As a (physical) trainer or coach, you are balancing training load on a daily basis. High physical loads improve fitness levels of the players, in turn enhancing the performance of your team. On the other hand, high training loads also increase the risk of overloading your players. In order to optimize the performance of teams, more and more clubs (also amateur clubs) are starting to use GPS-tracking systems and/or exertions and recovery forms. However, when you start monitoring your team in this way, a lot of information is coming your way. How are you going to handle all this data and, especially, how are you going to use this data to your advantage? To guide teams during this process, JOHAN Sports has added the Player Status module to the software platform. In the coming series of blogs, we will explain what the Player Status module is and how you can use it to get the most out of all your players.
Three training principles (a gradual increase of training load, supercompensation, and recovery status) are integrated into the Player Status module to guide you in determining the ideal amount of training load and the ideal variation of training load for your players. In this blog, we will discuss how the Player Status module (see Figure 1) determines the ideal training load for the players and how you can use the results of these analyses for optimizing the performance of your team.
Figure 1: Player status module
One of the principles of enhancing physical fitness is exposing players to higher physical loads than what they are used to (i.e. no pain, no gain). However, by increasing the load too much, too fast you also increase the risk of overloading the players. This will reduce their performance. Therefore, a gradual increase in load over time is important. The AC-ratio is able to help you determine the ideal amount of load for your players. This ratio compares the load that players are used to (chronic load) to the load that they are currently exposed to (acute load). When the acute load is more than 1.5 times higher (150%) than the chronic load, this increases the chance of overloading the players. In that case, the Player Status module will automatically make a detection and show the corresponding training suggestions (do’s and don’ts).
Figure 2: Detailed information of AC-ratio calculation.
There are, however, also some things you need to take in mind when working with the AC-ratio. First, 1.5 is not a magic number: this threshold is just a guideline. When a player has a detection on the AC-ratio, you shouldn’t automatically lower the training load for this player. Therefore, it is advised to check the AC-ratio score: the higher the AC-Ratio is, the higher the chances of overloading the player, and the higher the need to make adjustments to the training program. By using other input variables as well, such as exertion/recovery scores or by seeing that someone appears fatigued, you gain even more confidence that you should (or should not) change the program.
Let’s give an example of the above-described process. When you go to the Player Status module, you see that a player has a detection on the AC-ratio: an overload of sprinting. Before deciding on whether you are going to implement the training suggestions, you want to gather more information: AC-ratio score, recovery scores, and the goal of training for this week. Therefore, you check the AC-ratio score, which is 162% in this case (see figure 1). Furthermore, data also shows that the player reports normal scores for fatigue and muscle soreness when compared to his normal scoring tendency (see Figure 3). Since we are in pre-season (and thus we want to improve the fitness levels of the players), there is (only) a mild overload (162%) and the player reports normal values for fatigue and muscle soreness, the player is able to continue the periodization schedule. However, it is advised to check upon the recovery scores of this player more carefully in the coming days to make sure that he is responding well to the training program.
Figure 3: Detailed information of well-being variations.
The second thing to take into account is that the module needs 4 weeks of data to make a good estimation of the ideal training load. This means that in the pre-season period (or when a player is returning from an injury) the AC-ratio cannot directly be used to determine the ideal training load. In order to help you design a training program in this period, guidelines for the average team load for the first 4 weeks of pre-season are shown in the figure below. Furthermore, collecting recovery scores of the player can further help you in determining the optimal load of the players.
Figure 4: Guidelines for weekly load in the first 4 weeks of pre-season.
The Player Status module helps trainers and coaches determining the ideal amount of training load for the team. When there is a deviation from this ideal training load, an automated alert will show up and corresponding training suggestions will be given. When an alert on AC-ratio shows up for a player, try to integrate different aspects (AC-ratio score, well-being variations, and the goal of the week) to come to a final conclusion on whether to adjust his/her training program. Always remember that the data is there to get insights into the status of the players, and therefore help you make a final decision. However, the data should not make the decision for you!
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