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A machine learning approach to assess injury risk in elite youth football players

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Abstract
Purpose To assess injury risk in elite-level youth football (soccer) players based on anthropometric, motor coordination and physical performance measures with a machine learning model. Methods A total of 734 players in the U10 to U15 age categories (mean age, 11.7 +/- 1.7 yr) from seven Belgian youth academies were prospectively followed during one season. Football exposure and occurring injuries were monitored continuously by the academies' coaching and medical staff, respectively. Preseason anthropometric measurements (height, weight, and sitting height) were taken and test batteries to assess motor coordination and physical fitness (strength, flexibility, speed, agility, and endurance) were performed. Extreme gradient boosting algorithms (XGBoost) were used to predict injury based on the preseason test results. Subsequently, the same approach was used to classify injuries as either overuse or acute. Results During the season, half of the players (n= 368) sustained at least one injury. Of the first occurring injuries, 173 were identified as overuse and 195 as acute injuries. The machine learning algorithm was able to identify the injured players in the hold-out test sample with 85% precision, 85% recall (sensitivity) and 85% accuracy (f1 score). Furthermore, injuries could be classified as overuse or acute with 78% precision, 78% recall, and 78% accuracy. Conclusions Our machine learning algorithm was able to predict injury and to distinguish overuse from acute injuries with reasonably high accuracy based on preseason measures. Hence, it is a promising approach to assess injury risk among elite-level youth football players. This new knowledge could be applied in the development and improvement of injury risk management strategies to identify youth players with the highest injury risk.
Keywords
INJURY PREVENTION, ADOLESCENT, CHILD, SOCCER, SOCCER PLAYERS, MOTOR COORDINATION, MUSCLE INJURIES, CHILDREN, ASSOCIATION, FLEXIBILITY, RELIABILITY, VALIDITY, FITNESS

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MLA
Rommers, Nikki, et al. “A Machine Learning Approach to Assess Injury Risk in Elite Youth Football Players.” MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, vol. 52, no. 8, 2020, pp. 1745–51, doi:10.1249/mss.0000000000002305.
APA
Rommers, N., Rössler, R., Verhagen, E., Vandecasteele, F., Verstockt, S., Vaeyens, R., … Witvrouw, E. (2020). A machine learning approach to assess injury risk in elite youth football players. MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, 52(8), 1745–1751. https://doi.org/10.1249/mss.0000000000002305
Chicago author-date
Rommers, Nikki, Roland Rössler, Evert Verhagen, Florian Vandecasteele, Steven Verstockt, Roel Vaeyens, Matthieu Lenoir, Eva D’Hondt, and Erik Witvrouw. 2020. “A Machine Learning Approach to Assess Injury Risk in Elite Youth Football Players.” MEDICINE AND SCIENCE IN SPORTS AND EXERCISE 52 (8): 1745–51. https://doi.org/10.1249/mss.0000000000002305.
Chicago author-date (all authors)
Rommers, Nikki, Roland Rössler, Evert Verhagen, Florian Vandecasteele, Steven Verstockt, Roel Vaeyens, Matthieu Lenoir, Eva D’Hondt, and Erik Witvrouw. 2020. “A Machine Learning Approach to Assess Injury Risk in Elite Youth Football Players.” MEDICINE AND SCIENCE IN SPORTS AND EXERCISE 52 (8): 1745–1751. doi:10.1249/mss.0000000000002305.
Vancouver
1.
Rommers N, Rössler R, Verhagen E, Vandecasteele F, Verstockt S, Vaeyens R, et al. A machine learning approach to assess injury risk in elite youth football players. MEDICINE AND SCIENCE IN SPORTS AND EXERCISE. 2020;52(8):1745–51.
IEEE
[1]
N. Rommers et al., “A machine learning approach to assess injury risk in elite youth football players,” MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, vol. 52, no. 8, pp. 1745–1751, 2020.
@article{8650042,
  abstract     = {{Purpose To assess injury risk in elite-level youth football (soccer) players based on anthropometric, motor coordination and physical performance measures with a machine learning model. Methods A total of 734 players in the U10 to U15 age categories (mean age, 11.7 +/- 1.7 yr) from seven Belgian youth academies were prospectively followed during one season. Football exposure and occurring injuries were monitored continuously by the academies' coaching and medical staff, respectively. Preseason anthropometric measurements (height, weight, and sitting height) were taken and test batteries to assess motor coordination and physical fitness (strength, flexibility, speed, agility, and endurance) were performed. Extreme gradient boosting algorithms (XGBoost) were used to predict injury based on the preseason test results. Subsequently, the same approach was used to classify injuries as either overuse or acute. Results During the season, half of the players (n= 368) sustained at least one injury. Of the first occurring injuries, 173 were identified as overuse and 195 as acute injuries. The machine learning algorithm was able to identify the injured players in the hold-out test sample with 85% precision, 85% recall (sensitivity) and 85% accuracy (f1 score). Furthermore, injuries could be classified as overuse or acute with 78% precision, 78% recall, and 78% accuracy. Conclusions Our machine learning algorithm was able to predict injury and to distinguish overuse from acute injuries with reasonably high accuracy based on preseason measures. Hence, it is a promising approach to assess injury risk among elite-level youth football players. This new knowledge could be applied in the development and improvement of injury risk management strategies to identify youth players with the highest injury risk.}},
  author       = {{Rommers, Nikki and Rössler, Roland and Verhagen, Evert and Vandecasteele, Florian and Verstockt, Steven and Vaeyens, Roel and Lenoir, Matthieu and D’Hondt, Eva and Witvrouw, Erik}},
  issn         = {{0195-9131}},
  journal      = {{MEDICINE AND SCIENCE IN SPORTS AND EXERCISE}},
  keywords     = {{INJURY PREVENTION,ADOLESCENT,CHILD,SOCCER,SOCCER PLAYERS,MOTOR COORDINATION,MUSCLE INJURIES,CHILDREN,ASSOCIATION,FLEXIBILITY,RELIABILITY,VALIDITY,FITNESS}},
  language     = {{eng}},
  number       = {{8}},
  pages        = {{1745--1751}},
  title        = {{A machine learning approach to assess injury risk in elite youth football players}},
  url          = {{http://dx.doi.org/10.1249/mss.0000000000002305}},
  volume       = {{52}},
  year         = {{2020}},
}

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