JOHAN V5: Working with the JOHAN Sports Live GPS Tracking App
The JOHAN Live GPS Tracking App enables real-time monitoring (during training sessions or match) of both live GPS and heart rate variables.
Which players in your youth academy are ready for the next step? Deciding which players remain in the academy next season and which teams they will play in, is a tough decision. Several aspects need to be taken into account (tactical skills, motivation, anthropometrics, physical fitness etc.), before a final decision can be made. An excellent score on one of these aspects, does not automatically mean that the player is ready for the next step. Rather, all aspects should be of sufficient level. To make things even more complicated, the ability to excel on these aspects now, does not tell anything about the performance in 5 years. Are the best players now, also the best players in 5 years? To put the physical performance of players into context, we will provide benchmarks for youth teams in today’s blog.
Sufficient levels of physical fitness are needed for playing at an elite level. A YoYo-test or Interval Shuttle Run Test can be used to measure the endurance capacity of the players. However, an extremely high score on these tests does not make the best player: the goal is not to develop endurance athletes. Rather, the score on these tests should be sufficient to fulfill the demands of a match. But what are these demands for the different teams?
The benchmarks for the different levels for several speed zones is shown in figure 1. We see a gradual increase from the U16 teams towards to the U23 teams for all load variables. For which it should be mentioned that a higher deviation is expected for the U16 (and possibly U17) teams due to the growth spurt of the players. Some players who have already had their growth spurt (>6mm per month) are expected to outperform the players who are in their growth spurt. This also has implications for the selection process: players who have had their growth spurt but still do not meet the physical demands, might not reach the expected level. In contrary, players who have not had their growth spurt might not meet to physical demands yet, however, it might just be a matter of time before they do.
Furthermore, data from the Dutch professional level and the Premier League1 is added to the benchmarks to provide information on the physical demands the players are working towards. For which it is striking, that the Premier League makes the differences with the Dutch professional level at high speed running categories (sprint distance & high-intensity sprint distance). Therefore, we can conclude that at a higher level the difference is made in the intensity of a match rather than the volume of a match.
Now that we have seen what the differences between various levels are, the question remains on how to improve the performance of the players. Increasing the load of the conditional training to 120% of match demands improves the physical fitness of the players. Therefore, they will be able to increase their workload during the match!
The limitation of the current benchmarks is that they represent averages of the whole team. Whereas we know that there are positional differences in physical demands. Therefore, in the next blog, we will provide more information about the physical demands for different playing positions.
The selection process in a youth academy is a complex process. One of the factors that should be taken into account is the physical fitness of the players. Benchmarks can help you in this process by showing which players are physically ready to take the next step. However, especially in the U16 teams, the growth spurt should be taken into account as well: this influences the physical capacity of the players. Furthermore, we have seen that high-intensity variables are more discriminative at the professional level. Therefore, meeting the physical demands at the high speed running categories seems to be more important than the other categories.
Ease the selection process for your academy by using data!