RPE and its relationship with the risk of injury in footballers
Overtime, the competitive distance among elite football teams has shortened, so the focus is currently on those aspects that can tip the scale to one side or the other.
Professional basketball is evolving towards playing more matches, reaching up to 90 games per season.1 Because of this, the players’ training should help them to endure training loads on a regular basis, reduce the injury risk, mitigate fatigue symptoms, as well as achieving an optimal performance throughout the season.
That is why, according to the team’s game style and the players’ characteristics, physical demands will vary, and personalised training focused on boosting the game’s particular aspects will be required. In other words, the demands of a team with a “slow and exploratory” style will be different from the demands of a team with a greater pace in the game, with the implications this entails at a physical level and in decision-making. In this sense, monitoring physical demands in both training sessions and matches have become an important task to prescribe and monitor training sessions.
Current workload monitoring technology provides coaches with objective information to periodise training and thus look for adaptations according to the individual demands of each player and the accumulated load. The use of inertial and positioning sensors allows them to accurately analyse the physical demands and relate them to other variables.
Historically, scientific literature has connected external load data with the risk of injury, to try and answer the following question: “Does a greater workload result in a lower injury incidence?” Instead, the relation between training load and game performance has not been examined yet.
In basketball, there are indicators that combining game-related statistics (for example. two-point shots, defensive rebounds, assistances, steals, blocks…) through sequential analysis and subsets of structural equations, allow to establish a performance marker. For example, two of the most used ones are WinScore2 or performance index, also known as PIR. A recent study published by members of FC Barcelona’s Performance Area, Jairo Vázquez-Guerrero and Martí Casals1 in the international Research in Sports Medicine magazine, in collaboration with researcher Jaime Sampaio from CIDESD – University of Trás-os-Montes e Alto Douro (Portugal), has been able to answer two important issues about which there is not much information yet:
With the aim of answering these questions, the study analysed the training session physical demands of a Euroleague basketball team by using WIMU PRO inertial and positioning sensors and connecting them with different performance indexes as well.
Through a group analysis, the performance was classified into three categories: high, medium, and low. In the same way, the workload was also classified into three categories: high, medium, and low. Also, other sub-analyses were carried out according to the game position, to be able to analyse the relationship between physical demands and performance:
Undertaking a correspondence analysis, as seen in Figure 1a (point guards and shooting guards), a worse performance was related to a medium load (as they are in the same quadrant). Moreover, there was also correspondence between a low workload and a medium performance. On the other hand, high performance and high workloads were found to be independent of other categories as they stand in different quadrants. In other words, a match between higher workload and higher performance was not found for point guards and shooting guards.
If we analyse the quadrants in the forwards’ panel, Figure 1b, there’s a relation between low workloads and a better performance and between medium workloads and medium performance. On the other hand, high loads were associated with worse performance. In essence, this data suggest that, in forwards, one of the most versatile positions in basketball, a bad performance is related to higher workloads. Lastly, even though the relation in Figure 1c is less obvious, high workloads were not associated with a better performance.
Even though the analysed results belong to one single team and, therefore, they cannot be analysed generically, they allow to reflect on quantification and interpretation of physical demands in isolation without considering other aspects, such as tactic, technique, volitional emotional aspects… this is enough to better understand the performance during competition… Thus, according to the authors of the study, “these results allow to reflect on the need to use higher training loads during the competitive period, as well as the usefulness of training load quantification to ‘foresee’ the performance in the game. In this sense, a new door opens up to find variables that can be incorporated to the current performance monitoring models in the game.”
This study suggests that the relation between external training load and performance in matches varies according to the players’ position; besides, there is not a connection between a higher external load and a better performance. For that reason, according to the authors “the coaching staff should be aware of the fact that players need sufficient physical stimulation to properly endure the competition demands; however, after overcoming certain thresholds depending on the position, further increases in load may not lead to a better performance in the game.” More is not always the better. Therefore, in spite of its relevance, the interpretation of external load should be complemented with further validated measures that can be translated to the performance of the game.
Adrián Castillo García
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