The making of cricket package yorkr – Part 3

4. Runs vs deliveries plot

It can be seen from the plots below that Kohli is very consistent in the runs scored. The runs crowd near the regression curve. There is more variance in Dhoni and De Villiers performance. The band on either side of the regression curve represents the 95% confidence interval(A 95% confidence level means that 95% of the intervals would include the population parameter).

p1 <-batsmanRunsVsDeliveries(kohli,"Kohli")
p2 <- batsmanRunsVsDeliveries(dhoni, "Dhoni")
p3 <- batsmanRunsVsDeliveries(devilliers,"De Villiers")
grid.arrange(p1,p2,p3, ncol=3)

5. Total runs vs 4s vs 6s plot

The plots below show the runs (Total runs, Runs from 4s & Runs from sixes) vs the deliveries faced. Kohli scores more runs and more fours which can be evaluated from the slope of the blue and red regression lines (reaches 150+,50+) for Total runs and Runs from fours). De Villers has more Runs from sixes as can be seen the 3rd sub plot (green line)

kohli46 <- select(kohli,batsman,ballsPlayed,fours,sixes,runs)
p1 <- batsmanFoursSixes(kohli46,"Kohli")
dhoni46 <- select(dhoni,batsman,ballsPlayed,fours,sixes,runs)
p2 <- batsmanFoursSixes(dhoni46,"Dhoni")
devilliers46 <- select(devilliers,batsman,ballsPlayed,fours,sixes,runs)
p3 <- batsmanFoursSixes(devilliers46, "De Villiers")
grid.arrange(p1,p2,p3, ncol=3)

6. Batsmen dismissals

Interestingly it can be seen that Dhoni has remained unbeaten more often (47 times) than Kohli or De Villiers. Dhoni despite being a great runner between wickets has been run-out more often.

p1 <-batsmanDismissals(kohli,"Kohli")
p2 <- batsmanDismissals(dhoni, "Dhoni")
p3 <- batsmanDismissals(devilliers, "De Villiers")
grid.arrange(p1,p2,p3, ncol=3)

7. Batsmen Strike Rate

From the plot below Kohli has the best strike rate till 100 runs, the slope seems to steeper. De Villiers seems to do better after 100 runs.

p1 <-batsmanMeanStrikeRate(kohli,"Kohli")
p2 <- batsmanMeanStrikeRate(dhoni, "Dhoni")
p3 <- batsmanMeanStrikeRate(devilliers, "De Villiers")
grid.arrange(p1,p2,p3, ncol=3)

8. Batsmen moving average

Kohli’s and De Villiers’ form can be seen to be improving over the years. Dhoni seems to have hit a slump in recent times. But we have to keep in mind that he has the second highest ODI runs in India and is just behind Kohli

p1 <-batsmanMovingAverage(kohli,"Kohli")
p2 <- batsmanMovingAverage(dhoni, "Dhoni")
p3 <- batsmanMovingAverage(devilliers, "De Villiers")
grid.arrange(p1,p2,p3, ncol=3)

9. Batsmen against opposition

Kohli averages 50 runs against 6 countries, to Dhoni’s 4. Kohli performs well against Australia, New Zealand, West Indies,Pakistan,Bangladesh. Kohli’s performance against England has been mediocre. De Villiers averages around 50 with 5 countries

batsmanRunsAgainstOpposition(kohli,"Kohli")

batsmanRunsAgainstOpposition(dhoni, "Dhoni")

batsmanRunsAgainstOpposition(devilliers, "De Villiers")

10. Batsmen runs at different venues

Kohli’s favorite hunting grounds in ODI are Adelaide, Sydney, Western Australia, Wankhede. Dhoni’s best performances are at Lords, Sydney,Chepauk.

batsmanRunsVenue(kohli,"Kohli")

batsmanRunsVenue(dhoni, "Dhoni")

batsmanRunsVenue(devilliers, "De Villiers")

11. Batsmen runs predict

The plots below predict the number of deliveries needed by each batsmen to score runs shown. For this I have used classification trees based on deliveries and runs using the package rpart. From the plot for Kohli it can be seen that for <26 deliveries Kohli on an average scores 8 runs(7.6), for <58 deliveries scores 30 runs but for >58 deliveries scores around 52 runs. On the other hand De Villiers needs just over 40 deliveries to score 52 runs.

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(kohli,"Kohli")
batsmanRunsPredict(dhoni, "Dhoni")
batsmanRunsPredict(devilliers, "De Villiers")

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

12. Get team bowling details

The function below all the ODI matches between India or Australia and all other countries

c <- getTeamBowlingDetails("India",save=TRUE)
d <- getTeamBowlingDetails("Australia",save=TRUE)

13. Get wicket details

The functions below gets the data frame for each bowler

jadeja <- getBowlerWicketDetails(team="India",name="Jadeja")
harbhajan <- getBowlerWicketDetails(team="India",name="Harbhajan")
ashwin <- getBowlerWicketDetails(team="India",name="Ashwin")
johnson <-  getBowlerWicketDetails(team="Australia",name="Johnson")

14. Display data frame

The details of the data frame is shown below

knitr::kable(head(jadeja))

15. Bowler Economy rate

Harbhajan and Ashwin have a better economy rate than RA Jadeja

p1 <- bowlerEconomyRate(jadeja,"RA Jadeja")
p2<-bowlerEconomyRate(harbhajan, "Harbhajan")
p3<-bowlerEconomyRate(ashwin, "Ashwin")
p4<-bowlerEconomyRate(johnson, "MG Johnson")
grid.arrange(p1,p2,p3,p4, ncol=2)

15. Mean runs conceded by bowler

p1<-bowlerMeanRuns(jadeja,"RA Jadeja")
p2<-bowlerMeanRuns(harbhajan, "Harbhajan")
p3<-bowlerMeanRuns(ashwin, "Ashwin")
p4<-bowlerMeanRuns(johnson, "MG Johnson")
grid.arrange(p1,p2,p3,p4, ncol=2)

15. Moving average of bowler

From the plots below MG Johnson, Harbhajan and Ashwin have been performing very consistently. RA Jadeja bowling seems to be taking a nosedive, though he is at the top of all ODI bowlers of India

p1<-bowlerMovingAverage(jadeja,"RA Jadeja")
p2<-bowlerMovingAverage(harbhajan, "Harbhajan")
p3<-bowlerMovingAverage(ashwin, "Ashwin")
p4<-bowlerMovingAverage(johnson, "MG Johnson")
grid.arrange(p1,p2,p3,p4, ncol=2)

16. Wicket average

Jadeja has a better wicket average than Harbhajan and Ashwin.Jadeja and Ashwin average around 2 wickets Harbhajan averages 1.5 wickets(tendency to 2)

p1<-bowlerWicketPlot(jadeja,"RA Jadeja")
p2<-bowlerWicketPlot(harbhajan, "Harbhajan")
p3<-bowlerWicketPlot(ashwin, "Ashwin")
p4<-bowlerWicketPlot(johnson, "MG Johnson")
grid.arrange(p1,p2,p3,p4, ncol=2)

16. Wickets opposition

Jadeja’s best performances have been against England, Pakistan, New Zealand and Zimbabwe. For Harbhajan it has been New Zealand, Sri Lanka and Zimbabwe.

bowlerWicketsAgainstOpposition(jadeja,"RA Jadeja")

bowlerWicketsAgainstOpposition(harbhajan, "Harbhajan")

bowlerWicketsAgainstOpposition(ashwin, "Ashwin")

bowlerWicketsAgainstOpposition(johnson, "MG Johnson")

16. Wickets venue

The top 20 venues for each bowler is shown in the plots

bowlerWicketsVenue(jadeja,"RA Jadeja")

bowlerWicketsVenue(harbhajan, "Harbhajan")

bowlerWicketsVenue(ashwin, "Ashwin")

bowlerWicketsVenue(johnson, "MG Johnson")

16. Create a data frame with wickets and deliveries

jadeja1 <- getDeliveryWickets(team="India",name="Jadeja",save=FALSE)
harbhajan1 <- getDeliveryWickets(team="India",name="Harbhajan",save=FALSE)
ashwin1 <- getDeliveryWickets(team="India",name="Ashwin",save=FALSE)
johnson1 <- getDeliveryWickets(team="Australia",name="MG Johnson",save=FALSE)

17. Deliveries to wickets plots

The following plots try to predict the average number of deliveries required for the wickets taken. As in the batsman runs predict I have used classification trees between deliverie at which a wicket was taken. The package rpart was used for the classification. The internediate nodes are the number of deliveries and the leaf nodes are the wickets taken. Though the wickets are in decimal we can intepret the tree as follows For RA Jadeja <22 deliveries result in 1.2 (~1 wicket) and >22 to take 1.6 wicket (~2 wickets). Interestingly Harbhajan needs <9.5 deliveries to take a wicket. Harbhajan is more likely to provide a quicker breakthrough.

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(jadeja1,"RA Jadeja")
bowlerWktsPredict(harbhajan1,"Harbhajan Sigh")

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

Similarly MG Johnson can provide a breakthrough with just around 14 deliveries

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(ashwin1,"Ravichander Ashwin")
bowlerWktsPredict(johnson1,"MG Johnson")

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

ODI batsman

1. The top 2 ODI Indian batsman(Kohli and Dhoni) and De Villiers of South Africa were considered.
2. Kohli has a better strike rate till about 100 runs(steeper slope) and De Villiers beyond 100.
3. Dhoni has remained unbeaten more number of times than the other 2. It may have been possible that his average would have been higher if he had come in earlier
4. Kohli and De Villiers have performed consistently. Dhoni needs to get back his touch

ODI bowlers

1. RA Jadeja has a better wicket taking rate than Harbhajan and Ashwin.
2. Ashwin and Harbhajan have a better economy rate than Jadeja
3. Harbhjanan, Ashwin and MG Johnson have performed consistently while RA Jadeja’s performance has been on the decline.
4. Harbhajan and MG Johnson need around 11 balls to make a break through

This was probably the last set of functions for my cricket package yorkr. Over the next several weeks I will be cleaning up, documenting, refining the functions and removing any glitches. I hope to have the package released in the next 6-8 weeks

Also see

1. Cricket analytics with cricketr
2. Sixer: An R package cricketr’s new Shiny avatar

You may also like

1. What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress
2. The common alphabet of programming languages
3. A method to crowd source pothole marking on (Indian) roads
4. The Anomaly
5. Simulating the domino effect in Android using Box2D and AndEngine
6. Presentation on Wireless Technologies – Part 1
7. Natural selection of database technology through the years