Network analysis and intra-team activity in attacking phases of professional football

Gama, José; Passos, Pedro; Davids, Keith; Relvas, Hugo; Ribeiro, João; Vaz, Vasco; Dias, Gonçalo

doi:10.1080/24748668.2014.11868752

Cited by 60 publications

(63 citation statements)

References 5 publications

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“…Ao fornecer uma visão sistêmica e ecológica do jogo (8) , a Análise de Redes Sociais (ASR) pode fornecer um entendimento da conexão entre todos os subsistemas. A ASR fornece uma visão qualitativa e quantitativa dos dados através de um mapa das interconexões (arestas) entre um conjunto de atores, jogadores e/ou ações de jogo (nós), fornecendo informações acerca da relevância de cada agente e ligação na rede (16) .…”

Section: Introductionunclassified

A importância de diferenciar o KII do KIII no Voleibol feminino de alto nível

Afonso¹,

Laporta²,

Mesquita³

2017

RPCD

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RESUMOApesar do voleibol apresentar seis complexos de jogo (K), a maioria das pesquisas dividem em apenas dois K (KI e KII). Desta forma, muita informação pode ser perdida ou tratada de modo similar, não obstante a sua divergência. A ação de contra-ataque é um exemplo, em que ela pode ocorrer como transição do side-out (KII) e transição da transição (KIII).Assim, a nossa hipótese é que existem diferenças entre as variáveis analisadas nestes dois complexos no voleibol, merecendo uma exploração cuidada. Para o efeito, recorreu-se à Análise de Redes Sociais, especificamente à centralidade de autovetor, para obter uma visão sistémica de todas as ações e perceber a sua relevância no todo. 15The importance of differentiating KII and KIII in high-level women's volleyball ABSTRACT Although volleyball features six game complexes (K), most research divide into only two K (KI and KII). In this way, much information can be lost or treated in a similar way, despite its divergence. Counter-attack action is an example, where it can occur as a side-out (KII) transition and transition of transition (KIII). Thus, our hypothesis is that there are differences between the variables analyzed in these two complexes in volleyball, deserving a careful exploration. For this purpose, we used Social Network Analysis, specifically to eigenvector centrality, to obtain a systemic view of all actions and to perceive their relevance in the whole.Thirteen matches of the final phase of the World Grand Prix 2015 were analyzed, resulting in differences in the variables between the complexes. Specifically, the KII presented greater variability in its actions compared to KIII. The results also call for the inclusion of "out-of-system" situations in training.

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Section: Introductionunclassified

A importância de diferenciar o KII do KIII no Voleibol feminino de alto nível

Afonso¹,

Laporta²,

Mesquita³

2017

RPCD

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“…(McGarry, 2009). Along these lines, several studies have been unearthed that have looked into the relationship between passes between areas of play on the field instead of passes between players (Cintia, Giannotti, Pappalardo, Pedreschi & Malvaldi, 2015;Gama et al, 2014;Travassos et al, 2016). There is even the occasional study available that combines both sources of information (Narizuka, Yamamoto & Yamazaki, 2014).…”

Section: Métodomentioning

confidence: 99%

“…La incertidumbre contextual de las interacciones entre jugadoras o jugadores y su ubicación espacial supone una información imprescindible para comprender cualquier situación de juego (McGarry, 2009). En este sentido se han podido encontrar varios estudios que han estudiado la relación de pases entre zonas del campo en lugar de entre jugadores (Cintia, Giannotti, Pappalardo, Pedreschi, & Malvaldi, 2015;Gama et al, 2014;Travassos et al, 2016). Incluso existe algún estudio que combina ambas informaciones (Narizuka, Yamamoto, & Yamazaki, 2014).…”

Section: Introductionunclassified

Dinàmica de passades en el futbol segons la recuperació de la pilota

Vives¹,

Martín²,

Hileno³

et al. 2018

Apunts

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IntroducciónEl análisis del rendimiento deportivo se ha transformado en los últimos años por el gran volumen de datos y la velocidad con la que se adquieren. Su análisis ha permitido establecer indicadores de rendimiento, facilitando la toma de decisiones de los distintos miembros del AbstractThe aim of this study is to analyze the attacking behavior of the team Real Club Deportivo Español over the seasons . To achieve this, the proposal is made to evaluate networks depending on the areas of the field where the ball was passed until it reached the final third. The passing impetus in the team that can be ascertained from this network over the two seasons under analysis allows us to capture the dynamics of offensive play throughout said period. It can be observed that the closing down of the flanks and a sturdy inside defense lead to ball recovery in interior zones, thus facilitating attacking play with the ball and bringing about counterattacks down the wings. Irrespective of the recovery zone (own half or opponent's half), the progression of the attack emerged through a direct clearance along the flanks to feed finishing zones. The use of this type of analysis allows for the identification of the most likely channels to be chosen by a team to reach the final third. Future records that contemplate the type of recovery and ending, as well as the players taking part in the sequence would allow for the application of other techniques and methods, along with the analysis of the main components or factorial analysis to reduce the dimensional nature of the data involved, thus obtaining the correlation of all those categories that define each sequence or pattern of offensive play.

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“…A combined network for both teams in a match was used, meaning that players on different teams were connected by edges representing miskicks and interceptions. Gama, Passos, Davids, Relvas, Ribeiro, Vaz, and Dias (2014) used a network-based approach to identify the key players in the attacking phases of play, while Pina, Paulo, and Ara´ujo (2017) investigated the relationship between specific network metrics and teams' outcome of offensive plays, after controlling for the effect of total passes. In the latter paper, a hierarchical logistic regression model was developed using data from the group stage of the UEFA Champions League 2015/2016, and the extent to which network density, clustering coefficients and centralisation could predict a team's performance on offensive plays were examined.…”

Section: Introductionmentioning

confidence: 99%

Analyzing passing networks in association football based on the difficulty, risk, and potential of passes

Wiig

Håland

Stålhane

et al. 2019

International Journal of Computer Science in Sport

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This paper investigates the use of network analysis to identify key players on teams, and patterns of passing within teams, in association football. Networks are constructed based on passes made between players, and several centrality measures are investigated in combination with three different methods for evaluating individual passes. Four seasons of data from the Norwegian top division are used to identify key players and analyze matches from a selected team. The networks examined in this work have weights based on three different aspects of the passes made: their probability of being completed, the probability that the team keeps possession after the completed pass, and the probability of the pass being part of a sequence leading to a shot. The results show that using different metrics and network weights leads to the identification of key passers in different phases of play and in different positions on the pitch.

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Network analysis and intra-team activity in attacking phases of professional football

Cited by 60 publications

References 5 publications

A importância de diferenciar o KII do KIII no Voleibol feminino de alto nível

A importância de diferenciar o KII do KIII no Voleibol feminino de alto nível

Dinàmica de passades en el futbol segons la recuperació de la pilota

Analyzing passing networks in association football based on the difficulty, risk, and potential of passes

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