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Predicting Football Using R
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Data & Analytics
Presentation I gave to the Manchester R User Group about predicting results of football matches using R
Martin EastwoodFollow
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Predicting Football Using R
- 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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