Functional Assessment and Injury Risk in a Professional Soccer Team

Can NFL Combine Results be Used to Estimate NFL Defensive Players Longevity?

The National Football League Scouting Combine is an annual event held to evaluate football players. The results of the combine are used to identify talent and player potential. Our study aims to examine the relationship between combine drill performance (e.g., speed, power, agility, explosiveness) and career longevity. We performed a retrospective review of The National Football League combine test results for all defensive players from 2005-2015 to determine the association of each player's combine test results with career longevity. The position that had the most significant associations for 1-year status was linebackers, with weight, 10-yard dash, 20-yard dash, 40-yard dash, broad jump, 3-cone drill, and shuttle being significantly associated with 1-year roster status. The position that had the most significant associations for 5-year status was cornerbacks, with weight, 10-yard dash, 20-yard dash, 40-yard dash, 3-cone drill, and shuttle being significantly associated with 1-year roster status. The least number of significant associations was found for safeties, with no drill being associated with 1-year roster status and only height and shuttle time significantly associated with 5-year career longevity. Our study demonstrates the utility of the combine results to help estimate the career longevity of NFL defensive players.

Game Exposure, Player Characteristics, and Neuromuscular Performance Influence Injury Risk in Professional and Youth Field Hockey Players

BACKGROUND

Identifying factors that contribute to the occurrence of injury is an important step in designing training programs to minimize the risk of injury. However, despite high injury rates, variables contributing to injury in field hockey players remain relatively unexplored.

PURPOSE

To identify factors that may predict injury in professional and youth field hockey players.

STUDY DESIGN

Cohort study; Level of evidence, 2.

METHOD

Professional and youth hockey players completed preseason neuromuscular performance testing and were monitored for injuries, training, and game exposure throughout the subsequent 12-month indoor and outdoor hockey season. Least absolute shrinkage and selection operator analysis was conducted to identify injury risk factors. Receiver operating characteristic curves were then calculated to determine the individual predictive accuracy of the identified variables.

RESULTS

A total of 83 players (mean ± SD age, 20.7 ± 4.9 years; 34.9% female) with complete performance, injury, and exposure data were included for data analysis. Almost half of players (44.6%) sustained a time-loss injury during the season, and 73% of these injuries occurred in the lower limb. Playing more games and having an older age, asymmetrical and poor dynamic postural control, and better explosive performance were identified as jointly influencing the risk of injury. When considered individually, number of games played throughout the season was the most accurate predictor of injury risk (area under the curve [AUC] = 0.74; P < .001), while asymmetrical and poor dynamic postural control (AUCs = 0.61-0.65; P = .01-.04) and better explosive athletic performance (AUCs = 0.65-0.67; P < .01) were identified as moderate individual predictors of sustaining a general or lower limb injury.

CONCLUSION

A number of modifiable factors were individually and jointly associated with an increased injury risk in field hockey players, providing initial evidence for the design of targeted and sport-specific training programs to mitigate the risk of injury.

Combining Internal- and External-Training-Loads to Predict Non-Contact Injuries in Soccer

The large amount of features recorded from GPS and inertial sensors (external load) and well-being questionnaires (internal load) can be used together in a multi-dimensional non-linear machine learning based model for a better prediction of non-contact injuries. In this study we put forward the main hypothesis that the use of such models would be able to inform better about injury risks by considering the evolution of both internal and external loads over two horizons (one week and one month). Predictive models were trained with data collected by both GPS and subjective questionnaires and injury data from 40 elite male soccer players over one season. Various classification machine-learning algorithms that performed best on external and internal loads features were compared using standard performance metrics such as accuracy, precision, recall and the area under the receiver operator characteristic curve. In particular, tree-based algorithms based on non-linear models with an important interpretation aspect were privileged as they can help to understand internal and external load features impact on injury risk. For 1-week injury prediction, internal load features data were more accurate than external load features while for 1-month injury prediction, the best performances of classifiers were reached by combining internal and external load features.