Computing Similarity between a Pair of Trajectories

With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of identifying common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of identifying similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. Our model is robust under noise and outliers, it does not make any assumptions on the sampling rates on either trajectory, and it works even if they are partially observed. Additionally, the model also yields a scalar similarity score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We also present efficient algorithms for computing the similarity under our measures; the worst-case running time is quadratic in the number of sample points. Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing with it with earlier approaches, and illustrating many issues that arise in trajectory data. Our experiments show that our approach is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling

SoccerStories: A Kick-off for Visual Soccer Analysis

This article presents SoccerStories, a visualization interface to support analysts in exploring soccer data and communicating interesting insights. Currently, most analyses on such data relate to statistics on individual players or teams. However, soccer analysts we collaborated with consider that quantitative analysis alone does not convey the right picture of the game, as context, player positions and phases of player actions are the most relevant aspects. We designed SoccerStories to support the current practice of soccer analysts and to enrich it, both in the analysis and communication stages. Our system provides an overview+detail interface of game phases, and their aggregation into a series of connected visualizations, each visualization being tailored for actions such as a series of passes or a goal attempt. To evaluate our tool, we ran two qualitative user studies on recent games using SoccerStories with data from one of the world's leading live sports data providers. The first study resulted in a series of four articles on soccer tactics, by a tactics analyst, who said he would not have been able to write these otherwise. The second study consisted in an exploratory follow-up to investigate design alternatives for embedding soccer phases into word-sized graphics. For both experiments, we received a very enthusiastic feedback and participants consider further use of SoccerStories to enhance their current workflow

The offensive patterns causing disequilibrium in the defensive organization of the opponent leading to a goal scored in soccer

This study has an interest to understand and detect the offensive patterns of the most 2 teams with highest goal scored per game in the top 5 leagues and the effect on creating disequilibrium on the opponentopponent’s defensive lines. Thus, this allows to identify the interactions of the players between their teammates and their opponent. 76 out of 99 goals for Bayern Munich and 67 out of 90 goals for Atalanta were observed and analyzed using REOFUT protocol. Some similarities were detected between both teams using Chi Square Test to discover the association between different variables like Initial opponent behavior and Type of attack, Penultimate action and Penultimate invasive zone, Last action and Penultimate action with, X^2= 15.005, P=0.05, X^2= 31.932, P=0.006 X^2= 40.920, P= < respectively for Bayern Munich and X^2= 14.983a, P=0.045, X^2= 24.945a, P=0.034 and X^2= 20.696a, P=0.015, respectively for Atalanta As a conclusion, although the detection of the correlation between both team and opponentopponent’s behavior, number, pressure and space, various factors influence the patterns and playing dynamics which were not mentioned all in this study.Este estudo tem como objetivo compreender e detetar os pa drões ofensivos das duas equipas com maior número de golos marcados por jogo nas 5 principais ligas e o efeito na criação de desequilíbrio nas linhas defensivas do adversário. Com isso, torna se possível identificar as interações dos jogadores entre seus c ompanheiros e adversários. 76 de 99 golos do Bayern de Munique e 67 de 90 golos do Atalanta foram observados e analisados usando o protocolo REOFUT. Algumas semelhanças foram detectadas entre as equipas usando o Teste Qui Quadrado para descobrir a associ ação entre diferentes variáveis como comportamento inicial do oponente e tipo de ataque, penúltima ação e penúltima zona invasiva, última ação e penúltima ação com X^2= 15.005, P=0.05, X^2= 31.932, P=0.006 e X^2= 40.920, P= < respectivamente para o Bayern Munich e X^2= 14.983a, P=0.045, X^2= 24.945a, P=0.034 e X^2= 20.696a, P=0.015, respectivamente para o Atalanta Como conclusão, embora a detecção da correlação entre o comportamento da equipa e do adversário, número, pressão e espaço, vários fato res influenciam os padrões e a dinâmica de jogo que não foram mencionados neste estudo