Tag: ESPN Cricinfo

Cricketr learns new tricks : Performs fine-grained analysis of players

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Introduction

In this post, I introduce 2 new functions in my R package ‘cricketr’ (cricketr v0.22) see Re-introducing cricketr! : An R package to analyze performances of cricketers which enable granular analysis of batsmen and bowlers. They are

  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. Setp 2: 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

Note All the existing cricketr functions can be used on this smaller fine-grained data set for a closer analysis of players

Note 1: You have to call the above functions only once. You can reuse the CSV files in other functions

Important note: Don’t go too fine-grained by choosing just one opposition, in one of home/away/neutral and for too short a period. Too small a dataset may defeat the purpose of the analysis!

This post has been published in Rpubs and can be accessed at Cricketr learns new tricks

You can download a PDF version of this post at Cricketr learns new tricks

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!!

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1. Analyzing Tendulkar at 3 different stages of his career

The following functions analyze Sachin Tendulkar during 3 different periods of his illustrious career. a) 1st Jan 2001-1st Jan 2002 b) 1st Jan 2005-1st Jan 2006 c) 1st Jan 2012-1st Jan 2013

# Get the homeOrAway dataset for Tendulkar in matches
#Note: I have commented the lines to getPlayerDataHA() as I already have 
# CSV file
#df=getPlayerDataHA(35320,tfile="tendulkarTestHA.csv",matchType="Test")

# Get Tendulkar's data for 2001-02
df1=getPlayerDataOppnHA(infile="tendulkarHA.csv",outfile="tendulkarTest2001.csv",
                         startDate="2001-01-01",endDate="2002-01-01")

# Get Tendulkar's data for 2005-06
df2=getPlayerDataOppnHA(infile="tendulkarHA.csv",outfile="tendulkarTest2005.csv",

                                               startDate="2005-01-01",endDate="2006-01-01")

# Get Tendulkar's data for 20012-13
#df3=getPlayerDataOppnHA(infile="tendulkarHA.csv",outfile="tendulkarTest2012.csv",
#                        startDate="2012-01-01",endDate="2013-01-01")

`

1a Mean strike rate of Tendulkar in 2001,2005,2012

Note: Any of the cricketr R functions can be used on the fine-grained subset of data as below. The mean strike rate of Tendulkar is of the order of 60+ in 2001 which decreases to 50 and later to around 45

# Compute and plot mean strike rate of Tendulkar in the 3 periods
batsmanMeanStrikeRate ("./tendulkarTest2001.csv","Tendulkar-2001")

batsmanMeanStrikeRate ("./tendulkarTest2005.csv","Tendulkar-2005")

batsmanMeanStrikeRate ("./tendulkarTest2012.csv","Tendulkar-2012")

1b. Plot the performance of Tendulkar at venues during 2001,2005,2012

On an average Tendulkar score 60+ in 2001 and is really blazing. This performance decreases in 2005 and later in 2012

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("tendulkarTest2001.csv","Tendulkar-2001")
batsmanAvgRunsGround("tendulkarTest2005.csv","Tendulkar-2005")
batsmanAvgRunsGround("tendulkarTest2012.csv","Tendulkar-2012")

dev.off()


 




 




1c. Plot the performance of Tendulkar against different oppositions during 2001,2005,2012


Sachin uniformly scores 50+ against formidable oppositions in 2001. In 2005 this decreases to 40 in 2005 and in 2012 it is around 25


batsmanAvgRunsOpposition("tendulkarTest2001.csv","Tendulkar-2001")






batsmanAvgRunsOpposition("tendulkarTest2005.csv","Tendulkar-2005")






batsmanAvgRunsOpposition("tendulkarTest2012.csv","Tendulkar-2012")






1d. Plot the relative cumulative average and relative strike rate of Tendulkar in 2001,2005,2012


The plot below compares Tendulkar’s cumulative strike rate and cumulative average during 3 different stages of his career


    

  1. The cumulative average runs of Tendulkar is in the high 60+ in 2001, which drops to ~50 in 2005 and later plummets to the low 25s in 2012
    2. 

  2. The strike rate in 2001 for Tendulkar is amazing 60+
    3. 



frames=list("tendulkarTest2001.csv","tendulkarTest2005.csv","tendulkarTest2012.csv")
names=list("Tendulkar-2001","Tendulkar-2005","Tendulkar-2012")
relativeBatsmanCumulativeAvgRuns(frames,names)




relativeBatsmanCumulativeStrikeRate(frames,names)








2. Analyzing Virat Kohli’s performance against England in England in 2014 and 2018


The analysis below looks at Kohli’s performance against England in ‘away’ venues (England) in 2014 and 2018


# Get the homeOrAway data for Kohli in Test matches
#df=getPlayerDataHA(253802,tfile="kohliTestHA.csv",type="batting",matchType="Test")

# Get the subset if data of Kohli's performance against England in England in 2014
df=getPlayerDataOppnHA(infile="kohliTestHA.csv",outfile="kohliTestEng2014.csv",
   opposition=c("England"),homeOrAway=c("away"),startDate="2014-01-01",endDate="2015-01-01")

# Get the subset if data of Kohli's performance against England in England in 2018
df1=getPlayerDataOppnHA(infile="kohliHA.csv",outfile="kohliTestEng2018.csv",
   opposition=c("England"),homeOrAway=c("away"),startDate="2018-01-01",endDate="2019-01-01")






2a. Kohli’s performance at England grounds in 2014 & 2018


Kohli had a miserable outing to England in 2014 with a string of low scores. In 2018 Kohli pulls himself out of the morass


batsmanAvgRunsGround("kohliTestEng2014.csv","Kohli-Eng-2014")




batsmanAvgRunsGround("kohliTestEng2018.csv","Kohli-Eng-2018")








2a. Kohli’s cumulative average runs in 2014 & 2018


Kohli’s cumulative average runs in 2014 is in the low 15s, while in 2018 it is 70+. Kohli stamps his class back again and undoes the bad memories of 2014


batsmanCumulativeAverageRuns("kohliTestEng2014.csv", "Kohli-Eng-2014")




batsmanCumulativeAverageRuns("kohliTestEng2018.csv", "Kohli-Eng-2018")








3a. Compare the performances of Ganguly, Dravid and VVS Laxman against opposition in ‘away’ matches in Tests


The analyses below compares the performances of Sourav Ganguly, Rahul Dravid and VVS Laxman against Australia, South Africa, and England in ‘away’ venues between 01 Jan 2002 to 01 Jan 2008


#Get the HA data for Ganguly, Dravid and Laxman
#df=getPlayerDataHA(28779,tfile="gangulyTestHA.csv",type="batting",matchType="Test")
#df=getPlayerDataHA(28114,tfile="dravidTestHA.csv",type="batting",matchType="Test")
#df=getPlayerDataHA(30750,tfile="laxmanTestHA.csv",type="batting",matchType="Test")


# Slice the data 
df=getPlayerDataOppnHA(infile="gangulyTestHA.csv",outfile="gangulyTestAES2002-08.csv"
                       ,opposition=c("Australia", "England", "South Africa"),
                       homeOrAway=c("away"),startDate="2002-01-01",endDate="2008-01-01")


df=getPlayerDataOppnHA(infile="dravidTestHA.csv",outfile="dravidTestAES2002-08.csv"
                       ,opposition=c("Australia", "England", "South Africa"),
                       homeOrAway=c("away"),startDate="2002-01-01",endDate="2008-01-01")


df=getPlayerDataOppnHA(infile="laxmanTestHA.csv",outfile="laxmanTestAES2002-08.csv"
                       ,opposition=c("Australia", "England", "South Africa"),
                       homeOrAway=c("away"),startDate="2002-01-01",endDate="2008-01-01")






3b Plot the relative cumulative average runs and relative cumative strike rate


Plot the relative cumulative average runs and relative cumative strike rate of Ganguly, Dravid and Laxman


-Dravid towers over Laxman and Ganguly with respect to cumulative average runs. – Ganguly has a superior strike rate followed by Laxman and then Dravid


frames=list("gangulyTestAES2002-08.csv","dravidTestAES2002-08.csv","laxmanTestAES2002-08.csv")
names=list("GangulyAusEngSA2002-08","DravidAusEngSA2002-08","LaxmanAusEngSA2002-08")
relativeBatsmanCumulativeAvgRuns(frames,names)




relativeBatsmanCumulativeStrikeRate(frames,names)








4. Compare the ODI performances of Rohit Sharma, Joe Root and Kane Williamson against opposition


Compare the performances of Rohit Sharma, Joe Root and Kane williamson in away & neutral venues against Australia, West Indies and Soouth Africa


    

  • Joe Root piles us the runs in about 15 matches. Rohit has played far more ODIs than the other two and averages a steady 35+
    • 



# Get the ODI HA data for Rohit, Root and Williamson
#df=getPlayerDataHA(34102,tfile="rohitODIHA.csv",type="batting",matchType="ODI")
#df=getPlayerDataHA(303669,tfile="joerootODIHA.csv",type="batting",matchType="ODI")
#df=getPlayerDataHA(277906,tfile="williamsonODIHA.csv",type="batting",matchType="ODI")

# Subset the data for specific opposition in away and neutral venues
df=getPlayerDataOppnHA(infile="rohitODIHA.csv",outfile="rohitODIAusWISA.csv"
                       ,opposition=c("Australia", "West Indies", "South Africa"),
                      homeOrAway=c("away","neutral"))

df=getPlayerDataOppnHA(infile="joerootODIHA.csv",outfile="joerootODIAusWISA.csv"
                       ,opposition=c("Australia", "West Indies", "South Africa"),
                       homeOrAway=c("away","neutral"))

df=getPlayerDataOppnHA(infile="williamsonODIHA.csv",outfile="williamsonODIAusWiSA.csv"
                       ,opposition=c("Australia", "West Indies", "South Africa"),
                       homeOrAway=c("away","neutral"))






4a. Compare cumulative strike rates and cumulative average runs of Rohit, Root and Williamson


The relative cumulative strike rate of all 3 are comparable


frames=list("rohitODIAusWISA.csv","joerootODIAusWISA.csv","williamsonODIAusWiSA.csv")
names=list("Rohit-ODI-AusWISA","Joe Root-ODI-AusWISA","Williamson-ODI-AusWISA")
relativeBatsmanCumulativeAvgRuns(frames,names)




relativeBatsmanCumulativeStrikeRate(frames,names)








5. Plot the performance of Dhoni in T20s against specific opposition at all venues


Plot the performances of Dhoni against Australia, West Indies, South Africa and England


# Get the HA T20 data for Dhoni
#df=getPlayerDataHA(28081,tfile="dhoniT20HA.csv",type="batting",matchType="T20")

#Subset the data
df=getPlayerDataOppnHA(infile="dhoniT20HA.csv",outfile="dhoniT20AusWISAEng.csv"
                       ,opposition=c("Australia", "West Indies", "South Africa","England"),
                       homeOrAway=c("all"))






5a. Plot Dhoni’s performances in T20


Note You can use any of cricketr’s functions against the fine grained data


batsmanAvgRunsOpposition("dhoniT20AusWISAEng.csv","Dhoni")




batsmanAvgRunsGround("dhoniT20AusWISAEng.csv","Dhoni")




batsmanCumulativeStrikeRate("dhoniT20AusWISAEng.csv","Dhoni")




batsmanCumulativeAverageRuns("dhoniT20AusWISAEng.csv","Dhoni")








6. Compute and performances of Anil Kumble, Muralitharan and Warne in ‘away’ test matches


Compute the performances of Kumble, Warne and Maralitharan against New Zealand, West Indies, South Africa and England in pitches that are not ‘home’ pithes


# Get the bowling data for Kumble, Warne and Muralitharan in Test matches
#df=getPlayerDataHA(30176,tfile="kumbleTestHA.csv",type="bowling",matchType="Test")
#df=getPlayerDataHA(8166,tfile="warneTestHA.csv",type="bowling",matchType="Test")
#df=getPlayerDataHA(49636,tfile="muraliTestHA.csv",type="bowling",matchType="Test")


# Subset the data
df=getPlayerDataOppnHA(infile="kumbleTestHA.csv",outfile="kumbleTest-NZWISAEng.csv"
                       ,opposition=c("New Zealand", "West Indies", "South Africa","England"),
                       homeOrAway=c("away"))

df=getPlayerDataOppnHA(infile="warneTestHA.csv",outfile="warneTest-NZWISAEng.csv"
                       ,opposition=c("New Zealand", "West Indies", "South Africa","England"),
                       homeOrAway=c("away"))

df=getPlayerDataOppnHA(infile="muraliTestHA.csv",outfile="muraliTest-NZWISAEng.csv"
                       ,opposition=c("New Zealand", "West Indies", "South Africa","England"),
                       homeOrAway=c("away"))






6a. Plot the average wickets of Kumble, Warne and Murali


bowlerAvgWktsOpposition("kumbleTest-NZWISAEng.csv","Kumble-NZWISAEng-AN")




bowlerAvgWktsOpposition("warneTest-NZWISAEng.csv","Warne-NZWISAEng-AN")




bowlerAvgWktsOpposition("muraliTest-NZWISAEng.csv","Murali-NZWISAEng-AN")








6b. Plot the average wickets in different grounds of Kumble, Warne and Murali


bowlerAvgWktsGround("kumbleTest-NZWISAEng.csv","Kumblew")




bowlerAvgWktsGround("warneTest-NZWISAEng.csv","Warne")




bowlerAvgWktsGround("muraliTest-NZWISAEng.csv","murali")








6c. Plot the cumulative average wickets and cumulative economy rate of Kumble, Warne and Murali


    

  • Murali has the best economy rate followed by Kumble and then Warne
    • 

  • Again Murali has the best cumulative average wickets followed by Warne and then Kumble
    • 



frames=list("kumbleTest-NZWISAEng.csv","warneTest-NZWISAEng.csv","muraliTest-NZWISAEng.csv")
names=list("Kumble","Warne","Murali")
relativeBowlerCumulativeAvgEconRate(frames,names)




relativeBowlerCumulativeAvgWickets(frames,names)








7. Compute and plot the performances of Bumrah in 2016, 2017 and 2018 in ODIs


# Get the HA data for Bumrah in ODI in bowling
df=getPlayerDataHA(625383,tfile="bumrahODIHA.csv",type="bowling",matchType="ODI")


## [1] "Working..."


# Slice the data for periods 2016, 2017 and 2018
df=getPlayerDataOppnHA(infile="bumrahODIHA.csv",outfile="bumrahODI2016.csv",
                       startDate="2016-01-01",endDate="2017-01-01")

df=getPlayerDataOppnHA(infile="bumrahODIHA.csv",outfile="bumrahODI2017.csv",
                       startDate="2017-01-01",endDate="2018-01-01")

df=getPlayerDataOppnHA(infile="bumrahODIHA.csv",outfile="bumrahODI2018.csv",
                       startDate="2018-01-01",endDate="2019-01-01")






7a. Compute the performances of Bumrah in 2016, 2017 and 2018


    

  • Very clearly Bumrah is getting better at his art. His economy rate in 2018 is the best!!!
    • 

  • Bumrah has had a very prolific year in 2017. However all the years he seems to be quite effective
    • 



frames=list("bumrahODI2016.csv","bumrahODI2017.csv","bumrahODI2018.csv")
names=list("Bumrah-2016","Bumrah-2017","Bumrah-2018")
relativeBowlerCumulativeAvgEconRate(frames,names)




relativeBowlerCumulativeAvgWickets(frames,names)








8. Compute and plot the performances of Shakib, Bumrah and Jadeja in T20 matches for bowling


# Get the HA bowling data for Shakib, Bumrah and Jadeja
df=getPlayerDataHA(56143,tfile="shakibT20HA.csv",type="bowling",matchType="T20")


## [1] "Working..."


df=getPlayerDataHA(625383,tfile="bumrahT20HA.csv",type="bowling",matchType="T20")


## [1] "Working..."


df=getPlayerDataHA(234675,tfile="jadejaT20HA.csv",type="bowling",matchType="T20")


## [1] "Working..."


# Slice the data for performances against Sri Lanka, Australia, South Africa and England
df=getPlayerDataOppnHA(infile="shakibT20HA.csv",outfile="shakibT20-SLAusSAEng.csv"
                       ,opposition=c("Sri Lanka","Australia", "South Africa","England"),
                       homeOrAway=c("all"))
df=getPlayerDataOppnHA(infile="bumrahT20HA.csv",outfile="bumrahT20-SLAusSAEng.csv"
                       ,opposition=c("Sri Lanka","Australia", "South Africa","England"),
                       homeOrAway=c("all"))

df=getPlayerDataOppnHA(infile="jadejaT20HA.csv",outfile="jadejaT20-SLAusSAEng.csv"
                       ,opposition=c("Sri Lanka","Australia", "South Africa","England"),
                       homeOrAway=c("all"))






8a. Compare the relative performances of Shakib, Bumrah and Jadeja


    

  • Jadeja and Bumrah have comparable economy rates. Shakib is more expensive
    • 

  • Shakib pips Bumrah in number of cumulative wickets, though Bumrah is close behind
    • 



frames=list("shakibT20-SLAusSAEng.csv","bumrahT20-SLAusSAEng.csv","jadejaT20-SLAusSAEng.csv")
names=list("Shakib-SLAusSAEng","Bumrah-SLAusSAEng","Jadeja-SLAusSAEng")
relativeBowlerCumulativeAvgEconRate(frames,names)




relativeBowlerCumulativeAvgWickets(frames,names)








Conclusion


By getting the homeOrAway data for players using the profileNo, you can slice and dice the data based on your choice of opposition, whether you want matches that were played at home/away/neutral venues. Finally by specifying the period for which the data has to be subsetted you can create fine grained analysis.


Hope you have a great time with cricketr!!!




Also see


1. My book ‘Deep Learning from first principles:Second Edition’ now on Amazon

2. Cricpy takes a swing at the ODIs

3. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon

4. Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr

5. Big Data-2: Move into the big league:Graduate from R to SparkR

6. Rock N’ Roll with Bluemix, Cloudant & NodeExpress

7. A method to crowd source pothole marking on (Indian) roads

8. De-blurring revisited with Wiener filter using OpenCV


To see all posts click Index of posts

	


	



			
				


			

			
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. 

  2. Mean Strike Rate for runs scored in the given range
    3. 

  3. A histogram of runs frequency percentages in runs ranges For example
    4. 





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. 

  2. Player 2
    3. 

  3. Player 3
    4. 

  4. Player 4
    5. 







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. 

  2. Bowler2
    3. 

  3. Bowler3
    4. 

  4. Bowler4
    5. 



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. 

  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
    3. 







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

	


	



			
				


			

			
Cricpy adds team analytics to its arsenal!!
		
	
	

		
I can’t sit still and see another man slaving and working. I want to get up and superintend, and walk round with my hands in my pockets, and tell him what to do. It is my energetic nature. I can’t help it.


It always does seem to me that I am doing more work than I should do. It is not that I object to the work, mind you; I like work: it fascinates me. I can sit and look at it for hours. I love to keep it by me: the idea of getting rid of it nearly breaks my heart.


Let your boat of life be light, packed with only what you need – a homely home and simple pleasures, one or two friends, worth the name, someone to love and someone to love you, a cat, a dog, and a pipe or two, enough to eat and enough to wear, and a little more than enough to drink; for thirst is a dangerous thing.


                Three Men in a boat by Jerome K Jerome
                




Introduction


Cricpy, the python avatar of my R package was born about a 9 months back see Introducing cricpy:A python package to analyze performances of cricketers. Cricpy, like its R twin, can analyze performance of batsmen & bowlers in Test, ODI and T20 formats. About a week and a half back, I added team analytics to my R package cricketr see Cricketr adds team analytics to its repertoire!!!. If cricketr has team analysis functions, then can cricpy be far behind? So, I have included the same 8 functions which can perform Team analytics into cricpy also. Team performance analysis can be done for Test, ODI and T20 matches.


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


You should be able to install the package using pip install cricpy. Please be mindful of ESPN Cricinfo Terms of Use


There are 5 functions which are used internally 1) getTeamData b) getTeamNumber c) getMatchType d) getTeamDataHomeAway e) cleanTeamData


and the external functions which a) teamWinLossStatusVsOpposition b) teamWinLossStatusAtGrounds c) plotTimelineofhttps://drive.google.com/file/d/1l4nQsRZ0C2FyPosigZmo0t-kC2xZZ_wl/view?usp=sharingWinsLosses


All the above functions are common to Test, ODI and T20 teams


The data for a particular Team can be obtained with the getTeamDataHomeAway() function from the package. This will return a dataframe of the team’s win/loss status at home and away venues over a period of time. This can be saved as a CSV file. Once this is done, you can use this CSV file for all subsequent analysis


This post has been published at Rpubs at teamAnalyticsCricpy You can download the PDF version of this post at teamAnalyticsCricpy


As before you can get the help for any of the cricpy functions as below


import cricpy.analytics as ca


help(ca.teamWinLossStatusAtGrounds)


## Help on function teamWinLossStatusAtGrounds in module cricpy.analytics:
## 
## teamWinLossStatusAtGrounds(file, teamName, opposition=['all'], homeOrAway=['all'], matchType='Test', plot=False)
##     Compute the wins/losses/draw/tied etc for a Team in Test, ODI or T20 at venues
##     
##     Description
##     
##     This function computes the won,lost,draw,tied or no result for a team against other teams in home/away or neutral venues and either returns a dataframe or plots it for grounds
##     
##     Usage
##     
##     teamWinLossStatusAtGrounds(file,teamName,opposition=["all"],homeOrAway=["all"],
##                   matchType="Test",plot=FALSE)
##     Arguments
##     
##     file        
##     The CSV file for which the plot is required
##     teamName    
##     The name of the team for which plot is required
##     opposition  
##     Opposition is a vector namely ["all")] or ["Australia", "India", "England"]
##     homeOrAway  
##     This parameter is a vector which is either ["all")] or a vector of venues ["home","away","neutral"]
##     matchType   
##     Match type - Test, ODI or T20
##     plot        
##     If plot=FALSE then a data frame is returned, If plot=TRUE then a plot is generated
##     Value
##     
##     None
##     
##     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.in/
##     See Also
##     
##     teamWinLossStatusVsOpposition teamWinLossStatusAtGrounds plotTimelineofWinsLosses
##     
##     Examples
##     
##     ## Not run: 
##     #Get the team data for India for Tests
##     
##     df =getTeamDataHomeAway(teamName="India",file="indiaOD.csv",matchType="ODI")
##     ca.teamWinLossStatusAtGrounds("india.csv",teamName="India",opposition=c("Australia","England","India"),
##                               homeOrAway=c("home","away"),plot=TRUE)
##     
##     ## End(Not run)






1. Get team data




1a. Test


The teams in Test cricket are included below


    

  1. Afghanistan 2.Bangladesh 3. England 4. World 5. India 6. Ireland 7. New Zealand 8. Pakistan 9. South Africa 10.Sri Lanka 11. West Indies 12.Zimbabwe
    2. 



You can use this for the teamName paramater. This will return a dataframe and also save the file as a CSV , if save=True


Note: – Since I have already got the data as CSV files I am not executing the lines below


import cricpy.analytics as ca
# Get the data for the teams. Save as CSV
#indiaTest= ca.getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="indiaTest.csv",save=True,teamName="India")
#ca.getTeamDataHomeAway(teamName="South Africa", matchType="Test", file="southafricaTest.csv", save=True)
#ca.getTeamDataHomeAway(teamName="West Indies", matchType="Test", file="westindiesTest.csv", save=True)
#newzealandTest = ca.getTeamDataHomeAway(matchType="Test",file="newzealandTest.csv",save=True,teamName="New Zealand")






1b. ODI


The ODI teams in the world are below. The data for these teams can be got by names as shown below


    

  1. Afghanistan 2. Africa XI 3. Asia XI 4.Australia 5.Bangladesh
    2. 

  2. Bermuda 7. England 8. ICC World X1 9. India 11.Ireland 12. New Zealand 13. Pakistan       14. South Africa 15.Sri Lanka 17. West Indies 18. Zimbabwe 19 Canada    21. East Africa        22. Hong Kong 23.Ireland 24. Kenya 25. Namibia 26.Nepal 27.Netherlands 28. Oman 29.Papua New Guinea 30. Scotland 31 United Arab Emirates 32. United States of America
    3. 



import cricpy.analytics as ca
#indiaODI=  ca.getTeamDataHomeAway(dir=".",matchType="ODI",file="indiaODI.csv",save=True,teamName="India")
#englandODI =  ca.getTeamDataHomeAway(matchType="ODI",file="englandODI.csv",save=True,teamName="England")
#westindiesODI = ca.getTeamDataHomeAway(matchType="ODI",file="westindiesODI.csv",save=True,teamName="West Indies")
#irelandODI <- ca.getTeamDataHomeAway(matchType="ODI",file="irelandODI.csv",save=True,teamName="Ireland")






1c T20


The T20 teams in the world are


    

  1. Afghanistan 2. Australia 3. Bahrain 4. Bangladesh 5. Belgium 6. Belize
    2. 

  2. Bermuda 8.Botswana 9. Canada 11. Costa Rica 12. Germany 13. Ghana
    3. 

  3. Guernsey 15. Hong Kong 16. ICC World X1 17.India 18. Ireland 19.Italy
    4. 

  4. Jersey 21. Kenya 22.Kuwait 23.Maldives 24.Malta 25.Mexico 26.Namibia
    
27.Nepal 28.Netherlands 29. New Zealand 30.Nigeria 31.Oman 32. Pakistan
    
33.Panama 34.Papua New Guinea 35. Philippines 36.Qatar 37.Saudi Arabia
    
38.Scotland 39.South Africa 40.Spain 41.Sri Lanka 42.Uganda 43.United Arab Emirates United States of America 44.Vanuatu 45.West Indies
    5. 



import cricpy.analytics as ca
#southafricaT20 = ca.getTeamDataHomeAway(matchType="T20",file="southafricaT20.csv",save=True,teamName="South Africa")
#srilankaT20 = ca.getTeamDataHomeAway(matchType="T20",file="srilankaT20.csv",save=True,teamName="Sri Lanka")
#canadaT20 = ca.getTeamDataHomeAway(matchType="T20",file="canadaT20.csv",save=True,teamName="Canada")
#afghanistanT20 = ca.getTeamDataHomeAway(matchType="T20",file="afghanistanT20.csv",save=True,teamName="Afghanistan")








2 Analysis of Test matches


The functions below perform analysis of Test teams






2a. Wins vs Loss against opposition


This function performs analysis of Test teams against other teams at home/away or neutral venue. Note:- The opposition can be a list of opposition teams. Similarly homeOrAway can also be a list of home/away/neutral venues.


import cricpy.analytics as ca
# Get the performance of Indian test team against all teams at all venues as a dataframe
df =ca.teamWinLossStatusVsOpposition("indiaTest.csv",teamName="India",opposition=["all"], homeOrAway=["all"], matchType="Test", plot=False)
print(df)



## ha                   away  home
## Opposition   Result            
## Afghanistan  won      0.0   1.0
## Australia    draw    20.0  23.0
##              lost    58.0  26.0
##              tied     0.0   2.0
##              won     13.0  39.0
## Bangladesh   draw     3.0   0.0
##              won      9.0   2.0
## England      draw    35.0  48.0
##              lost    68.0  26.0
##              won     13.0  33.0
## New Zealand  draw    18.0  28.0
##              lost    16.0   4.0
##              won     10.0  28.0
## Pakistan     draw    29.0  34.0
##              lost    14.0  10.0
##              won      2.0  13.0
## South Africa draw    13.0   3.0
##              lost    20.0  10.0
##              won      6.0  15.0
## Sri Lanka    draw    11.0  14.0
##              lost    14.0   0.0
##              won     16.0  13.0
## West Indies  draw    39.0  35.0
##              lost    32.0  28.0
##              won     13.0  21.0
## Zimbabwe     draw     1.0   1.0
##              lost     4.0   0.0
##              won      5.0   6.0


# Plot the performance of Indian Test team  against all teams at all venues
ca.teamWinLossStatusVsOpposition("indiaTest.csv",teamName="India",opposition=["all"],homeOrAway=["all"],matchType="Test",plot=True)















# Get the performance of Australia against India, England and New Zealand at all venues in Tests




df =ca.teamWinLossStatusVsOpposition("southafricaTest.csv",teamName="South Africa",opposition=["India","England","New Zealand"],homeOrAway=["all"],matchType="Test",plot=False)
print(df)

#Plot the performance of Australia against England, India and New Zealand only at home (Australia) 


## ha                  away  home
## Opposition  Result            
## England     draw      43    55
##             lost      60    62
##             won       26    34
## India       draw       5    14
##             lost      16     6
##             won        7    19
## New Zealand draw      20     7
##             lost       2     6
##             won       14    29


ca.teamWinLossStatusVsOpposition("southafricaTest.csv",teamName="South Africa",opposition=["India","England","New Zealand"],homeOrAway=["home","away"],matchType="Test",plot=True)





 


2b Wins vs losses of Test teams against opposition at different venues


import cricpy.analytics as ca
# Get the  performance of Pakistan against India, West Indies, South Africa at all venues in Tests and show performances at the venues
df = ca.teamWinLossStatusAtGrounds("westindiesTest.csv",teamName="West Indies",opposition=["India","Sri Lanka","South Africa"],homeOrAway=["all"],matchType="Test",plot=False)
print(df)

# Plot the performance of New Zealand Test team against England, Sri Lanka and Bangladesh at all grounds playes 


## ha                         away  home
## Ground             Result            
## Ahmedabad          won      2.0   0.0
## Basseterre         draw     0.0   3.0
## Bengaluru          draw     2.0   0.0
##                    won      2.0   0.0
## Bridgetown         draw     0.0   6.0
##                    lost     0.0   6.0
##                    won      0.0  14.0
## Cape Town          draw     2.0   0.0
##                    lost     6.0   0.0
## Centurion          lost     6.0   0.0
## Chennai            draw     4.0   0.0
##                    lost     8.0   0.0
##                    won      3.0   0.0
## Colombo (PSS)      lost     2.0   0.0
## Colombo (RPS)      draw     2.0   0.0
## Colombo (SSC)      lost     4.0   0.0
## Delhi              draw     6.0   0.0
##                    lost     2.0   0.0
##                    won      3.0   0.0
## Durban             lost     6.0   0.0
## Galle              draw     1.0   0.0
##                    lost     4.0   0.0
## Georgetown         draw     0.0  10.0
## Gros Islet         draw     0.0   5.0
##                    lost     0.0   2.0
## Hyderabad (Deccan) lost     2.0   0.0
## Johannesburg       lost     4.0   0.0
## Kandy              lost     4.0   0.0
## Kanpur             draw     1.0   0.0
##                    won      3.0   0.0
## Kingston           draw     0.0   8.0
##                    lost     0.0   4.0
##                    won      0.0  15.0
## Kingstown          draw     0.0   2.0
## Kolkata            draw     7.0   0.0
##                    lost     6.0   0.0
##                    won      3.0   0.0
## Mohali             won      2.0   0.0
## Moratuwa           draw     1.0   0.0
## Mumbai             draw     7.0   0.0
##                    lost     6.0   0.0
##                    won      2.0   0.0
## Mumbai (BS)        draw     5.0   0.0
##                    won      2.0   0.0
## Nagpur             draw     2.0   0.0
## North Sound        lost     0.0   2.0
## Pallekele          draw     1.0   0.0
## Port Elizabeth     draw     1.0   0.0
##                    lost     2.0   0.0
##                    won      2.0   0.0
## Port of Spain      draw     0.0  12.0
##                    lost     0.0  12.0
##                    won      0.0  10.0
## Providence         lost     0.0   2.0
## Rajkot             lost     2.0   0.0
## Roseau             draw     0.0   2.0
## St John's          draw     0.0   6.0
##                    lost     0.0   2.0
##                    won      0.0   2.0


ca. teamWinLossStatusAtGrounds("newzealandTest.csv",teamName="New Zealand",opposition=["England","Sri Lanka","Bangladesh"],homeOrAway=["all"],matchType="Test",plot=True)



 




2c. Plot the time line of wins vs losses of Test teams against opposition at different venues during an interval


import cricpy.analytics as ca
# Plot the time line of wins/losses of India against Australia, West Indies, South Africa in away/neutral venues
#from 2000-01-01 to 2017-01-01
ca.plotTimelineofWinsLosses("indiaTest.csv",teamName="India",opposition=["Australia","West Indies","South Africa"],
                         homeOrAway=["away","neutral"], startDate="2000-01-01",endDate="2017-01-01")
#Plot the time line of wins/losses of Indian Test team from 1970 onwards






ca.plotTimelineofWinsLosses("indiaTest.csv",teamName="India",startDate="1970-01-01",endDate="2017-01-01")





3 ODI


The functions below perform analysis of ODI teams listed above




3a. Wins vs Loss against opposition ODI teams


This function performs analysis of ODI teams against other teams at home/away or neutral venue. Note:- The opposition can be a vector of opposition teams. Similarly homeOrAway can also be a vector of home/away/neutral venues.


import cricpy.analytics as ca
# Get the performance of West Indies in ODIs against all other ODI teams at all venues and retirn as a dataframe
df = ca.teamWinLossStatusVsOpposition("westindiesODI.csv",teamName="West Indies",opposition=["all"],homeOrAway=["all"],matchType="ODI",plot=False)
print(df)

# Plot the performance of West Indies in ODIs against Sri Lanka, India at all venues


## ha                   away  home  neutral
## Opposition   Result                     
## Afghanistan  lost     0.0   1.0      2.0
##              won      0.0   1.0      0.0
## Australia    lost    41.0  25.0      8.0
##              n/r      3.0   0.0      0.0
##              tied     1.0   2.0      0.0
##              won     35.0  18.0      7.0
## Bangladesh   lost     6.0   5.0      3.0
##              n/r      1.0   0.0      1.0
##              won     10.0   8.0      3.0
## Bermuda      won      0.0   0.0      1.0
## Canada       won      2.0   1.0      1.0
## England      lost    22.0  17.0     12.0
##              n/r      0.0   3.0      0.0
##              won     15.0  23.0      6.0
## India        lost    27.0  14.0     18.0
##              n/r      0.0   1.0      0.0
##              tied     1.0   0.0      1.0
##              won     27.0  20.0     15.0
## Ireland      lost     0.0   0.0      1.0
##              won      2.0   3.0      2.0
## Kenya        lost     0.0   0.0      1.0
##              won      3.0   0.0      2.0
## Netherlands  won      0.0   0.0      2.0
## New Zealand  lost    19.0   5.0      3.0
##              n/r      2.0   0.0      2.0
##              won     10.0  15.0      5.0
## P.N.G.       won      0.0   0.0      1.0
## Pakistan     lost    11.0  15.0     34.0
##              tied     1.0   2.0      0.0
##              won     14.0  16.0     41.0
## Scotland     won      0.0   0.0      3.0
## South Africa lost    20.0  17.0      7.0
##              n/r      1.0   0.0      0.0
##              tied     0.0   0.0      1.0
##              won      5.0   7.0      3.0
## Sri Lanka    lost     9.0   5.0     11.0
##              n/r      2.0   1.0      0.0
##              won      3.0   5.0     20.0
## U.A.E.       won      0.0   0.0      2.0
## Zimbabwe     lost     4.0   1.0      5.0
##              n/r      0.0   1.0      0.0
##              tied     1.0   0.0      0.0
##              won      9.0  15.0     12.0


ca.teamWinLossStatusVsOpposition("westindiesODI.csv",teamName="West Indies",opposition=["Sri Lanka", "India"],homeOrAway=["all"],matchType="ODI",plot=True)















#Plot the performance of Ireland in ODIs against Zimbabwe, Kenya, bermuda, UAE, Oman and Scotland at all venues





ca.teamWinLossStatusVsOpposition("irelandODI.csv",teamName="Ireland",opposition=["Zimbabwe","Kenya","Bermuda","U.A.E.","Oman","Scotland"],homeOrAway=["all"],matchType="ODI",plot=True)


 


3b Wins vs losses of ODI teams against opposition at different venues


import cricpy.analytics as ca
# Plot the performance of England ODI team against Bangladesh, West Indies and Australia at all venues
ca.teamWinLossStatusAtGrounds("englandODI.csv",teamName="England",opposition=["West Indies"],homeOrAway=["all"],matchType="ODI",plot=True)
























#Plot the performance of India against South Africa, West Indies and Australia at 'home' venues


ca.teamWinLossStatusAtGrounds("indiaODI.csv",teamName="India",opposition=["South Africa"],homeOrAway=["home"],matchType="ODI",plot=True)


 




3c. Plot the time line of wins vs losses of ODI teams against opposition at different venues during an interval



import cricpy.analytics as ca
#Plot the time line of wins/losses of Bangladesh ODI team between 2015 and 2019 against all other teams and at
# all venues
ca.plotTimelineofWinsLosses("bangladeshOD.csv",teamName="Bangladesh",startDate="2015-01-01",endDate="2019-01-01",matchType="ODI")























#Plot the time line of wins/losses of India ODI against Sri Lanka, Bangladesh from 2016 to 2019


ca.plotTimelineofWinsLosses("indiaODI.csv",teamName="India",opposition=["Sri Lanka","Bangladesh"],startDate="2016-01-01",endDate="2019-01-01",matchType="ODI")



 




4 Twenty 20


The functions below perform analysis of Twenty 20 teams listed above




4a. Wins vs Loss against opposition ODI teams


This function performs analysis of T20 teams against other T20 teams at home/away or neutral venue. Note:- The opposition can be a list of opposition teams. Similarly homeOrAway can also be a list of home/away/neutral venues.


import cricpy.analytics as ca
# Get the performance of South Africa T20 team against England, India and Sri Lanka at home grounds at England
df = ca.teamWinLossStatusVsOpposition("southafricaT20.csv",teamName="South Africa",opposition=["England","India","Sri Lanka"], homeOrAway=["home"], matchType="T20", plot=False)
print(df)

#Plot the performance of South Africa T20 against England, India and Sri Lanka at all venues


## ha                 home
## Opposition Result      
## England    lost       1
##            won        4
## India      lost       5
##            won        2
## Sri Lanka  lost       2
##            tied       1
##            won        3


ca.teamWinLossStatusVsOpposition("southafricaT20.csv",teamName="South Africa", opposition=["England","India","Sri Lanka"],homeOrAway=["all"],matchType="T20",plot=True)

























#Plot the performance of Afghanistan T20 teams against all oppositions




 


ca.teamWinLossStatusVsOpposition("afghanistanT20.csv",teamName="Afghanistan",opposition=["all"],homeOrAway=["all"],matchType="T20",plot=True)



 




4b Wins vs losses of T20 teams against opposition at different venues


# Compute the performance of Canada against all opposition at all venues and show by grounds. Return as dataframe
df =ca.teamWinLossStatusAtGrounds("canadaT20.csv",teamName="Canada",opposition=["all"],homeOrAway=["all"],matchType="T20",plot=False)
print(df)

# Plot the performance of Sri Lanka T20 team against India and Bangladesh in different venues at home/away and neutral


## ha                     home  neutral
## Ground         Result               
## Abu Dhabi      lost     0.0      1.0
## Belfast        lost     0.0      1.0
##                won      0.0      2.0
## Colombo (SSC)  lost     0.0      1.0
##                won      0.0      1.0
## Dubai (DSC)    lost     0.0      5.0
## ICCA Dubai     lost     0.0      2.0
##                won      0.0      1.0
## King City (NW) lost     3.0      0.0
##                tied     1.0      0.0
## Sharjah        lost     0.0      1.0


ca.teamWinLossStatusAtGrounds("srilankaT20.csv",teamName="Sri Lanka",opposition=["India", "Bangladesh"], homeOrAway=["all"], matchType="T20", plot=True)



 




4c. Plot the time line of wins vs losses of T20 teams against opposition at different venues during an interval


#Plot the time line of Sri Lanka T20 team agaibst all opposition
ca.plotTimelineofWinsLosses("srilankaT20.csv",teamName="Sri Lanka",opposition=["Australia", "Pakistan"], startDate="2013-01-01", endDate="2019-01-01",  matchType="T20")





















# Plot the time line of South Africa T20 between 2010 and 2015 against West Indies and Pakistan


ca.plotTimelineofWinsLosses("southafricaT20.csv",teamName="South Africa",opposition=["West Indies", "Pakistan"], startDate="2010-01-01", endDate="2015-01-01",  matchType="T20")





Conclusion


With the above additional functions cricpy can now analyze batsmen, bowlers and teams in all formats of the game (Test, ODI and T20).


Have fun with cricpy!!!


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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


Cricpy can now analyze performances of teams in Test, ODI and T20 cricket see Cricpy adds team analytics to its arsenal!!


This post is also hosted on Rpubs at Int


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.


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


Untitled






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


    

  1. Frequency percentage of runs in each run range over the whole career
    2. 

  2. Mean Strike Rate for runs scored in the given range
    3. 

  3. A histogram of runs frequency percentages in runs ranges
    4. 



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


    

  1. Virat Kohli: Runs – 2167, Average:49.25 ,Strike rate-136.11
    2. 

  2. MJ Guptill : Runs -2271, Average:34.4 ,Strike rate-132.88
    3. 

  3. Mohammed Shahzad :Runs – 1936, Average:31.22 ,Strike rate-134.81
    4. 

  4. BB McCullum : Runs – 2140, Average:35.66 ,Strike rate-136.21
    5. 



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


    

  1. Shakib Hasan:Wickets: 80, Average = 21.07, Economy Rate – 6.74
    2. 

  2. Mohammed Nabi : Wickets: 67, Average = 24.25, Economy Rate – 7.13
    3. 

  3. Rashid Khan: Wickets: 64, Average = 12.40, Economy Rate – 6.01
    4. 

  4. Imran Tahir : Wickets:62, Average – 14.95, Economy Rate – 6.77
    5. 







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


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