Classification and Definitions

Research has therefore focused on the development of “all-in” variables incorporating both speed and or accelerations (Gaudino et al., 2013;

Gaudino et al., 2014). The recent development of GNSS/LPS software incorporating specific algorithms has made it easy to retrieve composite variables. However, despite their increasing use, several composite variables such as dynamic stress load, high-intensity bursts, high metabolic load distance and repeated high-intensity need to be further studied and validated (Malone et al., 2018; Silva et al., 2018).

Within the composite variables, only estimated metabolic power has actually been examined against direct estimations of metabolic consumption, and a lack of concurrent validity was observed compared to direct metabolic cost measurements (Buchheit et al., 2015).

Total Distance

Total distance was one of the first GNSS variables studied and is the most cited in the literature (Cummins et al., 2013; Reilly, 1976). In soccer, this variable has been analyzed both in competition (Bangsbo et al., 2006;

Pettersen & Brenn, 2019) and in training (Reche-Soto et al., 2019; Zurutuza

& Castellano, 2020). Cricket players covered the greatest distance per competition game, with a fast bowler in One Day International cricket covering 15,903 m (Petersen et al., 2009).

Positional differences were evident within each sport, for example, backs in both rugby league (Austin & Kelly, 2013; Gabbett et al., 2012; McLellan et al., 2010) and rugby union (Hartwig et al., 2011; Venter et al., 2011) covered greater distances than forwards; however, the margin of difference was less in rugby union. McLellan et al. (2010) reported that elite rugby league backs covered 5,747 m or 16.9% more distance than forwards (4,774 m). By comparison, in rugby union, backs covered 7.6% more distance than forwards (6,471 and 5,853 m, respectively) (Suarez-Arrones et al., 2014).

Similarly, in AFL (Australian Football League), Brewer et al. (2010) found that midfielders and small forwards/backs covered greater total distances than other positions, with ruckmen recording the lowest distances.

According to Bosco and Vila (1991), distances close to 11 km are

covered in a soccer match. Bangsbo et al. (1991) found similar distances

but discovered differences between soccer defenders (10.1 km), soccer

forwards (10.5 km) and soccer midfielders (11.4 km). Data that agree with

the results were presented in later studies (Di Salvo et al., 2007; Mallo et al., 2015). As we have seen previously, total distance does not seem to be a sensitive variable to the evolution of soccer demands, since the results found in publications of more than 20 years ago are similar to the demands of current soccer, so that it can be affirmed that total distance does not make a difference regarding physical performance (Ekblom, 1986).

Furthermore, the results are conditioned by the age and competitive level of the players, showing that the top-level players travel 5% more (p<0.05) than the soccer players who compete in lower category competitions (10,860 ± 180 and 10,330 ± 260 m, respectively) (Mohr et al., 2003).

Similarly, under 18 soccer players run about 8,867 m compared to the 6,549 m covered by under 13 soccer players (27.2% difference) (Buchheit et al., 2010). It is evident that other factors should be considered such as intensity or frequency, together with total distance, to determine the performance of the soccer players (Carling et al., 2008).

Relative Distance

Commonly, we can find another way to express the distance relativized to playing time (m/min), which is considered more accurate for the quantification of the effort made in the match or training (Cabrera, 2019).

Distances represent the total volume of distance covered in a measured session, whereas relative distances represent the relative volume of the distance per minute of a measured session (Cummins et al., 2013). Similar to the total distance, the relative distance would be conditioned by the age of the players and the level of the competition in which the players participate (Lago-Peñas et al., 2009). A difference of 7.2% relative distance was evident between elite rugby union forwards and backs (66.7 vs. 71.9 m/min, respectively) (Cunniffe et al., 2009). The impact of age on distance travelled per minute was also observed in soccer, where under 16 years players covered 115.2 m/min, an additional 10% relative distance compared with that of under 13 years players (103.7 m/min) (Downs & Black, 1998).

Since it is evident that knowledge of the total distance is not a sufficient

measure for the assessment of physical demands (Miñano-Espin et al.,

2017), we need to check the meters travelled in different speed zones.

Speed Zones

These speed zones have been unevenly established in the literature (Bangsbo et al., 1991; Bradley et al., 2009; Mohr et al., 2003; Pirnay et al., 1991; Reilly, 1976), thus making it difficult to unify criteria and results.

They vary from Reilly (1976) with the classification of slow running, high speed and sprint, to authors like Bradley et al. (2009) proposing the classification of the speed sections in: stopped (0–0.6 km/h), walking (0.7–

7.1 km/h), jogging (7.2–14.3 km/h), running (14.4–19.7 km/h) high-speed running (19.8–25.1 km/h) and sprint (>25.1 km/h); and authors like Mohr et al. (2003) who propose: stopped (0–6 km/h), walking (6 km/h), jogging (8km/h), low speed (12 km/h), high speed (18 km/h) and sprint (30 km/h).

EPTS devices also offer data on the average and maximum speed of the players. Today, the companies in charge of developing and marketing EPTS devices make it possible to establish the number of zones that the user considers appropriate in their analysis software, thus allowing a real adaptation to the context.

Speed, High-Speed Running and Sprint

Peak speed is the highest speed recorded in a measured session. Another metric is the number and distance of high-speed runs, this determines how much of the total volume was covered at a certain speed, which is typically classified in “work-rate zones.” Regarding peak speed, as with distances, a recent wearable comparison study has shown that a shorter distance leads to lower validity in the overall distance covered. Thus, the validity of peak speed for short distances inherently suffers (Hoppe et al., 2018). The work- rate zones allow making assertions such as the time spent in each zone.

However, the distribution range of speeds for each zone heavily depends on the examined sport (Aughey & Falloon, 2010; Cummins et al., 2013;

Wisbey et al., 2010). Furthermore, certain researchers argue for individual, position-dependent zones, as players have different physical characteristics (Gabbett et al., 2012; McLellan et al., 2011). Another sensitive topic is the

“minimum effort duration” (Malone et al., 2017). This is a customizable

setting, which defines how long an activity must last to be identified as

such. For example, an activity is only labeled as running if the speed of the

measured activity is over 18 km/h over a period of 0.5 s. Then, the

minimum duration for the activity running is 0.5 s. This allows for filtering outliers, created by qualitatively bad GNSS data. However, this setting is not consistent for all types of activities. Therefore, every brand, device, and sometimes even different versions of the same device have different minimum effort durations for certain movements again increasing difficulty for comparing results or creating data archives over time.

Acceleration/Deceleration

Likewise, together with the distance travelled and its different classifications at different speeds, we find other variables that also inform us about the physical performance of soccer players, accelerations and decelerations. For this, the EPTS offer us the values of the speed change in m/s ² (Little & Williams, 2007; Osgnach et al., 2010). These accelerations and decelerations have been analyzed in terms of certain intensity categories, mainly taking into account absolute thresholds; although today also thresholds relative to the player’s maximum acceleration and deceleration capacity can be considered. In the literature and in the world of high performance, we can find these activities counted by number of events (accelerations and decelerations), distance traveled accelerating and decelerating, time in each intensity zone (all the previous ones also represented relative to playing time) and the maximum value of acceleration and deceleration (Aughey, 2010). Regarding the latter, the most used thresholds are 1–2 m/s ² ; 2–3 m/s ² and > 3 m/s ² (Ade et al., 2014;

Akenhead et al., 2013; Hodgson et al., 2014; Osgnach et al., 2010). These variables are used to assess neuromuscular demand and fatigue (Akenhead et al., 2013; Bradley et al., 2010; Méndez-Villanueva et al., 2012), the variable number of accelerations per minute (acc/min) being the external load indicator that is most related to intensity measurement through the subjective perception of effort (Gaudino et al., 2015). Another aspect to take into account is the initial speed prior to acceleration or deceleration, since it is considered a determining factor of maximum acceleration or deceleration capacity (Sonderegger et al., 2016).

Metabolic Power and High Metabolic Load Distance

Continuing with the above, and taking into account the proposal of authors Gaudino et al. (2014) and Osgnach et al. (2010), if we analyze the speed and accelerations/decelerations separately, we can run the risk of not reaching a global understanding of all the activity. The analysis of the distance travelled at different intensity thresholds could obviate the energy demands related to accelerating and decelerating (Gaudino et al., 2013;

Osgnach et al., 2010) underestimating the real demands (Akenhead et al., 2013; Manzi et al., 2014). This can lead us to understand that a player can develop a high metabolic load not only with actions that require high speed, but also in activities that involve accelerations and decelerations, even though there is no high locomotion speed (Gaudino et al., 2014; Osgnach et al., 2010). This proposal comes from smart research by di Prampero et al.

(2005), who demonstrated the physical/metabolic equivalence between accelerated/decelerated running and constant-speed uphill/downhill running. This finding lays the foundations for a new indicator to quantify physical load, metabolic power. Metabolic power is calculated from the (metabolic) energy cost and the speed of the activities carried out. Another index that we can find in the literature derived from MP is high metabolic load distance, the distance travelled when the metabolic power value is equal to or greater than 25.5 W/kg (Malone et al., 2016).

Metabolic power (or metabolic load distance) appears as the most frequently adopted composite variable. The aim of this metric is to calculate the total internal expenditure during training or competition (Osgnach et al., 2010). The calculations in a test that contained linear running trials were documented as reliable (Osgnach et al., 2010).

Another study testing in a confined circuit 19 m long, with the focus on short sprints and changes of direction came to a different conclusion (Buchheit et al., 2015). They stated that “locomotor-derived metabolic power underestimated very largely the actual net metabolic demands of the drills.” A recent review study confirms these findings and elaborates that

“recent research findings question the validity of this construct in the context of team-sport-specific movements” (Buchheit & Simpson, 2017).

More so, the authors state that it is only an incomplete measure of the

internal load and too broad a marker of the external load. The consensus

statement of Bourdon et al. (2017) evaluates the validity as low-medium

and the reliability of the metabolic power as medium.

Albeit the validity and reliability concerns, practitioners are applying these metrics. In the previously mentioned survey study, 41 elite soccer clubs have answered questions about their applied tracking systems and metrics (Akenhead & Nassis, 2016). There the clubs valued the following top five metrics for practice training load: acceleration variables, total distance, distance covered at speeds greater than 5 m/s, metabolic power variables, and heart rate exertion, whereas in a competition the top five training load variables of interest were: total distance, distance covered at speeds greater than 5.5 m/s, distance covered at speeds greater than 7.0 m/s, acceleration variables, and average speed. One of the main findings of the study was that “there is no universally adopted monitoring approach in high-level football” (Akenhead & Nassis, 2016).

As already mentioned, di Prampero et al. (2005) very smartly theorized the equivalence, in terms of both mechanical output and corresponding metabolic input, between flat accelerated (or decelerated) running and gradient constant-speed running. Gradient constant-speed running metabolic input, as metabolic cost, was already known (Margaria et al., 1963; Minetti et al., 2002). Osgnach et al. (2010) combined the theory and the data obtained in their research relatively easily, developing a new approach called the “metabolic GPS approach” (MGA). This methodology made it possible to estimate metabolic cost and power of running in soccer players during official matches (Osgnach et al., 2010) by putting instantaneous speed data into the developed MGA algorithm. For sake of clarity, metabolic power of running is obtained by multiplying its metabolic cost by its speed. Instantaneous speed data could be obtained by either traditional video-based match analysis systems (e.g., Prozone; Osgnach et al., 2010), IMU-enhanced GNSS receivers (Gaudino et al., 2013) or high- speed cameras (e.g., sampling at 210 Hz; Buglione & di Prampero, 2013).

Electronic Performance Tracking Systems (EPTS) represent a valid

alternative to these instruments to provide accurate instantaneous speed

data. Over the years, several further investigations making use of the MGA

have been published. As already mentioned, over the same years, MGA was

criticized for lack of accuracy and precision (Buchheit et al., 2015; Osgnach

et al., 2016). Relatively recently, MGA was updated by its original

developers taking walking and running into account separately and

considering the energy expenditure needed to overcome the air resistance

(di Prampero & Osgnach, 2018). Furthermore, these same authors provided

an estimate of disaggregated anaerobic and aerobic energy yields to whole

match metabolic expenditure (Osgnach & di Prampero, 2018). Almost at

the same time, other researchers developed an instantaneous speed-based

algorithm, alternative to the original one by Osgnach et al., applicable to a

broader speed range (Minetti & Pavei, 2018). Moreover, these same authors

updated their algorithm by taking forward and backward running into

account separately (Rasica et al., 2020). Future research making further use

of the above updated/new MGAs is expected (Table 9.1).

Table 9.1 Kinematical variables used to quantify external load in team sports