Forecasting Football Matches, February 14–21
J James Reade
13/02/2015
Introduction
This is the third in a series of forecasts of football match outcomes, following on from my efforts last week and the week before. The method of forecasts is unchanged from previous week. For next week I hope to add bookmaker prices in for reference purposes.
Loading the Data
As with previous weeks, the dataset 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 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.
library(knitr)
library(MASS)
date.1 <- "2015-02-13"
wd <- "/home/readejj/Dropbox/Teaching/Reading/ec313/2015/Football-forecasts/"
forecast.matches <- read.csv(paste(wd,"forecasts_",date.1,".csv",sep=""))
forecast.matches <- forecast.matches[is.na(forecast.matches$outcome)==F,]Forecast Model
The linear regression model is estimated here and reported:
res.eng <- read.csv(paste(wd,"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
## -1.0175 -0.2936 0.1390 0.3498 0.8446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.013e-01 7.141e-03 56.199 < 2e-16 ***
## E.1 4.051e-01 1.097e-02 36.910 < 2e-16 ***
## pts1 1.043e-03 4.308e-04 2.421 0.01549 *
## pts.D -2.848e-03 3.153e-04 -9.035 < 2e-16 ***
## pts.D.2 -1.393e-05 6.574e-06 -2.120 0.03404 *
## pld1 -1.721e-03 6.192e-04 -2.779 0.00545 **
## pld.D 3.401e-03 7.236e-04 4.700 2.61e-06 ***
## pld.D.2 -4.550e-05 3.131e-05 -1.454 0.14608
## gs1 5.058e-04 1.737e-04 2.912 0.00359 **
## gs.D -3.027e-05 1.551e-04 -0.195 0.84529
## gs.D.2 -1.637e-06 4.764e-06 -0.344 0.73116
## gd1 -6.785e-04 2.445e-04 -2.775 0.00552 **
## gd.D 3.424e-03 1.784e-04 19.191 < 2e-16 ***
## gd.D.2 -5.614e-06 2.379e-06 -2.359 0.01830 *
## pos1 7.934e-04 3.052e-04 2.599 0.00934 **
## pos.D -4.188e-04 2.583e-04 -1.621 0.10494
## pos.D.2 3.603e-05 1.189e-05 3.030 0.00244 **
## form1 7.654e-04 3.574e-04 2.142 0.03220 *
## form.D -2.168e-03 3.344e-04 -6.484 8.96e-11 ***
## form.D.2 -7.911e-05 3.044e-05 -2.599 0.00935 **
## tier1 2.000e-03 7.779e-04 2.572 0.01012 *
## tier.D -5.401e-02 3.172e-03 -17.028 < 2e-16 ***
## tier.D.2 -5.876e-03 1.277e-03 -4.601 4.21e-06 ***
## season.d -1.105e-03 3.132e-05 -35.283 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4008 on 215758 degrees of freedom
## (38021 observations deleted due to missingness)
## Multiple R-squared: 0.05682, Adjusted R-squared: 0.05672
## F-statistic: 565.1 on 23 and 215758 DF, p-value: < 2.2e-16The 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 1.911e+00 9.478e-03 201.5822
## pts1 4.334e-03 2.069e-03 2.0953
## pts.D -1.424e-02 1.503e-03 -9.4704
## pts.D.2 -7.916e-05 3.582e-05 -2.2098
## pld1 -9.536e-03 2.976e-03 -3.2039
## pld.D 1.860e-02 3.527e-03 5.2735
## pld.D.2 -2.538e-04 1.606e-04 -1.5802
## gs1 3.692e-03 8.455e-04 4.3665
## gs.D -5.924e-04 7.528e-04 -0.7869
## gs.D.2 -3.010e-06 2.544e-05 -0.1183
## gd1 -3.792e-03 1.186e-03 -3.1984
## gd.D 1.774e-02 8.713e-04 20.3634
## gd.D.2 7.060e-06 1.637e-05 0.4313
## pos1 3.107e-03 1.446e-03 2.1480
## pos.D -1.142e-03 1.240e-03 -0.9211
## pos.D.2 1.961e-04 5.900e-05 3.3241
## form1 3.467e-03 1.702e-03 2.0373
## form.D -9.760e-03 1.415e-03 -6.8966
## form.D.2 -2.759e-04 1.481e-04 -1.8633
## tier1 9.465e-03 3.693e-03 2.5627
## tier.D -2.825e-01 1.428e-02 -19.7836
## tier.D.2 -8.418e-03 6.901e-03 -1.2198
## season.d -5.405e-03 1.514e-04 -35.7005
##
## Intercepts:
## Value Std. Error t value
## 0|0.5 -0.1004 0.0170 -5.9140
## 0.5|1 1.0602 0.0170 62.4785
##
## Residual Deviance: 434042.87
## AIC: 434092.87
## (38021 observations deleted due to missingness)The Forecasts
FA Cup
First, our FA Cup forecasts:
facup.matches <- forecast.matches[forecast.matches$division=="English FA Cup",]
facup.matches$id <- 1:NROW(facup.matches)
par(mar=c(9,4,4,5)+.1)
plot(facup.matches$id,facup.matches$outcome,xaxt="n",xlab="",ylim=range(0,1),
main="Forecasts of Weekend FA Cup Matches",
ylab="Probability of Outcome")
lines(facup.matches$id,facup.matches$Ph,col=2,pch=15,type="p")
lines(facup.matches$id,facup.matches$Pd,col=3,pch=16,type="p")
lines(facup.matches$id,facup.matches$Pa,col=4,pch=17,type="p")
legend("topleft",ncol=4,pch=c(1,15,16,17),col=c(1:4),
legend=c("OLS","OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
abline(h=0.4,lty=3)
axis(1,at=facup.matches$id,labels=paste(facup.matches$team1,facup.matches$team2,sep=" v "),las=2,cex.axis=0.65)
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.
The forecasts suggest that the Blackburn-Stoke, Crystal Palace-Liverpool, West Brom-West Ham and Aston Villa-Leicester matches will be tight. I’m hopeful in future weeks that a “change of manager” variable will be added to the model to attempt to capture the likely boost Aston Villa will get having parted with their manager in the week. Derby have the highest forecast probability of winning in all the ties this weekend, followed by Manchester United at Preston.
An additional interesting insight is provided by bookmaker prices. We scrape prices from http://www.oddsportal.com/ and look at the average bookmaker price for each of the outcomes, converted into an implied probability, and plot below for the FA Cup:
bookies <- read.csv("/home/readejj/Dropbox/Teaching/Reading/ec313/2015/Football-forecasts/betting_prices_2015-02-13 22:54:19.850486.csv",stringsAsFactors=F)
bookies$odds1 <- as.numeric(bookies$odds1)## Warning: NAs introduced by coercionbookies$odds2 <- as.numeric(bookies$odds2)## Warning: NAs introduced by coercionbookies$odds3 <- as.numeric(bookies$odds3)## Warning: NAs introduced by coercionfacup.bk <- aggregate(bookies[regexpr("FA Cup",bookies$keywords)>-1,c("odds1","odds2","odds3")],
by=list(bookies$match[regexpr("FA Cup",bookies$keywords)>-1]),FUN=mean,na.rm=T)
facup.bk$Group.1 <- gsub("Manchester United","Man Utd",facup.bk$Group.1)
facup.bk$Group.1 <- gsub("Crystal Palace","C Palace",facup.bk$Group.1)
facup.bk$Group.1 <- gsub("Middlesbrough","Middlesbro",facup.bk$Group.1)
facup.bk$Group.1 <- gsub("Stoke City","Stoke",facup.bk$Group.1)
facup.bk$team1 <- gsub("^(.*?) - (.*?)$","\\1",facup.bk$Group.1)
facup.bk$team2 <- gsub("^(.*?) - (.*?)$","\\2",facup.bk$Group.1)
facup.matches <- merge(facup.matches,facup.bk,by=c("team1","team2"),all.x=T)
par(mar=c(9,4,4,5)+.1)
plot(facup.matches$id,facup.matches$Ph,xaxt="n",xlab="",ylim=range(0,1),col=2,pch=15,type="p",
main="Forecasts of Weekend FA Cup Matches",
ylab="Probability of Outcome")
lines(facup.matches$id,1/facup.matches$odds1,col=2,pch=0,type="p")
lines(facup.matches$id,facup.matches$Pd,col=3,pch=16,type="p")
lines(facup.matches$id,1/facup.matches$odds2,col=3,pch=1,type="p")
lines(facup.matches$id,facup.matches$Pa,col=4,pch=17,type="p")
lines(facup.matches$id,1/facup.matches$odds3,col=4,pch=2,type="p")
legend("topleft",ncol=4,pch=c(1,15,16,17),col=c(1:4),
legend=c("OLS","OL (home)","OL (draw)","OL (away)"),bty="n")
abline(h=0.5,lty=2)
abline(h=0.6,lty=3)
abline(h=0.7,lty=2)
abline(h=0.4,lty=3)
axis(1,at=facup.matches$id,labels=paste(facup.matches$team1,facup.matches$team2,sep=" v "),las=2,cex.axis=0.65)
Hence the bookmakers do not rate Derby’s likelihood of victory as highly as my model does, whilst bookmakers rate Man United’s chances at Preston even more highly than my model. This may simply be a manifestation of the favourite-longshot bias; similarly with the discrepancy in Arsenal’s likelihood of victory against Middlebrough.
Championship
Next, our Championship forecasts:
champ.matches <- forecast.matches[forecast.matches$division=="English Championship",]
champ.matches$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$team2,sep=" v "),las=2,cex.axis=0.65)
The biggest favourites to win in the Championship this weekend are Bournemouth in their clash with Huddersfield.
League One
Next, our League One forecasts:
lg1.matches <- forecast.matches[forecast.matches$division=="English League One",]
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)
League One sees a number of likely home wins, most likely of which is Bristol City when facing Peterborough.
League Two
Next, our League Two forecasts:
lg2.matches <- forecast.matches[forecast.matches$division=="English League Two",]
lg2.matches$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$team2,sep=" v "),las=2,cex.axis=0.65)
Football Conference
Next, our Football Conference forecasts:
conf.matches <- forecast.matches[forecast.matches$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)
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 | |
|---|---|---|---|---|---|---|---|---|
| 59 | 2015-02-14 | Conference North | Stockport | 0.7928062 | Hyde | 0.7205194 | 0.1711199 | 0.1083606 |
| 60 | 2015-02-14 | Conference North | Boston Utd | 0.6910559 | Bradford PA | 0.5789428 | 0.2354775 | 0.1855796 |
| 1 | 2015-02-14 | English Championship | Blackpool | 0.4804788 | Nottm Forest | 0.3333087 | 0.2814356 | 0.3852556 |
| 2 | 2015-02-14 | English Championship | Norwich | 0.6496203 | Wolves | 0.5317588 | 0.2520096 | 0.2162315 |
| 3 | 2015-02-14 | English Championship | Bolton | 0.4944355 | Watford | 0.3458833 | 0.2820528 | 0.3720639 |
| 4 | 2015-02-14 | English Championship | Sheff Wed | 0.5730854 | Brighton | 0.4243613 | 0.2773923 | 0.2982464 |
| 5 | 2015-02-14 | English Championship | Bournemouth | 0.7710514 | Huddersfield | 0.6821898 | 0.1904397 | 0.1273705 |
| 6 | 2015-02-14 | English Championship | Leeds | 0.6517284 | Millwall | 0.5248527 | 0.2541820 | 0.2209653 |
| 7 | 2015-02-14 | English Championship | Charlton | 0.4793118 | Brentford | 0.3301633 | 0.2812153 | 0.3886214 |
| 8 | 2015-02-14 | English Championship | Fulham | 0.4725960 | Ipswich | 0.3240754 | 0.2807117 | 0.3952129 |
| 98 | 2015-02-14 | English FA Cup | Blackburn | 0.4393591 | Stoke | 0.2862546 | 0.2751618 | 0.4385836 |
| 99 | 2015-02-14 | English FA Cup | C Palace | 0.4926068 | Liverpool | 0.3414431 | 0.2818824 | 0.3766745 |
| 100 | 2015-02-14 | English FA Cup | Derby | 0.7458782 | Reading | 0.6484037 | 0.2063754 | 0.1452209 |
| 101 | 2015-02-14 | English FA Cup | West Brom | 0.4978087 | West Ham | 0.3474745 | 0.2821015 | 0.3704240 |
| 9 | 2015-02-14 | English League One | Gillingham | 0.4663722 | MK Dons | 0.3193624 | 0.2802508 | 0.4003867 |
| 10 | 2015-02-14 | English League One | Peterborough | 0.5045922 | Rochdale | 0.3525335 | 0.2822134 | 0.3652531 |
| 11 | 2015-02-14 | English League One | Chesterfield | 0.7020723 | Leyton Orient | 0.5845705 | 0.2333202 | 0.1821093 |
| 12 | 2015-02-14 | English League One | Doncaster | 0.6901215 | Yeovil | 0.5704271 | 0.2386696 | 0.1909033 |
| 13 | 2015-02-14 | English League One | Scunthorpe | 0.4865076 | Swindon | 0.3393653 | 0.2817850 | 0.3788498 |
| 14 | 2015-02-14 | English League One | Walsall | 0.6638441 | Port Vale | 0.5386012 | 0.2497925 | 0.2116063 |
| 15 | 2015-02-14 | English League One | Bristol C | 0.6707691 | Sheff Utd | 0.5540402 | 0.2445596 | 0.2014003 |
| 16 | 2015-02-14 | English League One | Crewe | 0.5527338 | Fleetwood | 0.4052684 | 0.2797641 | 0.3149676 |
| 17 | 2015-02-14 | English League One | Oldham | 0.6872876 | Colchester | 0.5688543 | 0.2392495 | 0.1918963 |
| 18 | 2015-02-14 | English League One | Crawley | 0.4744462 | Barnsley | 0.3218589 | 0.2805028 | 0.3976383 |
| 19 | 2015-02-14 | English League Two | Burton | 0.6810356 | Oxford | 0.5569975 | 0.2435217 | 0.1994808 |
| 20 | 2015-02-14 | English League Two | Hartlepool | 0.4915018 | Stevenage | 0.3427982 | 0.2819399 | 0.3752619 |
| 21 | 2015-02-14 | English League Two | Luton | 0.7110453 | Carlisle | 0.5976611 | 0.2281587 | 0.1741802 |
| 22 | 2015-02-14 | English League Two | Mansfield | 0.5017062 | Northampton | 0.3508069 | 0.2821825 | 0.3670105 |
| 23 | 2015-02-14 | English League Two | Cheltenham | 0.4790124 | Bury | 0.3285376 | 0.2810908 | 0.3903716 |
| 24 | 2015-02-14 | English League Two | York | 0.5617495 | Tranmere | 0.4160725 | 0.2785119 | 0.3054156 |
| 25 | 2015-02-14 | English League Two | Wycombe | 0.6479863 | Newport Co | 0.5222602 | 0.2549803 | 0.2227595 |
| 26 | 2015-02-14 | English League Two | Shrewsbury | 0.6780475 | AFC W’bledon | 0.5580401 | 0.2431532 | 0.1988068 |
| 27 | 2015-02-14 | English League Two | Portsmouth | 0.5887559 | Exeter | 0.4492478 | 0.2732423 | 0.2775099 |
| 28 | 2015-02-14 | English League Two | Southend | 0.6936066 | Accrington | 0.5780157 | 0.2358293 | 0.1861550 |
| 29 | 2015-02-14 | English League Two | Plymouth | 0.5815134 | Cambridge U | 0.4372511 | 0.2753870 | 0.2873619 |
| 30 | 2015-02-14 | Football Conference | Altrincham | 0.5330155 | Forest Green | 0.3854939 | 0.2814209 | 0.3330852 |
| 31 | 2015-02-14 | Football Conference | Aldershot | 0.5800231 | Welling | 0.4386217 | 0.2751554 | 0.2862230 |
| 32 | 2015-02-14 | Football Conference | Kidderminster | 0.5483510 | Woking | 0.3983975 | 0.2804348 | 0.3211677 |
| 33 | 2015-02-14 | Football Conference | Halifax | 0.5538107 | Dover | 0.4060521 | 0.2796813 | 0.3142666 |
| 34 | 2015-02-14 | Football Conference | Southport | 0.4111772 | Macclesfield | 0.2667889 | 0.2705369 | 0.4626742 |
| 35 | 2015-02-14 | Football Conference | Eastleigh | 0.6228813 | Torquay | 0.4907618 | 0.2638941 | 0.2453441 |
| 36 | 2015-02-14 | Football Conference | Gateshead | 0.7818561 | Nuneaton | 0.6895111 | 0.1868477 | 0.1236412 |
| 37 | 2015-02-14 | Football Conference | Braintree | 0.6812044 | Alfreton | 0.5704052 | 0.2386777 | 0.1909171 |
| 38 | 2015-02-14 | Football Conference | Wrexham | 0.4318478 | Barnet | 0.2860870 | 0.2751274 | 0.4387856 |
| 39 | 2015-02-14 | Football Conference | Grimsby | 0.6043868 | Bristol R | 0.4671363 | 0.2695688 | 0.2632950 |
| 40 | 2015-02-14 | Football Conference | Lincoln | 0.6211194 | Chester | 0.4897203 | 0.2641632 | 0.2461166 |
| 41 | 2015-02-14 | Football Conference | Telford | 0.6219963 | Dartford | 0.4954374 | 0.2626654 | 0.2418971 |
| 80 | 2015-02-14 | Ryman Premier | Canvey Isl. | 0.6589050 | Grays | 0.5527878 | 0.2449957 | 0.2022165 |
| 103 | 2015-02-15 | English FA Cup | Arsenal | 0.6950703 | Middlesbro | 0.5827696 | 0.2340147 | 0.1832157 |
| 104 | 2015-02-15 | English FA Cup | Aston Villa | 0.5402466 | Leicester | 0.3893593 | 0.2811636 | 0.3294770 |
| 105 | 2015-02-15 | English FA Cup | Bradford | 0.3794712 | Sunderland | 0.2397159 | 0.2618640 | 0.4984201 |
| 102 | 2015-02-15 | English League Two | Morecambe | 0.5980164 | Dag & Red | 0.4577094 | 0.2715740 | 0.2707166 |
| 107 | 2015-02-16 | English FA Cup | Preston | 0.2991186 | Man Utd | 0.1741355 | 0.2281287 | 0.5977357 |
| 117 | 2015-02-17 | Conference South | Bath City | 0.7376143 | Farnborough | 0.6272198 | 0.2157993 | 0.1569809 |
| 108 | 2015-02-17 | English Championship | Rotherham | 0.4049631 | Derby | 0.2602807 | 0.2686986 | 0.4710207 |
| 109 | 2015-02-17 | English Championship | Reading | 0.6500941 | Wigan | 0.5184511 | 0.2561358 | 0.2254131 |
| 110 | 2015-02-17 | English Championship | Cardiff | 0.5225144 | Blackburn | 0.3739658 | 0.2819885 | 0.3440457 |
| 111 | 2015-02-17 | English League One | Doncaster | 0.6468889 | Crewe | 0.5249289 | 0.2541584 | 0.2209127 |
| 112 | 2015-02-17 | English League One | Scunthorpe | 0.5554547 | Chesterfield | 0.4119968 | 0.2790122 | 0.3089911 |
| 128 | 2015-02-17 | English League One | Notts Co | 0.4916847 | Sheff Utd | 0.3387332 | 0.2817531 | 0.3795137 |
| 129 | 2015-02-17 | English League One | Bristol C | 0.7467466 | Peterborough | 0.6412391 | 0.2096135 | 0.1491474 |
| 130 | 2015-02-17 | English League One | Colchester | 0.3826489 | MK Dons | 0.2451824 | 0.2638375 | 0.4909801 |
| 127 | 2015-02-17 | English League Two | Mansfield | 0.4494845 | Luton | 0.2977173 | 0.2773034 | 0.4249793 |
| 132 | 2015-02-18 | Conference North | Bradford PA | 0.4587552 | Stockport | 0.3100066 | 0.2791473 | 0.4108461 |
| 131 | 2015-02-18 | English Championship | Birmingham | 0.3868512 | Middlesbro | 0.2422403 | 0.2627898 | 0.4949699 |
| 137 | 2015-02-18 | English League One | Leyton Orient | 0.4766566 | Bradford | 0.3285868 | 0.2810947 | 0.3903185 |
| 136 | 2015-02-18 | Ryman Premier | Grays | 0.3640978 | Maidstone | 0.2261596 | 0.2564556 | 0.5173848 |