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

Spatial-temporal metrics to assess collective behavior in football: a systematic review and assessment of research quality and applicability.

Extensive research has been conducted to investigate collective behaviour of football players using spatial-temporal data. The purpose of this systematic review was to synthesise and evaluate the applicability of this research by reviewing information presented in previous studies and its capacity to clearly describe the analysis approaches and practical applications of findings. Eighty-five studies were included in the review with approaches assigned to 4 categories of metrics (1: spaces; 2: distances; 3) position; 4: numerical relations) and 2 analysis methods (predictability and synchronisation). The review identified that authors descriptions of metrics generally focussed on operationalised definitions and provided limited translation to game scenarios or coaching strategies. Similarly, a substantive percentage of studies (22%) did provide any practical applications, and where these were provided, they were generally broad and provided limited actionable information that could be used directly by practitioners to inform training. Where specific applications were provided these were consistent with a dynamic systems perspective of collective behaviour and focused on organismic, environmental and task constraints that could be manipulated. The findings of the present review highlight the innovative practices of the research base and identify several areas for development to increase understanding and uptake in practice

Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach

Effects of score line on match performance in professional soccer players

Investigating the impact of score line on performance in professional soccer players is of major interest to managers and coaches within professional soccer. However, the majority of studies investigating score line have omitted important components of performance such as team and opposition ability, goal difference and fatigue. Little is also known, regarding the impact of score line on the performance of different playing positions in different areas of the pitch. Thus, the aim of this thesis was to examine the effects of score line on the technical, physical and psychological components of performance across a number of variables (team ability, opposition ability, match location, pitch position, playing position) using new methods of automated tracking and data collection. The majority of participants were recruited from the English Premier League consisting of 1027 players and 501 games analysed across multiple seasons (2004-2005, 2007-2008, 2011-2012). The exception to this was Chapter 5 where the 75 participants were recruited from both EPL and Championship teams. Across all chapters playing position was characterised as striker (attacker), defender and midfielder and ability (both team and opposition) was defined as final league finish position. Chapter 3 revealed in the 5 minutes that preceded a goal, the scoring team played a significantly greater percentage of passes accurately (72.4 ± 12.7) compared to the average for the half (70.2 ± 7.5) (P 0.7). The results demonstrate good validity over a range of soccer specific movements and speeds, up to and including sprinting. In Chapter 7a multi-level regression revealed an inverted “u” shaped association between total distance covered and goal difference (GD), with greater distances covered when GD was zero and reduced distances when GD was either positive or negative. A similar “u” shaped association was found with high speed distance covered at home. In addition, distance covered (both at home and away) were predicted by playing position. All activity profiles (with the exception of sprint distance at home) were predicted by pitch location and time scored. Lastly, distance away from home and high speed running at home were predicted by opposition ability. In Chapter 7b multi-level regression revealed a “u” shaped association between passing accuracy and goal difference (GD) with greater accuracy occurring at extremes of GD e.g., when the score was either positive or negative. The same pattern was seen for corner accuracy away from home e.g., corner accuracy was lowest when the score was close with the lowest accuracy at extremes of GD. Although free-kicks were not associated with GD, team ability, playing position and pitch location were found to predict accuracy. No situational variables were found to predict cross accuracy. The results of this thesis suggest that a number of variables are associated with both the physical and technical performance of players in difference score lines and that such effects may be related to the perception of events (as shown in Chapter 5) rather than fatigue, ability or opposition as previously thought. The current study also highlighted the need for more sensitive score line definitions in which to consider score line effects using technological advancements such as automated tracking systems