8,197 research outputs found
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
Complex networks analysis in team sports performance: multilevel hypernetworks approach to soccer matches
Humans need to interact socially with others and the environment. These interactions
lead to complex systems that elude naïve and casuistic tools for understand these
explanations. One way is to search for mechanisms and patterns of behavior in our
activities. In this thesis, we focused on players’ interactions in team sports performance
and how using complex systems tools, notably complex networks theory and tools, can
contribute to Performance Analysis. We began by exploring Network Theory,
specifically Social Network Analysis (SNA), first applied to Volleyball (experimental
study) and then on soccer (2014 World Cup). The achievements with SNA proved
limited in relevant scenarios (e.g., dynamics of networks on n-ary interactions) and we
moved to other theories and tools from complex networks in order to tap into the
dynamics on/off networks. In our state-of-the-art and review paper we took an
important step to move from SNA to Complex Networks Analysis theories and tools,
such as Hypernetworks Theory and their structural Multilevel analysis. The method
paper explored the Multilevel Hypernetworks Approach to Performance Analysis in
soccer matches (English Premier League 2010-11) considering n-ary cooperation and
competition interactions between sets of players in different levels of analysis. We
presented at an international conference the mathematical formalisms that can express
the players’ relationships and the statistical distributions of the occurrence of the sets
and their ranks, identifying power law statistical distributions regularities and design
(found in some particular exceptions), influenced by coaches’ pre-match arrangement
and soccer rules.Os humanos necessitam interagir socialmente com os outros e com o
envolvimento. Essas interações estão na origem de sistemas complexos cujo
entendimento não é captado através de ferramentas ingénuas e casuísticas. Uma
forma será procurar mecanismos e padrões de comportamento nas atividades.
Nesta tese, o foco centra-se na utilização de ferramentas dos sistemas complexos,
particularmente no contributo da teoria e ferramentas de redes complexas, na
Análise do Desempenho Desportivo baseado nas interações dos jogadores de
equipas desportivas. Começámos por explorar a Teoria das Redes, especificamente
a Análise de Redes Sociais (ARS) no Voleibol (estudo experimental) e depois no
futebol (Campeonato do Mundo de 2014). As aplicações da ARS mostraram-se
limitadas (por exemplo, na dinâmica das redes em interações n-árias) o que nos
trouxe a outras teorias e ferramentas das redes complexas. No capítulo do estadoda-
arte e artigo de revisão publicado, abordámos as vantagens de utilização de
outras teorias e ferramentas, como a análise Multinível e Teoria das Híperredes.
No artigo de métodos, apresentámos a Abordagem de Híperredes Multinível na
Análise do Desempenho em jogos de futebol (Premier League Inglesa 2010-11)
considerando as interações de cooperação e competição nos conjuntos de
jogadores, em diferentes níveis de análise. Numa conferência internacional,
apresentámos os formalismos matemáticos que podem expressar as relações dos
jogadores e as distribuições estatísticas da ocorrência dos conjuntos e a sua ordem,
identificando regularidades de distribuições estatísticas de power law e design
(encontrado nalgumas exceções estatísticas específicas), promovidas pelos
treinadores na preparação dos jogos e constrangidas pelas regras do futebol
Signed Networks in Social Media
Relations between users on social media sites often reflect a mixture of
positive (friendly) and negative (antagonistic) interactions. In contrast to
the bulk of research on social networks that has focused almost exclusively on
positive interpretations of links between people, we study how the interplay
between positive and negative relationships affects the structure of on-line
social networks. We connect our analyses to theories of signed networks from
social psychology. We find that the classical theory of structural balance
tends to capture certain common patterns of interaction, but that it is also at
odds with some of the fundamental phenomena we observe --- particularly related
to the evolving, directed nature of these on-line networks. We then develop an
alternate theory of status that better explains the observed edge signs and
provides insights into the underlying social mechanisms. Our work provides one
of the first large-scale evaluations of theories of signed networks using
on-line datasets, as well as providing a perspective for reasoning about social
media sites
What’s next in complex networks? Capturing the concept of attacking play in invasive team sports
The evolution of performance analysis within sports sciences is tied to technology development and practitioner demands. However, how individual and collective patterns self-organize and interact in invasive team sports remains elusive. Social network analysis has been recently proposed to resolve some aspects of this problem, and has proven successful in capturing collective features resulting from the interactions between team members as well as a powerful communication tool. Despite these advances, some fundamental team sports concepts such as an attacking play have not been properly captured by the more common applications of social network analysis to team sports performance. In this article, we propose a novel approach to team sports performance centered on sport concepts, namely that of an attacking play. Network theory and tools including temporal and bipartite or multilayered networks were used to capture this concept. We put forward eight questions directly related to team performance to discuss how common pitfalls in the use of network tools for capturing sports concepts can be avoided. Some answers are advanced in an attempt to be more precise in the description of team dynamics and to uncover other metrics directly applied to sport concepts, such as the structure and dynamics of attacking plays. Finally, we propose that, at this stage of knowledge, it may be advantageous to build up from fundamental sport concepts toward complex network theory and tools, and not the other way around.info:eu-repo/semantics/acceptedVersio
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Deep learning has the potential to revolutionize sports performance, with
applications ranging from perception and comprehension to decision. This paper
presents a comprehensive survey of deep learning in sports performance,
focusing on three main aspects: algorithms, datasets and virtual environments,
and challenges. Firstly, we discuss the hierarchical structure of deep learning
algorithms in sports performance which includes perception, comprehension and
decision while comparing their strengths and weaknesses. Secondly, we list
widely used existing datasets in sports and highlight their characteristics and
limitations. Finally, we summarize current challenges and point out future
trends of deep learning in sports. Our survey provides valuable reference
material for researchers interested in deep learning in sports applications
Successful team synergies : a social network analysis on high performing soccer teams in the UEFA Champions League
A interacção sinergética entre colegas de uma equipa de futebol tem propriedades
susceptíveis de serem estudadas através da Social Network Analysis (SNA). A análise de redes formadas pelos passes de colegas de equipa tem demonstrado que o sucesso
colectivo está correlacionado com alta densidade de rede e coeficientes de clustering, bem como com centralização de rede reduzida. Apesar disso é importante evitar uma
simplificação excessiva no estudo deste fenómeno, nomeadamente a consideração por igual na obtenção das métricas de rede dos eventos que estão na origem quer da performance colectiva de sucesso quer de insucesso. No presente estudo, investigamos se a densidade, o coeficiente de clustering e a centralização das redes podem prever o sucesso ou o insucesso da performance de uma equipa no futebol. Analisámos 12 jogos do Grupo C da UEFA Champions League 2015/2016, utilizando registos públicos das transmissões de TV. Realizaram-se análises de notação para categorizar as sequências ofensivas como bem-sucedidas ou mal-sucedidas e para recolher os dados das redes de passe e subsequentes métricas. Utilizou-se um modelo de regressão logística hierárquica para prever o sucesso das sequências ofensivas a partir da densidade, do coeficiente de clustering e da centralização das redes, utilizando a variável total de passes como variável moderadora. Os resultados confirmaram o efeito independente das métricas de rede. A densidade, ao contrário do coeficiente de clustering e a centralização, foi um preditor significativo do sucesso das sequências ofensivas, tendo-se registado uma relação negativa entre densidade e sucesso de sequências ofensivas. Para além disso, densidades reduzidas foram associadas a um número superior de sequências ofensivas, embora maioritariamente mal-sucedidas. Por outro lado, altas densidades foram associadas a um número inferior de sequências ofensivas bem-sucedidas, mas também a um menor número total de sequências e de "perdas de posse de bola" sem que a equipa atacante tivesse conseguido entrar na zona de finalização. Uma análise individual por equipa indicou que a relação entre a
performance da equipa e a densidade é dependente da equipa. A aplicação de SNA aos desempenhos de sucesso e insucesso, de forma independente, de uma equipa de futebol é importante para minimizar uma possível simplificação excessiva das sinergias efectivas de uma equipa.The synergistic interaction between teammates in soccer has properties that can be
captured by Social Network Analysis (SNA). The analysis of networks formed by team players passing a ball in a match shows that team success is correlated with high network density and clustering coefficient, as well as with reduced network centralization.
However, oversimplification needs to be avoided, as network metrics events associated with success should not be considered equally to those that are not. In the present study, we investigated whether network density, clustering coefficient and centralization can predict successful or unsuccessful team performance. We analyzed 12 games of the Group Stage of UEFA Champions League 2015/2016 Group C by using public records from TV broadcasts. Notational analyses were performed to categorize attacking sequences as successful or unsuccessful, and to collect data on the ball-passing networks. The network metrics were then computed. A hierarchical logistic-regression model was used to predict the successfulness of the offensive plays from network density, clustering coefficient and centralization, by using the number of total passes as a moderator variable. Results confirmed the independent effect of network metrics. Density, but not clustering coefficient or centralization, was a significant predictor of the successfulness of offensive plays. We found a negative relation between density and successfulness of offensive plays.
However, reduced density was associated with a higher number of offensive plays, albeit
mostly unsuccessful. Conversely, high density was associated with a lower number of successful offensive plays, but also with overall fewer offensive plays and “ball possession losses” before the attacking team entered the finishing zone. An individual team analysis indicated that a relationship between team performance and density is team dependent.
Independent SNA of team performance is important to minimize the limitations of
oversimplifying effective team synergies
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