Lower Body Profiling and Increases in Speed, Power and Strength

If you are following on from my previous blog post, you would know that I have correlated data for many different exercises in the gym and how they relate to sprinting. I have concluded that there are many different strength qualities that relate to speed, and there is not one defining test that will accurately predict a persons speed.

Now that I am aware of the correlation between different exercises and the expression of speed I can go about creating individualised programs based on the athlete's profile to help them get faster! My area of interest lately has been to train the athlete in what they are lacking - or better their weak points. My theory when designing programs has been to ‘fill in the gaps’ and overcome any obvious weak points. So far I have had great improvements in my athletes’ in their respective weaknesses  which has translated to personal bests across the board.

For example, as seen below both of these athletes have drastically different profiles and it is my belief that they should therefore have different training programs. Player A tested to be very powerful, and moderately fast, however lacked maximal strength, so I have been programming him to increase strength in the lower body. Player B tested to be very strong (2.7xBW squat) and moderately fast, but lacked being able to translate his strength into speed and power (shown by his low RSI and average VJ), so his program focused on producing force and included more jumping and plyometric movements.

Here is an example of how I would differentiate the programs to highlight and improve on their weak areas while maintaining and improving their strengths.

Both programs are similar yet with obvious differences. Both contain strength work for the lower body however; different volumes, intensities and exercises are used to stress the system that needs the most work.

After profiling my athletes early in the year, most of the 8 week preseason was spent training on individualised programs similar to the one above. Here is a realistic improvement seen over the course of a preseason with actual players. It is interesting to see how their profiles moved around after specifically training a weakness.

I have found this type of profiling and then programming from it to be very effective to say the least. 49 out of 50 players increased performance in the 10m and 20m tests which I count as a win! 

Is how you feel a lie? Performance vs Subjective fatigue.

One of the Front Rowers demonstrating the RSI test that is performed weekly on the jump mat.

Monitoring and managing fatigue is an important part of running the physical conditioning for a team sport, as well as helping to prevent injuries through making sure recovery is optimised, having a team fresh or fatigued for game day may also impact the final score line. There are currently many methods available to do this, such as; RPE, HRV, GPS etc. The majority of which I have had some experience with or use currently.

I have been wanting to improve the way I monitor and manage my teams in-season fatigue and be able to compare it to other factors such as training load or physical performance. I initially set out to determine if I could predict fatigue/freshness through using a performance based test. I chose to use an RSI or reactive drop jump test. This is an area I have been interested for some time as I have many discussions with different colleagues about it, and have often heard of it being implemented in different settings.

I chose to use this test as it is very simple and easy to do, just pull the jump mat out and away you go. It doesn’t require a large amount of effort from the athletes and should not add to the accumulation of fatigue.

My hypothesis was that if a player performs poorly in the test (<90% of his best) then he would be fatigued and not be performing on the field. I the player is  performing well in the test then they would be performing well on the field.

This was only my hypothesis, so I didn’t let the results reflect training load or intensity, and the boys played and trained as normal, I just collected data in the background.

Simultaneously I collected GPS data, RPE data, as well as subjective player data about lower body soreness and general fatigue. This was collected using ‘One Tap Conditioning’ an Athlete Management System (AMS) here in Japan. These scores were taken on the same day as the RSI test, which was consistently a regular “Thursday” or G-3.

Upon review of the data I found my hypothesis to be completely incorrect. I found no correlation between training load (GPS or RPE) and performance on the jump test. Meaning that the athletes may jump high when training load was high and visa versa. This rings true for all data I collected, nothing seemed To correlate with performance in the test.

The most interesting conclusion that I came to was that there was actually positive relationship between lower body soreness and jump performance. Meaning that the more sore players were, there better they performed. Just let that sink in for a minute, the worse they reported feeling, the better they performed.

So why is this?

While I’m still working on that one, one possibility is that the harder you train, the better you perform, however your soreness and fatigue increases with the increased training load.  

While it is only a weak relationship, I did have a large playing group of 50 athletes to work with, all of whom participated in the research. Have a look at the correlation above, and please leave a comment!