Introduction
This is a post I have been wanting to write for several months, but had to put it off for one reason or another. In this post I use my R package cricketr to analyze the performance of All-rounder greats namely Kapil Dev, Ian Botham, Imran Khan and Richard Hadlee. All these players had talent that was natural and raw. They were good strikers of the ball and extremely lethal with their bowling. The ODI data for these players have been taken from ESPN Cricinfo.
Please be mindful of the ESPN Cricinfo Terms of Use
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!!
You can download the latest PDF version of the book at ‘Cricket analytics with cricketr and cricpy: Analytics harmony with R and Python-6th edition‘
You can also read this post at Rpubs as cricketr-AR. Dowload this report as a PDF file from cricketr-AR
Important note 1: The latest release of ‘cricketr’ now includes the ability to analyze performances of teams now!! See Cricketr adds team analytics to its repertoire!!!
Important note 2 : Cricketr can now do a more fine-grained analysis of players, see Cricketr learns new tricks : Performs fine-grained analysis of players
Important note 3: Do check out the python avatar of cricketr, ‘cricpy’ in my post ‘Introducing cricpy:A python package to analyze performances of cricketers”
Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricketr template from Github (which is the R Markdown file I have used for the analysis below). You will only need to make appropriate changes for the players you are interested in. Just a familiarity with R and R Markdown only is needed.
Important note: Do check out my other posts using cricketr at cricketr-posts
All Rounders
- Kapil Dev (Ind)
- Ian Botham (Eng)
- Imran Khan (Pak)
- Richard Hadlee (NZ)
I have sprinkled the plots with a few of my comments. Feel free to draw your conclusions! The analysis is included below
if (!require("cricketr")){
install.packages("cricketr",)
}
library(cricketr)
The data for any particular ODI player can be obtained with the getPlayerDataOD() function. To do you will need to go to ESPN CricInfo Playerand type in the name of the player for e.g Kapil Dev, etc. This will bring up a page which have the profile number for the player e.g. for Kapil Dev this would be http://www.espncricinfo.com/india/content/player/30028.html. Hence, Kapils’s profile is 30028. This can be used to get the data for Kapil Dev’s data as shown below. I have already executed the below 4 commands and I will use the files to run further commands
Plot of 4s, 6s and the scoring rate in ODIs
The 3 charts below give the number of
- 4s vs Runs scored
- 6s vs Runs scored
- Balls faced vs Runs scored
A regression line is fitted in each of these plots for each of the ODI batsmen
A. Kapil Dev
It can be seen that Kapil scores four 4’s when he scores 50. Also after facing 50 deliveries he scores around 43
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./kapil1.csv","Kapil")
batsman6s("./kapil1.csv","Kapil")
batsmanScoringRateODTT("./kapil1.csv","Kapil")
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dev.off()
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B. Ian Botham
Botham scores around 39 runs after 50 deliveries
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./botham1.csv","Botham")
batsman6s("./botham1.csv","Botham")
batsmanScoringRateODTT("./botham1.csv","Botham")
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dev.off()
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C. Imran Khan
Imran scores around 36 runs for 50 deliveries
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./imran1.csv","Imran")
batsman6s("./imran1.csv","Imran")
batsmanScoringRateODTT("./imran1.csv","Imran")
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dev.off()
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D. Richard Hadlee
Hadlee also scores around 30 runs facing 50 deliveries
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./hadlee1.csv","Hadlee")
batsman6s("./hadlee1.csv","Hadlee")
batsmanScoringRateODTT("./hadlee1.csv","Hadlee")
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dev.off()
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Relative Mean Strike Rate
Kapil tops the strike rate among all the all-rounders. This is really a revelation to me. This can also be seen in the original data in Kapil’s strike rate is at a whopping 95.07 in comparison to Botham, Inran and Hadlee who are at 79.1,72.65 and 75.50 respectively
par(mar=c(4,4,2,2))
frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBatsmanSRODTT(frames,names)
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Relative Runs Frequency Percentage
This plot shows that Imran has a much better average runs scored over the other all rounders followed by Kapil
frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeRunsFreqPerfODTT(frames,names)
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Relative cumulative average runs in career
It can be seen clearly that Imran Khan leads the pack in cumulative average runs followed by Kapil Dev and then Botham
frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBatsmanCumulativeAvgRuns(frames,names)
Relative cumulative average strike rate in career
In the cumulative strike rate Hadlee and Kapil run a close race.
frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBatsmanCumulativeStrikeRate(frames,names)
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Percent 4’s,6’s in total runs scored
The plot below shows the contrib
frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
runs4s6s <-batsman4s6s(frames,names)
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print(runs4s6s)
## Kapil Botham Imran Hadlee
## Runs(1s,2s,3s) 72.08 66.53 77.53 73.27
## 4s 21.98 25.78 17.61 21.08
## 6s 5.94 7.68 4.86 5.65
Runs forecast
The forecast for the batsman is shown below.
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfForecast("./kapil1.csv","Kapil")
batsmanPerfForecast("./botham1.csv","Botham")
batsmanPerfForecast("./imran1.csv","Imran")
batsmanPerfForecast("./hadlee1.csv","Hadlee")
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dev.off()
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## 1
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("./kapil1.csv","Kapil")
battingPerf3d("./botham1.csv","Botham")
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dev.off()
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par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./imran1.csv","Imran")
battingPerf3d("./hadlee1.csv","Hadlee")
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dev.off()
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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, 200,length=10)
Mins <- seq(30,220,length=10)
newDF <- data.frame(BF,Mins)
kapil <- batsmanRunsPredict("./kapil1.csv","Kapil",newdataframe=newDF)
botham <- batsmanRunsPredict("./botham1.csv","Botham",newdataframe=newDF)
imran <- batsmanRunsPredict("./imran1.csv","Imran",newdataframe=newDF)
hadlee <- batsmanRunsPredict("./hadlee1.csv","Hadlee",newdataframe=newDF)
The fitted model is then used to predict the runs that the batsmen will score for a hypotheticial Balls faced and Minutes at crease. It can be seen that Kapil is the best bet for a balls faced and minutes at crease followed by Botham.
batsmen <-cbind(round(kapil$Runs),round(botham$Runs),round(imran$Runs),round(hadlee$Runs))
colnames(batsmen) <- c("Kapil","Botham","Imran","Hadlee")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns
## BallsFaced MinsAtCrease Kapil Botham Imran Hadlee
## 1 10 30 16 6 10 15
## 2 31 51 33 22 22 28
## 3 52 72 49 38 33 42
## 4 73 93 65 54 45 56
## 5 94 114 81 70 56 70
## 6 116 136 97 86 67 84
## 7 137 157 113 102 79 97
## 8 158 178 130 117 90 111
## 9 179 199 146 133 102 125
## 10 200 220 162 149 113 139
Highest runs likelihood
The plots below the runs likelihood of batsman. This uses K-Means . A. Kapil Dev
batsmanRunsLikelihood("./kapil1.csv","Kapil")
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## Summary of Kapil 's runs scoring likelihood
## **************************************************
##
## There is a 34.57 % likelihood that Kapil will make 22 Runs in 24 balls over 34 Minutes
## There is a 17.28 % likelihood that Kapil will make 46 Runs in 46 balls over 65 Minutes
## There is a 48.15 % likelihood that Kapil will make 5 Runs in 7 balls over 9 Minutes
B. Ian Botham
batsmanRunsLikelihood("./botham1.csv","Botham")
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## Summary of Botham 's runs scoring likelihood
## **************************************************
##
## There is a 47.95 % likelihood that Botham will make 9 Runs in 12 balls over 15 Minutes
## There is a 39.73 % likelihood that Botham will make 23 Runs in 32 balls over 44 Minutes
## There is a 12.33 % likelihood that Botham will make 59 Runs in 74 balls over 101 Minutes
C. Imran Khan
batsmanRunsLikelihood("./imran1.csv","Imran")
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## Summary of Imran 's runs scoring likelihood
## **************************************************
##
## There is a 23.33 % likelihood that Imran will make 36 Runs in 54 balls over 74 Minutes
## There is a 60 % likelihood that Imran will make 14 Runs in 18 balls over 23 Minutes
## There is a 16.67 % likelihood that Imran will make 53 Runs in 90 balls over 115 Minutes
D. Richard Hadlee
batsmanRunsLikelihood("./hadlee1.csv","Hadlee")
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## Summary of Hadlee 's runs scoring likelihood
## **************************************************
##
## There is a 6.1 % likelihood that Hadlee will make 64 Runs in 79 balls over 90 Minutes
## There is a 42.68 % likelihood that Hadlee will make 25 Runs in 33 balls over 44 Minutes
## There is a 51.22 % likelihood that Hadlee will make 9 Runs in 11 balls over 15 Minutes
Average runs at ground and against opposition
A. Kapil Dev
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./kapil1.csv","Kapil")
batsmanAvgRunsOpposition("./kapil1.csv","Kapil")
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dev.off()
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B. Ian Botham
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./botham1.csv","Botham")
batsmanAvgRunsOpposition("./botham1.csv","Botham")
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dev.off()
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C. Imran Khan
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./imran1.csv","Imran")
batsmanAvgRunsOpposition("./imran1.csv","Imran")
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dev.off()
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D. Richard Hadlee
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./hadlee1.csv","Hadlee")
batsmanAvgRunsOpposition("./hadlee1.csv","Hadlee")
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dev.off()
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Moving Average of runs over career
The moving average for the 4 batsmen indicate the following
Kapil’s performance drops significantly while there is a slump in Botham’s performance. On the other hand Imran and Hadlee’s performance were on the upswing.
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("./kapil1.csv","Kapil")
batsmanMovingAverage("./botham1.csv","Botham")
batsmanMovingAverage("./imran1.csv","Imran")
batsmanMovingAverage("./hadlee1.csv","Hadlee")
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dev.off()
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Wicket Frequency percentage
This plot gives the percentage of wickets for each wickets (1,2,3…etc).
par(mfrow=c(1,4))
par(mar=c(4,4,2,2))
bowlerWktsFreqPercent("./kapil2.csv","Kapil")
bowlerWktsFreqPercent("./botham2.csv","Botham")
bowlerWktsFreqPercent("./imran2.csv","Imran")
bowlerWktsFreqPercent("./hadlee2.csv","Hadlee")
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dev.off()
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Wickets Runs plot
The plot below gives a boxplot of the runs ranges for each of the wickets taken by the bowlers.
par(mfrow=c(1,4))
par(mar=c(4,4,2,2))
bowlerWktsRunsPlot("./kapil2.csv","Kapil")
bowlerWktsRunsPlot("./botham2.csv","Botham")
bowlerWktsRunsPlot("./imran2.csv","Imran")
bowlerWktsRunsPlot("./hadlee2.csv","Hadlee")
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dev.off()
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Cumulative average wicket plot
Botham has the best cumulative average wicket touching almost 1.6 wickets followed by Hadlee
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerCumulativeAvgWickets("./kapil2.csv","Kapil")
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bowlerCumulativeAvgWickets("./botham2.csv","Botham")
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bowlerCumulativeAvgWickets("./imran2.csv","Imran")
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bowlerCumulativeAvgWickets("./hadlee2.csv","Hadlee")
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dev.off()
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par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerCumulativeAvgEconRate("./kapil2.csv","Kapil")
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bowlerCumulativeAvgEconRate("./botham2.csv","Botham")
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bowlerCumulativeAvgEconRate("./imran2.csv","Imran")
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bowlerCumulativeAvgEconRate("./hadlee2.csv","Hadlee")
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dev.off()
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Average wickets in different grounds and opposition
A. Kapil Dev
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./kapil2.csv","Kapil")
bowlerAvgWktsOpposition("./kapil2.csv","Kapil")
![]()
dev.off()
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B. Ian Botham
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./botham2.csv","Botham")
bowlerAvgWktsOpposition("./botham2.csv","Botham")
![]()
dev.off()
## null device
## 1
C. Imran Khan
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./imran2.csv","Imran")
bowlerAvgWktsOpposition("./imran2.csv","Imran")
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dev.off()
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## 1
D. Richard Hadlee
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./hadlee2.csv","Hadlee")
bowlerAvgWktsOpposition("./hadlee2.csv","Hadlee")
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dev.off()
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Relative Economy Rate against wickets taken
Hadlee has the best overall economy rate followed by Kapil Dev
frames <- list("./kapil2.csv","./botham2.csv","imran2.csv","hadlee2.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBowlingERODTT(frames,names)
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Relative cumulative average wickets of bowlers in career
This plot confirms the wicket taking ability of Botham followed by Hadlee
frames <- list("./kapil2.csv","./botham2.csv","imran2.csv","hadlee2.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBowlerCumulativeAvgWickets(frames,names)
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Relative cumulative average economy rate of bowlers
frames <- list("./kapil2.csv","./botham2.csv","imran2.csv","hadlee2.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBowlerCumulativeAvgEconRate(frames,names)
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Moving average of wickets over career
This plot shows that Hadlee has the best economy rate followed by Kapil
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerMovingAverage("./kapil2.csv","Kapil")
bowlerMovingAverage("./botham2.csv","Botham")
bowlerMovingAverage("./imran2.csv","Imran")
bowlerMovingAverage("./hadlee2.csv","Hadlee")
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dev.off()
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Wickets forecast
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerPerfForecast("./kapil2.csv","Kapil")
bowlerPerfForecast("./botham2.csv","Botham")
bowlerPerfForecast("./imran2.csv","Imran")
bowlerPerfForecast("./hadlee2.csv","Hadlee")
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dev.off()
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## 1
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