Forecasting Football Matches, March 13-19
J James Reade
13/03/2015
Introduction
dates <- c("2015-01-30","2015-02-06","2015-02-13","2015-02-20","2015-02-27","2015-03-06","2015-03-13")This is the number 7 in a series of forecasts of football match outcomes, following on from my efforts last week and the previous weeks. The method of forecasts is unchanged from previous week.
Bookmaker prices are compared with forecasts, potentially offering opportunities, should you sufficiently believe my forecasts to be better than those of bookmakers. Naturally, I make no such claim, and indeed since the bookmaker prices were collected, they may well have changed.
Loading the Data
The dataset used is all English matches recorded on http://www.soccerbase.com, which goes back to 1877 and the very first football matches. Experimentation will take place in time (i.e., not this week) with adjusting the estimation sample size, since it is not necessarily useful to have all matches back to 1877 when forecasting matches in 2015. The Elo ranks have been calculated since the very first matches, and hence historical information is retained, to the extent that it is useful in determining a team’s current strength, back throughout footballing history.
Forecast Model
A linear regression model is estimated here and reported:
library(knitr)
library(MASS)
date.1 <- tail(dates,1)
forecast.matches <- read.csv(paste("forecasts_",date.1,".csv",sep=""),stringsAsFactors=F)
forecast.matches <- forecast.matches[is.na(forecast.matches$outcome)==F,]
res.eng <- read.csv(paste("historical_",date.1,".csv",sep=""))
model <- lm(outcome ~ E.1 + pts1 + pts.D + pts.D.2 + pld1 + pld.D + pld.D.2 + gs1 + gs.D + gs.D.2
+ gd1 + gd.D + gd.D.2
+ pos1 + pos.D + pos.D.2 + form1 + form.D + form.D.2 + tier1 + tier.D + tier.D.2 + season.d,
data=res.eng)
summary(model)##
## Call:
## lm(formula = outcome ~ E.1 + pts1 + pts.D + pts.D.2 + pld1 +
## pld.D + pld.D.2 + gs1 + gs.D + gs.D.2 + gd1 + gd.D + gd.D.2 +
## pos1 + pos.D + pos.D.2 + form1 + form.D + form.D.2 + tier1 +
## tier.D + tier.D.2 + season.d, data = res.eng)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0268 -0.3114 0.1151 0.3369 0.8958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.308e-01 5.418e-03 61.059 < 2e-16 ***
## E.1 5.452e-01 6.051e-03 90.087 < 2e-16 ***
## pts1 9.775e-04 4.239e-04 2.306 0.02111 *
## pts.D -2.370e-03 3.077e-04 -7.703 1.33e-14 ***
## pts.D.2 -1.360e-05 6.468e-06 -2.102 0.03555 *
## pld1 -1.641e-03 6.094e-04 -2.693 0.00708 **
## pld.D 2.266e-03 7.111e-04 3.186 0.00144 **
## pld.D.2 -4.093e-05 3.083e-05 -1.327 0.18437
## gs1 4.757e-04 1.710e-04 2.782 0.00540 **
## gs.D -2.481e-04 1.526e-04 -1.626 0.10397
## gs.D.2 -5.669e-07 4.689e-06 -0.121 0.90376
## gd1 -5.980e-04 2.407e-04 -2.485 0.01297 *
## gd.D 2.451e-03 1.759e-04 13.929 < 2e-16 ***
## gd.D.2 -5.660e-06 2.342e-06 -2.416 0.01568 *
## pos1 7.670e-04 3.005e-04 2.552 0.01071 *
## pos.D -1.842e-03 2.514e-04 -7.327 2.37e-13 ***
## pos.D.2 3.753e-05 1.171e-05 3.206 0.00135 **
## form1 6.250e-04 3.518e-04 1.777 0.07565 .
## form.D 1.353e-03 2.960e-04 4.573 4.82e-06 ***
## form.D.2 -7.301e-05 2.995e-05 -2.437 0.01480 *
## tier1 1.920e-03 7.653e-04 2.509 0.01212 *
## tier.D -3.165e-02 2.862e-03 -11.059 < 2e-16 ***
## tier.D.2 -5.262e-03 1.258e-03 -4.184 2.86e-05 ***
## season.d -1.082e-03 3.081e-05 -35.115 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3948 on 216080 degrees of freedom
## (38337 observations deleted due to missingness)
## Multiple R-squared: 0.0852, Adjusted R-squared: 0.0851
## F-statistic: 874.9 on 23 and 216080 DF, p-value: < 2.2e-16An ordered logistic regression model is:
model.ord <- polr(as.factor(outcome) ~ E.1 + pts1 + pts.D + pts.D.2 + pld1 + pld.D + pld.D.2 +
gs1 + gs.D + gs.D.2 + gd1 + gd.D + gd.D.2 + pos1 + pos.D + pos.D.2 +
form1 + form.D + form.D.2 + tier1 + tier.D + tier.D.2 + season.d,
data=res.eng, method = "logistic")
summary(model.ord)##
## Re-fitting to get Hessian## Call:
## polr(formula = as.factor(outcome) ~ E.1 + pts1 + pts.D + pts.D.2 +
## pld1 + pld.D + pld.D.2 + gs1 + gs.D + gs.D.2 + gd1 + gd.D +
## gd.D.2 + pos1 + pos.D + pos.D.2 + form1 + form.D + form.D.2 +
## tier1 + tier.D + tier.D.2 + season.d, data = res.eng, method = "logistic")
##
## Coefficients:
## Value Std. Error t value
## E.1 2.637e+00 3.018e-02 87.39575
## pts1 4.200e-03 2.084e-03 2.01586
## pts.D -1.221e-02 1.521e-03 -8.02725
## pts.D.2 -7.571e-05 3.609e-05 -2.09768
## pld1 -9.174e-03 2.998e-03 -3.06047
## pld.D 1.288e-02 3.567e-03 3.60921
## pld.D.2 -2.356e-04 1.637e-04 -1.43912
## gs1 3.431e-03 8.511e-04 4.03088
## gs.D -1.580e-03 7.582e-04 -2.08343
## gs.D.2 -2.841e-07 2.556e-05 -0.01111
## gd1 -3.342e-03 1.193e-03 -2.80092
## gd.D 1.331e-02 8.796e-04 15.12806
## gd.D.2 7.347e-06 1.638e-05 0.44861
## pos1 3.062e-03 1.466e-03 2.08910
## pos.D -7.980e-03 1.240e-03 -6.43591
## pos.D.2 2.029e-04 5.945e-05 3.41353
## form1 2.846e-03 1.719e-03 1.65588
## form.D 7.041e-03 1.457e-03 4.83310
## form.D.2 -2.301e-04 1.494e-04 -1.53938
## tier1 9.319e-03 3.728e-03 2.49996
## tier.D -1.742e-01 1.505e-02 -11.57178
## tier.D.2 -6.675e-03 6.881e-03 -0.96999
## season.d -5.408e-03 1.526e-04 -35.42876
##
## Intercepts:
## Value Std. Error t value
## 0|0.5 0.2411 0.0264 9.1335
## 0.5|1 1.4299 0.0266 53.8245
##
## Residual Deviance: 428191.57
## AIC: 428241.57
## (38337 observations deleted due to missingness)The Forecasts
Premier League
prem.matches <- forecast.matches[forecast.matches$division=="English Premier",]
prem.matches <- prem.matches[order(prem.matches$date),]
prem.matches$id <- 1:NROW(prem.matches)
par(mar=c(9,4,4,5)+.1)
plot(prem.matches$id,prem.matches$outcome,xaxt="n",xlab="",ylim=range(0,1),
main="Forecasts of Weekend Premier League Matches",
ylab="Probability of Outcome")
lines(prem.matches$id,prem.matches$Ph,col=2,pch=15,type="p")
lines(prem.matches$id,prem.matches$Pd,col=3,pch=16,type="p")
lines(prem.matches$id,prem.matches$Pa,col=4,pch=17,type="p")
legend("topleft",ncol=4,pch=c(1,15,16,17),col=c(1:4),
legend=c("OLS","OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
abline(h=0.4,lty=3)
axis(1,at=prem.matches$id,labels=paste(prem.matches$team1,prem.matches$team2,sep=" v "),las=2,cex.axis=0.65)
if(NROW(prem.matches)>1) {
for(i in 2:NROW(prem.matches)){
if(prem.matches$date[i]!=prem.matches$date[i-1]) {
lines(rep(c(i-0.5),2),c(0,1),lty=2)
}
}
}
The coloured dots are forecasts from the ordered logistic regression model; the black circles are the forecasts from a simple OLS linear probability model. Hence the black circles are essentially a probability of a home win occurring (given the ordinal variable defined to capture all three outcomes), whereas the red squares are the probability of a home win, the green solid circles are the probability of a draw, and the blue triangles the probability of an away win. The home bias in football is notable in that the majority of red squares lie above blue triangles.
clean.data <- function(data) {
require(zoo)
colnames(data)[1] <- "Date.Time"
colnames(data)[-1] <- gsub("^.*?\\d+_(\\w+)[.]href.*?$","\\1",colnames(data)[-1])
data <- data[,-NCOL(data)]
data$Date.Time <- as.Date(substring(data$Date.Time,2),"%Y-%m-%d")
data <- data[is.na(data$Date.Time)==F,]
data <- data[order(data$Date.Time),]
for(i in colnames(data)[nchar(colnames(data))==2]) {
data[,i] <- gsub("<div class=.*?>(.*?)</div>","\\1",data[,i])
data[,i] <- gsub("<div class=.*?>(.*?)\\s+$","\\1",data[,i])
data[,i] <- gsub(">","",data[,i])
data[,i] <- gsub("^\\s+|\\s+$","",data[,i])
#need to turn fractional odds into decimal odds
numerator <- gsub("(\\d+)/(\\d+)$","\\1",data[,i])
denominator <- gsub("(\\d+)/(\\d+)$","\\2",data[,i])
data[,i] <- as.numeric(numerator)+1
data[numerator!=denominator,i] <- as.numeric(numerator[numerator!=denominator])/as.numeric(denominator[numerator!=denominator])+1
data[,i] <- na.locf(data[,i],na.rm=F)
}
return(data)
}
prem.bk <- data.frame()
prem.loc <- paste("/home/readejj/Dropbox/Research/Data for Ideas/Betting/football/",Sys.Date(),"/premier-league",sep="")
prem.bk.matches <- dir(prem.loc,pattern="*.csv")
for(i in prem.bk.matches) {
temp <- read.csv(paste(prem.loc,i,sep="/"),stringsAsFactors=F)
temp <- clean.data(temp)
temp$event <- gsub("[.]csv","",i)
prem.bk <- rbind(prem.bk,temp)
}## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericprem.bk <- prem.bk[order(prem.bk$event,prem.bk$Date.Time),]
prem.bk$mean <- rowMeans(prem.bk[colnames(prem.bk)[nchar(colnames(prem.bk))==2]],na.rm=T)
prem.bk$event <- gsub("man-utd","man utd",prem.bk$event)
prem.bk$event <- gsub("man-city","man city",prem.bk$event)
prem.bk$event <- gsub("aston-villa","aston villa",prem.bk$event)
prem.bk$event <- gsub("west-brom","west brom",prem.bk$event)
prem.bk$event <- gsub("west-ham","west ham",prem.bk$event)
prem.bk$event <- gsub("crystal-palace","c palace",prem.bk$event)
prem.bk$team1 <- gsub("^(.*?)-v-(.*?)-(.*?)$","\\1",prem.bk$event)
prem.bk$team2 <- gsub("^(.*?)-v-(.*?)-(.*?)$","\\2",prem.bk$event)
prem.bk$event <- gsub("^(.*?)-v-(.*?)-(.*?)$","\\3",prem.bk$event)
prem.bk$event.1 <- c(prem.bk$event[-1],NA)
prem.bk.h <- prem.bk[(prem.bk$event!=prem.bk$event.1 | is.na(prem.bk$event.1)==T) & prem.bk$event==prem.bk$team1,]
prem.bk.d <- prem.bk[prem.bk$event!=prem.bk$event.1 & regexpr("draw",prem.bk$event)>-1,]
prem.bk.a <- prem.bk[prem.bk$event!=prem.bk$event.1 & prem.bk$event==prem.bk$team2,]
prem.matches$team1 <- tolower(prem.matches$team1)
prem.matches$team2 <- tolower(prem.matches$team2)
prem.matches <- merge(prem.matches,prem.bk.h[c(colnames(prem.bk.h[nchar(colnames(prem.bk.h))==2]),"team1","team2")],by=c("team1","team2"),all.x=T)
colnames(prem.matches)[colnames(prem.matches) %in% colnames(prem.bk.h) & regexpr("team",colnames(prem.matches))==-1] <- paste(colnames(prem.matches)[colnames(prem.matches) %in% colnames(prem.bk.h) & regexpr("team",colnames(prem.matches))==-1],".h",sep="")
prem.matches <- merge(prem.matches,prem.bk.d[c(colnames(prem.bk.d[nchar(colnames(prem.bk.d))==2]),"team1","team2")],by=c("team1","team2"),all.x=T)
colnames(prem.matches)[colnames(prem.matches) %in% colnames(prem.bk.d) & regexpr("team",colnames(prem.matches))==-1] <- paste(colnames(prem.matches)[colnames(prem.matches) %in% colnames(prem.bk.d) & regexpr("team",colnames(prem.matches))==-1],".d",sep="")
prem.matches <- merge(prem.matches,prem.bk.a[c(colnames(prem.bk.a[nchar(colnames(prem.bk.a))==2]),"team1","team2")],by=c("team1","team2"),all.x=T)
colnames(prem.matches)[colnames(prem.matches) %in% colnames(prem.bk.a) & regexpr("team",colnames(prem.matches))==-1] <- paste(colnames(prem.matches)[colnames(prem.matches) %in% colnames(prem.bk.a) & regexpr("team",colnames(prem.matches))==-1],".a",sep="")
par(mar=c(9,4,4,5)+.1)
plot(prem.matches$id,prem.matches$Ph,xaxt="n",xlab="",ylim=range(0,1),col=2,pch=15,type="p",cex=1.5,
main="Forecasts of Weekend Premier League Matches",
ylab="Probability of Outcome")
for(i in colnames(prem.matches)[grep("[.]h",colnames(prem.matches))]) {
for(j in prem.matches$id) {
lines(j,1/prem.matches[prem.matches$id==j,i],col="darkred",pch=0,type="p",cex=0.5)
}
}
lines(prem.matches$id,prem.matches$Pd,col=3,pch=16,type="p",cex=1.5)
for(i in colnames(prem.matches)[grep("[.]d",colnames(prem.matches))]) {
for(j in prem.matches$id) {
lines(j,1/prem.matches[prem.matches$id==j,i],col="darkgreen",pch=0,type="p",cex=0.5)
}
}
lines(prem.matches$id,prem.matches$Pa,col=4,pch=17,type="p",cex=1.5)
for(i in colnames(prem.matches)[grep("[.]a",colnames(prem.matches))]) {
for(j in prem.matches$id) {
lines(j,1/prem.matches[prem.matches$id==j,i],col="darkblue",pch=0,type="p",cex=0.5)
}
}
legend("topleft",ncol=4,pch=c(1,15,16,17),col=c(1:4),
legend=c("OLS","OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
abline(h=0.4,lty=3)
axis(1,at=prem.matches$id,labels=paste(prem.matches$team1,prem.matches$team2,sep=" v "),las=2,cex.axis=0.65)
The darker coloured and smaller symbols are the range of bookmaker implied probabilities for outcomes. Unlike in previous weeks where I’ve simply plotted the mean bookmaker implied probability, here all bookmaker prices (usually 20) are plotted. By and large, they are not particularly widely spread, but then this is only a small sample of the total bookmakers pricing these matches.
In terms of making stronger picks than simply stating probabilities, as requested by some, here goes:
- Palace v QPR: My model seems to fancy Palace more than the bookies, hence that might be worth considering.
- Sunderland v Aston Villa: My model fancies Sunderland less than the bookmakers in what looks like a very tight match.
- Everton v Newcastle: Bookmakers seem to heavily favour Everton in this match, pricing them at nearly 60% likely to win whereas my model puts them at less than 40%. One of a number of matches that look tight (likely draws?) this weekend.
Hence I’d suggest the following home wins this weekend: Palace, Arsenal, Chelsea and Man United, then the following away wins: Man City and Liverpool. The games at West Brom, Sunderland, Leicester, and Everton look like draws.
Championship
Next, our Championship forecasts:
champ.matches <- forecast.matches[forecast.matches$division=="English Championship",]
champ.matches <- champ.matches[order(champ.matches$date),]
champ.matches$id <- 1:NROW(champ.matches)
par(mar=c(9,4,4,5)+.1)
plot(champ.matches$id,champ.matches$outcome,xaxt="n",xlab="",ylim=range(0,1),
main="Forecasts of Weekend Championship Matches",
ylab="Probability of Outcome")
lines(champ.matches$id,champ.matches$Ph,col=2,pch=15,type="p")
lines(champ.matches$id,champ.matches$Pd,col=3,pch=16,type="p")
lines(champ.matches$id,champ.matches$Pa,col=4,pch=17,type="p")
legend("topleft",ncol=4,pch=c(1,15,16,17),col=c(1:4),
legend=c("OLS","OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
axis(1,at=champ.matches$id,labels=paste(champ.matches$team1,champ.matches$team2,sep=" v "),las=2,cex.axis=0.65)
for(i in 2:NROW(champ.matches)){
if(champ.matches$date[i]!=champ.matches$date[i-1]) {
lines(rep(c(i-0.5),2),c(0,1),lty=2)
}
}
League One
Next, our League One forecasts:
lg1.matches <- forecast.matches[forecast.matches$division=="English League One",]
lg1.matches <- lg1.matches[order(lg1.matches$date),]
lg1.matches$id <- 1:NROW(lg1.matches)
par(mar=c(9,4,4,5)+.1)
plot(lg1.matches$id,lg1.matches$outcome,xaxt="n",xlab="",ylim=range(0,1),
main="Forecasts of Weekend League One Matches",
ylab="Probability of Outcome")
lines(lg1.matches$id,lg1.matches$Ph,col=2,pch=15,type="p")
lines(lg1.matches$id,lg1.matches$Pd,col=3,pch=16,type="p")
lines(lg1.matches$id,lg1.matches$Pa,col=4,pch=17,type="p")
legend("topleft",ncol=4,pch=c(1,15,16,17),col=c(1:4),
legend=c("OLS","OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
axis(1,at=lg1.matches$id,labels=paste(lg1.matches$team1,lg1.matches$team2,sep=" v "),las=2,cex.axis=0.65)
for(i in 2:NROW(lg1.matches)){
if(lg1.matches$date[i]!=lg1.matches$date[i-1]) {
lines(rep(c(i-0.5),2),c(0,1),lty=2)
}
}
League Two
Next, our League Two forecasts:
lg2.matches <- forecast.matches[forecast.matches$division=="English League Two",]
lg2.matches <- lg2.matches[order(lg2.matches$date),]
lg2.matches$id <- 1:NROW(lg2.matches)
par(mar=c(9,4,4,5)+.1)
plot(lg2.matches$id,lg2.matches$outcome,xaxt="n",xlab="",ylim=range(0,1),
main="Forecasts of Weekend League Two Matches",
ylab="Probability of Outcome")
lines(lg2.matches$id,lg2.matches$Ph,col=2,pch=15,type="p")
lines(lg2.matches$id,lg2.matches$Pd,col=3,pch=16,type="p")
lines(lg2.matches$id,lg2.matches$Pa,col=4,pch=17,type="p")
legend("topleft",ncol=4,pch=c(1,15,16,17),col=c(1:4),
legend=c("OLS","OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
axis(1,at=lg2.matches$id,labels=paste(lg2.matches$team1,lg2.matches$team2,sep=" v "),las=2,cex.axis=0.65)
for(i in 2:NROW(lg2.matches)){
if(lg2.matches$date[i]!=lg2.matches$date[i-1]) {
lines(rep(c(i-0.5),2),c(0,1),lty=2)
}
}
Football Conference
Next, our Football Conference forecasts:
conf.matches <- forecast.matches[forecast.matches$division=="Football Conference",]
conf.matches$id <- 1:NROW(conf.matches)
par(mar=c(9,4,4,5)+.1)
plot(conf.matches$id,conf.matches$outcome,xaxt="n",xlab="",ylim=range(0,1),
main="Forecasts of Weekend Football Conference Matches",
ylab="Probability of Outcome")
lines(conf.matches$id,conf.matches$Ph,col=2,pch=15,type="p")
lines(conf.matches$id,conf.matches$Pd,col=3,pch=16,type="p")
lines(conf.matches$id,conf.matches$Pa,col=4,pch=17,type="p")
legend("topleft",ncol=4,pch=c(1,15,16,17),col=c(1:4),
legend=c("OLS","OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
axis(1,at=conf.matches$id,labels=paste(conf.matches$team1,conf.matches$team2,sep=" v "),las=2,cex.axis=0.65)
for(i in 2:NROW(conf.matches)){
if(conf.matches$date[i]!=conf.matches$date[i-1]) {
lines(rep(c(i-0.5),2),c(0,1),lty=2)
}
}
List of all forecasts
For transparency, all forecasts are also listed as a table:
kable(forecast.matches[order(forecast.matches$date,forecast.matches$division),
c("date","division","team1","outcome","team2","Ph","Pd","Pa")])| date | division | team1 | outcome | team2 | Ph | Pd | Pa | |
|---|---|---|---|---|---|---|---|---|
| 1 | 2015-03-13 | English League Two | Newport Co | 0.7234953 | Cheltenham | 0.6091137 | 0.2273906 | 0.1634957 |
| 69 | 2015-03-14 | Conference North | Bradford PA | 0.6750727 | Stalybridge | 0.5540478 | 0.2490700 | 0.1968822 |
| 63 | 2015-03-14 | Conference South | Hayes & Y | 0.6838500 | Farnborough | 0.5719251 | 0.2424309 | 0.1856441 |
| 8 | 2015-03-14 | English Championship | Leeds | 0.6291843 | Nottm Forest | 0.4940288 | 0.2682059 | 0.2377653 |
| 9 | 2015-03-14 | English Championship | Watford | 0.8279223 | Reading | 0.7403601 | 0.1631368 | 0.0965031 |
| 10 | 2015-03-14 | English Championship | Charlton | 0.5298993 | Blackburn | 0.3777815 | 0.2881590 | 0.3340595 |
| 11 | 2015-03-14 | English Championship | Birmingham | 0.7885331 | Huddersfield | 0.6813175 | 0.1939862 | 0.1246963 |
| 12 | 2015-03-14 | English Championship | Middlesbro | 0.6902984 | Ipswich | 0.5699403 | 0.2431876 | 0.1868721 |
| 13 | 2015-03-14 | English Championship | Rotherham | 0.5156896 | Wigan | 0.3577441 | 0.2887554 | 0.3535005 |
| 14 | 2015-03-14 | English Championship | Bolton | 0.7778686 | Millwall | 0.6729782 | 0.1981019 | 0.1289199 |
| 15 | 2015-03-14 | English Championship | Norwich | 0.7007024 | Derby | 0.5859773 | 0.2369368 | 0.1770859 |
| 16 | 2015-03-14 | English Championship | Bournemouth | 0.8979064 | Blackpool | 0.8230274 | 0.1155075 | 0.0614651 |
| 17 | 2015-03-14 | English Championship | Sheff Wed | 0.6526918 | Fulham | 0.5263693 | 0.2585271 | 0.2151037 |
| 18 | 2015-03-14 | English Championship | Brighton | 0.5048770 | Wolves | 0.3518252 | 0.2887429 | 0.3594320 |
| 19 | 2015-03-14 | English Championship | Brentford | 0.6423660 | Cardiff | 0.5137805 | 0.2624777 | 0.2237418 |
| 20 | 2015-03-14 | English League One | Port Vale | 0.5444533 | Swindon | 0.3984057 | 0.2865723 | 0.3150221 |
| 21 | 2015-03-14 | English League One | Doncaster | 0.5617537 | Peterborough | 0.4146775 | 0.2846704 | 0.3006521 |
| 22 | 2015-03-14 | English League One | Scunthorpe | 0.5723732 | Sheff Utd | 0.4301487 | 0.2823626 | 0.2874887 |
| 23 | 2015-03-14 | English League One | Notts Co | 0.4487322 | Bradford | 0.2902210 | 0.2828876 | 0.4268915 |
| 24 | 2015-03-14 | English League One | Oldham | 0.4646691 | Barnsley | 0.3088598 | 0.2858334 | 0.4053068 |
| 25 | 2015-03-14 | English League One | Colchester | 0.5431059 | Crawley | 0.3950537 | 0.2868943 | 0.3180520 |
| 26 | 2015-03-14 | English League One | Preston | 0.8095830 | Crewe | 0.7273383 | 0.1701862 | 0.1024755 |
| 27 | 2015-03-14 | English League One | Bristol C | 0.7649958 | Gillingham | 0.6683277 | 0.2003694 | 0.1313029 |
| 28 | 2015-03-14 | English League One | Fleetwood | 0.5011693 | Rochdale | 0.3426391 | 0.2885452 | 0.3688156 |
| 29 | 2015-03-14 | English League One | Walsall | 0.7609811 | MK Dons | 0.6546584 | 0.2069169 | 0.1384247 |
| 30 | 2015-03-14 | English League One | Chesterfield | 0.7541168 | Coventry | 0.6453832 | 0.2112573 | 0.1433595 |
| 31 | 2015-03-14 | English League One | Leyton Orient | 0.6664997 | Yeovil | 0.5472725 | 0.2514796 | 0.2012479 |
| 32 | 2015-03-14 | English League Two | Northampton | 0.8258709 | Tranmere | 0.7232783 | 0.1723567 | 0.1043650 |
| 33 | 2015-03-14 | English League Two | Wycombe | 0.5538955 | Shrewsbury | 0.4025768 | 0.2861378 | 0.3112854 |
| 34 | 2015-03-14 | English League Two | York | 0.6778942 | Carlisle | 0.5592212 | 0.2471901 | 0.1935886 |
| 35 | 2015-03-14 | English League Two | Morecambe | 0.6797780 | Hartlepool | 0.5677194 | 0.2440287 | 0.1882519 |
| 36 | 2015-03-14 | English League Two | Cambridge U | 0.4748841 | Stevenage | 0.3218264 | 0.2872581 | 0.3909155 |
| 37 | 2015-03-14 | English League Two | Dag & Red | 0.5249049 | Southend | 0.3739798 | 0.2883459 | 0.3376743 |
| 38 | 2015-03-14 | English League Two | Exeter | 0.6705709 | AFC W’bledon | 0.5447955 | 0.2523455 | 0.2028590 |
| 39 | 2015-03-14 | English League Two | Mansfield | 0.5443846 | Bury | 0.3947255 | 0.2869245 | 0.3183499 |
| 40 | 2015-03-14 | English League Two | Burton | 0.7688148 | Accrington | 0.6659403 | 0.2015257 | 0.1325341 |
| 41 | 2015-03-14 | English League Two | Portsmouth | 0.5149639 | Luton | 0.3620817 | 0.2887089 | 0.3492094 |
| 42 | 2015-03-14 | English League Two | Oxford | 0.5667209 | Plymouth | 0.4200326 | 0.2839253 | 0.2960421 |
| 2 | 2015-03-14 | English Premier | C Palace | 0.7204743 | QPR | 0.6085778 | 0.2276185 | 0.1638037 |
| 3 | 2015-03-14 | English Premier | West Brom | 0.4479865 | Stoke | 0.2909345 | 0.2830207 | 0.4260449 |
| 4 | 2015-03-14 | English Premier | Arsenal | 0.8203821 | West Ham | 0.7212633 | 0.1734290 | 0.1053077 |
| 5 | 2015-03-14 | English Premier | Sunderland | 0.5280668 | Aston Villa | 0.3776528 | 0.2881659 | 0.3341813 |
| 6 | 2015-03-14 | English Premier | Burnley | 0.2668707 | Man City | 0.1561825 | 0.2218155 | 0.6220021 |
| 7 | 2015-03-14 | English Premier | Leicester | 0.5461477 | Hull | 0.3981881 | 0.2865939 | 0.3152180 |
| 43 | 2015-03-14 | Football Conference | Halifax | 0.4207691 | Bristol R | 0.2630043 | 0.2765242 | 0.4604715 |
| 44 | 2015-03-14 | Football Conference | Gateshead | 0.5112438 | Macclesfield | 0.3576106 | 0.2887561 | 0.3536333 |
| 45 | 2015-03-14 | Football Conference | Telford | 0.3339530 | Woking | 0.2000210 | 0.2508118 | 0.5491672 |
| 46 | 2015-03-14 | Football Conference | Aldershot | 0.6246361 | Southport | 0.4935773 | 0.2683299 | 0.2380928 |
| 47 | 2015-03-14 | Football Conference | Altrincham | 0.5792096 | Wrexham | 0.4322401 | 0.2820147 | 0.2857453 |
| 48 | 2015-03-14 | Football Conference | Dartford | 0.4870577 | Alfreton | 0.3334361 | 0.2881232 | 0.3784408 |
| 49 | 2015-03-14 | Football Conference | Nuneaton | 0.5198226 | Torquay | 0.3722660 | 0.2884191 | 0.3393149 |
| 50 | 2015-03-14 | Football Conference | Eastleigh | 0.6077846 | Barnet | 0.4729491 | 0.2736484 | 0.2534025 |
| 51 | 2015-03-14 | Football Conference | Forest Green | 0.7828496 | Braintree | 0.6785451 | 0.1953615 | 0.1260935 |
| 52 | 2015-03-14 | Football Conference | Kidderminster | 0.5950275 | Dover | 0.4510471 | 0.2785177 | 0.2704352 |
| 53 | 2015-03-14 | Football Conference | Lincoln | 0.7416156 | Welling | 0.6307570 | 0.2179277 | 0.1513153 |
| 54 | 2015-03-14 | Football Conference | Chester | 0.4515079 | Grimsby | 0.2961493 | 0.2839434 | 0.4199073 |
| 95 | 2015-03-14 | Ryman Premier | Canvey Isl. | 0.4735870 | Margate | 0.3147075 | 0.2865373 | 0.3987552 |
| 96 | 2015-03-14 | Ryman Premier | Maidstone | 0.7794122 | Lewes | 0.6984135 | 0.1853550 | 0.1162315 |
| 111 | 2015-03-15 | English Premier | Everton | 0.5014666 | Newcastle | 0.3501452 | 0.2887231 | 0.3611317 |
| 112 | 2015-03-15 | English Premier | Chelsea | 0.7622778 | Southampton | 0.6560296 | 0.2062682 | 0.1377023 |
| 113 | 2015-03-15 | English Premier | Man Utd | 0.6537786 | Tottenham | 0.5309838 | 0.2570227 | 0.2119935 |
| 119 | 2015-03-16 | English FA Cup | Reading | 0.5933102 | Bradford | 0.4613294 | 0.2763350 | 0.2623356 |
| 114 | 2015-03-16 | English Premier | Swansea | 0.3699298 | Liverpool | 0.2192042 | 0.2604437 | 0.5203521 |
| 166 | 2015-03-17 | Conference North | Stalybridge | 0.4973249 | Bradford PA | 0.3530898 | 0.2887530 | 0.3581572 |
| 120 | 2015-03-17 | English Championship | Cardiff | 0.3468817 | Bournemouth | 0.2061934 | 0.2540982 | 0.5397084 |
| 121 | 2015-03-17 | English Championship | Wigan | 0.3742418 | Watford | 0.2319241 | 0.2659205 | 0.5021554 |
| 122 | 2015-03-17 | English Championship | Wolves | 0.6097167 | Sheff Wed | 0.4702736 | 0.2742873 | 0.2554391 |
| 123 | 2015-03-17 | English Championship | Derby | 0.4962412 | Middlesbro | 0.3406414 | 0.2884730 | 0.3708857 |
| 124 | 2015-03-17 | English Championship | Blackburn | 0.7041553 | Brentford | 0.5850969 | 0.2372879 | 0.1776152 |
| 125 | 2015-03-17 | English Championship | Blackpool | 0.2891120 | Charlton | 0.1644623 | 0.2281040 | 0.6074337 |
| 126 | 2015-03-17 | English Championship | Millwall | 0.4009919 | Brighton | 0.2454101 | 0.2709872 | 0.4836027 |
| 127 | 2015-03-17 | English Championship | Huddersfield | 0.3115669 | Norwich | 0.1824553 | 0.2404291 | 0.5771156 |
| 128 | 2015-03-17 | English Championship | Reading | 0.4777877 | Birmingham | 0.3191461 | 0.2870040 | 0.3938499 |
| 129 | 2015-03-17 | English Championship | Ipswich | 0.5517435 | Bolton | 0.4120185 | 0.2850188 | 0.3029627 |
| 130 | 2015-03-17 | English League One | Chesterfield | 0.5986782 | Gillingham | 0.4594262 | 0.2767530 | 0.2638208 |
| 131 | 2015-03-17 | English League One | Oldham | 0.7564007 | MK Dons | 0.6506083 | 0.2088225 | 0.1405692 |
| 132 | 2015-03-17 | English League One | Scunthorpe | 0.5072944 | Bradford | 0.3533328 | 0.2887545 | 0.3579127 |
| 133 | 2015-03-17 | English League One | Preston | 0.6285725 | Peterborough | 0.5030996 | 0.2656484 | 0.2312520 |
| 134 | 2015-03-17 | English League One | Leyton Orient | 0.4395728 | Barnsley | 0.2866907 | 0.2822046 | 0.4311047 |
| 135 | 2015-03-17 | English League One | Notts Co | 0.4248245 | Rochdale | 0.2673367 | 0.2777106 | 0.4549527 |
| 136 | 2015-03-17 | English League One | Port Vale | 0.7282043 | Crawley | 0.6192013 | 0.2230422 | 0.1577566 |
| 137 | 2015-03-17 | English League One | Bristol C | 0.8482436 | Crewe | 0.7734410 | 0.1446510 | 0.0819080 |
| 138 | 2015-03-17 | English League One | Doncaster | 0.6404179 | Swindon | 0.5094816 | 0.2637742 | 0.2267442 |
| 139 | 2015-03-17 | English League One | Fleetwood | 0.6680099 | Coventry | 0.5398197 | 0.2540602 | 0.2061200 |
| 140 | 2015-03-17 | English League One | Colchester | 0.5154908 | Yeovil | 0.3624071 | 0.2887035 | 0.3488893 |
| 141 | 2015-03-17 | English League One | Walsall | 0.5623401 | Sheff Utd | 0.4154394 | 0.2845679 | 0.2999927 |
| 142 | 2015-03-17 | English League Two | Newport Co | 0.5815696 | Luton | 0.4357314 | 0.2814153 | 0.2828533 |
| 143 | 2015-03-17 | English League Two | Wycombe | 0.7821339 | Accrington | 0.6832066 | 0.1930451 | 0.1237482 |
| 144 | 2015-03-17 | English League Two | Mansfield | 0.7325188 | AFC W’bledon | 0.6161119 | 0.2243855 | 0.1595025 |
| 145 | 2015-03-17 | English League Two | Morecambe | 0.4149843 | Shrewsbury | 0.2582504 | 0.2751434 | 0.4666061 |
| 146 | 2015-03-17 | English League Two | Portsmouth | 0.6530356 | Cheltenham | 0.5288637 | 0.2577175 | 0.2134187 |
| 147 | 2015-03-17 | English League Two | York | 0.4690369 | Bury | 0.3134381 | 0.2863931 | 0.4001688 |
| 148 | 2015-03-17 | English League Two | Exeter | 0.4690897 | Stevenage | 0.3126050 | 0.2862959 | 0.4010991 |
| 149 | 2015-03-17 | English League Two | Northampton | 0.7765874 | Carlisle | 0.6792972 | 0.1949890 | 0.1257137 |
| 150 | 2015-03-17 | English League Two | Dag & Red | 0.5914935 | Plymouth | 0.4495627 | 0.2788173 | 0.2716200 |
| 151 | 2015-03-17 | English League Two | Oxford | 0.7150490 | Hartlepool | 0.6083759 | 0.2277043 | 0.1639198 |
| 152 | 2015-03-17 | English League Two | Cambridge U | 0.7401062 | Tranmere | 0.6289322 | 0.2187446 | 0.1523231 |
| 153 | 2015-03-17 | Football Conference | Chester | 0.7425367 | Dartford | 0.6312647 | 0.2176998 | 0.1510355 |
| 154 | 2015-03-17 | Football Conference | Halifax | 0.4228500 | Barnet | 0.2658218 | 0.2773035 | 0.4568746 |
| 155 | 2015-03-17 | Football Conference | Telford | 0.4140039 | Gateshead | 0.2664471 | 0.2774726 | 0.4560803 |
| 156 | 2015-03-17 | Football Conference | Alfreton | 0.6117566 | Lincoln | 0.4777932 | 0.2724614 | 0.2497454 |
| 157 | 2015-03-17 | Football Conference | Torquay | 0.5855895 | Braintree | 0.4440749 | 0.2798903 | 0.2760348 |
| 158 | 2015-03-17 | Football Conference | Dover | 0.5520752 | Altrincham | 0.4133926 | 0.2848406 | 0.3017669 |
| 159 | 2015-03-17 | Football Conference | Eastleigh | 0.7466898 | Wrexham | 0.6443562 | 0.2117327 | 0.1439111 |
| 180 | 2015-03-18 | English Championship | Nottm Forest | 0.6637535 | Rotherham | 0.5476472 | 0.2513479 | 0.2010049 |
| 181 | 2015-03-18 | English Championship | Fulham | 0.4896021 | Leeds | 0.3344860 | 0.2881829 | 0.3773311 |
| 182 | 2015-03-18 | English League Two | Burton | 0.5907256 | Southend | 0.4481357 | 0.2791015 | 0.2727627 |