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

Constantinou, Anthony C.; Fenton, Norman; Neil, Martin

doi:10.1016/j.knosys.2012.07.008

Cited by 115 publications

(76 citation statements)

References 38 publications

(42 reference statements)

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“…Joseph et al [18] compare between different techniques and show that a Bayesian network constructed by an expert provides best results among different machine learning algorithms. Incorporation of the expertise in football prediction models is studied further by Constantinou [3] and Constantinou et al [4]. The authors propose a Bayesian network model that allows for including impact of beliefs on, for example, the perceived current shape of a team, its fatigue or availability of key players.…”

Section: Prediction Of Future Gamesmentioning

confidence: 99%

How to improve a team's position in the FIFA ranking? A simulation study

Lašek

Szlávik

Gągolewski

et al. 2015

Journal of Applied Statistics

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In this paper, we study the efficacy of the official ranking for international football teams compiled by FIFA, the body governing football competition around the globe. We present strategies for improving a team's position in the ranking. By combining several statistical techniques, we derive an objective function in a decision problem of optimal scheduling of future matches. The presented results display how a team's position can be improved. Along the way, we compare the official procedure to the famous Elo rating system. Although it originates from chess, it has been successfully tailored to ranking football teams as well.

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Section: Prediction Of Future Gamesmentioning

confidence: 99%

How to improve a team's position in the FIFA ranking? A simulation study

Lašek

Szlávik

Gągolewski

et al. 2015

Journal of Applied Statistics

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“…Bayesian network can be used with Bayesian statistics to avoid the problem of data over fitting but it is not bounded to be used with Bayesian statistics (Constantinou et al 2012).…”

Section: Bayes Network (Bn)mentioning

confidence: 99%

Using machine learning techniques for rising star prediction in co-author network

et al. 2014

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Online bibliographic databases are powerful resources for research in data mining and social network analysis especially co-author networks. Predicting future rising stars is to find brilliant scholars/researchers in co-author networks. In this paper, we propose a solution for rising star prediction by applying machine learning techniques. For classification task, discriminative and generative modeling techniques are considered and two algorithms are chosen for each category. The author, co-authorship and venue based information are incorporated, resulting in eleven features with their mathematical formulations. Extensive experiments are performed to analyze the impact of individual feature, category wise and their combination w.r.t classification accuracy. Then, two ranking lists for top 30 scholars are presented from predicted rising stars. In addition, this concept is demonstrated for prediction of rising stars in database domain. Data from DBLP and Arnetminer databases (1996-2000 for wide disciplines) are used for algorithms' experimental analysis.

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“…Machine learning methods have been successfully applied in football. For example, in the prediction of match outcomes (Constantinou, Fenton, & Neil, 2012;Min, Kim, Choe, Eom, & McKay, 2008;Odachowski & Grekow, 2013;Strnad, Nerat, & Kohek, 2015;Tüfekci, 2016), analysis of team performance (Arruda Moura, Barreto Martins, & Augusto Cunha, 2013) or player's injury prediction (Arndt & Brefeld, 2016;Jelinek, Kelarev, Robinson, Stranieri, & Cornforth, 2014;Kampakis, 2011). However, the problem of characterizing and selecting players based on available data of performance using machine learning methods is an interesting and open field of research today.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system. [Modelo basado en agrupamiento de mixturas Gaussianas para caracterizar futbolistas utilizando el sistema de videojuegos FIFA de EA Sports].

Soto-Valero¹

2017

Rev. int. cienc. deporte

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The generation and availability of football data has increased considerably last decades, mostly due to its popularity and also because of technological advances. Gaussian mixture clustering models represents a novel approach to exploring and analyzing performance data in sports. In this paper, we use principal components analysis in conjunction with a model-based Gaussian clustering method with the purpose of characterizing professional football players. Our model approach is tested using 40 attributes from EA Sports' FIFA video game series system, corresponding to 7705 European players. Clustering results reveal a clear distinction among different performance indicators, representing four different roles in the team. Players were labeled according to these roles and a gradient tree boosting model was used for ranking attributes regarding to its importance. We found that the dribbling skill is the most discriminating variable among the different clustered players' profiles.Key words: association football; EA Sports' FIFA video game series system; machine learning; principal component analysis; Gaussian mixture clustering models; classification and regression trees. AbstractCorrespondencia/correspondence: César Soto-Valero Department of Computer Science, Universidad Central "Marta Abreu" de Las Villas, Cuba Email: cesarsotovalero@gmail.com En las últimas décadas se ha visto un incremento considerable en la generación y disponibilidad de datos de fútbol, esto se debe fundamentalmente a la popularidad de este deporte así como a los avances tecnológicos realizados en este campo. Los modelos de agrupamiento basados en mixturas Gaussianas representan un enfoque novedoso para explorar y analizar datos de desempeño deportivo. En el presente trabajo, se lleva a cabo una caracterización de jugadores profesionales de fútbol utilizando técnicas de análisis de componentes principales y agrupamiento basados en mixturas Gaussianas. El modelo presentado es comprobado utilizando datos del sistema de videojuegos FIFA de EA Sports, dichos datos representan 40 atributos correspondientes a 7705 futbolistas europeos. Los resultados del agrupamiento revelan una clara distinción entre algunos indicadores de desempeño, los cuales corresponden a cuatro roles diferentes en el equipo. Consecuentemente, los jugadores fueron etiquetados de acuerdo a estos roles y un modelo de árboles de gradiente ampliado fue utilizado para ordenar los atributos de acuerdo a su importancia. Como resultado se identificó a la habilidad de driblear como la variable que mejor discrimina entre los diferentes perfiles de jugadores.Palabras clave: fútbol; sistema de videojuegos FIFA de EA Sports; aprendizaje automátic; análisis de componentes principales; agrupamiento basado en modelos de mixturas Gaussianas; árboles de clasificación y regresión. Resumen

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

Cited by 115 publications

References 38 publications

How to improve a team's position in the FIFA ranking? A simulation study

How to improve a team's position in the FIFA ranking? A simulation study

Using machine learning techniques for rising star prediction in co-author network

A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system. [Modelo basado en agrupamiento de mixturas Gaussianas para caracterizar futbolistas utilizando el sistema de videojuegos FIFA de EA Sports].

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