BKViz: A Basketball Visual Analysis Tool

Losada, Antonio G.; Therón, Roberto; Benito-Santos, Alejandro

doi:10.1109/mcg.2016.124

Cited by 43 publications

(25 citation statements)

References 6 publications

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“…In addition, the winner (Golden State Warriors) and runner-up (Cleveland Cavaliers) of the NBA finals possessed pre-eminent or ‘all-star’ players with excellent coaching staff, components of a ‘Superteam’. 27 Importantly, ‘all-star’ players were more efficient than others as they were able to perceive environmental information and adapt their behaviour accordingly with less mistakes when deciding offensive and defensive actions. 28 Thus, the highly competitive and effective playing styles of ‘all-star’ players likely influenced the whole team’s style of play throughout the season.…”

Section: Discussionmentioning

confidence: 99%

Evolution of game-play characteristics within-season for the National Basketball Association

Zhang

Lorenzo

Woods

et al. 2019

International Journal of Sports Science & Coaching

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The aim of this study was to explore the (dis)similarity of game-play characteristics throughout an in-season period within the National Basketball Association. Thirteen performance-related indicators of all 1230 games of the regular 2016–2017 season in the National Basketball Association were analysed. Non-metric multidimensional scaling was used to examine (dis)similarity of team profiles. The two-dimensional multivariate matrix showed that team profiles generally presented similarity, while the beginning and ending of the season (October and April) showed relative dissimilarity. Although each team presented unique paths throughout the in-season period, the dominant teams in the National Basketball Association presented similar game styles. In addition, the game-play of the teams evolved into effective interactions in terms of offence and defence as the competition progressed while presenting an increased trend in the number of three-point field-goals made (p < 0.000, small effect size (η2) = 0.011) throughout the in-season period. The analytics performed in this study could be used practically to evaluate temporal changes of game-play characteristics in basketball as well as inform strategic periodization plans within season periods.

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Section: Discussionmentioning

confidence: 99%

Evolution of game-play characteristics within-season for the National Basketball Association

Zhang

Lorenzo

Woods

et al. 2019

International Journal of Sports Science & Coaching

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“…Some techniques have been developed to analyze some variables sets that can be acquired in real‐time, such as pedaling forces [KYY∗16] or the aerodynamic flows around a cyclist [MLT∗20]. There is also a host of systems that visualize some other aspects of team sports [AAB∗17, CEGH17, FMB∗15, LOC∗16, LTB16, PSBS12, SSS∗14, SHJ∗15]. However, most of the techniques can only be applied to closed and relatively small playgrounds since they rely on analyzing repeated actions of single players or the whole team structure, which is not the case of bicycle races.…”

Section: Related Workmentioning

confidence: 99%

TourVis: Narrative Visualization of Multi‐Stage Bicycle Races

Iriberri

Fort

Vázquez

2021

Computer Graphics Forum

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There are many multiple‐stage racing competitions in various sports such as swimming, running, or cycling. The wide availability of affordable tracking devices facilitates monitoring the position along with the race of all participants, even for non‐professional contests. Getting real‐time information of contenders is useful but also unleashes the possibility of creating more complex visualization systems that ease the understanding of the behavior of all participants during a simple stage or throughout the whole competition. In this paper we focus on bicycle races, which are highly popular, especially in Europe, being the Tour de France its greatest exponent. Current visualizations from TV broadcasting or real‐time tracking websites are useful to understand the current stage status, up to a certain extent. Unfortunately, still no current system exists that visualizes a whole multi‐stage contest in such a way that users can interactively explore the relevant events of a single stage (e.g. breakaways, groups, virtual leadership…), as well as the full competition. In this paper, we present an interactive system that is useful both for aficionados and professionals to visually analyze the development of multi‐stage cycling competitions.

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“…NBAVis [LSS13] consists of a scatterplot with multiple dimensions such as team, rebounds, assists, points, and blocks to explore NBA data. With Buckets [Bes16] and BKViz [LTB16], it is possible to explore the points a basketball team scored against each other team in the league, among many other facets of the data. Chung et al's knowledge‐based ranking system features multiple coordinated views to explore the multiple facets of rugby games [CPG*16].…”

Section: Visualization By Type: Box‐score Datamentioning

confidence: 99%

State of the Art of Sports Data Visualization

Périn

Vuillemot

Stolper

et al. 2018

Computer Graphics Forum

106

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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/.

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BKViz: A Basketball Visual Analysis Tool

Cited by 43 publications

References 6 publications

Evolution of game-play characteristics within-season for the National Basketball Association

Evolution of game-play characteristics within-season for the National Basketball Association

TourVis: Narrative Visualization of Multi‐Stage Bicycle Races

State of the Art of Sports Data Visualization

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