#Import data
my_dataEPL <- `FullEPLFinal.(1)`
View(my_dataEPL)
my_dataEPL <- my_dataEPL[c(4, 5, 8, 11)]
view(my_dataEPL)
my_dataEPL <- subset(my_dataEPL, HTResult!="D")
my_dataEPL <- subset(my_dataEPL, FTResult!="D")
View(my_dataEPL)
# Basically just subsetting a few times over. Removing Draws from the HTResult and FTResult,
# then only counting instances where the HTResult is the same as the FTResult
library(tidyverse)
HTMomentum <- my_dataEPL[c("Home", "Away", "HTResult", "FTResult")] %>%
subset(FTResult!= "D")%>%
subset(HTResult!="D") %>%
subset(HTResult==FTResult)
View(HTMomentum)
#This is counting where the HTResult does not match the FTresult, I just called it an 'upset'
HTUpset <- my_dataEPL[c("Home", "Away", "HTResult", "FTResult")] %>%
subset(FTResult!= "D") %>%
subset(HTResult!="D") %>%
subset(HTResult!=FTResult)
View (HTUpset)
#This counts how often a draw at HT leads to a draw at FT
HTMomentumDraws <- my_dataEPL[c("Home", "Away", "HTResult", "FTResult")] %>%
subset(FTResult=="D") %>%
subset(HTResult=="D") %>%
subset(HTResult==FTResult)
View(HTMomentumDraws)
#Using a histogram to show that teams that win in the half time results, generally win in the full time results.
HalfTimeResults_freq <- table(my_dataEPL$HTResult)
view(HalfTimeResults_freq)
#away winnings in HTR
my_dataEPL2 <- subset(my_dataEPL, HTResult!="H")
#teams that won in the FTR where away won in the HTR
FullTimeResults_freq_away <- table(my_dataEPL2$FTResult)
View (FullTimeResults_freq_away)
#data where home won in the HTR
my_dataEPL3 <- subset(my_dataEPL, HTResult!="A")
#Team that won in the FTR where home won in the HTR
FullTimeResults_freq_home <- table(my_dataEPL3$FTR)
#Pie chart showing the amount of game won by the home team when they were in the lead at halftime.
HomePieValues <- c(FullTimeResults_freq_home["H"], FullTimeResults_freq_home["A"])
percentlabels_H <- round(100*HomePieValues/sum(HomePieValues), 1)
HomePieLabels <- paste(percentlabels_H, "%", sep="")
HomePie <- pie(HomePieValues, labels = HomePieLabels,
main="Percentage of Home teams that win at FT \n when in the lead at HT",
col=rainbow(length(HomePieValues)))
Home_Legend <- legend("topright", c("Home Team wins at FT", "Home Team lose at FT"),
cex=1,
fill=rainbow(length(HomePieValues)))
#Pie Chart showing the amount of games won by the Away Team when they were in the lead at halftime.
AwayPieValues <- c(FullTimeResults_freq_away["A"], FullTimeResults_freq_away["H"])
percentlabels_A <- round(100*AwayPieValues/sum(AwayPieValues), 1)
AwayPieLabels <- paste(percentlabels_A, "%", sep="")
AwayPie <- pie(AwayPieValues, labels=AwayPieLabels,
main="Percentage of Away Teams that wins at FT \n when in the lead at HT",
col=rainbow(length(AwayPieValues)))
Away_Legend <- legend("topright",c("Away Team wins at FT","Away Team lose at FT"),
cex=1,
fill=rainbow(length(AwayPieValues)))
#SUMMARY:
# of winnings in HTR how many away won in HT and how many home won in HT
HalfTimeResults <- table(my_dataEPL$HTResult)
view(HalfTimeResults)
#Away winnings in HTR
AwayResultsHT<- subset(my_dataEPL, HTResult!="H")
view(AwayResultsHT)
#how many aways that won in the HTR also won in FTR
FTRW <- table(AwayResultsHT$FTResult)
View(FTRW)
#Table of HTR home winnings
HomeResultHT <- subset(my_dataEPL, HTResult!="A")
#how many home winnings continued to win in the second half
FTRWhome <- table(HomeResultHT$FTResult)
view(FTRWhome)
view(FTRWhome)
#SUMMARY:
#Looking at the half time results, out of 1884 matches played (excluding the draws), 1147 games were won by the home team and 737 games were won by the away team.
#HOME: Out of the 1147 home teams that won in the first half, 1086 home teams continued to win in the full time. Hence only 61 Home teams lost even after winning at half time.
#AWAY: Out of the 737 away teams that won in the first half, 645 away teams continued to win in the full time, hence only 92 away teams lost even after winning at half time.