This document summarises the forecast outcomes for the fourth weekly set of forecasts for English football match outcomes, following on from last week, and it provides outcomes for forecasts of matches over the last week as detailed in this document.

Two sets of forecasts are constructed, one using a simple linear regression method for a dependent variable taking the values 0 for an away win, 0.5 for a draw, and 1 for a home win. This linear regression method only yields a number which can be interpreted as a probability of an outcome occurring. The second, ordered logit method, treats each outcome in an ordered manner, from away win, to draw, to home win, and estimates a probability for each outcome.

The purpose of this document is to evaluate these forecasts, and begin to form a longer-term narrative about them.

Outcomes

To consider outcomes; we must load up the specific outcomes file for matches forecast over the weekend:

dates <- c("2015-01-30","2015-02-06","2015-02-13","2015-02-20")
date.1 <- dates[NROW(dates)]
recent.forecast.outcomes <- read.csv(paste("forecast_outcomes_",date.1,".csv",sep=""),stringsAsFactors=F)
forecast.matches <- read.csv(paste("forecasts_",date.1,".csv",sep=""))
forecast.matches <- forecast.matches[is.na(forecast.matches$outcome)==F,]
forecast.outcomes <- merge(forecast.matches[,c("match_id","outcome","Ph","Pd","Pa")],
                           recent.forecast.outcomes,by=c("match_id"),
                           suffixes=c(".forc",".final"))
forecast.outcomes <- forecast.outcomes[is.na(forecast.outcomes$outcome.final)==F,]

#all.forecast.outcomes <- data.frame()
#for(i in dates) {
#  temp.0 <- read.csv(paste(loc2,"forecast_outcomes_",i,".csv",sep=""),stringsAsFactors=F)
#  temp.1 <- read.csv(paste(loc2,"forecasts_",i,".csv",sep=""))
#  temp.1 <- temp.1[is.na(temp.1$outcome)==F,]
#  temp.2 <- merge(temp.1[,c("match_id","outcome","Ph","Pd","Pa")],
#                             temp.0,by=c("match_id"),
#                             suffixes=c(".forc",".final"))
#  forecast.outcomes <- rbind(temp.2[is.na(temp.2$outcome.final)==F,],forecast.outcomes)
#}

We also load up previous weeks’ forecasts and outcomes in order that we can begin to determine trends over time. This week was one particularly filled with surprise results, but that does not necessarily mean our forecast model need be amended. More, it reflects that football is intrinsically a very uncertain game. This, of course, is not to say that the model cannot be improved upon.

First, our Premier League forecasts:

prem.matches <- forecast.outcomes[forecast.outcomes$division=="English Premier",]
prem.matches$id <- 1:NROW(prem.matches)
par(mar=c(9,4,4,5)+.1)
plot(prem.matches$id,prem.matches$outcome.forc,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)
for(i in 1:NROW(prem.matches)) {
  if(prem.matches$outcome.final[i]==1) {
    lines(prem.matches$id[i],prem.matches$outcome.final[i],col=2,type="p",pch=0)
    lines(rep(i,2),c(prem.matches$Ph[i],prem.matches$outcome.final[i]),type="l",lty=2,col="red")
  } else if (prem.matches$outcome.final[i]==0.5) {
    lines(prem.matches$id[i],prem.matches$outcome.final[i],col=3,type="p",pch=1)
    lines(rep(i,2),c(prem.matches$Pd[i],prem.matches$outcome.final[i]),type="l",lty=2,col="green")
  } else {
    lines(prem.matches$id[i],prem.matches$outcome.final[i],col=4,type="p",pch=2)
    lines(rep(i,2),c(prem.matches$Pa[i],prem.matches$outcome.final[i]),type="l",lty=2,col="blue")
  }
}
axis(1,at=prem.matches$id,labels=paste(prem.matches$team1,prem.matches$team2,sep=" v "),las=2,cex.axis=0.65)

Outcomes are hollowed variants of their predicted probabilities; red empty circles are home wins, marked on at 1, green empty circles are draws, marked on at 0.5, and blue empty triangles are away wins. All are linked to their associated probability. This drawing doesn’t quite reflect actual forecast errors since each outcome is 1 if it happened, but nonetheless illustrates the weekend’s outcomes.

Chelsea being held by Burnley in a controversial match, and Swansea beating Man United were the stand-out results last weekend.

Next, our Championship forecasts:

champ.matches <- forecast.outcomes[forecast.outcomes$division=="English Championship",]
champ.matches$id <- 1:NROW(champ.matches)
par(mar=c(9,4,4,5)+.1)
plot(champ.matches$id,champ.matches$outcome.forc,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)
abline(h=0.4,lty=3)
for(i in 1:NROW(champ.matches)) {
  if(champ.matches$outcome.final[i]==1) {
    lines(champ.matches$id[i],champ.matches$outcome.final[i],col=2,type="p",pch=0)
    lines(rep(i,2),c(champ.matches$Ph[i],champ.matches$outcome.final[i]),type="l",lty=2,col="red")
  } else if (champ.matches$outcome.final[i]==0.5) {
    lines(champ.matches$id[i],champ.matches$outcome.final[i],col=3,type="p",pch=1)
    lines(rep(i,2),c(champ.matches$Pd[i],champ.matches$outcome.final[i]),type="l",lty=2,col="green")
  } else {
    lines(champ.matches$id[i],champ.matches$outcome.final[i],col=4,type="p",pch=2)
    lines(rep(i,2),c(champ.matches$Pa[i],champ.matches$outcome.final[i]),type="l",lty=2,col="blue")
  }
}
axis(1,at=champ.matches$id,labels=paste(champ.matches$team1,champ.matches$team2,sep=" v "),las=2,cex.axis=0.65)

Next, our League One forecasts:

lg1.matches <- forecast.outcomes[forecast.outcomes$division=="English League One",]
lg1.matches$id <- 1:NROW(lg1.matches)
par(mar=c(9,4,4,5)+.1)
plot(lg1.matches$id,lg1.matches$outcome.forc,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)
abline(h=0.4,lty=3)
for(i in 1:NROW(lg1.matches)) {
  if(lg1.matches$outcome.final[i]==1) {
    lines(lg1.matches$id[i],lg1.matches$outcome.final[i],col=2,type="p",pch=0)
    lines(rep(i,2),c(lg1.matches$Ph[i],lg1.matches$outcome.final[i]),type="l",lty=2,col="red")
  } else if (lg1.matches$outcome.final[i]==0.5) {
    lines(lg1.matches$id[i],lg1.matches$outcome.final[i],col=3,type="p",pch=1)
    lines(rep(i,2),c(lg1.matches$Pd[i],lg1.matches$outcome.final[i]),type="l",lty=2,col="green")
  } else {
    lines(lg1.matches$id[i],lg1.matches$outcome.final[i],col=4,type="p",pch=2)
    lines(rep(i,2),c(lg1.matches$Pa[i],lg1.matches$outcome.final[i]),type="l",lty=2,col="blue")
  }
}
axis(1,at=lg1.matches$id,labels=paste(lg1.matches$team1,lg1.matches$team2,sep=" v "),las=2,cex.axis=0.65)

Next, our League Two forecasts:

lg2.matches <- forecast.outcomes[forecast.outcomes$division=="English League Two",]
lg2.matches$id <- 1:NROW(lg2.matches)
par(mar=c(9,4,4,5)+.1)
plot(lg2.matches$id,lg2.matches$outcome.forc,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)
abline(h=0.4,lty=3)
for(i in 1:NROW(lg2.matches)) {
  if(lg2.matches$outcome.final[i]==1) {
    lines(lg2.matches$id[i],lg2.matches$outcome.final[i],col=2,type="p",pch=0)
    lines(rep(i,2),c(lg2.matches$Ph[i],lg2.matches$outcome.final[i]),type="l",lty=2,col="red")
  } else if (lg2.matches$outcome.final[i]==0.5) {
    lines(lg2.matches$id[i],lg2.matches$outcome.final[i],col=3,type="p",pch=1)
    lines(rep(i,2),c(lg2.matches$Pd[i],lg2.matches$outcome.final[i]),type="l",lty=2,col="green")
  } else {
    lines(lg2.matches$id[i],lg2.matches$outcome.final[i],col=4,type="p",pch=2)
    lines(rep(i,2),c(lg2.matches$Pa[i],lg2.matches$outcome.final[i]),type="l",lty=2,col="blue")
  }
}
axis(1,at=lg2.matches$id,labels=paste(lg2.matches$team1,lg2.matches$team2,sep=" v "),las=2,cex.axis=0.65)

Next, our Football Conference forecasts:

conf.matches <- forecast.outcomes[forecast.outcomes$division=="Football Conference",]
conf.matches$id <- 1:NROW(conf.matches)
par(mar=c(9,4,4,5)+.1)
plot(conf.matches$id,conf.matches$outcome.forc,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)
abline(h=0.4,lty=3)
for(i in 1:NROW(conf.matches)) {
  if(conf.matches$outcome.final[i]==1) {
    lines(conf.matches$id[i],conf.matches$outcome.final[i],col=2,type="p",pch=0)
    lines(rep(i,2),c(conf.matches$Ph[i],conf.matches$outcome.final[i]),type="l",lty=2,col="red")
  } else if (conf.matches$outcome.final[i]==0.5) {
    lines(conf.matches$id[i],conf.matches$outcome.final[i],col=3,type="p",pch=1)
    lines(rep(i,2),c(conf.matches$Pd[i],conf.matches$outcome.final[i]),type="l",lty=2,col="green")
  } else {
    lines(conf.matches$id[i],conf.matches$outcome.final[i],col=4,type="p",pch=2)
    lines(rep(i,2),c(conf.matches$Pa[i],conf.matches$outcome.final[i]),type="l",lty=2,col="blue")
  }
}
axis(1,at=conf.matches$id,labels=paste(conf.matches$team1,conf.matches$team2,sep=" v "),las=2,cex.axis=0.65)

Tabular Version

Numerically it is important to evaluate forecast errors.

For the OLS model:

forecast.outcomes$error <- forecast.outcomes$outcome.final - forecast.outcomes$outcome.forc
forecast.outcomes$error2 <- forecast.outcomes$error^2
forecast.outcomes$aerror <- abs(forecast.outcomes$error)
#summary(forecast.outcomes[forecast.outcomes$tier<=5,c("error","error2","aerror")])
summary(forecast.outcomes[,c("error","error2","aerror")])

##      error              error2             aerror       
##  Min.   :-0.77673   Min.   :0.001067   Min.   :0.03266  
##  1st Qu.:-0.48222   1st Qu.:0.077311   1st Qu.:0.27805  
##  Median : 0.26373   Median :0.166673   Median :0.40826  
##  Mean   : 0.02556   Mean   :0.187540   Mean   :0.39650  
##  3rd Qu.: 0.40236   3rd Qu.:0.286225   3rd Qu.:0.53500  
##  Max.   : 0.60602   Max.   :0.603310   Max.   :0.77673

For the ordered logit we must consider the three outcomes distinctly; for the home win:

forecast.outcomes$error.h <- forecast.outcomes$outcome.final - forecast.outcomes$Ph
forecast.outcomes$error.d <- as.numeric(forecast.outcomes$outcome.final==0.5) - forecast.outcomes$Pd
forecast.outcomes$error.a <- as.numeric(forecast.outcomes$outcome.final==0) - forecast.outcomes$Pa
forecast.outcomes$error.h2 <- forecast.outcomes$error.h^2
forecast.outcomes$error.d2 <- forecast.outcomes$error.d^2
forecast.outcomes$error.a2 <- forecast.outcomes$error.a^2
forecast.outcomes$aerror.h <- abs(forecast.outcomes$error.h)
forecast.outcomes$aerror.d <- abs(forecast.outcomes$error.d)
forecast.outcomes$aerror.a <- abs(forecast.outcomes$error.a)
#summary(forecast.outcomes[forecast.outcomes$tier<=5,c("error","error2","aerror")])
summary(forecast.outcomes[,c("error.h","error.d","error.a")])

##     error.h           error.d            error.a         
##  Min.   :-0.6867   Min.   :-0.28214   Min.   :-0.479309  
##  1st Qu.:-0.3330   1st Qu.:-0.27744   1st Qu.:-0.290193  
##  Median : 0.3649   Median :-0.26675   Median :-0.215367  
##  Mean   : 0.1576   Mean   :-0.07427   Mean   : 0.009257  
##  3rd Qu.: 0.5365   3rd Qu.:-0.21596   3rd Qu.: 0.614371  
##  Max.   : 0.7460   Max.   : 0.81457   Max.   : 0.874897

Considering here just the mean errors, the forecasts for home wins were biased downward much more than those for either the draw or away win; a positive forecast error suggests that the event occurs more often than the model predicts. Until we consider more forecasts, it is difficult to say whether this is simply an artifact of one particular week.

We can consider also, by division, forecast errors for our linear regression model:

library(knitr)
aggs <- aggregate(forecast.outcomes[,c("error","error2","aerror")],
          by=list(forecast.outcomes$division),FUN=mean,na.rm=T)
kable(aggs[c(4,1,2,3,5),])

	Group.1	error	error2	aerror
4	English League One	0.0779550	0.2030334	0.4121697
1	Conference North	-0.6022717	0.3627312	0.6022717
2	Conference South	0.4762125	0.2267783	0.4762125
3	English Championship	0.0322989	0.1961428	0.4079146
5	English League Two	0.0319687	0.2071820	0.4309823

The error column is the mean forecast error, the error2 column is the mean squared forecast error, and the aerror column is the absolute forecast error.

We do the same for each outcome for the ordered probit model:

library(knitr)
aggs <- aggregate(forecast.outcomes[,c("error.h","error.h2","aerror.h")],
          by=list(forecast.outcomes$division),FUN=mean,na.rm=T)
colnames(aggs) <- gsub("Group.1","Home win",colnames(aggs))
kable(aggs[c(4,1,2,3,5),])

	Home win	error.h	error.h2	aerror.h
4	English League One	0.2087273	0.2459764	0.4550829
1	Conference North	-0.4758152	0.2264001	0.4758152
2	Conference South	0.6244175	0.3898972	0.6244175
3	English Championship	0.1613308	0.2212022	0.4397543
5	English League Two	0.1711974	0.2354336	0.4445498

aggs <- aggregate(forecast.outcomes[,c("error.d","error.d2","aerror.d")],
          by=list(forecast.outcomes$division),FUN=mean,na.rm=T)
colnames(aggs) <- gsub("Group.1","Draw",colnames(aggs))
kable(aggs[c(4,1,2,3,5),])

	Draw	error.d	error.d2	aerror.d
4	English League One	-0.0819895	0.1534324	0.3444316
1	Conference North	-0.2675369	0.0715760	0.2675369
2	Conference South	-0.2818646	0.0794476	0.2818646
3	English Championship	-0.1302506	0.1264693	0.3158312
5	English League Two	-0.1360890	0.1368623	0.3334704

aggs <- aggregate(forecast.outcomes[,c("error.a","error.a2","aerror.a")],
          by=list(forecast.outcomes$division),FUN=mean,na.rm=T)
colnames(aggs) <- gsub("Group.1","Away win",colnames(aggs))
kable(aggs[c(4,1,2,3,5),])

	Away win	error.a	error.a2	aerror.a
4	English League One	-0.0385024	0.1986033	0.3891609
1	Conference North	0.7433520	0.5525723	0.7433520
2	Conference South	-0.3425529	0.1173425	0.3425529
3	English Championship	0.0314198	0.2050055	0.3899978
5	English League Two	0.0315583	0.2141886	0.4193710

Finally, we list all the forecasts again with outcomes:

kable(forecast.outcomes[order(forecast.outcomes$date,forecast.outcomes$division),
                       c("date","division","team1","goals1","goals2","team2",
                         "outcome.forc","Ph","Pd","Pa","outcome.final","error","error2","aerror")],digits=3)

	date	division	team1	goals1	goals2	team2	outcome.forc	Ph	Pd	Pa	outcome.final	error	error2	aerror
29	2015-02-20	English Championship	Wigan	0	3	Charlton	0.556	0.411	0.279	0.310	0.0	-0.556	0.309	0.556
64	2015-02-20	English League Two	Accrington	1	1	Cheltenham	0.665	0.540	0.249	0.211	0.5	-0.165	0.027	0.165
76	2015-02-21	Conference South	Farnborough	2	1	Eastbourne	0.524	0.376	0.282	0.343	1.0	0.476	0.227	0.476
11	2015-02-21	English Championship	Blackburn	1	1	Blackpool	0.730	0.626	0.216	0.158	0.5	-0.230	0.053	0.230
13	2015-02-21	English Championship	Brighton	4	3	Birmingham	0.587	0.450	0.273	0.277	1.0	0.413	0.170	0.413
15	2015-02-21	English Championship	Huddersfield	0	0	Cardiff	0.576	0.434	0.276	0.291	0.5	-0.076	0.006	0.076
18	2015-02-21	English Championship	Nottm Forest	4	1	Bolton	0.560	0.419	0.278	0.303	1.0	0.440	0.194	0.440
20	2015-02-21	English Championship	Brentford	3	1	Bournemouth	0.459	0.307	0.279	0.415	1.0	0.541	0.292	0.541
21	2015-02-21	English Championship	Middlesbro	0	1	Leeds	0.733	0.629	0.215	0.156	0.0	-0.733	0.538	0.733
22	2015-02-21	English Championship	Wolves	5	0	Rotherham	0.615	0.476	0.267	0.256	1.0	0.385	0.148	0.385
23	2015-02-21	English Championship	Watford	0	3	Norwich	0.602	0.466	0.270	0.264	0.0	-0.602	0.363	0.602
25	2015-02-21	English Championship	Derby	3	2	Sheff Wed	0.725	0.625	0.217	0.159	1.0	0.275	0.076	0.275
26	2015-02-21	English Championship	Millwall	0	0	Fulham	0.542	0.394	0.281	0.325	0.5	-0.042	0.002	0.042
32	2015-02-21	English Championship	Ipswich	0	1	Reading	0.698	0.587	0.232	0.181	0.0	-0.698	0.487	0.698
35	2015-02-21	English League One	Bradford	1	1	Walsall	0.651	0.521	0.255	0.224	0.5	-0.151	0.023	0.151
36	2015-02-21	English League One	Sheff Utd	2	2	Coventry	0.699	0.583	0.234	0.183	0.5	-0.199	0.040	0.199
37	2015-02-21	English League One	MK Dons	3	0	Peterborough	0.736	0.635	0.212	0.153	1.0	0.264	0.070	0.264
39	2015-02-21	English League One	Barnsley	2	0	Crewe	0.586	0.452	0.273	0.275	1.0	0.414	0.171	0.414
41	2015-02-21	English League One	Leyton Orient	3	0	Oldham	0.526	0.382	0.282	0.337	1.0	0.474	0.224	0.474
42	2015-02-21	English League One	Colchester	3	2	Bristol C	0.394	0.254	0.267	0.479	1.0	0.606	0.367	0.606
43	2015-02-21	English League One	Preston	2	0	Scunthorpe	0.634	0.504	0.260	0.236	1.0	0.366	0.134	0.366
44	2015-02-21	English League One	Port Vale	3	0	Doncaster	0.521	0.371	0.282	0.347	1.0	0.479	0.230	0.479
47	2015-02-21	English League One	Swindon	1	2	Crawley	0.777	0.687	0.188	0.125	0.0	-0.777	0.603	0.777
48	2015-02-21	English League One	Rochdale	1	0	Chesterfield	0.609	0.479	0.267	0.254	1.0	0.391	0.153	0.391
49	2015-02-21	English League One	Yeovil	2	2	Gillingham	0.467	0.318	0.280	0.402	0.5	0.033	0.001	0.033
50	2015-02-21	English League One	Fleetwood	2	1	Notts Co	0.612	0.473	0.268	0.259	1.0	0.388	0.151	0.388
51	2015-02-21	English League Two	AFC W’bledon	3	2	Luton	0.551	0.404	0.280	0.316	1.0	0.449	0.202	0.449
52	2015-02-21	English League Two	Dag & Red	1	3	Burton	0.498	0.349	0.282	0.369	0.0	-0.498	0.248	0.498
53	2015-02-21	English League Two	Bury	1	0	Hartlepool	0.714	0.609	0.224	0.168	1.0	0.286	0.082	0.286
54	2015-02-21	English League Two	Exeter	1	3	Plymouth	0.496	0.342	0.282	0.376	0.0	-0.496	0.246	0.496
55	2015-02-21	English League Two	Stevenage	4	2	Southend	0.560	0.415	0.279	0.306	1.0	0.440	0.194	0.440
56	2015-02-21	English League Two	Newport Co	0	1	Morecambe	0.635	0.498	0.262	0.240	0.0	-0.635	0.403	0.635
57	2015-02-21	English League Two	Tranmere	2	1	Shrewsbury	0.443	0.295	0.277	0.428	1.0	0.557	0.310	0.557
58	2015-02-21	English League Two	Oxford	3	0	Mansfield	0.603	0.465	0.270	0.265	1.0	0.397	0.158	0.397
59	2015-02-21	English League Two	Carlisle	2	3	Wycombe	0.480	0.333	0.281	0.386	0.0	-0.480	0.230	0.480
61	2015-02-21	English League Two	Cambridge U	2	6	Portsmouth	0.620	0.488	0.264	0.247	0.0	-0.620	0.384	0.620
62	2015-02-21	English League Two	Northampton	3	0	York	0.598	0.463	0.270	0.266	1.0	0.402	0.162	0.402
1	2015-02-21	English Premier	Sunderland	0	0	West Brom	0.537	0.389	0.281	0.330	0.5	-0.037	0.001	0.037
2	2015-02-21	English Premier	Man City	5	0	Newcastle	0.730	0.627	0.216	0.157	1.0	0.270	0.073	0.270
3	2015-02-21	English Premier	Hull	2	1	QPR	0.593	0.457	0.272	0.272	1.0	0.407	0.165	0.407
5	2015-02-21	English Premier	C Palace	1	2	Arsenal	0.435	0.285	0.275	0.440	0.0	-0.435	0.189	0.435
7	2015-02-21	English Premier	Swansea	2	1	Man Utd	0.442	0.290	0.276	0.434	1.0	0.558	0.311	0.558
8	2015-02-21	English Premier	Aston Villa	1	2	Stoke	0.482	0.330	0.281	0.389	0.0	-0.482	0.233	0.482
10	2015-02-21	English Premier	Chelsea	1	1	Burnley	0.778	0.692	0.185	0.122	0.5	-0.278	0.077	0.278
81	2015-02-21	FA Trophy	Wrexham	2	1	Torquay	0.567	0.419	0.278	0.303	1.0	0.433	0.188	0.433
65	2015-02-21	Football Conference	Bristol R	1	0	Altrincham	0.692	0.577	0.236	0.187	1.0	0.308	0.095	0.308
66	2015-02-21	Football Conference	Halifax	1	0	Braintree	0.605	0.467	0.269	0.263	1.0	0.395	0.156	0.395
67	2015-02-21	Football Conference	Dover	2	2	Southport	0.725	0.622	0.218	0.160	0.5	-0.225	0.051	0.225
68	2015-02-21	Football Conference	Alfreton	2	3	Aldershot	0.568	0.424	0.277	0.299	0.0	-0.568	0.323	0.568
69	2015-02-21	Football Conference	Barnet	1	3	Grimsby	0.620	0.488	0.264	0.247	0.0	-0.620	0.384	0.620
70	2015-02-21	Football Conference	Forest Green	3	0	Telford	0.730	0.626	0.216	0.158	1.0	0.270	0.073	0.270
73	2015-02-21	Football Conference	Nuneaton	0	0	Kidderminster	0.434	0.287	0.275	0.438	0.5	0.066	0.004	0.066
74	2015-02-21	Football Conference	Chester	0	1	Eastleigh	0.551	0.407	0.279	0.313	0.0	-0.551	0.304	0.551
4	2015-02-22	English Premier	Tottenham	2	2	West Ham	0.615	0.478	0.267	0.255	0.5	-0.115	0.013	0.115
6	2015-02-22	English Premier	Everton	2	2	Leicester	0.657	0.533	0.252	0.215	0.5	-0.157	0.025	0.157
9	2015-02-22	English Premier	Southampton	0	2	Liverpool	0.578	0.440	0.275	0.285	0.0	-0.578	0.334	0.578
77	2015-02-24	Conference North	Stockport	0	1	Barrow	0.602	0.476	0.268	0.257	0.0	-0.602	0.363	0.602
12	2015-02-24	English Championship	Millwall	1	3	Sheff Wed	0.520	0.369	0.282	0.349	0.0	-0.520	0.270	0.520
14	2015-02-24	English Championship	Middlesbro	1	0	Bolton	0.719	0.616	0.221	0.164	1.0	0.281	0.079	0.281
16	2015-02-24	English Championship	Brentford	4	0	Blackpool	0.708	0.601	0.227	0.172	1.0	0.292	0.085	0.292
17	2015-02-24	English Championship	Wigan	0	1	Cardiff	0.535	0.388	0.281	0.331	0.0	-0.535	0.286	0.535
19	2015-02-24	English Championship	Derby	2	0	Charlton	0.747	0.651	0.205	0.144	1.0	0.253	0.064	0.253
27	2015-02-24	English Championship	Ipswich	4	2	Birmingham	0.691	0.577	0.236	0.187	1.0	0.309	0.096	0.309
28	2015-02-24	English Championship	Brighton	2	0	Leeds	0.567	0.424	0.277	0.298	1.0	0.433	0.188	0.433
30	2015-02-24	English Championship	Wolves	3	0	Fulham	0.637	0.504	0.260	0.236	1.0	0.363	0.132	0.363
31	2015-02-24	English Championship	Watford	3	0	Rotherham	0.711	0.607	0.224	0.169	1.0	0.289	0.084	0.289
33	2015-02-24	English Championship	Blackburn	1	2	Norwich	0.516	0.366	0.282	0.352	0.0	-0.516	0.266	0.516
34	2015-02-24	English Championship	Huddersfield	3	0	Reading	0.576	0.434	0.276	0.290	1.0	0.424	0.179	0.424
38	2015-02-24	English League One	Swindon	2	1	Bradford	0.592	0.461	0.271	0.268	1.0	0.408	0.167	0.408
40	2015-02-24	English League One	Rochdale	1	2	Sheff Utd	0.538	0.399	0.280	0.321	0.0	-0.538	0.290	0.538
45	2015-02-24	English League One	Preston	1	0	Walsall	0.656	0.528	0.253	0.219	1.0	0.344	0.118	0.344
46	2015-02-24	English League One	Doncaster	1	3	Bristol C	0.489	0.339	0.282	0.380	0.0	-0.489	0.239	0.489
78	2015-02-24	English League One	Scunthorpe	0	1	Barnsley	0.687	0.566	0.240	0.194	0.0	-0.687	0.472	0.687
60	2015-02-24	English League Two	Accrington	1	0	Burton	0.462	0.311	0.279	0.410	1.0	0.538	0.290	0.538
63	2015-02-24	English League Two	Portsmouth	3	2	Tranmere	0.598	0.456	0.272	0.272	1.0	0.402	0.162	0.402
80	2015-02-24	English League Two	Cambridge U	0	0	AFC W’bledon	0.598	0.464	0.270	0.266	0.5	-0.098	0.010	0.098
71	2015-02-24	Football Conference	Gateshead	2	0	Kidderminster	0.621	0.487	0.265	0.249	1.0	0.379	0.143	0.379
72	2015-02-24	Football Conference	Grimsby	1	0	Telford	0.739	0.644	0.208	0.148	1.0	0.261	0.068	0.261
75	2015-02-24	Football Conference	Bristol R	2	1	Braintree	0.649	0.517	0.257	0.227	1.0	0.351	0.123	0.351
79	2015-02-24	Football Conference	Wrexham	0	0	Forest Green	0.533	0.385	0.281	0.333	0.5	-0.033	0.001	0.033
24	2015-02-25	English Championship	Nottm Forest	2	1	Bournemouth	0.416	0.268	0.271	0.461	1.0	0.584	0.341	0.584

Update: Forecasts and Outcomes, February 20–26 2015

J James Reade

27/02/2015

Outcomes

Tabular Version