https://www.academia.edu/17046449/Regression_models_of_sprint_vertical_jump_and_change_of_direction_performanceIt was the aim of the present study to expand on previous correlational analyses whichhave attempted to identify factors that influence performance of jumping, sprinting andchanging direction. This was achieved by employing a regression approach to obtainpredictor models which combined multiple anthropometric and biomechanicalvariables. Thirty rugby union players participated in the study (age: 24.2 ± 3.9yr;stature: 181.2 ± 6.6cm; mass: 94.2 ± 11.1kg). The athletes ability to sprint, jump andchange direction were assessed using a 30 m sprint-, vertical jump-, and 505 agility-test, respectively. Predictor variables were collected during maximum strength tests(1RM deadlift and squat) and performance of fast velocity resistance exercises (deadliftand jump squat) using sub-maximum loads (10 to 70% 1RM). Force, velocity, power and rate of force development values were measured during fast velocity exerciseswith the greatest value produced across the loads selected for further analysis.Anthropometric data, including lengths, widths and girths were collected using a 3Dbody scanner. Potential predictor variables were first identified using correlationalanalyses. Suitable variables were then regressed using a best subsets approach.Three factor models provided the most appropriate balance between explainedvariance and model complexity. Adjusted R2 values of 0.86, 0.82 and 0.67 wereobtained for sprint, jump and change of direction performance, respectively.Anthropometric measurements did not feature in any of the models due to their strongassociation with body mass. For each performance measure assessed, variance wasbest explained using maximum strength measures. Improvements in the models werethen obtained by including velocity and power values for jumping and springing, and byincluding rate of force development values for change of direction performance
results -
Performance in the vertical jump was best explained by an athlete’s maximum
strength capabilities and their ability to develop high velocities, whereas, performance in the 5 m
sprint and 505 agility tests were best explained by maximum strength scores and RFD. Predictor
models for 10 m and 30 m sprints featured primarily maximum strength scores and mechanical
power.
The results of this study demonstrate that a large amount of variance in performance of general
movement patterns common to most sports can be explained by an athlete’s relative maximum
strength and their ability to produce high outputs in certain biomechanical variables. Using the
adjusted coefficient of determination, between 70 to 80% of the variance in vertical jump, 10 m
sprint and 30 m sprint performance could be explained by relatively simple three factor models.
For the 505 agility and 5 m sprint tests the explained variance decreased to between 40 and 65%,
indicating that factors other than those assessed in the present study are important in determining
overall performance. More accurate models may require inclusion of technique related factors to
increase understanding of performance, particularly for the acceleration and change of direction
tasks. However, the higher within-individual variability measured in the 505 agility and 5 m
sprint tests may also have contributed to reduced explanatory power of the models.
However, the vertical jump features a single discrete movement in which
performance is determined by the velocity at take-off, which is approximated very closely by the
peak velocity obtained during the movement (22). In contrast, performances in the 5 m sprint and
505 agility test are dependent upon a more complex series of movements which progressively
increase the velocity of the body. Due to the cause and effect relationship between take-off
velocity and performance in the vertical jump, it is unsurprising that the regression models
identified peak velocity as a primary factor, especially as the testing movements were outwardly
similar to the performance action. For the 10 m and 30 m sprints the regression models
highlighted the combination of strength and power values as the best predictors of performance.
As the distance of the sprint increases, velocity and therefore contact time with the ground
decreases (10). Mechanical power may reflect an athlete’s ability to generate substantial ground
eaction forces over short time periods and their capacity to store and release mechanical energy
(31), all of which would be important in influencing performance in these sprints.
Practical Applications
From the results of this study, it is clear that the relative maximum strength of an athlete is the
basic quality which determines their ability to perform many fundamental sporting tasks.
However, relative maximum strength in isolation only explained approximately 35 to 65% of the
variation in performance of the selected jump, sprint and change of direction tests. Greater
understanding and predictive ability can be obtained by combining normalized maximum
strength values with biomechanical variables measured during performance of explosive
resistance exercises. For certain tests such as the vertical jump and 30 m sprint, as much as 90%
of the variation (as measured by the unadjusted coefficient of determination) in performance of a
field based sports team can be explained by combining the most suitable strength and
biomechanical variables. The results also indicate that different biomechanical variables relate
more closely with performance in certain sporting tasks compared to others. Therefore,
contemporary training practices which include periodized programs aimed at developing
maximum strength and multiple sections of the force-velocity curve appear warranted. This
result also suggests that the general pre-occupation with mechanical power as a general
descriptor of all biomechanical variables representing explosive athletic movements may be
overly simplistic. In certain activities, the ability to displace relatively light resistances at high
velocities or develop high RFD values may be more important to success than developing large
amounts of mechanical power. Importantly, the results of this study also suggest that when an
athlete has reached optimum body mass for their sport, further improvements in strength and
power should be achieved whilst trying to limit changes in their mass; as it is the relative values
of these variables and not absolute values which effect performance. Future models may wish to
include technique related variables to enhance understanding of performance in tasks such as the
5 m sprint and change of direction tests where unexplained variance was still relatively high. In
addition, more advanced body composition modelling including proportionality and segmental
masses may provide data which combine more effectively with the force and velocity related
variables identified in the present study.
