The seconds before shoting, visualized

I took the chance of looking into the "Advanced" data set for learning something of the ggplot2 package (I have been doing my visualizations with lattice until now.)

So here's what I have done.
I looked at where the ball was up till 20 seconds before a shot was attempted.
Click on the picture below for an enlarged version.

I don't have much to comment on this chart, except that I suppose it could be useful having this kind of visualization (or animated heatmaps) for a full season of data. That would allow to see where the action begins for a particular team (or for teams playing against a particular opponent.)

Below the same chart, with lines added for plays leading to a goal.

R code for managing the F24 dataset

Many times I have benefited from the work of great guys, who were so kind to share the results of their labor.

Particularly, I would not be a top baseball data analyst if not for Kyle Wilkomm's code at Baseball On a Stick.

I haven't had much time to do some analysis on the newly released advanced data set by MCFC Analytics and Opta, so I thought it could be a good idea to share the R code I wrote for massaging the provided XML file a bit.

So, for the R users who are a bit stuck with the file, here's some help (I hope).

The code that follows only works on the "big file", the one containing the play-by-play.

The resulting dataset contains 1673 events, with 121 variables.

Some more notes after the code.

library(XML)
library(plyr)
library(reshape)
 
fnm <- "c:/download/mcfc/Bolton_ManCityF24.xml" #file path & name
 
#utility function
grabAll <- function(XML.parsed, field){
  parse.field <- xpathSApply(XML.parsed, paste("//", field, "[@*]", sep=""))
  results <- t(sapply(parse.field, function(x) xmlAttrs(x)))
  if(typeof(results)=="list"){
    do.call(rbind.fill, lapply(lapply(results, t), data.frame, stringsAsFactors=F))
  } else {
    as.data.frame(results, stringsAsFactors=F)
  }
}
 
#XML Parsing
pbpParse <- xmlInternalTreeParse(fnm)
eventInfo <- grabAll(pbpParse, "Event")
eventParse <- xpathSApply(pbpParse, "//Event")
NInfo <- sapply(eventParse, function(x) sum(names(xmlChildren(x)) == "Q"))
QInfo <- grabAll(pbpParse, "Q")
EventsExpanded <- as.data.frame(lapply(eventInfo[,1:2], function(x) rep(x, NInfo)), stringsAsFactors=F)
QInfo <- cbind(EventsExpanded, QInfo)
names(QInfo)[c(1,3)] <- c("Eid", "Qid")
QInfo$value <- ifelse(is.na(QInfo$value),-1, QInfo$value)
Qual <- cast(QInfo, Eid ~ qualifier_id)
 
#final data set
events <- merge(eventInfo, Qual, by.x="id", by.y="Eid", all.x=T)

Created by Pretty R at inside-R.org


Variables coming from the qualifiers are named with numbers, and I know this is not a good practice.
If someone has turned the tables in the pdf file provided by Opta into a spreadsheet (or the good guys at Opta are willing to share those table in an easier to manage format), please share—as I have done with the code ;-). In that way, it would be easy to give columns meaningful (and good-practicesy) names.

Let me know if you find this useful, or if you have any comments.