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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-pr...
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...
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...
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,...
Questions?
Predicting Football Using R
Predicting Football Using R
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Predicting Football Using R

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Presentation I gave to the Manchester R User Group about predicting results of football matches using R

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Predicting Football Using R

  1. 1. Predicting Football Using R Martin Eastwood
  2. 2. £1Billion! bet on 2014 World Cup in UK According to http://www.theguardian.com/sport/2014/jun/02/cashing-in-world-cup-profit-brazil
  3. 3. 70%! of all sports betting world wide ! is football According to http://www.bbc.co.uk/sport/0/football/24354124
  4. 4. Not ! everybody ! loses to the bookmaker
  5. 5. The odds often reflect the! market rather than the true! probability According to http://www.bbc.co.uk/sport/0/football/24354124
  6. 6. Load The Data Into R
  7. 7. Average Goals Scored
  8. 8. Average Goals Conceded
  9. 9. Home Advantage
  10. 10. Goal Distribution
  11. 11. Goal Distribution
  12. 12. Creating The Model ! Xi,j ~ Poisson(ɑiβjƔ)! ! Yi,j ~ Poisson(ɑjβi)
  13. 13. Munge The Data
  14. 14. Fit The Model
  15. 15. Predicting Fixtures
  16. 16. Calculate Probability ! Per Goal
  17. 17. Calculate Probability ! Of Scores
  18. 18. Win / Loss / Draw
  19. 19. Over / Under
  20. 20. How well does it work?
  21. 21. Poor Accuracy For Low Scores
  22. 22. Poisson model assumes scores are independent of each other! ! Doesn’t account for teams motivation - e.g. playing for a draw
  23. 23. 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
  24. 24. Man City Vs Man Utd Man City = 1.88 (53%)& Draw = 4.13 (24%)& Away = 4.45 (23%)
  25. 25. 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
  26. 26. Questions?

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