Date: Friday December 11th at 4pm
Statistical concept of CUB models to the world of sports
16:00 The class of CUB models: a paradigm for rating data (by Domenico Piccolo)
16:10 CUB models and extensions: from theory to action (by Rosaria Simone)
16:40: Focus on two developments: Nonlinear CUB and Treatment of "don't know" responses (by Marica Manisera)
16:55: Future research on CUB models in Sports: some insights (by Paola Zuccolotto)
If interested in joining the session, please contact Christophe Ley at Christophe.Ley@UGent.be
Date: Friday December 4th at 2.10pm
Prevention of injuries, are we heading the right direction?
Physical activity and sports are an integral part of our society. Both have a positive effect on quality of life. However, it should at the same time be noted that due to the physical demands the injury rate/percentage in sports are high. Besides the consequences for the player, there are repercussions for the team and club. Injuries do not only lead to reduced performance, they cause financial losses as well. In order to minimize these negative consequences, prevention programs to predict injuries were developed. However, injuries show no tendency to decrease. As such, it can be concluded that injury prevention is currently not sufficiently adequate and in need for change. It is know that current approaches are not sufficiently addressing the complex and dynamic nature of sports injury aetiology, and this necessitates the need for integrating complex system approaches in sports injury prediction and prevention. Accordingly, due to the lack of suitable methodological approaches, the use of Artificial intelligence to identify complex patterns of interactions and the implementation of wearables to continuously monitor the athletes have been introduced[6,7]. Despite the promising future for the use of AI and the implementation of wearables, further research, based on longitudinal studies with large datasets and continuous monitoring, is warranted to establish the effectiveness and predictive performance of these statistical techniques and methods in the particular domain of sports injury risk identification.
Speaker: Evi Wezenbeek (Ghent University)
 Pfirrmann D, et al., Analysis of Injury Incidences in Male Professional Adult and Elite Youth Soccer Players. Hägglund M, et al., Injuries affect team performance negatively in professional football. Owen A, et al., Effect of an injury prevention program on muscle injuries in elite professional soccer. Ekstrand J, et al., Hamstring injuries have increased by 4% annually in men’s professional football, since 2001. Van Dyk N. et al., Prevention forecast: cloudy with a chance of injury. Bittencourt et al., Complex systems approach for sports injuries. Claudino et al., Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports.