Analysis of defensive playing styles in the professional Chinese Football Super League

Ruan, Lingfeng; Ge, Huanmin; Gómez, Miguel–Ángel; Shen, Yanfei; Gong, Bingnan; Cui, Yixiong

doi:10.1080/24733938.2022.2099964

Cited by 8 publications

(4 citation statements)

References 48 publications

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“…However, observing the variables that load the factors, we find that the variables of factor four (lost balls) can only appear in situations of defensive transition, while the variables of factor five (picking up free balls) can only appear in situations of offensive transition. Similarly, Ruan, Ge, Gómez, Shen, Gong, and Cui [ 50 ] give factors 6 and 8 the same name (defense of goalkeepers) even though, as the authors report, different teams have higher values in each of them. Moreover, in the study by Gómez, Mitrotasios, Armatas and Lago-Peñas [ 8 ], factor four was named counter-attack despite the fact that one of the loaded variables is lost balls, which cannot result in a counter-attack.…”

Section: Discussionmentioning

confidence: 99%

“…As examples, we mention the offside trap, which is a very common tactical tool of coaches [ 66 , 67 , 68 , 69 ], the passing tempo that gives very useful information about the playing style of a team [ 12 , 40 , 70 ], the counter-pressing [ 71 , 72 ] and so on. Further, only two studies employing factor analysis with PCA [ 47 , 50 ] had access to physical performance variables. However, they found no association between these variables and the styles emerging from technical-tactical variables.…”

Section: Discussionmentioning

confidence: 99%

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Identifying Soccer Teams’ Styles of Play: A Scoping and Critical Review

Plakias

Moustakidis²,

Kokkotis

et al. 2023

JFMK

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Identifying and measuring soccer playing styles is a very important step toward a more effective performance analysis. Exploring the different game styles that a team can adopt to enable a great performance remains under-researched. To address this challenge and identify new directions in future research in the area, this paper conducted a critical review of 40 research articles that met specific criteria. Following the 22-item Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, this scoping review searched for literature on Google Scholar and Pub Med database. The descriptive and thematic analysis found that the objectives of the identified papers can be classified into three main categories (recognition and effectiveness of playing styles and contextual variables that affect them). Critically reviewing the studies, the paper concluded that: (i) factor analysis seems to be the best technique among inductive statistics; (ii) artificial intelligence (AI) opens new horizons in performance analysis, and (iii) there is a need for further research on the effectiveness of different playing styles, as well as on the impact of contextual variables on them.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identifying Soccer Teams’ Styles of Play: A Scoping and Critical Review

Plakias

Moustakidis²,

Kokkotis

et al. 2023

JFMK

View full text Add to dashboard Cite

show abstract

“…Much effort has been devoted by football players and coaches as well as researchers in academia to improve the player’s performance and support the entire football industry. For example, numerous studies have been conducted on indicators of player’s technique [ 2 , 3 ], tactic [ 4 , 5 ], physical ability [ 6 , 7 ], physiological function [ 8 , 9 ], psychological state [ 10 , 11 ], among others to better understand football.…”

Section: Introductionmentioning

confidence: 99%

Analysis of football research trends using text network analysis

Kim

2024

PLoS ONE

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This study was aimed to identify football research trends in various periods. A total of 30,946 football papers were collected from a representative academic database and search engine, the ‘Web of Science’. Keyword refinement included filtering nouns, establishing synonyms and thesaurus, and excluding conjunctions, and the Cyram’s Netminer 4.0 software was used for network analysis. A centrality analysis was conducted by extracting the words corresponding to the top 2% of the main research topics to obtain the degree and eigenvector centralities. The most frequently mentioned research keywords were injury, performance, and club. Keyword performance showed the highest degree centrality (0.294) and keyword world and cup showed the highest eigenvector centrality (0.710). The keyword with the highest eigenvector degree changed from injury in the 1990s and world in the 2000s to cup since the 2010s. Although various studies on football injuries have been conducted, research on the sport itself has recently been conducted. This study provides fundamental information on football trends from research published over the past 30 years.

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“…These discrepancies might be explainable by distinguishing between intra-and inter-team differences in possession. On the one hand, better teams (teams that win more games) tend to play a more possession-based style (Gollan et al, 2018;Kempe et al, 2014;Ruan et al, 2022;Zhou et al, 2021). On the other hand, all teams have to get more proactive when trailing, which leads to higher ball possession percentages (Liu et al, 2016;Mao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Success-Score in Professional Soccer – Validation of a Dynamic Key Performance Indicator Combining Space Control and Ball Control within Goalscoring Opportunities

Brinkjans

Memmert

Imkamp

et al. 2022

International Journal of Computer Science in Sport

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Typical performance indicators in professional quantitative soccer analysis simplify complex matters, resulting in loss of information. Hence, a novel approach to characterize the performance of soccer teams was investigated: Success-Scores, combining space control with ball control and the correlation between the two. Success-Score Profiles were calculated for 14 games from the German Bundesliga. The dataset was split into two groups: all data points above resp. below the 80th percentile of Success-Scores. Subsequently, the relative goalscoring frequency in those two groups was compared. All data points were sorted according to their Success-Score and split into equally sized eighths. These groups were tested for a rank order correlation with the number of scored goals. Finally, the Success-Scores of two teams with different success levels as well as their opponents’ Success-Scores were compared. Results indicated significantly higher goalscoring frequencies above the 80th percentile for Success-Scores and a statistically significant rank order correlation between the Success-Scores and the number of scored goals, rs(6) = 0.73, p = .04. The more successful team showed significantly higher Success-Scores. This novel performance indicator shows significant connections to success defined as scoring goals and final ranking in elite soccer and therefore shows potential in reconizing underlying performance.

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Analysis of defensive playing styles in the professional Chinese Football Super League

Cited by 8 publications

References 48 publications

Identifying Soccer Teams’ Styles of Play: A Scoping and Critical Review

Identifying Soccer Teams’ Styles of Play: A Scoping and Critical Review

Analysis of football research trends using text network analysis

Success-Score in Professional Soccer – Validation of a Dynamic Key Performance Indicator Combining Space Control and Ball Control within Goalscoring Opportunities

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