Can We Predict Adductor Strain? A Predictive Analysis of a Major League Soccer (MLS) Cohort Spanning from 2019 to...

Background: Despite the high prevalence of adductor injury in soccer, there is limited injury-specific predictive modeling to identify common risk factors. The objective of this study was to create an adductor strain prediction model utilizing injury, game, and performance data collected from a cohort of professional Major League Soccer (MLS) players. Methods: We identified potential risk factors for soft tissue, non-contact adductor strain using a predictive machine learning model framework. Performance and injury data were collected between the 2019 to 2022 seasons of one professional MLS team. We utilized Random Forest (RF) machine learning models with Synthetic Minority Oversampling (SMOTE) to predict soft tissue, non-contact adductor strain injury amongst the cohort. Features chosen to be implemented in the model included injury, game, and performance data. Results: From the four models constructed in this study, the best performing model included Catapult Global Position System (GPS)/Internal Measurement Unit (IMU), strength, injury, and game data using a weekly structure determined by F1 score. Multiple models indicated that not having a previous injury lowers the odds of a future injury in the following week or month. Forwards had greater odds of injury whereas defenders had lower odds of injury. Greater hamstring max force lowered odds of injury whereas a greater amount of change of direction efforts increased the odds of injury in the following week or month. Adductor-to-abductor max strength ratio showed conflicting results regarding the odds of future injury. Conclusions: Through the utilization of RF and SMOTE, we were able to successfully predict adductor injuries in an MLS cohort utilizing injury, game, and performance metrics. Validation in a larger cohort would be highly recommended before utilizing the findings of this study in the design of injury prevention protocols.