Space and Control in Soccer

Abstract

In many team sports, the ability to control and generate space in dangerous areas on the pitch is crucial for the success of a team. This holds, in particular, for soccer. In this study, we revisit ideas from Fernandez and Bornn (2018) who introduced interesting space-related quantities including pitch control (PC) and pitch value. We identify influence of the player on the pitch with the movements of the player and turn their concepts into data-driven quantities that give rise to a variety of different applications. Furthermore, we devise a novel space generation measure to visualize the strategies of the team and player. We provide empirical evidence for the usefulness of our contribution and showcase our approach in the context of game analyses.

Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach

“…2D KDE plots have been used before, to investigate spatial trends of attacking possession in rugby, 12 and the team's space-related control on the pitch in soccer. 11 This highlights the general ability of KDE plots to visualize and summarize complex time series data. However, big data sets are required to provide sufficient data points to create such detailed visualizations.…”

“…However, big data sets are required to provide sufficient data points to create such detailed visualizations. For instance, the study of Martens et al 11 incorporated 54 soccer matches with 31,824 data points and that of Sawczuk et al 12 included 138 rugby matches comprising 99,966 data points. In the present study, we monitored competitive speed skaters for 2 consecutive seasons and obtained detailed sensor readings from heart rate monitors and detection loops on the ice rink, which resulted in a data set of 933 training sessions with 421,982 data points (at least 11,821 data points per training session type).…”

“…While KDE is regularly applied in an environmental 9 and health context, 10 applications in sports are sparse. To the best of our knowledge, only a few studies used KDE in a sport context, and only to describe player position on the pitch in soccer 11 and rugby. 12 It remains unknown whether KDE also reveals valuable information on the coupling between internal and external training load, which may benefit coaches and support staff in designing their training schemes.…”

Success-Score in Professional Soccer – Is there a sweet spot in the analysis of space and ball control?

“…Moreover, new limitations would inevitably ☛✠✞✁✂✚ ✛✌✂ ✡☛☞✘✂ ☎✒ ✁✓☛✄✂✁ ✑✞✕✌✟ ✝✞✒✒✂✠ ✝✂✓✂✆✝✞✆✕ ☎✆ ☛ ✟✂☛✑✔✁ ✟☛✄✟✞✄✁ ☛✆✝ ✁☎ ✑✞✕✌✟ ✟✌✂ ✡☛☞✘✂ ☎✒ spaces controlled by players that are offside. Furthermore, space control models incorporating player kinematics could improve the performance of the Success-Score and could be considered as potential improvements in the future (Caetano et al, 2021;Fernandez & Bornn, 2018;Martens et al, 2021;Spearman et al, 2017). When working with the Success-Score, awareness of the limitations resulting from position data is required.…”