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Objective <p>This study aimed to examine the injury profile of a professional football team over five consecutive seasons and assess the predictive value of the Functional Movement Screen (FMS), offering insights to optimize injury prevention strategies in professional football.</p> Design <p>Injury data for 169 players between the 2016–2017 and 2020–2021 seasons were recorded, including the number of missed training sessions, injury severity, and injury types. Descriptive statistics were used to analyze these factors. The relationship between preseason FMS composite scores, asymmetry findings, and injury profiles was assessed using Variance Inflation Factor (VIF) and Logistic Regression Analysis.</p> Results <p>Over the five seasons, the injury incidence was 7.76 injuries per 1,000 training hours (95% CI: 7.59–7.93), 15.47 injuries per 1,000 match hours (95% CI: 15.23–15.71), and 8.9 injuries per 1,000 combined hours (95% CI: 8.72- 9.0). Injury data, including severity, type, and training or match absence, were meticulously recorded and analyzed. The study established an injury profile for players over five consecutive seasons but found that FMS was ineffective in predicting injuries, either within individual seasons or across the entire period. This suggests that the FMS may not be a reliable tool for forecasting injury risk in high-performance football.</p> Conclusion <p>The injury frequency was 8.9 per 1,000&#xa0;h of exposure, with 26% of injuries classified as severe, leading to over 28 missed training days per injury. Given that the FMS test battery alone did not reliably predict injury risk, we recommend its use in combination with other multifactorial screening methods to enhance the accuracy of injury risk assessment. Hamstring injuries were the most common, while goalkeepers primarily experienced back issues. Factors such as age, height, and body mass may influence injury risk. These findings underscore the need for multifaceted injury prevention programs that consider a wider range of risk factors beyond FMS scores, including age, height, and body mass, to effectively manage and reduce the risk of injuries in professional football. Additionally, these insights can assist technical staff in managing training absences and planning player availability more effectively.</p>

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Exploring injury profiles in professional football: evidence from a five-year study and the role of the functional movement screen

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月28日

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  • Ahmet Bayrak

摘要

Objective

This study aimed to examine the injury profile of a professional football team over five consecutive seasons and assess the predictive value of the Functional Movement Screen (FMS), offering insights to optimize injury prevention strategies in professional football.

Design

Injury data for 169 players between the 2016–2017 and 2020–2021 seasons were recorded, including the number of missed training sessions, injury severity, and injury types. Descriptive statistics were used to analyze these factors. The relationship between preseason FMS composite scores, asymmetry findings, and injury profiles was assessed using Variance Inflation Factor (VIF) and Logistic Regression Analysis.

Results

Over the five seasons, the injury incidence was 7.76 injuries per 1,000 training hours (95% CI: 7.59–7.93), 15.47 injuries per 1,000 match hours (95% CI: 15.23–15.71), and 8.9 injuries per 1,000 combined hours (95% CI: 8.72- 9.0). Injury data, including severity, type, and training or match absence, were meticulously recorded and analyzed. The study established an injury profile for players over five consecutive seasons but found that FMS was ineffective in predicting injuries, either within individual seasons or across the entire period. This suggests that the FMS may not be a reliable tool for forecasting injury risk in high-performance football.

Conclusion

The injury frequency was 8.9 per 1,000 h of exposure, with 26% of injuries classified as severe, leading to over 28 missed training days per injury. Given that the FMS test battery alone did not reliably predict injury risk, we recommend its use in combination with other multifactorial screening methods to enhance the accuracy of injury risk assessment. Hamstring injuries were the most common, while goalkeepers primarily experienced back issues. Factors such as age, height, and body mass may influence injury risk. These findings underscore the need for multifaceted injury prevention programs that consider a wider range of risk factors beyond FMS scores, including age, height, and body mass, to effectively manage and reduce the risk of injuries in professional football. Additionally, these insights can assist technical staff in managing training absences and planning player availability more effectively.