Soccer is a game with worldwide appeal. Increasing numbers of participants are members of all age groups and skill levels. The game presents to the sports medicine practitioner a wide variety of musculoskeletal and medical problems. Soccer injuries increase in frequency as the age of participant increases, with a low incidence of injury in preadolescent players. Musculoskeletal injuries most commonly affect the lower extremities and include contusions, acute and chronic musculotendinous strains, and ligamentous injuries to the knee and ankle. Most injuries are minor and respond to analgesics, therapy modalities and exercise therapy. Groin pain is a common problem and particularly prevalent among soccer players owing to the game’s specific stresses. Other less common but important injuries include facial trauma, mild brain injury (concussion) and heat-related injury. Team physicians, athletic trainers and physical therapists need to possess a basic understanding of the most common injuries and problems in order to maximise safe participation for their athletes.

Common soccer injuries. Diagnosis, treatment and rehabilitation.

The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). 
Three investigations were conducted: 
1. Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football. For this investigation, three datasets of UEFA injury recordings were available. Different machine learning algorithms were used in order to build a predictive model. The performance of the machine learning models is then improved by using feature selection conducted through correlation-based subset feature selection and random forests. 
2. Predicting injuries in professional football using exposure records: The relationship between exposure (in training hours and match hours) in professional football athletes and injury incidence was studied. A common problem in football is understanding how the training schedule of an athlete can affect the chance of him getting injured. The task was to predict the number of days a player can train before he gets injured. 
3. Predicting intrinsic injury incidence using in-training GPS measurements: A significant percentage of football injuries can be attributed to overtraining and fatigue. GPS data collected during training sessions might provide indicators of fatigue, or might be used to detect very intense training sessions which can lead to overtraining. This research used GPS data gathered during training sessions of the first team of THFC, in order to predict whether an injury would take place during a week.

Predictive modelling of football injuries

Injuries are a common problem in professional football. A challenge that the medical team faces is to successfully predict the recovery time of an injured player. Current medical standards can only give vague predictions as to when a player will return to play. Obviously, making an accurate prediction as soon as possible would be helpful to the coach. This research tries to answer the question of whether it is possible to predict when a player will return to play, based on information at the moment of injury, while also comparing three machine learning methods for this task: support vector machines, Gaussian processes and neural networks. The tests were conducted on data from the professional football club of Tottenham Hotspur. The results demonstrate that this task can be completed with a reasonable amount of accuracy, without any method performing significantly better than the rest. Future directions and possible improvements are discussed.

Comparison of Machine Learning Methods for Predicting the Recovery Time of Professional Football Players After an Undiagnosed Injury.

The evidence base for sport-related concussive brain injury is reviewed in this paper. In the past, pathophysiological understanding of this common condition has been extrapolated from studies of severe brain trauma. More recent scientific study demonstrates that this approach is unsatisfactory, and the clinical features of concussion represent a predominantly functional brain injury rather than manifest by structural or neuropathological damage. Such understanding of this condition remains incomplete at this stage.

Evidence-Based review of sport-related concussion: clinical science.

/pdf/machine-learning-and-data-mining-for-sports-analytics-5th-2o8xbfzj7k.pdf

Machine Learning and Data Mining for Sports Analytics: 5th International Workshop, MLSA 2018, colocated with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings

Concussion is common in contact sports. There are two major management dilemmas. The first is to decide when an athlete is fit to resume competition. Factors useful for evaluating recovery such as psychometric testing ‐ a quantitative measure of recovery ‐ are discussed. Risks of premature return to competition are outlined. The second management dilemma arises when there is risk of cumulative, or permanent damage. The effect of repeated concussion is not well understood and prospective studies are urgently required.

A sports doctor's dilemma in concussion

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A sports doctor's dilemma in concussion