average\n\n 
Sachin Tendulkar 's Form Status: Out-of-Form because the p value: 0.000713  is less than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("./kohli.csv","Kohli")

## [1] "**************************** Form status of Kohli ****************************\n\n Population size: 117
  Mean of population: 50.35 \n Sample size: 13  Mean of sample: 53.77 SD of sample: 46.15 \n\n Null 
hypothesis H0 : Kohli 's sample average is within 95% confidence interval of population average\n 
Alternative hypothesis Ha : Kohli 's sample average is below the 95% confidence interval of population
 average\n\n Kohli 's Form Status: In-Form because the p value: 0.603244  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("./gavaskar.csv","Gavaskar")

## [1] "**************************** Form status of Gavaskar ****************************\n\n 
Population size: 125  Mean of population: 44.67 \n Sample size: 14  Mean of sample: 57.86 SD of sample:
 58.55 \n\n Null hypothesis H0 : Gavaskar 's sample average is within 95% confidence interval of population
 average\n Alternative hypothesis Ha : Gavaskar 's sample average is below the 95% confidence interval of 
population average\n\n Gavaskar 's Form Status: In-Form because the p value: 0.793276  is greater 
than alpha=  0.05 \n *******************************************************************************************\n\n"

#dev.off()

20. Performance 3D

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

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

20. Runs likelihood

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

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

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

batsmanRunsLikelihood("./kohli.csv","Kohli")

## Summary of  Kohli 's runs scoring likelihood
## **************************************************
## 
## There is a 20 % likelihood that Kohli  will make  143 Runs in  232 balls over 330  Minutes 
## There is a 33.85 % likelihood that Kohli  will make  51 Runs in  92 balls over  127  Minutes 
## There is a 46.15 % likelihood that Kohli  will make  11 Runs in  24 balls over 31  Minutes

batsmanRunsLikelihood("./gavaskar.csv","Gavaskar")

## Summary of  Gavaskar 's runs scoring likelihood
## **************************************************
## 
## There is a 33.81 % likelihood that Gavaskar  will make  69 Runs in  159 balls over 214  Minutes 
## There is a 8.63 % likelihood that Gavaskar  will make  172 Runs in  364 balls over  506  Minutes 
## There is a 57.55 % likelihood that Gavaskar  will make  13 Runs in  35 balls over 48  Minutes

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

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
tendulkar <- batsmanRunsPredict("./tendulkar.csv","Tendulkar",newdataframe=newDF)
kohli <- batsmanRunsPredict("./kohli.csv","Kohli",newdataframe=newDF)
gavaskar <- batsmanRunsPredict("./gavaskar.csv","Gavaskar",newdataframe=newDF)
batsmen <-cbind(round(tendulkar$Runs),round(kohli$Runs),round(gavaskar$Runs))
colnames(batsmen) <- c("Tendulkar","Kohli","Gavaskar")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns

##    BallsFaced MinsAtCrease Tendulkar Kohli Gavaskar
## 1          10           30         7     6        4
## 2          38           71        23    24       17
## 3          66          111        39    42       30
## 4          94          152        54    60       43
## 5         121          193        70    78       56
## 6         149          234        86    96       69
## 7         177          274       102   114       82
## 8         205          315       118   132       95
## 9         233          356       134   150      108
## 10        261          396       150   168      121
## 11        289          437       165   186      134
## 12        316          478       181   204      147
## 13        344          519       197   222      160
## 14        372          559       213   240      173
## 15        400          600       229   258      186

#dev.off()

Key findings

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

B ODI Cricket – Analysis of Tendulkar and Kohli

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

22 Get player data for ODIs

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

#dev.off()

23a Basic performance of Tendulkar in ODI

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

23b. Basic performance of Kohli in ODI

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

24. Performance forecast in ODIs

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

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

batsmanPerfForecast("./kohliOD.csv","Kohli")

25. Batting performance

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

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

battingPerf3d("./kohliOD.csv","Kohli")

26. Predicting runs scored for the ODI batsmen

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

BF <- seq( 10, 200,length=10)
Mins <- seq(30,220,length=10)
newDF <- data.frame(BF,Mins)
tendulkarDF <- batsmanRunsPredict("./tendulkarOD.csv","Tendulkar",newdataframe=newDF)
kohliDF <- batsmanRunsPredict("./kohliOD.csv","Kohli",newdataframe=newDF)
batsmen <-cbind(round(tendulkarDF$Runs),round(kohliDF$Runs))
colnames(batsmen) <- c("Tendulkar","Kohli")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns

##    BallsFaced MinsAtCrease Tendulkar Kohli
## 1          10           30         7     8
## 2          31           51        26    28
## 3          52           72        45    48
## 4          73           93        64    68
## 5          94          114        83    88
## 6         116          136       102   108
## 7         137          157       121   128
## 8         158          178       140   149
## 9         179          199       159   169
## 10        200          220       178   189

27. Runs likelihood for the ODI batsmen

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

par(mar=c(4,4,2,2))
batsmanRunsLikelihood("./tendulkarOD.csv","Tendulkar")

## Summary of  Tendulkar 's runs scoring likelihood
## **************************************************
## 
## There is a 18.09 % likelihood that Tendulkar  will make  111 Runs in  118 balls over 172  Minutes 
## There is a 28.39 % likelihood that Tendulkar  will make  53 Runs in  63 balls over  95  Minutes 
## There is a 53.52 % likelihood that Tendulkar  will make  13 Runs in  18 balls over 27  Minutes

batsmanRunsLikelihood("./kohliOD.csv","Kohli")

## Summary of  Kohli 's runs scoring likelihood
## **************************************************
## 
## There is a 31.41 % likelihood that Kohli  will make  63 Runs in  69 balls over 97  Minutes 
## There is a 49.74 % likelihood that Kohli  will make  13 Runs in  18 balls over  24  Minutes 
## There is a 18.85 % likelihood that Kohli  will make  116 Runs in  113 balls over 163  Minutes

28. Runs in different venues for the ODI batsmen

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

batsmanAvgRunsGround("./kohliOD.csv","Kohli")

28. Runs against different opposition for the ODI batsmen

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

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

batsmanAvgRunsOpposition("./kohliOD.csv","Kohli")

29. Moving average of runs for the ODI batsmen

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

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

batsmanMovingAverage("./kohliOD.csv","Kohli")

30. Cumulative average runs of ODI batsmen

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

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

batsmanCumulativeAverageRuns("./kohliOD.csv","Kohli")

31 Cumulative strike rate of ODI batsmen

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

batsmanCumulativeStrikeRate("./kohliOD.csv","Kohli")

32. Relative batsmen strike rate

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

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

#dev.off()

33. Relative Run Frequency percentages

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

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

#dev.off()

34. Relative cumulative average runs of ODI batsmen

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

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

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

#dev.off()

35. Relative cumulative strike rate of ODI batsmen

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

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

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

#dev.off()

36. Batsmen 4s and 6s

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

frames <- list("./tendulkarOD.csv","./kohliOD.csv")
names <- list("Tendulkar","Kohli")
batsman4s6s(frames,names)

##                Tendulkar Kohli
## Runs(1s,2s,3s)     66.29 69.67
## 4s                 29.65 25.90
## 6s                  4.06  4.43

#dev.off()

37. Check ODI batsmen form

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

checkBatsmanInForm("./tendulkar.csv","Tendulkar")

## [1] "**************************** Form status of Tendulkar ********
********************\n\n Population size: 294  Mean of population: 50.48 \n
 Sample size: 33  Mean of sample: 32.42 SD of sample: 29.8 \n\n 
Null hypothesis H0 : Tendulkar 's sample average is within 95% confidence
 interval of population average\n Alternative hypothesis 
Ha : Tendulkar 's sample average is below the 95% confidence interval 
of population average\n\n Tendulkar 's Form Status: Out-of-Form because the p value: 0.000713  is less than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("./kohli.csv","Kohli")

## [1] "**************************** Form status of Kohli ***********
*****************\n\n Population size: 117  Mean of population: 50.35 \n
 Sample size: 13  Mean of sample: 53.77 SD of sample: 46.15 \n\n 
Null hypothesis H0 : Kohli 's sample average is within 95% confidence 
interval of population average\n Alternative hypothesis 
Ha : Kohli 's sample average is below the 95% confidence interval 
of population average\n\n Kohli 's Form Status: In-Form because 
the p value: 0.603244  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

#dev.off()

Key Findings

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

Check out some other players with my R package cricketr

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

Also see

To see all posts click Index of posts

Pitching yorkpy … in the block hole – Part 4

A good programmer is someone who always looks both ways before crossing a one-way street. Doug Linder

There are two ways to write error-free programs; only the third one works. Alan J. Perlis

In order to understand recursion, one must first understand recursion. Anonymous

This is the fourth and final part of my Python package yorkpy. In this part yorkpy, the python avatar of my R package yorkr see Introducing cricket package yorkr: Part 1- Beaten by sheer pace!, develops wings and is prepared for take-off. The yorkpy package uses data from Cricsheet

You can clone/download the code at Github yorkpy
This post has been published to RPubs at yorkpy-Part4
You can download this post as PDF at IPLT20-yorkpy-part4
You can download all the data used in this post and the previous post at yorkpyData

This post is a continuation of the earlier posts on yorkpy

1. Pitching yorkpy . short of good length to IPL – Part 1 In this part I included functions that convert the yaml data of IPL matches into Pandas dataframe which are then saved as CSV. This part can perform analysis of individual IPL matches. Note The converted data is available at yorkpyData
2. Pitching yorkpy.on the middle and outside off-stump to IPL – Part 2 This part included functions to create a large data frame for head-to-head confrontation between any 2IPL teams says CSK-MI, DD-KKR etc, which can be saved as CSV. Analysis is then performed on these team-2-team confrontations. Note The converted data is available at yorkpyData
3. Pitching yorkpy.swinging away from the leg stump to IPL – Part 3 The 3rd part includes the performance of any IPL team against all other IPL teams. The data can also be saved as CSV.Note The converted data is available at yorkpyData

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

This 4th and final part includes analysis of batting and bowling performances of any IPL player. The batting and bowling details for all teams have already been converted and are available at IPLT20-Batting-BowlingDetails

This part includes the following new functions

Batsman functions

batsmanRunsVsDeliveries
batsmanFoursSixes
batsmanDismissals
batsmanRunsVsStrikeRate
batsmanMovingAverage
batsmanCumulativeAverageRuns
batsmanCumulativeStrikeRate
batsmanRunsAgainstOpposition
batsmanRunsVenue

Bowler functions

bowlerMeanEconomyRate
bowlerMeanRunsConceded
bowlerMovingAverage
bowlerCumulativeAvgWickets
bowlerCumulativeAvgEconRate
bowlerWicketPlot
bowlerWicketsAgainstOpposition
bowlerWicketsVenue

A. Batsman functions

1. Get IPL Team Batting details

The function below gets the overall IPL team batting details based on the CSV files that were saved for IPL T20 matches. This is currently also available in Github at yorkpyData. The batting details of the IPL team in each match is created and a huge data frame is created by combining the batting details from each match. This can be saved as a csv file with name as for e.g. Delhi Daredevils-BattingDetails.csv.

dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
#csk_details = yka.getTeamBattingDetails("Chennai Super Kings",dir=dir1, save=True)
#dd_details = yka.getTeamBattingDetails("Delhi Daredevils",dir=dir1,save=True)
#kkr_details = yka.getTeamBattingDetails("Kolkata Knight Riders",dir=dir1,save=True)

2. Get IPL batsman details

This function is used to get the individual IPL T20 batting record for a the specified batsman of the team as in the functions below.

For the batsmen functions below I have chosen Rishabh Pant, Kane Williamson and Ambati Rayudu for the analysis as they top the batting lists. You can choose any IPL batsmen for the analysis

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
rpant=yka.getBatsmanDetails(team,name,dir=dir1)

3 Batsman Runs vs Deliveries (in IPL matches)

This functions plots the runs vs deliveries faced for batsman

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsDeliveries(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsDeliveries(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsDeliveries(df,name)

4. Batsman fours and sixes (in IPL matches)

This plots the fours, sixes and the total runs for a batsman

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanFoursSixes(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanFoursSixes(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanFoursSixes(df,name)

5. Batsman dismissals (in IPL matches)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanDismissals(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanDismissals(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanDismissals(df,name)

6. Batsman Runs vs Strike Rate (in IPL matches)

The plots below give the Runs vs Strike rate for batsmen

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsStrikeRate(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsStrikeRate(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsStrikeRate(df,name)

7. Batsman Moving average of runs (in IPL matches)

The plots below compute and plot the moving average of batsmen

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanMovingAverage(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanMovingAverage(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanMovingAverage(df,name)

8. Batsman Cumulative average of runs (in IPL matches)

The functions below plot the cumulative average of the batsmen

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeAverageRuns(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeAverageRuns(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeAverageRuns(df,name)

9. Batsman Cumulative Strike Rate (in IPL matches)

The functions below plot the cumulative strike rate of the batsmen

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

10. Batsman performance against opposition (in IPL matches)

The plots below show how the batsmen performed against other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

11. Batsman performance at different venues (in IPL matches)

The plots below show how the batsmen performed at different venues

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVenue(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVenue(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVenue(df,name)

B. Bowler functions

12. Get bowling details in IPL matches

The function below gets the overall team IPL T20 bowling details based on the RData file available in IPL T20 matches. This is currently also available in Github at yorkpyData. The IPL T20 bowling details of the IPL team in each match is created, and a huge data frame is created by stacking the individual dataframes. This can be saved as a CSV file for e.g. Chennai Super Kings-BowlingDetails.csv

dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
#kkr_bowling = yka.getTeamBowlingDetails("Kolkata Knight Riders",dir=dir1,save=True)
#csk_bowling = yka.getTeamBowlingDetails("Chennai Super Kings",dir=dir1,save=True)
#kxip_bowling = yka.getTeamBowlingDetails("Kings XI Punjab",dir=dir1,save=True)

13. Get bowling details of the individual IPL bowlers

This function is used to get the individual bowling record for a specified bowler of the country as in the functions below.

The plots below deal with bowler’s performance. For this analysis I have chosen Amit Mishra, Piyush Chawla and Bhuvaneshwar Kumar for the analysis. You can chose any other IPL bowler

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
#df=yka.getBowlerWicketDetails(team,name,dir=dir1)

14. Bowler Economy Rate (in IPL matches)

The plots below show the economy rate of the selected bowlers

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanEconomyRate(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanEconomyRate(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanEconomyRate(df,name)

15. Bowler Mean Runs conceded (in IPL matches)

The plots below show the mean runs conceded by the selected bowlers

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanRunsConceded(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanRunsConceded(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanRunsConceded(df,name)

16. Moving average of wickets for bowler (in IPL matches)

The moving average of the bowlers are plotted below

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMovingAverage(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMovingAverage(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMovingAverage(df,name)

17. Cumulative average wickets for bowler (in IPL matches)

The cumulative average wickets for each bowler is computed and plotted

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgWickets(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgWickets(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgWickets(df,name)

18. Cumulative average economy rate for bowler (in IPL matches)

The plots below give the cumulative average economy rate for each bowler

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

19. Bowler wicket plot (in IPL matches)

The plots below give the over vs wickets for bowlers

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketPlot(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketPlot(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketPlot(df,name)

20. Bowler wicket against opposition (in IPL matches)

The performance of the bowlers against different IPL teams is shown below

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsAgainstOpposition(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsAgainstOpposition(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsAgainstOpposition(df,name)

21. Bowler wicket in different venues (in IPL matches)

The plots below show how the bowlers perform at different venues

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsVenue(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsVenue(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsVenue(df,name)

Note:You can clone/download the code at Github yorkpy

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

Conclusion: This concludes the python package yorkpy. Go ahead and give yorkpy a spin!

Also see
1. Take 4+: Presentations on ‘Elements of Neural Networks and Deep Learning’ – Parts 1-8
2. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
3. Hand detection through Haartraining: A hands-on approach
4.My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
5. Big Data-1: Move into the big league:Graduate from Python to Pyspark
6. Cricpy takes a swing at the ODIs

To see all posts click Index of posts

Pitching yorkpy…swinging away from the leg stump to IPL – Part 3

Clocks offer at best a convenient fiction They imply that time ticks steadily, predictably forward, when our experience shows that it often does the opposite: it stretches and compresses, skips a beat and doubles back.

                                 David Eagleman

Memory is the space in which a thing happens for a second time

                                 Paul Auster

Introduction

In this 3rd post, yorkpy, the python avatar of my R package yorkr develops more muscle. The first two posts of yorkpy were

1. Pitching yorkpy . short of good length to IPL – Part 1 This post dealt with function which perform analytics on an IPL match between any 2 IPL teams
2. Pitching yorkpy…on the middle and outside off-stump to IPL – Part 2 The second post dealt with analytics on all matches between any 2 IPL teams.

This third post deals with analyses and analytics of an IPL team in all matches against all other IPL teams. The data for yorkpy comes from Cricsheet. The data in Cricsheet are in the form of yaml files. These files have already been converted as dataframes and stored as CSV as seen in the earlier posts.You can download all the data used in this post and the previous post at yorkpyData

The signatures of yorkpy and yorkr are identical and will work in almost the same way. However there may be some unique functions in yorkr & yorkpy, based on what my thought process was on that day!

-You can clone/download the code at Github yorkpy
-This post has been published to RPubs at yorkpy-Part3
-Download this post as PDF at IPLT20-yorkpy-part3
-You can download all the data used in this post and the previous post at yorkpyData

The IPL T20 functions in yorkpy are shown below

2. Get data for all T20 matches between an IPL team and all other IPL teams

We can get all IPL T20 matches between an IPL team and all other teams using the function below. The dir parameter should point to the folder which has the IPL T20 csv files of the individual matches (see Pitching yorkpy…short of good length to IPL-Part 1). This function creates a data frame of all the IPL T20 matches between the IPL team and all other teams and and also saves the dataframe as CSV file if save=True. If save=False the dataframe is just returned and not saved.

import pandas as pd
import os
import yorkpy.analytics as yka
#dir1= "C:\\software\\cricket-package\\yorkpyPkg\\yorkpyData\\IPLConverted"
#getAllMatchesAllOpposition("Kolkata Knight Riders",dir=dir1,save=True)

3. Save data for all matches between an IPL team and all oppositions

This can be done locally using the function below. You could use this function to get combine all IPL matches of an IPL team against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
#dir1= "C:\\software\\cricket-package\\yorkpyPkg\\yorkpyData\\IPLConverted"
#saveAllMatchesAllOppositionIPLT20(dir1)

Note: In the functions below, I have randomly chosen an IPL team for the analyses. You are free to choose any IPL team for your analysis

4.Team Batsmen partnership in Twenty20 (all matches against all IPL teams – summary)

The function below computes the highest partnerships for an IPL team against all other IPL teams for e.g. the batsmen with the highest partnership from Chennai Super Kings in all matches against all other IPL teams. Any other IPL team could have also been chosen.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Chennai Super Kings-allMatchesAllOpposition.csv") 
csk_matches = pd.read_csv(path)

m=yka.teamBatsmenPartnershiAllOppnAllMatches(csk_matches,'Chennai Super Kings',report="summary")
print(m)

##         batsman  totalPartnershipRuns
## 42     SK Raina                  3699
## 28     MS Dhoni                  2986
## 25   MEK Hussey                  1768
## 24      M Vijay                  1600
## 36  S Badrinath                  1441

5. Team Batsmen partnership in Twenty20 (all matches against all IPL teams -detailed)

The function below gives the detailed breakup of partnerships for Mumbai Indian against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Mumbai Indians-allMatchesAllOpposition.csv")
mi_matches = pd.read_csv(path)
theTeam='Mumbai Indians'
m=yka.teamBatsmenPartnershiAllOppnAllMatches(mi_matches,theTeam,report="detailed", top=3)
print(m)

##        batsman  totalPartnershipRuns      non_striker  partnershipRuns
## 0    RG Sharma                3037.0        A Symonds            142.0
## 1    RG Sharma                3037.0      AC Blizzard              5.0
## 2    RG Sharma                3037.0         AJ Finch              2.0
## 3    RG Sharma                3037.0          AP Tare             32.0
## 4    RG Sharma                3037.0        AT Rayudu            566.0
## 5    RG Sharma                3037.0          BR Dunk              1.0
## 6    RG Sharma                3037.0      CJ Anderson            183.0
## 7    RG Sharma                3037.0        CM Gautam             22.0
## 8    RG Sharma                3037.0         DR Smith             50.0
## 9    RG Sharma                3037.0       GJ Maxwell              6.0
## 10   RG Sharma                3037.0         HH Gibbs            109.0
## 11   RG Sharma                3037.0        HH Pandya            105.0
## 12   RG Sharma                3037.0  Harbhajan Singh             86.0
## 13   RG Sharma                3037.0       JC Buttler            105.0
## 14   RG Sharma                3037.0     JEC Franklin             50.0
## 15   RG Sharma                3037.0       KA Pollard            633.0
## 16   RG Sharma                3037.0       KD Karthik            170.0
## 17   RG Sharma                3037.0        KH Pandya             34.0
## 18   RG Sharma                3037.0        KV Sharma             33.0
## 19   RG Sharma                3037.0      LMP Simmons            172.0
## 20   RG Sharma                3037.0       MEK Hussey             21.0
## 21   RG Sharma                3037.0       MJ Guptill             61.0
## 22   RG Sharma                3037.0   MJ McClenaghan              2.0
## 23   RG Sharma                3037.0           N Rana             25.0
## 24   RG Sharma                3037.0         PA Patel            103.0
## 25   RG Sharma                3037.0          RE Levi             25.0
## 26   RG Sharma                3037.0       SL Malinga              0.0
## 27   RG Sharma                3037.0     SR Tendulkar            208.0
## 28   RG Sharma                3037.0        SS Tiwary             27.0
## 29   RG Sharma                3037.0         TL Suman              7.0
## ..         ...                   ...              ...              ...
## 70  KA Pollard                2344.0      CJ Anderson             82.0
## 71  KA Pollard                2344.0        CM Gautam             16.0
## 72  KA Pollard                2344.0         DR Smith             10.0
## 73  KA Pollard                2344.0      DS Kulkarni             15.0
## 74  KA Pollard                2344.0        HH Pandya            158.0
## 75  KA Pollard                2344.0  Harbhajan Singh            158.0
## 76  KA Pollard                2344.0        J Suchith             26.0
## 77  KA Pollard                2344.0       JC Buttler             37.0
## 78  KA Pollard                2344.0     JEC Franklin             38.0
## 79  KA Pollard                2344.0        JP Duminy             63.0
## 80  KA Pollard                2344.0       KD Karthik             40.0
## 81  KA Pollard                2344.0        KH Pandya            111.0
## 82  KA Pollard                2344.0        KV Sharma             13.0
## 83  KA Pollard                2344.0      LMP Simmons             77.0
## 84  KA Pollard                2344.0       MEK Hussey             10.0
## 85  KA Pollard                2344.0       MG Johnson              1.0
## 86  KA Pollard                2344.0           N Rana             60.0
## 87  KA Pollard                2344.0         PA Patel             18.0
## 88  KA Pollard                2344.0          PP Ojha             12.0
## 89  KA Pollard                2344.0         R Dhawan             25.0
## 90  KA Pollard                2344.0        R McLaren             20.0
## 91  KA Pollard                2344.0        R Sathish             27.0
## 92  KA Pollard                2344.0        RG Sharma            587.0
## 93  KA Pollard                2344.0      RJ Peterson              0.0
## 94  KA Pollard                2344.0         S Dhawan             20.0
## 95  KA Pollard                2344.0       SL Malinga             14.0
## 96  KA Pollard                2344.0     SR Tendulkar             69.0
## 97  KA Pollard                2344.0        SS Tiwary             42.0
## 98  KA Pollard                2344.0         TL Suman              2.0
## 99  KA Pollard                2344.0           Z Khan              1.0
## 
## [100 rows x 4 columns]

6. Team Batsmen partnership in Twenty20 – Chart (all matches against all IPL teams)

The function below plots the partnerships of an IPL team against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Delhi Daredevils-allMatchesAllOpposition.csv")
dd_matches = pd.read_csv(path)
yka.teamBatsmenPartnershipAllOppnAllMatchesChart(dd_matches,'Delhi Daredevils', plot=True, top=4, partnershipRuns=100)

7.Team Batsmen partnership in Twenty20 – Dataframe (all matches against all IPL teams)

This function does not plot the data but returns the dataframe to the user to plot or manipulate.

Note: Many of the plots include an additional parameters for e.g. plot which is either True or False. The default value is plot=True. When plot=True the plot will be displayed. When plot=False the data frame will be returned to the user. The user can use this to create an interactive charts. The parameter top= specifies the number of top batsmen that need to be included in the chart, and partnershipRuns gives the minimum cutoff runs in partnwerships to be considered

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kochi Tuskers Kerala-allMatchesAllOpposition.csv")
ktk_matches = pd.read_csv(path)
m=yka.teamBatsmenPartnershipAllOppnAllMatchesChart(ktk_matches,'Kochi Tuskers Kerala', plot=False, top=3, partnershipRuns=100)
print(m)

##              batsman       non_striker  partnershipRuns
## 0        BB McCullum          BJ Hodge             17.0
## 1        BB McCullum  DPMD Jayawardene            160.0
## 2        BB McCullum         M Klinger             67.0
## 3        BB McCullum          PA Patel             40.0
## 4        BB McCullum         RA Jadeja             19.0
## 5        BB McCullum        VVS Laxman             41.0
## 6        BB McCullum  Y Gnaneswara Rao             13.0
## 7   DPMD Jayawardene       BB McCullum            152.0
## 8   DPMD Jayawardene          BJ Hodge             41.0
## 9   DPMD Jayawardene         KM Jadhav              4.0
## 10  DPMD Jayawardene         M Klinger             28.0
## 11  DPMD Jayawardene           OA Shah              9.0
## 12  DPMD Jayawardene          PA Patel             25.0
## 13  DPMD Jayawardene         RA Jadeja             18.0
## 14  DPMD Jayawardene          RV Gomez             10.0
## 15  DPMD Jayawardene        VVS Laxman             12.0
## 16          BJ Hodge       BB McCullum             18.0
## 17          BJ Hodge  DPMD Jayawardene             47.0
## 18          BJ Hodge         KM Jadhav              2.0
## 19          BJ Hodge           OA Shah             19.0
## 20          BJ Hodge          PA Patel             79.0
## 21          BJ Hodge         RA Jadeja             99.0
## 22          BJ Hodge          RV Gomez             21.0

8. Team batsmen versus bowler in Twenty20-Chart (all matches against all IPL teams)

The plots below provide information on how each of the top batsmen of the IPL team fared against the opposition bowlers of all other IPL teams.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Royal Challengers Bangalore-allMatchesAllOpposition.csv")
rcb_matches = pd.read_csv(path)
yka.teamBatsmenVsBowlersAllOppnAllMatches(rcb_matches,"Royal Challengers Bangalore",plot=True,top=3,runsScored=60)

9 Team batsmen versus bowler in Twenty20-Dataframe (all matches against all IPL teams)

This function provides the batting performance of an IPL team against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kings XI Punjab-allMatchesAllOpposition.csv")
kxip_matches = pd.read_csv(path)
m=yka.teamBatsmenVsBowlersAllOppnAllMatches(kxip_matches,'Kings XI Punjab',plot=False,top=2,runsScored=50)

print(m)

##        batsman            bowler  runsScored
## 0     SE Marsh        A Chandila        20.0
## 1     SE Marsh       A Choudhary         1.0
## 2     SE Marsh          A Kumble        37.0
## 3     SE Marsh          A Mishra         0.0
## 4     SE Marsh          A Mithun         9.0
## 5     SE Marsh           A Nehra        33.0
## 6     SE Marsh           A Singh         2.0
## 7     SE Marsh         A Symonds         5.0
## 8     SE Marsh         AA Chavan        19.0
## 9     SE Marsh   AA Jhunjhunwala        15.0
## 10    SE Marsh        AB Agarkar        27.0
## 11    SE Marsh          AB Dinda        31.0
## 12    SE Marsh       AB McDonald         9.0
## 13    SE Marsh         AC Thomas         1.0
## 14    SE Marsh        AD Mathews         7.0
## 15    SE Marsh        AD Russell         8.0
## 16    SE Marsh            AJ Tye         0.0
## 17    SE Marsh        AL Menaria         6.0
## 18    SE Marsh          AM Salvi         8.0
## 19    SE Marsh          AN Ahmed        16.0
## 20    SE Marsh           AS Raut         7.0
## 21    SE Marsh      Ankit Sharma         2.0
## 22    SE Marsh        Ankit Soni        11.0
## 23    SE Marsh           B Kumar        10.0
## 24    SE Marsh             B Lee         1.0
## 25    SE Marsh        BAW Mendis        11.0
## 26    SE Marsh           BB Sran         3.0
## 27    SE Marsh          BJ Hodge        18.0
## 28    SE Marsh      Basil Thampi        17.0
## 29    SE Marsh   C de Grandhomme         8.0
## ..         ...               ...         ...
## 235  DA Miller          R Sharma         7.0
## 236  DA Miller         R Tewatia         3.0
## 237  DA Miller     R Vinay Kumar        30.0
## 238  DA Miller         RA Jadeja        84.0
## 239  DA Miller         RD Chahar         3.0
## 240  DA Miller  RE van der Merwe         5.0
## 241  DA Miller  RN ten Doeschate         1.0
## 242  DA Miller          RP Singh        35.0
## 243  DA Miller       Rashid Khan         0.0
## 244  DA Miller         S Aravind         7.0
## 245  DA Miller            S Kaul        23.0
## 246  DA Miller         S Kaushik         8.0
## 247  DA Miller           S Ladda         6.0
## 248  DA Miller          S Nadeem        11.0
## 249  DA Miller          SK Raina         2.0
## 250  DA Miller        SL Malinga         9.0
## 251  DA Miller   SMSM Senanayake         6.0
## 252  DA Miller         SP Narine        10.0
## 253  DA Miller         SR Watson        16.0
## 254  DA Miller         STR Binny        14.0
## 255  DA Miller   Shakib Al Hasan         3.0
## 256  DA Miller          TA Boult        20.0
## 257  DA Miller        TG Southee        11.0
## 258  DA Miller          UT Yadav        51.0
## 259  DA Miller          VR Aaron        19.0
## 260  DA Miller          VS Malik         3.0
## 261  DA Miller         YK Pathan         0.0
## 262  DA Miller         YS Chahal        35.0
## 263  DA Miller      Yuvraj Singh        11.0
## 264  DA Miller            Z Khan         2.0
## 
## [265 rows x 3 columns]

10. Team batting scorecard(all matches against all IPL teams)

This function provides the overall scorecard for an IPL team in all matches against all other IPL teams. The batting scorecard shows the top batsmen for Kolkata Knight Riders below

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kolkata Knight Riders-allMatchesAllOpposition.csv")
kkr_matches = pd.read_csv(path)
scorecard=yka.teamBattingScorecardAllOppnAllMatches(kkr_matches,'Kolkata Knight Riders')
print(scorecard)

##              batsman    runs  balls   4s  6s          SR
## 19         G Gambhir  3035.0   2533  352  46  119.818397
## 17         YK Pathan  1893.0   1421  150  86  133.216045
## 22        RV Uthappa  1806.0   1311  200  54  137.757437
## 16         JH Kallis  1295.0   1237  128  23  104.688763
## 23         MK Pandey  1270.0   1048  103  38  121.183206
## 0         SC Ganguly  1031.0    977  105  36  105.527124
## 12         MK Tiwary  1002.0    921   86  23  108.794788
## 1        BB McCullum   882.0    754   92  32  116.976127
## 25          SA Yadav   608.0    474   54  21  128.270042
## 15          MS Bisla   543.0    518   60  16  104.826255
## 26        AD Russell   516.0    308   45  34  167.532468
## 4          DJ Hussey   511.0    417   31  28  122.541966
## 24   Shakib Al Hasan   498.0    399   44  15  124.812030
## 10          BJ Hodge   476.0    430   47  10  110.697674
## 11          CH Gayle   463.0    350   45  26  132.285714
## 18        EJG Morgan   444.0    373   45  16  119.034853
## 54           CA Lynn   378.0    250   30  23  151.200000
## 6          LR Shukla   374.0    320   31  15  116.875000
## 29  RN ten Doeschate   326.0    238   26  15  136.974790
## 21            DB Das   304.0    267   23  16  113.857678
## 3            WP Saha   298.0    213   24  12  139.906103
## 28         SP Narine   271.0    193   36  12  140.414508
## 13        AD Mathews   249.0    211   20   8  118.009479
## 33       Salman Butt   193.0    172   30   2  112.209302
## 41        MN van Wyk   167.0    135   19   1  123.703704
## 7         AB Agarkar   160.0    137   12   5  116.788321
## 20          R Bhatia   159.0    134   15   3  118.656716
## 51   C de Grandhomme   126.0     92   10   6  136.956522
## 39         CA Pujara   122.0    119   14   3  102.521008
## 40           OA Shah   115.0     96    7   5  119.791667
## ..               ...     ...    ...  ...  ..         ...
## 50         JO Holder    22.0     20    2   1  110.000000
## 65     Kuldeep Yadav    20.0     22    2   0   90.909091
## 71         BJ Haddin    18.0     11    2   1  163.636364
## 70   NM Coulter-Nile    14.0     13    0   2  107.692308
## 47          L Balaji    13.0     12    1   0  108.333333
## 55   SMSM Senanayake    10.0     17    0   0   58.823529
## 53          M Morkel     9.0      8    0   0  112.500000
## 62          AN Ghosh     7.0      8    1   0   87.500000
## 32           GB Hogg     7.0      6    0   0  116.666667
## 56        MV Boucher     6.0      6    0   0  100.000000
## 77     Azhar Mahmood     6.0      8    1   0   75.000000
## 78          DM Bravo     6.0      5    1   0  120.000000
## 68         SS Shaikh     6.0      7    1   0   85.714286
## 66          TA Boult     5.0      8    0   0   62.500000
## 76    Mohammed Shami     5.0     10    0   0   50.000000
## 80           P Dogra     5.0      8    0   0   62.500000
## 69     R Vinay Kumar     4.0      7    0   0   57.142857
## 75        AS Rajpoot     4.0      7    1   0   57.142857
## 43     Mandeep Singh     4.0     11    1   0   36.363636
## 37          AB Dinda     4.0      8    0   0   50.000000
## 79        PJ Sangwan     4.0      2    1   0  200.000000
## 73         R McLaren     3.0      6    0   0   50.000000
## 67         SB Bangar     2.0      9    0   0   22.222222
## 57       RS Gavaskar     2.0      8    0   0   25.000000
## 72     Shoaib Akhtar     2.0      8    0   0   25.000000
## 38  Mashrafe Mortaza     2.0      2    0   0  100.000000
## 63        BAW Mendis     1.0      2    0   0   50.000000
## 58           SE Bond     1.0      2    0   0   50.000000
## 44     CK Langeveldt     0.0      1    0   0    0.000000
## 30        PJ Cummins     0.0      2    0   0    0.000000
## 
## [81 rows x 6 columns]

10a. Team batting scorecard(all matches against all IPL teams)

The output below shows the Chennai Super Kings against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Chennai Super Kings-allMatchesAllOpposition.csv")
csk_matches = pd.read_csv(path)
scorecard=yka.teamBattingScorecardAllOppnAllMatches(csk_matches,'Chennai Super Kings')
print(scorecard)

##             batsman  runs  balls   4s   6s          SR
## 3          SK Raina  3699   2735  322  150  135.246801
## 5          MS Dhoni  2986   2199  218  126  135.788995
## 17       MEK Hussey  1768   1461  181   45  121.013005
## 11          M Vijay  1600   1289  141   66  124.127230
## 4       S Badrinath  1441   1245  154   28  115.742972
## 9         ML Hayden  1107    838  121   44  132.100239
## 18     F du Plessis  1081    867   92   29  124.682814
## 25         DR Smith   965    766  102   50  125.979112
## 26      BB McCullum   841    634   83   42  132.649842
## 6         JA Morkel   827    591   51   48  139.932318
## 20         DJ Bravo   706    543   54   30  130.018416
## 19        RA Jadeja   670    533   46   23  125.703565
## 0          PA Patel   516    529   67    7   97.542533
## 2        SP Fleming   196    171   27    3  114.619883
## 13         R Ashwin   190    208   19    1   91.346154
## 21         S Vidyut   145    115   21    3  126.086957
## 31          WP Saha   144    138    8    8  104.347826
## 1        S Anirudha   133    116    9    7  114.655172
## 33        DJ Hussey   116     96    8    6  120.833333
## 38           P Negi   116     77   10    5  150.649351
## 10         JDP Oram   106    107    6    5   99.065421
## 29        GJ Bailey    63     67    9    0   94.029851
## 22       A Flintoff    62     57    5    2  108.771930
## 8           MS Gony    50     39    2    5  128.205128
## 7   Joginder Sharma    36     30    1    2  120.000000
## 27         M Manhas    35     26    3    1  134.615385
## 28        MM Sharma    29     26    1    2  111.538462
## 23        SB Jakati    27     28    3    0   96.428571
## 12          JM Kemp    26     25    1    1  104.000000
## 14         L Balaji    22     35    1    1   62.857143
## 24     DE Bollinger    21     23    1    1   91.304348
## 41    CK Kapugedera    16     24    0    0   66.666667
## 37        CH Morris    14     17    0    0   82.352941
## 30       T Thushara    12     19    0    0   63.157895
## 42          M Ntini    11     19    2    0   57.894737
## 15   M Muralitharan     9     13    1    0   69.230769
## 32  KMDN Kulasekara     5      3    1    0  166.666667
## 34        SB Styris     5      2    1    0  250.000000
## 35       B Laughlin     4      9    0    0   44.444444
## 16          S Tyagi     3      4    0    0   75.000000
## 45  KB Arun Karthik     3      5    0    0   60.000000
## 36       AS Rajpoot     2      6    0    0   33.333333
## 43          RG More     2      2    0    0  100.000000
## 44         S Randiv     2      4    0    0   50.000000
## 39          A Nehra     1      7    0    0   14.285714
## 40         A Mukund     0      1    0    0    0.000000

11.Team Bowling scorecard (all matches against all IPL teams)

The output below gives the bowling performance of an IPL team against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Sunrisers Hyderabad-allMatchesAllOpposition.csv")
srh_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardAllOppnAllMatches(srh_matches,'Sunrisers Hyderabad')

## C:\Users\Ganesh\ANACON~1\lib\site-packages\yorkpy\analytics.py:564: SettingWithCopyWarning: 
## A value is trying to be set on a copy of a slice from a DataFrame.
## Try using .loc[row_indexer,col_indexer] = value instead
## 
## See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
##   df1['over']=df1.delivery.astype(int)
## C:\Users\Ganesh\ANACON~1\lib\site-packages\yorkpy\analytics.py:567: SettingWithCopyWarning: 
## A value is trying to be set on a copy of a slice from a DataFrame.
## Try using .loc[row_indexer,col_indexer] = value instead
## 
## See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
##   df1['runsConceded']=df1['runs'] + df1['wides'] + df1['noballs']

print(scorecard)

##               bowler  overs  runs  maidens  wicket   econrate
## 60       JP Faulkner     28   192        0      15   6.857143
## 83         MM Sharma     37   334        0      13   9.027027
## 119       SL Malinga     31   215        0      13   6.935484
## 123        SR Watson     30   281        0      13   9.366667
## 90   NM Coulter-Nile     24   166        0      12   6.916667
## 31          DJ Bravo     26   184        0      12   7.076923
## 135         UT Yadav     37   297        0      12   8.027027
## 125   Sandeep Sharma     32   280        0      11   8.750000
## 75          M Morkel     25   195        0       9   7.800000
## 81    MJ McClenaghan     24   175        0       9   7.291667
## 5           AB Dinda     23   165        0       9   7.173913
## 55        JD Unadkat     20   167        0       8   8.350000
## 36       DS Kulkarni     28   200        0       8   7.142857
## 25         CH Morris     24   190        0       7   7.916667
## 101         R Bhatia     18   128        0       7   7.111111
## 70     Kuldeep Yadav     16   129        0       7   8.062500
## 11          AR Patel     27   208        0       7   7.703704
## 122        SP Narine     43   282        0       7   6.558140
## 141        YS Chahal     26   224        0       6   8.615385
## 44   Harbhajan Singh     39   264        0       6   6.769231
## 96         PP Chawla     21   140        0       6   6.666667
## 4            A Zampa      4    19        0       6   4.750000
## 126  Shakib Al Hasan     14    99        1       6   7.071429
## 80        MG Johnson     20   155        0       6   7.750000
## 59         JP Duminy     10    80        0       5   8.000000
## 58         JO Holder     15   113        0       5   7.533333
## 92           P Kumar     23   173        0       5   7.521739
## 100         R Ashwin     28   142        0       5   5.071429
## 2           A Mishra     18   144        0       4   8.000000
## 106    R Vinay Kumar     19   154        0       4   8.105263
## ..               ...    ...   ...      ...     ...        ...
## 6     AD Mascarenhas      4    25        0       0   6.250000
## 13        Ankit Soni      2    31        0       0  15.500000
## 132          TM Head      1    11        0       0  11.000000
## 10          AN Ahmed      6    63        0       0  10.500000
## 131       TM Dilshan      1    10        0       0  10.000000
## 134     Tejas Baroka      3    33        0       0  11.000000
## 73          M Ashwin      1     6        0       0   6.000000
## 109        RG Sharma      1     5        0       0   5.000000
## 22      Basil Thampi      2    21        0       0  10.500000
## 23           C Munro      1     8        0       0   8.000000
## 68         KV Sharma      2    19        0       0   9.500000
## 77           M Vijay      4    24        0       0   6.000000
## 66         KJ Abbott      3    34        0       0  11.333333
## 65         KH Pandya      2    17        0       0   8.500000
## 82          MM Patel      3    22        0       0   7.333333
## 62          K Rabada      4    59        0       0  14.750000
## 85        MP Stoinis      3    28        0       0   9.333333
## 54         JA Morkel      3    35        0       0  11.666667
## 46          I Sharma      8    64        0       0   8.000000
## 94        PJ Cummins      4    37        0       0   9.250000
## 95        PJ Sangwan      8    82        0       0  10.250000
## 103        R Sathish      1     9        0       0   9.000000
## 38          DW Steyn      2    17        0       0   8.500000
## 108          RG More      2    28        0       0  14.000000
## 34         DJG Sammy      2    18        0       0   9.000000
## 33     DJ Muthuswami      2    20        0       0  10.000000
## 32          DJ Hooda      5    45        0       0   9.000000
## 24          CH Gayle      3    24        0       0   8.000000
## 116        SA Abbott      2    21        0       0  10.500000
## 72         LR Shukla      2    28        0       0  14.000000
## 
## [144 rows x 6 columns]

11a.Team Bowling scorecard (all matches against all IPL teams)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Rajasthan Royals-allMatchesAllOpposition.csv")
rr_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardAllOppnAllMatches(rr_matches,'Rajasthan Royals')
print(scorecard)

##                bowler  overs  runs  maidens  wicket   econrate
## 2            A Mishra     63   426        0      29   6.761905
## 66          JA Morkel     38   301        0      16   7.921053
## 129     R Vinay Kumar     48   406        0      15   8.458333
## 135          RP Singh     41   255        0      14   6.219512
## 95        MF Maharoof     23   139        0      14   6.043478
## 118         PP Chawla     45   353        0      14   7.844444
## 130         RA Jadeja     32   227        0      14   7.093750
## 50           DW Steyn     43   232        0      13   5.395349
## 56    Harbhajan Singh     45   341        0      12   7.577778
## 1            A Kumble     21   108        1      12   5.142857
## 159        SL Malinga     49   363        0      12   7.408163
## 60          IK Pathan     37   279        0      11   7.540541
## 82         KA Pollard     21   201        0      11   9.571429
## 119           PP Ojha     46   426        0      11   9.260870
## 121          R Ashwin     29   222        0      11   7.655172
## 22            B Kumar     31   233        0      11   7.516129
## 3             A Nehra     32   214        0      11   6.687500
## 41           DJ Bravo     30   292        0      10   9.733333
## 110           P Kumar     48   329        1      10   6.854167
## 58           I Sharma     37   284        0       9   7.675676
## 168   Shakib Al Hasan     25   153        0       9   6.120000
## 87           L Balaji     33   277        0       9   8.393939
## 122          R Bhatia     19   121        0       8   6.368421
## 48        DS Kulkarni     21   148        0       8   7.047619
## 101         MM Sharma     20   142        0       8   7.100000
## 174          UT Yadav     25   203        0       8   8.120000
## 15           AR Patel     16   110        0       7   6.875000
## 133         RJ Harris     16   132        0       7   8.250000
## 72          JH Kallis     37   254        0       7   6.864865
## 192            Z Khan     33   213        0       7   6.454545
## ..                ...    ...   ...      ...     ...        ...
## 170      Shoaib Ahmed      2    19        0       0   9.500000
## 54          GS Sandhu      4    49        0       0  12.250000
## 139          RV Gomez      1     9        0       0   9.000000
## 163         SPD Smith      0     5        0       0        inf
## 115       PC Valthaty      3    35        0       0  11.666667
## 34        CJ Anderson      4    26        0       0   6.500000
## 81         K Upadhyay      3    29        0       0   9.666667
## 79             K Goel      1    11        0       0  11.000000
## 28          BJ Rohrer      1    12        0       0  12.000000
## 78    Joginder Sharma      2    23        0       0  11.500000
## 99          MK Tiwary      2    28        0       0  14.000000
## 26       BE Hendricks      4    57        0       0  14.250000
## 102          MR Marsh      1    10        0       0  10.000000
## 106       NL McCullum      3    22        0       0   7.333333
## 113        P Prasanth      1    18        0       0  18.000000
## 114           P Suyal      4    45        0       0  11.250000
## 46      DP Vijaykumar      1    10        0       0  10.000000
## 154         SB Styris      2    14        0       0   7.000000
## 71       JEC Franklin      3    32        0       0  10.666667
## 70          JE Taylor      3    22        0       0   7.333333
## 18       Ankit Sharma      4    33        0       0   8.250000
## 134  RN ten Doeschate      2    14        0       0   7.000000
## 16       Abdur Razzak      2    29        0       0  14.500000
## 65           J Theron      6    48        0       0   8.000000
## 146          S Narwal      2    17        0       0   8.500000
## 63            J Botha      1    19        0       0  19.000000
## 149           S Tyagi      8    65        0       0   8.125000
## 151         SB Bangar      2    20        0       0  10.000000
## 13           AM Nayar      2     7        0       0   3.500000
## 0      A Ashish Reddy      3    22        0       0   7.333333
## 
## [193 rows x 6 columns]

12. Team Bowling wicket kind -Chart (all matches against all IPL teams)

The functions compute and display the kind of wickets taken(bowled, caught, lbw etc) by an IPL team in all matches against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Gujarat Lions-allMatchesAllOpposition.csv")
gl_matches = pd.read_csv(path)
yka.teamBowlingWicketKindAllOppnAllMatches(gl_matches,'Gujarat Lions',plot=True,top=5,wickets=2)

13. Team Bowling wicket kind -Dataframe (all matches against all IPL teams)

This gives the type of wickets taken for an IPL team against all other IPL teams.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Rising Pune Supergiants-allMatchesAllOpposition.csv")
rps_matches = pd.read_csv(path)
m=yka.teamBowlingWicketKindAllOppnAllMatches(rps_matches,'Rising Pune Supergiants',plot=False,top=4,wickets=10)
print(m)

##           bowler               kind  wickets
## 0        A Nehra             caught        4
## 1        A Nehra            run out        2
## 2      MM Sharma             caught        3
## 3      MM Sharma  caught and bowled        1
## 4      MM Sharma            run out        1
## 5      SR Watson             bowled        1
## 6      SR Watson             caught        4
## 7  KW Richardson             caught        3
## 8  KW Richardson       retired hurt        1

14 Team Bowler vs Batman -Plot (all matches against all IPL teams)

The function below gives the performance of bowlers against batsmen ,in all matches against another IPL team.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Rising Pune Supergiants-allMatchesAllOpposition.csv")
rps_matches = pd.read_csv(path)
yka.teamBowlersVsBatsmenAllOppnAllMatches(rps_matches,'Rising Pune Supergiants',plot=True,top=5,runsConceded=10)

15 Team Bowler vs Batman – Dataframe (all matches against all IPL teams)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Deccan Chargers-allMatchesAllOpposition.csv")
dc_matches = pd.read_csv(path)
m=yka.teamBowlersVsBatsmenAllOppnAllMatches(dc_matches,'Deccan Chargers',plot=False,top=2,runsConceded=30)
print(m)

##        bowler          batsman  runsConceded
## 0     P Kumar   A Ashish Reddy           6.0
## 1     P Kumar        A Symonds          15.0
## 2     P Kumar      AA Bilakhia          12.0
## 3     P Kumar  AA Jhunjhunwala           1.0
## 4     P Kumar     AC Gilchrist          20.0
## 5     P Kumar    Anirudh Singh          11.0
## 6     P Kumar         B Chipli           1.0
## 7     P Kumar         CL White          11.0
## 8     P Kumar     DB Ravi Teja          15.0
## 9     P Kumar        DJ Harris           2.0
## 10    P Kumar         DR Smith           5.0
## 11    P Kumar       FH Edwards           3.0
## 12    P Kumar         HH Gibbs          46.0
## 13    P Kumar         J Theron           0.0
## 14    P Kumar        JP Duminy           4.0
## 15    P Kumar    KC Sangakkara          15.0
## 16    P Kumar        MD Mishra           4.0
## 17    P Kumar         PA Patel           9.0
## 18    P Kumar        RG Sharma          36.0
## 19    P Kumar        RJ Harris           3.0
## 20    P Kumar         S Dhawan          37.0
## 21    P Kumar          S Sohal           6.0
## 22    P Kumar        SB Styris           6.0
## 23    P Kumar    Shahid Afridi           0.0
## 24    P Kumar         TL Suman          22.0
## 25    P Kumar       VVS Laxman           5.0
## 26    P Kumar  Y Venugopal Rao           1.0
## 27  PP Chawla   A Ashish Reddy           2.0
## 28  PP Chawla        A Symonds          35.0
## 29  PP Chawla  AA Jhunjhunwala           6.0
## 30  PP Chawla     AC Gilchrist           4.0
## 31  PP Chawla         B Chipli           8.0
## 32  PP Chawla         CL White          16.0
## 33  PP Chawla     DB Ravi Teja          30.0
## 34  PP Chawla        DJ Harris           9.0
## 35  PP Chawla        DNT Zoysa           1.0
## 36  PP Chawla         HH Gibbs          30.0
## 37  PP Chawla        JP Duminy          10.0
## 38  PP Chawla    KC Sangakkara           1.0
## 39  PP Chawla         MR Marsh           1.0
## 40  PP Chawla         PA Patel           4.0
## 41  PP Chawla         PA Reddy           8.0
## 42  PP Chawla        RG Sharma          50.0
## 43  PP Chawla         S Dhawan          33.0
## 44  PP Chawla        SB Bangar           1.0
## 45  PP Chawla         TL Suman          17.0
## 46  PP Chawla       VVS Laxman           7.0
## 47  PP Chawla  Y Venugopal Rao           3.0

16 Team Wins and Losses – Summary (all matches against all IPL teams)

The function below computes and plots the number of wins and losses between an IPL team and all other IPL teams in all matches. The summary just gives the wins, losses and ties

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Chennai Super Kings-allMatchesAllOpposition.csv")
csk_matches = pd.read_csv(path)
team1='Chennai Super Kings'
yka.plotWinLossByTeamAllOpposition(csk_matches,team1,plot="summary")

16a Team Wins and Losses – Detailed (all matches against all IPL teams)

The function below computes and plot the number of wins and losses between an IPL team and all other IPL teams in all matches. This gives a breakup of which team won against this team.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Chennai Super Kings-allMatchesAllOpposition.csv")
csk_matches = pd.read_csv(path)
team1='Chennai Super Kings'
yka.plotWinLossByTeamAllOpposition(csk_matches,team1,plot="detailed")

16b Team Wins and Losses – Summary (all matches against all IPL teams)

This plot gives the wins vs losses of MI against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Mumbai Indians-allMatchesAllOpposition.csv")
mi_matches = pd.read_csv(path)
team1='Mumbai Indians'
yka.plotWinLossByTeamAllOpposition(mi_matches,team1,plot="summary")

16c Team Wins and Losses – Detailed (all matches against all IPL teams)

The function below computes and plot the number of wins and losses between an IPL team and all other IPL teams in all matches. This gives the breakup of MI wins, losses and ties

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Mumbai Indians-allMatchesAllOpposition.csv")
mi_matches = pd.read_csv(path)
team1='Mumbai Indians'
yka.plotWinLossByTeamAllOpposition(mi_matches,team1,plot="detailed")

17 Team Wins by win type (all matches against all IPL teams)

This function shows how the win happened whether by runs or by wickets in all matches played against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Royal Challengers Bangalore-allMatchesAllOpposition.csv")
rcb_matches = pd.read_csv(path)
yka.plotWinsByRunOrWicketsAllOpposition(rcb_matches,'Royal Challengers Bangalore')

18 Team Wins by toss decision (summary) (all matches against all IPL teams)

This show how Royal Challengers Bangalore fared when it chose to field on winning the toss

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Royal Challengers Bangalore-allMatchesAllOpposition.csv")
rcb_matches = pd.read_csv(path)
yka.plotWinsbyTossDecisionAllOpposition(rcb_matches,'Royal Challengers Bangalore',tossDecision='field',plot='summary')

18a. Team Wins by toss decision (detailed) (all matches against all IPL teams)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kings XI Punjab-allMatchesAllOpposition.csv")
kxip_matches = pd.read_csv(path)
yka.plotWinsbyTossDecisionAllOpposition(kxip_matches,'Kings XI Punjab',tossDecision='field',plot='detailed')

19 Team Wins by toss decision (summary) (all matches against all IPL teams)

This plot shows how Mumbai Indians fared when it chose to bat on winning the toss against all other IPL teams.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Delhi Daredevils-allMatchesAllOpposition.csv")
mi_rcb_matches = pd.read_csv(path)
yka.plotWinsbyTossDecisionAllOpposition(mi_rcb_matches,'Mumbai Indians',tossDecision='bat',plot='summary')

20 Team Wins by toss decision (detailed)(all matches against all IPL teams)

This plot shows how Kings X1 Punjab fared when it chose to bat on winning the toss

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kings XI Punjab-allMatchesAllOpposition.csv")
kxip_matches = pd.read_csv(path)
yka.plotWinsbyTossDecisionAllOpposition(kxip_matches,'Kings XI Punjab',tossDecision='bat',plot='detailed')

Feel free to clone/download the code from Github yorkpy

Conclusion

This post included analysis of an IPL team against all other IPL teams. You can download the data for this and the earlier posts from [yorkpyData](https://github.com/tvganesh/yorkpyData

The code can be cloned/downloaded from Github

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

To be continued. Watch this space!

Also see
1. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
2. My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
3. Designing a Social Web Portal
4. Computer Vision: Ramblings on derivatives, histograms and contours
5. Introducing cricket package yorkr: Part 3-Foxed by flight!
6. The making of Total Control Android game

To see all posts click Index of posts

Cricpy takes guard for the Twenty20s

“There are two ways to write error-free programs; only the third one works.”” Alan J. Perlis

“Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the universe trying to produce bigger and better idiots. So far, the universe is winning. ” Rick Cook

“My software never has bugs. It just develops random features.” Anon

“If you make an ass out of yourself, there will always be someone to ride you.” Bruce Lee

Introduction

This is the 3rd and final post on cricpy, and is a continuation to my 2 earlier posts

1. Introducing cricpy:A python package to analyze performances of cricketers
2.Cricpy takes a swing at the ODIs

Cricpy, is the python avatar of my R package ‘cricketr’. To know more about my R package cricketr see Re-introducing cricketr! : An R package to analyze performances of cricketers

With this post cricpy, like cricketr, now becomes omnipotent, and is now capable of handling Test, ODI and T20 matches.

Cricpy uses the statistics info available in ESPN Cricinfo Statsguru.

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

This post is also hosted on Rpubs at Cricpy takes guard for the Twenty 20s. You can also download the pdf version of this post at cricpy-TT.pdf

You can fork/clone the package at Github cricpy

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

Untitled

The cricpy package

The data for a particular player in Twenty20s can be obtained with the getPlayerDataTT() function. To do this you will need to go to T20 Batting and T20 Bowling and click the player you are interested in This will bring up a page which have the profile number for the player e.g. for Virat Kohli this would be http://www.espncricinfo.com/india/content/player/253802.html. Hence,this can be used to get the data for Virat Kohlias shown below

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

You can fork/clone the package at Github cricpy

Note: The charts are self-explanatory and I have not added much of my own interpretation to it. Do look at the plots closely and check out the performances for yourself.

1 Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy.analytics as ca

2. Invoking functions with Python package cricpy

import cricpy.analytics as ca 
ca.batsman4s("./kohli.csv","Virat Kohli")

3. Getting help from cricpy – Python

import cricpy.analytics as ca 
help(ca.getPlayerDataTT)

## Help on function getPlayerDataTT in module cricpy.analytics:
## 
## getPlayerDataTT(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2, 3], result=[1, 2, 3, 5], create=True)
##     Get the Twenty20 International player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory~
##     
##     Description
##     
##     Get the Twenty20 player data given the profile of the batsman/bowler. The allowed inputs are home,away, neutralboth and won,lost,tied or no result of matches. The data is stored in a <player>.csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerDataTT(profile, opposition="",host="",dir = "./data", file = "player001.csv", 
##     type = "batting", homeOrAway = c(1, 2, 3), result = c(1, 2, 3,5))
##     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 Virat Kohli this turns out to be 253802 http://www.espncricinfo.com/india/content/player/35263.html. Hence the profile for Sehwag is 35263
##     opposition  
##     The numerical value of the opposition country e.g.Australia,India, England etc. The values are Afghanistan:40,Australia:2,Bangladesh:25,England:1,Hong Kong:19,India:6,Ireland:29, New Zealand:5,Pakistan:7,Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27, West Indies:4, Zimbabwe:9; Note: If no value is entered for opposition then all teams are considered
##     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, South Africa:3,Sri Lanka:8,United States of America:11,West Indies:4, Zimbabwe:9 Note: If no value is entered for host then all host countries are considered
##     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. kohli.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 vector with either or all 1,2, 3. 1 is for home 2 is for away, 3 is for neutral venue
##     result      
##     This is a vector that can take values 1,2,3,5. 1 - won match 2- lost match 3-tied 5- no result
##     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
##     
##     bowlerWktRateTT getPlayerData
##     
##     Examples
##     
##     ## Not run: 
##     # Only away. Get data only for won and lost innings
##     kohli =getPlayerDataTT(253802,dir="../cricketr/data", file="kohli1.csv",
##     type="batting")
##     
##     # Get bowling data and store in file for future
##     ashwin = getPlayerDataTT(26421,dir="../cricketr/data",file="ashwin1.csv",
##     type="bowling")
##     
##     kohli =getPlayerDataTT(253802,opposition = 2,host=2,dir="../cricketr/data", 
##     file="kohli1.csv",type="batting")

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

4. Get the Twenty20 player data for a player using the function getPlayerDataOD()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. kohli.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 getPlayerDataTT for all subsequent analyses

import cricpy.analytics as ca
#kohli=ca.getPlayerDataTT(253802,dir=".",file="kohli.csv",type="batting")
#guptill=ca.getPlayerDataTT(226492,dir=".",file="guptill.csv",type="batting")
#shahzad=ca.getPlayerDataTT(419873,dir=".",file="shahzad.csv",type="batting")
#mccullum=ca.getPlayerDataTT(37737,dir=".",file="mccullum.csv",type="batting")

Included below are some of the functions that can be used for ODI batsmen and bowlers. For this I have chosen, Virat Kohli, ‘the run machine’ who is on-track for breaking many of the Test, ODI and Twenty20 records

5 Virat Kohli’s performance – Basic Analyses

The 3 plots below provide the following for Virat Kohli in T20s

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

import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("./kohli.csv","Virat Kohli")

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

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

6. More analyses

import cricpy.analytics as ca
ca.batsman4s("./kohli.csv","Virat Kohli")

ca.batsman6s("./kohli.csv","Virat Kohli")

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

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

7. 3D scatter plot and prediction plane

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

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

8. Average runs at different venues

The plot below gives the average runs scored by Kohli at different grounds. The plot also the number of innings at each ground as a label at x-axis.

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("./kohli.csv","Virat Kohli")

9. Average runs against different opposing teams

This plot computes the average runs scored by Kohli against different countries.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("./kohli.csv","Virat Kohli")

10 . Highest Runs Likelihood

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

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("./kohli.csv","Virat Kohli")

11. A look at the Top 4 batsman – Kohli, Guptill, Shahzad and McCullum

The following batsmen have been very prolific in Twenty20 cricket and will be used for the analyses

Virat Kohli: Runs – 2167, Average:49.25 ,Strike rate-136.11
MJ Guptill : Runs -2271, Average:34.4 ,Strike rate-132.88
Mohammed Shahzad :Runs – 1936, Average:31.22 ,Strike rate-134.81
BB McCullum : Runs – 2140, Average:35.66 ,Strike rate-136.21

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

12. Box Histogram Plot

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

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("./kohli.csv","Virat Kohli")

ca.batsmanPerfBoxHist("./guptill.csv","M J Guptill")

ca.batsmanPerfBoxHist("./shahzad.csv","M Shahzad")

ca.batsmanPerfBoxHist("./mccullum.csv","BB McCullum")

13 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 Twenty20 batsmen.

import cricpy.analytics as ca
ca.batsmanMovingAverage("./kohli.csv","Virat Kohli")

ca.batsmanMovingAverage("./guptill.csv","M J Guptill")
#ca.batsmanMovingAverage("./shahzad.csv","M Shahzad") # Gives error. Check!

ca.batsmanMovingAverage("./mccullum.csv","BB McCullum")

14 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career.Kohli’s average tops around 45 runs around 43 innings, though there is a dip downwards

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeAverageRuns("./guptill.csv","M J Guptill")

ca.batsmanCumulativeAverageRuns("./shahzad.csv","M Shahzad")

ca.batsmanCumulativeAverageRuns("./mccullum.csv","BB McCullum")

15 Cumulative Average strike rate of batsman in career

Kohli, Guptill and McCullum average a strike rate of 125+

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeStrikeRate("./guptill.csv","M J Guptill")

ca.batsmanCumulativeStrikeRate("./shahzad.csv","M Shahzad")

ca.batsmanCumulativeStrikeRate("./mccullum.csv","BB McCullum")

16 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman. Kohli is way above all the other 3 batsmen. Behind Kohli is McCullum and then Guptill

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullumn"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

17. Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show that Kohli tops the overall strike rate followed by McCullum and then Guptill

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullum"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

18. 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 3D prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

ca.battingPerf3d("./guptill.csv","M J Guptill")

ca.battingPerf3d("./shahzad.csv","M Shahzad")

ca.battingPerf3d("./mccullum.csv","BB McCullum")

19. 3D plot of Runs vs Balls Faced and Minutes at Crease

Guptill and McCullum have a large percentage of sixes in comparison to the 4s. Kohli has a relative lower number of 6s

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullum"]
ca.batsman4s6s(frames,names)

20. Predicting Runs given Balls Faced and Minutes at Crease

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

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
kohli= ca.batsmanRunsPredict("./kohli.csv",newDF,"Kohli")

print(kohli)

##             BF        Mins        Runs
## 0    10.000000   30.000000   14.753153
## 1    37.857143   70.714286   55.963333
## 2    65.714286  111.428571   97.173513
## 3    93.571429  152.142857  138.383693
## 4   121.428571  192.857143  179.593873
## 5   149.285714  233.571429  220.804053
## 6   177.142857  274.285714  262.014233
## 7   205.000000  315.000000  303.224414
## 8   232.857143  355.714286  344.434594
## 9   260.714286  396.428571  385.644774
## 10  288.571429  437.142857  426.854954
## 11  316.428571  477.857143  468.065134
## 12  344.285714  518.571429  509.275314
## 13  372.142857  559.285714  550.485494
## 14  400.000000  600.000000  591.695674

21 Analysis of Top Bowlers

The following 4 bowlers have had an excellent career and will be used for the analysis

Shakib Hasan:Wickets: 80, Average = 21.07, Economy Rate – 6.74
Mohammed Nabi : Wickets: 67, Average = 24.25, Economy Rate – 7.13
Rashid Khan: Wickets: 64, Average = 12.40, Economy Rate – 6.01
Imran Tahir : Wickets:62, Average – 14.95, Economy Rate – 6.77

22. Get the bowler’s data

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

import cricpy.analytics as ca
#shakib=ca.getPlayerDataTT(56143,dir=".",file="shakib.csv",type="bowling")
#nabi=ca.getPlayerDataOD(25913,dir=".",file="nabi.csv",type="bowling")
#rashid=ca.getPlayerDataOD(793463,dir=".",file="rashid.csv",type="bowling")
#tahir=ca.getPlayerDataOD(40618,dir=".",file="tahir.csv",type="bowling")

23. Wicket Frequency Plot

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

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("./shakib.csv","Shakib Al Hasan")

ca.bowlerWktsFreqPercent("./nabi.csv","Mohammad Nabi")

ca.bowlerWktsFreqPercent("./rashid.csv","Rashid Khan")

ca.bowlerWktsFreqPercent("./tahir.csv","Imran Tahir")

24. Wickets Runs plot

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

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("./shakib.csv","Shakib Al Hasan")

ca.bowlerWktsRunsPlot("./nabi.csv","Mohammad Nabi")

ca.bowlerWktsRunsPlot("./rashid.csv","Rashid Khan")

ca.bowlerWktsRunsPlot("./tahir.csv","Imran Tahir")

25 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues.

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("./shakib.csv","Shakib Al Hasan")

ca.bowlerAvgWktsGround("./nabi.csv","Mohammad Nabi")

ca.bowlerAvgWktsGround("./rashid.csv","Rashid Khan")

ca.bowlerAvgWktsGround("./tahir.csv","Imran Tahir")

26 Average wickets against different opposition

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

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("./shakib.csv","Shakib Al Hasan")

ca.bowlerAvgWktsOpposition("./nabi.csv","Mohammad Nabi")

ca.bowlerAvgWktsOpposition("./rashid.csv","Rashid Khan")

ca.bowlerAvgWktsOpposition("./tahir.csv","Imran Tahir")

27 Wickets taken moving average

From the plot below it can be see

import cricpy.analytics as ca
ca.bowlerMovingAverage("./shakib.csv","Shakib Al Hasan")

ca.bowlerMovingAverage("./nabi.csv","Mohammad Nabi")

ca.bowlerMovingAverage("./rashid.csv","Rashid Khan")

ca.bowlerMovingAverage("./tahir.csv","Imran Tahir")

28 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. Rashid Khan has been the most effective with almost 2.28 wickets per match

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("./shakib.csv","Shakib Al Hasan")

ca.bowlerCumulativeAvgWickets("./nabi.csv","Mohammad Nabi")

ca.bowlerCumulativeAvgWickets("./rashid.csv","Rashid Khan")

ca.bowlerCumulativeAvgWickets("./tahir.csv","Imran Tahir")

29 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. Rashid Khan has the nest economy rate followed by Mohammed Nabi

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("./shakib.csv","Shakib Al Hasan")

ca.bowlerCumulativeAvgEconRate("./nabi.csv","Mohammad Nabi")

ca.bowlerCumulativeAvgEconRate("./rashid.csv","Rashid Khan")

ca.bowlerCumulativeAvgEconRate("./tahir.csv","Imran Tahir")

30 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate is given below. It can be seen that Rashid Khan has the best economy rate followed by Mohammed Nabi and then Imran Tahir

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

31 Relative Economy Rate against wickets taken

Rashid Khan has the best figures for wickets between 2-3.5 wickets. Mohammed Nabi pips Rashid Khan when takes a haul of 4 wickets.

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlingER(frames,names)

32 Relative cumulative average wickets of bowlers in career

Rashid has the best performance with cumulative average wickets. He is followed by Imran Tahir in the wicket haul, followed by Shakib Al Hasan

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

33. Key Findings

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

Here are the main findings from the analysis above

Analysis of Top 4 batsman

The analysis of the Top 4 test batsman Kohli, Guptill, Shahzad and McCullum
1.Kohli has the best overall cumulative average runs and towers over everybody else
2. Kohli, Guptill and McCullum has a very good strike rate of around 125+
3. Guptill and McCullum have a larger percentage of sixes as compared to Kohli
4. Rashid Khan has the best cumulative average wickets, followed by Imran Tahir and then Shakib Al Hasan
5. Rashid Khan is the most economical bowler, followed by Mohammed Nabi

You can fork/clone the package at Github cricpy

Conclusion

Cricpy now has almost all the functions and functionalities of my R package cricketr. There are still a few more features that need to be added to cricpy. I intend to do this as and when I find time.

Go ahead, take cricpy for a spin! Hope you enjoy the ride!

Watch this space!!!

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

To see all posts click Index of Posts

Cricpy takes a swing at the ODIs

No computer has ever been designed that is ever aware of what it’s doing; but most of the time, we aren’t either.” Marvin Minksy

“The competent programmer is fully aware of the limited size of his own skull. He therefore approaches his task with full humility, and avoids clever tricks like the plague” Edgser Djikstra

Introduction

In this post, cricpy, the Python avatar of my R package cricketr, learns some new tricks to be able to handle ODI matches. To know more about my R package cricketr see Re-introducing cricketr! : An R package to analyze performances of cricketers

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

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

To know how to use cricpy see Introducing cricpy:A python package to analyze performances of cricketers. To the original version of cricpy, I have added 3 new functions for ODI. The earlier functions work for Test and ODI.

This post is also hosted on Rpubs at Cricpy takes a swing at the ODIs. You can also down the pdf version of this post at cricpy-odi.pdf

You can fork/clone the package at Github cricpy

Untitled

The cricpy package

The data for a particular player in ODI can be obtained with the getPlayerDataOD() function. To do you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Virat Kohli, Virendar Sehwag, Chris Gayle etc. This will bring up a page which have the profile number for the player e.g. for Virat Kohli this would be http://www.espncricinfo.com/india/content/player/253802.html. Hence, Kohli’s profile is 253802. This can be used to get the data for Virat Kohlis shown below

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

You can fork/clone the package at Github cricpy

Note: The charts are self-explanatory and I have not added much of my owy interpretation to it. Do look at the plots closely and check out the performances for yourself.

1 Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy.analytics as ca

2. Invoking functions with Python package crlcpy

import cricpy.analytics as ca 
ca.batsman4s("./kohli.csv","Virat Kohli")

3. Getting help from cricpy – Python

import cricpy.analytics as ca 
help(ca.getPlayerDataOD)

## Help on function getPlayerDataOD in module cricpy.analytics:
## 
## getPlayerDataOD(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2, 3], result=[1, 2, 3, 5], create=True)
##     Get the One day 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
##     
##     getPlayerDataOD(profile, opposition="",host="",dir = "../", file = "player001.csv", 
##     type = "batting", homeOrAway = c(1, 2, 3), result = c(1, 2, 3,5))
##     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 Virender Sehwag this turns out to be http://www.espncricinfo.com/india/content/player/35263.html. Hence the profile for Sehwag is 35263
##     opposition      The numerical value of the opposition country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,Bermuda:12, England:1,Hong Kong:19,India:6,Ireland:29, Netherlands:15,New Zealand:5,Pakistan:7,Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27, West Indies:4, Zimbabwe:9; Africa XI:405 Note: If no value is entered for opposition then all teams are considered
##     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,Ireland:29,Malaysia:16,New Zealand:5,Pakistan:7, Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27,West Indies:4, Zimbabwe:9 Note: If no value is entered for host then all host countries are considered
##     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 vector with either or all 1,2, 3. 1 is for home 2 is for away, 3 is for neutral venue
##     result      
##     This is a vector that can take values 1,2,3,5. 1 - won match 2- lost match 3-tied 5- no result
##     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 getPlayerData
##     
##     Examples
##     
##     
##     ## Not run: 
##     # Both home and away. Result = won,lost and drawn
##     sehwag =getPlayerDataOD(35263,dir="../cricketr/data", file="sehwag1.csv",
##     type="batting", homeOrAway=[1,2],result=[1,2,3,4])
##     
##     # Only away. Get data only for won and lost innings
##     sehwag = getPlayerDataOD(35263,dir="../cricketr/data", file="sehwag2.csv",
##     type="batting",homeOrAway=[2],result=[1,2])
##     
##     # Get bowling data and store in file for future
##     malinga = getPlayerData(49758,dir="../cricketr/data",file="malinga1.csv",
##     type="bowling")
##     
##     # Get Dhoni's ODI record in Australia against Australua
##     dhoni = getPlayerDataOD(28081,opposition = 2,host=2,dir=".",
##     file="dhoniVsAusinAusOD",type="batting")
##     
##     ## End(Not run)

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

4. Get the ODI player data for a player using the function getPlayerDataOD()

import cricpy.analytics as ca
#sehwag=ca.getPlayerDataOD(35263,dir=".",file="sehwag.csv",type="batting")
#kohli=ca.getPlayerDataOD(253802,dir=".",file="kohli.csv",type="batting")
#jayasuriya=ca.getPlayerDataOD(49209,dir=".",file="jayasuriya.csv",type="batting")
#gayle=ca.getPlayerDataOD(51880,dir=".",file="gayle.csv",type="batting")

5 Virat Kohli’s performance – Basic Analyses

The 3 plots below provide the following for Virat Kohli

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

import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("./kohli.csv","Virat Kohli")

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

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

6. More analyses

import cricpy.analytics as ca
ca.batsman4s("./kohli.csv","Virat Kohli")

ca.batsman6s("./kohli.csv","Virat Kohli")

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

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

7. 3D scatter plot and prediction plane

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

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

Average runs at different venues

The plot below gives the average runs scored by Kohli at different grounds. The plot also the number of innings at each ground as a label at x-axis.

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("./kohli.csv","Virat Kohli")

9. Average runs against different opposing teams

This plot computes the average runs scored by Kohli against different countries.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("./kohli.csv","Virat Kohli")

10 . Highest Runs Likelihood

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("./kohli.csv","Virat Kohli")

A look at the Top 4 batsman – Kohli, Jayasuriya, Sehwag and Gayle

The following batsmen have been very prolific in ODI cricket and will be used for the analyses

Virat Kohli: Runs – 10232, Average:59.83 ,Strike rate-92.88
Sanath Jayasuriya : Runs – 13430, Average:32.36 ,Strike rate-91.2
Virendar Sehwag :Runs – 8273, Average:35.05 ,Strike rate-104.33
Chris Gayle : Runs – 9727, Average:37.12 ,Strike rate-85.82

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

12. Box Histogram Plot

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

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("./kohli.csv","Virat Kohli")

ca.batsmanPerfBoxHist("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanPerfBoxHist("./gayle.csv","Chris Gayle")

ca.batsmanPerfBoxHist("./sehwag.csv","Virendar Sehwag")

13 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 (ignore the dip at the end of all plots. Need to check why this is so!). Kohli’s performance has been steadily improving over the years, so has Sehwag. Gayle seems to be on the way down

import cricpy.analytics as ca
ca.batsmanMovingAverage("./kohli.csv","Virat Kohli")

ca.batsmanMovingAverage("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanMovingAverage("./gayle.csv","Chris Gayle")

ca.batsmanMovingAverage("./sehwag.csv","Virendar Sehwag")

14 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Kohli seems to be getting better with time and reaches a cumulative average of 45+. Sehwag improves with time and reaches around 35+. Chris Gayle drops from 42 to 35

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeAverageRuns("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanCumulativeAverageRuns("./gayle.csv","Chris Gayle")

ca.batsmanCumulativeAverageRuns("./sehwag.csv","Virendar Sehwag")

15 Cumulative Average strike rate of batsman in career

Sehwag has the best strike rate of almost 90. Kohli and Jayasuriya have a cumulative strike rate of 75.

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeStrikeRate("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanCumulativeStrikeRate("./gayle.csv","Chris Gayle")

ca.batsmanCumulativeStrikeRate("./sehwag.csv","Virendar Sehwag")

16 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman . It can be seen that Virat Kohli towers above all others in the runs. He is followed by Chris Gayle and then Sehwag

import cricpy.analytics as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percentages for each 10 run bucket. The plot below show Sehwag has the best strike rate, followed by Jayasuriya

import cricpy.analytics as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

18. 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 3D prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

ca.battingPerf3d("./jayasuriya.csv","Sanath jayasuriya")

ca.battingPerf3d("./gayle.csv","Chris Gayle")

ca.battingPerf3d("./sehwag.csv","Virendar Sehwag")

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

From the plot below it can be seen that Sehwag has more runs by way of 4s than 1’s,2’s or 3s. Gayle and Jayasuriya have large number of 6s

import cricpy.analytics as ca
frames = ["./sehwag.csv","./kohli.csv","./gayle.csv","./jayasuriya.csv"]
names = ["Sehwag","Kohli","Gayle","Jayasuriya"]
ca.batsman4s6s(frames,names)

20. Predicting Runs given Balls Faced and Minutes at Crease

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

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
kohli= ca.batsmanRunsPredict("./kohli.csv",newDF,"Kohli")

print(kohli)

##             BF        Mins        Runs
## 0    10.000000   30.000000    6.807407
## 1    37.857143   70.714286   36.034833
## 2    65.714286  111.428571   65.262259
## 3    93.571429  152.142857   94.489686
## 4   121.428571  192.857143  123.717112
## 5   149.285714  233.571429  152.944538
## 6   177.142857  274.285714  182.171965
## 7   205.000000  315.000000  211.399391
## 8   232.857143  355.714286  240.626817
## 9   260.714286  396.428571  269.854244
## 10  288.571429  437.142857  299.081670
## 11  316.428571  477.857143  328.309096
## 12  344.285714  518.571429  357.536523
## 13  372.142857  559.285714  386.763949
## 14  400.000000  600.000000  415.991375

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

21 Analysis of Top Bowlers

The following 4 bowlers have had an excellent career and will be used for the analysis

Muthiah Muralitharan:Wickets: 534, Average = 23.08, Economy Rate – 3.93
Wasim Akram : Wickets: 502, Average = 23.52, Economy Rate – 3.89
Shaun Pollock: Wickets: 393, Average = 24.50, Economy Rate – 3.67
Javagal Srinath : Wickets:315, Average – 28.08, Economy Rate – 4.44

How do Muralitharan, Akram, Pollock and Srinath compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

22. Get the bowler’s data

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

import cricpy.analytics as ca
#akram=ca.getPlayerDataOD(43547,dir=".",file="akram.csv",type="bowling")
#murali=ca.getPlayerDataOD(49636,dir=".",file="murali.csv",type="bowling")
#pollock=ca.getPlayerDataOD(46774,dir=".",file="pollock.csv",type="bowling")
#srinath=ca.getPlayerDataOD(34105,dir=".",file="srinath.csv",type="bowling")

23. Wicket Frequency Plot

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

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("./murali.csv","M Muralitharan")

ca.bowlerWktsFreqPercent("./akram.csv","Wasim Akram")

ca.bowlerWktsFreqPercent("./pollock.csv","Shaun Pollock")

ca.bowlerWktsFreqPercent("./srinath.csv","J Srinath")

24. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken. Murali’s median runs for wickets ia around 40 while Akram, Pollock and Srinath it is around 32+ runs. The spread around the median is larger for these 3 bowlers in comparison to Murali

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("./murali.csv","M Muralitharan")

ca.bowlerWktsRunsPlot("./akram.csv","Wasim Akram")

ca.bowlerWktsRunsPlot("./pollock.csv","Shaun Pollock")

ca.bowlerWktsRunsPlot("./srinath.csv","J Srinath")

25 Average wickets at different venues

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

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("./murali.csv","M Muralitharan")

ca.bowlerAvgWktsGround("./akram.csv","Wasim Akram")

ca.bowlerAvgWktsGround("./pollock.csv","Shaun Pollock")

ca.bowlerAvgWktsGround("./srinath.csv","J Srinath")

26 Average wickets against different opposition

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

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("./murali.csv","M Muralitharan")

ca.bowlerAvgWktsOpposition("./akram.csv","Wasim Akram")

ca.bowlerAvgWktsOpposition("./pollock.csv","Shaun Pollock")

ca.bowlerAvgWktsOpposition("./srinath.csv","J Srinath")

27 Wickets taken moving average

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

import cricpy.analytics as ca
ca.bowlerMovingAverage("./murali.csv","M Muralitharan")

ca.bowlerMovingAverage("./akram.csv","Wasim Akram")

ca.bowlerMovingAverage("./pollock.csv","Shaun Pollock")

ca.bowlerMovingAverage("./srinath.csv","J Srinath")

28 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. Muralitharan has consistently taken wickets at an average of 1.6 wickets per game. Shaun Pollock has an average of 1.5

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("./murali.csv","M Muralitharan")

ca.bowlerCumulativeAvgWickets("./akram.csv","Wasim Akram")

ca.bowlerCumulativeAvgWickets("./pollock.csv","Shaun Pollock")

ca.bowlerCumulativeAvgWickets("./srinath.csv","J Srinath")

29 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. Pollock is the most economical, followed by Akram and then Murali

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("./murali.csv","M Muralitharan")

ca.bowlerCumulativeAvgEconRate("./akram.csv","Wasim Akram")

ca.bowlerCumulativeAvgEconRate("./pollock.csv","Shaun Pollock")

ca.bowlerCumulativeAvgEconRate("./srinath.csv","J Srinath")

30 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate shows that Pollock is the most economical of the 4 bowlers. He is followed by Akram and then Murali

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

31 Relative Economy Rate against wickets taken

Pollock is most economical vs number of wickets taken. Murali has the best figures for 4 wickets taken.

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlingER(frames,names)

32 Relative cumulative average wickets of bowlers in career

The plot below shows that McGrath has the best overall cumulative average wickets. While the bowlers are neck to neck around 130 innings, you can see Muralitharan is most consistent and leads the pack after 150 innings in the number of wickets taken.

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

33. Key Findings

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

Here are the main findings from the analysis above

Analysis of Top 4 batsman

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

Kohli is a mean run machine and has been consistently piling on runs. Clearly records will lay shattered in days to come for Kohli
Virendar Sehwag has the best strike rate of the 4, followed by Jayasuriya and then Kohli
Shaun Pollock is the most economical of the bowlers followed by Wasim Akram
Muralitharan is the most consistent wicket of the lot.

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

Also see
1. Architecting a cloud based IP Multimedia System (IMS)
2. Exploring Quantum Gate operations with QCSimulator
3. Dabbling with Wiener filter using OpenCV
4. Deep Learning from first principles in Python, R and Octave – Part 5
5. Big Data-2: Move into the big league:Graduate from R to SparkR
6. Singularity
7. Practical Machine Learning with R and Python – Part 4
8. Literacy in India – A deepR dive
9. Modeling a Car in Android

To see all posts click Index of Posts

Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket

In my recent post, My travels through the realms of Data Science, Machine Learning, Deep Learning and (AI), I had recounted my journey in the domains of of Data Science, Machine Learning (ML), and more recently Deep Learning (DL) all of which are useful while analyzing data. Of late, I have come to the realization that there are many facets to data. And to glean insights from data, Data Science, ML and DL alone are not sufficient and one needs to also have a good handle on linear programming and optimization. My colleague at IBM Research also concurred with this view and told me he had arrived at this conclusion several years ago.

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!!

While ML & DL are very useful and interesting to make inferences and predictions of outputs from input variables, optimization computes the choice of input which results in maximizing or minimizing the output. So I made a small course correction and started on a course from India’s own NPTEL Introduction to Linear Programming by Prof G. Srinivasan of IIT Madras (highly recommended!). The lectures are delivered with remarkable clarity by the Prof and I am just about halfway through the course (each lecture is of 50-55 min duration), when I decided that I needed to try to formulate and solve some real world Linear Programming problem.

As usual, I turned towards cricket for some appropriate situations, and sure enough it was there in the open. For this LP formulation I take International T20 and IPL, though International ODI will also work equally well. You can download the associated code and data for this from Github at LP-cricket-analysis

In T20 matches the captain has to make choice of how to rotate bowlers with the aim of restricting the batting side. Conversely, the batsmen need to take advantage of the bowling strength to maximize the runs scored.

Note:
a) A simple and obvious strategy would be
– If the ith bowler’s economy rate is less than the economy rate of the jth bowler i.e.
$er_{i}$ < $er_{j}$ then have bowler ‘i’ to bowl more overs as his/her economy rate is better

b)A better strategy would be to consider the economy rate of each bowler against each batsman. How often have we witnessed bowlers with a great bowling average get thrashed time and again by the same batsman, or a bowler who is generally very poor being very effective against a particular batsman. i.e. $er_{ij}$ < $er_{ik}$ where the jth bowler is more effective than the kth bowler against the ith batsman. This now becomes a linear optimization problem as we can have several combinations of number of overs X economy rate for different bowlers and we will have to solve this algorithmically to determine the lowest score for bowling performance or highest score for batting order.

This post uses the latter approach to optimize bowling change and batting lineup.

Let is take a hypothetical situation
Assume there are 3 bowlers – $bwlr_{1},bwlr_{2},bwlr_{3}$
and there are 4 batsmen – $bman_{1},bman_{2},bman_{3},bman_{4}$

Let the economy rate $er_{ij}$ be the Economy Rate of the jth bowler to the ith batsman. Also if remaining overs for the bowlers are $o_{1},o_{2},o_{3}$
and the total number of overs left to be bowled are
$o_{1}+o_{2}+o_{3} = N$ then the question is

a) Given the economy rate of each bowler per batsman, how many overs should each bowler bowl, so that the total runs scored by all the batsmen are minimum?

b) Alternatively, if the know the individual strike rate of a batsman against the individual bowlers, how many overs should each batsman face with a bowler so that the total runs scored is maximized?

1. LP Formulation for bowling order

Let the economy rate $er_{ij}$ be the Economy Rate of the jth bowler to the ith batsman.
Objective function : Minimize –
$er_{11}*o_{11} + er_{12}*o_{12} +..+er_{1n}*o_{1n}+ er_{21}*o_{21} + er_{22}*o_{22}+.. + er_{22}*o_{2n}+ er_{m1}*o_{m1}+..+ er_{mn}*o_{mn}$
i.e.
$\sum_{i=1}^{i=m}\sum_{j=1}^{i=n}er_{ij}*o_{ij}$
Constraints
Where $o_{j}$ is the number of overs remaining for the jth bowler against ‘k’ batsmen
$o_{j1} + o_{j2} + .. o_{jk} < o_{j}$
and if the total number of overs remaining to be bowled is N then
$o_{1} + o_{2} +...+ o_{k} = N$ or
$\sum_{j=1}^{j=k} o_{j} =N$
The overs that any bowler can bowl is $o_{j} >=0$

2. LP Formulation for batting lineup

Let the strike rate $sr_{ij}$ be the Strike Rate of the ith batsman to the jth bowler
Objective function : Maximize –
$sr_{11}*o_{11} + sr_{12}*o_{12} +..+ sr_{1n}*o_{1n}+ sr_{21}*o_{21} + sr_{22}*o_{22}+.. sr_{2n}*o_{2n}+ sr_{m1}*o_{m1}+..+ sr_{mn}*o_{mn}$
i.e.
$\sum_{i=1}^{i=4}\sum_{j=1}^{i=3}sr_{ij}*o_{ij}$
Constraints
Where $o_{j}$ is the number of overs remaining for the jth bowler against ‘k’ batsmen
$o_{j1} + o_{j2} + .. o_{jk} < o_{j}$
and the total number of overs remaining to be bowled is N then
$o_{1} + o_{2} +...+ o_{k} = N$ or
$\sum_{j=1}^{j=k} o_{j} =N$
The overs that any bowler can bowl is
$o_{j} >=0$

lpSolveAPI– For this maximization and minimization problem I used lpSolveAPI.

Below I take 2 simple examples (example1 & 2) to ensure that my LP formulation and solution is correct before applying it on real T20 cricket data (Intl. T20 and IPL)

3. LP formulation (Example 1)

Initially I created a test example to ensure that I get the LP formulation and solution correct. Here the er1=4 and er2=3 and o1 & o2 are the overs bowled by bowlers 1 & 2. Also o1+o2=4 In this example as below

o1 o2 Obj Fun(=4o1+3o2)
1 3 13
2 2 14
3 1 15

library(lpSolveAPI)

library(dplyr)

library(knitr)
lprec <- make.lp(0, 2)
a <-lp.control(lprec, sense="min")
set.objfn(lprec, c(4, 3))  # Economy Rate of 4 and 3 for er1 and er2
add.constraint(lprec, c(1, 1), "=",4)  # o1 + o2 =4
add.constraint(lprec, c(1, 0), ">",1)  # o1 > 1
add.constraint(lprec, c(0, 1), ">",1)  # o2 > 1
lprec

## Model name: 
##             C1    C2       
## Minimize     4     3       
## R1           1     1   =  4
## R2           1     0  >=  1
## R3           0     1  >=  1
## Kind       Std   Std       
## Type      Real  Real       
## Upper      Inf   Inf       
## Lower        0     0

b <-solve(lprec)
get.objective(lprec) # 13

## [1] 13

get.variables(lprec) # 1    3

## [1] 1 3

Note 1: In the above example 13 runs is the minimum that can be scored and this requires

LP solution:
Minimum runs=13

o1=1
o2=3

Note 2:The numbers in the columns represent the number of overs that need to be bowled by a bowler to the corresponding batsman.

4. LP formulation (Example 2)

In this formulation there are 2 bowlers and 2 batsmen o11,o12 are the oves bowled by bowler 1 to batsmen 1 & 2 and o21, o22 are the overs bowled by bowler 2 to batsmen 1 & 2 er11=4, er12=2,er21=2,er22=5 o11+o12+o21+o22=5

The solution for this manually computed is o11, o12, o21, o22 Runs
where B11, B12 are the overs bowler 1 bowls to batsman 1 and B21 and B22 are overs bowler 2 bowls to batsman 2

o11 o12 o21 o22 Runs=(4*o11+2*o12+2*o21+5*o22)
1 1 1 2 18
1 2 1 1 15
2 1 1 1 17
1 1 2 1 15

lprec <- make.lp(0, 4)
a <-lp.control(lprec, sense="min")
set.objfn(lprec, c(4, 2,2,5))
add.constraint(lprec, c(1, 1,0,0), "<=",8)
add.constraint(lprec, c(0, 0,1,1), "<=",7)
add.constraint(lprec, c(1, 1,1,1), "=",5)
add.constraint(lprec, c(1, 0,0,0), ">",1)
add.constraint(lprec, c(0, 1,0,0), ">",1)
add.constraint(lprec, c(0, 0,1,0), ">",1)
add.constraint(lprec, c(0, 0,0,1), ">",1)
lprec

## Model name: 
##             C1    C2    C3    C4       
## Minimize     4     2     2     5       
## R1           1     1     0     0  <=  8
## R2           0     0     1     1  <=  7
## R3           1     1     1     1   =  5
## R4           1     0     0     0  >=  1
## R5           0     1     0     0  >=  1
## R6           0     0     1     0  >=  1
## R7           0     0     0     1  >=  1
## Kind       Std   Std   Std   Std       
## Type      Real  Real  Real  Real       
## Upper      Inf   Inf   Inf   Inf       
## Lower        0     0     0     0

b<-solve(lprec)
get.objective(lprec)

## [1] 15

get.variables(lprec)

## [1] 1 2 1 1

Note: In the above example 15 runs is the minimum that can be scored and this requires

LP Solution:
Minimum runs=15

o11=1
o12=2
o21=1
o22=1

It is possible to keep the minimum to other values and solves also.

5. LP formulation for International T20 India vs Australia (Batting lineup)

To analyze batting and bowling lineups in the cricket world I needed to get the ball-by-ball details of runs scored by each batsman against each of the bowlers. Fortunately I had already created this with my R package yorkr. yorkr processes yaml data from Cricsheet. So I copied the data of all matches between Australia and India in International T20s. You can download my processed data for International T20 at Inswinger

load("Australia-India-allMatches.RData")
dim(matches)

## [1] 3541   25

The following functions compute the ‘Strike Rate’ of a batsman as

S R = 1 / o v e r s * \sum R u n s S c o r e d

Also the Economy Rate is computed as

E R = 1 / o v e r s * \sum R u n s C o n c e d e d

Incidentally the SR=ER

# Compute the Strike Rate of the batsman
computeSR <- function(batsman1,bowler1){
    a <- matches %>% filter(batsman==batsman1 & bowler==bowler1) 
    a1 <- a %>% summarize(totalRuns=sum(runs),count=n()) %>% mutate(SR=(totalRuns/count)*6)
    a1
}

# Compute the Economy Rate of the batsman
computeER <- function(batsman1,bowler1){
    a <- matches %>% filter(batsman==batsman1 & bowler==bowler1) 
    a1 <- a %>% summarize(totalRuns=sum(runs),count=n()) %>% mutate(ER=(totalRuns/count)*6)
    a1
}

Here I compute the Strike Rate of Virat Kohli, Yuvraj Singh and MS Dhoni against Shane Watson, Brett Lee and MA Starc

 # Kohli
kohliWatson<- computeSR("V Kohli","SR Watson")

kohliWatson

##   totalRuns count       SR
## 1        45    37 7.297297

kohliLee <- computeSR("V Kohli","B Lee")
kohliLee

##   totalRuns count       SR
## 1        10     7 8.571429

kohliStarc <- computeSR("V Kohli","MA Starc")
kohliStarc

##   totalRuns count       SR
## 1        11     9 7.333333

# Yuvraj
yuvrajWatson<- computeSR("Yuvraj Singh","SR Watson")
yuvrajWatson

##   totalRuns count       SR
## 1        24    22 6.545455

yuvrajLee <- computeSR("Yuvraj Singh","B Lee")
yuvrajLee

##   totalRuns count       SR
## 1        12     7 10.28571

yuvrajStarc <- computeSR("Yuvraj Singh","MA Starc")
yuvrajStarc

##   totalRuns count SR
## 1        12     8  9

# MS Dhoni
dhoniWatson<- computeSR("MS Dhoni","SR Watson")
dhoniWatson

##   totalRuns count       SR
## 1        33    28 7.071429

dhoniLee <- computeSR("MS Dhoni","B Lee")
dhoniLee

##   totalRuns count  SR
## 1        26    20 7.8

dhoniStarc <- computeSR("MS Dhoni","MA Starc")
dhoniStarc

##   totalRuns count   SR
## 1        11     8 8.25

When we consider the batting lineup, the problem is one of maximization. In the LP formulation below V Kohli has a SR of 7.29, 8.57, 7.33 against Watson, Lee & Starc
Yuvraj has a SR of 6.5, 10.28, 9 against Watson, Lee & Starc
and Dhoni has a SR of 7.07, 7.8, 8.25 against Watson, Lee and Starc

The constraints are Watson, Lee and Starc have 3, 4 & 3 overs remaining respectively. The total number of overs remaining to be bowled is 9.The other constraints could be that a bowler bowls at least 1 over etc.

Formulating and solving

# 3 batsman x 3 bowlers
lprec <- make.lp(0, 9)
# Maximization
a<-lp.control(lprec, sense="max")

# Set the objective function
set.objfn(lprec, c(kohliWatson$SR, kohliLee$SR,kohliStarc$SR,
                   yuvrajWatson$SR,yuvrajLee$SR,yuvrajStarc$SR,
                   dhoniWatson$SR,dhoniLee$SR,dhoniStarc$SR))

#Assume the  bowlers have 3,4,3 overs left respectively
add.constraint(lprec, c(1, 1,1,0,0,0, 0,0,0), "<=",3)
add.constraint(lprec, c(0,0,0,1,1,1,0,0,0), "<=",4)
add.constraint(lprec, c(0,0,0,0,0,0,1,1,1), "<=",3)
#o11+o12+o13+o21+o22+o23+o31+o32+o33=8 (overs remaining)
add.constraint(lprec, c(1,1,1,1,1,1,1,1,1), "=",9) 


add.constraint(lprec, c(1,0,0,0,0,0,0,0,0), ">=",1) #o11 >=1
add.constraint(lprec, c(0,1,0,0,0,0,0,0,0), ">=",0) #o12 >=0
add.constraint(lprec, c(0,0,1,0,0,0,0,0,0), ">=",0) #o13 >=0
add.constraint(lprec, c(0,0,0,1,0,0,0,0,0), ">=",1) #o21 >=1
add.constraint(lprec, c(0,0,0,0,1,0,0,0,0), ">=",1) #o22 >=1
add.constraint(lprec, c(0,0,0,0,0,1,0,0,0), ">=",0) #o23 >=0
add.constraint(lprec, c(0,0,0,0,0,0,1,0,0), ">=",1) #o31 >=1
add.constraint(lprec, c(0,0,0,0,0,0,0,1,0), ">=",0) #o32 >=0
add.constraint(lprec, c(0,0,0,0,0,0,0,0,1), ">=",0) #o33 >=0

lprec

## Model name: 
##   a linear program with 9 decision variables and 13 constraints

b <-solve(lprec)
get.objective(lprec) #

## [1] 77.16418

get.variables(lprec) #

## [1] 1 2 0 1 3 0 1 0 1

This shows that the maximum runs that can be scored for the current strike rate is 77.16 runs in 9 overs The breakup is as follows

This is also shown below

get.variables(lprec) #

## [1] 1 2 0 1 3 0 1 0 1

This is also shown below

e <- as.data.frame(rbind(c(1,2,0,3),c(1,3,0,4),c(1,0,1,2)))
names(e) <- c("S Watson","B Lee","MA Starc","Overs")
rownames(e) <- c("Kohli","Yuvraj","Dhoni")
e

LP Solution:
Maximum runs that can be scored by India against Australia is:77.164 if the 9 overs to be faced by the batsman are as below

##        S Watson B Lee MA Starc Overs
## Kohli         1     2        0     3
## Yuvraj        1     3        0     4
## Dhoni         1     0        1     2

#Total overs=9

Note: This assumes that the batsmen perform at their current Strike Rate. Howvever anything can happen in a real game, but nevertheless this is a fairly reasonable estimate of the performance

Note 2:The numbers in the columns represent the number of overs that need to be bowled by a bowler to the corresponding batsman.

Note 3:You could try other combinations of overs for the above SR. For the above constraints 77.16 is the highest score for the given number of overs

6. LP formulation for International T20 India vs Australia (Bowling lineup)

For this I compute how the bowling should be rotated between R Ashwin, RA Jadeja and JJ Bumrah when taking into account their performance against batsmen like Shane Watson, AJ Finch and David Warner. For the bowling performance I take the Economy rate of the bowlers. The data is the same as above

computeSR <- function(batsman1,bowler1){
    a <- matches %>% filter(batsman==batsman1 & bowler==bowler1) 
    a1 <- a %>% summarize(totalRuns=sum(runs),count=n()) %>% mutate(SR=(totalRuns/count)*6)
    a1
}
# RA Jadeja
jadejaWatson<- computeER("SR Watson","RA Jadeja")
jadejaWatson

##   totalRuns count       ER
## 1        60    29 12.41379

jadejaFinch <- computeER("AJ Finch","RA Jadeja")
jadejaFinch

##   totalRuns count       ER
## 1        36    33 6.545455

jadejaWarner <- computeER("DA Warner","RA Jadeja")
jadejaWarner

##   totalRuns count       ER
## 1        23    11 12.54545

# Ashwin
ashwinWatson<- computeER("SR Watson","R Ashwin")
ashwinWatson

##   totalRuns count       ER
## 1        41    26 9.461538

ashwinFinch <- computeER("AJ Finch","R Ashwin")
ashwinFinch

##   totalRuns count   ER
## 1        63    36 10.5

ashwinWarner <- computeER("DA Warner","R Ashwin")
ashwinWarner

##   totalRuns count       ER
## 1        38    28 8.142857

# JJ Bunrah
bumrahWatson<- computeER("SR Watson","JJ Bumrah")
bumrahWatson

##   totalRuns count  ER
## 1        22    20 6.6

bumrahFinch <- computeER("AJ Finch","JJ Bumrah")
bumrahFinch

##   totalRuns count       ER
## 1        25    19 7.894737

bumrahWarner <- computeER("DA Warner","JJ Bumrah")
bumrahWarner

##   totalRuns count ER
## 1         2     4  3

As can be seen from above RA Jadeja has a ER of 12.4, 6.54, 12.54 against Watson, AJ Finch and Warner also Ashwin has a ER of 9.46, 10.5, 8.14 against Watson, Finch and Warner. Similarly Bumrah has an ER of 6.6,7.89, 3 against Watson, Finch and Warner
The constraints are Jadeja, Ashwin and Bumrah have 4, 3 & 4 overs remaining and the total overs remaining to be bowled is 10.

Formulating solving the bowling lineup is shown below

lprec <- make.lp(0, 9)
a <-lp.control(lprec, sense="min")

# Set the objective function
set.objfn(lprec, c(jadejaWatson$ER, jadejaFinch$ER,jadejaWarner$ER,
                   ashwinWatson$ER,ashwinFinch$ER,ashwinWarner$ER,
                   bumrahWatson$ER,bumrahFinch$ER,bumrahWarner$ER))

add.constraint(lprec, c(1, 1,1,0,0,0, 0,0,0), "<=",4) # Jadeja has 4 overs
add.constraint(lprec, c(0,0,0,1,1,1,0,0,0), "<=",3)   # Ashwin has 3 overs left
add.constraint(lprec, c(0,0,0,0,0,0,1,1,1), "<=",4)   # Bumrah has 4 overs left
add.constraint(lprec, c(1,1,1,1,1,1,1,1,1), "=",10) # Total overs = 10
add.constraint(lprec, c(1,0,0,0,0,0,0,0,0), ">=",1)
add.constraint(lprec, c(0,1,0,0,0,0,0,0,0), ">=",0)
add.constraint(lprec, c(0,0,1,0,0,0,0,0,0), ">=",1)
add.constraint(lprec, c(0,0,0,1,0,0,0,0,0), ">=",0)
add.constraint(lprec, c(0,0,0,0,1,0,0,0,0), ">=",1)
add.constraint(lprec, c(0,0,0,0,0,1,0,0,0), ">=",0)
add.constraint(lprec, c(0,0,0,0,0,0,1,0,0), ">=",0)
add.constraint(lprec, c(0,0,0,0,0,0,0,1,0), ">=",1)
add.constraint(lprec, c(0,0,0,0,0,0,0,0,1), ">=",0)

lprec

## Model name: 
##   a linear program with 9 decision variables and 13 constraints

b <-solve(lprec)
get.objective(lprec) #

## [1] 73.58775

get.variables(lprec) #

## [1] 1 2 1 0 1 1 0 1 3

The minimum runs that will be conceded by these 3 bowlers in 10 overs is 73.58 assuming the bowling is rotated as follows

e <- as.data.frame(rbind(c(1,0,0),c(2,1,1),c(1,1,3),c(4,2,4)))
names(e) <- c("RA Jadeja","R Ashwin","JJ Bumrah")
rownames(e) <- c("S Watson","AJ Finch","DA Warner","Overs")
e

LP Solution:
Minimum runs that will be conceded by India against Australia is 73.58 in 10 overs if the overs bowled are as follows

##           RA Jadeja R Ashwin JJ Bumrah
## S Watson          1        0         0
## AJ Finch          2        1         1
## DA Warner         1        1         3
## Overs             4        2         4

#Total overs=10

7. LP formulation for IPL (Mumbai Indians – Kolkata Knight Riders – Bowling lineup)

As in the case of International T20s I also have processed IPL data derived from my R package yorkr. yorkr. yorkr processes yaml data from Cricsheet. The processed data for all IPL matches can be downloaded from GooglyPlus

load("Mumbai Indians-Kolkata Knight Riders-allMatches.RData")
dim(matches)

## [1] 4237   25

# Compute the Economy Rate of the Mumbai Indian bowlers against Kolkata Knight Riders

# Gambhir
gambhirMalinga <- computeER("G Gambhir","SL Malinga")
gambhirHarbhajan <- computeER("G Gambhir","Harbhajan Singh")
gambhirPollard <- computeER("G Gambhir","KA Pollard")

#Yusuf Pathan
yusufMalinga <- computeER("YK Pathan","SL Malinga")
yusufHarbhajan <- computeER("YK Pathan","Harbhajan Singh")
yusufPollard <- computeER("YK Pathan","KA Pollard")

#JH Kallis
kallisMalinga <- computeER("JH Kallis","SL Malinga")
kallisHarbhajan <- computeER("JH Kallis","Harbhajan Singh")
kallisPollard <- computeER("JH Kallis","KA Pollard")

#RV Uthappa
uthappaMalinga <- computeER("RV Uthappa","SL Malinga")
uthappaHarbhajan <- computeER("RV Uthappa","Harbhajan Singh")
uthappaPollard <- computeER("RV Uthappa","KA Pollard")

Here

gambhirMalinga, yusufMalinga, kallisMalinga, uthappaMalinga is the ER of Malinga against Gambhir, Yusuf Pathan, Kallis and Uthappa
gambhirHarbhajan, yusufHarbhajan, kallisHarbhajan, uthappaHarbhajan is the ER of Harbhajan against Gambhir, Yusuf Pathan, Kallis and Uthappa
gambhirPollard, yusufPollard, kallisPollard, uthappaPollard is the ER of Kieron Pollard against Gambhir, Yusuf Pathan, Kallis and Uthappa

The constraints are Malinga, Harbhajan and Pollard have 4 overs each and remaining overs to be bowled is 10.

Formulating and solving this for the bowling lineup of Mumbai Indians against Kolkata Knight Riders

 library("lpSolveAPI")
 lprec <- make.lp(0, 12)
 a=lp.control(lprec, sense="min")
 
 set.objfn(lprec, c(gambhirMalinga$ER, yusufMalinga$ER,kallisMalinga$ER,uthappaMalinga$ER,
                    gambhirHarbhajan$ER,yusufHarbhajan$ER,kallisHarbhajan$ER,uthappaHarbhajan$ER,
                    gambhirPollard$ER,yusufPollard$ER,kallisPollard$ER,uthappaPollard$ER))
 
 add.constraint(lprec, c(1,1,1,1, 0,0,0,0, 0,0,0,0), "<=",4)
 add.constraint(lprec, c(0,0,0,0,1,1,1,1,0,0,0,0), "<=",4)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,1,1,1,1), "<=",4)
 add.constraint(lprec, c(1,1,1,1,1,1,1,1,1,1,1,1), "=",10)
 
 add.constraint(lprec, c(1,0,0,0,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,1,0,0,0,0,0,0,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,1,0,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,1,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,1,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,1,0,0,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,1,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,1,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,1,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,1,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,0,1,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,0,0,1), ">=",0)
 
 lprec

## Model name: 
##   a linear program with 12 decision variables and 16 constraints

 b=solve(lprec)
 get.objective(lprec) #

## [1] 55.57887

 get.variables(lprec) #

##  [1] 3 1 0 0 0 1 0 1 3 1 0 0

e <- as.data.frame(rbind(c(3,1,0,0,4),c(0, 1, 0,1,2),c(3, 1, 0,0,4)))
names(e) <- c("Gambhir","Yusuf","Kallis","Uthappa","Overs")
rownames(e) <- c("Malinga","Harbhajan","Pollard") 
e

LP Solution: Mumbai Indians can restrict Kolkata Knight Riders to 55.87 in 10 overs
if the overs are bowled as below

##           Gambhir Yusuf Kallis Uthappa Overs
## Malinga         3     1      0       0     4
## Harbhajan       0     1      0       1     2
## Pollard         3     1      0       0     4

#Total overs=10

8. LP formulation for IPL (Mumbai Indians – Kolkata Knight Riders – Batting lineup)

As I mentioned it is possible to perform a maximation with the same formulation since computeSR<==>computeER

This just flips the problem around and computes the maximum runs that can be scored for the batsman’s Strike rate (this is same as the bowler’s Economy rate) i.e.

gambhirMalinga, yusufMalinga, kallisMalinga, uthappaMalinga is the SR of Gambhir, Yusuf Pathan, Kallis and Uthappa against Malinga
gambhirHarbhajan, yusufHarbhajan, kallisHarbhajan, uthappaHarbhajan is the SR of Gambhir, Yusuf Pathan, Kallis and Uthappa against Harbhajan
gambhirPollard, yusufPollard, kallisPollard, uthappaPollard is the SR of Gambhir, Yusuf Pathan, Kallis and Uthappa against Kieron Pollard.

The constraints are Malinga, Harbhajan and Pollard have 4 overs each and remaining overs to be bowled is 10.

 library("lpSolveAPI")
 lprec <- make.lp(0, 12)
 a=lp.control(lprec, sense="max")
 
 a <-set.objfn(lprec, c(gambhirMalinga$ER, yusufMalinga$ER,kallisMalinga$ER,uthappaMalinga$ER,
                    gambhirHarbhajan$ER,yusufHarbhajan$ER,kallisHarbhajan$ER,uthappaHarbhajan$ER,
                    gambhirPollard$ER,yusufPollard$ER,kallisPollard$ER,uthappaPollard$ER))
 
 
 add.constraint(lprec, c(1,1,1,1, 0,0,0,0, 0,0,0,0), "<=",4)
 add.constraint(lprec, c(0,0,0,0,1,1,1,1,0,0,0,0), "<=",4)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,1,1,1,1), "<=",4)
 add.constraint(lprec, c(1,1,1,1,1,1,1,1,1,1,1,1), "=",11)
 
 add.constraint(lprec, c(1,0,0,0,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,1,0,0,0,0,0,0,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,1,0,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,1,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,1,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,1,0,0,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,1,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,1,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,1,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,1,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,0,1,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,0,0,1), ">=",0)
 lprec

## Model name: 
##   a linear program with 12 decision variables and 16 constraints

 b=solve(lprec)
 get.objective(lprec) #

## [1] 94.22649

 get.variables(lprec) #

##  [1] 0 3 0 0 0 1 0 3 0 1 3 0

e <- as.data.frame(rbind(c(0,3,0,0,3),c(0, 1, 0,3,4),c(0, 1, 3,0,4)))
names(e) <- c("Gambhir","Yusuf","Kallis","Uthappa","Overs")
rownames(e) <- c("Malinga","Harbhajan","Pollard") 
e

LP Solution: Kolkata Knight Riders can score a maximum of 94.22 in 11 overs against Mumbai Indians
if the the number of overs KKR face is as below

##           Gambhir Yusuf Kallis Uthappa Overs
## Malinga         0     3      0       0     3
## Harbhajan       0     1      0       3     4
## Pollard         0     1      3       0     4

#Total overs=11

Conclusion: It is possible to thus determine the optimum no of overs to give to a specific bowler based on his/her Economy Rate with a particular batsman. Similarly one can determine the maximum runs that can be scored by a batsmen based on their strike rate with bowlers. Cricket like many other games is a game of strategy, skill, talent and some amount of luck. So while the LP formulation can provide some direction, one must be aware anything could happen in a game of cricket!

Thoughts, comments, suggestions welcome!

Also see
1. Inswinger: yorkr swings into International T20s
2. Working with Node.js and PostgreSQL
3. Simulating the domino effect in Android using Box2D and AndEngine
4. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
5. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
6. A Cloud medley with IBM Bluemix, Cloudant DB and Node.js

To see all posts see Index of Posts

The cricketr package

1. Install the cricketr package

2. For Test cricket

2a. For ODI cricket

2b For Twenty 20 cricket

Analysis of batsmen

Sachin Tendulkar’s performance – Basic Analyses

3. Basic analyses

4. More analyses

5.Boxplot histogram plot

6. Contribution to won and lost matches

7, Performance at home and overseas

8. Batsman average at different venues

9. Batsman average against different opposition

10. Runs Likelihood of batsman

11. Moving Average of runs in career

12. Cumulative Average runs of batsman in career

13. Cumulative Average strike rate of batsman in career

14. Future Runs forecast

15. Relative Mean Strike Rate plot

16. Relative Runs Frequency plot

17. Relative cumulative average runs in career

18. Relative cumulative average strike rate in career

19. Check Batsman In-Form or Out-of-Form

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

21. Predicting Runs given Balls Faced and Minutes at Crease

Analysis of bowlers

21. Wicket Frequency Plot

22. Wickets Runs plot

23. Average wickets at different venues

24. Average wickets against different opposition

25. Wickets taken moving average

26. Cumulative Wickets taken

27. Cumulative Economy rate

28. Future Wickets forecast

29. Contribution to matches won and lost

30. Performance home and overseas.

31 Relative Wickets Frequency Percentage

32 Relative Economy Rate against wickets taken

33 Relative cumulative average wickets of bowlers in career

34 Relative cumulative average economy rate of bowlers

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

Key Findings

Analysis of batsman

Analysis of bowlers

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Introduction

1 Load the cricketr package

A Test cricket – Analysis of Gavaskar, Tendulkar and Kohli

2. Get player data

3a. Basic analyses for Tendulkar

3b Basic analyses for Kohli

3c Basic analyses for Gavaskar

4a.More analyses for Tendulkar

4b. More analyses for Kohli

4c More analyses for Gavaskar

5 Performance of batsmen on different grounds

6. Performance if batsmen against different Opposition

7. Get player data special

8 Get contribution of batsmen in matches won and lost

9 Performance of batsmen at home and overseas

10. Moving average of runs

11 Boxplot and histogram of runs

12 Cumulative average Runs for batsmen

13. Cumulative average strike rate of batsmen

14 Performance forecast of batsmen

15. Relative strike rate of batsmen

16. Relative Runs frequency of batsmen

17. Relative cumulative average runs of batsmen

18. Relative cumulative average strike rate

19. Check batsman in form

20. Performance 3D

20. Runs likelihood

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

Key findings

B ODI Cricket – Analysis of Tendulkar and Kohli

22 Get player data for ODIs

23a Basic performance of Tendulkar in ODI