Machine Learning for Understanding and Predicting Injuries in Football

Area of Science:

• Sports Science
• Biomechanical Engineering
• Data Science

Background:

• Understanding the relationship between training and competition load and injury in football is essential for athlete adaptation, fatigue assessment, and injury prevention.
• Technological advancements have enabled the collection of extensive data for analysis and injury prediction in sports.

Purpose of the Study:

• To examine recent research on player load and injury relationships using machine learning.
• To describe the analyses, algorithms, key findings, and model fits in current studies.
• To identify limitations and future directions for systematic evaluation and unified conclusions.

Main Methods:

• Review of recent research employing machine learning for player load and injury analysis.
• Description of various statistical methods and algorithms used in data analysis.
• Comparison of model fits and key findings from different studies.

Main Results:

• Machine learning is increasingly used to analyze the complex player load and injury relationship.
• Current studies face limitations due to the vast array of variables, data imbalance, model fitting issues, and lack of multi-season data.
• A systematic evaluation of findings and unified conclusions are currently limited.

Conclusions:

• Machine learning holds significant potential for addressing the training load and injury paradox in sports.
• Addressing current limitations, such as data consistency and multi-season analysis, is crucial for future advancements.
• Enhanced and systematic analysis of athlete data through machine learning can provide future solutions.