# The Dixon-Coles model, part 4: A trick to speed up estimation

In the previous installments in this series on implementing the Dixon-Coles model I complained a bit about the time it took to estimate the parameters. In the original implementation in part 1 it took about 40 seconds. Now 40 seconds is not much to complain about, there are a whole lot of other models and algorithms that takes much much longer time to fit (for my master’s I had some computations that took several months). Still, I wanted to make a few improvements.

The approach I described in part 3 is quite acceptable, I think, especially since it takes less than a second to fit the model. But still, I wanted to make some improvements to my original implementation.

There are several reasons for the estimation procedure being slow. I used a general purpose optimizer instead of a tailor-made algorithm, and I didn’t provide the optimizer with a function of the derivative of the model likelihood function, nor the function defining the constraint. This means that the optimizer have to estimate the derivatives by doing a lot of evaluations of the two functions with slight changes in the parameters. The most important speed bump, however, is probably due to how I implemented the constraint that all the average of the attack parameters should equal 1.

The alabama package I used relied on a technique called Lagrange multipliers, which is a very general method for constrained optimization. Instead of relying on general constrained optimization procedures, there is a trick commonly used in linear models with sum-to-zero constrained categorical parameters that we also can use here.

There has been some discussion and confusion in the comments about how categorical variables are coded and how R presents the results of the glm function. A thorough discussion of this is best left for another time, but let me explain how the sum-to-zero constraint is implemented in linear models. We will fit the model with this constraint and then make some adjustments later on to get the correct average-is-one constraint.

The sum-to-zero constraint basically says that the sum of all the parameters for a categorical variable must equal to zero:

$$\sum_{i=1} \theta_i = 0$$

If we for example have three levels, we can write out the equation like this:

$$\theta_1 + \theta_2 + \theta_3 = 0$$

If we subtract $$\theta_3$$ and multiply both sides of the equation by minus 1 we get

$$– \theta_1 – \theta_2 = \theta_3$$

Notice how we can write one of the parameters as a simple linear function of the other parameters. We can use this result to construct the design matrix for the categorical variable, incorporating the sum-to-zero constraint (exactly which parameter or level we chose to be a function of the others doesn’t matter, the end results does not differ). Suppose we have the following observations of a three-level categorical variable:

$$\begin{bmatrix} A & A & B & B & C & C \end{bmatrix}^T$$

We can then construct the following design matrix:

$$\begin{bmatrix} 1 & 0 \\ 1 & 0 \\ 0 & 1 \\ 0 & 1 \\ -1 & -1 & \\ -1 & -1 & \end{bmatrix}$$

Notice that we only need two columns (i.e. two variables) to encode the three levels. Since the last parameter is a function of the two other it is redundant. Also notice how the observations in the two last rows, corresponding to the $$C$$ observations, will influence the estimation of all the other parameters for this variable. When the two parameters are estimated, the last parameter can be obtained using the result from above relating the last parameter to all the other.

In the Dixon-Coles paper they used the constraint that the average of the attack parameters should be 1. This is not quite the same as the sum-to-zero constraint, but for prediction, it does not matter exactly which constraint we use. Anyway, I will explain later how we can fix this.

To use this trick in the Dixon-Coles implementation we need to make the following changes to our code from part 1. Obviously the first thing we need to change is how the design matrices in the DCmodelData function is computed. We need four matrices now, since the number of parameters estimated directly is different for the attack and defense parameters. Notice how I chose the last of team that appear last in the team.names vector. The teams get sorted alphabetically, so for the 2011-12 Premier League data this is is Wolves.

DCmodelData <- function(df){

team.names <- unique(c(levels(df$HomeTeam), levels(df$AwayTeam)))

# attack, with sum-to-zero constraint
## home
hm.a <- model.matrix(~ HomeTeam - 1, data=df)
hm.a[df$HomeTeam == team.names[length(team.names)], ] <- -1 hm.a <- hm.a[,1:(length(team.names)-1)] # away am.a <- model.matrix(~ AwayTeam -1, data=df) am.a[df$AwayTeam == team.names[length(team.names)], ] <- -1
am.a <- am.a[,1:(length(team.names)-1)]

# defence, same as before
hm.d <- model.matrix(~ HomeTeam - 1, data=df)
am.d <- model.matrix(~ AwayTeam -1, data=df)

return(list(homeTeamDMa=hm.a, homeTeamDMd=hm.d,
awayTeamDMa=am.a, awayTeamDMd=am.d,
homeGoals=df$FTHG, awayGoals=df$FTAG,
teams=team.names))
}


Some changes to the DCoptimFn function is also needed, so it properly handles the changes we made to the design matrices.

# I don't bother showing the rest of the function
nteams <- length(DCm$teams) attack.p <- matrix(params[3:(nteams+1)], ncol=1) #one column less defence.p <- matrix(params[(nteams+2):length(params)], ncol=1) # need to multiply with the correct matrices lambda <- exp(DCm$homeTeamDMa %*% attack.p + DCm$awayTeamDMd %*% defence.p + home.p) mu <- exp(DCm$awayTeamDMa %*% attack.p + DCm$homeTeamDMd %*% defence.p)  We also need to make a the appropriate adjustments to the vectors with the initial parameter values, so that they have the correct lengths. dcm <- DCmodelData(data) nteams <- length(dcm$teams)

#initial parameter estimates
attack.params <- rep(.1, times=nteams-1) # one less parameter
defence.params <- rep(-0.8, times=nteams)
home.param <- 0.06
rho.init <- 0.03
par.inits <- c(home.param, rho.init, attack.params, defence.params)

#informative names
#skip the last team
names(par.inits) <- c('HOME', 'RHO',
paste('Attack', dcm$teams[1:(nteams-1)], sep='.'), paste('Defence', dcm$teams, sep='.'))


With these changes we can simply use the built-in optim function in R. There is no need for the DCattackConstr function anymore, or a third party package, since we built the constraint right into the design matrices.

res <- optim(par=par.inits, fn=DCoptimFn, DCm=dcm, method='BFGS')


This takes about 6-7 seconds on my laptop, a decent improvement to the 40 seconds it took before. If you take a look at the resulting parameter estimates in res$par you will see that the attack parameter for Wolves is missing. As I explained earlier, this parameter is easy to find. It is also easy to correct all the parameter estimates so that they become the same as if we had a mean-is-one constraint on the attack parameters. This is done by increasing the attack parameters by one, and decreasing the defense parameters by one. The reason it is that simple is that the sum-to-zero constraint is equivalent with a mean-is-zero constraint. parameters <- res$par

#compute Wolves attack parameter
missing.attack <- sum(parameters[3:(nteams+1)]) * -1

#put it in the parameters vector
parameters <- c(parameters[1:(nteams+1)], missing.attack, parameters[(nteams+2):length(parameters)])
names(parameters)[nteams+2] <- paste('Attack.', dcm$teams[nteams], sep='') #increase attack by one parameters[3:(nteams+2)] <- parameters[3:(nteams+2)] + 1 #decrease defence by one parameters[(nteams+3):length(parameters)] <- parameters[(nteams+3):length(parameters)] - 1  # The Dixon-Coles model for predicting football matches in R (part 3) About a moth ago Martin Eastwood of the pena.lt/y blog put up some slides from a talk he gave about predicting football results in R. He presented in detail the independent Poisson regression model, and how to implement it. He also briefly mentioned and showed the bivariate adjustments in the Dixon-Coles model. I was curious about how he had implemented it since I had just finished my own implementation. In the comments he said that he used a two-stage approach, first estimating the attack and defense parameters using the independent Poisson model, and then estimating the rho parameter by it self. This method may be less accurate than fitting the complete model, but it will probably be more accurate than the independent Poisson model. It is without a doubt faster and easier to implement. We start with loading the data, and then making a new data.frame that contains two rows per match, as described in my post about the independent Poisson model. dta <- read.csv('FAPL1112.csv') # Data formated for the independent model # Store in new variable, we need the data in original format later dta.indep <- data.frame(Team=as.factor(c(as.character(dta$HomeTeam),
as.character(dta$AwayTeam))), Opponent=as.factor(c(as.character(dta$AwayTeam),
as.character(dta$HomeTeam))), Goals=c(dta$FTHG, dta$FTAG), Home=c(rep(1, dim(dta)[1]), rep(0, dim(dta)[1])))  Now fit the model: m <- glm(Goals ~ Home + Team + Opponent, data=dta.indep, family=poisson())  Since we now have estimated the attack, defense and home parameters we can use the built-in functions in R to calculate the expected home and away scores (lambda and mu). To calculate lambda and mu, we use the fitted function. I organized the data so that all the rows with the goals scored by the home team comes before all the rows with the goals by the away teams. Whats more, the match in the first row in the home team part corresponds to the match in the first row in the away team part, so it is easy to get the corresponding expectations correct. expected <- fitted(m) home.expected <- expected[1:nrow(dta)] away.expected <- expected[(nrow(dta)+1):(nrow(dta)*2)]  To estimate the rho parameter we can use the tau and DClogLik function we defined in part 1. We just wrap it inside a function we pass to the built in optimizer in R: DCoptimRhoFn <- function(par){ rho <- par[1] DClogLik(dta$FTHG, dta$FTAG, home.expected, away.expected, rho) } res <- optim(par=c(0.1), fn=DCoptimRhoFn, control=list(fnscale=-1), method='BFGS')  The optimization finishes in an instant. As before we get the parameter values by looking at res$par. The estimated rho parameter is -0.126, which is reassuringly not that different from the -0.134 we got from the full model. This is is also about the same values Justin Worrall gets at his sportshacker.net blog.

To make predictions we can reuse most of the code from part 2. The only substantial difference is how we calculate the expected goals, which is a bit simpler this time:

# Expected goals home
lambda <- predict(m, data.frame(Home=1, Team='Bolton', Opponent='Blackburn'), type='response')

# Expected goals away
mu <- predict(m, data.frame(Home=0, Team='Blackburn', Opponent='Bolton'), type='response')


This two-stage approach is much faster and simpler. We don’t have to manually create the design matrices and use matrix algebra to calculate the expected scores. We also don’t have to write as much code to keep track of all the parameters. I haven’t really compared all the different models against each other, so I can’t say which one makes the best predictions, but my guess is that this two-stage approach gives results similar to the fully specified Dixon-Coles model.