Take home points:
- The average jump height of team Gloria Junior improved 20% or 6.11 centimeters.
- This was accomplished in a 5 month period. They did one specific strength training session per week plus a series of exercises to be implemented during their regular gymnastics trainings.
- We used a mix of squatting, deadlifting and pressing movements with a special emphasis on glutes and core. Plyometrics and jumping and landing technique were also part of the program.
- A big percentage of the improvement can be attributed to two factors: One is the gymnasts still being in a growing phase (they are 13 to 15 years old). The other one is due to the fact that they had no previous history of strength training, which elicits a high initial response.
Here’s a peek of how we trained:
Top 3 Jumpers:
When I see an athlete jumping high, my blood pressure raises.
Ever since I played sports myself, I put a lot of effort into improving my jumps and later the jumps of the athletes I have worked with.
By the end of January 2016, I started strength training Gloria Junior from PNV/Attitude clubs.
The focus of my intervention was increasing jump height, and that’s what we measured and trained for.
The benefits of getting stronger transfer to jumping, but also include enhancements in general sports skills as well as decreases in risk of injury (Suchomel, Nimphius, & Stone, 2016).
While jumping is only a very small part of success in gymnastics, it is still an important one.
Flexibility isn’t enough if power isn’t in place, as there won’t even be enough time to express the movement in the air and land safely.
Lately, I have been developing an interest in statistics and data analysis, so I decided to play with the numbers.
Please notice I am a beginner in this area.
For those interested, keep on reading (it gets a bit technical).
To develop jumps in a young gymnast, we need a combination of explosive movements and heavy-resistance strength training (Maffiuletti et al., 2016).
That’s what we did with Gloria Junior.
In the past, the times when I have failed to improve the jumping height of athletes, it’s been due to:
- Lack of consistency. You can’t strength train 6 weeks, stop training, train another 6 weeks, stop again and expect to increase or even maintain jumping power. Strength and power need to be developed and maintained as high as possible year round.
- Poor Nutrition. Fat don’t fly. Any body mass that is not directly contributing to propulsive forces is actually hindering your power. Overweight gymnasts won’t jump high.
No training system will overcome these 2 premises.
With Gloria Junior, I have had the opportunity to work for 5 months on a semi-consistent basis.
I wasn’t able to fit strength training as often as I would have liked, but we kept it fairly regular.
This eliminates constraint number one (lack of consistency).
When it comes to bodyweight, they are visibly lean. This eliminates the second constraint (sub-optimal fat levels).
This is a Jackpot for me.
Now I can really strength train them and help them get the results they want.
I decided to use Mladen Janovic approach to statistical analysis (a big influence of mine).
We tested a countermovement jump using a validated contact platform from Chronojump.
Here are the results of the test and the re-test:
To see the jump scores, look into the column that says “ABK (Cm)” in blue, and the column that says “ABK (Cm)” in yellow (test and re-test).
In the lower table, you can see the average score of the team (mean), the standard deviation (SD) and the best and worst jumps (Min and Max).
For averages, I didn’t take into account the athletes in red, as they missed one of the tests due to injury/sickness.
I have also included the z-scores, which is a measure that says how good each gymnast can jump compared to the average of the team.
A z-score above 0 means you can jump more than the average, and a z-score under 0 means you jump less than the team average.
While you could already see this directly from the jump height in centimeters, it is a very useful measure to deeper analyze the data. Keep reading.
Now, the next step was to visualize the improvements in absolute change (how many centimeters they improved) as well as in relative change (what percentage of improvement).
I also added each individual and mean z-scores.
You can see this on the 2 right columns:
Realize that weight loss or weight gain, having a bad day, wherein the menstrual cycle the athlete is, error in the measurement and many other factors all can affect test scores positively or negatively.
Results in tests are just an opportunity to see trends, not a definitive judgment in performance (leave that for competition time).
Let’s look now at correlations:
Looking at the table, we see almost no correlation between test and absolute and relative changes.
This means that both the gymnasts with high and low initial scores improved a lot.
This is a sign that they are still beginner athletes (in terms of strength).
As they become more advanced, it will be a lot harder to see improvements over time.
We should then see a negative correlation between jumping skill and magnitude of improvement.
To make the data easier to understand, we can create some simple data visualization.
One way to do it is using scatter plots, or points distributed along a vertical and/or horizontal axis.
Below is a scatter plot of the initial test and relative change or improvement.
The more on the left an athlete is, the less they jumped in the initial test.
The higher an athlete is, the more they improved in terms of percentage change.
I also added a trend line, which is showing a non-significant skew towards low initial test scores having a bigger relative change.
Next one is a scatter plot of the relative change in z-scores-.
Here we can see who improved the most compared to the average of the team (0 in the vertical axis):
Now this is where it gets interesting.
I divided the gymnasts into high and low responders.
We can accomplish this by looking at the z-scores of the initial test and the relative change.
This way we end up with a very useful quadrant where we can rapidly see:
- Gymnasts with a low initial ability that responded poorly to the training program.
- Gymnasts with a low initial ability that responded well to the training program.
- Gymnasts with a high initial ability that responded poorly to the training program.
- Gymnasts with a high initial ability that responded well to the training program.
This is very cool because it will allow us to individualize the training a lot more, putting gymnasts into groups depending on their characteristics.
We need to change the training for some gymnasts while others can keep training in the same manner.
We also can decide how much strength training is necessary for each of them according to their individual response.
Maybe some gymnasts can focus on gymnastics skills and less in strength while others should focus on strength and less in skills.
Again, these are just trends and we have to be very careful when interpreting tests results.
But provided that we don’t make false conclusions, this is very valuable information.
With that said, I am confident in this approach.
Expect high-flying gymnasts next season.
Click to check the references
Maffiuletti, N. A., Aagaard, P., Blazevich, A. J., Folland, J., Tillin, N., & Duchateau, J. (2016). Rate of force development: physiological and methodological considerations. European Journal of Applied Physiology, 116(6), 1091–116. doi:10.1007/s00421-016-3346-6
Mladen Janovic. (n.d.). Complementary Training. Retrieved from www.complementarytraining.net
Suchomel, T. J., Nimphius, S., & Stone, M. H. (2016). The Importance of Muscular Strength in Athletic Performance. Sports Medicine, 1–31. doi:10.1007/s40279-016-0486-0
Sergio Navadijo is a physical preparation expert and owner of Entrena. He has a university background in Sports Science and a proven track record of success with athletes over the last decade. He works with gymnasts of all levels that want to get stronger and stay away from injuries. Contact him to get help achieving your goals.