The influence of opponent level on professional soccer players’ training and match performance assessed by using wearable sensor technology

Abstract

Purpose. The study aim was 2-fold: to quantify and compare the weekly external training load that preceded matches; to compare in-match activities depending on the opponent level (top, middle, bottom) in a top-level team from the first professional Asian national league. Methods. The load for 6 matches played against top-, 11 against middle-, and 11 against bottom-level teams was monitored. With a 15-Hz Global Positioning System, total duration, total distance, high-speed (18-23 km), DecZ2 (-2 to -4 m • s -2 ), DecZ3 (< -4 m • s -2 ), player load, and metabolic power were collected in 12 players. Results. DecZ3 showed higher values against top-level compared with middle-(effect size [ES] = 0.91) and bottom-level opponents (ES = 1.50). The training was significantly longer against middle-level compared with top-and bottom-level opponents (all, p 0.001). Total distance was bigger against middle-level compared with top-(p = 0.011, ES = -0.92) and bottom-level opponents (p = 0.027, ES = 1.50). AccZ2 presented higher values when middle-level came close compared with bottom-level opponents (p = 0.05, ES = 0.79). Conclusions. Opponent's level influences the load experienced by soccer players during matches. Total distance, highspeed running distance, AccZ1, and AccZ2 exhibited higher training values when a win or a draw approached. Decelerations in all zones were highest in matches against top-level teams.

Playing position and match location affect the number of high-intensity efforts more than the quality of the opposition in elite football players

“…The current study found the greatest number of ACC and DEC actions were observed in matches against higher standard opposition, but no significant difference was observed. This aligns with agrees with Rago, Silva [ 29 ] and Nobari, Ramachandran [ 28 ]. Generally, an increased density between players is expected when competing against better quality opponents, which may decrease the space available for players to run, which may predispose players to perform more high-intensity ACC and DEC actions as less time and space is available to make quick decisions to perform subsequent actions [ 27 ].…”

“…When playing against higher-standard teams, elite football players covered a greater distance by high-intensity running (HIR), which consists of high-speed running (HSR) (≥ 19.8–25.1 km/h −1 ) and sprinting (> 25.2 km/h −1 ) [ 4 ], whereas other research highlighted that increased running demands were observed when playing against similar-level opponents [ 27 ]. A recent study observed that DEC (< –4 m∙s −2 ) showed higher values against top-level compared with middle- and bottom-level opponents [ 28 ]. As a limited number of studies have investigated the effect of OS on high-intensity ACC and DEC actions [ 12 , 26 , 28 , 29 ], it restricts any definitive conclusions assessing the effect of those actions.…”

“…A recent study observed that DEC (< –4 m∙s −2 ) showed higher values against top-level compared with middle- and bottom-level opponents [ 28 ]. As a limited number of studies have investigated the effect of OS on high-intensity ACC and DEC actions [ 12 , 26 , 28 , 29 ], it restricts any definitive conclusions assessing the effect of those actions.…”

Match running performance is influenced by possession and team formation in an English Premier League team

“…Also, this study did not examine the outcome of effective playing time on locomotor performance [ 43 ]. In addition, other contextual factors such as opponent level [ 44 ], match location [ 20 ], match outcome [ 45 ] and match half were not considered. For example, when playing against top-level teams, players covered greater distance of high-speed running and sprinting [ 46 ].…”

Soccer’s AI transformation: deep learning’s analysis of soccer’s pandemic research evolution

“…Considering past experiences when humanity has developed science and technology to overcome crises, Garfin (2020) predicted that the pandemic period would also see remarkable changes. Therefore, it is expected that AI, robotics, immersive virtual technology, metaverse, blockchain, and all-encompassing WEB 3.0 technologies will be applied to the field of soccer in the process of turning the current crisis into an opportunity ( Hotaran et al, 2022 ; Lorabi, 2022 ; Nobari et al, 2022 ). In this process, soccer technology research will continue to be an independent field of study.…”