Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
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()
## null device
## 1
- 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()
## 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
- 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()
## 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
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
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.