Forecasting Football Matches, April 10-15
J James Reade
10/04/2015
Introduction
This is number 9 in a series of forecasts of football match outcomes, following on from my efforts a couple of weeks back 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 (12 noon, Friday 10 April), 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
## -1.0276 -0.3117 0.1147 0.3370 0.8961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.314e-01 5.416e-03 61.189 < 2e-16 ***
## E.1 5.440e-01 6.047e-03 89.965 < 2e-16 ***
## pts1 9.870e-04 4.233e-04 2.332 0.01973 *
## pts.D -2.384e-03 3.074e-04 -7.754 8.93e-15 ***
## pts.D.2 -1.424e-05 6.462e-06 -2.204 0.02754 *
## pld1 -1.673e-03 6.088e-04 -2.748 0.00599 **
## pld.D 2.262e-03 7.107e-04 3.182 0.00146 **
## pld.D.2 -4.058e-05 3.083e-05 -1.316 0.18810
## gs1 4.813e-04 1.709e-04 2.817 0.00484 **
## gs.D -2.412e-04 1.524e-04 -1.582 0.11357
## gs.D.2 -3.868e-07 4.683e-06 -0.083 0.93417
## gd1 -5.943e-04 2.405e-04 -2.471 0.01346 *
## gd.D 2.461e-03 1.758e-04 13.997 < 2e-16 ***
## gd.D.2 -5.447e-06 2.341e-06 -2.327 0.01997 *
## pos1 7.822e-04 3.004e-04 2.604 0.00921 **
## pos.D -1.849e-03 2.513e-04 -7.360 1.85e-13 ***
## pos.D.2 3.740e-05 1.170e-05 3.196 0.00139 **
## form1 6.268e-04 3.516e-04 1.782 0.07468 .
## form.D 1.333e-03 2.958e-04 4.507 6.59e-06 ***
## form.D.2 -7.375e-05 2.993e-05 -2.464 0.01374 *
## tier1 1.854e-03 7.644e-04 2.426 0.01528 *
## tier.D -3.180e-02 2.862e-03 -11.114 < 2e-16 ***
## tier.D.2 -5.241e-03 1.258e-03 -4.167 3.09e-05 ***
## season.d -1.085e-03 3.078e-05 -35.231 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3948 on 216405 degrees of freedom
## (38631 observations deleted due to missingness)
## Multiple R-squared: 0.0852, Adjusted R-squared: 0.0851
## F-statistic: 876.3 on 23 and 216405 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 2.631e+00 3.015e-02 87.28056
## pts1 4.236e-03 2.081e-03 2.03596
## pts.D -1.229e-02 1.520e-03 -8.08540
## pts.D.2 -7.968e-05 3.605e-05 -2.21049
## pld1 -9.305e-03 2.994e-03 -3.10811
## pld.D 1.284e-02 3.566e-03 3.60251
## pld.D.2 -2.328e-04 1.638e-04 -1.42168
## gs1 3.455e-03 8.504e-04 4.06247
## gs.D -1.537e-03 7.574e-04 -2.02935
## gs.D.2 7.485e-07 2.553e-05 0.02932
## gd1 -3.313e-03 1.192e-03 -2.77927
## gd.D 1.335e-02 8.788e-04 15.19278
## gd.D.2 8.727e-06 1.636e-05 0.53347
## pos1 3.132e-03 1.465e-03 2.13780
## pos.D -8.021e-03 1.239e-03 -6.47335
## pos.D.2 2.023e-04 5.942e-05 3.40420
## form1 2.856e-03 1.718e-03 1.66279
## form.D 6.935e-03 1.456e-03 4.76416
## form.D.2 -2.345e-04 1.493e-04 -1.57028
## tier1 8.995e-03 3.723e-03 2.41641
## tier.D -1.750e-01 1.505e-02 -11.62672
## tier.D.2 -6.569e-03 6.882e-03 -0.95445
## season.d -5.419e-03 1.525e-04 -35.53556
##
## Intercepts:
## Value Std. Error t value
## 0|0.5 0.2381 0.0264 9.0255
## 0.5|1 1.4269 0.0266 53.7409
##
## Residual Deviance: 428892.08
## AIC: 428942.08
## (38631 observations deleted due to missingness)The Forecasts
simpleplot <- function(div) {
matches <- forecast.matches[forecast.matches$division==div,]
matches <- matches[order(matches$date),]
matches$id <- 1:NROW(matches)
par(mar=c(9,4,4,5)+.1)
plot(matches$id,matches$outcome,xaxt="n",xlab="",ylim=range(0,1),
main=paste("Forecasts of Weekend ",div," Matches",sep=""),
ylab="Probability of Outcome")
lines(matches$id,matches$Ph,col=2,pch=15,type="p")
lines(matches$id,matches$Pd,col=3,pch=16,type="p")
lines(matches$id,matches$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=matches$id,labels=paste(matches$team1,matches$team2,sep=" v "),las=2,cex.axis=0.65)
for(i in 2:NROW(matches)){
if(matches$date[i]!=matches$date[i-1]) {
lines(rep(c(i-0.5),2),c(0,1),lty=2)
}
}
}
bkplot <- function(div,div.short) {
matches <- forecast.matches[forecast.matches$division==div,]
matches <- matches[order(matches$date),]
matches$id <- 1:NROW(matches)
bk <- data.frame()
loc <- paste("/home/readejj/Dropbox/Research/Data for Ideas/Betting/football/",Sys.Date(),"/",div.short,sep="")
bk.matches <- dir(loc,pattern="*.csv")
for(i in bk.matches) {
temp <- read.csv(paste(loc,i,sep="/"),stringsAsFactors=F)
temp <- clean.data(temp)
temp$event <- gsub("[.]csv","",i)
bk <- rbind(bk,temp)
}
bk <- bk[order(bk$event,bk$Date.Time),]
bk$mean <- rowMeans(bk[colnames(bk)[nchar(colnames(bk))==2]],na.rm=T)
#bk$event <- gsub("-"," ",bk$event)
bk$event <- gsub("sheffield-wednesday","sheff wed",bk$event)
bk$event <- gsub("nottingham-forest","nottm forest",bk$event)
bk$event <- gsub("middlesbrough","middlesbro",bk$event)
bk$event <- gsub("port-vale","port vale",bk$event)
bk$event <- gsub("man-utd","man utd",bk$event)
bk$event <- gsub("man-city","man city",bk$event)
bk$event <- gsub("aston-villa","aston villa",bk$event)
bk$event <- gsub("west-brom","west brom",bk$event)
bk$event <- gsub("west-ham","west ham",bk$event)
bk$event <- gsub("crystal-palace","c palace",bk$event)
bk$event <- gsub("sheffield-utd","sheff utd",bk$event)
bk$event <- gsub("cambridge-utd","cambridge u",bk$event)
bk$event <- gsub("dagenham-redbridge","dag & red",bk$event)
bk$event <- gsub("mk-dons","mk dons",bk$event)
bk$event <- gsub("notts-county","notts co",bk$event)
bk$event <- gsub("newport-county","newport co",bk$event)
bk$event <- gsub("afc-wimbledon","afc w'bledon",bk$event)
bk$event <- gsub("york-city","york",bk$event)
bk$team1 <- gsub("^(.*?)-v-(.*?)-(.*?)$","\\1",bk$event)
bk$team2 <- gsub("^(.*?)-v-(.*?)-(.*?)$","\\2",bk$event)
bk$event <- gsub("^(.*?)-v-(.*?)-(.*?)$","\\3",bk$event)
bk$event.1 <- c(bk$event[-1],NA)
bk.h <- bk[(bk$event!=bk$event.1 | is.na(bk$event.1)==T) & bk$event==bk$team1,]
bk.d <- bk[bk$event!=bk$event.1 & regexpr("draw",bk$event)>-1,]
bk.a <- bk[bk$event!=bk$event.1 & bk$event==bk$team2,]
matches$team1 <- tolower(matches$team1)
matches$team2 <- tolower(matches$team2)
matches <- merge(matches,bk.h[c(colnames(bk.h[nchar(colnames(bk.h))==2]),"team1","team2")],by=c("team1","team2"),all.x=T)
colnames(matches)[colnames(matches) %in% colnames(bk.h) & regexpr("team",colnames(matches))==-1] <- paste(colnames(matches)[colnames(matches) %in% colnames(bk.h) & regexpr("team",colnames(matches))==-1],".h",sep="")
matches <- merge(matches,bk.d[c(colnames(bk.d[nchar(colnames(bk.d))==2]),"team1","team2")],by=c("team1","team2"),all.x=T)
colnames(matches)[colnames(matches) %in% colnames(bk.d) & regexpr("team",colnames(matches))==-1] <- paste(colnames(matches)[colnames(matches) %in% colnames(bk.d) & regexpr("team",colnames(matches))==-1],".d",sep="")
matches <- merge(matches,bk.a[c(colnames(bk.a[nchar(colnames(bk.a))==2]),"team1","team2")],by=c("team1","team2"),all.x=T)
colnames(matches)[colnames(matches) %in% colnames(bk.a) & regexpr("team",colnames(matches))==-1] <- paste(colnames(matches)[colnames(matches) %in% colnames(bk.a) & regexpr("team",colnames(matches))==-1],".a",sep="")
par(mar=c(9,4,4,5)+.1)
plot(matches$id,matches$Ph,xaxt="n",xlab="",ylim=range(0,1),col=2,pch=15,type="p",cex=1.5,
main=paste("Forecasts of Weekend ",div," Matches",sep=""),
ylab="Probability of Outcome")
for(i in colnames(matches)[grep("[.]h",colnames(matches))]) {
for(j in matches$id) {
lines(j,1/matches[matches$id==j,i],col="darkred",pch=0,type="p",cex=0.5)
}
}
lines(matches$id,matches$Pd,col=3,pch=16,type="p",cex=1.5)
for(i in colnames(matches)[grep("[.]d",colnames(matches))]) {
for(j in matches$id) {
lines(j,1/matches[matches$id==j,i],col="darkgreen",pch=0,type="p",cex=0.5)
}
}
lines(matches$id,matches$Pa,col=4,pch=17,type="p",cex=1.5)
for(i in colnames(matches)[grep("[.]a",colnames(matches))]) {
for(j in matches$id) {
lines(j,1/matches[matches$id==j,i],col="darkblue",pch=0,type="p",cex=0.5)
}
}
legend("topleft",ncol=3,pch=c(15,16,17),col=c(2:4),
legend=c("OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
abline(h=0.4,lty=3)
axis(1,at=matches$id,labels=paste(matches$team1,matches$team2,sep=" v "),las=2,cex.axis=0.65)
}
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)
}Premier League
simpleplot("English Premier")
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.
bkplot("English Premier","premier-league")## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
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 24) are plotted.
In terms of making stronger picks than simply stating probabilities, as requested by some, here goes:
- My model backs Man United much more strongly than the bookmakers do against Man City.
- My model backs Crystal Palace more strongly than do the bookmakers at Sunderland. I have two explanations for this; my model does not factor in any slacking that “safe” teams like Palace may indulge in, nor does it have any “new manager” effect.
- My model backs Swansea more strongly than Everton.
Championship
Next, our Championship forecasts:
simpleplot("English Championship")
This week I’m adding in Championship bookmaker prices:
bkplot("English Championship","championship")
The big divergences here are:
- My model backs Derby, Middlesbrough, Ipswich, Sheffield Wednesday and Reading much less than the bookmakers.
- My model backs Leeds and Nottingham Forest slightly more than the bookmakers.
League One
Next, our League One forecasts:
simpleplot("English League One")
bkplot("English League One","league-1")
Adding in bookmakers at League One level, the main differences are:
- My model backs Port Vale much more strongly against MK Dons; I suspect that this is an issue with my model, as it continually under-predicts MK Dons.
- My model backs Peterborough much more strongly than the bookmakers at Swindon.
- My model backs Scunthorpe much less strongly than the bookmakers to beat Crawley at home.
League Two
Next, our League Two forecasts:
simpleplot("English League Two")
bkplot("English League Two","league-2")
At League Two level:
- My model backs Cambridge to beat Accrington much more than bookmakers do.
- My model backs Morcambe to beat Portsmouth more strongly than bookmakers do.
- My model backs Southend to beat Tranmere more strongly than bookmakers do.
Football Conference
Next, our Football Conference forecasts:
simpleplot("Football Conference")
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 | 2015-04-10 | English Championship | Fulham | 0.5867834 | Wigan | 0.4415506 | 0.2803591 | 0.2780903 |
| 2 | 2015-04-10 | English Championship | Brighton | 0.2959805 | Bournemouth | 0.1729436 | 0.2341287 | 0.5929277 |
| 71 | 2015-04-11 | Conference North | Stalybridge | 0.4593461 | Boston Utd | 0.3010595 | 0.2847255 | 0.4142150 |
| 72 | 2015-04-11 | Conference North | Stockport | 0.6622523 | Gainsborough | 0.5431513 | 0.2529107 | 0.2039380 |
| 59 | 2015-04-11 | Conference South | Hayes & Y | 0.5020577 | Bath City | 0.3518142 | 0.2887356 | 0.3594502 |
| 10 | 2015-04-11 | English Championship | Derby | 0.5819815 | Brentford | 0.4443685 | 0.2798281 | 0.2758034 |
| 11 | 2015-04-11 | English Championship | Bolton | 0.4312179 | Norwich | 0.2827221 | 0.2813799 | 0.4358980 |
| 12 | 2015-04-11 | English Championship | Middlesbro | 0.7156617 | Rotherham | 0.6196352 | 0.2228486 | 0.1575163 |
| 13 | 2015-04-11 | English Championship | Birmingham | 0.4933596 | Wolves | 0.3394792 | 0.2884188 | 0.3721020 |
| 14 | 2015-04-11 | English Championship | Ipswich | 0.7460879 | Blackpool | 0.6696775 | 0.1997098 | 0.1306127 |
| 15 | 2015-04-11 | English Championship | Sheff Wed | 0.5326384 | Charlton | 0.3770952 | 0.2881883 | 0.3347165 |
| 16 | 2015-04-11 | English Championship | Leeds | 0.6155337 | Cardiff | 0.4763877 | 0.2728040 | 0.2508083 |
| 17 | 2015-04-11 | English Championship | Millwall | 0.2614884 | Watford | 0.1556839 | 0.2214166 | 0.6228995 |
| 18 | 2015-04-11 | English Championship | Reading | 0.3851924 | Blackburn | 0.2322778 | 0.2660551 | 0.5016671 |
| 19 | 2015-04-11 | English Championship | Nottm Forest | 0.7096703 | Huddersfield | 0.6029825 | 0.2299748 | 0.1670426 |
| 20 | 2015-04-11 | English League One | Oldham | 0.4890115 | Sheff Utd | 0.3341311 | 0.2881558 | 0.3777132 |
| 21 | 2015-04-11 | English League One | Port Vale | 0.6310434 | MK Dons | 0.5161733 | 0.2617390 | 0.2220877 |
| 22 | 2015-04-11 | English League One | Yeovil | 0.5331003 | Notts Co | 0.3796439 | 0.2880483 | 0.3323079 |
| 23 | 2015-04-11 | English League One | Walsall | 0.4883421 | Fleetwood | 0.3305857 | 0.2879384 | 0.3814759 |
| 24 | 2015-04-11 | English League One | Swindon | 0.5223208 | Peterborough | 0.3755776 | 0.2882646 | 0.3361578 |
| 25 | 2015-04-11 | English League One | Doncaster | 0.5506341 | Rochdale | 0.4023056 | 0.2861605 | 0.3115339 |
| 26 | 2015-04-11 | English League One | Coventry | 0.5940021 | Colchester | 0.4513732 | 0.2784452 | 0.2701815 |
| 27 | 2015-04-11 | English League One | Crewe | 0.4971961 | Leyton Orient | 0.3398167 | 0.2884328 | 0.3717505 |
| 28 | 2015-04-11 | English League One | Preston | 0.5543306 | Bristol C | 0.4037826 | 0.2859986 | 0.3102187 |
| 29 | 2015-04-11 | English League One | Barnsley | 0.4821956 | Chesterfield | 0.3262855 | 0.2876278 | 0.3860866 |
| 30 | 2015-04-11 | English League One | Scunthorpe | 0.5749679 | Crawley | 0.4339496 | 0.2817178 | 0.2843326 |
| 31 | 2015-04-11 | English League One | Gillingham | 0.5441754 | Bradford | 0.3943666 | 0.2869506 | 0.3186828 |
| 32 | 2015-04-11 | English League Two | Newport Co | 0.4807392 | Bury | 0.3210175 | 0.2871762 | 0.3918063 |
| 33 | 2015-04-11 | English League Two | Luton | 0.4415806 | Burton | 0.2850808 | 0.2818718 | 0.4330474 |
| 34 | 2015-04-11 | English League Two | Wycombe | 0.8069030 | Cheltenham | 0.7148977 | 0.1767919 | 0.1083104 |
| 35 | 2015-04-11 | English League Two | Cambridge U | 0.6811304 | Accrington | 0.5645931 | 0.2451974 | 0.1902094 |
| 36 | 2015-04-11 | English League Two | York | 0.7053379 | Hartlepool | 0.5941557 | 0.2336275 | 0.1722168 |
| 37 | 2015-04-11 | English League Two | Morecambe | 0.6198479 | Portsmouth | 0.4796936 | 0.2719794 | 0.2483269 |
| 38 | 2015-04-11 | English League Two | Shrewsbury | 0.7250929 | Exeter | 0.6180704 | 0.2235310 | 0.1583986 |
| 39 | 2015-04-11 | English League Two | Carlisle | 0.4462679 | Dag & Red | 0.2884658 | 0.2825455 | 0.4289887 |
| 40 | 2015-04-11 | English League Two | Stevenage | 0.6017732 | Northampton | 0.4598365 | 0.2766574 | 0.2635061 |
| 41 | 2015-04-11 | English League Two | Southend | 0.8165246 | Tranmere | 0.7101341 | 0.1792889 | 0.1105770 |
| 42 | 2015-04-11 | English League Two | AFC W’bledon | 0.4776292 | Oxford | 0.3181323 | 0.2868951 | 0.3949726 |
| 43 | 2015-04-11 | English League Two | Plymouth | 0.6183988 | Mansfield | 0.4960058 | 0.2676537 | 0.2363405 |
| 3 | 2015-04-11 | English Premier | Swansea | 0.6246042 | Everton | 0.4874359 | 0.2699790 | 0.2425851 |
| 4 | 2015-04-11 | English Premier | Tottenham | 0.7629533 | Aston Villa | 0.6594419 | 0.2046422 | 0.1359159 |
| 5 | 2015-04-11 | English Premier | Southampton | 0.7595162 | Hull | 0.6587052 | 0.2049934 | 0.1363014 |
| 6 | 2015-04-11 | English Premier | West Ham | 0.4639240 | Stoke | 0.3085476 | 0.2857854 | 0.4056670 |
| 7 | 2015-04-11 | English Premier | West Brom | 0.6014977 | Leicester | 0.4606373 | 0.2764818 | 0.2628809 |
| 8 | 2015-04-11 | English Premier | Sunderland | 0.4032194 | C Palace | 0.2478659 | 0.2718236 | 0.4803105 |
| 9 | 2015-04-11 | English Premier | Burnley | 0.2202478 | Arsenal | 0.1290936 | 0.1982616 | 0.6726448 |
| 44 | 2015-04-11 | Football Conference | Chester | 0.4656753 | Dover | 0.3124147 | 0.2862660 | 0.4013193 |
| 45 | 2015-04-11 | Football Conference | Bristol R | 0.8141887 | Southport | 0.7256853 | 0.1710686 | 0.1032461 |
| 46 | 2015-04-11 | Football Conference | Telford | 0.5225283 | Lincoln | 0.3761909 | 0.2882344 | 0.3355747 |
| 47 | 2015-04-11 | Football Conference | Woking | 0.7057248 | Gateshead | 0.5918014 | 0.2345868 | 0.1736118 |
| 48 | 2015-04-11 | Football Conference | Dartford | 0.5430642 | Nuneaton | 0.3939866 | 0.2869850 | 0.3190284 |
| 49 | 2015-04-11 | Football Conference | Forest Green | 0.6832997 | Macclesfield | 0.5632835 | 0.2456854 | 0.1910311 |
| 50 | 2015-04-11 | Football Conference | Braintree | 0.4936029 | Aldershot | 0.3364790 | 0.2882808 | 0.3752402 |
| 51 | 2015-04-11 | Football Conference | Eastleigh | 0.7696715 | Alfreton | 0.6893966 | 0.1899360 | 0.1206674 |
| 52 | 2015-04-11 | Football Conference | Grimsby | 0.7656390 | Wrexham | 0.6655195 | 0.2017253 | 0.1327551 |
| 53 | 2015-04-11 | Football Conference | Torquay | 0.5279120 | Altrincham | 0.3819404 | 0.2879093 | 0.3301503 |
| 54 | 2015-04-11 | Football Conference | Welling | 0.4575021 | Kidderminster | 0.3005845 | 0.2846525 | 0.4147630 |
| 55 | 2015-04-11 | Football Conference | Barnet | 0.7605087 | Halifax | 0.6610811 | 0.2038589 | 0.1350600 |
| 93 | 2015-04-11 | Ryman Premier | Maidstone | 0.6537448 | Grays | 0.5553071 | 0.2486107 | 0.1960822 |
| 110 | 2015-04-12 | English Premier | QPR | 0.2325452 | Chelsea | 0.1372553 | 0.2058581 | 0.6568866 |
| 111 | 2015-04-12 | English Premier | Man Utd | 0.6870530 | Man City | 0.5651716 | 0.2449812 | 0.1898472 |
| 113 | 2015-04-13 | English Premier | Liverpool | 0.7867825 | Newcastle | 0.6873256 | 0.1909789 | 0.1216955 |
| 155 | 2015-04-14 | Conference North | Hyde | 0.3601157 | Stockport | 0.2295661 | 0.2649498 | 0.5054841 |
| 152 | 2015-04-14 | Conference South | Bath City | 0.5497797 | Ebbsfleet | 0.4022475 | 0.2861668 | 0.3115857 |
| 115 | 2015-04-14 | English Championship | Middlesbro | 0.5530180 | Wolves | 0.4069038 | 0.2856415 | 0.3074547 |
| 116 | 2015-04-14 | English Championship | Bolton | 0.5636805 | Charlton | 0.4167254 | 0.2843858 | 0.2988888 |
| 117 | 2015-04-14 | English Championship | Derby | 0.7576580 | Blackpool | 0.6985482 | 0.1852828 | 0.1161690 |
| 118 | 2015-04-14 | English Championship | Sheff Wed | 0.5512843 | Brentford | 0.4011796 | 0.2862808 | 0.3125396 |
| 119 | 2015-04-14 | English Championship | Birmingham | 0.4735992 | Blackburn | 0.3150378 | 0.2865666 | 0.3983956 |
| 120 | 2015-04-14 | English Championship | Brighton | 0.6717369 | Huddersfield | 0.5513314 | 0.2500383 | 0.1986303 |
| 121 | 2015-04-14 | English Championship | Reading | 0.2642809 | Bournemouth | 0.1564866 | 0.2220463 | 0.6214671 |
| 122 | 2015-04-14 | English Championship | Leeds | 0.4026147 | Norwich | 0.2542053 | 0.2738944 | 0.4719003 |
| 123 | 2015-04-14 | English Championship | Millwall | 0.4776314 | Wigan | 0.3159557 | 0.2866669 | 0.3973774 |
| 124 | 2015-04-14 | English Championship | Ipswich | 0.6015650 | Cardiff | 0.4651941 | 0.2754610 | 0.2593449 |
| 125 | 2015-04-14 | English League One | Fleetwood | 0.7343645 | MK Dons | 0.6243432 | 0.2207796 | 0.1548771 |
| 126 | 2015-04-14 | English League One | Preston | 0.7538480 | Gillingham | 0.6566829 | 0.2059547 | 0.1373624 |
| 127 | 2015-04-14 | English League One | Rochdale | 0.6742830 | Swindon | 0.5521083 | 0.2497609 | 0.1981308 |
| 128 | 2015-04-14 | English League One | Walsall | 0.6108732 | Crawley | 0.4754366 | 0.2730379 | 0.2515255 |
| 129 | 2015-04-14 | English League One | Yeovil | 0.3159803 | Sheff Utd | 0.1880171 | 0.2438786 | 0.5681043 |
| 130 | 2015-04-14 | English League One | Notts Co | 0.4387817 | Barnsley | 0.2807439 | 0.2809528 | 0.4383033 |
| 131 | 2015-04-14 | English League One | Scunthorpe | 0.6179367 | Port Vale | 0.4824661 | 0.2712742 | 0.2462597 |
| 132 | 2015-04-14 | English League One | Coventry | 0.5044856 | Oldham | 0.3497899 | 0.2887108 | 0.3614992 |
| 133 | 2015-04-14 | English League One | Peterborough | 0.7304723 | Crewe | 0.6260560 | 0.2200212 | 0.1539228 |
| 134 | 2015-04-14 | English League One | Bradford | 0.3956876 | Bristol C | 0.2447895 | 0.2707624 | 0.4844481 |
| 135 | 2015-04-14 | English League One | Leyton Orient | 0.5329839 | Doncaster | 0.3854267 | 0.2876752 | 0.3268981 |
| 136 | 2015-04-14 | English League One | Chesterfield | 0.8002104 | Colchester | 0.6965026 | 0.1863292 | 0.1171682 |
| 137 | 2015-04-14 | English League Two | Bury | 0.5138621 | Shrewsbury | 0.3553619 | 0.2887539 | 0.3558842 |
| 138 | 2015-04-14 | English League Two | York | 0.6458332 | Morecambe | 0.5146992 | 0.2621917 | 0.2231090 |
| 139 | 2015-04-14 | English League Two | Cheltenham | 0.4145106 | Cambridge U | 0.2567252 | 0.2746749 | 0.4685999 |
| 140 | 2015-04-14 | English League Two | Stevenage | 0.7048149 | Portsmouth | 0.5834766 | 0.2379272 | 0.1785962 |
| 141 | 2015-04-14 | English League Two | Southend | 0.6897802 | Newport Co | 0.5664725 | 0.2444934 | 0.1890341 |
| 142 | 2015-04-14 | English League Two | Plymouth | 0.6659539 | AFC W’bledon | 0.5439127 | 0.2526471 | 0.2034402 |
| 143 | 2015-04-14 | English League Two | Wycombe | 0.6072650 | Exeter | 0.4711714 | 0.2740684 | 0.2547603 |
| 144 | 2015-04-14 | English League Two | Hartlepool | 0.6081170 | Accrington | 0.4690774 | 0.2745631 | 0.2563595 |
| 145 | 2015-04-14 | English League Two | Oxford | 0.5567980 | Northampton | 0.4072423 | 0.2856015 | 0.3071562 |
| 146 | 2015-04-14 | English League Two | Dag & Red | 0.6323771 | Luton | 0.4993134 | 0.2667257 | 0.2339609 |
| 147 | 2015-04-14 | English League Two | Mansfield | 0.7002314 | Tranmere | 0.5736883 | 0.2417499 | 0.1845619 |
| 148 | 2015-04-14 | Football Conference | Torquay | 0.5385028 | Lincoln | 0.3920867 | 0.2871522 | 0.3207611 |
| 149 | 2015-04-14 | Football Conference | Welling | 0.3909812 | Wrexham | 0.2380482 | 0.2683053 | 0.4936465 |
| 150 | 2015-04-14 | Football Conference | Halifax | 0.4954849 | Eastleigh | 0.3388176 | 0.2883904 | 0.3727920 |
| 151 | 2015-04-14 | Football Conference | Southport | 0.6036338 | Braintree | 0.4617415 | 0.2762377 | 0.2620208 |
| 166 | 2015-04-15 | English Championship | Nottm Forest | 0.3929701 | Watford | 0.2415202 | 0.2695936 | 0.4888861 |
| 167 | 2015-04-15 | English Championship | Fulham | 0.6127951 | Rotherham | 0.4753418 | 0.2730611 | 0.2515971 |
| 168 | 2015-04-15 | English League Two | Burton | 0.7943569 | Carlisle | 0.7001598 | 0.1844558 | 0.1153843 |