yorkr crashes the IPL party! - Part 4

Batsman functions

1. batsmanRunsVsDeliveries
2. batsmanFoursSixes
3. batsmanDismissals
4. batsmanRunsVsStrikeRate
5. batsmanMovingAverage
6. batsmanCumulativeAverageRuns
7. batsmanCumulativeStrikeRate
   
8. batsmanRunsAgainstOpposition
9. batsmanRunsVenue
10. batsmanRunsPredict

Bowler functions

1. bowlerMeanEconomyRate
2. bowlerMeanRunsConceded
3. bowlerMovingAverage
4. bowlerCumulativeAvgWickets
   
5. bowlerCumulativeAvgEconRate
6. bowlerWicketPlot
7. bowlerWicketsAgainstOpposition
8. bowlerWicketsVenue
9. bowlerWktsPredict

library(yorkr)
library(gridExtra)
library(rpart.plot)
library(dplyr)
library(ggplot2)
rm(list=ls())

1. Get IPL Team Batting details

The function below gets the overall IPL team batting details based on the RData file available in IPL T20 matches. This is currently also available in Github at [IPL-T20-matches] (https://github.com/tvganesh/yorkrData/tree/master/IPL/IPL-T20-matches). The batting details of the IPL team in each match is created and a huge data frame is created by rbinding the individual dataframes. This can be saved as a RData file

setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-matches")
csk_details <- getTeamBattingDetails("Chennai Super Kings",dir=".", save=TRUE)
dd_details <- getTeamBattingDetails("Delhi Daredevils",dir=".",save=TRUE)
kkr_details <- getTeamBattingDetails("Kolkata Knight Riders",dir=".",save=TRUE)
mi_details <- getTeamBattingDetails("Mumbai Indians",dir=".",save=TRUE)
rcb_details <- getTeamBattingDetails("Royal Challengers Bangalore",dir=".",save=TRUE)

2. Get IPL batsman details

This function is used to get the individual IPL T20 batting record for a the specified batsman of the team as in the functions below. For analyzing the batting performances I have chosen the top IPL T20 batsmen from the teams. This was based to a large extent on batting scorecard functions from yorkr crashes the IPL party!:Part 3 The top IPL batsmen chosen are the ones below

1. Suresh Raina (CSK)
2. MS Dhoni (CSK)
3. Virendar Sehwag (DD)
4. Rohit Sharma (MI)
5. Gautham Gambhir (KKR)
6. Virat Kohli (RCB)

setwd("C:/software/cricket-package/cricsheet/cleanup/IPL/part4")
raina <- getBatsmanDetails(team="Chennai Super Kings",name="SK Raina",dir=".")

## [1] "./Chennai Super Kings-BattingDetails.RData"

dhoni <- getBatsmanDetails(team="Chennai Super Kings",name="MS Dhoni")

## [1] "./Chennai Super Kings-BattingDetails.RData"

sehwag <-  getBatsmanDetails(team="Delhi Daredevils",name="V Sehwag",dir=".")

## [1] "./Delhi Daredevils-BattingDetails.RData"

gambhir <-  getBatsmanDetails(team="Kolkata Knight Riders",name="G Gambhir",dir=".")

## [1] "./Kolkata Knight Riders-BattingDetails.RData"

rsharma <-  getBatsmanDetails(team="Mumbai Indians",name="RG Sharma",dir=".")

## [1] "./Mumbai Indians-BattingDetails.RData"

kohli <-  getBatsmanDetails(team="Royal Challengers Bangalore",name="V Kohli",dir=".")

## [1] "./Royal Challengers Bangalore-BattingDetails.RData"

3. Runs versus deliveries (in IPL matches)

Sehwag has a superb strike rate. It can be seen that Sehwag averages around 80 runs for around 40 deliveries followed by Rohit Sharma. Raina and Dhoni average around 60 runs

p1 <-batsmanRunsVsDeliveries(raina, "SK Raina")
p2 <-batsmanRunsVsDeliveries(dhoni,"MS Dhoni")
p3 <-batsmanRunsVsDeliveries(sehwag,"V Sehwag")
p4 <-batsmanRunsVsDeliveries(gambhir,"G Gambhir")
p5 <-batsmanRunsVsDeliveries(rsharma,"RG Sharma")
p6 <-batsmanRunsVsDeliveries(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

4. Batsman Total runs, Fours and Sixes (in IPL matches)

Dhoni leads in the runs made from sixes in comparison to the others

raina46 <- select(raina,batsman,ballsPlayed,fours,sixes,runs)
p1 <-batsmanFoursSixes(raina46, "SK Raina")
dhoni46 <- select(dhoni,batsman,ballsPlayed,fours,sixes,runs)
p2 <-batsmanFoursSixes(dhoni46,"MS Dhoni")
sehwag46 <- select(sehwag,batsman,ballsPlayed,fours,sixes,runs)
p3 <-batsmanFoursSixes(sehwag46,"V Sehwag")
gambhir46 <- select(gambhir,batsman,ballsPlayed,fours,sixes,runs)
p4 <-batsmanFoursSixes(gambhir46,"G Gambhir")
rsharma46 <- select(rsharma,batsman,ballsPlayed,fours,sixes,runs)
p5 <-batsmanFoursSixes(rsharma46,"RG Sharma")
kohli46 <- select(kohli,batsman,ballsPlayed,fours,sixes,runs)
p6 <-batsmanFoursSixes(kohli46,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

5. Batsman dismissals (in IPL matches)

The type of dismissal for each batsman is shown below

p1 <-batsmanDismissals(raina, "SK Raina")
p2 <-batsmanDismissals(dhoni,"MS Dhoni")
p3 <-batsmanDismissals(sehwag,"V Sehwag")
p4 <-batsmanDismissals(gambhir,"G Gambhir")
p5 <-batsmanDismissals(rsharma,"RG Sharma")
p6 <-batsmanDismissals(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

6. Runs versus Strike Rate (in IPL matches)

Raina, Dhoni and Kohli have an increasing strike rate with more runs scored

p1 <-batsmanRunsVsStrikeRate(raina, "SK Raina")
p2 <-batsmanRunsVsStrikeRate(dhoni,"MS Dhoni")
p3 <-batsmanRunsVsStrikeRate(sehwag,"V Sehwag")
p4 <-batsmanRunsVsStrikeRate(gambhir,"G Gambhir")
p5 <-batsmanRunsVsStrikeRate(rsharma,"RG Sharma")
p6 <-batsmanRunsVsStrikeRate(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

7. Batsman moving average (in IPL matches)

Rohit Sharma seems to maintain an average of almost 30 runs, while Dhoni and Kohli average around 25.

p1 <-batsmanMovingAverage(raina, "SK Raina")
p2 <-batsmanMovingAverage(dhoni,"MS Dhoni")
p3 <-batsmanMovingAverage(sehwag,"V Sehwag")
p4 <-batsmanMovingAverage(gambhir,"G Gambhir")
p5 <-batsmanMovingAverage(rsharma,"RG Sharma")
p6 <-batsmanMovingAverage(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

8. Batsman cumulative average (in IPL matches)

The cumulative runs average of Raina, Gambhir, Kohli and Rohit Sharma are around 28-30 runs.Dhoni drops to 25

p1 <-batsmanCumulativeAverageRuns(raina, "SK Raina")
p2 <-batsmanCumulativeAverageRuns(dhoni,"MS Dhoni")
p3 <-batsmanCumulativeAverageRuns(sehwag,"V Sehwag")
p4 <-batsmanCumulativeAverageRuns(gambhir,"G Gambhir")
p5 <-batsmanCumulativeAverageRuns(rsharma,"RG Sharma")
p6 <-batsmanCumulativeAverageRuns(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

9. Cumulative Average Strike Rate (in IPL matches)

As seen above Sehwag has a phenomenal cumulative strike rate of around 150, followed by Dhoni around 130, then we Raina and finally Kohli.

p1 <-batsmanCumulativeStrikeRate(raina, "SK Raina")
p2 <-batsmanCumulativeStrikeRate(dhoni,"MS Dhoni")
p3 <-batsmanCumulativeStrikeRate(sehwag,"V Sehwag")
p4 <-batsmanCumulativeStrikeRate(gambhir,"G Gambhir")
p5 <-batsmanCumulativeStrikeRate(rsharma,"RG Sharma")
p6 <-batsmanCumulativeStrikeRate(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

10. Batsman runs against opposition (in IPL matches)

The following charts show the performance of te batsmen against opposing IPL teams

#Kohli's best performances are against New Zealand and Sri Lanka
batsmanRunsAgainstOpposition(raina, "SK Raina")

batsmanRunsAgainstOpposition(dhoni,"MS Dhoni")

batsmanRunsAgainstOpposition(sehwag,"V Sehwag")

batsmanRunsAgainstOpposition(gambhir,"G Gambhir")

batsmanRunsAgainstOpposition(rsharma,"RG Sharma")

batsmanRunsAgainstOpposition(kohli,"V Kohli")

11. Runs at different venues (in IPL matches)

The plots below give the performances of the batsmen at different grounds.

batsmanRunsVenue(raina, "SK Raina")

batsmanRunsVenue(dhoni,"MS Dhoni")

batsmanRunsVenue(sehwag,"V Sehwag")

batsmanRunsVenue(gambhir,"G Gambhir")

batsmanRunsVenue(rsharma,"RG Sharma")

batsmanRunsVenue(kohli,"V Kohli")

12. Predict number of runs to deliveries (in IPL matches)

The plots below use rpart classification tree to predict the number of deliveries required to score the runs in the leaf node.

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(raina, "SK Raina")
batsmanRunsPredict(dhoni,"MS Dhoni")
batsmanRunsPredict(sehwag,"V Sehwag")

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(gambhir,"G Gambhir")
batsmanRunsPredict(rsharma,"RG Sharma")
batsmanRunsPredict(kohli,"V Kohli")

13. Get bowling details in IPL matches

The function below gets the overall team IPL T20 bowling details based on the RData file available in IPL T20 matches. This is currently also available in Github at [yorkrData] (https://github.com/tvganesh/yorkrData/tree/master/IPL/IPL-T20-matches). The IPL T20 bowling details of the IPL team in each match is created, and a huge data frame is created by rbinding the individual dataframes. This can be saved as a RData file

setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-matches")
kkr_bowling <- getTeamBowlingDetails("Kolkata Knight Riders",dir=".",save=TRUE)
csk_bowling <- getTeamBowlingDetails("Chennai Super Kings",dir=".",save=TRUE)
kxip_bowling <- getTeamBowlingDetails("Kings XI Punjab",dir=".",save=TRUE)
mi_bowling <- getTeamBowlingDetails("Mumbai Indians",dir=".",save=TRUE)
rcb_bowling <- getTeamBowlingDetails("Royal Challengers Bangalore",dir=".",save=TRUE)
rr_bowling <- getTeamBowlingDetails("Rajasthan Royals",dir=".",save=TRUE)
fl <- list.files(".","BowlingDetails.RData")
file.copy(fl, "C:/software/cricket-package/cricsheet/cleanup/IPL/part4")

## [1] FALSE FALSE FALSE FALSE FALSE FALSE

file.remove(fl)

## [1] TRUE TRUE TRUE TRUE TRUE TRUE

14. Get bowling details of the individual IPL bowlers

This function is used to get the individual bowling record for a specified bowler of the country as in the functions below. For analyzing the bowling performances the following cricketers have been chosen based on the bowling scorecard from my post yorkr crashes the IPL party ! – Part 3

1. Ravichander Ashwin (CSK)
2. DJ Bravo (CSK)
3. PP Chawla (KXIP)
4. Harbhajan Singh (MI)
5. R Vinay Kumar (RCB)
6. SK Trivedi (RR)

setwd("C:/software/cricket-package/cricsheet/cleanup/IPL/part4")
ashwin <- getBowlerWicketDetails(team="Chennai Super Kings",name="R Ashwin",dir=".")
bravo <-  getBowlerWicketDetails(team="Chennai Super Kings",name="DJ Bravo",dir=".")
chawla <-  getBowlerWicketDetails(team="Kings XI Punjab",name="PP Chawla",dir=".")
harbhajan <-  getBowlerWicketDetails(team="Mumbai Indians",name="Harbhajan Singh",dir=".")
vinay <-  getBowlerWicketDetails(team="Royal Challengers Bangalore",name="R Vinay Kumar",dir=".")
sktrivedi <-  getBowlerWicketDetails(team="Rajasthan Royals",name="SK Trivedi",dir=".")

15. Bowler Mean Economy Rate (in IPL matches)

Ashwin & Chawla have the best economy rates of in the IPL teams, followed by Harbhajan Singh

p1<-bowlerMeanEconomyRate(ashwin,"R Ashwin")
p2<-bowlerMeanEconomyRate(bravo, "DJ Bravo")
p3<-bowlerMeanEconomyRate(chawla, "PP Chawla")
p4<-bowlerMeanEconomyRate(harbhajan, "Harbhajan Singh")
p5<-bowlerMeanEconomyRate(vinay, "R Vinay")
p6<-bowlerMeanEconomyRate(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

16. Bowler Mean Runs conceded (in IPL matches)

p1<-bowlerMeanRunsConceded(ashwin,"R Ashwin")
p2<-bowlerMeanRunsConceded(bravo, "DJ Bravo")
p3<-bowlerMeanRunsConceded(chawla, "PP Chawla")
p4<-bowlerMeanRunsConceded(harbhajan, "Harbhajan Singh")
p5<-bowlerMeanRunsConceded(vinay, "R Vinay")
p6<-bowlerMeanRunsConceded(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

17. Bowler Moving average (in IPL matches)

Harbhajan’s moving average is the best hovering around 2 wickets

p1<-bowlerMovingAverage(ashwin,"R Ashwin")
p2<-bowlerMovingAverage(bravo, "DJ Bravo")
p3<-bowlerMovingAverage(chawla, "PP Chawla")
p4<-bowlerMovingAverage(harbhajan, "Harbhajan Singh")
p5<-bowlerMovingAverage(vinay, "R Vinay")
p6<-bowlerMovingAverage(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

17. Bowler cumulative average wickets (in IPL matches)

The cumulative average tells a different story. DJ Bravo and R Vinay have a cumulative average of 2 wickets. All others are around 1.5

p1<-bowlerCumulativeAvgWickets(ashwin,"R Ashwin")
p2<-bowlerCumulativeAvgWickets(bravo, "DJ Bravo")
p3<-bowlerCumulativeAvgWickets(chawla, "PP Chawla")
p4<-bowlerCumulativeAvgWickets(harbhajan, "Harbhajan Singh")
p5<-bowlerCumulativeAvgWickets(vinay, "R Vinay")
p6<-bowlerCumulativeAvgWickets(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

18. Bowler cumulative Economy Rate (ER) (in IPL matches)

Ashwin & Harbhajan have the best cumulative economy rate

p1<-bowlerCumulativeAvgEconRate(ashwin,"R Ashwin")
p2<-bowlerCumulativeAvgEconRate(bravo, "DJ Bravo")
p3<-bowlerCumulativeAvgEconRate(chawla, "PP Chawla")
p4<-bowlerCumulativeAvgEconRate(harbhajan, "Harbhajan Singh")
p5<-bowlerCumulativeAvgEconRate(vinay, "R Vinay")
p6<-bowlerCumulativeAvgEconRate(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

19. Bowler wicket plot (in IPL matches)

The plot below gives the average wickets versus number of overs

p1<-bowlerWicketPlot(ashwin,"R Ashwin")
p2<-bowlerWicketPlot(bravo, "DJ Bravo")
p3<-bowlerWicketPlot(chawla, "PP Chawla")
p4<-bowlerWicketPlot(harbhajan, "Harbhajan Singh")
p5<-bowlerWicketPlot(vinay, "R Vinay")
p6<-bowlerWicketPlot(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

20. Bowler wicket against opposing IPL teams

bowlerWicketsAgainstOpposition(ashwin,"R Ashwin")

bowlerWicketsAgainstOpposition(bravo, "DJ Bravo")

bowlerWicketsAgainstOpposition(chawla, "PP Chawla")

bowlerWicketsAgainstOpposition(harbhajan, "Harbhajan Singh")

bowlerWicketsAgainstOpposition(vinay, "R Vinay")

bowlerWicketsAgainstOpposition(sktrivedi, "SK Trivedi")

21. Bowler wicket at cricket grounds in IPL

bowlerWicketsVenue(ashwin,"R Ashwin")

bowlerWicketsVenue(bravo, "DJ Bravo")

## Warning: Removed 7 rows containing missing values (geom_bar).

bowlerWicketsVenue(chawla, "PP Chawla")

bowlerWicketsVenue(harbhajan, "Harbhajan Singh")

bowlerWicketsVenue(vinay, "R Vinay")

## Warning: Removed 2 rows containing missing values (geom_bar).

bowlerWicketsVenue(sktrivedi, "SK Trivedi")

## Warning: Removed 3 rows containing missing values (geom_bar).

22. Get Delivery wickets for IPL bowlers

This function creates a dataframe of deliveries and the wickets taken

setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-matches")
ashwin1 <- getDeliveryWickets(team="Chennai Super Kings",dir=".",name="R Ashwin",save=FALSE)
bravo1 <- getDeliveryWickets(team="Chennai Super Kings",dir=".",name="DJ Bravo",save=FALSE)
chawla1 <- getDeliveryWickets(team="Kings XI Punjab",dir=".",name="PP Chawla",save=FALSE)
harbhajan1 <- getDeliveryWickets(team="Mumbai Indians",dir=".",name="Harbhajan Singh",save=FALSE)
vinay1 <- getDeliveryWickets(team="Royal Challengers Bangalore",dir=".",name="R Vinay",save=FALSE)
sktrivedi1 <- getDeliveryWickets(team="Rajasthan Royals",dir=".",name="SK Trivedi",save=FALSE)

23. Predict number of deliveries to wickets in IPL T20

#Ashwin takes <6.5 deliveries for a wicket while Bravo takes around 5.5 deliveries. Don't get hung up on the .5 delivery. We can just take it that Bravo will provide a breakthrough quicker than Ashwin
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))

bowlerWktsPredict(ashwin1,"R Ashwin")
bowlerWktsPredict(bravo1, "DJ Bravo")

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(chawla1, "PP Chawla")
bowlerWktsPredict(harbhajan1, "Harbhajan Singh")

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(vinay1, "R Vinay")
bowlerWktsPredict(sktrivedi1, "SK Trivedi")