# Training Tasks vs. Match Demands: Do Football Drills Replicate Worst-Case Scenarios?
###### Abstract  
This study analyses the physical performance variables involved in different training tasks aimed at replicating the worst-case scenarios (WCSs) observed during official matches in professional football, with a focus on playing positions and occurrences within a \(1\) min period. Data were collected from 188 training sessions and 42 matches of a Spanish Second Division team during the 2021/2022 season. All data were reported on a per-player basis. GPS tracking devices were used to record physical variables such as total distance, high-speed running (HSR), sprints, accelerations, accelerations, and high metabolic load distance (HMLD). Players were grouped according to their match positions: central defenders, wide players, midfielders and forwards. The results showed that none of the training tasks fully replicated the physical demands of match play. However, task TYPEs 11 (Large-Sided Games) and 9 (small-sided games with orientation and transition) were the closest to match demands, particularly in terms of accelerations and decelerations. Although differences were observed across all variables, the most pronounced discrepancies were observed in sprint and HSR variables, where training tasksfailed to reach 60% of match demands. These findings highlight the need to design more specific drills that simulate the intensity of WCS, allowing for more accurate weekly training load planning. This study offers valuable contributions for optimising performance and reducing injury risk in professional footballers during the competitive period. 2025 15 8172 2  
elite-level matches. Such analyses have identified key performance metrics, including total distance covered, high-speed running (HSR) distance, the frequency and intensity of accelerations and decelerations, peak velocity, and PlayerLoad(tm) [1; 3]. These metrics underscore the explosive and metabolically demanding nature of competition, providing reference values essential for aligning training tasks with match demands [4]. The individualisation of training is a key principle for maximising adaptation and minimising the risk of overtraining [5]. A critical factor in this optimisation process is the consideration of the specific physical demands associated with each playing position on the pitch. Distinct tactical responsibilities and movement patterns inherent to different positions result in differentiated physical demand profiles [6; 7]. Furthermore, even within the same position, individual players may perform successfully with varying performance profiles. Neglecting these positional variations or failing to provide adequate training stimuli for key aspects of physical preparation when designing training tasks may lead to insufficient readiness for the specific demands of match play, thereby increasing the risk of fatigue or injury [5]. Within the specific context of training tasks, it is essential to evaluate their contribution to developing the capacity to meet the peak demands of match play [8]. The manipulation of key variables, such as pitch dimensions and the number of players involved, significantly influences the physical demands elicited [9]. Each training task elicits distinct physiological and neuromuscular responses, with varying implications for the development of players' physical capacities [3]. However, accumulating training volume alone does not ensure optimal preparation for the most demanding phases of competition. The selection of specific training scenarios that replicate the peak physical demands of match play is therefore a fundamental aspect of designing truly effective training programmes [5; 10]. This necessitates exposing players to scenarios capable of reproducing the intensities observed during the most physically demanding moments of competition [7]. Evidence suggests that athletes who are adapted to training loads comparable to the peak demands of competition may experience a lower incidence of injury compared to those exposed to lower training loads [1].