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Predicting Football Using R

I recently gave a presentation to the Manchester R Users' Group discussing how to predict football results using R. My presentation gave a brief overview of how to create a Poisson model in R and apply the Dixon and Coles adjustment to it to account for dependance in the scores.

The slides are below for anybody interested and contain enough example R code to get you started. Unfortunately, there are no slide notes though but hopefully the slides should be descriptive enough to get you going!

Example code from the presentation can be found at my GitHub account

Predicting Football Using R from Martin Eastwood


Anonymous - November 3, 2014

Can you explain the +/- Dixon/Coles adjustment?

Martin Eastwood - November 3, 2014

Sure, if you are interested in the theory behind it better than I recommend reading Dixon And Coles paper where they propose their adjustment to account for dependency between the scores –

Peter - November 4, 2014

Is it possible for you to share the R code, how you implemented this adjustment in the model?

Martin Eastwood - November 4, 2014

Hi Peter – I’m not planing on adding the Dixon and Coles adjustment to the code as it was intended just as a simple demonstration rather than a full model. The adjustment requires carrying out an optimisation to estimate rho, which in turns requires a cost function etc so it increases the complexity of the example considerably.

Jonas - November 3, 2014

Do you apply the Dixon & Coles adjustment to the probabilities you got from the independent goals model? Do you estimate the rho parameter independently of the other parameters then?

Martin Eastwood - November 3, 2014

Hi Jonas, yes that’s right. You’ll need to run an optimisation to get rho and then use that to modify the probabilities from the Poisson model.

Jonas - November 3, 2014

That’s a neat trick, probably much easier than to fit the comlete Dixon & Coles model :)

Seth Dobson - November 4, 2014

Hi Martin! Thanks for posting this. Looking forward to trying it out on the SPFL.

Have you ever tried the fbRanks package in R?

Martin Eastwood - November 4, 2014

I didn’t even know it existing, will take a look!

Ian - March 12, 2014

After running the glm function to create the model, am I correct in saying that the values under "Estimate" are the attack/defence strengths? So "teamAston Villa" would have be an attack strength of -0.53733 and "opponentAston Villa" would be a defence strength of 0.36923?

Is there just an overall attack/defence strength and not a home attack/defence strength and an away attack/defence strength?

Martin Eastwood - March 12, 2014

IIRC yes this example gives you an attack/defence strength per team plus an estimate for home field advantage too.

William - August 09, 2016

Hi Martin,

I have written a dixon-coles model in python using scipy.optimize.minimize to minimize the log-likelihood as explained in and calculate the parameters however i appear to be getting defense parameters that are too high so my model over predicts the number of goals, i was wondering if you also used scipy.optimize.minimize or if you minimized the likelihood function in a different way?

Martin Eastwood - August 13, 2016

Hi William - I use R's optim function but it should be the same thing. Sounds like their may be a bug in your code somewhere?

James - January 09, 2017

Thanks for your work. It's truly great. However, I've encountered this error in the presentation named above

df <- apply(df, 2, function(row){ + data.frame(team=c(row[\'HomeTeam\'], row[\'AwayTeam\']), + opponent=c(row[\'AwayTeam\'], row[\'HomeTeam\']), + goals=c(row[\'FTHG\'], row[\'FTAG\']), + home=c(1, 0)) + }) Error in apply(df, 2, function(row) { : dim(X) must have a positive length

Martin Eastwood - January 09, 2017

Hi James - that error typically appears in R when a dataframe gets coerced into a flat vector and therefore is just considered an array rather than an object with columns and rows that the apply function can be used against

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