Activities

Date: Friday May 21st at 4 pm

Introducing Regularisation to Generalised Joint Regression Modelling and its Application to Football and Sports

When modelling the bivariate outcome of football matches and other sports, many different approaches regarding dependency have been investigated. We propose the use of copula regression via the powerful GJRM (Generalised Joint Regression Models) framework in R by Giampiero Marra and Rosalba Radice and present its use for modelling match results. Motivated by the application to football and FIFA World Cups in particular, we introduce two types of useful penalties. The first tackles a very specific issue occurring in sport tournaments and leagues (or other competitive situations), while the second is a Lasso-approximation yielding general sparsity.

The talk will be given by Hendrik van der Wurp from the Technical University of Dortmund.



Date: Friday April 23rd at 3.30 pm

Enduring Love: the Long-Term Effect of a New Stadium on Attendance at Professional English Soccer

Since 1988, 22 of the 92 of the english professional football clubs have acquired a new stadium. We estimate the causal effect of the new facility on attendance using a difference-in-difference model. We find that a new stadium raises attendance by around 20% on average, and this effect is sustained over nearly two decades. This result contrasts with the "Honeymoon effect" identified in the US literature, where attendance quickly reverts to the mean. We explain this by the promotion and relegation system. Unlike the closed major leagues, a new stadium represents an opportunity for a club in a lower division to generate increased revenue from fans, hire better players, win promotion and sustain a higher league position on average.

The talk will be given by Stefan Szymanski from the University of Michigan. The organization of this webinar is handled by James Reade from the University of Reading.



Date: Friday March 12th at 4 pm (or 3.30 pm)

Machine Learning in Orthopaedic Sports Medicine - Clinical Translation from the Registry to the Clinic

While the clinical application of machine learning to several health-care disciplines has increased considerably over recent years, it remains in its infancy in orthopaedic sports medicine. This presentation will review the basics of machine learning, give examples of how it has impacted other areas of orthopaedic surgery, and illustrate how machine learning can be applied to existing national knee ligament registers. Completed and ongoing collaborative studies between the University of Minnesota and the University of Oslo will be discussed, including a demonstration of how an in-clinic calculator was developed capable of estimating the risk of ACL reconstruction failure at a patient-specific level. Focusing on clinical translation of machine learning techniques, future opportunities within sports medicine will also be presented to stimulate idea generation and ultimately improve patient care.

R. Kyle Martin MD FRCSC is an orthopaedic sports medicine surgeon with the University of Minnesota. Originally from Canada, Dr. Martin completed his orthopaedic training at the University of Manitoba in 2017. He then travelled to Oslo, Norway where he spent one-year in a clinical fellowship with professor Lars Engebretsen at the University of Oslo and the Oslo Sports Trauma Research Center. Following the Oslo fellowship, Dr. Martin then completed a second clinical fellowship at Mayo Clinic in Rochester, MN. His current clinical practice revolves around knee, hip, and shoulder injuries and arthroscopy, while his research focus is on machine learning and its clinical applications to the field of sports medicine.



Date: Friday February 26th at 3.30 pm

Real-Time Skeleton Detection for Visual Sports Analysis and ... You

Dr. Manuel Stein presents a system for automated data acquisition and analysis from simple video recordings of team sport matches. The proposed system focuses on extracting movement data as well as body poses for players and allows tracking of ball movement in the case of ball-based forms of team sports. Furthermore, Dr. Manuel Stein will provide insights into his novel system for automatically displaying complex and advanced 2.5D visualizations superimposed on the original video recordings. As CEO & Co-Founder of Subsequent, he is especially interested in new project ideas and would like to hear about how you would like to make use of such a system.



Date: Friday January 22nd at 4pm

Shoe cushioning, body mass and running biomechanics as risk factors for running injury: A randomised trial with 800+ recreational runners


The objective of the event is to present a large randomised trial on the relationship between shoecushioning, running biomechanics and the risk of running-related injury to researchers interested ininjury aetiology, injury prediction and data sciences. The second part of the webinar will specificallyfocus on the challenges and opportunities resulting from the large dataset collected in this project, aswell as on recommendations for future research in injury prevention.


  1. Introduction: Running-related injury research[1] Daniel Theisen (ALAN)

  2. The study design, data collection and descriptive data[2] Laurent Malisoux (LIH)

  3. Recently published results

a. Effect of shoe cushioning and body mass on injury risk[3]

b. Effect of cushioning on running biomechanics[4]

  1. Let’s move to prediction

a. Predicting cumulative load using a wearable device[5] Anne Backes (LIH)

b. Predicting running-related injury using machine learning – Hans van Eetvelde (UGent)


[1]Theisen D, Nielsen R, Malisoux L. The relationship between running shoes and running injuries: Choosing between a complicated truth and a simple lie. In: Ley C, Dominicy Y, editors. Science meets sports: When statistics are more than numbers. 1 ed: Cambridge Scholars Publishing; 2020. p. 123-146.[2]Malisoux L, Delattre N, Urhausen A, Theisen D. Shoe cushioning, body mass and running biomechanics as risk factors for running injury: a study protocol for a randomised controlled trial. BMJ Open 2017; 7(8):e017379.[3]Malisoux L, Delattre N, Urhausen A, Theisen D. Shoe Cushioning Influences the Running Injury Risk According to Body Mass: A Randomized Controlled Trial Involving 848 Recreational Runners. Am J Sports Med2020; 48(2):473-480.[4]Malisoux L, Delattre N, Meyer C, Gette P, Urhausen A, Theisen D. Effect of shoe cushioning on landing impact forces and spatiotemporal parameters during running: results from a randomized trial including 800+ recreational runners. Eur J Sport Sci 2020;10.1080/17461391.2020.1809713:1-9.[5]Backes A, Skejø SD, Gette P, Nielsen RØ, Sørensen H, Morio C, et al. Predicting cumulative load during running using field based measures. ‐Scandinavian Journal of Medicine & Science in Sports2020;10.1111/sms.13796.



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[1]. 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[2]. In order to minimize these negative consequences, prevention programs to predict injuries were developed[3]. However, injuries show no tendency to decrease[4]. As such, it can be concluded that injury prevention is currently not sufficiently adequate and in need for change[5]. 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[6]. 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)


[1] Pfirrmann D, et al., Analysis of Injury Incidences in Male Professional Adult and Elite Youth Soccer Players.[2] Hägglund M, et al., Injuries affect team performance negatively in professional football.[3] Owen A, et al., Effect of an injury prevention program on muscle injuries in elite professional soccer.[4] Ekstrand J, et al., Hamstring injuries have increased by 4% annually in men’s professional football, since 2001.[5] Van Dyk N. et al., Prevention forecast: cloudy with a chance of injury.[6] Bittencourt et al., Complex systems approach for sports injuries.[7] Claudino et al., Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports.