cricketr adapts to the Twenty20 International!

Introduction

This should be last in the series of posts based on my R package cricketr. That is, unless some bright idea comes trotting along and light bulbs go on around my head.

In this post cricketr adapts to the Twenty20 International format. Now cricketr can handle stats from all 3 formats of the game namely Test matches, ODIs and Twenty20 International from ESPN Cricinfo. You should be able to install the package from GitHub and use the many of the functions available in the package.

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‘

Untitled

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”

You can also read this post at Rpubs as twenty20-cricketr. Download this report as a PDF file from twenty20-cricketr.pdf

Do check out my interactive Shiny app implementation using the cricketr package – Sixer – R package cricketr’s new Shiny avatar

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

I have chosen the Top 4 batsmen and top 4 bowlers based on ICC rankings and/or number of matches played.

Batsmen

Virat Kohli (Ind)
Faf du Plessis (SA)
A J Finch (Aus)
Brendon McCullum (Aus)

Bowlers

Samuel Badree (WI)
Sunil Narine (WI)
Ravichander Ashwin (Ind)
Ajantha Mendis (SL)

I have explained the plots and added my own observations. Please feel free to draw your conclusions!

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 Virat Kohli, Sunil Narine etc. This will bring up a page which have the profile number for the player e.g. for Virat Kohli this would be http://www.espncricinfo.com/india/content/player/253802.html.

The package can be installed directly from CRAN

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

or from Github

library(devtools)
install_github("tvganesh/cricketr")
library(cricketr)

kohli <- getPlayerDataTT(253802,dir="..",file="kohli.csv",type="batting")

The analysis is included below

Analyses of Batsmen

The following plots gives the analysis of the 4 ODI batsmen

Virat Kohli (Ind) – Innings-26, Runs-972, Average-46.28,Strike Rate-131.70
Faf du Plessis (SA) – Innings-24, Runs-805, Average-42.36,Strike Rate-135.75
A J Finch (Aus) – Innings-22, Runs-756, Average-39.78,Strike Rate-152.41
Brendon McCullum (NZ) – Innings-70, Runs-2140, Average-35.66,Strike Rate-136.21

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. Virat Kohli
– The 1st plot shows that Kohli approximately hits about 5 4’s on his way to the 50s
– The 2nd box plot of no of 6s and runs shows the range of runs when Kohli scored 1,2 or 4 6s. The dark line in the box shows the average runs when he scored those number of 6s. So when he scored 1 6 the average runs he scored was 45
– The 3rd plot shows the number of runs scored against the balls faced. It can be seen when Kohli faced 50 balls he had scored around ~ 70 runs

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./kohli.csv","Kohli")
batsman6s("./kohli.csv","Kohli")
batsmanScoringRateODTT("./kohli.csv","Kohli")

dev.off()

## null device 
##           1

B. Faf du Plessis

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./plessis.csv","Du Plessis")
batsman6s("./plessis.csv","Du Plessis")
batsmanScoringRateODTT("./plessis.csv","Du Plessss")

dev.off()

## null device 
##           1

C. A J Finch

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./finch.csv","A J Finch")
batsman6s("./finch.csv","A J Finch")
batsmanScoringRateODTT("./finch.csv","A J Finch")

dev.off()

## null device 
##           1

D. Brendon McCullum

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./mccullum.csv","McCullum")
batsman6s("./mccullum.csv","McCullum")
batsmanScoringRateODTT("./mccullum.csv","McCullum")

dev.off()

## null device 
##           1

Relative Mean Strike Rate

This plot shows the Mean Strike Rate of the batsman in each run range. It can be seen the A J Finch has the best strike rate followed by B McCullum.

par(mar=c(4,4,2,2))
frames <- list("./kohli.csv","./plessis.csv","finch.csv","mccullum.csv")
names <- list("Kohli","Du Plessis","Finch","McCullum")
relativeBatsmanSRODTT(frames,names)

Relative Runs Frequency Percentage

The plot below provides the average runs scored in each run range 0-5,5-10,10-15 etc. Clearly Kohli has the most runs scored in most of the runs ranges. . This is also evident in the fact that Kohli has the highest average. He is followed by McCullum

frames <- list("./kohli.csv","./plessis.csv","finch.csv","mccullum.csv")
names <- list("Kohli","Du Plessis","Finch","McCullum")
relativeRunsFreqPerfODTT(frames,names)

Percent 4’s,6’s in total runs scored

The plot below shows the percentage of runs scored by way of 4s and 6s for each batsman. Du Plessis has the highest percentage of 4s, McCullum has the highest 6s. Finch has the highest percentage of 4s & 6s – 25.37 + 15.64= 41.01%

rames <- list("./kohli.csv","./plessis.csv","finch.csv","mccullum.csv")
names <- list("Kohli","Du Plessis","Finch","McCullum")
runs4s6s <-batsman4s6s(frames,names)

print(runs4s6s)

##                Kohli Du Plessis Finch McCullum
## Runs(1s,2s,3s) 64.29      64.55 58.99    61.45
## 4s             27.78      24.38 25.37    22.87
## 6s              7.94      11.07 15.64    15.69

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 then fitted based on the Balls Faced and Minutes at Crease to give the runs scored

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./kohli.csv","Kohli")
battingPerf3d("./plessis.csv","Du Plessis")

dev.off()

## null device 
##           1

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./finch.csv","A J Finch")
battingPerf3d("./mccullum.csv","McCullum")

dev.off()

## null device 
##           1

Predicting Runs given Balls Faced and Minutes at Crease

A hypothetical Balls faced and Minutes at Crease is used to predict the runs scored by each batsman based on the computed prediction plane

BF <- seq( 5, 70,length=10)
Mins <- seq(5,70,length=10)
newDF <- data.frame(BF,Mins)

kohli <- batsmanRunsPredict("./kohli.csv","Kohli",newdataframe=newDF)
plessis <- batsmanRunsPredict("./plessis.csv","Du Plessis",newdataframe=newDF)
finch <- batsmanRunsPredict("./finch.csv","A J Finch",newdataframe=newDF)
mccullum <- batsmanRunsPredict("./mccullum.csv","McCullum",newdataframe=newDF)

The predicted runs is displayed. As can be seen Finch has the best overall strike rate followed by McCullum.

batsmen <-cbind(round(kohli$Runs),round(plessis$Runs),round(finch$Runs),round(mccullum$Runs))
colnames(batsmen) <- c("Kohli","Du Plessis","Finch","McCullum")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns

##    BallsFaced MinsAtCrease Kohli Du Plessis Finch McCullum
## 1           5            5     2          1     5        3
## 2          12           12    12         10    22       16
## 3          19           19    22         19    40       28
## 4          27           27    31         28    57       41
## 5          34           34    41         37    74       54
## 6          41           41    51         47    91       66
## 7          48           48    60         56   108       79
## 8          56           56    70         65   125       91
## 9          63           63    79         74   142      104
## 10         70           70    89         84   159      117

Highest runs likelihood

The plots below the runs likelihood of batsman. This uses K-Means Kohli has the highest likelihood of scoring runs 34.2% likely to score 66 runs. Du Plessis has 25% likelihood to score 53 runs, A. Virat Kohli

batsmanRunsLikelihood("./kohli.csv","Kohli")

## Summary of  Kohli 's runs scoring likelihood
## **************************************************
## 
## There is a 23.08 % likelihood that Kohli  will make  10 Runs in  10 balls over 13  Minutes 
## There is a 42.31 % likelihood that Kohli  will make  29 Runs in  23 balls over  30  Minutes 
## There is a 34.62 % likelihood that Kohli  will make  66 Runs in  47 balls over 63  Minutes

B. Faf Du Plessis

batsmanRunsLikelihood("./plessis.csv","Du Plessis")

## Summary of  Du Plessis 's runs scoring likelihood
## **************************************************
## 
## There is a 62.5 % likelihood that Du Plessis  will make  14 Runs in  11 balls over 19  Minutes 
## There is a 25 % likelihood that Du Plessis  will make  53 Runs in  40 balls over  50  Minutes 
## There is a 12.5 % likelihood that Du Plessis  will make  94 Runs in  61 balls over 90  Minutes

C. A J Finch

batsmanRunsLikelihood("./finch.csv","A J Finch")

## Summary of  A J Finch 's runs scoring likelihood
## **************************************************
## 
## There is a 20 % likelihood that A J Finch  will make  95 Runs in  54 balls over 70  Minutes 
## There is a 25 % likelihood that A J Finch  will make  42 Runs in  27 balls over  35  Minutes 
## There is a 55 % likelihood that A J Finch  will make  8 Runs in  8 balls over 12  Minutes

D. Brendon McCullum

batsmanRunsLikelihood("./mccullum.csv","McCullum")

## Summary of  McCullum 's runs scoring likelihood
## **************************************************
## 
## There is a 50.72 % likelihood that McCullum  will make  11 Runs in  10 balls over 13  Minutes 
## There is a 28.99 % likelihood that McCullum  will make  36 Runs in  27 balls over  37  Minutes 
## There is a 20.29 % likelihood that McCullum  will make  74 Runs in  48 balls over 70  Minutes

Moving Average of runs over career

The moving average for the 4 batsmen indicate the following. It must be noted that there is not sufficient data yet on Twenty20 Internationals. Kpohli, Du Plessis and Finch average only 26 innings while McCullum has close to 70. So the moving average while an indication will regress towards the mean over time.

The moving average of Kohli and Du Plessis is on the way up.
McCullum has a consistent performance while Finch had a brief burst in 2013-2014

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("./kohli.csv","Kohli")
batsmanMovingAverage("./plessis.csv","Du Plessis")
batsmanMovingAverage("./finch.csv","A J Finch")
batsmanMovingAverage("./mccullum.csv","McCullum")

dev.off()

## null device 
##           1

Analysis of bowlers

Samuel Badree (WI) – Innings-22, Runs -464, Wickets – 31, Econ Rate : 5.39
Sunil Narine (WI)- Innings-31,Runs-666, Wickets – 38 , Econ Rate : 5.70
Ravichander Ashwin (Ind)- Innings-26, Runs- 732, Wickets – 25, Econ Rate : 7.32
Ajantha Mendis (SL)- Innings-39, Runs – 952,Wickets – 66, Econ Rate : 6.45

The plot shows the frequency with which the bowlers have taken 1,2,3 etc wickets. The most wickets taken is by Ajantha Mendis (6 wickets)

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("./badree.csv","Badree")
bowlerWktsFreqPercent("./mendis.csv","Mendis")
bowlerWktsFreqPercent("./narine.csv","Narine")
bowlerWktsFreqPercent("./ashwin.csv","Ashwin")

dev.off()

## null device 
##           1

Wickets Runs plot

The plot below gives a boxplot of the runs ranges for each of the wickets taken by the bowlers. The ends of the box indicate the 25% and 75% percentile of runs scored for the wickets taken and the dark balck line is the average runs conceded.

par(mfrow=c(1,4))
par(mar=c(4,4,2,2))
bowlerWktsRunsPlot("./badree.csv","Badree")
bowlerWktsRunsPlot("./mendis.csv","Mendis")
bowlerWktsRunsPlot("./narine.csv","Narine")
bowlerWktsRunsPlot("./ashwin.csv","Ashwin")

dev.off()

## null device 
##           1

This plot below shows the average number of deliveries needed by the bowler to take the wickets (1,2,3 etc)

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktRateTT("./badree.csv","Badree")
bowlerWktRateTT("./mendis.csv","Mendis")

dev.off()

## null device 
##           1

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktRateTT("./narine.csv","Narine")
bowlerWktRateTT("./ashwin.csv","Ashwin")

dev.off()

## null device 
##           1

Relative bowling performance

The plot below shows that Narine has the most wickets in the 2 -4 range followed by Mendis

frames <- list("./badree.csv","./mendis.csv","narine.csv","ashwin.csv")
names <- list("Badree","Mendis","Narine","Ashwin")
relativeBowlingPerf(frames,names)

Relative Economy Rate against wickets taken

The economy rate can be deduced as follows from the plot below. Narine has a good economy rate around 1 & 4 wickets, Ashwin around 2 wickets and Badree around 3. wickets

frames <- list("./badree.csv","./mendis.csv","narine.csv","ashwin.csv")
names <- list("Badree","Mendis","Narine","Ashwin")
relativeBowlingERODTT(frames,names)

Relative Wicket Rate

The relative wicket rate plots the mean number of deliveries needed to take the wickets namely (1,2,3,4). For e.g. Narine needed an average of 22 deliveries to take 1 wicket and 22.5,23.2, 24 deliveries to take 2,3 & 4 wickets respectively

frames <- list("./badree.csv","./mendis.csv","narine.csv","ashwin.csv")
names <- list("Badree","Mendis","Narine","Ashwin")
relativeWktRateTT(frames,names)

Moving average of wickets over career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerMovingAverage("./badree.csv","Badree")
bowlerMovingAverage("./mendis.csv","Mendis")
bowlerMovingAverage("./narine.csv","Narine")
bowlerMovingAverage("./ashwin.csv","Ashwin")

## null device 
##           1

Key findings

Here are some key conclusions

Twenty 20 batsmen

Kohli has the a very consistent performance scoring high runs in the different run ranges. Kohli also has a 34.2% likelihood to score 6 runs. He is followed by McCullum for consisten performance
Finch has a best strike rate followed by McCullum.
Du Plessis has the highest percentage of 4s and McCullum has the percentage of 6s. Finch is superior in the percentage of runs scored in 4s and 6s
For a hypothetical balls faced and minutes at crease, Finch does best followed by McCullum
Kohli’s & Du Plessis Twenty20 career is on a upswing. Can they maintain the momentum. McCullum is consistent

Twenty20 bowlers

Narine has the highest wickets percentage for different wickets taken followed by Mendis
Mendis has taken 1,2,3,4,6 wickets in 24 deliveries
Narine has the lowest economy rate for 1 & 4 wickets, Ashwin for 2 wickets and Badree for 3 wickets. Mendis is comparatively expensive
Narine needed the least deliveries to get 1 (22.5) & 2 (23.2) wickets, Mendis needed 20.5 deliveries and Ashwin 19 deliveries for 4 wickets

Key takeaways 1. If all the above batsment and bowlers were in the same team we expect

Finch would be most useful when the run rate has to be greatly accelerated followed by McCullum
If the need is to consolidate, then Kohli is the best man for the job followed by McCullum
Overall McCullum is the best bet for Twenty20
When it comes to bowling Narine wins hands down as he has the most wickets, a good economy rate and a very good attack rate. So Narine is great bet for providing a vital breakthrough.

Also see my other posts in R

cricketr plays the ODIs!

Published in R bloggers: cricketr plays the ODIs

Introduction

In this post my package ‘cricketr’ takes a swing at One Day Internationals(ODIs). Like test batsman who adapt to ODIs with some innovative strokes, the cricketr package has some additional functions and some modified functions to handle the high strike and economy rates in ODIs. As before I have chosen my top 4 ODI batsmen and top 4 ODI bowlers.

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‘

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”

Do check out my interactive Shiny app implementation using the cricketr package – Sixer – R package cricketr’s new Shiny avatar

You can also read this post at Rpubs as odi-cricketr. Dowload this report as a PDF file from odi-cricketr.pdf

Important note: Do check out my other posts using cricketr at cricketr-posts

Virendar Sehwag (Ind)
AB Devilliers (SA)
Chris Gayle (WI)
Glenn Maxwell (Aus)

Bowlers

Mitchell Johnson (Aus)
Lasith Malinga (SL)
Dale Steyn (SA)
Tim Southee (NZ)

I have sprinkled the plots with a few of my comments. Feel free to draw your conclusions! The analysis is included below

The profile for Virender Sehwag is 35263. This can be used to get the ODI data for Sehwag. For a batsman the type should be “batting” and for a bowler the type should be “bowling” and the function is getPlayerDataOD()

The package can be installed directly from CRAN

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

or from Github

library(devtools)
install_github("tvganesh/cricketr")
library(cricketr)

The One day data for a particular player can be obtained with the getPlayerDataOD() function. To do you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Virendar Sehwag, etc. This will bring up a page which have the profile number for the player e.g. for Virendar Sehwag this would be http://www.espncricinfo.com/india/content/player/35263.html. Hence, Sehwag’s profile is 35263. This can be used to get the data for Virat Sehwag as shown below

sehwag <- getPlayerDataOD(35263,dir="..",file="sehwag.csv",type="batting")

Analyses of Batsmen

The following plots gives the analysis of the 4 ODI batsmen

Virendar Sehwag (Ind) – Innings – 245, Runs = 8586, Average=35.05, Strike Rate= 104.33
AB Devilliers (SA) – Innings – 179, Runs= 7941, Average=53.65, Strike Rate= 99.12
Chris Gayle (WI) – Innings – 264, Runs= 9221, Average=37.65, Strike Rate= 85.11
Glenn Maxwell (Aus) – Innings – 45, Runs= 1367, Average=35.02, Strike Rate= 126.69

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. Virender Sehwag

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./sehwag.csv","Sehwag")
batsman6s("./sehwag.csv","Sehwag")
batsmanScoringRateODTT("./sehwag.csv","Sehwag")

dev.off()

## null device 
##           1

B. AB Devilliers

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./devilliers.csv","Devillier")
batsman6s("./devilliers.csv","Devillier")
batsmanScoringRateODTT("./devilliers.csv","Devillier")

dev.off()

## null device 
##           1

C. Chris Gayle

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./gayle.csv","Gayle")
batsman6s("./gayle.csv","Gayle")
batsmanScoringRateODTT("./gayle.csv","Gayle")

dev.off()

## null device 
##           1

D. Glenn Maxwell

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./maxwell.csv","Maxwell")
batsman6s("./maxwell.csv","Maxwell")
batsmanScoringRateODTT("./maxwell.csv","Maxwell")

dev.off()

## null device 
##           1

Relative Mean Strike Rate

In this first plot I plot the Mean Strike Rate of the batsmen. It can be seen that Maxwell has a awesome strike rate in ODIs. However we need to keep in mind that Maxwell has relatively much fewer (only 45 innings) innings. He is followed by Sehwag who(most innings- 245) also has an excellent strike rate till 100 runs and then we have Devilliers who roars ahead. This is also seen in the overall strike rate in above

par(mar=c(4,4,2,2))
frames <- list("./sehwag.csv","./devilliers.csv","gayle.csv","maxwell.csv")
names <- list("Sehwag","Devilliers","Gayle","Maxwell")
relativeBatsmanSRODTT(frames,names)

Relative Runs Frequency Percentage

Sehwag leads in the percentage of runs in 10 run ranges upto 50 runs. Maxwell and Devilliers lead in 55-66 & 66-85 respectively.

frames <- list("./sehwag.csv","./devilliers.csv","gayle.csv","maxwell.csv")
names <- list("Sehwag","Devilliers","Gayle","Maxwell")
relativeRunsFreqPerfODTT(frames,names)

Percentage of 4s,6s in the runs scored

The plot below shows the percentage of runs made by the batsmen by ways of 1s,2s,3s, 4s and 6s. It can be seen that Sehwag has the higheest percent of 4s (33.36%) in his overall runs in ODIs. Maxwell has the highest percentage of 6s (13.36%) in his ODI career. If we take the overall 4s+6s then Sehwag leads with (33.36 +5.95 = 39.31%),followed by Gayle (27.80+10.15=37.95%)

Percent 4’s,6’s in total runs scored

The plot below shows the contrib

frames <- list("./sehwag.csv","./devilliers.csv","gayle.csv","maxwell.csv")
names <- list("Sehwag","Devilliers","Gayle","Maxwell")
runs4s6s <-batsman4s6s(frames,names)

print(runs4s6s)

##                Sehwag Devilliers Gayle Maxwell
## Runs(1s,2s,3s)  60.69      67.39 62.05   62.11
## 4s              33.36      24.28 27.80   24.53
## 6s               5.95       8.32 10.15   13.36

Runs forecast

The forecast for the batsman is shown below.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfForecast("./sehwag.csv","Sehwag")
batsmanPerfForecast("./devilliers.csv","Devilliers")
batsmanPerfForecast("./gayle.csv","Gayle")
batsmanPerfForecast("./maxwell.csv","Maxwell")

dev.off()

## null device 
##           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("./sehwag.csv","V Sehwag")
battingPerf3d("./devilliers.csv","AB Devilliers")

dev.off()

## null device 
##           1

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./gayle.csv","C Gayle")
battingPerf3d("./maxwell.csv","G Maxwell")

dev.off()

## null device 
##           1

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)

sehwag <- batsmanRunsPredict("./sehwag.csv","Sehwag",newdataframe=newDF)
devilliers <- batsmanRunsPredict("./devilliers.csv","Devilliers",newdataframe=newDF)
gayle <- batsmanRunsPredict("./gayle.csv","Gayle",newdataframe=newDF)
maxwell <- batsmanRunsPredict("./maxwell.csv","Maxwell",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 Maxwell sets a searing pace in the predicted runs for a given Balls Faced and Minutes at crease followed by Sehwag. But we have to keep in mind that Maxwell has only around 1/5th of the innings of Sehwag (45 to Sehwag’s 245 innings). They are followed by Devilliers and then finally Gayle

batsmen <-cbind(round(sehwag$Runs),round(devilliers$Runs),round(gayle$Runs),round(maxwell$Runs))
colnames(batsmen) <- c("Sehwag","Devilliers","Gayle","Maxwell")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns

##    BallsFaced MinsAtCrease Sehwag Devilliers Gayle Maxwell
## 1          10           30     11         12    11      18
## 2          31           51     33         32    28      43
## 3          52           72     55         52    46      67
## 4          73           93     77         71    63      92
## 5          94          114    100         91    81     117
## 6         116          136    122        111    98     141
## 7         137          157    144        130   116     166
## 8         158          178    167        150   133     191
## 9         179          199    189        170   151     215
## 10        200          220    211        190   168     240

Highest runs likelihood

The plots below the runs likelihood of batsman. This uses K-Means It can be seen that Devilliers has almost 27.75% likelihood to make around 90+ runs. Gayle and Sehwag have 34% to make 40+ runs. A. Virender Sehwag

A. Virender Sehwag

batsmanRunsLikelihood("./sehwag.csv","Sehwag")

## Summary of  Sehwag 's runs scoring likelihood
## **************************************************
## 
## There is a 35.22 % likelihood that Sehwag  will make  46 Runs in  44 balls over 67  Minutes 
## There is a 9.43 % likelihood that Sehwag  will make  119 Runs in  106 balls over  158  Minutes 
## There is a 55.35 % likelihood that Sehwag  will make  12 Runs in  13 balls over 18  Minutes

B. AB Devilliers

batsmanRunsLikelihood("./devilliers.csv","Devilliers")

## Summary of  Devilliers 's runs scoring likelihood
## **************************************************
## 
## There is a 30.65 % likelihood that Devilliers  will make  44 Runs in  43 balls over 60  Minutes 
## There is a 29.84 % likelihood that Devilliers  will make  91 Runs in  88 balls over  124  Minutes 
## There is a 39.52 % likelihood that Devilliers  will make  11 Runs in  15 balls over 21  Minutes

C. Chris Gayle

batsmanRunsLikelihood("./gayle.csv","Gayle")

## Summary of  Gayle 's runs scoring likelihood
## **************************************************
## 
## There is a 32.69 % likelihood that Gayle  will make  47 Runs in  51 balls over 72  Minutes 
## There is a 54.49 % likelihood that Gayle  will make  10 Runs in  15 balls over  20  Minutes 
## There is a 12.82 % likelihood that Gayle  will make  109 Runs in  119 balls over 172  Minutes

D. Glenn Maxwell

batsmanRunsLikelihood("./maxwell.csv","Maxwell")

## Summary of  Maxwell 's runs scoring likelihood
## **************************************************
## 
## There is a 34.38 % likelihood that Maxwell  will make  39 Runs in  29 balls over 35  Minutes 
## There is a 15.62 % likelihood that Maxwell  will make  89 Runs in  55 balls over  69  Minutes 
## There is a 50 % likelihood that Maxwell  will make  6 Runs in  7 balls over 9  Minutes

Average runs at ground and against opposition

A. Virender Sehwag

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./sehwag.csv","Sehwag")
batsmanAvgRunsOpposition("./sehwag.csv","Sehwag")

dev.off()

## null device 
##           1

B. AB Devilliers

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./devilliers.csv","Devilliers")
batsmanAvgRunsOpposition("./devilliers.csv","Devilliers")

dev.off()

## null device 
##           1

C. Chris Gayle

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./gayle.csv","Gayle")
batsmanAvgRunsOpposition("./gayle.csv","Gayle")

dev.off()

## null device 
##           1

D. Glenn Maxwell

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./maxwell.csv","Maxwell")
batsmanAvgRunsOpposition("./maxwell.csv","Maxwell")

dev.off()

## null device 
##           1

Moving Average of runs over career

The moving average for the 4 batsmen indicate the following

1. The moving average of Devilliers and Maxwell is on the way up.
2. Sehwag shows a slight downward trend from his 2nd peak in 2011
3. Gayle maintains a consistent 45 runs for the last few years

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("./sehwag.csv","Sehwag")
batsmanMovingAverage("./devilliers.csv","Devilliers")
batsmanMovingAverage("./gayle.csv","Gayle")
batsmanMovingAverage("./maxwell.csv","Maxwell")

dev.off()

## null device 
##           1

Check batsmen in-form, out-of-form

Maxwell, Devilliers, Sehwag are in-form. This is also evident from the moving average plot
Gayle is out-of-form

checkBatsmanInForm("./sehwag.csv","Sehwag")

## *******************************************************************************************
## 
## Population size: 143  Mean of population: 33.76 
## Sample size: 16  Mean of sample: 37.44 SD of sample: 55.15 
## 
## Null hypothesis H0 : Sehwag 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Sehwag 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Sehwag 's Form Status: In-Form because the p value: 0.603525  is greater than alpha=  0.05"
## *******************************************************************************************

checkBatsmanInForm("./devilliers.csv","Devilliers")

## *******************************************************************************************
## 
## Population size: 111  Mean of population: 43.5 
## Sample size: 13  Mean of sample: 57.62 SD of sample: 40.69 
## 
## Null hypothesis H0 : Devilliers 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Devilliers 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Devilliers 's Form Status: In-Form because the p value: 0.883541  is greater than alpha=  0.05"
## *******************************************************************************************

checkBatsmanInForm("./gayle.csv","Gayle")

## *******************************************************************************************
## 
## Population size: 140  Mean of population: 37.1 
## Sample size: 16  Mean of sample: 17.25 SD of sample: 20.25 
## 
## Null hypothesis H0 : Gayle 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Gayle 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Gayle 's Form Status: Out-of-Form because the p value: 0.000609  is less than alpha=  0.05"
## *******************************************************************************************

checkBatsmanInForm("./maxwell.csv","Maxwell")

## *******************************************************************************************
## 
## Population size: 28  Mean of population: 25.25 
## Sample size: 4  Mean of sample: 64.25 SD of sample: 36.97 
## 
## Null hypothesis H0 : Maxwell 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Maxwell 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Maxwell 's Form Status: In-Form because the p value: 0.948744  is greater than alpha=  0.05"
## *******************************************************************************************

Analysis of bowlers

Mitchell Johnson (Aus) – Innings-150, Wickets – 239, Econ Rate : 4.83
Lasith Malinga (SL)- Innings-182, Wickets – 287, Econ Rate : 5.26
Dale Steyn (SA)- Innings-103, Wickets – 162, Econ Rate : 4.81
Tim Southee (NZ)- Innings-96, Wickets – 135, Econ Rate : 5.33

Malinga has the highest number of innings and wickets followed closely by Mitchell. Steyn and Southee have relatively fewer innings.

To get the bowler’s data use

malinga <- getPlayerDataOD(49758,dir=".",file="malinga.csv",type="bowling")

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("./mitchell.csv","J Mitchell")
bowlerWktsFreqPercent("./malinga.csv","Malinga")
bowlerWktsFreqPercent("./steyn.csv","Steyn")
bowlerWktsFreqPercent("./southee.csv","southee")

dev.off()

## null device 
##           1

Wickets Runs plot

The plot below gives a boxplot of the runs ranges for each of the wickets taken by the bowlers. M Johnson and Steyn are more economical than Malinga and Southee corroborating the figures above

par(mfrow=c(1,4))
par(mar=c(4,4,2,2))

bowlerWktsRunsPlot("./mitchell.csv","J Mitchell")
bowlerWktsRunsPlot("./malinga.csv","Malinga")
bowlerWktsRunsPlot("./steyn.csv","Steyn")
bowlerWktsRunsPlot("./southee.csv","southee")

dev.off()

## null device 
##           1

Average wickets in different grounds and opposition

A. Mitchell Johnson

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./mitchell.csv","J Mitchell")
bowlerAvgWktsOpposition("./mitchell.csv","J Mitchell")

dev.off()

## null device 
##           1

B. Lasith Malinga

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./malinga.csv","Malinga")
bowlerAvgWktsOpposition("./malinga.csv","Malinga")

dev.off()

## null device 
##           1

C. Dale Steyn

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./steyn.csv","Steyn")
bowlerAvgWktsOpposition("./steyn.csv","Steyn")

dev.off()

## null device 
##           1

D. Tim Southee

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./southee.csv","southee")
bowlerAvgWktsOpposition("./southee.csv","southee")

dev.off()

## null device 
##           1

Relative bowling performance

The plot below shows that Mitchell Johnson and Southee have more wickets in 3-4 wickets range while Steyn and Malinga in 1-2 wicket range

frames <- list("./mitchell.csv","./malinga.csv","steyn.csv","southee.csv")
names <- list("M Johnson","Malinga","Steyn","Southee")
relativeBowlingPerf(frames,names)

Relative Economy Rate against wickets taken

Steyn had the best economy rate followed by M Johnson. Malinga and Southee have a poorer economy rate

frames <- list("./mitchell.csv","./malinga.csv","steyn.csv","southee.csv")
names <- list("M Johnson","Malinga","Steyn","Southee")
relativeBowlingERODTT(frames,names)

Moving average of wickets over career

Johnson and Steyn career vs wicket graph is on the up-swing. Southee is maintaining a reasonable record while Malinga shows a decline in ODI performance

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerMovingAverage("./mitchell.csv","M Johnson")
bowlerMovingAverage("./malinga.csv","Malinga")
bowlerMovingAverage("./steyn.csv","Steyn")
bowlerMovingAverage("./southee.csv","Southee")

dev.off()

## null device 
##           1

Wickets forecast

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerPerfForecast("./mitchell.csv","M Johnson")
bowlerPerfForecast("./malinga.csv","Malinga")
bowlerPerfForecast("./steyn.csv","Steyn")
bowlerPerfForecast("./southee.csv","southee")

dev.off()

## null device 
##           1

Check bowler in-form, out-of-form

All the bowlers are shown to be still in-form

checkBowlerInForm("./mitchell.csv","J Mitchell")

## *******************************************************************************************
## 
## Population size: 135  Mean of population: 1.55 
## Sample size: 15  Mean of sample: 2 SD of sample: 1.07 
## 
## Null hypothesis H0 : J Mitchell 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : J Mitchell 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "J Mitchell 's Form Status: In-Form because the p value: 0.937917  is greater than alpha=  0.05"
## *******************************************************************************************

checkBowlerInForm("./malinga.csv","Malinga")

## *******************************************************************************************
## 
## Population size: 163  Mean of population: 1.58 
## Sample size: 19  Mean of sample: 1.58 SD of sample: 1.22 
## 
## Null hypothesis H0 : Malinga 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Malinga 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Malinga 's Form Status: In-Form because the p value: 0.5  is greater than alpha=  0.05"
## *******************************************************************************************

checkBowlerInForm("./steyn.csv","Steyn")

## *******************************************************************************************
## 
## Population size: 93  Mean of population: 1.59 
## Sample size: 11  Mean of sample: 1.45 SD of sample: 0.69 
## 
## Null hypothesis H0 : Steyn 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Steyn 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Steyn 's Form Status: In-Form because the p value: 0.257438  is greater than alpha=  0.05"
## *******************************************************************************************

checkBowlerInForm("./southee.csv","southee")

## *******************************************************************************************
## 
## Population size: 86  Mean of population: 1.48 
## Sample size: 10  Mean of sample: 0.8 SD of sample: 1.14 
## 
## Null hypothesis H0 : southee 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : southee 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "southee 's Form Status: Out-of-Form because the p value: 0.044302  is less than alpha=  0.05"
## *******************************************************************************************

***************

Key findings

Here are some key conclusions ODI batsmen

AB Devilliers has high frequency of runs in the 60-120 range and the highest average
Sehwag has the most number of innings and good strike rate
Maxwell has the best strike rate but it should be kept in mind that he has 1/5 of the innings of Sehwag. We need to see how he progress further
Sehwag has the highest percentage of 4s in the runs scored, while Maxwell has the most 6s
For a hypothetical Balls Faced and Minutes at creases Maxwell will score the most runs followed by Sehwag
The moving average of indicates that the best is yet to come for Devilliers and Maxwell. Sehwag has a few more years in him while Gayle shows a decline in ODI performance and an out of form is indicated.

ODI bowlers

Malinga has the highest played the highest innings and also has the highest wickets though he has poor economy rate
M Johnson is the most effective in the 3-4 wicket range followed by Southee
M Johnson and Steyn has the best overall economy rate followed by Malinga and Steyn 4 M Johnson and Steyn’s career is on the up-swing,Southee maintains a steady consistent performance, while Malinga shows a downward trend

Hasta la vista! I’ll be back!
Watch this space!

Also see my other posts in R