Using network science to analyze football passing networks: dynamics, space, time and the multilayer nature of the game

From the diversity of applications of Network Science, in this Opinion Paper we are concerned about its potential to analyze one of the most extended group sports: Football (soccer in U.S. terminology). As we will see, Network Science allows addressing different aspects of the team organization and performance not captured by classical analyses based on the performance of individual players. The reason behind relies on the complex nature of the game, which, paraphrasing the foundational paradigm of complexity sciences "can not be analyzed by looking at its components (i.e., players) individually but, on the contrary, considering the system as a whole" or, in the classical words of after-match interviews "it's not just me, it's the team".Comment: 7 pages, 1 figur

A visual analytics approach for passing strateggies analysis in soccer using geometric features

Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol

PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this paper, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by {\sf PlayeRank} and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank -- i.e. searching players and player versatility --- showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Tracking the ball location is essential for automated game analysis in complex ball-centered team sports such as football. However, it has always been a challenge for image processing-based techniques because the players and other factors often occlude the view of the ball. This study proposes an automated machine learning-based method for predicting the ball location from players' behavior on the pitch. The model has been built by processing spatial information of players acquired from optical tracking data. Optical tracking data include samples from 300 matches of the 2017-2018 season of the Turkish Football Federation's Super League. We use neural networks to predict the ball location in 2D axes. The average coefficient of determination of the ball tracking model on the test set both for the x-axis and the y-axis is accordingly 79% and 92%, where the mean absolute error is 7.56 meters for the x-axis and 5.01 meters for the y-axi

Shared affordances guide interpersonal synergies in sport teams

This chapter focuses on the technologies for monitoring interpersonal coordination in team sports as this is an area that is receiving growing interest. They can be categorized into those based on: signal propagation sensing, inertial sensors, vision/image-based systems, and electro-magnetic tracking. The chapter provides an overview of the technologies available for studying interpersonal coordination, highlighting the key measurement principles. Vision systems can be categorized as marker based or non-marker based. This chapter talks about the capabilities and availability of technologies that can be used to assess interpersonal coordination are developing rapidly. Technologies such as mobile phones containing Global Positioning System (GPS) and inertial sensors offer considerable potential. These and other developing technologies offer the possibility of extending the scale and frequency of interpersonal coordination analyses in both research and real-world contexts. The chapter also explains the Global Navigation Satellite System (GNSS) that is a system of satellites provides positioning over the entire globe.info:eu-repo/semantics/acceptedVersio

Team performance according to ball possession characteristics : a social networks approach

Over the last few years, football entered in a period of accelerated access to large amount of match analysis data. Social networks have been adopted to reveal the structure and organization of the web of interactions, such as the players passing distribution tendencies. In this study we investigated the influence of ball possession characteristics in the competitive success of Spanish La Liga teams. The sample was composed by OPTA passing distribution raw data (n=269,055 passes) obtained from 380 matches involving all the 20 teams of the 2012/2013 season. Then, we generated 760 adjacency matrixes and their corresponding social networks using Node XL software. For each network we calculated three team performance measures to evaluate ball possession tendencies: graph density, average clustering and passing intensity. Three levels of competitive success were determined using two-step cluster analysis based on two input variables: the total points scored by each team and the scored per conceded goals ratio. Our analyses revealed significant differences between competitive performances on all the three team performance measures (p < .001). Bottom-ranked teams had less number of connected players (graph density) and triangulations (average clustering) than intermediate and top-ranked teams. However, all the three clusters diverged in terms of passing intensity, with top-ranked teams having higher number of passes per possession time, than intermediate and bottom-ranked teams. Finally, similarities and dissimilarities in team signatures of play between the 20 teams were displayed using Cohen’s effect size. In sum, findings suggest the competitive performance was influenced by the density and connectivity of the teams, mainly due to the way teams use their possession time to give intensity to their game.Ao longo dos últimos anos, o futebol entrou num período de acesso rápido a uma grande quantidade de dados de análise de jogo. As redes sociais têm sido adoptadas para revelar a estrutura e organização da rede de interacções, como as tendências de passe dos jogadores. Neste estudo investigou-se a influência das características posse de bola no sucesso competitivo das equipas Espanholas de La Liga. A amostra foi composta por dados brutos da distribuição de passe da OPTA (n = 269.055 passes) obtidos a partir de 380 jogos onde estão envolvidas todas as 20 equipas da temporada 2012/2013. Então, geramos 760 matrizes de adjacência e as suas redes sociais correspondentes, utilizando o software Node XL. Para cada rede foram calculadas três medidas de desempenho da equipa de forma a avaliar as tendências da posse de bola: graph density, average clustering e passing intensity. Foram identificados três níveis de sucesso competitivo utilizando uma análise de grupos a dois níveis com base em duas variáveis: O total de pontos marcados por cada equipa e o rácio de golos marcados por golos sofridos. A nossa análise revelou diferenças significativas entre desempenhos competitivos em todas as três medidas de desempenho da equipa (p <0,001). As equipas classificadas no fundo do ranking apresentaram menor número de jogadores conectados (graph density) e triangulações (average clustering) do que as equipas com ranking intermédio e de topo. No entanto, todos os três grupos divergiram em termos de intensidade de passe (passing intensity), sendo que as equipas de topo do ranking têm um maior número de passes por tempo de posse de bola, do que as equipas com ranking intermédio ou baixo. Finalmente, foram encontradas semelhanças e diferenças nos padrões de jogo das 20 equipas utilizando Cohen’s effect size. Em suma, os resultados sugerem que o desempenho competitivo foi influenciado pela densidade e conectividade das equipas (Graph density and average clustering, respectivamente), principalmente devido à forma como as equipas usam o seu tempo de posse de bola para dar intensidade ao seu jogo