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Abstract(s)
O Futebol é o desporto mais popular do mundo e está a no decurso de um processo de implementação do analytics na tomada de decisão. Este estudo quantitativo tem como objetivo identificar um conjunto de Indicadores de Performance Técnicos para os jogadores de 7 posições diferentes e analisar o valor da sua performance agregada. As posições usadas são: Guarda-redes, Defesa Lateral e Central, Médio Centro, Ofensivo, Ala e Avançados.
De forma a realizar estas análises, os dados de jogo relativos às ações técnicas dos jogadores da temporada de 2016/2017 da Primeira Liga foram extraidos de uma plataforma online e, com eles, uma base de dados foi criada no Access.
A identificação dos indicadores foi conseguida com sucesso através de uma análise de clustering, que utilizou a técnica de K-Means e uma análise de quantis. Estes estudos revelaram 16 Indicadores de Performance Técnicos que variavam e tinham diferentes relevâncias de posição para posição.
Seguidamente, um modelo de Data Envelopment Analysis (DEA) foi usado para compilar os indicadores técnicos e analizar o valor de performance agregada de cada jolgador, usando esse indicador compósito. Este último estudo apresentou resultados, contudo, devido a limitações relacionadas com o peso atribuído aos outputs, estes não são os mais precisos.
O presente estudo teve como base o artigo académico de Hughes, et al. (2012), “Moneyball and soccer - an analysis of the key performance indicators of elite male soccer players by position” e tentou provar analiticamente o que só tinha sido empiricamente referido na literatura anterior.
Football is the most popular sport in the world and it is undergoing a process of implementing analytics in decision making. This quantitative study aims to identify a set of Technical Performance Indicators for players of 7 different positions and analyse the aggregate performance of the players of those positions. The positions used were: Goalkeeper, Full Back, Centre Back, Holding Midfielder, Attacking Midfielder, Wide Midfielder and Striker. To be able to conduct these analyses, the match data of the technical actions of the Portuguese Primeira Liga 2016/2017 season players were extracted from an online platform and an Access database was developed. The identification of the indicators was successfully achieved by a clustering analysis, which used the K-Means technique, and a quantile analysis. These studies revealed 16 Technical Performance Indicators that varied and had different values of relevance from position to position. Afterwards, a Data Envelopment Analysis (DEA) model was used to compile the technical indicators and analyse the aggregated performance of the players using that composite indicator. This last study presented results, however, due to limitations related to the weight awarded to the outputs, they are not the most accurate. The present research study was based on the academic article from Hughes, et al. (2012), “Moneyball and soccer - an analysis of the key performance indicators of elite male soccer players by position” and it tried to prove analytically what was only empirically stated in literature before.
Football is the most popular sport in the world and it is undergoing a process of implementing analytics in decision making. This quantitative study aims to identify a set of Technical Performance Indicators for players of 7 different positions and analyse the aggregate performance of the players of those positions. The positions used were: Goalkeeper, Full Back, Centre Back, Holding Midfielder, Attacking Midfielder, Wide Midfielder and Striker. To be able to conduct these analyses, the match data of the technical actions of the Portuguese Primeira Liga 2016/2017 season players were extracted from an online platform and an Access database was developed. The identification of the indicators was successfully achieved by a clustering analysis, which used the K-Means technique, and a quantile analysis. These studies revealed 16 Technical Performance Indicators that varied and had different values of relevance from position to position. Afterwards, a Data Envelopment Analysis (DEA) model was used to compile the technical indicators and analyse the aggregated performance of the players using that composite indicator. This last study presented results, however, due to limitations related to the weight awarded to the outputs, they are not the most accurate. The present research study was based on the academic article from Hughes, et al. (2012), “Moneyball and soccer - an analysis of the key performance indicators of elite male soccer players by position” and it tried to prove analytically what was only empirically stated in literature before.
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Keywords
Analytics no futebol Indicadores de performance técnicos Análise de performance Football analytics Technical performance indicators Performance analysis