Design

In the current study, a longitudinal descriptive design with natural groups was followed [23] to describe the CentF and COD of elite soccer players during one season. En este sentido, Ninguna influencia respecto a la dinámica natural de la competición fue realiza, dando un tratamiento ecológico al estudio. In this regard, the study did not influence the natural dynamics of the competition in any way, thus giving it an ecological treatment. The data record refers to the 2017–2018 season and the matches played by an elite professional team in national (Russian Premier League and Russian Cup) and international (UEFA Champions League and UEFA Europa League) competitions. A total of 38 matches were recorded, of which 7 were played as locals, 17 as visitors and 14 as neutrals. In addition, 15 of the analyzed matches were in a national competition, 7 in an international competition and 16 were friendly matches. All the matches were played on outdoor natural grass and artificial turf pitches in accordance with FIFA rules. During the study, the analyzed team used a 4-4-2 formation.

Participants

Thirty elite-level professional soccer players participated voluntarily in the present study (age: 26.57±5.56 years; height: 1.82±0.05 m.; body mass: 77.2±2.76 kg.; fat percentage: 8.44±1.08%; BMI: 22.31±1.32 kg/m²). For the characterization of the sample, the participating players were divided into five specific positions: (a) central defender (n = 4), (b) lateral defender (n = 5), (c) central midfielder (n = 8), (d) lateral midfielder (n = 5) and (e) forward (n = 8). To be included in the analysis the participants had to meet the following criteria: (i) the players had to play for the full duration of the game (~90min); (ii) not suffer an injury during the game; (iii) play in the same position throughout the game; (iv) be familiar with their playing position; and (v) goalkeepers were excluded from the analysis as they present a different internal and external workload profile from field players.

The study, which was conducted according to the Declaration of Helsinki, was approved by the Institution Review Board of the University of Murcia (Reg. Code: 2595/2019). Participants were informed of the risks and discomforts associated with testing and provided written informed consent.

Procedures

Height was measured to the nearest 0.5 cm during a maximal inhalation using a wall-mounted stadiometer (SECA, Hamburg, Germany). Body mass and fat percentage were obtained with an 8-electrode segmental body composition monitor SC-240 model (TANITA, Tokyo, Japan). The accuracy for both variables has been analyzed in previous research and demonstrated acceptable results for estimating percentage of body fat mass when compared with DXA [24].

All team players wore inertial measurement devices (IMUs) (81x45x16 mm; 65gr.) with 10Hz GPS tracking system technology, four tri-axial accelerometers (±16, ±16, ±32 and ±400 G) and three 3D gyroscopes (±2000; ±2000 and ±4000 degrees/second) (WIMU PRO^™, Almeria, Spain) in order to assess motion pattern data measurement during each match. The sampling frequency of inertial sensors can be configured between 10 and 1000 Hz. The gyroscope and accelerometer were configured at a sampling frequency of 100 Hz. The GNSS model used, that recorded at a sampling frequency of 18 Hz, has been validated previously [25] and has the FIFA certificate for use in professional soccer competitions. Besides, a previous evaluation was carried out with this model and has demonstrated very large to nearly perfect inter-unit reliability (ICC = 0.75–0.96) and good accuracy results (bias, counterclockwise = -2.19 N; clockwise = 1.75 N; typical error of measurement < 5%) for CentF variable during four different tracks (circle-6m radius, circle-9.15m radius, circle-12m radius, and combined track with zig-zag locomotion) and at different speeds (walking, high-intensity running and sprinting) (unpublished data). Only days when data collection occurred in conditions that were considered good for gathering valid and reliable GPS data [26] were included (2 matches were eliminated). During monitoring 12.8 ± 2.5 satellites were connected and Global Navigation Satellite System (GNSS) horizontal geometric dilution of precision (HGDOP) was 0.96±0.14, therefore almost ideal [27].

Output variables

Statistical analysis

The descriptive statistics were calculated and reported as mean (M) ± standard deviations of the mean (SD) on each variable. Then, an exploratory analysis of the data was performed in order to confirm a normal distribution. A one-way ANOVA was used to detect differences among player positions. The Bonferroni post-hoc test was used to identify the source of any significant differences. A t-test was used to identify differences between laterality and the zone of the field. The magnitude of the differences was qualitatively interpreted using partial omega squared (ω_p²) as follows: >0.01 small; >0.06 moderate and >0.14 large; and Cohen’s d (d) as follows: trivial (0–0.19), small (0.20–0.49), medium (0.50–0.79), or large (0.80 and above) [31].

Relationships between the CentF and COD performed with the anthropometrical variables were assessed using Pearson’s product-moment correlation (r). The magnitude of the correlation coefficients was deemed as very low (r²< 0.1), low (0.1 <r²< 0.3), moderate (0.3 <r²< 0.5), high (0.5 <r²< 0.7), very high (0.7 <r²< 0.9), nearly perfect (r²> 0.9) and perfect (r² = 1) (Hopkins et al., 2009). Analyses were carried out using IBM SPSS Statistics (release 24.0; SPSS Inc., Chicago IL, USA) and figures were designed using GraphPad Prism (release 7; GraphPad Software, La Jolla CA, USA). Significance was established as p< 0.05 level.

Playing position

COD is a recently used variable that could be affected by speed, neuromuscular and mechanical properties of the lower limbs, pivot leg reactive strength or technique [32]. In this study, differences were found by playing position. Central midfielders and forwards presented higher demands in repeated COD ability at sprint and high intensity that could be explained by their specific role and in-field zone of movement [7, 20, 33, 34], that usually requires non-linear trajectories due to the higher number of opponents who surround them. Additionally, the limited space in all directions as well as fewer moments spent standing still, require them to change direction not only more often but with greater intensity compared to lateral midfielders or lateral defenders [7].

On the other hand, the highest centripetal force generated in CODs was performed by central defenders who also recorded the greatest number of CODs. This indicates that the profile of non-linear locomotion by central defenders starts at a low speed (walking or jogging) and needs to achieve maximum acceleration with the purpose of covering the defensive line when forwards and wide midfielders of the opposing team overtake their teammates (central and lateral defenders) [7, 20, 33, 34]. Therefore, playing positions determine the specific CODs, centripetal force and repeated COD ability during competitive matches and should be considered when designing specific training workloads in non-linear displacements. The results discussed specify that central midfielders and forwards should be exposed during training to multiple tasks which require frequent and multiple changes of direction due to limited space and opposition from the opponent. Exposure to these preconceived scenarios during training may help to specifically prepare these players for the demands related to CODs in volume and intensity during competition. These tasks will not only prepare the player from a physical-technical but also tactical point of view where they will develop multiple responses to situations that vary in time and space. These short-term periods should be part of injury prevention protocols [35].

Laterality

In the present study, laterality was evidenced by the fact that significant differences were reported in the players with right dominance vs. left dominance in the lateral zones, where they presented greater centripetal forces on the lateral side of their dominance. In this respect, previous research by Carey et al. [36] revealed a strong bias toward the use of the right foot in right-footed players and toward the use of the left foot in left-footed players across all soccer-related behaviors. Dominant foot use was highest for set-pieces (i.e., free kicks, penalty kicks, and corner kicks), dribbling, and passing (~85%). Hence, players utilize the non-dominant foot only when facing intense pressure from opponents.

This bias was reduced when the level of the players was higher, finding that two-footed players have an advantage for advancement to higher competitive levels [36]. For this reason, no differences were found in central zone players in the preference of COD direction, and a small difference was found only in lateral zone players. Therefore, for practical applications of training, this suggests that during the performance of curvilinear trajectories the inside leg works as a pivot and is usually the leg opposite to the direction of the turn or run [11] and should be trained specifically. The modification of the domination of CODs in the lateral zone could be produced by the tactics used by the team when interior locomotion predominates in the lateral zones.

Finally, as argued in the introduction section, CODs and the forces involved in high intensity actions are fundamental in soccer kinematics because these short-term actions may be a key parameter of soccer performance [35]. These actions usually open up clear scoring opportunities [37, 38] or allow the player to have a better position to shoot, unmark, pass or score [39]. The development of individualized training drills considering these external load variables should be addressed by the coaches and technical staff of professional teams in order to prepare not only their physical and technical abilities to face this type of actions but also to prevent potential injuries [40]. Besides, this type of variables may be included in future studies that analyze soccer performance or team players workload [28].

Body composition

Age, body mass, height, fat mass and BMI of the players in this study were within the range previously reported in the literature for male elite soccer players (age: 22.1–28.1 years, body mass: 69.4–83.4 kg, height: 1.71–1.83, fat mass: 8.0–13.2, BMI: 20.8–22.9) [41]. Although previous studies showed the importance of anthropometric characteristics as an attribute for soccer performance in women elite players [42] and youth male players [43], few studies were found with the association between anthropometric characteristics and variables of soccer performance or players workload index in male elite players.

The present results show a directly significant relationship between body mass, height, and BMI with respect to CentF_MAX and CentF_AVG (r = 0.13–0.27; p<0.05). This could be explained because the weight of the subject is included in the calculation of the centripetal force generated [11]. Thus, it is possible that higher body mass, height, and BMI could contribute significantly to greater values of this kind of workload. However, an inversely significant relationship was reported between CentF_MAX (r = -0.19) and CentF_AVG (r = -0.15) with respect to the percentage of fat. In contrast, the percentage of fat showed lower significance with respect to body mass [41] in women players. Although there is a relationship between high BMI and higher CentF_MAX and CentF_AVG, and an inverse relationship between percentage of fat with these variables, future studies should be carried out to confirm this finding due to the small lineal relation found between variables.

The previous information could help us to make practical recommendations to aid in the analysis and interpretation of body composition data in professional soccer. But in practical applications, possible variations in body composition could occur during the competitive season and must be taken into account, suggesting a need for continuous monitoring of this component of fitness across the entire season [44].