1d:T7bd,# Predict the runs for the batsman given the Balls Faced and Minutes in crease Fit a linear regression plane between Runs scored and Minutes in Crease and Balls Faced. This will be used to predict the batsman runs for time in crease and balls faced ```r batsmanRunsPredict(file, name="A Coverdrive", newdataframe) ``` ## Arguments - `file`: This is the .csv file obtained with an initial getPlayerData() - `name`: Name of the batsman - `newdataframe`: This is a data frame with 2 columns BF(Balls Faced) and Mins(Minutes) ## Details More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI ## Returns Returns a data frame with the predicted runs for the Balls Faced and Minutes at crease ## References https://www.espncricinfo.com/ci/content/stats/index.html https://gigadom.in/ ## Author(s) Tinniam V Ganesh ## Note Maintainer: Tinniam V Ganesh ## See Also `batsmanMovingAverage` `battingPerf3d` `batsmanContributionWonLost` ## Examples ```r ## Not run: # Get or use the .csv obtained with getPlayerData() # tendulkar <- getPlayerData(35320,file="tendulkar.csv",type="batting", # homeOrAway=c(1,2), result=c(1,2,4)) # Use a single value for BF and Mins BF <- 30 Mins <- 20 # retrieve the file path of a data file installed with cricketr pathToFile <- system.file("data", "tendulkar.csv", package = "cricketr") batsmanRunsPredict(pathToFile,"Sachin Tendulkar",newdataframe=data.frame(BF,Mins)) #or give a data frame #BF <- seq(20,200, length=15) #Mins <- seq(30,220,length=15) #values <- batsmanRunsPredict("../cricketr/data/tendulkar.csv","Sachin Tendulkar", #newdataframe=data.frame(BF,Runs) #print(values) # Note: The above example uses the file tendulkar.csv from the /data directory. However # you can use any directory as long as the data file exists in that directory. # The general format is pkg-function(pathToFile,par1,...) ## End(Not run) ```1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.5002

batsmanRunsPredict function