Cricpy performs granular analysis of players

“Gold medals aren’t really made of gold. They’re made of sweat, determination, & a hard-to-find alloy called guts.” Dan Gable

“It doesn’t matter whether you are pursuing success in business, sports, the arts, or life in general: The bridge between wishing and accomplishing is discipline” Harvey Mackay

“I won’t predict anything historic. But nothing is impossible.” Michael Phelps

Introduction

In this post, I introduce 2 new functions in my Python package ‘cricpy’ (cricpy v0.20) see Introducing cricpy:A python 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. Step 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 reducedsubset 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 cricpy functions can be used on this smaller fine-grained data set for a closer analysis of players

This post has been published in Rpubs and can be accessed at Cricpy performs granular analysis of players

You can download a PDF version of this post at Cricpy performs granular analysis of players

I have also updated the cricpy template with these lastest changes. See cricpy-template

1. Analyzing Rahul Dravid at 3 different stages of his career

The following functions analyze Rahul Dravid during 3 different periods of his illustrious career. a) 1st Jan 2001-1st Jan 2002 b) 1st Jan 2004-1st Jan 2005 c) 1st Jan 2009-1st Jan 2010

import cricpy.analytics as ca
# Get the homeOrAway dataset for Dravid in matches
# Note:Since I have already got the data I reuse the CSV file
#df=ca.getPlayerDataHA(28114,tfile="dravidTestHA.csv",matchType="Test")

# Get Dravid's data for 2001-02
df1=ca.getPlayerDataOppnHA(infile="dravidTestHA.csv",outfile="dravidTest2001.csv",startDate="2001-01-01",endDate="2002-01-01")

# Get Dravid's data for 2004-05
df2=ca.getPlayerDataOppnHA(infile="dravidTestHA.csv",outfile="dravidTest2004.csv", startDate="2004-01-01",endDate="2005-01-01")

# Get Dravid's data for 2009-10
df3=ca.getPlayerDataOppnHA(infile="dravidTestHA.csv",outfile="dravidTest2009.csv",startDate="2009-01-01",endDate="2010-01-01")

1a. Plot the performance of Dravid at venues during 2001,2004,2009

Note: Any of the cricpy functions can be used on the fine-grained subset of data as below.

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("dravidTest2001.csv","Dravid-2001")

ca.batsmanAvgRunsGround("dravidTest2004.csv","Dravid-2004")

ca.batsmanAvgRunsGround("dravidTest2009.csv","Dravid-2009")


1b. Plot the performance of Dravid against different oppositions during 2001,2004,2009

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("dravidTest2001.csv","Dravid-2001")

ca.batsmanAvgRunsOpposition("dravidTest2004.csv","Dravid-2004")


ca.batsmanAvgRunsOpposition("dravidTest2009.csv","Dravid-2009")


1c. Plot the relative cumulative average and relative strike rate of Dravid in 2001,2004,2009

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

import cricpy.analytics as ca
frames=["dravidTest2001.csv","dravidTest2004.csv","dravidTest2009.csv"]
names=["Dravid-2001","Dravid-2004","Dravid-2009"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

 

ca.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

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

# Get the homeOrAway data for Kohli in Test matches
df=ca.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=ca.getPlayerDataOppnHA(infile="kohliTestHA.csv",outfile="kohliTestEng2014.csv",  opposition=["England"],homeOrAway=["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=ca.getPlayerDataOppnHA(infile="kohliTestHA.csv",outfile="kohliTestEng2018.csv",
   opposition=["England"],homeOrAway=["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

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("kohliTestEng2014.csv","Kohli-Eng-2014")
ca.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

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("kohliTestEng2014.csv", "Kohli-Eng-2014")

ca.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

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

# Slice the data 
df=ca.getPlayerDataOppnHA(infile="gangulyTestHA.csv",outfile="gangulyTestAES2002-08.csv" ,opposition=["Australia", "England", "South Africa"],                        homeOrAway=["away"],startDate="2002-01-01",endDate="2008-01-01")
df=ca.getPlayerDataOppnHA(infile="dravidTestHA.csv",outfile="dravidTestAES2002-08.csv" ,opposition=["Australia", "England", "South Africa"],                        homeOrAway=["away"],startDate="2002-01-01",endDate="2008-01-01")
df=ca.getPlayerDataOppnHA(infile="laxmanTestHA.csv",outfile="laxmanTestAES2002-08.csv",opposition=["Australia", "England", "South Africa"],                       homeOrAway=["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

import cricpy.analytics as ca
frames=["gangulyTestAES2002-08.csv","dravidTestAES2002-08.csv","laxmanTestAES2002-08.csv"]
names=["GangulyAusEngSA2002-08","DravidAusEngSA2002-08","LaxmanAusEngSA2002-08"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

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

# Subset the data for specific opposition in away and neutral venues
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\compat\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
##   from pandas.core import datetools
df=ca.getPlayerDataOppnHA(infile="rohitODIHA.csv",outfile="rohitODIAusWISA.csv"
                       ,opposition=["Australia", "West Indies", "South Africa"],
                      homeOrAway=["away","neutral"])
df=ca.getPlayerDataOppnHA(infile="joerootODIHA.csv",outfile="joerootODIAusWISA.csv"
                       ,opposition=["Australia", "West Indies", "South Africa"],
                       homeOrAway=["away","neutral"])
df=ca.getPlayerDataOppnHA(infile="williamsonODIHA.csv",outfile="williamsonODIAusWiSA.csv",opposition=["Australia", "West Indies", "South Africa"],                    homeOrAway=["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

import cricpy.analytics as ca
frames=["rohitODIAusWISA.csv","joerootODIAusWISA.csv","williamsonODIAusWiSA.csv"]
names=["Rohit-ODI-AusWISA","Joe Root-ODI-AusWISA","Williamson-ODI-AusWISA"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

ca.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

import cricpy.analytics as ca
# Get the HA T20 data for Dhoni
#df=ca.getPlayerDataHA(28081,tfile="dhoniT20HA.csv",type="batting",matchType="T20")
#Subset the data
df=ca.getPlayerDataOppnHA(infile="dhoniT20HA.csv",outfile="dhoniT20AusWISAEng.csv",opposition=["Australia", "West Indies", "South Africa","England"],                homeOrAway=["all"])

5a. Plot Dhoni’s performances in T20

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

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("dhoniT20AusWISAEng.csv","Dhoni")

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

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

ca.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

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

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

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

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

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

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("kumbleTest-NZWISAEng.csv","Kumble-NZWISAEng-AN")

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

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

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

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("kumbleTest-NZWISAEng.csv","Kumble")

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

ca.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
import cricpy.analytics as ca
frames=["kumbleTest-NZWISAEng.csv","warneTest-NZWISAEng.csv","muraliTest-NZWISAEng.csv"]
names=["Kumble","Warne","Murali"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

ca.relativeBowlerCumulativeAvgWickets(frames,names)

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

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

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

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

df=ca.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
import cricpy.analytics as ca
frames=["bumrahODI2016.csv","bumrahODI2017.csv","bumrahODI2018.csv"]
names=["Bumrah-2016","Bumrah-2017","Bumrah-2018"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

ca.relativeBowlerCumulativeAvgWickets(frames,names)

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

import cricpy.analytics as ca
# Get the HA bowling data for Shakib, Bumrah and Jadeja
#df=ca.getPlayerDataHA(56143,tfile="shakibT20HA.csv",type="bowling",matchType="T20")
#df=ca.getPlayerDataHA(625383,tfile="bumrahT20HA.csv",type="bowling",matchType="T20")
#df=ca.getPlayerDataHA(234675,tfile="jadejaT20HA.csv",type="bowling",matchType="T20")

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

df=ca.getPlayerDataOppnHA(infile="jadejaT20HA.csv",outfile="jadejaT20-SLAusSAEng.csv"                      ,opposition=["Sri Lanka","Australia", "South Africa","England"],   homeOrAway=["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
import cricpy.analytics as ca
frames=["shakibT20-SLAusSAEng.csv","bumrahT20-SLAusSAEng.csv","jadejaT20-SLAusSAEng.csv"]
names=["Shakib-SLAusSAEng","Bumrah-SLAusSAEng","Jadeja-SLAusSAEng"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

ca.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 cricpy!!!

Also see
1. My book ‘Cricket analytics with cricketr and cricpy’ is now on Amazon
2. The 3rd paperback & kindle editions of my books on Cricket, now on Amazon
3. Exploring Quantum Gate operations with QCSimulator
4. Deep Learning from first principles in Python, R and Octave – Part 6
5. Natural selection of database technology through the years
6. Pitching yorkpy … short of good length to IPL – Part 1
7. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
8. Practical Machine Learning with R and Python – Part 3

To see all posts click Index of posts

Updated:Analyzing performance of cricketers and cricket teams with cricketr templates

Note: I have included the latest set of functions that perform granular analysis of batsmen and bowlers to the cricketr template below! You can download this RMarkdown file from Github at 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 performancs 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

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 directly with

1. Install the cricketr package

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

The cricketr package includes some pre-packaged sample (.csv) files. You can use these sample to test functions as shown below

# Retrieve the file path of a data file installed with cricketr
#pathToFile <- system.file("data", "tendulkar.csv", package = "cricketr")
#batsman4s(pathToFile, "Sachin Tendulkar")

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

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

2. For Test cricket

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

2a. For ODI cricket

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

2b For Twenty 20 cricket

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

Important Note 1 This needs to be done only once for a player. This function stores the player’s data in a CSV file (for e.g. tendulkar.csv as above) which can then be reused for all other functions. Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerData for all subsequent analyses

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

Sachin Tendulkar’s performance – Basic Analyses

The 3 plots below provide the following for Tendulkar

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges For example

3. Basic analyses

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

4. More analyses

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

# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")
dev.off()
## null device 
##           1
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#batsman4s("./player2.csv","Player2")
#batsman6s("./player2.csv","Player2")
#batsmanMeanStrikeRate("./player2.csv","Player2")
# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")
dev.off()
## null device 
##           1
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#batsman4s("./player3.csv","Player3")
#batsman6s("./player3.csv","Player3")
#batsmanMeanStrikeRate("./player3.csv","Player3")
# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")

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

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

5.Boxplot histogram plot

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

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

6. Contribution to won and lost matches

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

#player1sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player1sp.csv",ttype="batting")
#player2sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player2sp.csv",ttype="batting")
#player3sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player3sp.csv",ttype="batting")
#player4sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player4sp.csv",ttype="batting")
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanContributionWonLost("player1sp.csv","Player1")
#batsmanContributionWonLost("player2sp.csv","Player2")
#batsmanContributionWonLost("player3sp.csv","Player3")
#batsmanContributionWonLost("player4sp.csv","Player4")
dev.off()
## null device 
##           1

7, Performance at home and overseas

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

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanPerfHomeAway("player1sp.csv","Player1")
#batsmanPerfHomeAway("player2sp.csv","Player2")
#batsmanPerfHomeAway("player3sp.csv","Player3")
#batsmanPerfHomeAway("player4sp.csv","Player4")
dev.off()
## null device 
##           1

8. Batsman average at different venues

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanAvgRunsGround("./player1.csv","Player1")
#batsmanAvgRunsGround("./player2.csv","Player2")
#batsmanAvgRunsGround("./player3.csv","Ponting")
#batsmanAvgRunsGround("./player4.csv","Player4")
dev.off()
## null device 
##           1

9. Batsman average against different opposition

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanAvgRunsOpposition("./player1.csv","Player1")
#batsmanAvgRunsOpposition("./player2.csv","Player2")
#batsmanAvgRunsOpposition("./player3.csv","Ponting")
#batsmanAvgRunsOpposition("./player4.csv","Player4")
dev.off()
## null device 
##           1

10. Runs Likelihood of batsman

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanRunsLikelihood("./player1.csv","Player1")
#batsmanRunsLikelihood("./player2.csv","Player2")
#batsmanRunsLikelihood("./player3.csv","Ponting")
#batsmanRunsLikelihood("./player4.csv","Player4")
dev.off()
## null device 
##           1

11. Moving Average of runs in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanMovingAverage("./player1.csv","Player1")
#batsmanMovingAverage("./player2.csv","Player2")
#batsmanMovingAverage("./player3.csv","Ponting")
#batsmanMovingAverage("./player4.csv","Player4")
dev.off()
## null device 
##           1

12. Cumulative Average runs of batsman in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanCumulativeAverageRuns("./player1.csv","Player1")
#batsmanCumulativeAverageRuns("./player2.csv","Player2")
#batsmanCumulativeAverageRuns("./player3.csv","Ponting")
#batsmanCumulativeAverageRuns("./player4.csv","Player4")
dev.off()
## null device 
##           1

13. Cumulative Average strike rate of batsman in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanCumulativeStrikeRate("./player1.csv","Player1")
#batsmanCumulativeStrikeRate("./player2.csv","Player2")
#batsmanCumulativeStrikeRate("./player3.csv","Ponting")
#batsmanCumulativeStrikeRate("./player4.csv","Player4")
dev.off()
## null device 
##           1

14. Future Runs forecast

Here are plots that forecast how the batsman will perform in future. In this case 90% of the career runs trend is uses as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated runs trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

Take a look at the runs forecasted for the batsman below.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanPerfForecast("./player1.csv","Player1")
#batsmanPerfForecast("./player2.csv","Player2")
#batsmanPerfForecast("./player3.csv","Player3")
#batsmanPerfForecast("./player4.csv","Player4")
dev.off()
## null device 
##           1

15. Relative Mean Strike Rate plot

The plot below compares the Mean Strike Rate of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following

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

16. Relative Runs Frequency plot

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

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

17. Relative cumulative average runs in career

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

18. Relative cumulative average strike rate in career

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

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

The below computation uses Null Hypothesis testing and p-value to determine if the batsman is in-form or out-of-form. For this 90% of the career runs is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the batsman continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the batsman is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

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

This is done for the Top 4 batsman

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

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

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

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
#battingPerf3d("./player1.csv","Player1")
#battingPerf3d("./player2.csv","Player2")
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
#battingPerf3d("./player3.csv","Player3")
#battingPerf3d("./player4.csv","player4")
dev.off()
## null device 
##           1

21. Predicting Runs given Balls Faced and Minutes at Crease

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

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
#Player1 <- batsmanRunsPredict("./player1.csv","Player1",newdataframe=newDF)
#Player2 <- batsmanRunsPredict("./player2.csv","Player2",newdataframe=newDF)
#ponting <- batsmanRunsPredict("./player3.csv","Player3",newdataframe=newDF)
#sangakkara <- batsmanRunsPredict("./player4.csv","Player4",newdataframe=newDF)
#batsmen <-cbind(round(Player1$Runs),round(Player2$Runs),round(Player3$Runs),round(Player4$Runs))
#colnames(batsmen) <- c("Player1","Player2","Player3","Player4")
#newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
#colnames(newDF) <- c("BallsFaced","MinsAtCrease")
#predictedRuns <- cbind(newDF,batsmen)
#predictedRuns

Analysis of bowlers

  1. Bowler1
  2. Bowler2
  3. Bowler3
  4. Bowler4

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

21. Wicket Frequency Plot

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

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

22. Wickets Runs plot

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

23. Average wickets at different venues

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

24. Average wickets against different opposition

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

25. Wickets taken moving average

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

dev.off()
## null device 
##           1

26. Cumulative Wickets taken

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

27. Cumulative Economy rate

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

28. Future Wickets forecast

Here are plots that forecast how the bowler will perform in future. In this case 90% of the career wickets trend is used as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated wickets trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

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

29. Contribution to matches won and lost

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

#bowler1sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler1sp.csv",ttype="bowling")
#bowler2sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler2sp.csv",ttype="bowling")
#bowler3sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler3sp.csv",ttype="bowling")
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerContributionWonLost("bowler1sp","Bowler1")
#bowlerContributionWonLost("bowler2sp","Bowler2")
#bowlerContributionWonLost("bowler3sp","Bowler3")
dev.off()
## null device 
##           1

30. Performance home and overseas.

This can only be done for Test matches

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

31 Relative Wickets Frequency Percentage

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

32 Relative Economy Rate against wickets taken

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

33 Relative cumulative average wickets of bowlers in career

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

34 Relative cumulative average economy rate of bowlers

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

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

The below computation uses Null Hypothesis testing and p-value to determine if the bowler is in-form or out-of-form. For this 90% of the career wickets is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the bowler continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the bowler is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

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

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

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

36. Performing granular analysis of batsmen and bowlers

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

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

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

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

8. 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!!

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

“He felt that his whole life was some kind of dream and he sometimes wondered whose it was and whether they were enjoying it.”

“The ships hung in the sky in much the same way that bricks don’t.”

“We demand rigidly defined areas of doubt and uncertainty!”

“For a moment, nothing happened. Then, after a second or so, nothing continued to happen.”

“The Answer to the Great Question… Of Life, the Universe and Everything… Is… Forty-two,’ said Deep Thought, with infinite majesty and calm.”

                 The Hitchhiker's Guide to the Galaxy - Douglas Adams

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!!

Untitled

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. The strike rate in 2001 for Tendulkar is amazing 60+
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. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges For example

3. Basic analyses

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

4. More analyses

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

# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")
dev.off()
## null device 
##           1
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#batsman4s("./player2.csv","Player2")
#batsman6s("./player2.csv","Player2")
#batsmanMeanStrikeRate("./player2.csv","Player2")
# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")
dev.off()
## null device 
##           1
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#batsman4s("./player3.csv","Player3")
#batsman6s("./player3.csv","Player3")
#batsmanMeanStrikeRate("./player3.csv","Player3")
# For ODI and T20
#batsmanScoringRateODTT("./player1.csv","Player1")

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

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

5.Boxplot histogram plot

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

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

6. Contribution to won and lost matches

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

#player1sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player1sp.csv",ttype="batting")
#player2sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player2sp.csv",ttype="batting")
#player3sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player3sp.csv",ttype="batting")
#player4sp <- getPlayerDataSp(xxxx,tdir=".",tfile="player4sp.csv",ttype="batting")
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanContributionWonLost("player1sp.csv","Player1")
#batsmanContributionWonLost("player2sp.csv","Player2")
#batsmanContributionWonLost("player3sp.csv","Player3")
#batsmanContributionWonLost("player4sp.csv","Player4")
dev.off()
## null device 
##           1

7, Performance at home and overseas

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

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanPerfHomeAway("player1sp.csv","Player1")
#batsmanPerfHomeAway("player2sp.csv","Player2")
#batsmanPerfHomeAway("player3sp.csv","Player3")
#batsmanPerfHomeAway("player4sp.csv","Player4")
dev.off()
## null device 
##           1

8. Batsman average at different venues

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanAvgRunsGround("./player1.csv","Player1")
#batsmanAvgRunsGround("./player2.csv","Player2")
#batsmanAvgRunsGround("./player3.csv","Ponting")
#batsmanAvgRunsGround("./player4.csv","Player4")
dev.off()
## null device 
##           1

9. Batsman average against different opposition

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanAvgRunsOpposition("./player1.csv","Player1")
#batsmanAvgRunsOpposition("./player2.csv","Player2")
#batsmanAvgRunsOpposition("./player3.csv","Ponting")
#batsmanAvgRunsOpposition("./player4.csv","Player4")
dev.off()
## null device 
##           1

10. Runs Likelihood of batsman

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanRunsLikelihood("./player1.csv","Player1")
#batsmanRunsLikelihood("./player2.csv","Player2")
#batsmanRunsLikelihood("./player3.csv","Ponting")
#batsmanRunsLikelihood("./player4.csv","Player4")
dev.off()
## null device 
##           1

11. Moving Average of runs in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanMovingAverage("./player1.csv","Player1")
#batsmanMovingAverage("./player2.csv","Player2")
#batsmanMovingAverage("./player3.csv","Ponting")
#batsmanMovingAverage("./player4.csv","Player4")
dev.off()
## null device 
##           1

12. Cumulative Average runs of batsman in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanCumulativeAverageRuns("./player1.csv","Player1")
#batsmanCumulativeAverageRuns("./player2.csv","Player2")
#batsmanCumulativeAverageRuns("./player3.csv","Ponting")
#batsmanCumulativeAverageRuns("./player4.csv","Player4")
dev.off()
## null device 
##           1

13. Cumulative Average strike rate of batsman in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanCumulativeStrikeRate("./player1.csv","Player1")
#batsmanCumulativeStrikeRate("./player2.csv","Player2")
#batsmanCumulativeStrikeRate("./player3.csv","Ponting")
#batsmanCumulativeStrikeRate("./player4.csv","Player4")
dev.off()
## null device 
##           1

14. Future Runs forecast

Here are plots that forecast how the batsman will perform in future. In this case 90% of the career runs trend is uses as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated runs trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

Take a look at the runs forecasted for the batsman below.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
#batsmanPerfForecast("./player1.csv","Player1")
#batsmanPerfForecast("./player2.csv","Player2")
#batsmanPerfForecast("./player3.csv","Player3")
#batsmanPerfForecast("./player4.csv","Player4")
dev.off()
## null device 
##           1

15. Relative Mean Strike Rate plot

The plot below compares the Mean Strike Rate of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following

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

16. Relative Runs Frequency plot

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

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

17. Relative cumulative average runs in career

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

18. Relative cumulative average strike rate in career

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

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

The below computation uses Null Hypothesis testing and p-value to determine if the batsman is in-form or out-of-form. For this 90% of the career runs is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the batsman continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the batsman is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

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

This is done for the Top 4 batsman

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

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

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

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
#battingPerf3d("./player1.csv","Player1")
#battingPerf3d("./player2.csv","Player2")
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
#battingPerf3d("./player3.csv","Player3")
#battingPerf3d("./player4.csv","player4")
dev.off()
## null device 
##           1

21. Predicting Runs given Balls Faced and Minutes at Crease

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

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
#Player1 <- batsmanRunsPredict("./player1.csv","Player1",newdataframe=newDF)
#Player2 <- batsmanRunsPredict("./player2.csv","Player2",newdataframe=newDF)
#ponting <- batsmanRunsPredict("./player3.csv","Player3",newdataframe=newDF)
#sangakkara <- batsmanRunsPredict("./player4.csv","Player4",newdataframe=newDF)
#batsmen <-cbind(round(Player1$Runs),round(Player2$Runs),round(Player3$Runs),round(Player4$Runs))
#colnames(batsmen) <- c("Player1","Player2","Player3","Player4")
#newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
#colnames(newDF) <- c("BallsFaced","MinsAtCrease")
#predictedRuns <- cbind(newDF,batsmen)
#predictedRuns

Analysis of bowlers

  1. Bowler1
  2. Bowler2
  3. Bowler3
  4. Bowler4

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

21. Wicket Frequency Plot

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

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

22. Wickets Runs plot

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

23. Average wickets at different venues

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

24. Average wickets against different opposition

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

25. Wickets taken moving average

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

dev.off()
## null device 
##           1

26. Cumulative Wickets taken

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

27. Cumulative Economy rate

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

28. Future Wickets forecast

Here are plots that forecast how the bowler will perform in future. In this case 90% of the career wickets trend is used as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated wickets trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

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

29. Contribution to matches won and lost

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

#bowler1sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler1sp.csv",ttype="bowling")
#bowler2sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler2sp.csv",ttype="bowling")
#bowler3sp <- getPlayerDataSp(xxxx,tdir=".",tfile="bowler3sp.csv",ttype="bowling")
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
#bowlerContributionWonLost("bowler1sp","Bowler1")
#bowlerContributionWonLost("bowler2sp","Bowler2")
#bowlerContributionWonLost("bowler3sp","Bowler3")
dev.off()
## null device 
##           1

30. Performance home and overseas.

This can only be done for Test matches

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

31 Relative Wickets Frequency Percentage

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

32 Relative Economy Rate against wickets taken

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

33 Relative cumulative average wickets of bowlers in career

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

34 Relative cumulative average economy rate of bowlers

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

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

The below computation uses Null Hypothesis testing and p-value to determine if the bowler is in-form or out-of-form. For this 90% of the career wickets is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the bowler continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the bowler is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

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

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

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

36. Performing granular analysis of batsmen and bowlers

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

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

37. GetPlayerDataHA (Batsmen, Tests)

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

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

38. GetPlayerDataHA (Bowlers, Tests)

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

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

39. GetPlayerDataHA (Batsmen, ODI)

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

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

40. GetPlayerDataHA (Bowlers, ODI)

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

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

41. GetPlayerDataHA (Batsmen, T20)

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

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

42. GetPlayerDataHA (Bowlers, T20)

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

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

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

B. Performances of teams

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

1a. Get Test team data

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

1b. Get ODI team data

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

1c. Get T20 team data

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

2a. Test – Analyzing test performances against opposition

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

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

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

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

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

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

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

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

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

3a. ODI – Analyzing test performances against opposition

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

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

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

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

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

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

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

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

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

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

4a. T20 – Analyzing test performances against opposition

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

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

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

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

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

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

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

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

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

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

Key Findings

Analysis of batsman

Analysis of bowlers

Analysis of teams

Conclusion

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

Have fun with cricketr!!

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

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

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. 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
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. Bermuda 8.Botswana 9. Canada 11. Costa Rica 12. Germany 13. Ghana
  3. Guernsey 15. Hong Kong 16. ICC World X1 17.India 18. Ireland 19.Italy
  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
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

Cricketr adds team analytics to its repertoire!!!

And she’s got brains enough for two, which is the exact quantity the girl who marries you will need.

“I’m not absolutely certain of the facts, but I rather fancy it’s Shakespeare who says that it’s always just when a fellow is feeling particularly braced with things in general that Fate sneaks up behind him with the bit of lead piping.”

“A melancholy-looking man, he had the appearance of one who has searched for the leak in life’s gas-pipe with a lighted candle.”

“It isn’t often that Aunt Dahlia lets her angry passions rise, but when she does, strong men climb trees and pull them up after them.”

“Some minds are like soup in a poor restaurant – better left unstirred.”

                                      P.G. Wodehouse

Introduction

My R package cricketr had its genesis about 4 years ago, sometime around June 2015. There were some minor updates afterwards and the package performed analytics on cricketers (Test, ODI and T20) based on data from ESPN Cricinfo see Re-introducing cricketr! : An R package to analyze performances of cricketers. Now, in the latest release of cricketr, I have included 8 functions which can perform Team analytics. Team analysis can be done for Test, ODI and T20 teams.

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 for players (batsmen & bowlers) and also teams (Test, ODI and T20)

You should be able to install the package directly from CRAN. Please be mindful of ESPN Cricinfo Terms of Use

A total of 8 new functions which deal with team analytics has been included in the latest release.

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

and the external functions which are
a) teamWinLossStatusVsOpposition
b) teamWinLossStatusAtGrounds
c) plotTimelineofWinsLosses

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

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

#help(teamWinLossStatusVsOpposition)
Compute the wins/losses/draw/tied etc for a Team in Test, ODI or T20 against opposition
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 against opposition
Usage
teamWinLossStatusVsOpposition(file,teamName,opposition=c("all"),homeOrAway=c("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 c("all") or c("Australia", "India", "England")
homeOrAway	
This parameter is a vector which is either c("all") or a vector of venues c("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")
teamWinLossStatusAtGrounds("india.csv",teamName="India",opposition=c("Australia","England","India"),
                          homeOrAway=c("home","away"),plot=TRUE)
## End(Not run)

This post has been published at RPubs and is available at TeamAnalyticsWithCricketr

You can download PDF version of this post at TeamAnalyticsWithCricketr

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

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

# Get the data for the teams. Save as CSV
#indiaTest <-getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="indiaTest.csv",save=TRUE,teamName="India")
#australiaTest <- getTeamDataHomeAway(matchType="Test",file="australiaTest.csv",save=TRUE,teamName="Australia")
#pakistanTest <- getTeamDataHomeAway(matchType="Test",file="pakistanTest.csv",save=TRUE,teamName="Pakistan")
#newzealandTest <- 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. Bermuda 7. England 8. ICC World X1 9. India 11.Ireland 12. New Zealand
  3. Pakistan 14. South Africa 15. Sri Lanka 17. West Indies 18. Zimbabwe
  4. Canada 21. East Africa 22. Hong Kong 23.Ireland 24. Kenya 25. Namibia
  5. Nepal 27.Netherlands 28. Oman 29.Papua New Guinea 30. Scotland
  6. United Arab Emirates 32. United States of America
#indiaODI <- getTeamDataHomeAway(matchType="ODI",file="indiaODI.csv",save=TRUE,teamName="India")
#englandODI <- getTeamDataHomeAway(matchType="ODI",file="englandODI.csv",save=TRUE,teamName="England")
#westindiesODI <- getTeamDataHomeAway(matchType="ODI",file="westindiesODI.csv",save=TRUE,teamName="West Indies")
#irelandODI <- 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.Bermuda 8.Botswana 9. Canada 11. Costa Rica 12. Germany 13. Ghana
14.Guernsey 15. Hong Kong 16. ICC World X1 17.India 18. Ireland 19.Italy
20.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

#southafricaT20 <- getTeamDataHomeAway(matchType="T20",file="southafricaT20.csv",save=TRUE,teamName="South Africa")
#srilankaT20 <- getTeamDataHomeAway(matchType="T20",file="srilankaT20.csv",save=TRUE,teamName="Sri Lanka")
#canadaT20 <- getTeamDataHomeAway(matchType="T20",file="canadaT20.csv",save=TRUE,teamName="Canada")
#afghanistanT20 <- 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 vector of opposition teams. Similarly homeOrAway can also be a vector of home/away/neutral venues.

# Get the performance of Indian test team against all teams at all venues as a dataframe
df <- teamWinLossStatusVsOpposition("india.csv",teamName="India",opposition=c("all"),homeOrAway=c("all"),matchType="Test",plot=FALSE)
head(df)
## # A tibble: 6 x 4
## # Groups:   Opposition, Result [4]
##   Opposition  Result ha    count
##   <chr>       <chr>  <chr> <int>
## 1 Afghanistan won    home      1
## 2 Australia   draw   away     20
## 3 Australia   draw   home     23
## 4 Australia   lost   away     58
## 5 Australia   lost   home     26
## 6 Australia   tied   home      2
# Plot the performance of Indian Test team  against all teams at all venues
teamWinLossStatusVsOpposition("indiaTest.csv",teamName="India",opposition=c("all"),homeOrAway=c("all"),matchType="Test",plot=TRUE)

# Get the performance of Australia against India, England and New Zealand at all venues in Tests
df <-teamWinLossStatusVsOpposition("australiaTest.csv",teamName="Australia",opposition=c("India","England","New Zealand"),homeOrAway=c("all"),matchType="Test",plot=FALSE)

#Plot the performance of Australia against England, India and New Zealand only at home (Australia) 
teamWinLossStatusVsOpposition("australiaTest.csv",teamName="Australia",opposition=c("India","England","New Zealand"),homeOrAway=c("home"),matchType="Test",plot=TRUE)

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Untitled

 

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

# Get the  performance of Pakistan against India, West Indies, South Africa at all venues in Tests and show performances at the venues
df <- teamWinLossStatusAtGrounds("pakistanTest.csv",teamName="Pakistan",opposition=c("India","West Indies","South Africa"),homeOrAway=c("all"),matchType="Test",plot=FALSE)
head(df)
## # A tibble: 6 x 4
## # Groups:   Ground, Result [6]
##   Ground     Result ha      count
##   <chr>      <chr>  <chr>   <int>
## 1 Abu Dhabi  draw   neutral     2
## 2 Abu Dhabi  won    neutral     4
## 3 Ahmedabad  draw   away        2
## 4 Bahawalpur draw   home        1
## 5 Basseterre won    away        2
## 6 Bengaluru  draw   away        5
# Plot the performance of New Zealand Test team against England, Sri Lanka and Bangladesh at all grounds playes 
teamWinLossStatusAtGrounds("newzealandTest.csv",teamName="New Zealand",opposition=c("England","Sri Lanka","Bangladesh"),homeOrAway=c("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

# 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
plotTimelineofWinsLosses("indiaTest.csv",team="India",opposition=c("Australia","West Indies","South Africa"),
                         homeOrAway=c("away","neutral"), startDate="2000-01-01",endDate="2017-01-01")

#Plot the time line of wins/losses of Indian Test team from 1970 onwards
plotTimelineofWinsLosses("indiaTest.csv",team="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.

# Get the performance of West Indies in ODIs against all other ODI teams at all venues and retirn as a dataframe
df <- teamWinLossStatusVsOpposition("westindiesODI.csv",teamName="West Indies",opposition=c("all"),homeOrAway=c("all"),matchType="ODI",plot=FALSE)
head(df)
## # A tibble: 6 x 4
## # Groups:   Opposition, Result [3]
##   Opposition  Result ha      count
##   <chr>       <chr>  <chr>   <int>
## 1 Afghanistan lost   home        1
## 2 Afghanistan lost   neutral     2
## 3 Afghanistan won    home        1
## 4 Australia   lost   away       41
## 5 Australia   lost   home       25
## 6 Australia   lost   neutral     8
# Plot the performance of West Indies in ODIs against Sri Lanka, India at all venues
teamWinLossStatusVsOpposition("westindiesODI.csv",teamName="West Indies",opposition=c("Sri Lanka", "India"),homeOrAway=c("all"),matchType="ODI",plot=TRUE)

 

#Plot the performance of Ireland in ODIs against Zimbabwe, Kenya, bermuda, UAE, Oman and Scotland at all venues
teamWinLossStatusVsOpposition("irelandODI.csv",teamName="Ireland",opposition=c("Zimbabwe","Kenya","Bermuda","U.A.E.","Oman","Scotland"),homeOrAway=c("all"),matchType="ODI",plot=TRUE)

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

# Plot the performance of England ODI team against Bangladesh, West Indies and Australia at all venues
teamWinLossStatusAtGrounds("englandODI.csv",teamName="England",opposition=c("Bangladesh","West Indies","Australia"),homeOrAway=c("all"),matchType="ODI",plot=TRUE)

#Plot the performance of India against South Africa, West Indies and Australia at 'home' venues
teamWinLossStatusAtGrounds("indiaODI.csv",teamName="India",opposition=c("South Africa","West Indies","Australia"),homeOrAway=c("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

#Plot the time line of wins/losses of Bangladesh ODI team between 2015 and 2019 against all other teams and at
# all venues
plotTimelineofWinsLosses("bangladeshOD.csv",team="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
plotTimelineofWinsLosses("indiaODI.csv",team="India",opposition=c("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 vector of opposition teams. Similarly homeOrAway can also be a vector of home/away/neutral venues.

# Get the performance of South Africa T20 team against England, India and Sri Lanka at home grounds at England
df <- teamWinLossStatusVsOpposition("southafricaT20.csv",teamName="South Africa",opposition=c("England","India","Sri Lanka"),homeOrAway=c("home"),matchType="T20",plot=FALSE)

#Plot the performance of South Africa T20 against England, India and Sri Lanka at all venues
teamWinLossStatusVsOpposition("southafricaT20.csv",teamName="South Africa",opposition=c("England","India","Sri Lanka"),homeOrAway=c("all"),matchType="T20",plot=TRUE)

#Plot the performance of Afghanistan T20 teams against all oppositions
teamWinLossStatusVsOpposition("afghanistanT20.csv",teamName="Afghanistan",opposition=c("all"),homeOrAway=c("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 <-teamWinLossStatusAtGrounds("canadaT20.csv",teamName="Canada",opposition=c("all"),homeOrAway=c("all"),matchType="T20",plot=FALSE)
head(df)
## # A tibble: 6 x 4
## # Groups:   Ground, Result [6]
##   Ground        Result ha      count
##   <chr>         <chr>  <chr>   <int>
## 1 Abu Dhabi     lost   neutral     1
## 2 Belfast       lost   neutral     1
## 3 Belfast       won    neutral     2
## 4 Colombo (SSC) lost   neutral     1
## 5 Colombo (SSC) won    neutral     1
## 6 Dubai (DSC)   lost   neutral     5
# Plot the performance of Sri Lanka T20 team against India and Bangladesh in different venues at home/away and neutral
teamWinLossStatusAtGrounds("srilankaT20.csv",teamName="Sri Lanka",opposition=c("India", "Bangladesh"), homeOrAway=c("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
plotTimelineofWinsLosses("srilankaT20.csv",team="Sri Lanka",opposition=c("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
plotTimelineofWinsLosses("southafricaT20.csv",team="South Africa",opposition=c("West Indies", "Pakistan"), startDate="2010-01-01", endDate="2015-01-01",  matchType="T20")

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The book includes the following chapters

CONTENTS

Introduction 7
1. Cricket analytics with cricketr 9
1.1. Introducing cricketr! : An R package to analyze performances of cricketers 10
1.2. Taking cricketr for a spin – Part 1 48
1.2. cricketr digs the Ashes! 69
1.3. cricketr plays the ODIs! 97
1.4. cricketr adapts to the Twenty20 International! 139
1.5. Sixer – R package cricketr’s new Shiny avatar 168
1.6. Re-introducing cricketr! : An R package to analyze performances of cricketers 178
1.7. cricketr sizes up legendary All-rounders of yesteryear 233
1.8. cricketr flexes new muscles: The final analysis 277
1.9. The Clash of the Titans in Test and ODI cricket 300
1.10. Analyzing performances of cricketers using cricketr template 338
2. Cricket analytics with cricpy 352
2.1 Introducing cricpy:A python package to analyze performances of cricketers 353
2.2 Cricpy takes a swing at the ODIs 405
Analysis of Top 4 batsman 448
2.3 Cricpy takes guard for the Twenty20s 449
2.4 Analyzing batsmen and bowlers with cricpy template 490
9. Average runs against different opposing teams 493
3. Other cricket posts in R 500
3.1 Analyzing cricket’s batting legends – Through the mirage with R 500
3.2 Mirror, mirror … the best batsman of them all? 527
4. Appendix 541
Cricket analysis with Machine Learning using Octave 541
4.1 Informed choices through Machine Learning – Analyzing Kohli, Tendulkar and Dravid 542
4.2 Informed choices through Machine Learning-2 Pitting together Kumble, Kapil, Chandra 555
Further reading 569
Important Links 570

Also see
1. My book “Deep Learning from first principles” now on Amazon
2. Practical Machine Learning with R and Python – Part 1
3. Revisiting World Bank data analysis with WDI and gVisMotionChart
4. Natural language processing: What would Shakespeare say?
5. Optimal Cloud Computing
6. Pitching yorkpy … short of good length to IPL – Part 1
7. Computer Vision: Ramblings on derivatives, histograms and contours

To see all posts click Index of posts