Concluding Remarks and Lessons Learned

The methods of training load data collection and interpretation in team sports vary widely, possibly reflecting the actual procedures adopted in practical settings. In particular, the lack of uniformity in classifying speed and acceleration thresholds limits comparisons among studies.

From the information provided in this chapter, different recommendations are given for the choice of kinematical variables for load monitoring in sport (soccer):

There are three kinematical variables: total distance and speed, accelerations/ decelerations and composite variables.

Total distance does not seem to be a sensitive enough variable for the evolution of soccer demands.

Distances travelled in different speed zones provide more valuable information to assess physical demands.

Accelerations/decelerations (which are related to concentric/eccentric forces given that force equals mass times acceleration/deceleration) provide very valuable information to assess physical demands in terms of ability both to achieve required speed/stop and to sustain corresponding metabolic power (a composite variable).

All measures based on GNSS, time–motion analysis, and accelerometry are characterized by medium-high validity and medium reliability.

The original metabolic power acceleration/deceleration-based algorithm was characterized by low-medium validity and medium reliability.

Relatively recently, the original algorithm was updated (improved) to take into account (1) walking and running separately, (2) air resistance effect, (3) disaggregated anaerobic and aerobic energy yields, (4) broader speed range, and (5) forward and backward running separately.

Further research is expected.

To date, practitioners value the following top five metrics for load

monitoring in training: acceleration variables, total distance, distance

covered at speeds greater than 5 m/s, metabolic power variables, and

heart rate exertion. In competition, the ranking is 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.

There is no universally adopted monitoring approach in high-level soccer.

Future research may also consider assessing kinematic variables for

testing/training the team as a single entity.

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10 Collective Tactical Variables

Asier Gonzalez-Artetxe and Asier Los Arcos

Introduction

The presence of the adversary means that the decision-making dimension of the players is crucial to solve the motor problem. During duels between individuals (i.e., opposition sports) such as combat sports (e.g., judo, taekwondo, and wrestling) and racquet sports (e.g., individual tennis, badminton, and squash), players should hide their intentions while decoding body signals (Parlebas, 1999) of the adversary to intuit their intentions. In addition, the players should hide the intentions of their partners, which are completely conditioned by the motor interaction with the adversary, during duels between teams (i.e., cooperative–

opposition sports) such as ice hockey, basketball, and soccer.

Coaching staffs and sports scientists can register the motor manifestations of the players objectively (i.e., motor behavior) (Parlebas, 1999) during opposition and cooperative–opposition sports by EPTS. In addition to body data related to space (e.g., displacement and orientation) and time (e.g., position, orientation, and acceleration) (Parlebas, 1999) that facilitate external training load assessment (Bourdon et al., 2017), these systems allow for the assessment of body data in reference to others (i.e., partners and/or adversaries) (Lutz et al., 2020; Parlebas, 1999). Based on the positional data (x–y coordinates) of each player in the space at a certain time, it is possible to objectively observe the influence between players (i.e., tactical behavior). Team managers and research heads then infer the meaning of these motor manifestations from their own interpretation. The objectivity of the motor behavior (e.g., a displacement of a player in the space at a certain time) does not impose an evident nor univocal meaning (Parlebas, 1999).

Coaching staffs and sports scientists should carry out conscientious methods of

conceptualization and classification to perform an optimal tactical assessment of

positional data. They should be able to differentiate tactical behavior variables

according to different types or criteria of classification. Additionally, they have to

be aware of the practical applications of each variable to increase and optimize

their exploration degree during the assessment of the training process and the

competition, and the relationship between both.

Classification and Definitions

A classification responds to a desire for inventory and organization, as well as the search for intelligibility of a collection of objects or phenomena (Parlebas, 1999, p. 46). In this case, the identification and classification of the tactical behavior variables based on positional data could help coaching staffs and sports scientists to properly assess the tactical response during cooperation–opposition sports, namely team sports. Since the classification criteria depend on the point of view, different classifications have been suggested for tactical behavior (Low et al., 2020; McGarry et al., 2002; Rico-González et al., 2020a, 2020b, 2021a, 2021b;

Travassos et al., 2013). On the one hand, several studies have used a geometric criterion, specifically the geometrical primitives (i.e., the node, the line, and the area) to classify tactical variables (Low et al., 2020; Rico-González et al., 2020a, 2020b, 2021a, 2021b). On the other hand, the number of players has also been used, ranging from the individual to the team level (Low et al., 2020; McGarry et al., 2002; Travassos et al., 2013).

Researchers in sports sciences commonly use the classification based on a geometrical criterion (Low et al., 2020; Rico-González et al., 2020a, 2020b, 2021a, 2021b). This proposal differentiates three families according to the same number of geometrical primitives: (a) the node, (b) the line, and (c) the area. At a practical level, the node is related to the geometrical center (GC) (Rico-González et al., 2020b), also named centroid (Frencken et al., 2011), center of gravity (Lames et al., 2010), spatial center (Bourbousson et al., 2010), and center of the team (Frencken & Lemmink, 2009). The GC represents, in a single point computed by EPTS software that considers the x and y coordinates of the players, the relative positioning of each team in forward–backward and side-to-side movements (Araújo & Davids, 2016). The line is related to the distance between points, with each point representing the position of a player or the GC in the space, a relevant location of the playing space (e.g., basket), or the position of the mobile (Rico-González et al., 2020a). The area refers to the use of the space (i.e., occupation, influence, and dominance) by several players at each point in time (Rico-González et al., 2021b).

The number of players assessed as a whole is another criterion widely used by sports scientists to classify tactical behavior variables based on positional data (Low et al., 2020; McGarry et al., 2002; Travassos et al., 2013). Although all players constantly interact with one another in any team sport, the decomposition of the team into substructures or subsystems (Gréhaigne et al., 1997) is used to assess relevant and special interactions among players (Rico-González et al., 2020a). Particularly for soccer, different analysis levels have been suggested:

dyadic level, to investigate one-versus-one duels; subgroup level, to assess the