SlideShare a Scribd company logo
Predicting Football 
Using R 
Martin Eastwood
£1Billion! 
bet on 2014 World Cup in UK 
According to http://www.theguardian.com/sport/2014/jun/02/cashing-in-world-cup-profit-brazil
70%! 
of all sports betting world wide ! 
is football 
According to http://www.bbc.co.uk/sport/0/football/24354124
Not ! 
everybody ! 
loses to the 
bookmaker
The odds often reflect the! 
market rather than the true! 
probability 
According to http://www.bbc.co.uk/sport/0/football/24354124
Load The Data Into R
Average Goals Scored
Average Goals Conceded
Home Advantage
Goal Distribution
Goal Distribution
Creating The Model 
! 
Xi,j ~ Poisson(ɑiβjƔ)! 
! 
Yi,j ~ Poisson(ɑjβi)
Munge The Data
Fit The Model
Predicting Fixtures
Calculate Probability ! 
Per Goal
Calculate Probability ! 
Of Scores
Win / Loss / Draw
Over / Under
How well does it 
work?
Poor Accuracy For Low Scores
Poisson model assumes 
scores are independent of 
each other! 
! 
Doesn’t account for teams 
motivation - e.g. playing for a 
draw
Applying the Dixon & 
Coles Adjustment 
x=y=0: 1-λμρ& 
x=0, y=1: 1+λρ& 
x=1, y=0: 1+μρ& 
x=1, y=1: 1-ρ& 
otherwise: 1
Man City Vs Man Utd 
Man City = 1.88 (53%)& 
Draw = 4.13 (24%)& 
Away = 4.45 (23%)
Conclusions 
Poisson model with the Dixon & Coles 
adjustment is simple yet relatively 
accurate! 
! 
! 
Other parameters, such as weather, 
injuries, can be added to improve 
accuracy! 
! 
! 
Needs combining with an effective staking 
strategy though
Questions?

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