Correlation Between Sprint Speed and Agility

The statistical analysis that was used to evaluate the data was the rate of variability of values F-test, this verified whether there was a variance of the value-time. The researcher also used a non-parametric t-test from the results of the F-test to evaluate if there were any differences between the two samples of data, they then used the Spearmann correlation coefficient test to evaluate the relationship between the two separate tests. The results showed that there was no significant difference between basketball, volleyball and football players when looking at agility, the results also showed that there was no significant relationship found between either the basketball players and football players. (Horička, Hianik and Šimonek, 2014)

When looking at the results that were shown in this article it can show that there will be no significant difference when looking into the agility across multiple sports, the results that are given in this article could also show to be accurate in other sports and not just football.

General Research

The first general research topic is looking at the effect of speed, agility and quickness training programs have on increasing speed, agility and acceleration, the study used 26 football players which were separated into 2 groups of 13. The first group was given a SAQ training program while the second group where given a conventional 8-week training program. The data was collected using a 30-metre sprint test, the agility T-test and a 10-metre run test during pre and post testing. The study used a quasi-experimental method and the research design that was used was a matching only design. Data was taken of participants average pace before and after exercise the results showed a 5.9% increase in speed as well as a 5.6% increase in agility and a 6% increase in acceleration.

The results also showed that there was a significant effect from the SAQ group in improving speed, agility and acceleration. This study showed that by using a SAQ training programme agility, speed and acceleration can all be improved, when looking at how this can have an impact on the study being done it shows that by training on improving the components of agility and speed both will increase. (Azmi and Kusnanik, 2018)

The final general research being looked at is the specify of acceleration, maximum speed and agility in professional football players this study used 106 professional football players all male from nationwide divisions 1 and 2, the participants were aged between 18-36. The tests used were the 10-metre sprint tests, the flying 20-metre test and the Zigzag test. Pearson’s correlation coefficient was used to analyse the relationship between the 3 variables. The mean and standard deviation of the tests were also analysed. This research found that the performances in all three tests were significantly correlated however the coefficients of determination between the tests were only 39% for acceleration and maximum speed, 12% for acceleration and agility and 21% between maximum speed and agility leading the researcher to believe that acceleration, maximum speed and agility are speed specific qualities and relatively unrelated to each other. (Little and Williams, 2005) When comparing this to the hypothesis in this research it should mean that sprint speed and agility are significantly correlated however if determining the coefficient, it would mean that the values would not signify if the results are detrimental to one another and are in fact separate qualities in themselves

Specific Research

A study that researched the correlation between SAQ in young elite soccer players used 25 elite under-16 Serbian football players to see whether there was any correlation between speed, agility and quickness. This study used a 10-metre sprint, flying 20 metre sprints, the Zigzag test both with and without a football. This researcher used infrared photocells Uno Lux to measure all tests, the participants would start each test from a standing still position before the test began. All the tests that the participants took part in where done in one day during the morning time and they would only have one attempt to do each test. The Zigzag test was done both with a ball and without a ball to replicate real playing conditions and to assess if the agility of the participants would change when they were taking part in the test with a ball compared to without.

The researcher used statistical analysis to analyse the data and also used the Kolmogrov-Smirnov normal distribution test to see if the data was normally distributed, they also used Spearman product-moment correlation to determine the relationship of the tested variables. The researcher also looked into if individual playing positions would affect testing results however there was no significant differences found when testing each variable to a player’s position. This study found that there was no significant relationship between the 10-metre sprint and the Zigzag test without the ball however found that there was a significant relationship between the Zigzag test both with and without the football to the 30-metre and flying 20-metre sprint tests. The 10-metre sprint test was found to have a significant relationship with the Zigzag test with the football. From this research it enabled a method of training to be applied to improve SAQ and found that it was mainly represented by the movements during which control and contact are at a minimum with the football. Which meant that a loss of specificity was seen between exercise and demands in the demonstration both during testing and in a game. (Sporiš et al., 2011)

When looking at this study and comparing it to a previous study testing similar variables both researchers found that there was no significant relationship between the 10-metre sprint and agility without a football however both found there was a significant relationship with a football. When evaluating the findings of this research article it would show that the hypothesis that sprint speed would affect agility should show as true however the methods for doing so could also show that the 10-metre sprint test will not show a significant relationship between the two variables.

The second specific research study being looked into is the correlation between agility and sprinting according to student age. The main reason the researcher looked into this topic was to assess sprinting and agility and to determine the relationship between the two motor skills in elementary school children. The research was done on 176 children 88 boys and 88 girls the students where split into three separate groups, group 1 had 48 boys and 50 girls, group 2 had 15 boys and 23 girls and group 3 had 25 boys and 15 girls. The testing was carried out over a two-day period, the participants where all made familiar with the tests and where given trial runs on separate days, the tests that where done where the 15-metre sprint test which participants where allowed 3 trials and the researcher allowed for a 2-minute rest between each rep. The Modified Agility Test (MAT) was used as the test for agility, which involves participants sprinting at maximal effort from one cone to another that was placed directly in front of them they would then have to turn to the left and run to another cone before turning around and running past the second cone to a third cone, they would have to turn back to run to cone 2 then back to the starting position. The cones where placed precisely 5-metres apart from one another and participants would have to touch the top of each cone as they get to it. The researcher gave participants 3 trials and the fastest times for both tests would have been used for analysis.

The researcher used the Shapiro-Wilk test for normality and found that all variables were normal, an ANOVA test was used for the comparison of gender, the results found that there were significant differences between results in comparison of gender in groups 1 and 2. A Pearson’s correlation coefficient test was done to determine the relationship between the agility test and the sprint tests, these results found that there was a significant but moderate correlation between sprint speed in all groups. From these results it is clear to see that there is a correlation between sprint speed, when assessing the results from this research study and comparing to the hypothesis given it shows that sprint speed should affect agility. (Yanci et al., 2014)

Reliability and Validity

This research study was done to test the reliability of a repeated-sprint test for field hockey, the researched used 6 trials of a 30 metre sprint sprints, the researcher used 10 highly trained male hockey players. The participants were given 2 familiarisation trials prior to testing and the researcher repeated the 6 sprints on two separate days 7 days apart from one another, the testing was done on the same time of day as the first test. The day prior to the testing the participants were given a light training session at a low-intensity, the participants were given an active running warm up and then asked to perform 3-5 short distant sprints after those sprints they would have a 5-minute rest before participating in the test. The researcher calculated the scores by taking the total sprint time dividing it by the ideal time and then multiplying it by 100, the ideal time was the best time each participant ran. The researcher used Anova to analyse the variance with repeated measures and Newman-Keuls post hoc was used to determine the time points an effect was observed. The researcher found that sprint times were reliable however the percent sprint decrement was less reliable. (Spencer et al., 2006)

A study was done to test the validity and reliability of the Illinois agility test was conducted using 194 male national level football players, these players were split into 4 groups depending on age, the groups were under 8’s, under 10’s, under 12’s and under 14’s. The participants took part in two separate Illinois agility tests, the researcher gave 72 hours between tests. All participants took part in four trials for familiarisation two weeks prior to testing and all the testing was performed outdoors on artificial football pitch at the same time of day. All participants performed three trials during the test and retest and the best performance was used for further analysis. All participants were given 3-minute recovery periods between test trials. The data was tested for normal distribution using the Shapiro-Wilk test, a systematic bias was investigated using a dependant t-test to evaluate whether there was a significant difference between test retest sample mean’s.