Predicting Football Using R
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The document discusses predicting football match outcomes using a Poisson model, indicating that 70% of global sports betting is on football. It outlines the process of loading data, fitting the model, and applying adjustments to account for factors like team motivation and historical performance. Ultimately, it concludes that while the Poisson model with Dixon & Coles adjustments is relatively accurate, its effectiveness can be enhanced by incorporating additional variables and a good staking strategy.
Predicting Football Using R
- 1. Predicting Football Using R Martin Eastwood
- 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. 70%! of all sports betting world wide ! is football According to http://www.bbc.co.uk/sport/0/football/24354124
- 4. Not ! everybody ! loses to the bookmaker
- 5. The odds often reflect the! market rather than the true! probability According to http://www.bbc.co.uk/sport/0/football/24354124
- 8. Load The Data Into R
- 11. Home Advantage
- 14. Creating The Model ! Xi,j ~ Poisson(ɑiβjƔ)! ! Yi,j ~ Poisson(ɑjβi)
- 15. Munge The Data
- 16. Fit The Model
- 18. Calculate Probability ! Per Goal
- 19. Calculate Probability ! Of Scores
- 20. Win / Loss / Draw
- 21. Over / Under
- 22. How well does it work?
- 23. Poor Accuracy For Low Scores
- 24. Poisson model assumes scores are independent of each other! ! Doesn’t account for teams motivation - e.g. playing for a draw
- 25. 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
- 26. Man City Vs Man Utd Man City = 1.88 (53%)& Draw = 4.13 (24%)& Away = 4.45 (23%)
- 27. 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
- 28. Questions?