Abstract
Observational methodology provides a rigorous yet flexible framework for capturing behaviors over time to allow for the performance of subsequent diachronic analyses of the data captured. Theme is a specialized software program that detects hidden temporal behavioral patterns (T-patterns) within data sets. It is increasingly being used to analyze performance in soccer and other sports. The aim of this study was to show how to select and interpret T-patterns generated by the application of three “quantitative” sort options in Theme and three “qualitative” filters established by the researchers. These will be used to investigate whether 7-a-side (F7) or 8-a-side (F8) soccer is best suited to the learning and skills development needs of 7- and 8-year-old male soccer players. The information contained in the T-patterns generated allowed us to characterize patterns of play in children in this age group. For both formats, we detected technical-tactical behaviors showing that children of this age have difficulty with first-touch actions and controlling the ball after a throw-in. We also found that ball control followed by a pass or a shot at the goal are common in the central corridor of the pitch. Further, depth of play is achieved by ball control, followed by dribbling and a pass or shot. In F8, we saw that depth of play was achieved through ball control, followed by dribbling and passing of one or more opponents leading to a pass or shot. However, in F7, we saw that players succeeded in advancing from their goal area to the rival goal area through a sequence of actions.
Highlights
Data acquisition, data mining, and context-aware analysis have become crucial areas of research in team ball games, such as soccer [1]
C1) single touch and regulatory throw-in/kick-in; C12) attempt to control the ball with 2 or more touches resulting in loss of ball; C2) control of ball followed by a pass or shot -regardless of whether the ball reaches a team member or is recovered by an opponent; C23)
FDFT) free kick for team being observed; FDFJ) offside for team being observed; FFSB) throw-in for team being observed; FFSE) corner kick for team being observed; FFSP) goal kick for team being observed; CDFT) free kick against team being observed; CDFJ) offside against team being observed; CFFB) throw-in against team being observed; CFFF) corner kick or goal kick against team being observed; GF) goal scored by team being observed; GC) goal conceded by team being observed; SN) neutral kick
Summary
Data mining, and context-aware analysis have become crucial areas of research in team ball games, such as soccer [1]. Game analysis is changing at a dizzying pace, with constant improvements to automatic recording and annotation systems that enable the immediate acquisition of large amounts of complex data [2]. The potential commercial applications of these systems are further driving the development of increasingly small and precise sensor modalities and tracking devices. These can provide valuable information for clubs that can afford to deploy this technology. Data on player positions and displacements, need to be enriched with other types of data if they are to truly improve our understanding of the game. Insights into how technical skills are applied to tactical game situations, for example, would be potentially relevant to most studies of this kind [3]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
