Detecting Injury Risk Factors with Algorithmic Models in Elite Women's Pathway Cricket

Area of Science:

• Sports Medicine
• Data Science in Sports
• Injury Epidemiology

Background:

• Identifying cricket injury risk factors is crucial for player development and performance.
• Traditional statistical models may not capture complex, non-linear relationships in injury data.

Purpose of the Study:

• To explore the utility of algorithmic models in identifying novel injury risk factors in elite young female cricketers.
• To compare findings from machine learning models with conventional statistical approaches.

Main Methods:

• Retrospective cohort analysis of 17 players on the England and Wales Cricket Board women's international development pathway (aged 14-23).
• Utilized supervised learning (decision tree, random forest) and generalized linear mixed effect models.
• Assessed associations between various risk factors (e.g., training load, physical performance metrics) and injury occurrence.

Main Results:

• Supervised learning models did not predict injury but identified significant risk factors.
• The best generalized linear mixed effect model highlighted smoothed differential 7-day load, average broad jump scores, and 20m speed as significant predictors (P<0.001).
• Identified novel injury risk factors not previously apparent through conventional analysis.

Conclusions:

• Algorithmic models can uncover previously unrecognized injury risk factors in cricket.
• Findings provide a basis for targeted injury prevention strategies and future confirmatory research in this population.