Football Predictions Based on a Fuzzy Model with Genetic and Neural Tuning

Ротштейн, А. П.; Posner, M. J. M.; Rakityanskaya, A. B.

doi:10.1007/s10559-005-0098-4

Cited by 64 publications

(35 citation statements)

References 15 publications

(20 reference statements)

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“…al., 2002) the authors claimed that a genetic programming based technique was superior in predicting football outcomes to other two methods based on fuzzy models and neural networks. More recently, (Rotshtein et al, 2005) claimed that acceptable match simulation results can be obtained by tuning fuzzy rules using parameters of fuzzy-term membership functions and rule weights by a combination of genetic and neural optimisation techniques.…”

Section: Introductionmentioning

confidence: 99%

pi-football: A Bayesian network model for forecasting Association Football match outcomes

Constantinou

Fenton

Neil

2012

Knowledge-Based Systems

115

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A Bayesian network is a graphical probabilistic belief network that represents the conditional dependencies among uncertain variables, which can be both objective and subjective. We present a Bayesian network model for forecasting Association Football matches in which the subjective variables represent the factors that are important for prediction but which historical data fails to capture. The model (pi-football) was used to generate forecasts about the outcomes of the English Premier League (EPL) matches during season 2010/11 (but is easily extended to any football league). Forecasts were published online at www.pi-football.com prior to the start of each match. In this paper, we demonstrate that a) using an appropriate measure of forecast accuracy, the subjective information improved the model such that posterior forecasts were on par with bookmakers' performance; b) using a standard profitability measure with discrepancy levels at ≥ 5%, the model generates profit under maximum, mean, and common bookmakers' odds, even allowing for the bookmakers' built-in profit margin.Hence, compared with other published football forecast models, pi-football not only appears to be exceptionally accurate, but it can also be used to 'beat the bookies'.

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Section: Introductionmentioning

confidence: 99%

pi-football: A Bayesian network model for forecasting Association Football match outcomes

Constantinou

Fenton

Neil

2012

Knowledge-Based Systems

115

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“…Rothstein et al [3] used fuzzy logic in formalizing football predictions, and genetic and neural optimization techniques in tuning their fuzzy model. Koning [4] took a Bayesian network approach with Markov chains and the Monte-Carlo method, estimating the quality of football teams using this model.…”

Section: Related Workmentioning

confidence: 99%

Football Result Prediction with Bayesian Network in Spanish League-Barcelona Team

Owramipur¹,

Eskandarian²,

Mozneb³

2013

IJCTE

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Abstract-The problem of modeling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian Network (BN) to predict results of football matches. During the last decade, Bayesian networks (and probabilistic graphical models in general) have become very popular in artificial intelligence. In this paper, we look at the performance of a BN in the area of predicting the result of football matches involving Barcelona FC. The period under study was the 2008-2009 season in Spanish football league and we test its performance. We get necessary information about football states from valid websites. This BN is used for prediction of football results in future matches.

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“…al. compared the forecasting ability of both genetic algorithms and neural networks (Rotshtein et al, 2005). They first set about classifying the wins into one of five categories: big loss, small loss, draw, small win and big win, where a big loss would be in the range of 3 to 5 point deficit, small loss a 1 to 2 point deficit, etc.…”

Section: Soccermentioning

confidence: 99%