European Journal of Sport Science ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tejs20 Association of the Vertical and Horizontal ForceVelocity Profile and Acceleration With Change of Direction Ability in Various Sports Andrés Baena-Raya , Alberto Soriano-Maldonado , Filipe Conceição , Pedro Jiménez-Reyes & Manuel A. Rodríguez-Pérez To cite this article: Andrés Baena-Raya , Alberto Soriano-Maldonado , Filipe Conceição , Pedro Jiménez-Reyes & Manuel A. Rodríguez-Pérez (2020): Association of the Vertical and Horizontal Force-Velocity Profile and Acceleration With Change of Direction Ability in Various Sports, European Journal of Sport Science, DOI: 10.1080/17461391.2020.1856934 To link to this article: https://doi.org/10.1080/17461391.2020.1856934 Accepted author version posted online: 26 Nov 2020. Submit your article to this journal View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tejs20 Publisher: Taylor & Francis & European College of Sport Science Journal: European Journal of Sport Science DOI: 10.1080/17461391.2020.1856934 ASSOCIATION VELOCITY OF THE PROFILE VERTICAL AND AND ACCELERATION HORIZONTAL WITH FORCE- CHANGE OF DIRECTION ABILITY IN VARIOUS SPORTS Authors: Andrés Baena-Raya1, 2 Alberto Soriano-Maldonado1, 2 Filipe Conceição3,4 Pedro Jiménez-Reyes5* Manuel A. Rodríguez-Pérez1, 2* * Dr. Jiménez-Reyes and Dr. Rodríguez-Pérez contributed equally to this work. Affiliations: 1 Department of Education, Faculty of Education Sciences, University of Almería, Almería, Spain 2 SPORT Research Group (CTS-1024), CERNEP Research Center, University of Almería, Almería, Spain. 3 Center of Research, Education, Innovation and Intervention in Sport, Faculty of Sports, University of Porto, Porto, Portugal. 4 LABIOMEP-Porto Biomechanics Laboratory, University of Porto, Porto, Portugal. 5 Center for Sport Studies, Rey Juan Carlos University, Madrid. Spain. Address correspondence to Alberto Soriano-Maldonado, SPORT Research Group (CTS-1024), Department of Education, Faculty of Education Sciences, University of Almería, Carretera de Sacramento, s/n, 04120, La Cañada, Almería, Spain (email: [email protected]). ABSTRACT This study aimed to assess the association of the mechanical variables derived from the force-velocity (FV) profile (i.e. theoretical maximal force [F0], velocity [V0] and maximal power output [Pmax]) with change of direction (COD) performance in soccer, basketball and tennis players. Fifty-four male athletes (soccer n = 23; tennis n = 16; basketball n = 15) were assessed for the vertical (Vrt) and horizontal (Hzt) FV profiles, COD with the dominant (D) and nondominant (ND) legs, using the modified 505 test, and sprint. Hzt FV profile parameters showed stronger associations with performance than Vrt FV profile in the three sports. Specifically, the Hzt parameter most strongly associated with COD performance was F0 in tennis (r = -0.83; p<0.001) and Pmax in soccer and basketball (r = -0.79; p<0.001). Associations between sprint times and COD test ranged from (r = 0.73 to 0.82) in soccer players, (r = 0.74 to 0.87) in tennis players and (r = 0.62 to 0.85) in basketball players, respectively (p<0.05). Considering the whole sample and the random effect of the type of sports, an improvement in sprint acceleration (i.e. one N/kg increase in F0 and one W/kg in Pmax) was associated with 0.15 s and -0.04 s to complete the 505 test, respectively. In conclusion, our results suggest the potential usefulness of assessing the Hzt FV profile to maximize acceleration capabilities through training interventions which, in turn, may translate into improved COD performance. However, further longitudinal and experimental research is needed to confirm this hypothesis. Key words: Strength, acceleration, training, assessment. INTRODUCTION Game speed and the number of high-intensity activities such as sprinting, changing of direction (COD) and jumping performed during match-play has significantly increased in the last decade in sports like soccer (Barnes et al., 2014), basketball (García et al., 2020) or tennis (Fernandez-Fernandez et al., 2009). While attacking and defending, athletes are required to continually accelerate, decelerate and make quick changes in speed and direction in very short periods of time (Salaj & Markovic, 2011). Consequently, sprint and COD are determinant outcomes in a wide variety of sports (Brughelli et al., 2008), which has led researchers to focus on identifying the physical determinants of these complex abilities to enhance them through training interventions. Interestingly, recent studies (Nimphius et al., 2010; Pereira et al., 2018; Spiteri et al., 2015) found that stronger and more powerful athletes usually sprint faster, jump higher and change direction quicker than their weaker counterparts. Similarly, Spiteri et al. (2014) reported strong associations between vertical eccentric, concentric and isometric strength capabilities with faster 180º COD times. In contrast, inconsistent influence on COD performance has been also reported regardless of the strength (Loturco et al., 2018) or power levels (Spiteri et al., 2014) of the players. All these prior studies used either the squat one repetition maximum (1RM), jump height with a single load, or sprint times, which do not provide a comprehensive description of the muscular determinants of ballistic push-off performance compared to the entire force-velocitypower spectrum (Morin & Samozino, 2016). The force-velocity (FV) relationship has recently been proposed to identify the lower limbs mechanical capabilities to produce force in vertical jumping and sprinting (Jiménez-Reyes, Samozino, Pareja-Blanco, et al., 2017; Samozino et al., 2016). The FV relationship characterizes the mechanical limits of the entire lower limb neuromuscular system to produce force, velocity and power and it is summarized through the theoretical maximal force (F0), theoretical maximal velocity (V0), maximal power output (Pmax) and the slope of the FV relationship (FV slope) (Samozino et al., 2012). Thus, including the FV profile may provide an integrative mechanical representation of the athlete’s maximal capabilities in ballistic performance. Interestingly, an optimal FV slope (a combination of F0 and V0 that maximizes ballistic performance for a given Pmax) exists for each individual, which helps to prescribe individualized training programs (Jiménez-Reyes, Samozino, Brughelli, et al., 2017). Essentially, COD maneuvers are determined by the athlete’s ability to decelerate and reaccelerate the body rapidly (Chaouachi et al., 2012). This is especially relevant in sharp cuts such as 180º COD in which the athlete requires a combination of multiple strength components to shift their momentum during the deceleration phase (linear velocity is close to zero in 180º COD) by applying substantial forces onto the ground, in both vertical and horizontal directions to accelerate the body forward in a new direction (Delaney et al., 2015; Spiteri et al., 2015). Since the similar mechanical demand is required during jumping and sprinting (Morin & Samozino, 2016), assessing the individual mechanical properties underpinning jumping and sprint performance might provide a better understanding of the physical capacities that determine COD maneuvers in order to optimize training program prescription in soccer, tennis and basketball. Note that it can be easily conducted in field practice through accessible practical devices and simple inputs (i.e. anthropometric data and a single maximal jump/sprint) representing a time efficient strategy to measure athlete’s maximal capability to produce force, power and velocity in common sport context (Morin & Samozino, 2016; Romero-Franco et al., 2017). However, to the best of our knowledge, no study has explored the association of COD performance with the vertical (Vrt) and horizontal (Hzt) profiles. The aim of the present study was to assess the association of the mechanical variables derived from the force-velocity profile (F0, V0 or Pmax) with COD performance in different sports. Since sprint running and COD present similar mechanical demands during the acceleration phase in which athletes need to produce and apply substantial horizontal external force (Dos’Santos et al., 2019; Morin & Samozino, 2016), we hypothesized that the F0 and Pmax obtained from the Hzt FV profile would be the mechanical variables more associated with COD performance (since these variables are determinant for short sprint acceleration) among the sports as well as the magnitude of the association would be weaker for the Vrt FV profile. MATHERIAL AND METHODS Study design and participants In this cross-sectional study, a total of fifty-four male athletes volunteered to participate in this study. The characteristics of the subjects are presented in Table 1. Soccer and basketball participants were semi-professional players competing at third division leagues on their respective sports, whereas tennis players were competing at national level. None of the subjects exhibited any type of injury or limitation that might affect the tested performance. All subjects were informed of the risks and benefits of the study and gave their written consent before the initiation of the study. This study was reviewed and approved by the ethics committee of the University of Almería. Testing procedures Athletes were evaluated in-season since FV profile parameters seems to be compromised at both pre-season and the end of the competitive period (Jiménez-Reyes et al., 2020). No familiarization session was included because all participants were well familiarized with the testing procedures as part of their routine assessment. The FV profile, sprint and COD testing procedures were carried out the same day for each group. Prior to the tests, all subjects performed a standardized warm-up protocol including 5 min jogging and 5 min of lower limb dynamic stretching. The specific warm-up consisted of several trials of both unloaded and loaded countermovement jumps (CMJ) before jumping test and 3 progressive sprints of 30-m at increase running velocities before the sprinting test. 1. Vertical FV Profile test. To determine the individual FV relationship, participants performed maximal CMJ without external loads and against two to three external loads ranging from 10 to 70 kg. The heaviest external load (66.05 ± 8.31 kg) allowed each subject to jump approximately 12 cm (García-Ramos et al., 2018). The test was performed with a Smith Machine (Multipower Fitness Line, Peroga, Spain). Two valid trials were performed with each load with 2 minutes of rest between trials and 4-5 minutes between loading conditions (Jiménez-Reyes, Samozino, Brughelli, et al., 2017) which makes possible to evaluate a few athletes at the same time. Therefore, the maximum time needed to full assess vertical FV profile is about 15 min for two-three players. Jump height was estimated from the flight time recorded the OptoJump infrared platform (Microgate, Bolzano, Italy). Before each jump, participants were instructed to stand up straight keeping their hands on the hips for jumps without external loads and on the bar for loaded jumps. Thereafter, they initiated squatted until a crouching position at 90º knee angle, followed by a maximal vertical jump. To avoid any influence of the knee angle on the FV relationship, an elastic band was attached to the Smith Machine so that athlete’s buttock had to contact it at individual 90º knee angle (Janicijevic et al., 2019). The trials were checked by an experienced researcher, being repeated if these requirements were not met. Individual FV profile (F0, V0, FV slope, and Pmax) were obtained following the simple method (Jiménez-Reyes, Samozino, Pareja-Blanco, et al., 2017; Samozino et al., 2008). The FV relationship parameters were determined through a linear regression model: F(V) = F0 – aV, in which F0 represents the force-intercept and a is the slope of the F-v relationship. The velocityintercept (V0 = F0/a) and maximum power (Pmax = F0·V0/4) were also calculated. 2. Horizontal FV Profile test. To determine the individual FV relationship, participants performed two maximal sprints of 30-m with 4 min of recovery time between trials. Despite length court is limited in these sports, the entire sprint, and consequently, the entire FV profile was measured to check the underlying mechanical capabilities and get information of how the athlete is applying horizontal force during sprint at low and high running velocities. All data were collected using a Stalker Acceleration Testing System (ATS) II radar device (Model: Stalket ATS II Version 5.0.2.1; Applied Concepts, Dallas, TX, USA). The radar device was attached to a tripod 10-m from the starting line at a height of one m corresponding approximately to the height of participants’ centre of mass. The radar device sampled velocity-time data at 46.9 Hz. Participants initiated the sprint from a crouching position (staggered-stance). The velocity-time data was used to determine individual FV relationship (i.e. the theoretical maximum values of force: F0, V0 and Pmax) according to Samozino’s validated method (Samozino et al., 2016). F0 and Pmax were normalized to body mass. Moreover, five pairs of photocells (Microgate, Bolzano, Italy) were positioned at the starting line and the distances of 5-, 10-, 15- and 20-m to also obtain the split times. 3. COD test. COD performance was assessed by the modified 505 test (Taylor et al., 2019). Athletes began the test 0.3-m behind the pair of photocells placed on the starting line. The set of cones were set at 5-m from the start position. Athletes were instructed to accelerate as fast as possible along the 5-m distance, placing either their right or left foot on the line, pivot and sprint through the finish (photocells placed at starting position). Two trials were completed for each pivot foot, using the fastest time for statistical analysis. Data are presented as dominant (D) and nondominant (ND) side. Statistical analysis Descriptive data are presented as means and standard deviations. The distribution of the main variables was tested by histograms and the Shapiro-Wilk test. Pearson’s correlation coefficients (r) were used to assess the association of the mechanical variables derived from the Vrt and Hzt FV profiles and the different sprint times with COD performance separately in the three sports. To understand the extent to which the orientation of the FV profile was a potential confounder of the associations under study, we additionally used partial correlations adjusting for the slope of the FV relationship . Qualitative interpretations of the r coefficients are provided as defined by Hopkins, Marshall, Batterham, & Hanin (2009): trivial (0.00-0.09), small (0.10-0.29), moderate (0.30-0.49), large (0.50-0.69), very large (0.70-0.89), nearly perfect (0.90-0.99) and perfect (1.00). In addition, we conducted linear mixed models with the whole sample (n=54) to provide a robust overall message regardless of the sport. The COD time was entered as dependent variable, the mechanical variables derived from the FV profile as fixed effects, and the type of sport as random effects in separate models. Statistical significance was set at p<0.05. Statistical analyses were performed with SPSS version 25.0 (SPSS, Chicago, Illinois, USA). RESULTS The relationship of the mechanical variables derived from the Vrt and Hzt FV profiles with COD in the distinct groups of athletes is presented in Table 2. Importantly, adjusting for the slope (i.e. which represents the orientation of the FV profile) modified many correlation coefficients. Therefore, the results described below and the interpretations correspond to the partial correlation analysis in Table 2. Hzt FV profile associations were higher than Vrt FV profile in the three sports. Specifically, the parameters most strongly associated with 505 COD performance were Hzt F0 in tennis players (r = -0.83; p<0.001) and Pmax in both soccer and basketball players (r = -0.79; p<0.001) as well as Vrt F0 in tennis (r = -0.63; p<0.05) and basketball players (r = -0.58; p<0.05), respectively. Table 3 displays the association of sprint times with COD. The magnitude of the correlations ranged from (r = 0.73 to r = 0.82) in soccer players, (r = 0.74 to r = 0.87) in tennis players and (r = 0.62 to r = 0.85) in basketball players, respectively (p<0.05). Linear mixed model analyses revealed that, considering the whole sample and the random effect of the type of sport (i.e. the association might be different across sports) there was a consistent association so that higher scores on the mechanical variables derived from the Vrt and (more significantly) Hzt FV profiles were associated with a lower COD times (Table 4). DISCUSSION This study was designed to assess the association of the mechanical variables derived from the FV profile (F0, V0 or Pmax) with COD performance in soccer, basketball and tennis. The main findings of this study indicate that higher performance in both FV profile parameters were significantly associated with lower COD times, both in the whole sample and in the different sports evaluated. As expected, the Hzt FV profile (i.e. F0, Pmax and V0) was more strongly associated than the Vrt FV which, together with the strong correlations observed between sprint times and COD performance, support the key role of acceleration capabilities on COD speed (Loturco, Pereira, et al., 2019). Another relevant finding is that including the slope of the FV profile (which represents the orientation of FV towards force or velocity) significantly modified the strength of the association of some parameters derived from both FV profiles with COD performance as shown in Table 2, indicating the slope must be considered in the analyses. Thus, the magnitude of the correlation between the Vrt FV profile and COD performance were higher, especially for F0 and V0 when assuming an equal FV Slope for all athletes, whereas the V0 was the Hzt FV profile parameter that showed a greater increment in the correlation. Success in COD mainly depends on the individual capacity to apply high levels of force effectively onto the ground to overcome the moment of inertia. Previous studies have reported strong (Nimphius et al., 2010) to very strong (Spiteri et al., 2014) correlations of vertical measurements (i.e. maximal dynamic strength assessed during 1 RM back squat) with 505 COD test performance in softball and basketball female players whereas Loturco et al. (2018) and Freitas et al. (2019) found that soccer and rugby players with higher power values assessed by jump squats showed better COD performance. Our results also highlight F0 and Pmax capabilities as the vertical mechanical variables most strongly associated with COD performance among the different groups, especially for tennis and basketball. However, as we firstly hypothesized, Hzt FV profile associations were higher than Vrt FV profile in the three sports, highlighting the predominance of horizontal force application during COD tasks (Dos’Santos et al., 2019). Despite jump and sprint information has been traditionally used interchangeably, Jiménez-Reyes et al. (2018) and Marcote-Pequeño et al. (2019) observed weak associations between the FV profile obtained in jumping and sprinting in high level to elite athletes, suggesting the influence of the force-vector and force application. In this regard, Hzt rather than Vrt FV profile assessment would be more recommendable since it evaluates athlete’s ability to accelerate the body in a forward direction during a linear sprint (which is also determinant during the propulsive phase of the COD maneuver (Sayers, 2015)). Dos’Santos et al. (2019) recently reported that faster athletes in 505 test displayed greater horizontal mean and peak propulsive ground reaction forces (GRF) over penultimate and final foot contact. Interestingly, our findings suggest that the horizontal force application might be determinant not only during braking and plantar phases of the 180º COD but also to rapidly accelerate the body in the propulsive phase. Specifically, the strong association of F0 with COD performance in tennis players might be attributed to the mechanical demands of the sport which requires the player to continually accelerate over very short distances to hit the ball (Fernandez-Fernandez et al., 2009). This might lead to a superior ability to produce great amount of horizontal net force (i.e. F0) than medium level soccer and basketball players, as Jiménez-Reyes et al. (2018) previously reported (same findings are displayed in Table 1). Conversely, the Pmax was the parameter most strongly associated with both COD tests performance in soccer players, closely followed by F0. Although previous studies have reported that soccer players with greater maximum acceleration rates from zero to 5-m or higher sprint velocities demonstrate shorter COD time (Loturco, Jeffreys, et al., 2019; Loturco, Pereira, et al., 2019; Pereira et al., 2018), the present findings strengthen that relationship with COD performance by also integrating the mechanical properties of sprint acceleration performance (Samozino et al., 2016). Finally, basketball players reported a nearly similar association between F0 and Pmax with COD performance, regardless of the pivoting leg. Interestingly, basketball players usually perform 180º turns in short distances to position themselves between their opponents during defensive actions (e.g. backdoor cut) (Spiteri et al., 2015) so that greater force and power capabilities may help them maintaining a defensive ready position (Spiteri et al., 2014) From a practical point of view, it seems reasonable that practitioners from these three sports aiming to enhance acceleration ability should develop their athletes’ ability to produce high levels of horizontal force and power, which in turn, may translate into improved COD performance (due to the greater acceleration capabilities). We additionally support this by providing an estimation of the magnitude of association of FV profile variables with COD time in the whole sample. Specifically, for the Hzt FV profile we estimated that one N/kg increase in F0 and one m/s increase in V0 were associated with -0.15 s and -0.14 s to complete the 505 test, whereas increasing one W/kg in Pmax was associated with -0.04 s to complete the COD pivoting with both legs. The present results are in agreement with previous research (Loturco, Jeffreys, et al., 2019; Loturco, Pereira, et al., 2019; Pereira et al., 2018) supporting the strong association of different sprint times (i.e., 5-, 10-, 15- and 20-m) with COD time. Although sprint times undoubtedly explains a large variability of COD performance, the Hzt FV profile might still add valuable information about the mechanical variables underlying short sprint acceleration. For instance, two players may have the same 5-m or 20-m split time but different F0 or, what is more, the same F0 but different mechanical effectiveness values which is determinant in the acceleration phase (Morin & Samozino, 2016). In this example, prescribing a similar training program for these two players with the aim to optimize athlete’s acceleration ability might result in suboptimal adaptations for maximal linear velocity, since the specific Hzt FV profile mechanical variables underlying short sprint acceleration would not be addressed. Therefore, assessing the sprint FV profile might help coaches to describe athlete’s acceleration ability and prescribe specific training program to improve the acceleration and linear velocity, which might enhance COD ability. This study has limitations that must be underlined. The cross-sectional design precludes establishing causal relationships and these results must be contrasted in future prospective research. In particular, assessing whether optimizing the FV profiles through specific training programs translates into an improved COD performance (due to the improvement in acceleration capabilities) is warranted. Moreover, further research with larger samples is needed to confirm these findings. Finally, technical components (e.g. penultimate and final foot contact, trunk inclination or hip angle) should be considered since are involved in these maneuvers (Dos’Santos et al., 2019). The stronger association of the Hzt FV profile with COD performance both in the whole sample and in the different sports evaluated suggest that vertical and horizontal information should not be used interchangeably. Interestingly, sprint acceleration F0 and Pmax were strongly associated with COD performance. Since acceleration capabilities play a key role on COD speed, practitioners should focus on developing these mechanical variables through training programs. 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All Soccer players Tennis players Basketball players (n = 54) (n = 23) (n = 16) (n = 15) Age (years) 22.20 ± 4.41 25.35 ± 3.55 17.25 ± 2.11 22.67 ± 2.13 Height (cm) 181.05 ± 8.17 177.87 ± 5.96 177.75 ± 7.83 189.47 ± 5.11 Body mass (kg) 75.69 ± 11.36 73.44 ± 7.48 67.61 ± 7.86 87.75 ± 9.78 Vrt F0 33.04 ± 5.23 34.96 ± 4.81 32.01 ± 4.44 31.18 ± 5.96 Vrt Pmax 31.50 ± 3.85 29.95 ± 3.93 33.24 ± 3.19 32.03 ± 3.63 Vrt V0 3.91 ± 0.81 3.48 ± 0.61 4.24 ± 0.79 4.23 ± 0.83 Hzt F0 7.04 ± 0.84 6.99 ± 0.54 7.63 ± 1.08 6.47 ± 0.44 Hzt Pmax 15.22 ± 1.84 15.83 ± 1.18 15.29 ± 2.61 14.20 ± 1.26 Hzt V0 8.71 ± 0.69 9.13 ± 0.50 8.02 ± 0.70 8.82 ± 0.28 505 - D 2.44 ± 0.11 2.41 ± 0.05 2.47 ± 0.14 2.44 ± 0.14 505 - ND 2.51 ± 0.12 2.48 ± 0.06 2.51 ± 0.14 2.55 ± 0.15 T_5m 1.41 ± 0.07 1.41 ± 0.04 1.39 ± 0.87 1.46 ± 0.04 T_10m 2.19 ± 0.09 2.16 ± 0.05 2.18 ± 0.13 2.24 ± 0.07 T_15m 2.87 ± 0.12 2.82 ± 0.07 2.90 ± 0.16 2.92 ± 0.09 T_20m 3.51 ± 0.14 3.44 ± 0.09 3.55 ± 0.19 3.56 ± 0.11 Data are mean ± SD. D, dominant; ND, nondominant; Vrt, vertical; Hzt, horizontal; F0, theoretical maximal force; Pmax, maximal power output; V0, theoretical maximal velocity. Table 2. Correlation between vertical and horizontal FV profile variables and performance variables generated from Pearson and partial correlations. VERTICAL FV PROFILE PEARSON CORRELATION PARTIAL CORRELATION F0 Pmax V0 F0 Pmax V0 All -0.279* -0.378** -0.028 -0.446** -0.439** -0.273* Soccer -0.229 -0.370 -0.115 -0.410 -0.398 -0.371 Tennis 0.037 -0.546* -0.267 -0.632* -0.548* -0.209 Basketball -0.210 -0.509 -0.067 -0.577* -0.516 -0.194 All -0.245* -0.339** -0.040 -0.375** -0.396** -0.293* Soccer -0.260 -0.274 -0.021 -0.332 -0.320 -0.309 Tennis -0.267 -0560* -0.288 -0.637* -0.550* -0.207 Basketball -0.263 -0.476 -0.050 -0.453 -0.500 -0.359 505 - D 505 - ND HORIZONTAL FV PROFILE F0 Pmax V0 F0 Pmax V0 All -0.639** -0.729** -0.261* -0.589** -0.697** -0.456** Soccer -0.684** -0.758** -0.073 -0.680** -0.744** -0.203 Tennis -0.933** -0.786** -0.267 -0.830** -0.654* -0.419 Basketball -0.750** -0.793** -0.596* -0.763** -0.794** -0.612* All -0.667** -0.708** -0.182 -0.635** -0.666** -0.602** Soccer -0.714** -0.805** -0.098 -0.703** -0.793** -0.247 Tennis -0.941** -0.790** -0.288 -0.841** -0.665* -0.426 Basketball -0.626* -0.614* -0.369 -0.638* -0.615* -0.375 505 - D 505 - ND D, dominant; ND, nondominant; F0, theoretical maximal force; Pmax, maximal power output; V0, theoretical maximal velocity; *Correlation is significant (p < 0.05). **Correlation is significant (p < 0.001). Table 3. Pearson correlation coefficients assessing the association of sprint times with change of direction performance. T_5m T_10m T_15m T_20m All 0.676** 0.714** 0.717** 0.715** Soccer 0.744** 0.777** 0.734** 0.731** Tennis 0.864** 0.786 ** 0.737* 0.717* 505 - D Basketball 0.793** 0.810** 0.793** 0.848** All 0.715** 0.711** 0.693** 0.681** Soccer 0.782** 0.817** 0.798** 0.770** Tennis 0.870** 0.790** 0.743* 0.721* Basketball 0.660* 0.647* 0.621* 0.663* 505 - ND D, dominant; ND, nondominant; *Correlation is significant (p < 0.05). **Correlation is significant (p < 0.001). Table 4. Linear Mixed Model assessing the association of mechanical variables derived from the vertical (Vrt) and horizontal (Hzt) Force-Velocity profile with change of direction (COD) and sprint performance in the whole sample (n=54). Vrt Perform Stand FV Coeffic ance ard Prof ient variable Error ile 95% IC P Hzt Stand FV Coeffic ard Prof ient Error ile 95% IC P - , - , 0.00 <0.00 0.008 0.03 0.00 -0.146 0.021 0.18 0.10 F0 4* 1** 8 8 8 4 - , - , 0.01 <0.00 0.00 0.02 505-D Pmax -0.011 0.004 0.01 Pmax -0.042 0.006 0.05 1* 1** 9 3 4 9 - , - , 0.04 0.25 <0.00 -0.054 0.047 0.14 -0.134 0.025 0.18 0.08 V0 V0 1 8 1** 9 5 3 - , - , 0.00 <0.00 -0.024 0.008 0.04 0.00 -0.152 0.022 0.19 0.10 F0 F0 6* 1** 1 7 6 8 - , - , 0.00 <0.00 0.00 0.03 505-ND Pmax -0.013 0.004 0.02 Pmax -0.044 0.007 0.05 6* 1** 2 4 7 1 - , - , 0.01 0.08 <0.00 -0.088 0.050 0.18 -0.137 0.027 0.19 0.08 V0 V0 3 6 1** 9 1 3 * significance at (p < 0.05). ** significance at (p < 0.001); IC = confidence interval; D, dominant; ND, nondominant; Vrt, vertical; Hzt, horizontal; F0, theoretical maximal force; Pmax, maximal power output; V0, theoretical maximal velocity. F0 -0.023 The analyses were adjusted for the slope (representing the orientation) of the FV profile.
