TY - JOUR
T1 - Multicriteria analysis of football match performances
T2 - Composition of probabilistic preferences applied to the English premier league 2015/2016
AU - Principe, Vitor
AU - Gavião, Luiz Octávio
AU - Henriques, Roberto
AU - Lobo, Victor
AU - Lima, Gilson Brito Alves
AU - Sant’anna, Annibal Parracho
N1 - Principe, V., Gavião, L. O., Henriques, R., Lobo, V., Lima, G. B. A., & Sant’anna, A. P. (2017). Multicriteria analysis of football match performances: Composition of probabilistic preferences applied to the English premier league 2015/2016. Pesquisa Operacional, 37(2), 333-363. https://doi.org/10.1590/0101-7438.2017.037.02.0333
PY - 2017/5/1
Y1 - 2017/5/1
N2 - This article aims to analyze the technical performance of football teams in the FA Premier League during the 2015/2016 season. Data of twenty clubs over 38matches for each club are considered using 23 variables. These variables have been explored in the football literature and address different features of technical performance. The different configuration of the data for teams in detached segments motivated the multi-criteria approach, which enables identification of strong and weak sectors in each segment. The uncertainty as to the outcome of football matches and the imprecision of the measures indicated the use of Composition of Probabilistic Preferences (CPP) to model the problem. “R” software was used in the modeling and computation. The CPP global scores obtained were more consistent with the final classification than those of other methods. CPP scores revealed different performances of particular groups of variables indicating aspects to be improved and explored.
AB - This article aims to analyze the technical performance of football teams in the FA Premier League during the 2015/2016 season. Data of twenty clubs over 38matches for each club are considered using 23 variables. These variables have been explored in the football literature and address different features of technical performance. The different configuration of the data for teams in detached segments motivated the multi-criteria approach, which enables identification of strong and weak sectors in each segment. The uncertainty as to the outcome of football matches and the imprecision of the measures indicated the use of Composition of Probabilistic Preferences (CPP) to model the problem. “R” software was used in the modeling and computation. The CPP global scores obtained were more consistent with the final classification than those of other methods. CPP scores revealed different performances of particular groups of variables indicating aspects to be improved and explored.
KW - Football
KW - Match analysis
KW - Probabilistic composition of preferences
UR - http://www.scopus.com/inward/record.url?scp=85030776886&partnerID=8YFLogxK
U2 - 10.1590/0101-7438.2017.037.02.0333
DO - 10.1590/0101-7438.2017.037.02.0333
M3 - Article
AN - SCOPUS:85030776886
SN - 0101-7438
VL - 37
SP - 333
EP - 363
JO - Pesquisa Operacional
JF - Pesquisa Operacional
IS - 2
ER -