The Most Demanding Exercise in Different Training Tasks in Professional Female Futsal: A Mid-Season Study through Principal Component Analysis

Citation: Rico-González, M.; Puche-Ortuño, D.; Clemente, F.M.; Aquino, R.; Pino-Ortega, J. The Most Demanding Exercise in Different Training Tasks in Professional Female Futsal: A Mid-Season Study through Principal Component Analysis. Healthcare 2022,10, 838. https:// doi.org/10.3390/healthcare10050838 Academic Editor: Maria Chiara Gallotta Received: 24 March 2022 Accepted: 28 April 2022 Published: 2 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). healthcare Article The Most Demanding Exercise in Different Training Tasks in Professional Female Futsal: A Mid-Season Study through Principal Component Analysis Markel Rico-González 1, Daniel Puche-Ortuño 2, Filipe Manuel Clemente 3,4,5 , Rodrigo Aquino 6,* and JoséPino-Ortega 2,7 1Department of Didactics of Musical, Plastic and Corporal Expression, University of the Basque Country, UPV-EHU, 48940 Leioa, Spain; [email protected] 2Department of Physical Activity and Sport, Faculty of Sport Science, University of Murcia, 30100 Murcia, Spain; [email protected] (D.P.-O.); [email protected] (J.P.-O.) 3 Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal; [email protected] 4Instituto de Telecomunicações, Delegação da Covilhã, 1049-001 Lisboa, Portugal 5Research Center in Sports Performance, Recreation, Innovation and Technology (SPRINT), 4960-320 Melgaço, Portugal 6LabSport, Post-Graduate Program in Physical Education, Centre of Physical Education and Sports, Federal University of Espírito Santo, Vitória 29075-910, Brazil 7BIOVETMED & SPORTSCI Research Group, University of Murcia, 30100 Murcia, Spain *Correspondence: [email protected] Abstract: The contextual factors related to training tasks can play an important role in how a player performs and, subsequently, in how a player trains to face a competition. To date, there has been no study that has investigated the most demanding exercise in different training tasks in female futsal. Therefore, this study aimed to determine the most demanding efforts during different training tasks in a cohort study conducted in professional biological women futsal players using principal component analysis (PCA). A total of 14 elite women futsal players (age = 24.34 ± 4.51 years; height = 1.65 ± 0.60 m; body mass = 63.20 ± 5.65 kg) participated in this study. Seventy training sessions of an elite professional women’s team were registered over five months (pre-season and in-season). Different types of exercises were grouped into six clusters: preventive exercises; analytical situations; exercises in midcourt; exercises in 3/4 of the court; exercises in full court; superiorities/inferiorities. Each exercise cluster was composed of 5–7 principal components (PCs), considering from 1 to 5 main variables forming each, explaining from 65 to 75% of the physical total variance. A total of 13–19 sub-variables explained the players’ efforts in each training task group. The first PCs to explain the total variance of training load were as follows: preventive exercises (accelerations; ~31%); analytical situations (impacts; ~23%); exercises in midcourt (high-intensity efforts; ~28%); exercises in 3/4of the court (~27%) and superiorities/inferiorities (~26%) (aerobic/anaerobic components); exercises in full court (anaerobic efforts; ~24%). The PCs extracted from each exercise cluster provide evidence that may assist researchers and coaches during training load monitoring. The descriptive values of the training load support a scientific base to assist coaches in the planning of training schedules. Keywords: indoor football; data mining; data reduction techniques; women; load monitoring 1. Introduction Futsal (also known as indoor football) is an intermittent exercise, in which repeated efforts occur interspaced by periods of short recovery [ 1 , 2 ]. High-demanding efforts are associated with futsal, considering that, in this game, a lot of quick transitions of possession of the ball occur with implications for physical demands [ 3 ]. In the specific case of women’s Healthcare 2022,10, 838. https://doi.org/10.3390/healthcare10050838 https://www.mdpi.com/journal/healthcare Healthcare 2022,10, 838 2 of 13 soccer, total distance can vary between 1659 and 1547 m per half, while 41–42% of the distance is covered between 6.1 and 12 km/h, 6–7% between 15.5 and 18.3 km/h and 4–5% above 18 km/h [ 4 ]. The maximal sprint speed in women’s futsal is between 26 and 31 km/h, depending on the half [ 4 ]. Therefore, elite women futsal players usually possess high levels of maximal oxygen uptake (~58.7 mL/kg/min) [ 4 ] and sprinting abilities, since they can achieve times of 1.6s, 2.98s and 4.16s in 10, 20 and 30 m sprint tests [5]. Therefore, understanding the training context is important for characterizing regular stimuli on futsal players and identifying how they are managed for achieving specific goals [ 6 ]. Although there is a possible non-linear effect between load and individual responses to stimuli in futsal [ 7 ], it is expected that specific determinants of intensity can explain some performance and biological variations [ 8 , 9 ]. As an example, a study conducted on women soccer players examined the changes in salivary immunoglobin A, cortisol and upper respiratory tract infection and tested the relationship with training intensity [ 10 ]. In this study [ 10 ], it was found that a higher training intensity had significant relationships with an increase in upper respiratory tract infection, thus suggesting the impact of the training process on acute physiological and immunological responses. Similarly, a study conducted in women’s soccer revealed that moderate-intensity training enhanced cytokine patterns [11]. Naturally, the contextual factors associated with the futsal competitive schedule [ 12 ], moment of the season [ 13 ] and match demands will modulate the training process. Thus, it is expected that different training drills occurring in training scenarios may result in different outcomes, and the determinants of exercise can be different based on the futsal drill typology [ 6 , 14 , 15 ]. The variation in those determinants should be understood considering the dose–response effect. Despite the importance of considering the dose–response effect of specific intervention programs on changes occurring in fitness variables in futsal players [ 16 , 17 ], it is also important to understand the characteristics of the training intensity distribution in the microcycle [ 18 ] and the individual effects of specific training exercises on the players [ 19 ]. Although there has been some research describing the variations in intensity within and between microcycles [ 18 , 19 ], it did not consider the specific impact of types of exercises and the contribution of types of exercises for the intensity imposed on the players. Although not found in women’s futsal, this characterization of the intensity imposed by different types of exercises was described in soccer, suggesting that the highest volume of training and the greatest locomotor demands (physical demands) imposed on the players were based on game-based drills [20]. Descriptive analysis is important to understand how the intensity is distributed across the different training drill types. However, relationships between drills and locomotor demands may improve the capacity to understand the training process’s determinant outcomes. Considering the great number of variables typically extracted by microelectromechanical systems, it is important to find a reduction technique that allows identifying the most important variables to be analyzed. One of the approaches to do that is applying principal component analysis (PCA) [ 21 ] that, in this context, may play a role as a data reduction technique to highlight the most demanding efforts. In male futsal players, PCA allowed identifying that using 3–4 principal components was the most appropriate approach to determine players’ performance in matches [ 22 ]. In the case of women’s futsal, PCA allowed identifying 22 external and internal intensity measures that may be used for intensity monitoring during official matches [ 23 ]. Despite these findings regarding PCA in official matches, there is no related study (as far as we know) that has applied PCA in training drills and sessions of women futsal players. This may help to understand which measures are important to detect for monitoring training intensity in women’s futsal training sessions. Therefore, the purpose of this study was to determine the most demanding efforts during different training tasks in a cohort study conducted in professional female futsal players considering a range of local positioning system (LPS) (e.g., distances covered at different velocities, accelerations and decelerations) and heart rate-based variables. Healthcare 2022,10, 838 3 of 13 2. Materials and Methods 2.1. Design The data were registered from daily player monitoring in which the athletes’ motion was routinely measured throughout the season. Measures were gathered by a real-time motion tracking system which includes a local positioning system (LPS) device, based on UWB technology, and an inertial measurement unit (IMU; WIMU PROTM, RealTrack Systems, Almeria, Spain). In addition, one GARMIN HRM-PRO chest strap (Garmin Ltd., Olathe, KS, USA) was located below the chest. Data were sent to the WIMU PRO device, which stores all data sets. WIMI PRO devices were daily attached to the players’ upper back in a pocket attached to a tight-fitting garment, placed between the scapulae at the T2–T4 levels to avoid unwanted movements. The tight-fitting garment was the same for each player in each game. The same routine was implemented each day, and both protocol and task control were daily checked by the same team staff. In this cohort study, all tasks considered throughout the season during 70 training sessions (from September to February) were clustered into different groups depending on their characteristics (see Table 1). During these training sessions, an LPS and microelectromechanical system (MEMS) were used to extract information from more than 250 variables, which may be summarized as accelerations/decelerations, short-time efforts, distance covered at different intensities and physiological variables such as heart rate. Through a multivariate reduction technique (PCA), the more relevant variables were extracted, highlighting the most demanding efforts by professional female futsal players in each group of tasks. Table 1. Training tasks grouped into exercise clusters. Cluster Characteristics Task Description 1 Preventive exercises (individual) - Plyometric - Strength circuits - Tabata - Proprioception - Eccentric - Reactive velocity - Velocity/accelerative 2 Analytical situations - Analytical tactical movements with and without opponents - Corners (without opposition) - Throw-ins - Shot on goal from fouls - Shot on goal from the penalty spot 3 Exercises in midcourt (20 ×20 m) - Games (with and without wildcard) from 3 vs. 3 to 6 vs. 6 - Corner strategy (exercise with opposition starting from a corner) 4 Exercises in 3/4of the court (20 ×28 m) - Games (with and without wildcard) from 3 vs. 3 to 6 vs. 6 - Counterattack exercises 5 Exercises in full court (20 ×40 m) - Games (with and without wildcard) from 3 vs. 3 to 6 vs. 6 - Counterattack exercises 6 Superiorities/inferiorities - Situations with numerical superiorities/inferiorities 2.2. Participants Data were collected from 14 professional biological women futsal players (means ± standard deviations: age = 24.34 ± 4.51; height = 1.65 ± 0.60 m; body mass = 63.20 ± 5.65 kg) from a Spanish 1st division top-level team during the 2020–2021 pre-season and in-season Healthcare 2022,10, 838 4 of 13 periods (from September to February). This sample was considered due to the high number of individual observations and the real-world scientific practice in high-level settings. Players were allowed to participate in a training session if they were healthy and they did not have injuries or did not participate in a post-injury readaptation program. Due to the critical situation as a result of severe acute respiratory syndrome (SARS-CoV-2), players were excluded from the analysis when they were confined. All players were notified of the aim of the study and procedures in accordance with the Declaration of Helsinki. The study was approved by the ethical committee of the University of Murcia (protocol code 3180/2020). 2.3. Data Collection To conduct a strict description of the use of technology, a recently published survey was followed [ 24 ]. For the use of an ultra-wideband (UWB)-based LPS, 21 points out of 23 were explained, while for the use of a MEMS, 17 points out of 20 were detailed. The rest of the items cannot be explained because the information was not available to the authors. 2.4. Measures The UWB technology operates on a much wider frequency band than other traditional radio communication technologies (at least 0.5 GHz), and a previous study did not report any problems in UWB-based tracking system accuracy in multipath conditions (i.e., 28 devices turned on) (Bastida Castillo et al., 2018). This system holds both FIFA International Match Standard and Quality certificates [ 25 ]. Each device consists of an internal microprocessor, 2 GB flash memory and a high-speed USB interface, to record, store and upload data. 2.4.1. Ultra-Wideband From the UWB distance covered at different intensities, velocity and load indicators were registered (see Table 2). The data were recorded in a training space far from metallic materials. The UWB system was composed of a reference system and tracked devices carried by the players. The antennae are transmitters and receivers of radio frequency signals. The antennae (mainly the master antenna) computerize the position of the devices that are in the play area, while the device receives that calculation using time difference of arrival (TDOA). The eight antennae were installed five hours before the match, forming an octagon for better signal emission (4.5 m from the perimeter line for antennae located in the corners, and 5.5 m from the perimeter line for the antennae located in the middle of the court and behind the goals) and reception at a height of 3 m, and held by a tripod. Once installed, they were switched on one by one, with the master antenna turned on last. From that moment, it was necessary to respect a 5-min protocol to avoid technology lock [ 24 ]. To allow data time synchronization, the master antenna managed the time using a common clock which allows data recording at the same time. When all devices were switched on in the center of the reference system, a process of automatic recognition between antennae and devices was carried out for 1 min. In this study, the raw data were recorded at an 18 Hz sampling frequency because low frequencies have been shown to have a lower quality of measurement, and 18 Hz with UWB has not shown less accuracy because of noise problems. The conditions were maintained with low temperatures, humidity gradients and slow air circulation to allow easier positioning. This UWB system has demonstrated valid and reliable measures during continuous situations [26]. Healthcare 2022,10, 838 5 of 13 Table 2. External and internal training load variables. Abbreviation Sub-Variables 1Distance covered at different intensities Dist (m·min−1)Distance covered Expl dist Distance covered at explosive intensity HSR Abs (m·min−1)High-speed running HIBD (m·min−1) High-intensity break distance with DEC > 2 m·s−2 2Heart rate-related variables HR % (50–60) Time spent from 50 to 60 % of maximum heart rate HR % (70–80) Time spent from 70 to 80 % of maximum heart rate HR % (80–90) Time spent from 80 to 90 % of maximum heart rate HR % (90–95) Time spent from 90 to 95 % of maximum heart rate HR % (>95) Time spent up to 95 % of maximum heart rate MAX HR (bpm) Maximum heart rate achieved Rel HR % % of heart rate scored per minute 3Velocities Vel Abs (0–6) (m·min−1) Absolute distance covered from 0 to 6 m·min−1 Vel Abs (18–21) (m·min−1) Absolute distance covered from 18 to 21 m·min−1 Vel Abs (21–24) (m·min−1) Absolute distance covered from 21 to 24 m·min−1 Vel Max Maximum velocity achieved by a player 4Accelerations Acc/min Number of accelerations per minute Dist Acc Distance accelerating MAX Acc (m·s2)Maximum acceleration Acc Abs (0–1)/min Absolute accelerations lower than 1 per minute Acc Abs (1–2)/min Absolute accelerations from 1 to 2 per minute Acc Abs (3–4)/min Absolute accelerations from 3 to 4 per minute Acc Abs (4–5)/min Absolute accelerations from 4 to 5 per minute Acc Abs (5–6)/min Absolute accelerations from 5 to 6 per minute Acc Abs (6–10)/min Absolute accelerations from 6 to 10 per minute 5Decelerations Dec Abs (−1, 0)/min Absolute decelerations lower than 1 per minute Dec Abs (−2, −1)/min Absolute decelerations from 1 to 2 per minute Dec Abs (−4, −3)/min Absolute decelerations from 3 to 4 per minute Dec Abs (−5, −4)/min Absolute decelerations from 4 to 5 per minute Dec Abs (−6, −5)/min Absolute decelerations from 5 to 6 per minute Dec Abs (−10, −6)/min Absolute decelerations from 6 to 10 per minute 6Impacts Impacts (0–3) G Impacts at intensity lower than 3 G Impacts (3–5) G Absolute decelerations from 3 to 5 G Impacts (0–3)/min From 0 to 3 impacts per minute Impacts (3–5)/min From 3 to 5 impacts per minute Impacts (5–8)/min From 5 to 8 impacts per minute 7 Landings Landing (5–8)/min From 5 to 8 landings per minute 8Load indicators Player Load /min Player load extracted from accelerometer’s 3 axes Power Metabolic Energy consumed kg·s HML (10–25.5) (m) Distance covered from 10 to 25.5 m at 25.5 W/kg, which corresponds to 5.5 m/s2or significant acceleration/deceleration efforts HML (25.5–35) (m) Distance covered from 25.5 to 35 m at 25.5 W/kg, which corresponds to 5.5 m/s2or significant acceleration/deceleration efforts DSL/min Dynamic strength load. Total impacts with high intensity of 2 G. HBD (m·min−1) Note: G = 9.8 m/s2. 2.4.2. Inertial Measurement Units From the IMU, acceleration, deceleration, impact and landing-related variables were registered (see Table 2). The validity of the inertial measurement unit (IMU; WIMU PROTM, Real Track Systems, Almeria, Spain) was assessed in a previous study [ 27 ]. The results of the validity in this study were satisfactory and had a bias of 0.0006 ± 0.0018 s. The Healthcare 2022,10, 838 6 of 13 calculation of the velocity was conducted through differential Doppler and the acceleration was calculated from velocity. Finally, the minimum effort duration and minimum speed for avoiding unrealistic data mentioned were defined by the manufacturer to avoid outliers. 2.5. Performed Training Task Clusters and Variables Positional data recorded during training tasks (Table 1) extracted from PCA are summarized in Table 2. The training task clustering followed a technical classification proposed by team staff, which focused on the rationale of the classification. Further, the task clusters were established depending on the efforts in each of them. In cluster number one, warmup exercises were classified, which players performed at the beginning of each training session. In cluster number two, the harbor exercises were established (i.e., low physical stress), where team staff halted the exercise to explain tactical information. Clusters three, four and five focused the classification’s rationale on the number of players involved (individual training tasks, pure opposition, opposition and collaboration and opposition and collaboration with numerical unbalances). Additionally, tasks with opposition and collaboration (from 3 vs. 3 to 6 vs. 6) were classified into 3 clusters depending on space (i.e., midcourt, 3/4 of the full court and full court). These clusters included the most tactical and technical training enrichment exercises for the futsal players, close to the real situations. Finally, cluster number six refers to all these exercises, usually with a high-stress situation, where players with superiority/inferiority need to make decisions with high fatigue and risk. The training exercises non-characterized by task constraints were excluded from the analysis. In addition, abbreviations and definitions from the variables extracted from registered training tasks and that formed PCs are described in Table 2. See Figure 1for details regarding the experimental protocol. Healthcare 2022, 10, x FOR PEER REVIEW 7 of 15 Figure 1. The experimental protocol and method. 2.6. Statistical Analysis To describe the protocol of PCA, the survey purposed by Rojas-Valverde et al. [28] was followed. The protocol was explained step by step in Oliva-Lozano et al. [29], which contains the following steps: field data collection, software outcome (inclusion of all variables measured), correlation matrix exploration (all variables correlated between each other), assumption confirmation (variables were centered and scaled (Z-Score), suitability confirmed by KMO and Bartlett sphericity values), PCA (eigenvalues greater than 1 were included for extraction), loading groupings in each PC (loadings > 0.7 were considered for PCA grouping and inclusion) and final PCA outcome (each PCA was presented and grouped by loading) [29]. From more than 250 variables recorded, 12 (cluster 1), 32 (cluster 2), 32 (cluster 3), 28 (cluster 4), 27 (cluster 5) and 31 (cluster 6) variables were explored using a correlation matrix in order to select the most representative variables, and those with correlations r < 0.7 between variables were considered for extraction [30]. After exclusion of variables with variance = 0, 22-21 variables were scaled and centered using ZScores. PCA suitability was confirmed through the Kaiser–Meyer–Olkin value (KMO = 0.64–0.78) and Bartlett sphericity test significance (p < 0.05) [31]. Eigenvalues > 1 were considered for the extraction for each principal component, and a varimax orthogonal rotation method was used to identify high correlations between components to offer different information. PC loadings > 0.6 were considered for extraction, and when a cross-loading was found between PCs, only the highest factor loading was retained [31]. 3. Results From 250 variables extracted from the tracking system and MEMS, 8 main variables and load indicators were extracted, which contain a total of 42 sub-variables: (1) distance covered at different intensities (Dist. (m·min −1 ), Expl dist and HSR Abs (m/min)); (2) heart rate-related variables (maximum HR, relative HR and time spent at heart rates of different intensities); (3) velocity at different intensities (from 0 to 6, from 18 to 21, from 21 to 24 and maximum velocity); (4) variables related to accelerations (Acc/min, distance accelerating, maximum acceleration and absolute accelerations at different intensities (five Figure 1. The experimental protocol and method. 2.6. Statistical Analysis To describe the protocol of PCA, the survey purposed by Rojas-Valverde et al. [ 28 ] was followed. The protocol was explained step by step in Oliva-Lozano et al. [ 29 ], which contains the following steps: field data collection, software outcome (inclusion of all Healthcare 2022,10, 838 7 of 13 variables measured), correlation matrix exploration (all variables correlated between each other), assumption confirmation (variables were centered and scaled (Z-Score), suitability confirmed by KMO and Bartlett sphericity values), PCA (eigenvalues greater than 1 were included for extraction), loading groupings in each PC (loadings > 0.7 were considered for PCA grouping and inclusion) and final PCA outcome (each PCA was presented and grouped by loading) [ 29 ]. From more than 250 variables recorded, 12 (cluster 1), 32 (cluster 2), 32 (cluster 3), 28 (cluster 4), 27 (cluster 5) and 31 (cluster 6) variables were explored using a correlation matrix in order to select the most representative variables, and those with correlations r < 0.7 between variables were considered for extraction [ 30 ]. After exclusion of variables with variance = 0, 22–21 variables were scaled and centered using Z-Scores. PCA suitability was confirmed through the Kaiser–Meyer–Olkin value (KMO = 0.64–0.78) and Bartlett sphericity test significance (p< 0.05) [ 31 ]. Eigenvalues > 1 were considered for the extraction for each principal component, and a varimax orthogonal rotation method was used to identify high correlations between components to offer different information. PC loadings > 0.6 were considered for extraction, and when a cross-loading was found between PCs, only the highest factor loading was retained [31]. 3. Results From 250 variables extracted from the tracking system and MEMS, 8 main variables and load indicators were extracted, which contain a total of 42 sub-variables: (1) distance covered at different intensities (Dist. (m · min −1 ), Expl dist and HSR Abs (m/min)); (2) heart rate-related variables (maximum HR, relative HR and time spent at heart rates of different intensities); (3) velocity at different intensities (from 0 to 6, from 18 to 21, from 21 to 24 and maximum velocity); (4) variables related to accelerations (Acc/min, distance accelerating, maximum acceleration and absolute accelerations at different intensities (five levels)); (5) decelerating-related variables (six levels of decelerations at different intensities); (6) impacts (measured in both G (9.8 m · s 2 ) and number per minute (four levels)); (7) a variable related to landing (from 5 to 8 landings per minute); and (8) six load indicators (player load, power metabolic, HML (two levels), DSL, HBD and HIBD). However, the contributions of these sub-variables to explaining players’ external and internal load responses differed between types of exercises (Table 3). Descriptive statistics (e.g., mean, median, standard deviation, percentile) of each variable extracted from PCA for each exercise cluster are described in Supplementary File S1. Table 3. Representation of principal component analysis from external and internal load responses in different training tasks performed by elite professional female futsal players during mid-season. PC 1234567 Cluster 1 (preventive exercises) Eigenvalue 11.5 9.8 7.0 5.7 5.2 % Variance 31.4 42.9 52.8 59.9 65.6 70.7 HR % (50–60) 0.798 HR % (70–80) 0.771 HR % (90–95) 0.792 MAX Acc (m/s2)0.766 Acc Abs (2–3)/min 0.780 Acc Abs (4–5)/min 0.798 Acc Abs (5–6)/min 0.759 Dec Abs (−6, −5)/min 0.716 Impacts (0–3) G 0.850 Impacts (0–3) min Impacts (3–5)/min 0.874 Impacts (5–8)/min 0.917 Impacts (8–100)/min 0.950 Landing (5–8)/min 0.729 Healthcare 2022,10, 838 8 of 13 Table 3. Cont. PC 1234567 Cluster 2 (analytical situations) Eigenvalue 13.8 10.5 8.2 6.9 5.3 % Variance 23.4 37.3 47.7 55.9 62.8 68.1 HR % (50–60) 0.736 HR % (80–90) 0.817 DSL/min 0.766 Vel Abs (0–6) (m·min−1)0.705 Acc/min 0.903 Acc Abs (0–1)/min 0.903 Acc Abs (1–2)/min 0.737 Acc Abs (2–3)/min 0.766 Acc Abs (4–5)/min 0.799 Dec Abs (−5, −4)/min 0.801 Impacts (0–3) G 0.926 Impacts (3–5) G 0.734 Impacts (0–3) min 00.752 Impacts (5–8)/min 0.855 Cluster 3 (exercises in midcourt) Eigenvalue 14.9 9.4 7.3 6.4 4.9 4.2 % Variance 28.4 43.3 52.8 60.1 66.5 71.4 75.6 Expl dist (m) 0.867 HR % (50–60) 0.736 HR % (80–90) 0.720 HR % (90–95) 0.802 HR % (>95) 0.880 HIBD (m·min−1)0.859 Vel Abs (18–21) (m·min−1)0.803 Acc/min 0.900 Dist Acc .726 MAX Acc (m/s2)0.730 Acc Abs (3–4)/min 0.708 Acc Abs (5–6)/min 0.738 Acc Abs (6–10)/min 0.840 Dec Abs (−1, 0)/min Dec Abs (−2, −1)/min 0.919 Impacts (0–3) G 0.956 Impacts (3–5) G 0.949 Impacts (0–3) min 0.775 Impacts (5–8)/min 0.837 Cluster 4 (exercises in 3/4of the court) Eigenvalue 14.0 10.8 7.1 5.3 4.6 4.0 % Variance 26.9 40.8 51.6 58.7 64.0 68.6 72.6 Expl dist (m) 0.725 HSR Abs (m·min−1)0.807 HR % (50–60) 0.713 HR % (70–80) 0.842 HR % (80–90) 0.764 Acc/min 0.885 Healthcare 2022,10, 838 9 of 13 Table 3. Cont. PC 1234567 Acc Abs (2–3)/min 0.700 Acc Abs (3–4)/min 0.882 Acc Abs (4–5)/min 0.760 Acc Abs (6–10)/min 0.852 Dec Abs (−1, 0)/min 0.854 Impacts (3–5) G 0.802 Impacts (5–8)/min 0.928 Player Load /min 0.816 Power Metabolic (kg · s) 0.779 Cluster 5 (exercises in full court) Eigenvalue 16.3 10.9 7.7 6.2 % Variance 24.5 40.8 51.8 59.5 65.8 Expl dist (m) 0.920 HML (10–25.5) (m) 0.911 HML (25.5–35) (m 0.861 HBD (m/min) 0.854 Vel Abs (18–21) (m·min−1)0.748 Acc/min 0.839 Acc Abs (5–6)/min 0.714 Acc Abs (6–10)/min 0.854 Dec Abs (−2, −1)/min 0.909 Dec Abs (−6, −5)/min 0.783 Dec Abs (−10, −6)/min 0.704 Impacts (0–3) G 0.880 Impacts (5–8)/min 0.944 Cluster 6 (superiorities/inferiorities) Eigenvalue 14.0 10.7 8.9 5.7 5.1 4.2 % Variance 26.4 40.4 51.1 60.0 65.7 70.9 75.1 Expl dist (m) 0.700 Dist (m·min−1)0.778 HR % (50–60) 0.745 HR % (70–80) 0.765 MAX HR (bpm) 0.795 Rel HR % 0.885 Vel Abs (18–21) (m·min−1)0.867 Acc/min 0.781 Dist Acc 0.727 MAX Acc (m·s2)0.727 Acc Abs (0–1)/min 0.852 Acc Abs (2–3)/min 0.799 Acc Abs (3–4)/min 0.864 Acc Abs (4–5)/min 0.763 Dec Abs (−4, −3)/min 0.752 Dec Abs (−5, −4)/min 0.743 Impacts (0–3) min 0.787 The preventive exercise cluster (exercise cluster 1) was composed of 6 PCs formed by 14 variables, which explained a total of 70% of players’ behavior during these exercises. In this case, the first PC was composed of five sub-variables related to accelerations: MAX Acc (m · s 2 ), Acc Abs (2–3)/min, Acc Abs (4–5)/min, Acc Abs (5–6)/min and Dec Abs ( − 6, − 5)/min. In addition, cluster 2 was composed of 6 PCs formed by 14 variables, which explained a total of 68% of players’ physical behavior during these exercises. In this case,