Objective We performed a systematic review and meta-analysis of epidemiological data of injuries in professional male football. Method Forty-four studies have reported the incidence of injuries in football. Two reviewers independently extracted data and assessed trial quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement and Newcastle Ottawa Scale. Studies were combined in a pooled analysis using a Poisson random effects regression model. Results The overall incidence of injuries in professional male football players was 8.1 injuries/1000 hours of exposure. Match injury incidence (36 injuries/1000 hours of exposure) was almost 10 times higher than training injury incidence rate (3.7 injuries/1000 hours of exposure). Lower extremity injuries had the highest incidence rates (6.8 injuries/1000 hours of exposure). The most common types of injuries were muscle/tendon (4.6 injuries/1000 hours of exposure), which were frequently associated with traumatic incidents. Minor injuries (1–3 days of time loss) were the most common. The incidence rate of injuries in the top 5 European professional leagues was not different to that of the professional leagues in other countries (6.8 vs 7.6 injuries/1000 hours of exposure, respectively). Conclusions Professional male football players have a substantial risk of sustaining injuries, especially during matches.

As to analyze and compare the behavior of numerous machine learning methods to select the best-performing injury risk factor model to identify athlete at risk for lower extremity muscle injuries (MUSINJ). Methods A total of 132 male professional soccer and handball players underwent a preseason screening evaluation that included personal, psychological, and neuromuscular measures. Furthermore, injury surveillance was used to capture all the MUSINJ occurring in the 2013/2014 seasons. The predictive ability of several models built by applying a range of learning techniques were analyzed and compared. Results There were 32 MUSINJ over the follow-up period, 21 (65.6%) of which corresponded to the hamstrings, 3 to the quadriceps (9.3%), 4 to the adductors (12.5%), and 4 to the triceps surae (12.5%). A total of 13 injures occurred during training and 19 during competition. Three players were injured twice during the observation period so the first injury was used, leaving 29 MUSINJ that were used to develop the predictive models. The model generated by the SmooteBoost technique with a cost-sensitive ADTree as the base classifier reported the best evaluation criteria (area under the receiver operating characteristic curve score, 0.747; true positive rate, 65.9%; true negative rate, 79.1) and hence was considered the best for predicting MUSINJ. Conclusions The prediction model showed moderate accuracy for identifying professional soccer and handball players at risk for MUSINJ. Therefore, the model developed might help in the decision-making process for injury prevention....

Hamstring strain injury (HSI) is one of the most prevalent and severe injury in professional soccer. The purpose was to analyze and compare the predictive ability of a range of machine learning techniques to select the best performing injury risk factor model to identify professional soccer players at high risk of HSIs. A total of 96 male professional soccer players underwent a pre-season screening evaluation that included a large number of individual, psychological and neuromuscular measurements. Injury surveillance was prospectively employed to capture all the HSI occurring in the 2013/2014 season. There were 18 HSIs. Injury distribution was 55.6% dominant leg and 44.4% non-dominant leg. The model generated by the SmooteBoostM1 technique with a cost-sensitive ADTree as the base classifier reported the best evaluation criteria (area under the receiver operating characteristic curve score=0.837, true positive rate=77.8%, true negative rate=83.8%) and hence was considered the best for predicting HSI. The prediction model showed moderate to high accuracy for identifying professional soccer players at risk of HSI during pre-season screenings. Therefore, the model developed might help coaches, physical trainers and medical practitioners in the decision-making process for injury prevention.