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  • Daniel Kamenetzky


Sport’s data always has been available to be gathered. Today there is a dispute between the data “believers” and “detractors”. There is not possible athlete development and sports performance without data. Those that know what and how to observe and measure, can understand the development process and design the best strategies for their athletes. Those that negate the need to use it will soon find themselves out of the sports business.

Therefore, the strategic integration of relevant data from all areas of performance will determine the quality and results of a sport’s academy. In the example that I present here, I used the data from a descriptive paper that provides anthropometric data from the Chivas soccer club academy players.

My purpose in the next few articles is to illustrate how data from different areas of performance, organized and integrated appropriately, can tell the story of your players. And how its interpretation should direct the professional staff towards training and competitive design decisions.

I analyzed here the data from the scientific paper: “Anthropometric and Body Composition Profile of Young Professional Soccer Players” published in the Journal of Strength and Conditioning Research in 2020 by Marıa F. Bernal Orozco and collaborators. To provide my interpretation and to tell my story, I decided to compare the player's profile, with the data of a Canadian population’s anthropometric characteristics for each age group (we should have clear that this data includes sedentary and physically active people altogether). I choose also this reference group because they are similar to the European population where most clubs aspire to sell their Academy players.

Over the next few articles, I will present different aspects of the Chivas story. In this one, I organized the values of 5 skinfolds that are compared with the average (point 0,00 in the graph) of the Canadian population for age and sex.

We can see in the graph that some skinfolds are above the average for the age in the positive side. This means that the Chivas athletes have an adipose tissue bigger than the general population. Since increased stores of body fat in soccer players are detrimental for performance, in situations as we describe, a review of the Academy’s processes is mandatory.

Even in the 2nd Division (the upper-performance level), we can see skinfolds that are inconsistent with the conditional and structural requirements of the game. Considering that those players probably were at the academy for at least 5-6 years, they should present values at least 2 standard deviations lower than the average in the graph (down in this graph). As a reference point for you, an international level soccer athlete presents at least -3 standard deviation for the adipose mass compared to the general population for age and sex. This is why we can see lean body structures in professional soccer players like this as the result of about 8 years of Academy development:

What is the most probable story that we can read behind the Chivas data? (it should be corroborated with information that is not publicly available, so allow me to infer the most probable one):

1) Possible consequences of high adipose tissue in the soccer players, among others, are:

· early fatigue,

· increased risk of injuries for overload,

· decreased running and other movement speed,

· increased lactate levels in training and competition can slow down the decision-making capacity.

2) The possible reasons for those levels of adipose stores in highly trained soccer players, among others are:

· Insufficient volumes of low-intensity training (4-5 mmol lactic acid or sub-aerobic area of running),

· Inadequate nutritional proportionality, quantity and/or quality,

· Inadequate training load proportionality,

· Inadequate technical capacity (increasing the intensity of the activity with a decrement in the demand for fat sources of energy).

What can the academy staff do with this data and the “story” that it generates (of course there are different possible stories to tell)? How could they increase strategic value with the integration of this type of data and variables from other performance areas like conditional and physiological? How to use this data for competitive strategy design? How can this information improve the story that the eventing and GPS data provide? Those are some critical questions to be made and that only multidisciplinary information can answer for the benefit of training and competition design.

If you have any questions about how to use data in your Soccer Academy, please email me at



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