Tuesday, 2 August 2016

Predicting injury in football

ISTM recently ran a blog writing competition that was open to PhD students and young researchers with a view to improving their lay-writing skills and helping ISTM to play a greater role in the public dissemination of its research.   After concluding the competition we will be publishing each entry in turn over the coming months. The 1st prize winner of our competition was Fraser Philp a PhD student at ISTM.  Fraser's PhD focuses on identifying within current practice and research, methods used for predicting injury and performance within football and Fraser used this topic as the theme for his blog post.


Association football or soccer is one of the most popular international sports, with approximately 200,000 professionals and a further 240 million amateur male and female participants worldwide. Football is England’s largest national team sport, with men’s and women’s football being the first and third largest team sports respectively. Associated with the high levels of participation in football is a high level of injury risk. As many as 47% of footballers have been forced to retire from the game due to injury throughout the season, an average outfield player is expected to sustain at least 1-2 injuries resulting in them being unavailable for 1 competitive game. High rates of injury can negatively impact on the performance of an individual. Likewise an increased number of individuals sustaining injury within a team can negatively affect team performance, which, in a competitive league can have further consequences.

Given the problems associated with injury, the medical and sports science teams who work with professional football teams try to minimise injuries occurring. One of the ways they attempt to do this is through screening. Screening can involve exercise tests and measures of physical performance that are used in an attempt to identify injury risk factors. These tests are usually carried out before the competitive season starts, in a period known as pre-season, and during the competitive season itself. Despite the widespread use of these tests and measures, many of them have not been and compared against other methods of measurement for validation purposes. 

My research project is aimed at comparing and providing numerical values to one of the exercise screening tests that is commonly used. The screening test being evaluated is the Functional Movement Systems (FMS) screen, and I will be using a video motion capture system, Vicon (©Vicon Motion Systems Ltd) for my evaluation. The FMS is partly made up of 7 exercise tests, in which the participant is required to complete the movements, a maximum of 3 times. These tests include things such as a squat with their arms above their head, lunges and other physical tests. The quality of the movements is then scored by an assessor and the participant is given a score for each test. The final score is then used to identify injury risk, with a lower score indicating a higher risk of injury. I have chosen to evaluate the FMS against a motion capture system as this has not been done yet and there are some limitations experienced when using the FMS test. The movement test takes into consideration some patterns of movement but does not describe the angles that are achieved at the joints. It is also difficult for the person assessing to observe multiple joints, whilst at the same time, scoring the movement and identify variations in movement patterns. These problems arise because the assessor has essentially a limited 2 dimensional view and of a complex dimensional. The use of motion capture can help with some of these challenges.

Motion capture is more widely known for its use in the movie industry in virtual recreation. It is also used as the gold standard measurement in hospital settings for measuring walking patterns and human movement patterns in people with neurological disorders. In order to measure the movements of the FMS with motion capture cameras, some additional preparation is needed. This requires placing reflective markers on selected body parts of the person. This is because the motion capture camera’s only pick up reflections from the infrared light that they send out. These markers can then be virtually recreated providing an outline of the person’s body parts on which they were placed. Once this has been done we are able to see the angles achieved by the participant in all 3 dimensions i.e. how much they bent their knee or how much their hip was rotating. It also allows for a description of the movement patterns that are occurring across the joints when the participant completes the FMS. Furthermore we can also attribute numerical values to the rules and scoring criteria of the FMS exercise tests.

Figure above shows the process after placing the markers on and then virtualy recreating them.

Alongside this we have monitored a football team over one competitive season and will investigate whether there is a link between the measurements we took and the injuries they sustained. Within this analysis we will also be investigating things such as the amount they trained, the surface they trained on and what their match fixtures were like. Hopefully a better understanding of all these factors will allow for fewer injuries in footballers.

Written by Fraser Philp, PhD Student, ISTM
(1st Prize in the ISTM Blog Post Competition 2016)


  1. Ibet889 – Trang ca do bong da uy tín
    - Đăng ký tài khoản tham gia cực kỳ nhanh chóng
    - Rút nạp tiền chỉ mất 5 phút
    - Đa dạng kèo nhà cái
    - Dịch vụ hỗ trợ khách hàng 24/7
    - Chương trình khuyến mãi siêu hấp dẫn
    >>> Xem chi tiết tại : hướng dẫn soi kèo bóng đá