Athletes are exposed to various physical and mental stressors, such as high training loads and defeats. For professional sports organizations, maintaining and improving the resilience of athletes is a top priority to prevent performance decrements and physical or mental problems. The aim of this project is to gain novel insights into resilience, and to provide athletes and coaches with concrete feedback on the resilience of athletes.
Resilience can be defined as an athlete’s ability to return to the previous level of functioning, following a stressor. The resilience of an athlete can change over time and is individual-specific. Although sports organizations increasingly measure physical and mental data of athletes, they lack the resources to integrate these data and to use it to determine the resilience of individual athletes. Our innovative project fills this gap, and answers two main questions: 1) By integrating daily fluctuations in the physical and mental states of athletes, can we accurately predict resilience lapses (e.g., injuries, mental problems)? 2) Do coaches and athletes benefit from personalized feedback on the physical and mental resilience of athletes?
To answer these questions, we established an interdisciplinary collaboration between psychologists, human movement scientists, and data scientists. Furthermore, we collaborate with sports clubs that have high-quality resources (measurement infrastructure and personnel) to obtain daily measures of physical and mental states of athletes: The Dutch premier division soccer clubs PSV, Vitesse, and FC Groningen. From year 1 of our project, based on heart rate and position data of the athletes, we can measure their physical states and stressors (e.g., duration and intensity of trainings and matches). In addition, we utilize a phone app to track the mental states of athletes (e.g., mood, motivation). This information is integrated in a secured data platform that we have developed in the past years. We apply data mining algorithms to detect “deviations” in the physical and mental states of individual athletes, as possible warning signals for a resilience lapse (research question 1).
In years 2 and 3, the automated algorithms will be connected to an app, so that coaches and athletes are provided with feedback. More specifically, athletes and coaches will receive personalized insights into their physical and mental states, and signals when vulnerabilities are detected by the algorithms. This information can help athletes and coaches to adjust training and match schedules to individual athletes. We will evaluate the effect of the feedback (research question 2) by comparing occurrences of resilience lapses in athletes before they started using the feedback (year 1), and while using the feedback (year 2 and 3).
Year 4 is devoted to disseminating our knowledge to other sports. We will organize workshops for embedded scientists of different sports on how to implement similar data platforms and apps. Finally, via our platform and the Sport Data Valley, where we will store our data and analyses, our data and algorithms can be (re-)used by other researchers.