Analyzing cricketers’ and cricket team’s performances with 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. Additionally this template can also be used for analyzing performances of teams in Test, ODI and T20 matches using my R package cricketr. To see actual usage of functions related to players in the R package cricketr see Introducing cricketr! : An R package to analyze performances of cricketers and associated posts on cricket in Index of posts. For the analyses on team performances see https://gigadom.in/2019/06/21/cricpy-adds-team-analytics-to-its-repertoire/

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

You can download this RMarkdown file from Github at cricketr-template

The cricketr package

The cricketr package has several functions that perform several different analyses on both batsman and bowlers. The package can also analyze performances of teams 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.

In addition performances of teams against different oppositions at different venues can be computed and plotted. The timeline of wins & losses can be plotted.

A. Performances of batsmen and bowlers

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 as below

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")

Important Note 1: 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

Important Note 2: The same set of functions can be used for Tests, ODI and T20s. I have mentioned wherever you may need special functions for ODI and T20 below

Sachin Tendulkar’s performance – Basic Analyses

The 3 plots below provide the following for Tendulkar

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges 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()
## null device 
##           1
  1. Player 1
  2. Player 2
  3. Player 3
  4. 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()
## null device 
##           1
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()
## null device 
##           1
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()
## null device 
##           1
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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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

  1. Bowler1
  2. Bowler2
  3. Bowler3
  4. 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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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()
## null device 
##           1

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

Note: The check for the form status of the bowlers indicate

#checkBowlerInForm("./bowler1.csv","Bowler1")
#checkBowlerInForm("./bowler2.csv","Bowler2")
#checkBowlerInForm("./bowler3.csv","Bowler3")
dev.off()
## null device 
##           1

36. Performing granular analysis of batsmen and bowlers

To perform granular analysis of batsmen and bowlers do the following 2 steps

  1. Step 1: getPlayerDataHA – This function is a wrapper around getPlayerData(), getPlayerDataOD() and getPlayerDataTT(), and adds an extra column ‘homeOrAway’ which says whether the match was played at home/away/neutral venues. A CSV file is created with this new column.
  2. Step2:getPlayerDataOppnHA – This function allows you to slice & dice the data for batsmen and bowlers against specific oppositions, at home/away/neutral venues and between certain periods. This reduced subset of data can be used to perform analyses. A CSV file is created as an output based on the parameters of opposition, home or away and the interval of time

37. GetPlayerDataHA (Batsmen, Tests)

#This saves a file playerTestHA.csv
#df=getPlayerDataHA(<profileNo>,tfile="playerTestHA.csv",type="batting", matchType="Test")

#Use the generate file to create a subset of data
#df1=getPlayerDataOppnHA(infile="playerTestHA.csv",outfile="playerTestFile1.csv",
#                         startDate=<start Date>,endDate=<end Date>)

38. GetPlayerDataHA (Bowlers, Tests)

#This saves a file playerTestHA.csv
#df=getPlayerDataHA(<profileNo>,tfile="playerTestHA.csv",type="bowling", matchType="Test")

#Use the generate file to create a subset of data
#df1=getPlayerDataOppnHA(infile="playerTestHA.csv",outfile="playerTestFile1.csv",
#                         startDate=<start Date>,endDate=<end Date>)

39. GetPlayerDataHA (Batsmen, ODI)

#This saves a file playerTestHA.csv
#df=getPlayerDataHA(<profileNo>,tfile="playerODIHA.csv",type="batting", matchType="ODI")

#Use the generate file to create a subset of data
#df1=getPlayerDataOppnHA(infile="playerODIHA.csv",outfile="playerODIFile1.csv",
#                         startDate=<start Date>,endDate=<end Date>)

40. GetPlayerDataHA (Bowlers, ODI)

#This saves a file playerTestHA.csv
#df=getPlayerDataHA(<profileNo>,tfile="playerODIHA.csv",type="bowling", matchType="ODI")

#Use the generate file to create a subset of data
#df1=getPlayerDataOppnHA(infile="playerODIHA.csv",outfile="playerODIFile1.csv",
#                         startDate=<start Date>,endDate=<end Date>)

41. GetPlayerDataHA (Batsmen, T20)

#This saves a file playerTestHA.csv
#df=getPlayerDataHA(<profileNo>,tfile="playerT20HA.csv",type="batting", matchType="T20")

#Use the generate file to create a subset of data
#df1=getPlayerDataOppnHA(infile="playerT20HA.csv",outfile="playerT20File1.csv",
#                         startDate=<start Date>,endDate=<end Date>)

42. GetPlayerDataHA (Bowlers, T20)

#This saves a file playerTestHA.csv
#df=getPlayerDataHA(<profileNo>,tfile="playerT20HA.csv",type="bowling", matchType="T20")

#Use the generate file to create a subset of data
#df1=getPlayerDataOppnHA(infile="playerT20HA.csv",outfile="playerT20File1.csv",
#                         startDate=<start Date>,endDate=<end Date>)

Important Note Once you get the subset of data for batsmen and bowlers playerTestFile1.csv, playerODIFile1.csv or playerT20File1.csv , you can use any of the cricketr functions on the subset of data for a fine-grained analysis

B. Performances of teams

The following functions will get the team data for Tests, ODI and T20s

1a. Get Test team data

#country1Test= getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="country1Test.csv",save=True,teamName="Country1")
#country2Test= getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="country2Test.csv",save=True,teamName="Country2")
#country3Test= getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="country3Test.csv",save=True,teamName="Country3")

1b. Get ODI team data

#team1ODI=  getTeamDataHomeAway(dir=".",matchType="ODI",file="team1ODI.csv",save=True,teamName="team1")
#team2ODI=  getTeamDataHomeAway(dir=".",matchType="ODI",file="team2ODI.csv",save=True,teamName="team2")
#team3ODI=  getTeamDataHomeAway(dir=".",matchType="ODI",file="team3ODI.csv",save=True,teamName="team3")

1c. Get T20 team data

#team1T20 = getTeamDataHomeAway(matchType="T20",file="team1T20.csv",save=True,teamName="team1")
#team2T20 = getTeamDataHomeAway(matchType="T20",file="team2T20.csv",save=True,teamName="team2")
#team3T20 = getTeamDataHomeAway(matchType="T20",file="team3T20.csv",save=True,teamName="team3")

2a. Test – Analyzing test performances against opposition

# Get the performance of Indian test team against all teams at all venues as a dataframe
#df <- teamWinLossStatusVsOpposition("country1Test.csv",teamName="Country1",opposition=c("all"),homeOrAway=c("all"),matchType="Test",plot=FALSE)
#head(df)

# Plot the performance of Country1 Test team  against all teams at all venues
#teamWinLossStatusVsOpposition("country1Test.csv",teamName="Country1",opposition=c("all"),homeOrAway=c("all"),matchType="Test",plot=TRUE)

# Plot the performance of Country1 Test team  against specific teams at home/away venues
#teamWinLossStatusVsOpposition("country1Test.csv",teamName="Country1",opposition=c("Country2","Country3","Country4"),homeOrAway=c("home","away","neutral"),matchType="Test",plot=TRUE)

2b. Test – Analyzing test performances against opposition at different grounds

# Get the performance of Indian test team against all teams at all venues as a dataframe
#df <- teamWinLossStatusAtGrounds("country1Test.csv",teamName="Country1",opposition=c("all"),homeOrAway=c("all"),matchType="Test",plot=FALSE)
#head(df)

# Plot the performance of Country1 Test team  against all teams at all venues
#teamWinLossStatusAtGrounds("country1Test.csv",teamName="Country1",opposition=c("all"),homeOrAway=c("all"),matchType="Test",plot=TRUE)

# Plot the performance of Country1 Test team  against specific teams at home/away venues
#teamWinLossStatusAtGrounds("country1Test.csv",teamName="Country1",opposition=c("Country2","Country3","Country4"),homeOrAway=c("home","away","neutral"),matchType="Test",plot=TRUE)

2c. Test – Plot time lines of wins and losses

#plotTimelineofWinsLosses("country1Test.csv",team="Country1",opposition=c("all"), #startDate="1970-01-01",endDate="2017-01-01")
#plotTimelineofWinsLosses("country1Test.csv",team="Country1",opposition=c("Country2","Count#ry3","Country4"), homeOrAway=c("home",away","neutral"), startDate=<start Date> #,endDate=<endDate>)

3a. ODI – Analyzing test performances against opposition

#df <- teamWinLossStatusVsOpposition("team1ODI.csv",teamName="Team1",opposition=c("all"),homeOrAway=c("all"),matchType="ODI",plot=FALSE)
#head(df)

# Plot the performance of team1  in ODIs against Sri Lanka, India at all venues
#teamWinLossStatusVsOpposition("team1ODI.csv",teamName="Team1",opposition=c("all"),homeOrAway=c(all"),matchType="ODI",plot=TRUE)

# Plot the performance of Team1 ODI team  against specific teams at home/away venues
#teamWinLossStatusVsOpposition("team1ODI.csv",teamName="Team1",opposition=c("Team2","Team3","Team4"),homeOrAway=c("home","away","neutral"),matchType="ODI",plot=TRUE)

3b. ODI – Analyzing test performances against opposition at different venues

#df <- teamWinLossStatusAtGrounds("team1ODI.csv",teamName="Team1",opposition=c("all"),homeOrAway=c("all"),matchType="ODI",plot=FALSE)
#head(df)

# Plot the performance of Team1s in ODIs specific ODI teams at all venues
#teamWinLossStatusAtGrounds("team1ODI.csv",teamName="Team1",opposition=c("all"),homeOrAway=c(all"),matchType="ODI",plot=TRUE)

# Plot the performance of Team1 against specific ODI teams at home/away venues
#teamWinLossStatusAtGrounds("team1ODI.csv",teamName="Team1",opposition=c("Team2","Team3","Team4"),homeOrAway=c("home","away","neutral"),matchType="ODI",plot=TRUE)

3c. ODI – Plot time lines of wins and losses

#Plot the time line of wins/losses of Bangladesh ODI team between 2 dates all venues
#plotTimelineofWinsLosses("team1ODI.csv",team="Team1",startDate=<start date> ,endDa#te=<end date>,matchType="ODI")

#Plot the time line of wins/losses against specific opposition between 2 dates
#plotTimelineofWinsLosses("team1ODI.csv",team="Team1",opposition=c("Team2","Team2"), homeOrAway=c("home",away","neutral"), startDate=<start date>,endDate=<end date> ,matchType="ODI")

4a. T20 – Analyzing test performances against opposition

#df <- teamWinLossStatusVsOpposition("teamT20.csv",teamName="Team1",opposition=c("all"),homeOrAway=c("all"),matchType="T20",plot=FALSE)
#head(df)

# Plot the performance of Team1 in T20s  against  all opposition at all venues
#teamWinLossStatusVsOpposition("teamT20.csv",teamName="Team1",opposition=c("all"),homeOrAway=c(all"),matchType="T20",plot=TRUE)

# Plot the performance of T20 Test team  against specific teams at home/away venues
#teamWinLossStatusVsOpposition("teamT20.csv",teamName="Team1",opposition=c("Team2","Team3","Team4"),homeOrAway=c("home","away","neutral"),matchType="T20",plot=TRUE)

4b. T20 – Analyzing test performances against opposition at different venues

#df <- teamWinLossStatusAtGrounds("teamT20.csv",teamName="Team1",opposition=c("all"),homeOrAway=c("all"),matchType="T20",plot=FALSE)
#head(df)

# Plot the performance of Team1s in ODIs specific ODI teams at all venues
#teamWinLossStatusAtGrounds("teamT20.csv",teamName="Team1",opposition=c("all"),homeOrAway=c(all"),matchType="T20",plot=TRUE)

# Plot the performance of Team1 against specific ODI teams at home/away venues
#teamWinLossStatusAtGrounds("teamT20.csv",teamName="Team1",opposition=c("Team2","Team3","Team4"),homeOrAway=c("home","away","neutral"),matchType="T20",plot=TRUE)

4c. T20 – Plot time lines of wins and losses

#Plot the time line of wins/losses of Bangladesh ODI team between 2 dates all venues
#plotTimelineofWinsLosses("teamT20.csv",team="Team1",startDate=<start date> ,endDa#te=<end date>,matchType="T20")

#Plot the time line of wins/losses against specific opposition between 2 dates
#plotTimelineofWinsLosses("teamT20.csv",team="Team1",opposition=c("Team2","Team2"), homeOrAway=c("home",away","neutral"), startDate=<start date>,endDate=<end date> ,matchType="T20")

Key Findings

Analysis of batsman

Analysis of bowlers

Analysis of teams

Conclusion

Using the above template, analysis can be done for both batsmen and bowlers in Test, ODI and T20. Also analysis of any any team in Test, ODI and T20 against other specific opposition, at home/away and neutral venues can be performed.

Have fun with cricketr!!

Also see
1. Practical Machine Learning with R and Python – Part 5
2. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
3. yorkr crashes the IPL party ! – Part 1
4. Deep Learning from first principles in Python, R and Octave – Part 6
5. Cricpy takes a swing at the ODIs
6. Bull in a china shop – Behind the scenes in Android
7. Eliminating the Performance Drag
To see all posts click Index of posts

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