Visual soccer match analysis using spatiotemporal positions of players

Machado, Vinícius; Leite, Roger A.; Moura, Felipe Arruda; Cunha, Sérgio Augusto; Sadlo, Filip; Comba, João Luiz Dihl

doi:10.1016/j.cag.2017.08.006

Cited by 28 publications

(31 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Shorter trajectories (trails) also aim at reducing visual clutter, and are used in sports when entities are simultaneously moving such as during boat races [BC16], soccer games [AAB*16] (see Figure ), or swimming races [CM16]. Trajectories are often used as visual signatures of phases of play [MLM*17, PVF13b]. These small multiples can represent segmented and abstracted sports events, such as football catches [Tim17], which then act as small visual summaries of particular game events.…”

Section: Visualization By Type: Tracking Datamentioning

confidence: 99%

State of the Art of Sports Data Visualization

Périn

Vuillemot

Stolper

et al. 2018

Computer Graphics Forum

106

View full text Add to dashboard Cite

In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially-growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real-world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in-game actions and trajectories), and meta-data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high-level discussion of sports visualization research informed by our analysis-identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/.

show abstract

Section: Visualization By Type: Tracking Datamentioning

confidence: 99%

State of the Art of Sports Data Visualization

Périn

Vuillemot

Stolper

et al. 2018

Computer Graphics Forum

106

View full text Add to dashboard Cite

show abstract

“…It has been demonstrated that they can be successfully applied to various problem domains. [4][5][6][7] Fundamentally important is the interaction with graphical data representations, which supports integrating the human capability of recognizing visual patterns (inference capability) and a user's knowledge about the data analysis process. Visual analytics solutions assist users in creating adequate mental models of complex data, supporting the perception of global characteristics and identification of local behavior through user-driven exploration.…”

Section: Introductionmentioning

confidence: 99%

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

Soriano-Vargas

Hamann

Oliveira

2019

Information Visualization

View full text Add to dashboard Cite

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

show abstract

“…Our approach bears similarity to the one adopted by Machado et al (2017), who present visual designs based on pixel-oriented layouts, called Player Attribute Heatmap (PAH), to summarize a soccer match using as input players positions over time. The PAH visualization, illustrated in Figure 16, is a matrix display of the player positions during part of the match (often a half-time), conveying how player positions and tactical schemes change along the match.…”

Section: Exploratory Visualization Of Multivariate Time Seriesmentioning

confidence: 99%

“…Normalized time series line graphs of the variables can also be inspected, with lines colored according to its variable's cluster. This view is associated with an array showing the corresponding matrix values, simulating a timeline, as shown in Figure 42, our version of the temporal pixel-oriented layout proposed by Machado et al (2017). These views facilitate identifying common patterns between values in different time windows.…”

Section: Variable Correlation Matrixmentioning

confidence: 99%

See 1 more Smart Citation

Visual exploration to support the identification of relevant attributes in time-varying multivariate data

Vargas¹

View full text Add to dashboard Cite

This work is dedicated to my husband Oscar Cuadros Linares, to my father Raul Soriano Filinich, and to my mother, Dora Vargas Núñez de Soriano. Foremost, I wish to give my heartfelt thanks to a very special person, my husband Oscar Cuadros. Thanks for his unconditional love, his unflagging encouragement, patience, and continued support that made the culmination of this thesis possible. I greatly value his insightful comments, the stimulating discussions, that helped me to keep things in perspective, the sleepless nights we were working together before deadlines and for all the fun we have had on this journey. I would like to express my special appreciation and my sincere gratitude to my advisor Prof. Maria Cristina Ferreira de Oliveira. Thanks (i) for the continuous support and patience during all my Ph.D. study and research, especially in the most critical periods, (ii) for her helpful and priceless advice, and (iii) for her insightful comments. Also, I would like to thank Prof. Bernd Hamann for receiving me as a visiting research scholar at the University of California, Davis. Thanks for his guidance, for supporting my research ideas, and for his insightful discussions about the research in the last months. Both of them, Prof. Cristina and Prof. Bernd, were and remain my best role models for a scientist and mentor. ¡Muchas gracias! I would like to extend my sincerest gratitude to the people who mean a lot to me, my parents Raul Soriano Filinich and Dora Vargas Núñez de Soriano. Thanks for their unconditional love, their emotional support, for all of the sacrifices that you have made on my behalf. Also, I express my thanks to my sister Laura, my little heart Fabio and my grandaunt Marlene Núñez (Pipito). I would also like to dedicate this thesis to the memory of my grandmother Mamá Nelly Núñez. It was hard to say goodbye from afar. Even though the Alzheimer's demons attacked her, she treasured the memory of her granddaughter Aurea. Many times she talked with me about her granddaughter(me) with so much love, and joy because she(I) would visit her, not knowing that it was me. My mind and my heart treasure the great memories we had. Rest in peace. I would like to thank all my friends from Brazil, Spain, United States and Peru, especially the Reginas girls, the Pipocas girls, and the Chelas guys, who always support me, give me good advice and make my life colorful. Finally, I acknowledge the financial support provided by the State of São Paulo Research Foundation (FAPESP) grants 12/24537-0 and 15/12831-0. "It will hurt. It will take time. It will require dedication. It will require willpower. You will need to make healthy decisions. It requires sacrifice. You will need to push your body to its max. There will be temptation. But, I promise you, when you reach your goal, it's worth it. " (Unknown)

show abstract

Visual soccer match analysis using spatiotemporal positions of players

Cited by 28 publications

References 14 publications

State of the Art of Sports Data Visualization

State of the Art of Sports Data Visualization

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

Visual exploration to support the identification of relevant attributes in time-varying multivariate data

Contact Info

Product

Resources

About