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

Owramipur, Farzin; Eskandarian, Parinaz; Mozneb, Faezeh Sadat

doi:10.7763/ijcte.2013.v5.802

Cited by 36 publications

(19 citation statements)

References 14 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As fundamental theories have failed to make any reliable analysis or predictions around football, most contemporary research has focused on probabilistic models (e.g., Owramipur et al, 2013 ). Nevertheless, and despite the success of some of these methods, they rely only on the overall information retrieved from previous matches, without considering on-the-fly information retrieved during a current match, such as each athlete’s positional and physiological data, nor producing any macroscopic tactical metric inherent to such data (see Couceiro et al, 2016 ) for a critical analysis of these methods.…”

Section: Introductionmentioning

confidence: 99%

Team Synergies in Sport: Theory and Measures

2016

View full text Add to dashboard Cite

Individual players act as a coherent unit during team sports performance, forming a team synergy. A synergy is a collective property of a task-specific organization of individuals, such that the degrees of freedom of each individual in the system are coupled, enabling the degrees of freedom of different individuals to co-regulate each other. Here, we present an explanation for the emergence of such collective behaviors, indicating how these can be assessed and understood through the measurement of key system properties that exist, considering the contribution of each individual and beyond These include: to (i) dimensional compression, a process resulting in independent degree of freedom being coupled so that the synergy has fewer degrees of freedom than the set of components from which it arises; (ii) reciprocal compensation, if one element do not produce its function, other elements should display changes in their contributions so that task goals are still attained; (iii) interpersonal linkages, the specific contribution of each element to a group task; and (iv), degeneracy, structurally different components performing a similar, but not necessarily identical, function with respect to context. A primary goal of our analysis is to highlight the principles and tools required to understand coherent and dynamic team behaviors, as well as the performance conditions that make such team synergies possible, through perceptual attunement to shared affordances in individual performers. A key conclusion is that teams can be trained to perceive how to use and share specific affordances, explaining how individual’s behaviors self-organize into a group synergy. Ecological dynamics explanations of team behaviors can transit beyond mere ratification of sport performance, providing a comprehensive conceptual framework to guide the implementation of diagnostic measures by sport scientists, sport psychologists and performance analysts. Complex adaptive systems, synergies, group behaviors, team sport performance, ecological dynamics, performance analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

Team Synergies in Sport: Theory and Measures

2016

View full text Add to dashboard Cite

show abstract

“…*Correspondingauthor: aidam@uthm.edu.my the football matches outcome. However, the football prediction model with the application of Bayesian approach has been carried out in [2,3]. Both research proposed the same Bayesian algorithm as they claims that the Bayesian Networks become very popular in artificial intelligence and it is the most powerful algorithm as compared to other techniques in predicting football match outcome.…”

Section: Related Workmentioning

confidence: 99%

“…In [2], the Bayesian Network performance is tested in the area of predicting the football match result that involving the Barcelona FC in Spanish league (La Liga) for the season 2008-2009 while [3] predict the football match result for three seasons of English Premier League(EPL) and the accuracy of the prediction model were tested using the k-fold cross validation. Next, [4] developed other football predicting model that can generate forecast on the football match outcome for English Premier League (EPL) in season 2010-2011 which is called pi-rating model.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Approach to Classification of Football Match Outcome

Rahman¹,

Fauzi²,

Razali³

2018

IJIE

View full text Add to dashboard Cite

“…Some scientific studies were conducted in this area. Owramipur et al [12] intend to make the prediction of the results of the Barcelona games in the 2008/2009 season. Joseph et al [13] Effected identical to the previous work, but the team under study was Tottenham.…”

Section: Football Gambling Support Systems and Related Workmentioning

confidence: 99%

Pervasive Decision Support to Predict Football Corners and Goals by Means of Data Mining

Gomes

Portela

Santos

2016

New Advances in Information Systems and Technologies

View full text Add to dashboard Cite

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

show abstract

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

Cited by 36 publications

References 14 publications

Team Synergies in Sport: Theory and Measures

Team Synergies in Sport: Theory and Measures

Bayesian Approach to Classification of Football Match Outcome

Pervasive Decision Support to Predict Football Corners and Goals by Means of Data Mining

Contact Info

Product

Resources

About