Players’ conditional response during competition, for example, distance covered at a run, has traditionally been described using the average value covered (in metres per minute) during a half or full game. Later on, shorter game periods were studied (e.g. 15 minutes in Robinson, O’Donoghue, Wooster, 2011; or five minutes in Bradley & Noakes, 2013), which showed more intense periods. However, it has recently been proven (Gabbet et al., 2016) that these values taken from static periods may not represent maximum demand scenarios or MDS. Recently, the application of the rolling technique (Varley et al., 2012) has allowed researchers to confirm the existence of more demanding conditional scenarios than the average values used up to now (Are small-sided games the solution to all our problems?).

This technique examines second by second (or frame by frame, depending on the sampling unit) the chosen period or interval (e.g. 1, 3, 5 or 10 minutes) which is used to determine the highest values of physical variables used as a reference (e.g. distance covered at a speed greater than 14 Km·h-1). Both quantities – the period or established timeframe and the chosen physical performance variable – follow the mathematical relationship indicated in the power law (Katz and Katz, 1999). Thus, when the timeframe is larger, the relative value of the conditional variable decreases. In football, for example, when the timeframe is close to 15 minutes, the conditional variable values are very similar to the average values for a partial or complete game (Lacome et al., 2018).

As well as refining the description of MDS (Martín-García et al., 2018), sports scientists are also beginning to take an interest in understanding whether these scenarios can be replicated in the training process. Specifically, they are asking whether there are play-related tasks that allow them to be replicated (Lacome et al., 2018). This paper explores this idea by applying two original concepts: first, it connects the MDS from multiple variables simultaneously; and second, it compares the MDS from positional play in relative terms to the MDS recorded by each player in competition (e.g. distance covered in % with respect to MDS in competition for the same variable).

The participants were 21 players from FC Barcelona’s reserve team during the 2015-2016 season, and they were grouped by standard positions: centre backs (CD, n=4), full backs (FB, n=6), midfielders (MF, n=3), attacking midfielders (AMF, n=3), and forwards (FW, n=5). The time windows and positional play studied were: 1) 5-minute windows for small-sided games (SSG) [SSG5, players per team = 5, goalkeepers = 2, dimensions= 33*40m, and duration = 6 ±1 min; SSG6, players per team = 6, goalkeepers = 2, wild cards = 1, dimensions= 33*40m, and duration = 6 ±1 min] and 10 minutes for long-sided games (LSG) [SSG9, players per team = 9, goalkeepers = 2, dimensions = 72*65 m, and duration = 12 ±3 min; SSG10, players per team = 10, goalkeepers = 2, dimensions = 105*65 m, and duration = 11 ±3 min] and in competitive matches, which had a timeframe of 45 minutes. The variables analysed represented the different movement systems (locomotor, mechanical, and energetic), such as total distance covered or distance covered at high speed, accelerations/decelerations, or variables related to metabolic power, respectively. The main results are shown in the following figures (1, 2 and 3). The charts give a percentage with respect to periods of maximum demand in the game compared to distance in metres per minute (Figure 1), distance at more than 25 km·h-1 (Figure 2) and number of accelerations, or ACC (Figure 3). The red dashes represent 100% and indicates the limit up to which MDS would be replicated in competition.