SoccerCPD

Kim, Hyunsung; Kim, Bit; Dongwook, Chung,; Yoon, Jinsung; Ko, Sang‐Ki

doi:10.1145/3534678.3539150

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Secondly, in three studies [ 40 , 41 , 42 ], their purpose was not clearly stated. Finally, in three studies [ 45 , 46 , 47 ], the characteristics of the sample (and the data) are not completely clear.…”

Section: Discussionmentioning

confidence: 99%

“…Particularly, five studies [20,21,29,40,42] compare players based on vectors without describing any specific playing styles associated with different positions in the team formation. Similarly, two studies [41,47] that employed other computational methods did not provide descriptions of player styles. This does not necessarily imply that these investigations lack quality, but it suggests that they may have different objectives or purposes.…”

Section: Theoretical Framework For Players' Playing Stylesmentioning

confidence: 99%

“…They drop deep into midfield to create space and disrupt the opposition's defensive structure. They are technically skilled, good at dribbling, and have excellent vision to create scoring opportunities for themselves and teammates [47,67].…”

Section: False Ninementioning

confidence: 99%

See 2 more Smart Citations

Identifying Soccer Players’ Playing Styles: A Systematic Review

Plakias

Moustakidis²,

Kokkotis

et al. 2023

JFMK

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Identifying playing styles in football is highly valuable for achieving effective performance analysis. While there is extensive research on team styles, studies on individual player styles are still in their early stages. Thus, the aim of this systematic review was to provide a comprehensive overview of the existing literature on player styles and identify research areas required for further development, offering new directions for future research. Following the PRISMA guidelines for systematic reviews, we conducted a search using a specific strategy across four databases (PubMed, Scopus, Web of Science, and SPORTDiscus). Inclusion and exclusion criteria were applied to the initial search results, ultimately identifying twelve studies suitable for inclusion in this review. Through thematic analysis and qualitative evaluation of these studies, several key findings emerged: (a) a lack of a structured theoretical framework for player styles based on their positions within the team formation, (b) absence of studies investigating the influence of contextual variables on player styles, (c) methodological deficiencies observed in the reviewed studies, and (d) disparity in the objectives of sports science and data science studies. By identifying these gaps in the literature and presenting a structured framework for player styles (based on the compilation of all reported styles from the reviewed studies), this review aims to assist team stakeholders and provide guidance for future research endeavors.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Framework For Players' Playing Stylesmentioning

confidence: 99%

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Identifying Soccer Players’ Playing Styles: A Systematic Review

Plakias

Moustakidis²,

Kokkotis

et al. 2023

JFMK

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“…For example, a video assistant referee system has been deployed in football over 100 competitions including the 2022 FIFA World Cup to support the referee team to reduce misjudgments 1 . On the other hand, player evaluation and tactic investigation have improved the effectiveness in multiple sports, such as football [1,7,23,36], baseball [16,24], basketball [4,47], and badminton [3,43]. To train models for these applications, the datasets are mainly collected from either videos [9,50] or sensors [15,18,29] to extract the enormous variable metadata in the matches.…”

Section: Introductionmentioning

confidence: 99%

ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis

Wang

Huang

İk

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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With the recent progress in sports analytics, deep learning approaches have demonstrated the effectiveness of mining insights into players' tactics for improving performance quality and fan engagement. This is attributed to the availability of public groundtruth datasets. While there are a few available datasets for turnbased sports for action detection, these datasets severely lack structured source data and stroke-level records since these require highcost labeling efforts from domain experts and are hard to detect using automatic techniques. Consequently, the development of artificial intelligence approaches is significantly hindered when existing models are applied to more challenging structured turn-based sequences. In this paper, we present ShuttleSet, the largest publiclyavailable badminton singles dataset with annotated stroke-level records. It contains 104 sets, 3,685 rallies, and 36,492 strokes in 44 matches between 2018 and 2021 with 27 top-ranking men's singles and women's singles players. ShuttleSet is manually annotated with a computer-aided labeling tool to increase the labeling efficiency and effectiveness of selecting the shot type with a choice of 18 distinct classes, the corresponding hitting locations, and the locations of both players at each stroke. In the experiments, we provide multiple benchmarks (i.e., stroke influence, stroke forecasting, and movement forecasting) with baselines to illustrate the practicability of using ShuttleSet for turn-based analytics, which is expected to stimulate both academic and sports communities. Over the past two years, a visualization platform has been deployed to illustrate the variability of analysis cases from ShuttleSet for coaches to delve into players' tactical preferences with human-interactive interfaces, which was also used by national badminton teams during multiple international high-ranking matches.

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“…the sprinter and the other players should be processed in a permutation-invariant manner since the order of input players to the model is not important. Thus, inspired by a recent study that also handled trajectories in multi-agent sports [Kim et al, 2023], we employ Set Transformer [Lee et al, 2019] to extract the context-aware embedding of a given situation while securing the permutation-invariance of input players. To be specific, let p 1 be the considered sprinter and P 1 = {p 1 , .…”

Section: Others (Oth)mentioning

confidence: 99%

Republic of Korea (South Korea)

Kim¹

The Crime of Aggression

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Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem. Existing methods require reference images to feed style information into semantic layouts, which indicates that the style is constrained by the given image. In this paper, we propose a model named RUCGAN for user controllable semantic image synthesis, which utilizes a singular color to represent the style of a specific semantic region. The proposed network achieves reference-free semantic image synthesis by injecting color as userdesired styles into each semantic layout, and is able to synthesize semantic images with unusual colors. Extensive experimental results on various challenging datasets show that the proposed method outperforms existing methods, and we further provide an interactive UI to demonstrate the advantage of our approach for style controllability. The codes and UI are available at: https://github.com/BenjaminJonghyun/RUCGAN

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SoccerCPD

Cited by 5 publications

References 15 publications

Identifying Soccer Players’ Playing Styles: A Systematic Review

Identifying Soccer Players’ Playing Styles: A Systematic Review

ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis

Republic of Korea (South Korea)

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