Predicting sports events from past results
Towards effective betting on football matches
University of Twente
P.O. Box 217, 7500AE Enschede
A s ystem for predicting the results of football matches that
beats the bookmakers’ odds is presented. The predictions for
the matches are based on previous results of the teamsinvolved.
Prediction, odds, bookmakers, football, probability, profit,
Weka, Bayesian networks, regression analysis.
Betting on sports events has gained a lot of popularity recently
and bookmakers and betting offices are now omnipresent at the
internet. One of the most popular sports to bet on is football,
because hundreds of football matches are played everyweek in
professional competitions around the globe.
be possible to predict football matches even more accurately
and therefore gain a higher profit by betting. Goddard , in
2004, did use a much bigger feature set for his football match
predictions. This feature set includes the geographical distance
between the teams involved and their average spectator
attendances. However, the profitof their system was a lot lower
than the one by Buchdahl. We believe the main reason for this
is that they simply bet an amount of money on the most
probable outcome for each match. Reasonable though this may
seem, it is not too hard to understand that, because of
bookmakers’ unfairness, it may sometimes be better not to bet
on a match at all. In our investigation an attempt will be madeto accurately choose which bets to take on in order to maximize
1.2 Research Questions
The method is always the same, the bettor wagers an amount of
money on a bet of their choice. If their prediction turns out to be
correct they win an amount of money, equal to the odds of the
bet, per unit of money invested. For instance, if a bettor wagers
3 units of money on a bet withodds 1.75, they will win
3*1.75=5.25, yielding a net profit of 2.25. If the prediction is
incorrect the bettor’s wager is lost. The odds bookmakers use
for their bets are based on probability. If the probability of a
certain outcome showing up is 0.2, the corresponding odds
would be 1/0.2 = 5, meaning every unit bet will result in 2 units
returned if the bet is won. Obviously, bookmakers liketo make
a little profit, so when they believe the probability of a certain
outcome showing up is 0.2, they will not use the statistically
fair odds of 5, but will rather adjust this to for instance 4.5.
The main question of this research is:
Because of this “unfairness” by the bookmakers, making profit
by betting on football results is not all that easy. One would so
to speak have to“beat” the bookmakers’ odds. In order to do
this, accurate probabilities for a home win, a draw or an away
win must be calculated for each match. Section 2 and 3 give a
detailed description of how this is done.
1.3.1 Database and setup
1.1 Related Research
Lots of research on this topic has already been done. In 2003,
Buchdahl  investigated the quality of bookmakers’ odds in
all kindsof sports, including football. He concluded that, when
enough data is used for training a prediction system, even a
small feature set will suffice to beat the bookmakers’ odds in
football. With a larger and more intelligent feature set, it should
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14thTwente Student Conference on IT, January 21st, 2011, Enschede, The
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