# Predicting sports events from past results

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1 Predicting sports events from past results Towards effective betting on football Douwe Buursma University of Twente P.O. Box 217, 7500AE Enschede The Netherlands ABSTRACT A system for predicting the results of football that beats the bookmakers odds is presented. The predictions for the are based on previous results of the teams involved. Keywords Prediction, odds, bookmakers, football, probability, profit, Weka, Bayesian networks, regression analysis. 1. INTRODUCTION 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 are played every week in professional competitions around the globe. 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 with odds 1.75, they will win 3*1.75=5.25, yielding a net profit of 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 like to 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. 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. 1.1 Related Research Lots of research on this topic has already been done. In 2003, Buchdahl [2] investigated the quality of bookmakers odds in all kinds of 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 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. 14 th Twente Student Conference on IT, January 21 st, 2011, Enschede, The Netherlands. Copyright 2010, University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science. be possible to predict football even more accurately and therefore gain a higher profit by betting. Goddard [5], 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 profit of 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 made to accurately choose which bets to take on in order to maximize the profit. 1.2 Research Questions The main question of this research is: - How can we effectively bet on football by using features from past results? To answer this main question, several sub-questions will have to be answered. What features are important when predicting the outcome of a football match? How can we calculate the probabilities for the outcomes of a match from these features? Knowing these probabilities, what bets should we take on to maximize the profit? 1.3 Approach Database and setup To predict football accurately several things are needed. First of all, a set of features containing the most valuable information about previous must be collected. To do this, a database containing data from the past 15 years for the Dutch football competition is used. This database is available on The database contains all full-time and half-time score lines and the bookmaker odds for most of the. From these data lots of features, such as number of goals scored in the last 7 ; number of home wins in the last 10 etc., can be extracted. In Section 2 it is described in more detail what features are used and how they are collected. After collecting a vast number of features, we will then let Weka ( [11] determine what features are important. Weka is a tool that uses machine learning algorithms in order to calculate the correlation between certain data. Some of the most important machine learning algorithms used in this investigation are the Bayesian network[1] and forms of linear and logistic regression[10]. Basically what these algorithms do, is calculate how a given vector of outcomes is determined by a vector of numbers corresponding to these outcomes. For our investigation we will

5 7. ACKNOWLEDGEMENTS First of all I would like to thank Maarten Fokkinga and all track members for their help during the whole project. I would also like to thank Mannes Poel for his help and assistance with the Weka Toolkit. 8. REFERENCES [1] Ben-Gal I., Bayesian Networks, in Ruggeri F., Faltin F. & Kenett R., Encyclopedia of Statistics in Quality & Reliability, Wiley & Sons. [2] Buchdal, J., Rating Systems For Fixed Odds Football Match Prediction, from Fixed Odds Sports Betting: The Essential Guide. [3] Carmichael, F. and Thomas, D., Home-field effect and team performance: evidence from English premiership football. Journal of Sports Economics. v6 i [4] Goddard, J., Modelling Football Match Results and the Efficiency of Fixed-Odds Betting, Working Paper, Department of Economics, Swansea University. [5] Goddard, J. and Asimakopoulos, I., Forecasting football results and the efficiency of fixed-odds betting. Journal of Forecasting. v [6] Halicioglu, F., Forecasting the professional team sporting events: evidence from Euro 2000 and 2004 football tournaments, in: Fifth International Conference on Sports and Culture: Economic, Management and Marketing Aspects. [7] Joseph, A., Fenton, N.E., Neil, M., Predicting football results using Bayesian nets and other machine learning techniques (version with full set of models and data). [8] Koning, R.H., Balance in competition in Dutch soccer. The Statistician. v49 i [9] Rue, H., Salvesen, Ø., Prediction and retrospective analysis of soccer in a league, preprint Statistics 10. [10] Sykes, A.O., An introduction to Regression Analysis. ion.pdf [11] Witten I.H. and Frank E., Data Mining: Practical Machine Learning, Tools and Techniques with Java Implementations, Chapter 8.

6 APPENDIX A. FEATURE SELECTION In this appendix we present the full results of all evaluations done in Section 2. A.1 Match History Section 2.1 dealt with determining the best number of to consider for the feature set. Below, in table 4 is a comprehensive list of results, showing basically that the larger the number of considered, the better the classifiers perform. But it can also be seen that the performance does not improve by much when increasing the number of further than 20. Therefore this number is chosen as the optimal value. Table 4. Classification performance when considering x Class. /x --> ClassViaReg 51,81% 51,88% 52,55% 53,64% 53,53% MultiCC 51,81% 51,88% 52,59% 53,42% 53,46% RotationFor 50,47% 51,11% 51,69% 51,82% 53,00% LogitBoost 51,00% 51,63% 52,37% 52,62% 52,55% BayesNet 49,69% 50,80% 51,80% 52,55% 52,66% NaiveBayes 51,01% 51,47% 52,14% 53,05% 53,07% Class. /x --> ClassViaReg 54,17% 54,23% 54,86% 54,97% 55,14% MultiCC 54,24% 54,39% 54,84% 54,92% 55,46% RotationFor 53,50% 53,27% 54,04% 54,04% 54,68% LogitBoost 52,96% 53,53% 54,00% 54,39% 55,30% BayesNet 53,61% 53,58% 54,38% 54,11% 55,24% NaiveBayes 53,36% 53,79% 54,43% 54,82% 55,02% A.2 Classifier selection Section 2.2 subsequently deals with choosing the best classifier. Table 5 shows the performance of all classifiers on all chosen feature selections. The feature list is also given below. (1) Goals scored by home team in its latest 20 (2) Goals scored by away team in its latest 20 (3) Goals conceded by home team in its latest 20 (4) Goals conceded by away team in its latest 20 (5) Average number of points gained by home team in its latest 20 (6) Average number of points gained by away team in its latest 20 (7) Number of home won by home team in its latest 20 (8) Number of away won by away team in its latest 20 (9) Number of goals scored in home by home team in its latest 20 (10) Number of goals scored in away by away team in its latest 20 Table 5. Performance of all classifiers when using different selections of features Classifier/features--> 1-10 A.3 Feature selection The first part of the feature selection dealt with the same feature set used in the previous section, in which, one at a time, features were left out. Table 6 shows the performance of our chosen classifier when one of the features was left out. Table 6. Classification performance with one feature left out 1-6,9, ,2 ClassViaReg 55,00% 55,05% 54,96% 53,59% MultiCC 54,86% 54,98% 54,88% 53,73% RotationFor 53,61% 54,57% 54,57% 53,06% LogitBoost 53,88% 54,62% 54,48% 53,30% BayesNet 53,64% 54,55% 54,45% 53,59% NaiveBayes 53,64% 54,38% 54,43% 53,37% HomeWins 48,66% 48,66% 48,66% 48,66% Classifier/features--> 3,4 5, ClassViaReg 53,42% 54,40% 54,86% 53,13% MultiCC 53,40% 54,38% 54,84% 52,92% RotationFor 52,92% 54,00% 54,04% 53,56% LogitBoost 53,35% 53,78% 54,00% 53,28% BayesNet 53,28% 54,57% 54,38% 52,82% NaiveBayes 53,23% 54,36% 54,43% 52,99% HomeWins 48,66% 48,66% 48,66% 48,66% Feature left out Performance none 55,00% 1 54,93% 2 54,96% 3 54,93% 4 54,96% 5 54,74% 6 55,00% 7 55,05% 8 55,09% 9 55,00% 10 54,88%

7 B. DATA EVALUATION In this appendix we describe the formulas behind the calculating of the probabilities used for the betting strategies. Also the results of the betting strategies are shown in full detail. B.1 Calculating probabilities The formulas the classifier comes up with for each of the outcomes are the following (the numbers between the parentheses indicate the features from the earlier given feature list): Home win: * (1) * (2) * (3) * (4) * (5) * (6) * (9) * (18) Draw: * (2) * (4) * (9) * (10) Away win: * (1) * (2) * (3) * (4) * (5) * (6) * (10) * (18) After the formulas had been evaluated for all in the database, these numbers (which we will for convenience sake call a, b and c) were normalized (into a norm, b norm and c norm ) as follows: a norm = a/(a+b+c) b norm = b/(a+b+c) c norm = c/(a+b+c) B.2 Betting strategies Table 7 shows the results of all the betting strategies that were evaluated. Table 7. Performance of the betting strategies Strategy last ½ season last season all 7 seasons I II III-0 or IV III III III III III IV IV IV IV IV IV

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