Kinematics of Dominant and Non-Dominant Leg Soccer Kick - Lab Report Example

 1. Introduction In as much as many players in soccer desire to have the ability to use both legs effectively for kicking ball in soccer, many players have one leg dominating the other. Greater percentage of the velocity is produced by the extension of the knee. This demonstrates the fact that stronger motion creates better opportunities for increasing the speed before touching the ball. From the inherent analysis, the dominant leg produces greater motion in the hip-joint, making it able to create more speed of the shank before touching the ball, and leads to better shot kick. In this research, a comparison will be made between the kinematics of the kicking of the dominant leg and that of the non-dominant leg. This study intends to study the kick for shooting the ball and for accomplishing low-drive ball. The objective of this study is to apply the information that improves the skills and level of performance of the non-dominant leg of the soccer player. Further to that, this study aims at examining findings on the biomechanics used in soccer kicks for the identification of new aspects and factors influencing the performance of soccer kicks. Soccer game is among the most popular sports globally. The offensive action in the soccer game is the soccer kick. The team with the majority of shooting stands greater opportunities to win the game. In this regard, it is very essential to enhance the soccer kicking technique in the training programs among the young energetic soccer players. 2. Literature Review The effectiveness of instep soccer kicks is affected by various factors such as the type of kick, the kicking distance from the goal, atmospheric pressure (air resistance) and the method of main kick as per the description of the biomechanical analysis. Earlier studies have explored the details of biomechanics in the soccer movement. New transformations have taken place in the performance of soccer kick such as the three-dimensional kinematics. Other movements include the joint-moments, which drive the joint movement, football performance mechanisms and various factors affecting the biomechanics of soccer kick. 2.1. Instep Soccer Kick Kinematics The elementary kinematics of lower limbs in the instep soccer kicks have previously gone through assessment. These demand the evaluation of the angular kicking position, the angular velocity and the joint linear kinematics involved. The linear velocity of the ankle hips and the knee in soccer kicks at the start of the movement to point of touching the ground, then finally to the ball impact (Mizrahi et al, 2000). 2.2. Angular Velocities Pattern Several studies of soccer-kick biomechanics argue that during at the backswing stage, the angular speed of the thigh very small whereas the shank velocity is a negative value owing to the shank’s back movement. According to Carre et al (2002), in the start of the forward swing, the thigh produces a positive angular speed. This comes about because the thigh suddenly moves forward as the shank suddenly moves back until it attains maximum flexion. During the continuation of the forward movement of the leg, the shank and the thigh go forward. 2.3. Dominant and Non-Dominant Kicking Isokinetic dynamometry leaves out the general impact of qualities that may influence the measures of the imbalance. Earlier research works suggested that functional tests are able to detect the difference between the dominant leg and the non-dominant leg. Studies further finds that a simple field test (five - hop) detects the importance of dominant and non-dominant disparities (Manolopoulos, Papadopoulos and Kellis, 2006). Many experts in the soccer sport attempt to evaluate a wide spectrum of field tests in the assessment of the limb function. This provides a cost-effective alternative evidence based practice for the practitioners. The examination of single-leg hop test is strongly linked to the isokinetic assessment outcome, whose application was however suggested. Nunome et al (2002) argues that the practice of triple-hop was thus seen to be a good predicting factor of the strength and kicking performance. Another vital matter is that the observations of stronger and accurate kicking performance majorly take place in the maximum instep kicks. However, in the soccer game, a strong kick cannot be considered as a successful kick unless it accurately aims at the goal. In this case, the pattern of muscle activation around various joints becomes more complicated while the player uses the dominant leg to achieve the finest control of the lower limb motion. For example, there is a different amount of accuracy and strength in the muscle activity as the player kicks the ball against a low or high goal target. According to Apriantono et al (2006), this derives the necessary areas and methods of adjustments in the muscle actions as player use the almost diagonal technique in relation to the target. 2.4. Joint Segmental Move Apparently, joint movement in the process of kicking is driven by a simultaneous enabling of a great collection of muscles. Arguing from a point of view of an anatomy, certain muscle groups generate the simultaneous movement in the joint segment of the dominant leg. Early research by Kellis, Katis and Gissis (2004) in the matters of muscles has referred to this phenomenon as the soccer paradox. When both the agonist and the antagonist muscles act at the same time, they generate two opposite forces around the joint, causing a lower resultant joint movement. This may serve to improve the joint stability but makes the movement inefficient. 3. Method 3.1. Experimental Technique to the Project The current study applied a classical design in the investigation standard autonomous and functional tests in the field including leg press, horizontal and vertical moves are able to display muscular strength and disparities among the lower limbs in the same way as that of the isokinetic dynamometry. This study seeks to test the relationship between the uses of dominant legs in football to the effectiveness of the game as opposed to the non-dominant legs (Orchard, 2002). It is consequently essential to do assessment on the strengths of the legs and the qualities of the relevant field practices in an independent way, in order to examine the likelihood of collecting a series of tests. For the many tests applied, the performance of every leg was found for use in the subsequent assessment using the results of the left and right leg (Rahnama et al, 2003). At the same time, it tested the strength of the dominant leg (strongest and most accurate leg) against the non-dominant (weakest and less accurate leg). This was to assist in the examination of the disparity between the limbs. 3.2. Statistical Software This study used SPSS as the main statistical tool for analysis. The statistical methods used included the Correlational analyses to determine links between the disparities ratios (imbalance ratios) computed from the outcomes of the isokinetic dynamometry to that computed from the functional field practices and tests. The second statistical test was descriptive statistics, which was meant to show all the standard statistics including the mean and standard deviation of the variables. The third test was the regression analyses, which like the correlation analysis, shows the coefficient of association between the dominance of leg and the kick performance. 3.3. Variable Selection The dominant leg is represented in the data by variable “Side”, with values 1 for dominant and 0 for non-dominant leg. In the first test, the objective is to assess the joint ROM and peak flexion for the right foot and the left foot.. The variables used were: Participant Hip ROM (°) Knee ROM (°) Ankle ROM (°) Hip Flex. (°) Knee Flex. (°) Ankle Flex. (°) Side The study questions investigated in this approach were what noticed about the ranges of motion of the left and right foot, how this might affect the kicking technique and how could to improve the players’ ROM. The second test involved the assessment of the peak velocities for the left foot and the right foot. The variables used were: Participant Shoulder Velo. Hip Velo. Knee Velo. Ankle Velo. Foot Velo. Ball Velo. Side One of the study questions being investigated in this is the summation of speed principle / proximal to distal sequencing. The second question was whether the data support these principles or not. The third question was the possible effects of the velocity to their kicking technique and performance and does the ball dissipate any of the energy/velocity. The final test was an assessment of the time to peak against the cumulative time. The variables used were: Participant Shoulder Hip Knee Ankle Foot Ball Side 4. Results 4.1. Correlation Analysis In the comparison between the dominant leg and non-dominant leg, there was a significant difference in all the tests. The first test presented the highest coefficient of correlation as +0.725. In the second test, the coefficient of correlation between leg dominance and the performance was +0.838. The third test had the highest coefficient of correlation as +0.923. The right leg appeared to be dominant while left leg was the non-dominant. With the positive coefficient of significant magnitude, the dominant leg performed better than the non-dominant leg. The output is presented in the Appendix 2 section. 4.2. Regression Analysis In the comparison between the dominant leg and non-dominant leg, there was a significant difference in all the tests. The first test presented the highest coefficient of regression as 0.472. In the second test, the coefficient of regression between leg dominance and the performance was +0.369. The third test had the highest coefficient of correlation as +0.817. The right leg appeared to be dominant while left leg was the non-dominant. With the positive coefficient of significant magnitude, the dominant leg performed better than the non-dominant leg. The output is presented in the Appendix 2 section. 4.3. Descriptive Statistics In the comparison between the dominant leg and non-dominant leg, there was a significant difference in all the tests. The first test presented the highest standard deviation as 10.18749, with a mean of 79.2464. In the second test, the analysis of the dominant leg, the standard deviation was 17.62925 with a mean of 15.4501. In the second test, the analysis of the dominant leg, the standard deviation was 0.513 with a mean of 0.50. With the positive significant values of standard deviation, the dominant leg performed better than the non-dominant leg. The output is presented in the Appendix 2 section. 5. Discussion The main objective of this study is to evaluate the significance of various one-sided field practices on the assessment of the imbalance of strength in muscles of the lower limbs. The study involved a comparison of a wide range of practical soccer game with the ordinary approach of isokinetic dynamometry in the assessment of the imbalance in the lower. There was a remarkable difference between the muscle strength in the dominant as well as the non-dominant legs tested in all the three instances. However, there was a slight difference noticed between the performance of the right legs and the left limbs. The outcome shows that the field tests were successful in the detection of the strength of the lower limb to substantiate the hypotheses this study. This study did not find any significant relation linking the isokinetic variables and the field exercise variables. This suggests that the ratio of strength imbalance between the dominant legs and the non-dominant legs was dependent on the measures of their distinct strengths. The results give more support for the findings of muscular imbalance in soccer game among other sports that depend on the dominance of one leg. It found significant relationship between the Dominant and non-dominant limbs in the peak as well as the average force attained in the kicking process bilateral jumps, and the flexion peak but no great significance between the right and the left limbs. The current study made the same finding of significant differences observable between the performances of the dominant legs against the non-dominant legs. Furthermore, the percentage values of the imbalance were as low as 4 % for the non-dominant and 12 % in the dominant legs. In view of the above observation, one of the reasons for the existence of considerable differences between the left and right legs because of the minimal similarity of soccer game among the participants regardless of the sufficient sample size. Majority of the participants in this study preferred the application of right side to kick and throw. The imbalance in strength and accuracy between the dominant and the non-dominant legs in soccer was however not because of the choice of the side to use. In the contrary, the exercise had an influence of various sporting activities, causing the massive development of muscles and strength in the dominant legs. There was also no way to nullify the difference in strength between the left and the right legs. 6. Recommendation 6.1. Observed Difference Indeed, it was observable that the majority of the players in soccer games preferred to use one leg accurately. For those who used the non-dominant legs, their performance was a matter of trial and error in targeting the goal posts. There was a variation in the strength dominance and performance quality between the right and the left leg for all the three tests. The homogeneous sample suggested that the dominance and strength inequity styles could have been more consistent toward the preferred and the non-preferred legs. This study recommends the dire need for the detailed investigation of possible disparity trends that can possible develop in soccer and other sporting activities, and on how to maximize the potential of players by optimizing the use of dominant limbs. 6.2. Coaching Information Football players who can use both legs effectively usually have leverage in games because they have more options and styles to apply. They do not necessarily have to switch their legs while shooting. Additionally, they can target the goal post from any angle and from both sides, with an equal amount of strength to apply in both legs when shooting the ball. This is very essential for the coaches especially those who work with soccer team of young people. It is important to know how to handle, teach, motivate and train children that they can be able to use both legs instead of merely relying on one leg. However, the mode of teaching them should be more inspiring than compelling. They should not be forced to use the two legs. Some of the best coaching tips include being liberal to the trainees, using repetitions to make them perfect in the use of the non dominant legs, avoidance of forceful use of non dominant legs. The coach can also prepare a number of drills trainings in which the trainees exclusively use the non dominant legs. 6.2.1. Be liberal It is not beneficial to use force on the children to make them use the non dominant legs. When trainees are given the freedom to use the limbs of their choice, they will develop individual interest in the use of non dominant legs and become perfe4ct. This is not only essential for the foot ball but also in various conditions that require the use of legs. Children do not like many restrictions to develop. They are quick to learn because their minds and physical bodies are in the developing stages. 6.2.2. Repetition of Words. Coaches ought to organize small play programs in which the players are prohibited from using their dominant legs. Touching the ball in this case becomes a penalty and the player is expected to surrender the ball to the opponent. However, coaches should avoid this game for children below nine years of age. One mistake among many coaches is the imposition of their own ideas and expectations on the players, most of which are unrealistic, and cause fatigue on the children. 6.2.3. Avoid the use of Force Forcing children makes them develop fear and loose self confidence and expression. Forcing a child to use the non dominant leg eliminates the natural instinct in the child. Naturally, players use their dominant legs. It takes time to enable them adapt to the non dominant legs. Through encouragement, the players are inspired to develop more beneficial skills and abilities. The starting point ought to be very simple exercises such as passing the ball, with emphasis on use of non-dominant legs. 6.2.4. Sample training drills Coaches can use simple exercise in their trainings to enable the players have multiple contacts with the ball, using the non-dominant legs. For example, coaches can let each of the players to shoot 10 times consecutively towards the goal post using the non dominant legs, with minimal time to think. The players become more acquainted to shooting with any leg. This pattern transfers to the muscles and becomes permanent controlling factors for muscle strength. Summary Coaches can make it easier for the players to learn the application of non-dominant legs because naturally, people use two legs for walking. A player just trains the foot to aim at a target goal post for a number of hours per day. Most importantly, it is vital to train both legs at the same time, using the dominant leg 25% of the time and using the non dominant leg 75 times. Concentrating on the dominant ball 100 % creates the risk of making the dominant leg become non dominant. 7. References Kellis E., Katis A., Gissis I. (2004) Knee biomechanics of the support leg in soccer kicks from three angles of approach. Medicine and Science in Sports and Exercise 36, 1017-1028. Manolopoulos E., Papadopoulos C., Kellis E. (2006) Effects of combined strength and kick coordination training on soccer kick biomechanics in amateur players. Scandinavian Journal of Medicine and Science in Sports 16, 102-110. Apriantono T., Nunome H., Ikegami Y., Sano S. (2006) The effect of muscle fatigue on instep kicking kinetics and kinematics in association football. Journal of Sports Sciences 24, 951-960. Carre M., Asai T., Akatsuka T., Haake S. (2002). The curve kick of a footbal II: flight through the air. Sports Engineering 5, 193-200. Mizrahi J., Verbitsky O., Isakov E., Daily D. (2000) Effect of fatigue on leg kinematics and impact acceleration in long distance running. Human Movement Science 19, 139-151. Nunome H., Asai T., Ikegami Y., Sakurai S. (2002) Three-dimensional kinetic analysis of side-foot and instep soccer kicks. Medicine and Science in Sports and Exercise 34, 2028-2036. Orchard J., Walt S., McIntosh A., Garlick D. (2002) Muscle activity during the drop punt kick. In: Science and Football IV. Eds: Sprinks W., Reilly T., Murphy A., editors. London: Taylor and Francis; 32-43. Rahnama N., Reilly T., Lees A., Graham-Smith P. (2003) Muscle fatigue induced by exercise simulating the work rate of competitive soccer. Journal of Sports Sciences 21, 993-942. 8. Appendix 8.1. Data 8.1.1. JOINT ROMs AND PEAK FLEXION (º) Left Participant Hip Knee Ankle Hip Flex. Knee Ankle   ROM (°) ROM (°) ROM (°) (°) Flex. (°) Flex. (°) Side 1 72 78 70 95 106 86 0 2 75 90 69 71 95 88 0 3 78 87 72 81 92 90 0 4 69 81 70 88 100 87 0 5 78 80 69 83 102 84 0 6 69 79 71 75 94 83 0 7 67 81 75 84 98 86 0 8 70 75 69 85 91 84 0 9 65 78 70 86 104 90 0 10 72.468 91.368333 64.178 71.294 116.43 67.398 0 MEAN 71.5468 82.036833 69.9178 81.9294 99.843 84.5398 0 SD 4.41166536 5.5050694 2.7249488 7.6053241 7.7150532 6.4868462 0 Right Participant Hip Knee Ankle Hip Flex. Knee Ankle   ROM (°) ROM (°) ROM (°) (°) Flex. (°) Flex. (°) Side 1 1.95 2.56 6.12 9.23 12.32 21.89 1 2 1.96 2.87 5.98 9.12 11.50 22.01 1 3 1.87 3.12 5.78 11.22 12.54 22.15 1 4 1.77 3.01 5.96 10.12 11.87 21.97 1 5 1.93 2.95 6.32 9.87 12.54 21.86 1 6 1.90 3.14 6.12 10.32 11.45 22.14 1 7 2.01 2.99 6.14 11.65 12.08 21.99 1 8 1.85 2.77 5.99 9.87 11.45 22.00 1 9 1.89 2.78 6.45 10.25 10.87 22.50 1 10 3.0256 3.7664 10.5802 13.61 18.1188 23.8224 1 MEAN 66.0236 76.456 68.267 82.455 91.6028 81.9876 1 SD 10.3296882 13.095522 4.8657078 5.5810418 8.5608872 2.773963 1 8.1.2. PEAK VELOCITIES (m.s-1) Left Participant Shoulder Hip Knee Ankle Foot Ball   Velo. Velo. Velo. Velo. Velo. Velo. Side 1 1.78 2.22 5.43 8.56 10.41 20.95 0 2 1.59 2.15 5.16 8.15 10.98 20.78 0 3 1.62 2.41 4.94 7.98 11.12 21.12 0 4 1.66 2.10 5.22 9.00 11.21 20.99 0 5 1.70 2.44 4.88 8.45 9.87 21.34 0 6 1.74 2.21 4.12 7.84 10.21 21.25 0 7 1.65 2.16 3.94 8.22 10.34 20.89 0 8 1.66 2.09 3.84 8.49 11.02 20.96 0 9 1.74 2.16 4.15 8.34 9.45 21.04 0 10 3.044 3.72 8.796 14.41 17.772 24.41 0 MEAN 71.5468 82.036833 69.9178 81.9294 99.843 84.5398 0 SD 4.41166536 5.5050694 2.7249488 7.6053241 7.7150532 6.4868462 0 Right Participant Hip Knee Ankle Hip Flex. Knee Ankle   ROM (°) ROM (°) ROM (°) (°) Flex. (°) Flex. (°) Side 1 62 68 66 85 90 84 1 2 69 70 65 86 95 86 1 3 54 79 60 82 94 79 1 4 59 80 69 84 93 80 1 5 64 75 70 82 90 82 1 6 66 73 75 72 86 84 1 7 61 69 68 78 88 83 1 8 63 72 76 80 84 81 1 9 70 67 64 82 83 84 1 10 92.236 111.56 69.67 93.55 113.028 76.876 1 MEAN 66.0236 76.456 68.267 82.455 91.6028 81.9876 1 SD 10.3296882 13.095522 4.8657078 5.5810418 8.5608872 2.773963 1 8.1.3. TIME TO PEAK/CUMMULATIVE TIME (s) Left Participant Hip Knee Ankle Hip Flex. Knee Ankle   ROM (°) ROM (°) ROM (°) (°) Flex. (°) Flex. (°) Side 1 72 78 70 95 106 86 0 2 75 90 69 71 95 88 0 3 78 87 72 81 92 90 0 4 69 81 70 88 100 87 0 5 78 80 69 83 102 84 0 6 69 79 71 75 94 83 0 7 67 81 75 84 98 86 0 8 70 75 69 85 91 84 0 9 65 78 70 86 104 90 0 10 72.468 91.368333 64.178 71.294 116.43 67.398 0 MEAN 71.5468 82.036833 69.9178 81.9294 99.843 84.5398 0 SD 4.41166536 5.5050694 2.7249488 7.6053241 7.7150532 6.4868462 0 Right Participant Hip Knee Ankle Hip Flex. Knee Ankle   ROM (°) ROM (°) ROM (°) (°) Flex. (°) Flex. (°) Side 1 62 68 66 85 90 84 1 2 69 70 65 86 95 86 1 3 54 79 60 82 94 79 1 4 59 80 69 84 93 80 1 5 64 75 70 82 90 82 1 6 66 73 75 72 86 84 1 7 61 69 68 78 88 83 1 8 63 72 76 80 84 81 1 9 70 67 64 82 83 84 1 10 92.236 111.56 69.67 93.55 113.028 76.876 1 MEAN 66.0236 76.456 68.267 82.455 91.6028 81.9876 1 SD 10.3296882 13.095522 4.8657078 5.5810418 8.5608872 2.773963 1 8.2. Output 8.2.1. Test 1 Descriptive Statistics N Minimum Maximum Mean Std. Deviation Variance Statistic Statistic Statistic Statistic Std. Error Statistic Statistic HipRom 20 54.00 92.24 68.7852 1.84106 8.23349 67.790 KneeRom 20 67.00 111.56 79.2464 2.27799 10.18749 103.785 AnkleRom 20 60.00 76.00 69.0924 .87889 3.93051 15.449 HipFlex 20 71.00 95.00 82.1922 1.45302 6.49811 42.225 KneeFlex 20 83.00 116.43 95.7229 2.00971 8.98772 80.779 AnkleFlex 20 67.40 90.00 83.2637 1.12453 5.02905 25.291 Side 20 0 1 .50 .115 .513 .263 Valid N (listwise) 20 Correlations HipRom KneeRom AnkleRom HipFlex KneeFlex AnkleFlex Side HipRom Pearson Correlation 1 .725** .174 .217 .534* -.024 -.344 Sig. (2-tailed) .000 .462 .357 .015 .918 .137 N 20 20 20 20 20 20 20 KneeRom Pearson Correlation .725** 1 .063 .118 .725** -.331 -.281 Sig. (2-tailed) .000 .793 .620 .000 .153 .230 N 20 20 20 20 20 20 20 AnkleRom Pearson Correlation .174 .063 1 -.037 -.157 .348 -.215 Sig. (2-tailed) .462 .793 .877 .508 .133 .362 N 20 20 20 20 20 20 20 HipFlex Pearson Correlation .217 .118 -.037 1 .286 .221 .041 Sig. (2-tailed) .357 .620 .877 .222 .349 .862 N 20 20 20 20 20 20 20 KneeFlex Pearson Correlation .534* .725** -.157 .286 1 -.385 -.470* Sig. (2-tailed) .015 .000 .508 .222 .094 .036 N 20 20 20 20 20 20 20 AnkleFlex Pearson Correlation -.024 -.331 .348 .221 -.385 1 -.260 Sig. (2-tailed) .918 .153 .133 .349 .094 .268 N 20 20 20 20 20 20 20 Side Pearson Correlation -.344 -.281 -.215 .041 -.470* -.260 1 Sig. (2-tailed) .137 .230 .362 .862 .036 .268 N 20 20 20 20 20 20 20 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1249.884 6 208.314 9.505 .000a Residual 284.918 13 21.917 Total 1534.802 19 a. Predictors: (Constant), Side, HipFlex, AnkleRom, KneeRom, AnkleFlex, HipRom b. Dependent Variable: KneeFlex Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 114.092 28.734 3.971 .001 HipFlex .472 .173 .341 2.726 .016 AnkleFlex -.722 .272 -.404 -2.660 .019 AnkleRom -.317 .291 -.138 -1.087 .295 KneeRom .369 .125 .418 2.955 .010 Side -8.795 2.337 -.502 -3.763 .002 a. Dependent Variable: KneeFlex 8.2.2. Test 2 Descriptive Statistics N Minimum Maximum Mean Std. Deviation Variance Statistic Statistic Statistic Statistic Std. Error Statistic Statistic ShoulderVelo 20 1.59 3.04 1.9170 .08984 .40179 .161 HipVelo 20 2.09 3.77 2.6808 .11565 .51722 .268 KneeVelo 20 3.84 66.00 8.5668 3.03330 13.56531 184.018 AnkleVelo 20 7.84 85.00 13.3045 3.78977 16.94836 287.247 FootVelo 20 9.45 90.00 15.4501 3.94202 17.62925 310.790 BallVelo 20 20.78 67.40 23.9614 2.29242 10.25204 105.104 Side 20 0 1 .50 .115 .513 .263 Valid N (listwise) 20 Correlations ShoulderVelo HipVelo KneeVelo AnkleVelo FootVelo BallVelo Side ShoulderVelo Pearson Correlation 1 .838** .701** .708** .713** .698** -.252 Sig. (2-tailed) .000 .001 .000 .000 .001 .284 N 20 20 20 20 20 20 20 HipVelo Pearson Correlation .838** 1 .556* .564** .558* .553* -.624** Sig. (2-tailed) .000 .011 .010 .011 .012 .003 N 20 20 20 20 20 20 20 KneeVelo Pearson Correlation .701** .556* 1 .999** .999** .999** -.266 Sig. (2-tailed) .001 .011 .000 .000 .000 .257 N 20 20 20 20 20 20 20 AnkleVelo Pearson Correlation .708** .564** .999** 1 .999** .999** -.264 Sig. (2-tailed) .000 .010 .000 .000 .000 .261 N 20 20 20 20 20 20 20 FootVelo Pearson Correlation .713** .558* .999** .999** 1 .999** -.245 Sig. (2-tailed) .000 .011 .000 .000 .000 .298 N 20 20 20 20 20 20 20 BallVelo Pearson Correlation .698** .553* .999** .999** .999** 1 -.263 Sig. (2-tailed) .001 .012 .000 .000 .000 .262 N 20 20 20 20 20 20 20 Side Pearson Correlation -.252 -.624** -.266 -.264 -.245 -.263 1 Sig. (2-tailed) .284 .003 .257 .261 .298 .262 N 20 20 20 20 20 20 20 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1995.436 6 332.573 2.799E3 .000a Residual 1.545 13 .119 Total 1996.980 19 a. Predictors: (Constant), Side, FootVelo, ShoulderVelo, HipVelo, AnkleVelo, KneeVelo b. Dependent Variable: BallVelo Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 17.412 .685 25.407 .000 ShoulderVelo -.129 .592 -.005 -.218 .830 HipVelo -.203 .487 -.010 -.418 .683 KneeVelo .369 .160 .489 2.310 .038 AnkleVelo .284 .126 .469 2.247 .043 FootVelo .029 .126 .050 .230 .822 Side -.092 .292 -.005 -.317 .756 a. Dependent Variable: BallVelo 8.2.3. Test 3 Descriptive Statistics N Minimum Maximum Mean Std. Deviation Variance Statistic Statistic Statistic Statistic Std. Error Statistic Statistic Shoulder 20 .00 .00 .0000 .00000 .00000 .000 Hip 20 .07 .16 .1134 .00588 .02630 .001 Knee 20 .16 .40 .2714 .01234 .05520 .003 Ankle 20 .28 .44 .3536 .01018 .04554 .002 Foot 20 .28 .47 .3930 .01082 .04838 .002 Ball 20 .32 .48 .4090 .00921 .04117 .002 Side 20 0 1 .50 .115 .513 .263 Valid N (listwise) 20 Correlations Shoulder Knee Hip Ankle Foot Ball Side Shoulder Pearson Correlation .a .a .a .a .a .a .a Sig. (2-tailed) . . . . . . N 20 20 20 20 20 20 20 Knee Pearson Correlation .a 1 .369 .685** .555* .444* .441 Sig. (2-tailed) . .110 .001 .011 .050 .052 N 20 20 20 20 20 20 20 Hip Pearson Correlation .a .369 1 .424 .070 .118 .739** Sig. (2-tailed) . .110 .062 .768 .620 .000 N 20 20 20 20 20 20 20 Ankle Pearson Correlation .a .685** .424 1 .726** .723** .526* Sig. (2-tailed) . .001 .062 .000 .000 .017 N 20 20 20 20 20 20 20 Foot Pearson Correlation .a .555* .070 .726** 1 .923** .086 Sig. (2-tailed) . .011 .768 .000 .000 .719 N 20 20 20 20 20 20 20 Ball Pearson Correlation .a .444* .118 .723** .923** 1 .188 Sig. (2-tailed) . .050 .620 .000 .000 .427 N 20 20 20 20 20 20 20 Side Pearson Correlation .a .441 .739** .526* .086 .188 1 Sig. (2-tailed) . .052 .000 .017 .719 .427 N 20 20 20 20 20 20 20 a. Cannot be computed because at least one of the variables is constant. **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression .029 5 .006 22.576 .000a Residual .004 14 .000 Total .032 19 a. Predictors: (Constant), Side, Foot, Knee, Hip, Ankle b. Dependent Variable: Ball Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .102 .037 2.722 .017 Hip -.074 .208 -.047 -.356 .727 Knee -.166 .094 -.222 -1.765 .099 Ankle .091 .165 .100 .549 .591 Foot .817 .131 .961 6.254 .000 Side .015 .012 .186 1.216 .244 a. Dependent Variable: Ball Read More