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Neste estudo procurou-se analisar os padrões de jogo ofensivos no futebol, por intermédio da análise de redes sociais. Analisou-se também a forma como o número de oponentes que cercam o jogador em posse de bola, pode influenciar os padrões ofensivos de jogo. Foram analisados quatro jogos oficiais da Liga Português, registando-se 52 sequências de padrão de jogo. A posição no campo, assim como os passes efetuados e a sua direção, foram codificados tendo em conta um sistema de notação com 18 zonas iguais. O número de adversários na mesma área da bola foi também registrado. O primeiro passe foi codificado como curto (um passe para uma zona circundante), ou longo (um passe para uma zona que tem pelo menos uma zona de intervalo). Para a análise de rede, foi utilizando o software SocNetV 0,81. As variáveis centralidade de intermediação (% BC) e centralidade de entrada e saída (IDC% e ODC%), foram computados para medir a influência dos jogadores na rede de fluxo. Os resultados revelaram que o jogador com maior número de bolas recuperadas foi o Lateral Direito (31,37% ODC) e o jogador que recebeu mais passes foi o médio-defensivo (35,29% IDC). O segundo jogador que recebeu mais passes foi o Ponta de Lança (21,57% IDC). O jogador com mais influência sobre o padrão de jogo da equipa foi o Médio Defensivo (75,40% BC). As zonas com maior número de passes recebidos após a recuperação da bola foram, as duas zonas centrais defensivas mais próximas à linha de meio-campo (% IDC 50), seguindo-se a primeira zona à direita do meio-campo ofensivo (19,22% IDC). Registaram-se 60% de passes curtos e 40% de passes longos. A análise dos passes curtos e longos revelou, que o passe curto tende a ser executado quando 1 a 2 opositores rodeiam a bola e o passe longo tende a ser executado quando 3 a 4 opositores rodeiam a bola. Os resultados sugerem que a equipa em análise tem dois padrões preferenciais para este momento de jogo: i) Organiza o jogo, tendo o médio defensivo como principal elemento para receber bolas na zona central defensiva do campo, apresentando a maior influência sobre o rede de passes, e ii) o jogo directo, tendo como referência o ponta de lança para bolas mais longas, sobre a primeira zona central ofensiva, ou na primeira zona ofensiva sobre o corredor lateral direito. Os resultados também sugerem que o número de jogadores que cercam a bola, influenciam a decisão do tipo de passe utilizado (curto ou longo). Usando este tipo de metodologia é possível identificar e quantificar os padrões de jogo de uma equipa, fornecendo dados confiáveis que podem ajudar os treinadores a melhorar o desempenho das suas equipas.
In this study we aimed to analyse the offensive patterns of play in association football using social networks analysis (Duch, Waitzman, & Amaral, 2010). It was also analyzed how the number of opponents that surround the player in ball possession can influence the offensive patterns of play. Four competitive matches from the Portuguese League were analyzed. A total of 52 offensive patterns of play were noted. Passes performance were coded and position in the field was identified using a notation system with 18 equal zones. The number of opponents on the same area of the ball was registered. The first pass was coded as short (a pass to a surrounding zone) or long (a pass to a zone that has at least one interval zone). Network analysis and graph network was performed using SocNetV 0.81 software. Betweeness (%BC) and In-Out centralities (%IDC and %ODC) were computed to measure the player’s influence in the flow network. Results revealed that the player that recovers more balls was the defensive wing (%ODC 31.37) and the player that received more passes was the defensive midfielder (%IDC 35.29). The second player that received more passes was the centre forward (%IDC 21.57). The player with more influence on the pattern of play was the centre forward (%BC 75.40). The zones with more passes after ball recovering were the two defensive central zones closest to the midfield line (%IDC 50) followed by first central and right zones on attacking midfield (%IDC 19.22). It was also observed 60% of short and 40% of long passes. Results of short and long passes revealed that a short pass tend to be performed when 1 to 2 opponents surround the ball and the long pass tend to be performed when 3 to 4 opponents surround the ball. It is suggested that the team in analysis have two preferential patterns of play for this moment of game: i) supported play, with the defensive midfielder being the player that received more balls on the defensive central zone of the field and present the highest influence on the network of passing balls, and ii) direct play, with the centre forward being the player that received more long balls on the first offensive central zone or on the first right wing zone of the offensive midfield. Results also suggested that the number of players that surround the ball allow the emergence of a short or long pass. Using this type of methodology is possible to better identify and quantify the patterns of play of a team, providing reliable data that help coaches to improve performance of their teams.
In this study we aimed to analyse the offensive patterns of play in association football using social networks analysis (Duch, Waitzman, & Amaral, 2010). It was also analyzed how the number of opponents that surround the player in ball possession can influence the offensive patterns of play. Four competitive matches from the Portuguese League were analyzed. A total of 52 offensive patterns of play were noted. Passes performance were coded and position in the field was identified using a notation system with 18 equal zones. The number of opponents on the same area of the ball was registered. The first pass was coded as short (a pass to a surrounding zone) or long (a pass to a zone that has at least one interval zone). Network analysis and graph network was performed using SocNetV 0.81 software. Betweeness (%BC) and In-Out centralities (%IDC and %ODC) were computed to measure the player’s influence in the flow network. Results revealed that the player that recovers more balls was the defensive wing (%ODC 31.37) and the player that received more passes was the defensive midfielder (%IDC 35.29). The second player that received more passes was the centre forward (%IDC 21.57). The player with more influence on the pattern of play was the centre forward (%BC 75.40). The zones with more passes after ball recovering were the two defensive central zones closest to the midfield line (%IDC 50) followed by first central and right zones on attacking midfield (%IDC 19.22). It was also observed 60% of short and 40% of long passes. Results of short and long passes revealed that a short pass tend to be performed when 1 to 2 opponents surround the ball and the long pass tend to be performed when 3 to 4 opponents surround the ball. It is suggested that the team in analysis have two preferential patterns of play for this moment of game: i) supported play, with the defensive midfielder being the player that received more balls on the defensive central zone of the field and present the highest influence on the network of passing balls, and ii) direct play, with the centre forward being the player that received more long balls on the first offensive central zone or on the first right wing zone of the offensive midfield. Results also suggested that the number of players that surround the ball allow the emergence of a short or long pass. Using this type of methodology is possible to better identify and quantify the patterns of play of a team, providing reliable data that help coaches to improve performance of their teams.
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Keywords
Futebol Análise de jogo Padrões de jogo Futebol - Defesa-ataque - Equipa Desporto - Redes sociais