# Elo ratings in football

I have previously written about some statistical methods for rating football teams and to predict the result of future matches. One was the last squares method and another was the Poisson regression method. None of these methods make good enough predictions. One problem with them is that they don’t incorporate a time perspective. Matches played a year ago is given equal importance as the most recent one. This could however be incorporated by weighing the the older matches less than newer matches. One other problem that I mentioned in the second post about Poisson regression is that teams are treated as categoricals which makes it hard to model the fact that a team’s ability changes over time.

One different kind of method that has been employed a lot in the recent years is the Elo rating system, which were originally developed for rating chess players. The method is rather simple, but I will not explain it in detail here since there are many good explanations of it elsewhere. Wikipedia has a very thorough coverage. The basic principle is that the difference in ratings between the two opposing teams provide a prediction for the result each game. The rating is then updated based on how the teams perform. If a team performs better than expected the rating increases, if they perform worse than expected the rating decrease. How much the rating changes depends on an update factor (often referred to as the K-factor).

Chess and football are of course different in many ways so the method for rating chess players is not directly suitable for rating football teams. The relative simplicity of the Elo system makes it easy to tweak and adjust to better fit football by incorporating things like home field advantage and goal difference. There are many sites around the Internet who provide different variants of Elo ratings, like the World Football Elo Ratings for national teams and Club Elo and Euro Club Index for club teams. FIFA even uses its own Elo system in its Womans World Ranking.

There has even been some research into different football rating systems. A paper titled The predictive power of ranking systems in association football (pdf) by Jan Lasek and others compared different rating systems. Their conclusion was that the different Elo type systems in general were better at predicting match outcomes than other types of rating systems.

I figured I wanted to implement a simple Elo rating system for rating football teams. There is already a package in R, PlayerRatings, which implements several different rating systems based on Elo. In my simple implementation there is no adjustment for goal difference, but I have support for home field advantage. All teams start with an initial rating of 1500. Here is what I got when I calculated the ratings for Premier League in November 2012 based on data going back to 1993. I used an update factor 24 without any home field advantage. There is no particular reason for this as I did this mostly as a proof of concept.

Rating (November 2012)
Man United 1807
Man City 1767
Chelsea 1696
Arsenal 1658
Tottenham 1645
Everton 1640
Newcastle 1613
Fulham 1591
Liverpool 1567
West Brom 1562
Leeds 1552
Wigan 1543
Swansea 1526
Sunderland 1524
Stoke 1521
Middlesboro 1516
Norwich 1509
Aston Villa 1498
West Ham 1494
Birmingham 1493
Blackpool 1483
Ipswich 1481
Bolton 1479
Charlton 1470
Sheffield United 1458
Blackburn 1450
Sheffield Weds 1447
Coventry 1447
Middlesbrough 1443
QPR 1440
Barnsley 1439
Portsmouth 1438
Southampton 1437
Oldham 1436
Crystal Palace 1433
Leicester 1433
Nott’m Forest 1430
Hull 1422
Burnley 1418
Wolves 1414
Wimbledon 1413
Watford 1411
Swindon 1404
Derby 1297

The table seems reasonable I think except for a couple of things. There is a problem related to relegation and promotion. Since I have used data back to 1993 every team who has played in the Premier League is given a rating. If a team is relegated to the Championship, their rating will no longer be updated. We can see that this creates some strange results. Take the two lowest rated teams for example. Derby has not been in the Premier League since the 2007-2008 season. Swindon, which is rated about 100 points higher than Derby, has not played in the Premier League since 1993-1994 season! Swindon now play in the fourth level of the English league system. So the ratings for the teams not in the Premier League should be considered invalid.

Relegation and promotion also creates a problem with inflated ratings. The Elo system is created so that the total number of points in the league should be constant. When a team is promoted they start with an initial rating of 1500, and if they later gets relegated they will probably have lost some of those points to the other teams in the league. In fact, we see that many of the teams with ratings less than 1500 no longer plays in the Premier League. The points they have lost are still in present in the league even though the team isn’t. This means that over time the average ratings of the teams in the league will increase.

The code I have written takes a data frame as input and works “out of the box” with data from football-data.co.uk. If you are going to use it yourself you have to make sure the data is sorted by date as the rating function just loops from top to bottom.

Here is how you can use it:

```dta <- read.csv("yourdata.csv")
elo <- eloRating(data=dta)
print(elo)
```

And here is the code:

```
eloRating <- function(home="HomeTeam", away="AwayTeam", homeGoals="FTHG",
awayGoals="FTAG", data, kfactor=24, initialRating=1500,

#Make a list to hold ratings for all teams
all.teams <- levels(as.factor(union(levels(as.factor(data[[home]])),
levels(as.factor(data[[away]])))))

ratings <- as.list(rep(initialRating, times=length(all.teams)))
names(ratings) <- all.teams

#Loop trough data and update ratings
for (idx in 1:dim(data)[1]){

#get current ratings
homeTeamName <- data[[home]][idx]
awayTeamName <- data[[away]][idx]
awayTeamRating <- as.numeric(ratings[awayTeamName])

#calculate expected outcome
expectedHome <- 1 / (1 + 10^((awayTeamRating - homeTeamRating)/400))
expectedAway <- 1 - expectedHome

#Observed outcome
goalDiff <- data[[homeGoals]][idx] - data[[awayGoals]][idx]
if (goalDiff == 0){
resultHome <- 0.5
resultAway <- 0.5
}
else if (goalDiff < 0){
resultHome <- 0
resultAway <- 1
}
else if (goalDiff > 0){
resultHome <- 1
resultAway <- 0
}

#update ratings
ratings[homeTeamName] <- as.numeric(ratings[homeTeamName]) + kfactor*(resultHome - expectedHome)
ratings[awayTeamName] <- as.numeric(ratings[awayTeamName]) + kfactor*(resultAway - expectedAway)
}

#prepare output
ratingsOut <- as.numeric(ratings)
names(ratingsOut) <- names(ratings)
ratingsOut <- sort(ratingsOut, decreasing=TRUE)

return(ratingsOut)
}
```