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  • Daniel Kamenetzky

DATA AS A PRIORITY REQUIREMENT FOR THE DEVELOPMENT OF THE SOCCER PLAYER

Updated: May 7, 2020


During the developmental stage of the young athlete, and then in the period of adult performance maintenance, the obtaining of data that describes the biological and sport evolution is critical to be able to design a competitive career. Soccer development models that do not consider an objective analysis of the athlete's adaptations and growth, impede the design of individualized and specific training.

In the academy period (or “semillero” as we call it in Argentina), it is when the greatest changes in number and quality occur in the biological and sport "structures" of the players. Obtaining data describing the most relevant systems in development will allow a specific training design according to individual needs. Also, the possibility of generating databases and integrated analyzes in order to understand the cause-effect produced with each player.

I propose 6 areas to evaluate longitudinally, and some of the fundamental variables to consider for analysis. I present some examples applied with teams in the United States, with which I have worked as a methodology consultant, to illustrate the importance and value while designing a developmental soccer player process. The data and analysis presented here come from my own evaluations of the players during the 2018-19 season.

Temporary laboratories can be generated in a simple way in the same places where athletes train, and thus facilitate the logistics of the data collection model:

1) Body composition (I suggest using ISAK protocols) and biological maturation analysis. Minimum variables that are used in regular pediatric practice to assess maturation and development: weight, height, 3 skinfolds (triceps, biceps, gastrocnemius), two bone diameters (elbow and knee), 2 perimeters at the level of the skinfolds (arm, calf). Tanner maturation stages to estimate the biological age.

2) Analysis of conditional capacities: for soccer, the ideal is to evaluate at least the speed (from 10mts to 30mts depending on age and its functional capabilities) and vertical jump (as an estimator of the lower body strength);

3) Nutrition analysis: through a 3-5 day survey;

4) Analysis of movement: speed and long-distance running technique (both are well-differentiated);

5) Analysis of habits and behaviors: survey of rest habits, study, perception of hunger and fatigue, etc. (to be designed based on the coach's interest)

6) Analysis of competition: by video and data.

 

Below I offer some examples of how these data can be transformed into valuable information for the design of training and care of players (prevention of injuries and control of healthy biological maturation):

1) Comparison of anthropometric variables with population standards by age, sex, and sport. The use of Z graphs is very useful since it allows us to quickly differentiate subgroups according to the studied variable. We see in the graphs below the distribution of the players (each number in the x-axis is a player) with respect to the team average (thick black line) and the general population average of that variable (red line). A large number of players in this sample present skinfolds (representative of total body adipose mass) above the general population average (a group of girls of the same age that includes profiles of different levels of activity and physical capacity from sedentary to athletes). We would expect to see that the female soccer players in this group, most of whom had a training history of more than 6 years, had the skinfolds below the red line. If they were high-performance athletes we would expect to see that they were at this age at least 3 standard deviations below the general population average. The fact that this is the profile that the team presents could indicate, among other things, that: 1) nutrition is inadequate; 2) the quantity and/or quality of low-intensity aerobic work is inadequate; 3) the total amount of work does not agree with the type of nutritional intake. It is to be expected, then, that these data be integrated into the conditional and nutritional data to establish the best way to help each athlete to change their body composition in the direction of achieving a highly competitive profile.


2) Comparison of conditional variables with population standards by age, sex, and sport.

The comparison of the conditionals performances, in this case 20 mts. speed, allows the coach to establish workgroups based on physiological needs as well as designing game strategies depending on the amplitude of variation of the player's performance. If we incorporate the analysis of the player’s positions we could see if the different team lines (defense, mid, attack) can move at the same speed for the design of attack and defense tactics, and thus prevent for example unwanted spaces between lines or players. The same criteria or others can be used when analyzing any conditional variable (speed, strength, endurance) separately or integrated.

3) Integration of anthropometric and maturational variables with conditional variables: Now, in order to better understand the conditional variable studied, in our example is running speed, we must consider the causes that limit it in order to establish which work model will be most relevant for each player. In the graph below we see the relationship between adipose mass and speed. We can thus interpret for example: -That player # 18, although similar in speed to player # 9, have different adipose mass values. Therefore to improve the speed of these players we will have two different strategies according to each profile. For # 18 the options can be specific works of speed repetitions over short distances, strength work, and/or change of running mechanics (conditional and/or neuromuscular). We will have to assess which strategy is the best from integrating the variable “speed” with the analysis of movement and muscle strength.


As you can see in the 3 graphs above, her calf muscle area is the largest in the team, with which we can infer that the amount of muscle will not be a limitation, but perhaps the capacity to generate strength from that muscle is. However, as we see in the last vertical jump graph (VJ vs. adipose mass), this player also has the highest jumping value in the group, therefore, most likely, her muscular strength is not a limitation for running either. We conclude that speed repetitions over short distances will initially have priority in this player training strategy. For player # 9, the immediate main strategy will be to reduce her adipose body mass to eliminate ballast mass and thus improve the strength-body mass ratio to increase speed capacity. Since we can see that the calf muscle area and her vertical jump (a measure of leg strength) are in the team average, therefore they will not be initially limiting speed performance.

4) Nutritional analysis associated with training loads and the biological age of the player. Through the nutritional analysis, I have been able to observe that the players of these teams in school-age have a limited carbohydrate intake. The main causes of this deficiency are absent or scarce breakfasts; minimum lunches on the go and quick snacks. In this way, they arrive at their workouts with insufficient glucose reserves for the frequent demands of soccer practices. Due to the probable disinformation that coaches have of this variable, they subject their players in many cases to physiological loads that generate glycogenic emptying. As a consequence, the coordination processes lose efficiency limiting the technical and tactical results while increasing the risk of injury (see article: HOW TO REDUCE THE ECONOMIC "NON-PRODUCTIVE" EXPENDITURE THAT THE PLAYERS GENERATE FOR THEIR CLUBS). At the same time, training loads are transformed into "competition", due to the scarce energy resources available, with the growth processes. This situation puts pressure on developing biological systems and exacerbates the risk of injury and of generating overtraining syndrome.


5) Biomechanics analysis of soccer techniques. Biomechanical analysis integrated with body composition, conditional capacities, and nutrition generates a comprehensive profile of causes-effects of great value when interpreting sports performance and designing specific training models. In the case of the player in the photos below, it is observed how the landing in front of the center of gravity generates stopping forces which will decrease the speed. If we do not take into account this mechanical characteristic, we could make the mistake of subjecting this athlete (to improve her speed or endurance) to the repetition of numerous running series which would increase her risk of musculoskeletal injury. In addition, we could incur in helping the permanent fixing of inefficient mechanical actions (the athlete in the photos is not related to the examples above, it is a different case). This is how we see in high-performance athletes injuries associated with mechanics that could have been avoided (see article LUIS SUAREZ. INJURIES AND SANCTIONS REMOVE 50% OF THE COMPETITION).


6) Analysis of the behavioral variables associated with training loads. Finally, standardized questions like those implemented during the youth team assessments I present here can provide the coach with helpful information. In the example below we can see that the player states that he is hungry just at the time of his practice (5:30 pm, which corroborates what was found in the nutritional survey), with the consequent risks already explained above. Also, their effort and interest in training outside practice hours to increase their conditional capacity are limited (conditional work in the academy categories of US clubs is insufficient in many cases), which may indicate lack of time or resources to do it, lack of interest or knowledge of how to organize training. We can help our athletes by learning more about their individual behaviors.


The variables proposed here and their suggested integrations are just the beginning of a great opportunity for the creativity of the coaching staff. The sooner and more is known about each player, the greater the possibility of objective design to direct the performance of each athlete towards their maximum potential.

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