A key development in sports over the last three decades has been the increased use of scientific methods to improve the preparation and participation performance in elite competitions. In this context, international sport is undergoing a revolution fueled by the rapidly increasing availability of athlete quantification data, sensor technology, and advanced analytic software. These recent innovations have enabled close monitoring of athlete performance across all training sessions and matches, facilitating a better understanding of training methods that benefit athletes and coaches alike, i.e., facilitating a much deeper understanding of training methods and empowering both players and involved personnel to quantify individual and team strengths and weaknesses, give physical performance feedback, psychological status and wellness fluctuations, educate, plan training content in micro- and macrocycles, and avoid overuse injuries.
In this context, soccer (also known as association football) is an open-loop sport with many parameters that can be analyzed. The performance is a multifactorial construct with a dynamic and stochastic nature, where players’ physical performances (e.g., high-speed activities) are affected by external factors (e.g., ball possession and period of the season), which consequently causes a fluctuation between sessions. Several studies have reported on the effect of variability between players, positions, and matches.
The data we present in our SoccerMon dataset was collected and used by professional teams during the 2020 and 2021 seasons in the Norwegian women´s elite soccer league (“Toppserien”). In particular, the teams used the \pmsys athlete monitoring system to log subjective parameters, including training load, wellness, sickness, and injuries after every session, in addition to wellness once a day and sickness and injuries whenever they occurred. Moreover, during the training sessions, the players used wearable tracking equipment (STATSports APEX) to monitor total distance, high-speed running distance, sprint distance, accelerations and decelerations, and peak speed.
Download
All files can be viewed and downloaded in our OSF repository available here: https://osf.io/uryz9/
Terms of Use
The dataset is licensed under CC-By Attribution 4.0 International.
Contact
Please contact cise@simula.no for any questions regarding the dataset.