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PREDICTION OF THE RESULTS OF FOOTBALL GAMES BASED ON FUZZY MODEL must read
Authors: Alexander Rotshtein, Morton Posner, Hanna Rakytyanska
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The model of the football game result prediction, which uses information about previous results of opponent teams is proposed. The model is based on the method of nonlinear dependencies identi fication using fuzzy knowledge bases. The reasonable results of simulation can be reached by fuzzy rules tuning using tourna ment tables data. The tuning procedure implies choosing fuzzy terms membership functions pirameters and rules weights by means of genetic and neural optimization technique combination. The proposed model can be used for commercial programs making prediction of the football games results in bookmaker’s offices.
STOCHASTIC MODELLING OF SOCCER MATCH RESULTS must read
Authors: Stephen Dobson(University of the West Indies), John Goddard (University of Wales Swansea)
Diagnostic tests for normality, heteroscedasticity and structural stability are reported for an ordered probit model applied to an English league soccer match results data set. The unsystematic component in match results is found to be normal and homoscedastic, but the ordered probit model parameters reflecting relative team strengths are found to change significantly within the soccer season. Monte Carlo methods are used to test for short term persistence effects in sequences of consecutive results. Evidence of negative persistence is obtained......
Statistical Modelling For Soccer Games: The Greek League must read
Authors: Dimitris Karlis, Ioannis Ntzoufras
In recent years, sports related industries have grown dramatically to record larger and larger revenues. Betting on the results of athletic competitions is very popular in all countries of the European Community and the electronic facilities allow participation in such bets from all over the world. In this paper we explore the possibility of developing statistical models for predicting the outcome of a soccer game. Some results based on careful examination of a large number of games are presented and used for modelling soccer data of the Greek league for season 1997-98. These models are used for interpretation of team performances and prediction of the results in future games.
Statistical models for knock-out soccer tournaments must read
Sports events and tournament competitions provide excellent opportunities for model building and using basic statistical methodology in an interesting way. In this paper, a logistic regression model using seed positions (conceived through a seeding coefficient) is applied to European soccer Cups tournament data in order to predict the probability of winning the tournament for each one of the participating teams, and the predicted probabilities of each team reaching a certain leg.
The Perron-Frobenius Theorem and the Ranking of Football Teams must read
Four diffrent methods to rank football teams
Fuzzy Logic and Its Application in Football Team Ranking must read
Authors: Wenyi Zeng, Junhong Li
Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T7, T3, T1, T9, T10, T8, T11, T12, T2, T6, T5, T4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range.
Soft Computing-Based Result Prediction of Football Games must read
Authors: A. Tsakonas,G. Dounias, S. Shtovba and V. Vivdyuk
Soft computing methods for result prediction of football games based on fuzzy rules, neural networks and genetic programming techniques, are proposed in the article. The models are taking into account the following features of football teams: difference of infirmity factors; difference of dynamics profile; difference of ranks; host factor; personal score of the teams. Testing shows that the proposed models achieve a satisfactory estimation of the actual game results. The current work concludes with the recommendation of soft-computing techniques as a powerful approach, either for the creation of result prediction models of diverse sport championships, or as effective data extrapolation mechanisms in case of limited available statistics.
Soft Computing-Based Result Prediction of Football Games must read
In this paper we look at statistical models for predicting the outcome of football matches in a league. That is, our aim is to find a stati stical model which,based on the game-history so far in a season, can predict the outcome of next round’s matches. Many such models exist, but we are not aware of a thorough com-parison of the models’ merits as betting models. In this paper we look at some clas-sical models, extract their key ingredients, and use those as a basis to propose a new model. The different models are compared by simulating bets being made on matches in the English Premier League.
Prediction on Soccer Matches using Multi-Layer Perceptron must read
Soccer has becoming increasingly popular over the years. During the last decade, soccer’s biggest event, the FIFA World Cup,has attracted millions of fans worldwide; infact, the viewership of the FIFA World Cup match arguably surpasses the Super Bowl event as soccer is the more widely played sport in the world. To showcase its international reach, the World Cup was hosted in Japan and South Korea in 2002. South Africa hosted its first World Cup in 2010, and Brazil will host the next World Cup in 2014. Even in the Middle East, soccer appeals to the audience in that part of the world, with Qatar hosting the World Cup in 2022. The exposure of soccer to different continents highlights the growing popularity for the sport that originated in Europe. With great viewership comes great opportunity for soccer viewers to bet on scores too. This is especially true in Asia, where soccer betting is common among soccer fans. For instance, in Singapore, the Singapore Pools, which is Singapore’s legalized gambling institution, allows Singapore citizens to bet on almost anything, with soccer betting being one of its largest revenue generators. These bets range from predicting the outcome of the score, the exact result of the score, the winning margin and whether a certain player will score first. They are based on analyses including whether a certain player is playing, the coach’s record against the opposition and the strategies that the coach has been using. These factors are all based on human analyses with a tinge of biasness in them, which is inevitable for any sport that involves human judgment.
Predicting Outcomes of Association Football Matches Based on Individual Players' Performance must read
Authors: Johanne Birgitte Linde, Marius Lokketangen
This master’s thesis concludes our five years study in Computer Science, at the Norwegian University of Science and Technology. Predicting the outcome of football matches is a research area where it is possi-ble to earn a lot of money, if the generated predictions are accurate enough. In this thesis we develop three prediction models, based on a model proposed by Rue and Salvesen. Our models are scaled versions of the original model, where the scal-ing factors are determined by the strength of the players participating in a match. They are modelled as Bayesian networks, where the predictions are found by the Markov chain Monte Carlo method Gibbs sampling. The models are applied to the betting market for three seasons, using three different betting strategies, along with the unscaled Rue and Salvesen model. Over these three seasons, our best model, the GoalScaled model, is able to outperform the baseline Rue and Salvesen model and earn money in all seasons.
Predicting football results using Bayesian nets and other machine learning techniques must read
Authors: A. Joseph , N.E. Fenton, M. Neil
Bayesian networks (BNs) provide a means for representing, displaying, and making available in a usable form the knowledge of experts in a given field. In this paper, we look at the performance of an expert constructed BN compared with other machine learning (ML) techniques for predicting the outcome (win, lose, or draw) of matches played by Tottenham Hotspur Football Club. The period under study was 1995–1997 – the expert BN was constructed at the start of that period, based almost exclusively on subjective judgement. Our objective was to determine retrospectively the comparative accuracy of the expert BN compared to some alternative ML models that were built using data from the two-year period. The additional ML techniques considered were: MC4, a decision tree learner; Naive Bayesian learner; Data Driven Bayesian (a BN whose structure and node probability tables are learnt entirely from data); and a K-nearest neighbour learner. The results show that the expert BN is generally superior to the other techniques for this domain in predictive accu-racy. The results are even more impressive for BNs given that, in a number of key respects, the study assumptions place them at a disad-vantage. For example, we have assumed that the BN prediction is ‘incorrect’ if a BN predicts more than one outcome as equally most likely (whereas, in fact, such a prediction would prove valuable to somebody who could place an ‘each way’ bet on the outcome). Although the expert BN has now long been irrelevant (since it contains variables relating to key players who have retired or left the club) the results here tend to confirm the excellent potential of BNs when they are built by a reliable domain expert. The ability to provide accu-rate predictions without requiring much learning data are an obvious bonus in any domain where data are scarce. Moreover, the BN was relatively simple for the expert to build and its structure could be used again in this and similar types of problems
An Improved Prediction System for Football a Match Result must read
Authors: A. Joseph , N.E. Fenton, M. Neil
Predictive systems have been employed to predict events and results in virtually all walks of life. Football results prediction in particular has gained popularity in recent years. Statistical approaches have shown complex and low prediction results. Data mining tools with insufficient features, however, have also yielded low predictions. In our research, knowledge discovery in databases (KDD) has been used to develop a football match result predictive model by gathering 9 features that affect the outcome of football matches. We constructed a more comprehensive system with an improved prediction accuracy by using the features that directly affect the result of a football match. Our prediction system for football match results was implemented using both artificial neural network (ANN) and logistic regression (LR) techniques with Rapid iner as a data mining tool. The technique yielded 85% and 93% prediction accuracy for ANN and LR techniques respectively. With this output, it is observed that the prediction accuracy is higher than those of existing systems
Game ON! Predicting English Premier League Match Outcomes must read
Authors: Aditya Srinivas Timmaraju,Aditya Palnitkar and Vikesh Khanna
Among the different club-based soccer leagues in the world, the English Premier League (EPL), broadcast in 212 territories to 643 million homes, is the most fol-lowed. In this report, we attempt to predict match outcomes in EPL. One of the key challenges of this problem is the high incidence of drawn games in EPL. We identify desirable characteristics for features that are relevant to this problem. We draw parallels between our choice of features and those in state-of-the-art video search and retrieval algorithms. We demonstrate that our methods offer superior performance compared to existing methods, soccer pundits and the betting mar-kets. We also share a few insights we gained from this interesting exploration
Relational Learning for Football-Related Predictions Outcomes must read
Authors: Jan Van Haaren and Guy Van den Broeck
Association football has recently seen some radical changes,leading to higher nancial stakes, further professionalization and technical advances. This gave rise to large amounts of data becoming avail-able for analysis. Therefore, we propose football-related predictions asan interesting application for relational learning. We argue that footballdata is highly structured and most naturally represented in a relationalway. Furthermore, we identify interesting learning tasks which require arelational approach, such as link prediction or structured output learning. Early experiments show that this relational approach is competitivewith a propositionalized approach for the prediction of individual football matches' goal diference.
Combining Human and Machine Intelligence for Making Predictions must read
Authors: Yiftach Nagar and Thomas W. Malone
Statistical models almost always yield predictions that are more accurate than those of human experts. However, humans are better at data acquisition and at recognizing atypical circumstances. We use prediction markets to combine predictions from groups of humans and artificial-intelligence agents and show that they are more robust than those from groups of humans or agents alone .