Analysis of the worst-case scenarios in an elite football team: Towards a better understanding and application.

This study investigated the variability in the worst-case scenario (WCS) and suggested a framework to improve the definition and guide further investigation. Optical tracking data from 26 male players across 38 matches were analysed to determine the WCS for total distance, high-speed running (>5.5 m.s-1) and sprinting (>7.0 m.s-1) using a 3-minute rolling window. Position, total output, previous epoch, match half, time of occurrence, classification of starter vs substitute, and minutes played were modelled as selected contextual factors hypothesized to have associations with the WCS. Linear mixed effects models were used to account for cross-sectional observations and repeated measures. Unexplained variance remained high (total distance R2 = 0.53, high-speed running R2 = 0.53 and sprinting R2 = 0.40). Intra-individual variability was also high (total distance CV = 4.6-8.2%; high-speed CV = 15.6-37.8% and Sprinting CV = 21.1-76.4%). The WCS defined as the maximal physical load in a given time-window, produces unstable metrics lacking context, with high variability. Furthermore, training drills targetting this metric concurrently across players may not have representative designs and may underprepare athletes for complete match demands and multifaceted WCS scenarios. Using WCS as benchmarks (reproducing similar physical activity for training purposes) is conceptually questionable.

Scanning, Contextual Factors, and Association With Performance in English Premier League Footballers: An Investigation Across a Season.

Scanning in football (soccer) denotes an active head movement where a player's face is temporarily directed away from the ball to gather information in preparation for subsequently engaging with the ball. The aim of this study was to learn more about the ways that 27 elite professional football players in an English Premier League club use scanning in competitive matches, the conditions under which this behavior is exhibited, and the relationships between these behaviors and performance. Players were filmed across 21 matches, producing a total number of 9,574 individual ball possessions for analysis. Close-up video analyses of scanning show positional differences (with central midfielders and central defenders scanning most frequently, forwards least) and contextual differences (with relatively lower scanning frequency in situations with tight opponent pressure, in positions wide in the field and closer to the opponent's goal, and under certain game state conditions). Players scan more frequently prior to giving passes than when they dribble, shoot, or only receive it, as well as prior to more long/forward passes compared to short/backward ones, although these differences are small. A Bayesian hierarchical model, which accounts for individual player differences and pass difficulty, suggests that the more a player scans, the higher the probability of completing a pass. In conclusion, match demands are likely to constrain the extent to which highly elite players scan, and scanning seems to have a small, but positive role in elite football players' performance.