```
##########################################################################################
# Designed and developed by Tinniam V Ganesh
# Date : 8 Aug 2015
# Function: relativeBatsmanSRODTT
# This function computes and plot relative Mean Strike Rates of
# batsmen vs RUns scored for ODI and Twenty20
#
###########################################################################################
relativeBatsmanSRODTT <- function(frames, names) {
col1 <- rainbow(length(frames))
for(i in 1:length(frames))
{
# Clean batsman data
batsman <- clean(frames[[i]])
# Create a vector of runs with intervals of 15
maxi <- (max(batsman$Runs/15) + 1) *15
v <- seq(0,maxi,by=15)
a <- hist(batsman$Runs,breaks=v,plot=FALSE)
# Compute the Mean Strike Rate for each run range
SR <- NULL
for(j in 2:length(a$breaks)) {
b <- batsman$Runs > a$breaks[j-1] & batsman$Runs <= a$breaks[j]
c <- batsman[b,]
SR[j-1] <- mean(as.numeric(as.character(c$SR)))
}
# Find all intervals where there is no data i.e. NAN
b <- !is.na(SR)
#Subset and remove the NANs for counts
c <- a$mid[b]
#Subset and remove the NANs for Strike Rate
SR <- SR[b]
par(mar=c(4,4,1,1))
if(i==1) {
plot(c,predict(loess(SR~c)),xlab="Runs",ylab="Mean Strike Rate",
xlim=c(0,250), ylim=c(40,250), type="l",lty=1,lwd=3, col=col1[i],
main="Relative Mean Strike Rate")
} else {
lines(c,predict(loess(SR~c)),col=col1[i],lwd=3)
}
}
type = rep(1,length(frames))
width = rep(2.5,length(frames))
legend(x="topright",legend=names, lty=type,
lwd=width,col=col1,bty="n",cex=0.8)
mtext("Data source-Courtesy:ESPN Cricinfo", side=1, line=0, adj=1.0, cex=0.8, col="blue")
}
```

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