This document summarises the forecast outcomes for the second 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")
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 FA Cup forecasts:

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

The weekend was another one for surprising results in the FA Cup; Blackburn beat Stoke, Bradford beat Sunderland, and to a lesser extent, Reading beat Derby. Furthermore, at least one game expected to be tight was anything but, with West Brom beating West Ham 4-0. The favourites did progress between Arsenal and Middlesbrough, Liverpool and Crystal Palace, Aston Villa and Leicester, and Man United at Preston. Additionally, of course, bookmakers suffered forecast errors this week.

It is worth noting that one of the issues I have identified with these forecasts previously is that they appear somewhat conservative, but when unexpected results do occur, this means forecast errors are smaller.

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)

In the Championship, perhaps the most surprising result was Bournemouth being held at home by Huddersfield.

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)

League One added to the theme of unexpected results with all of the top three teams (Swindon, Bristol City and MK Dons) losing.

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)

In League Two, again by and large results followed predictions, with the exceptions of Hartlepool’s win over Northampton, and away wins for Carlisle, Plymouth and Dagenham and Redbridge.

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.   :-0.781856   Min.   :0.0000029   Min.   :0.001706  
##  1st Qu.:-0.491639   1st Qu.:0.0935982   1st Qu.:0.305933  
##  Median : 0.104093   Median :0.2057101   Median :0.453510  
##  Mean   :-0.004839   Mean   :0.2152598   Mean   :0.419424  
##  3rd Qu.: 0.419604   3rd Qu.:0.3115055   3rd Qu.:0.558110  
##  Max.   : 0.620529   Max.   :0.6112989   Max.   :0.781856

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.   :-0.6895   Min.   :-0.28221   Min.   :-0.49842  
##  1st Qu.:-0.3408   1st Qu.:-0.27911   1st Qu.:-0.34131  
##  Median : 0.2487   Median :-0.25556   Median :-0.19914  
##  Mean   : 0.1286   Mean   :-0.09274   Mean   : 0.04749  
##  3rd Qu.: 0.5624   3rd Qu.:-0.22814   3rd Qu.: 0.62262  
##  Max.   : 0.7603   Max.   : 0.80956   Max.   : 0.87636

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:

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 FA Cup	0.1138051	0.2680492	0.4982060
1	Conference North	0.3524609	0.1437739	0.3524609
2	Conference South	0.2623857	0.0688462	0.2623857
3	English Championship	-0.0447275	0.1255953	0.2859019
5	English League One	-0.0124267	0.2639907	0.4969437

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.

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
102	2015-02-14	Conference North	Stockport	2	0	Hyde	0.793	0.721	0.171	0.108	1.0	0.207	0.043	0.207
103	2015-02-14	Conference North	Boston Utd	5	0	Bradford PA	0.691	0.579	0.235	0.186	1.0	0.309	0.095	0.309
1	2015-02-14	English Championship	Blackpool	4	4	Nottm Forest	0.480	0.333	0.281	0.385	0.5	0.020	0.000	0.020
6	2015-02-14	English Championship	Norwich	2	0	Wolves	0.650	0.532	0.252	0.216	1.0	0.350	0.123	0.350
9	2015-02-14	English Championship	Bolton	3	4	Watford	0.494	0.346	0.282	0.372	0.0	-0.494	0.244	0.494
12	2015-02-14	English Championship	Sheff Wed	0	0	Brighton	0.573	0.424	0.277	0.298	0.5	-0.073	0.005	0.073
14	2015-02-14	English Championship	Bournemouth	1	1	Huddersfield	0.771	0.682	0.190	0.127	0.5	-0.271	0.073	0.271
18	2015-02-14	English Championship	Leeds	1	0	Millwall	0.652	0.525	0.254	0.221	1.0	0.348	0.121	0.348
20	2015-02-14	English Championship	Charlton	3	0	Brentford	0.479	0.330	0.281	0.389	1.0	0.521	0.271	0.521
22	2015-02-14	English Championship	Fulham	1	2	Ipswich	0.473	0.324	0.281	0.395	0.0	-0.473	0.223	0.473
123	2015-02-14	English FA Cup	Blackburn	4	1	Stoke	0.439	0.286	0.275	0.439	1.0	0.561	0.314	0.561
126	2015-02-14	English FA Cup	C Palace	1	2	Liverpool	0.493	0.341	0.282	0.377	0.0	-0.493	0.243	0.493
128	2015-02-14	English FA Cup	Derby	1	2	Reading	0.746	0.648	0.206	0.145	0.0	-0.746	0.556	0.746
133	2015-02-14	English FA Cup	West Brom	4	0	West Ham	0.498	0.347	0.282	0.370	1.0	0.502	0.252	0.502
27	2015-02-14	English League One	Gillingham	4	2	MK Dons	0.466	0.319	0.280	0.400	1.0	0.534	0.285	0.534
30	2015-02-14	English League One	Peterborough	2	1	Rochdale	0.505	0.353	0.282	0.365	1.0	0.495	0.245	0.495
32	2015-02-14	English League One	Chesterfield	2	3	Leyton Orient	0.702	0.585	0.233	0.182	0.0	-0.702	0.493	0.702
33	2015-02-14	English League One	Doncaster	3	0	Yeovil	0.690	0.570	0.239	0.191	1.0	0.310	0.096	0.310
36	2015-02-14	English League One	Scunthorpe	3	1	Swindon	0.487	0.339	0.282	0.379	1.0	0.513	0.264	0.513
37	2015-02-14	English League One	Walsall	0	1	Port Vale	0.664	0.539	0.250	0.212	0.0	-0.664	0.441	0.664
39	2015-02-14	English League One	Bristol C	1	3	Sheff Utd	0.671	0.554	0.245	0.201	0.0	-0.671	0.450	0.671
42	2015-02-14	English League One	Crewe	2	0	Fleetwood	0.553	0.405	0.280	0.315	1.0	0.447	0.200	0.447
43	2015-02-14	English League One	Oldham	0	1	Colchester	0.687	0.569	0.239	0.192	0.0	-0.687	0.472	0.687
45	2015-02-14	English League One	Crawley	5	1	Barnsley	0.474	0.322	0.281	0.398	1.0	0.526	0.276	0.526
49	2015-02-14	English League Two	Burton	2	0	Oxford	0.681	0.557	0.244	0.199	1.0	0.319	0.102	0.319
52	2015-02-14	English League Two	Hartlepool	1	3	Stevenage	0.492	0.343	0.282	0.375	0.0	-0.492	0.242	0.492
55	2015-02-14	English League Two	Luton	1	0	Carlisle	0.711	0.598	0.228	0.174	1.0	0.289	0.083	0.289
57	2015-02-14	English League Two	Mansfield	1	1	Northampton	0.502	0.351	0.282	0.367	0.5	-0.002	0.000	0.002
60	2015-02-14	English League Two	Cheltenham	1	2	Bury	0.479	0.329	0.281	0.390	0.0	-0.479	0.229	0.479
61	2015-02-14	English League Two	York	2	0	Tranmere	0.562	0.416	0.279	0.305	1.0	0.438	0.192	0.438
64	2015-02-14	English League Two	Wycombe	1	2	Newport Co	0.648	0.522	0.255	0.223	0.0	-0.648	0.420	0.648
65	2015-02-14	English League Two	Shrewsbury	2	0	AFC W’bledon	0.678	0.558	0.243	0.199	1.0	0.322	0.104	0.322
68	2015-02-14	English League Two	Portsmouth	1	0	Exeter	0.589	0.449	0.273	0.278	1.0	0.411	0.169	0.411
69	2015-02-14	English League Two	Southend	1	2	Accrington	0.694	0.578	0.236	0.186	0.0	-0.694	0.481	0.694
71	2015-02-14	English League Two	Plymouth	2	0	Cambridge U	0.582	0.437	0.275	0.287	1.0	0.418	0.175	0.418
73	2015-02-14	Football Conference	Altrincham	2	2	Forest Green	0.533	0.385	0.281	0.333	0.5	-0.033	0.001	0.033
76	2015-02-14	Football Conference	Aldershot	2	1	Welling	0.580	0.439	0.275	0.286	1.0	0.420	0.176	0.420
77	2015-02-14	Football Conference	Kidderminster	1	1	Woking	0.548	0.398	0.280	0.321	0.5	-0.048	0.002	0.048
79	2015-02-14	Football Conference	Halifax	3	2	Dover	0.554	0.406	0.280	0.314	1.0	0.446	0.199	0.446
82	2015-02-14	Football Conference	Southport	1	1	Macclesfield	0.411	0.267	0.271	0.463	0.5	0.089	0.008	0.089
83	2015-02-14	Football Conference	Eastleigh	1	2	Torquay	0.623	0.491	0.264	0.245	0.0	-0.623	0.388	0.623
86	2015-02-14	Football Conference	Gateshead	1	2	Nuneaton	0.782	0.690	0.187	0.124	0.0	-0.782	0.611	0.782
87	2015-02-14	Football Conference	Braintree	2	1	Alfreton	0.681	0.570	0.239	0.191	1.0	0.319	0.102	0.319
89	2015-02-14	Football Conference	Wrexham	1	0	Barnet	0.432	0.286	0.275	0.439	1.0	0.568	0.323	0.568
92	2015-02-14	Football Conference	Grimsby	0	1	Bristol R	0.604	0.467	0.270	0.263	0.0	-0.604	0.365	0.604
94	2015-02-14	Football Conference	Lincoln	0	1	Chester	0.621	0.490	0.264	0.246	0.0	-0.621	0.386	0.621
96	2015-02-14	Football Conference	Telford	2	3	Dartford	0.622	0.495	0.263	0.242	0.0	-0.622	0.387	0.622
105	2015-02-14	Ryman Premier	Canvey Isl.	1	1	Grays	0.659	0.553	0.245	0.202	0.5	-0.159	0.025	0.159
119	2015-02-15	English FA Cup	Arsenal	2	0	Middlesbro	0.695	0.583	0.234	0.183	1.0	0.305	0.093	0.305
120	2015-02-15	English FA Cup	Aston Villa	2	1	Leicester	0.540	0.389	0.281	0.329	1.0	0.460	0.211	0.460
124	2015-02-15	English FA Cup	Bradford	2	0	Sunderland	0.379	0.240	0.262	0.498	1.0	0.621	0.385	0.621
53	2015-02-15	English League Two	Morecambe	2	3	Dag & Red	0.598	0.458	0.272	0.271	0.0	-0.598	0.358	0.598
130	2015-02-16	English FA Cup	Preston	1	3	Man Utd	0.299	0.174	0.228	0.598	0.0	-0.299	0.089	0.299
97	2015-02-17	Conference South	Bath City	7	4	Farnborough	0.738	0.627	0.216	0.157	1.0	0.262	0.069	0.262
7	2015-02-17	English Championship	Rotherham	3	3	Derby	0.405	0.260	0.269	0.471	0.5	0.095	0.009	0.095
16	2015-02-17	English Championship	Reading	0	1	Wigan	0.650	0.518	0.256	0.225	0.0	-0.650	0.423	0.650
24	2015-02-17	English Championship	Cardiff	1	1	Blackburn	0.523	0.374	0.282	0.344	0.5	-0.023	0.001	0.023
25	2015-02-17	English League One	Doncaster	2	1	Crewe	0.647	0.525	0.254	0.221	1.0	0.353	0.125	0.353
48	2015-02-17	English League One	Scunthorpe	2	0	Chesterfield	0.555	0.412	0.279	0.309	1.0	0.445	0.198	0.445
113	2015-02-17	English League One	Notts Co	1	2	Sheff Utd	0.492	0.339	0.282	0.380	0.0	-0.492	0.242	0.492
114	2015-02-17	English League One	Bristol C	2	0	Peterborough	0.747	0.641	0.210	0.149	1.0	0.253	0.064	0.253
117	2015-02-17	English League One	Colchester	0	1	MK Dons	0.383	0.245	0.264	0.491	0.0	-0.383	0.146	0.383
111	2015-02-17	English League Two	Mansfield	1	0	Luton	0.449	0.298	0.277	0.425	1.0	0.551	0.303	0.551
99	2015-02-18	Conference North	Bradford PA	2	0	Stockport	0.459	0.310	0.279	0.411	1.0	0.541	0.293	0.541
4	2015-02-18	English Championship	Birmingham	1	1	Middlesbro	0.387	0.242	0.263	0.495	0.5	0.113	0.013	0.113
109	2015-02-18	English League One	Leyton Orient	0	2	Bradford	0.477	0.329	0.281	0.390	0.0	-0.477	0.227	0.477

Update: Forecasts and Outcomes, February 14–21 2015

J James Reade

20/02/2015

Outcomes

Tabular Version