Technical and physical match performance of teams in the 2018 FIFA World Cup: Effects of two different playing styles

Yi, Qing; Gómez, Miguel Ángel; Wang, Lei; Huang, Gan; Zhang, Hengliang; Liu, Hongyou

doi:10.1080/02640414.2019.1648120

Cited by 51 publications

(43 citation statements)

References 34 publications

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Section: Technical Variables and Situational Variablesmentioning

confidence: 99%

“…Twenty-five technical performance-related match actions or events were chosen as variables in the present study and were divided into four groups (Table 1) based on previous studies [18,[34][35][36]. Definitions of these variables can be found in the previous studies [11,20,36]. The technical match data was analyzed under the following five situational variables: (1) competition stage: group stage and knockout stage; (2) match location: home and away; (3) quality of team: teams that qualified into the knockout stage and teams that didn't qualify into the knockout stage; (4) quality of opponent: opponents that qualified into the knockout stage and opponents that didn't qualify into the knockout stage; and (5) match outcome: win, draw and lose.…”

Section: Technical Variables and Situational Variablesmentioning

confidence: 99%

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Evaluation of the Technical Performance of Football Players in the UEFA Champions League

Ruano

Liu

et al. 2020

IJERPH

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This study aimed to assess the technical match performance of top-class football players in a long-term perspective. Technical performance profiles of players according to five playing positions (central defender, full back, wide midfielder, central midfielder, forward) and five situational variables (competition stage, match location, quality of team, quality of opponent, match outcome) were established. Technical match data of players in the UEFA Champions League from season 2009-2010 to 2016-2017 were analyzed. The true effects of positional and situational variables on players' technical performance were evaluated by the non-clinical magnitude-based inference. Results showed that the effect of competition stage on player's performance was negligible. Quality of team, quality of opponent and match outcome revealed the strongest effects on player's performance (ES: −0.42 ± 0.10-0.59 ± 0.10) while the effect of match location was relatively lower (ES: −0.32 ± 0.10-0.23 ± 0.07). The number of variables that showed statistical differences under five competing contexts for wide midfielders and forwards were higher than those of central defenders, full backs, and central midfielders. Differences of players' match performance could mainly be identified in variables related to goal scoring, passing, and organizing, these findings may provide important insights for coaches and analysts during the match preparation and training session.

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Section: Technical Variables and Situational Variablesmentioning

confidence: 99%

Section: Technical Variables and Situational Variablesmentioning

confidence: 99%

Evaluation of the Technical Performance of Football Players in the UEFA Champions League

Ruano

Liu

et al. 2020

IJERPH

Self Cite

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“…High pressure, low pressure, pressure on wide areas, and pressure on central areas were the defensive styles of play identified (Fernandez-Navarro et al, 2016). Kempe et al (2014) showed that successful teams prefer possession play, and similarly, Yi et al (2019) showed that teams using possession play scored higher for the variables related to goal scoring, attacking and passing. However, according to other studies that explored further the characteristics of the possession style of play, by analyzing the factors that explain the self-organization in possession team play (Chassy, 2013), and examining the success of this possession play (Collet, 2013); the former study showed that possession itself did not predict shooting opportunities, and in the same way, the later demonstrated that possession was a poor predictor of performance when controlling for team quality and home advantage.…”

Section: Variable Definitionmentioning

confidence: 99%

Tactical Variables Related to Gaining the Ball in Advanced Zones of the Soccer Pitch: Analysis of Differences Among Elite Teams and the Effect of Contextual Variables

et al. 2020

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Attacking tactical variables have been commonly studied in soccer to analyze teams' performance. However, few studies investigated defensive tactical variables during match-play and the influence of contextual variables on them. The aims of the present study were (1) to examine the defensive behaviors of soccer teams when gaining the ball in advanced zones of the pitch and (2) to evaluate the effect of contextual variables on these defensive behaviors. A sample of 1,095 defensive pieces of play initiated in the opposing half of the pitch obtained from 10 matches of the season 2010/11 of La Liga and involving 13 teams was collected using the semiautomated tracking system Amisco Pro. Five defensive tactical variables, the outcome of defensive pieces of play, and contextual variables (i.e., match status, venue, quality of opposition, and match period) were recorded for every defensive piece initiated in the opposing half of the pitch. Results showed that there were significant differences among teams in the outcome of defensive pieces of play originating from the opposing half (χ 2 = 111.87, p < 0.01, ϕ c = 0.22), and in the outcome of offensive pieces of play following ball gains (χ 2 = 49.92, p < 0.001, ϕ c = 0.22). Cluster analysis revealed four groups describing different defensive behaviors from high-pressure to a defense close to their own goal. Match status (χ 2 = 25.87, p < 0.05, ϕ c = 0.11) and quality of opposition (χ 2 = 21.19, p < 0.05, ϕ c = 0.10) were the contextual variables that showed a significant effect on defensive pieces of play initiated in the opposite half of the pitch. Teams winning gained more balls in the zone close to their own goal, and losing teams gained more balls in advanced zones of the pitch. Moreover, the greater the quality of the opponent the lesser the chance of gaining the ball in advanced zones of the pitch. Neither venue or match period influenced the defensive pieces of play analyzed. Soccer teams could employ a similar analysis to improve their performance and prepare for opposition teams in competition.

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“…Due to privacy concerns, the competition, the teams and the players used in this example are presented anonymised. During the match, players were tracked with a temporal resolution of 25Hz, by a camera-based tracking system with high accuracy, similar to other verified systems [57] used in similar studies [58,59]. This dataset was collected as the official dataset of the respective competition and contains 144,086 (x,y,t)-coordinates for each of the 22 players on the field.…”

Section: Case Study: Dataset and Reference Fragment Selectionmentioning

confidence: 99%

Spatial movement pattern recognition in soccer based on relative player movements

et al. 2020

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Knowledge of spatial movement patterns in soccer occurring on a regular basis can give a soccer coach, analyst or reporter insights in the playing style or tactics of a group of players or team. Furthermore, it can support a coach to better prepare for a soccer match by analysing (trained) movement patterns of both his own as well as opponent players. We explore the use of the Qualitative Trajectory Calculus (QTC), a spatiotemporal qualitative calculus describing the relative movement between objects, for spatial movement pattern recognition of players movements in soccer. The proposed method allows for the recognition of spatial movement patterns that occur on different parts of the field and/or at different spatial scales. Furthermore, the Levenshtein distance metric supports the recognition of similar movements that occur at different speeds and enables the comparison of movements that have different temporal lengths. We first present the basics of the calculus, and subsequently illustrate its applicability with a real soccer case. To that end, we present a situation where a user chooses the movements of two players during 20 seconds of a real soccer match of a 2016-2017 professional soccer competition as a reference fragment. Following a pattern matching procedure, we describe all other fragments with QTC and calculate their distance with the QTC representation of the reference fragment. The top-k most similar fragments of the same match are presented and validated by means of a duo-trio test. The analyses show the potential of QTC for spatial movement pattern recognition in soccer.

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Technical and physical match performance of teams in the 2018 FIFA World Cup: Effects of two different playing styles

Cited by 51 publications

References 34 publications

Evaluation of the Technical Performance of Football Players in the UEFA Champions League

Evaluation of the Technical Performance of Football Players in the UEFA Champions League

Tactical Variables Related to Gaining the Ball in Advanced Zones of the Soccer Pitch: Analysis of Differences Among Elite Teams and the Effect of Contextual Variables

Spatial movement pattern recognition in soccer based on relative player movements

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