Is Milk an Effective Option for Recovery?
Recovery is one of the main processes to improve sports performance. Within the many factors that condition a good recovery, nutrition is one of the main ones.
To schedule and prescribe training throughout the season, we need to know the physical demands that occur during competition as accurately as possible, so that exercises can be set that prepare the players to withstand real game situations. Therefore, adequate adjustment of training to the demands of each sport is an important requirement in order to optimise performance and in turn reduce the risk of injury.
Technology allows us to quantify in real time a large number of variables related to in-game physical requirements. The technological advances of positioning devices over the last two decades mean that they are now widely used by most professional clubs. This is particularly so for the global positioning system, commonly known as GPS, which is used to analyse a wide range of parameters related to the external loads of outdoor athletes. However, a local positioning system, or LPS, a technology that is not as well-known or widely studied as GPS, can be used to analyse the positioning and performance variables of players with greater validity and reliability in indoor sports such as basketball, handball and futsal.1 These systems provide coaches and fitness trainers with a large amount of data with which to perform analyses that will help them to quantify the demands required during matches and training sessions. However, technology is just an instrument and it does not automatically report the necessary parameters with which to quantify and schedule training sessions. Technology must be accompanied by knowledge of what is going to be measured, how it is measured and why it is measured. It is data analysis methods that tell us whether a technology is useful and provides valid information for it to be used by the coaching staff, or whether it simply offers a large number of variables that are unlikely to have a positive impact on performance without proper analysis and interpretation.
For example, in basketball, where players perform actions involving major neuromuscular and metabolic demands within a dynamic and complex game system, traditional methods that assess physical demands may underestimate the game phases when players face the greatest demands. This approach based on the calculation of averages for a data series might lead to training programmes that do not take into account the most demanding phases of games, thus affecting player performance and the risk of injury.
In order to study the real impact of the most demanding scenarios within a game, the so-called maximum demanding scenarios, Jairo Vázquez-Guerrero and Franc Garcia, members of the FC Barcelona Performance Area, have recently published the results of a ground-breaking study in which they analyse the maximum physical demands of 21 professional basketball players during a friendly match using a method based on the calculation of rolling averages2. To do this, they used a local positioning system based on ultra-broadband technology (Realtrack Systems, WIMU PRO) and the calculation of rolling averages for each parameter of interest related to physical load (sprinting, distance covered with accelerations and decelerations at high intensity, number of sprinting actions and number of high-intensity accelerations) using 60-second periods. These maximum recorded values were compared with traditional averages.
The results showed differences of between 103.4% and 848.4% between the traditional averaging method and the most demanding scenarios for all the physical demands examined. For example, according to the rolling average method, the players covered a total distance of 141.3 m/min and a high speed distance of 25.4 m/min, while with the traditional method they covered 66.3 m/min and 3.2 m/min respectively. This data represents an increase of 113.1% for total distance per minute and 686.4% for sprinting distance per minute. As for high-intensity acceleration and deceleration actions, the players performed 8.8 acceleration actions and 8.2 deceleration actions in the maximum demand scenarios compared to the actions recorded using the traditional method, 2.5 and 2.1 respectively. This represents an increase of 252% for the number of high-intensity accelerations and 290.5% for the number of high-intensity decelerations recorded by the rolling average method. So, the results indicate that the traditional averaging method grossly underestimates the maximum physical demands during a game of basketball.
Therefore, according to the authors of the study, “the traditional approach should be complemented with the analysis of the most demanding scenarios in order to gain a better understanding of the physical demands that occur during basketball games. These results can help both coaches and fitness trainers to improve training schedules and prescription processes based on the quantification of the load, since they can be used to design more effective training and recovery exercises”. It is therefore shown that technology in itself does not solve problems, but instead needs to be complemented by a human team that knows how to interpret the needs and assess possible solutions.