Exploring Injury Profiles in Professional Football: Evidence from a Five-Year Study and the Role of the Functional Movement Screen

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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 hours of exposure, with 26% of injuries classified as severe, leading to over 28 missed training days per injury. FMS scores and asymmetry indicators did not reliably predict injuries. 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. Football Injury Functional movement screen Sports Injury profile Injury prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introductıon Sports injuries can diminish an individual's participation in sports activities and reduce their performance level, potentially ending a sports career [1]. Complex contact sports such as football, which demand high physical, technical, and tactical skills [2], significantly increase the risk of injury [3,4]. Some injuries in football are devastating for players and can jeopardize their sports careers. Moreover, injuries entail substantial financial costs. It is estimated that the average cost of a top-level professional team player sidelined for one month is approximately €500,000. Therefore, quantifying injury cases in professional football is critical [5,6]. In professional football, the injury incidence is reported to vary from 4.8 to 14.4 injuries per 1000 hours of football play, with injuries during matches ranging from 22.7 to 43.5 per 1000 hours and during training sessions from 2.8 to 11.2 per 1000 hours [7]. Because lower numbers of injuries are associated with team success, minimizing injuries and reducing lost playtime are crucial [4,8]. Determining the level of injury risk is of great importance in football, a sport with such a high injury rate. Sports injuries are associated with both intrinsic and extrinsic factors. In football, although it is difficult to control some extrinsic factors, such as contact injuries, specific predictable intrinsic factors that lead to non-contact injuries can be identified [9,10]. Coaches, sports physicians, and physiotherapists must implement injury prevention programs to support the health and continuous development of athlete [11]. To create injury prevention programs, it is necessary to accurately determine injury profiles specific to a sport. However, interventions related to the prevention of non-contact muscular injuries have been found to be inadequate [12]. There is a pressing need for further research to establish injury profiles in various sports. Identifying the most common and severe injuries, as well as understanding where (anatomically) and when (during matches or training sessions) they typically occur, will enable coaches, physiotherapists, and physicians to prioritize specific measures to prevent or reduce the risk of such injuries [6]. Despite the complexity and multifactorial nature of injury mechanisms, there are various intrinsic risk factors that may potentially increase the risk of injury (e.g., previous injury history, joint instability, muscle strength imbalances, and anatomical asymmetries, etc.) [13,14]. Musculoskeletal screening tests are designed to identify these risk factors so that medical professionals can implement appropriate training strategies (prehabilitation) aimed at reducing injury cases. One of the most well-known injury prediction tools, the Functional Movement Screen (FMS), was developed by Cook et al. in 1997[15]. The FMS is a battery of seven tests that evaluate fundamental movement patterns to identify dysfunctional, asymmetric, and painful movements that may contribute to future injuries [16,17,18]. During the FMS test, the presence or absence of pain and the number of asymmetries observed can be determined, with a composite score of ≤14 indicating an increased risk of injury [19]. In the literature, FMS tests are conducted at the beginning of the season and used as a method for injury risk analysis [20,21]. However, discussions continue regarding the accuracy of the FMS in predicting injuries. While some studies support a relationship between FMS scores and injury [22-25], others suggest that it cannot be used as a method of injury risk analysis [26- 29]. There is a gap in the literature regarding such a longitudinal study, as the debate on the injury predictive value of the Functional Movement Screen (FMS) continues, while existing studies lack comprehensive data on injury type, location, and severity. Thus, this study aims to address these gaps and better evaluate whether FMS can predict injuries by providing a more detailed and long-term analysis of these factors in professional football. Despite extensive research into sports injuries, there remains a lack of comprehensive, long-term studies that examine both injury profiles and the predictive efficacy of FMS across multiple seasons in professional football.Additionally, studies are needed to shed light on topics such as changes in FMS scores along with the evolving sports season and the impact of age. This study aimed to reveal the injury profiles of football players over a 5-year period by examining injury types, severities, and the number of training sessions missed, and to lay the groundwork for future injury prevention studies. Furthermore, injury risk predictions will be generated using pre-season FMS tests, and the predictive value of the FMS in assessing injury risk will be evaluated through long-term injury monitoring. Today, critical perspectives on the FMS test have expanded, and it is believed that this study will contribute to the literature on this subject. This research aims to achieve the following objectives: -To examine injury profiles in a professional football team over five seasons, analyzing factors such as injury incidence, severity, missed training days, and injury location. -To evaluate the long-term predictive value of the Functional Movement Screen (FMS) and determine whether FMS is a reliable tool for injury prediction in professional football players. -To address contextual factors influencing injury risk (match schedule, UEFA competition participation, training load variations) and contribute to injury management strategies. -To fill existing gaps in the literature and provide new findings for the development of long-term injury profiling and prediction methods. Method Participants Our study is based on data collected from professional football players from a football team in the Turkish Super League during the 2016–2017, 2017–2018, 2018–2019, 2019–2020, and 2020–2021 seasons. This was a prospective cohort study in which the players' injury status and functional movement analysis results were tracked to assess injury prediction. Injury risks were determined using pre-season FMS tests, and the accuracy of the injury analyses was evaluated at the end of each season. Players who remained with the team for the full season were included in the study, whereas those who left the team mid-season were excluded. A total of 169 players were included in the study, with 33 players in the 2016–2017 season, 33 players in the 2017–2018 season, 31 players in the 2018–2019 season, 35 players in the 2019–2020 season, and 37 players in the 2020–2021 season. However, players who did not complete an entire season were excluded: 2 players in the 2016–2017 season, 2 players in the 2017–2018 season, 10 players in the 2018–2019 season, 8 players in the 2019–2020 season, and 6 players in the 2020–2021 season (In this study, the excluded football players participated in the FMS test at the beginning of the season but were transferred to other teams shortly thereafter. They were not included in the study due to their exposure to different training loads in their new teams and, more importantly, the inability to track their injury status) (Fig. 1 ). In order to ensure standardization in study outcomes and the integrity of the collected data, football players who did not remain with the club for a full season and those who joined during the mid-season were not included in the study. Consent forms were obtained from all football players for the tests to be conducted at the beginning of the season and for the recording of injury data. Injury Identification Throughout the season, all injuries sustained by the players were recorded. These injuries were diagnosed by the club's sports physicians and/or relevant medical doctors from the associated hospitals. Only injuries that prevented players from participating in training/match for over 72 hours (injuries resulting in 3 days or less of missed training/match were not considered) were recorded [ 30 ]. Injuries were categorized as minimal (1–3 days of absence), minor (4–7 days of absence), moderate (8–28 days of absence), or severe/major (> 28 days of absence) [ 31 ]. Diagnoses made by the sports physician or a relevant specialist on the medical team were further documented by a sports physiotherapist, who was also part of the health team. Each injury was recorded immediately after the incident using a standard injury form, which included the type of injury, injury location, duration of absence from training due to the injury, and timing of the injury. Injury data in the sports club were recorded daily using a standardized form, and an Excel database was created. These records were archived at the end of each season. In this study, the definition of injury severity was based on the recommendations of the International Consensus Group on Injury, defined as the number of days from the date of injury to the player’s full return to team training and readiness for match selection [ 32 ]. Injury Follow-Up Period The risk and type of injury may vary throughout the football season [ 33 ]; therefore, the study period should encompass the entire season or multiple seasons, including both the pre-season and competitive season [ 31 , 34 ]. In this study, players were followed over five full seasons (2016–2021). Of the 169 players observed during the 2016–2017, 2017–2018, 2018–2019, 2019–2020, and 2020–2021 seasons, 133 players missed training due to injuries. Calculation of Injury Incidence The incidence of injuries per 1,000 training and match hours were calculated using the following formula: 1000 Incidence = (Total hours of exposure to training and matches Number of injuries​) ×1000 [ 35 ]. Injury Risk Assessment / Functional Movement Screen FMS tests were administered at the start of the season following the players' holiday period. During the FMS testing, each player was recorded from two different angles. All assessments were conducted at the facilities of the respective sports clubs. Before testing, the players completed a standardized warm-up led by the club’s fitness trainer. The FMS consists of seven subtests: deep squat, hurdle step, in-line lunge, shoulder mobility, trunk stability push-up, active straight leg raise, and rotary stability [ 36 ]. Five of the subtests (hurdle step, in-line lunge, shoulder mobility, active straight leg raise, and rotary stability) were performed on both sides of the body, and any asymmetries observed between sides were recorded as asymmetric findings. To calculate the FMS composite score, each of the seven tests was scored on a scale of 0 to 3. A score of 0 indicates the presence of pain during movement; a score of 1 is given if the individual is unable to perform the movement; a score of 2 is assigned if the individual completes the movement but with some form of compensation; and a score of 3 is awarded if the movement is performed correctly [ 36 ]. For tests completed on both sides of the body, the lowest score was recorded as the final score. Scores from all seven subtests were combined to yield a composite score of 21. FMS was conducted using standard FMS test kits (Functional Movement Systems Inc., Virginia, USA). FMS tests were administered by a certified physiotherapist who had received FMS training. Prior to the tests, the club informed the players about the testing process. To eliminate the effects of fatigue, players did not participate in training the day before the tests, which were conducted during the pre-season. The players’ instances of missed training and the reasons for their absence were documented. Instances of illness or contact-related injuries, which could cause missed training, were recorded but excluded from the FMS comparison. Only non-contact injuries were considered in the comparison with the FMS evaluations. Statistical Analysis Data are presented as mean and standard deviation. Skewness-Kurtosis and Shapiro-Wilk tests were used to assess whether the groups showed normal distribution. Independent Samples T-test was used to analyze the differences between demographic data and injury, while Mann-Whitney U test was used for FMS and asymmetry. while logistic regression was applied to evaluate the FMS’s predictive validity regarding injury. Statistical analyses were performed using IBM SPSS Statistics 25 (IBM Corporation), Jamovi (The Jamovi project, 2024. Jamovi (Version 2.5) Computer Software. Retrieved from https://www.jamovi.org ). The relationship between FMS scores, asymmetry, injury occurrence, and missed training days was analyzed using the Mann-Whitney U test. A significance level of 0.05 was set for all analyses. Results The demographic characteristics of the football players included in this study are presented in Table 2 . The age, body mass, height and body mass index of players who sustained injuries were similar to those of players who did not (p > 0.05). Table 1 Demographic information, fms score and asymmetry data normality test Skewness Kurtosis Shapiro-Wilk (p değeri) Age (years) -,0,268 0,150 0,142 Weight (kg) -0,40 0,547 0,311 Height (mt) -0,749 0,608 0,278 Body Mass İndex 0,57 -0,426 0,287 Fms Score -4,186 31,746 0,000 Asymmetry 2,290 6,862 0,000 It was determined that the Skewness-Kurtosis (+ 1.50 and − 1.50) was between and Shapiro-Wilk (p > 0.05) test showed that the players' age, weight, height and body mass index showed normal distribution, but the players' fms score and asymmetry values ​​did not show normal distribution. (p < 0.005) (Table 1 ). Table 2 Demographic characteristics Injury Status Injuried (n = 137) Non-Injuried (n = 32) t value p value Age (years) 28 ± 4,05 26 ± 4,48 2,852 0,005 Weight (kg) 79,3 ± 3,97 76,6 ± 4,94 1,157 0,042 Height (mt) 1,83 ± 0,55 1,81 ± 0,57 1,688 0,249 Body Mass İndex 23,40 ± 1,24 23,23 ± 1,38 0,597 0,553 Training Status Training (n = 133) Non-Training (n = 36) t value p value Age (years) 28 ± 3,93 25,2 ± 4,57 -3,263 0,002 Weight (kg) 78,2 ± 3,98 76,7 ± 4,80 -1,863 0,064 Height (mt) 1,83 ± 0,55 1,81 ± 0,56 -1,503 0,135 Body Mass İndex 23,40 ± 1,24 23,33 ± 1,37 -0,182 0,856 Independent t-test was performed for normally distributed demographic data. In the injury cases, comparisons between injured and uninjured players based on preseason FMS scores revealed the following: No significant differences were found for height (p = 0.249), body mass (p = 0.042), body mass index (p = 0.553). However, a significant difference was found in age (p = 0.005), injured players differed in age from uninjured players (Table 2 ). In terms of missed training days, comparisons between players who missed and did not miss training based on preseason FMS scores revealed the following: No significant differences were found for height (p = 0.135), body mass (p = 0.064), body mass index (p = 0.856), However, a significant difference was found in age (p = 0.002), players who missed training differed in age from players who did not miss training (Table 2 ). Table 3 Injury status analysis of fms score and asymmetry data Injury Status Mean Std.Deviation U value P value Z value R value Fms Score 15,83 2,225 2118,5 0,761 -0,304 -0,025 Asymmetry 0,43 0,615 2012,5 0,361 -0,848 -0,072 Training Status Mean Std.Deviation U value P value Z value R value Fms Score 15,83 2,225 2341,0 0,834 -0,210 -0,016 Asymmetry 0,43 0,615 2167,5 0,306 -1,024 -0,078 Mann-Whitney U test was performed for fms scores and asymmetry data that did not show normal distribution. In injury cases, it was found that Fms scores had no significant effect on injury status. (U = 2118.5, z=-0.304, p = 0.761, R=-0.025). It was also found that asymmetry had no significant effect on injury cases. (U = 2012.5, z=-0.848, p = 0.361, R=-0.072). Since FMS and Asymmetry P values ​​were greater than 0.05, it was determined that fms score and asymmetry had no effect on injury. It was found that Fms scores had no significant effect on missed training days. (U = 2341.0, z=-0.210, p = 0.834, R=-0.016). It was also found that asymmetry had no significant effect on missed training days. (U = 2167.5, z=-1.024, p = 0.306, R=-0.078). When injury status and missed training days were analyzed together, FMS score, asymmetry, body mass, body mass index, and height did not differ significantly between injured and uninjured players. However, there was a significant age difference between injured and uninjured athletes in both injury and missed training days scenarios (p < 0.05) (Table 3 ). Analysis of five years of data indicated that FMS scores and asymmetry findings did not significantly differ between injured and non-injured players, nor in the number of missed training days (p > 0.05). Across the seasons from to 2016–2017 to 2020–2021, 137 of the 169 players experienced injuries (Table 3 ). The highest injury count coincided with a period of fewer matches. Specifically, during the 2018–2019 season, the highest number of injuries occurred, despite fewer matches being played compared to other seasons. Similarly, in the 2019–2020 season, there was a notably low number of matches, yet injury counts remained high relative to other seasons. This suggests a possible disconnect between match frequency and injury incidence, highlighting that factors other than the number of matches may contribute significantly to injury risk (Fig. 2 ). Over the five seasons, players missed a total of 4,573 training sessions due to injuries and illnesses (training losses lasting less than 72 h were excluded). The breakdown of missed training sessions is as follows: 116 days missed due to illness, 550 days missed due to contact injuries, and 3,907 days missed due to non-contact injuries. As seen in the table, approximately 85% of the total missed training sessions were due to non-contact injuries. Additionally, it is noteworthy that missed training sessions due to illness became apparent only after the 2018–2019 season, indicating a possible change in health-related absences in the latter seasons (Fig. 3 ). Over the five seasons, the players experienced 224 injuries, with the most frequent injuries occurring in the thigh region, particularly affecting the hamstring muscles. The most common injury type recorded was hamstring muscle injury, accounting for 26% of all injuries. Knee issues: predominantly meniscal injuries, followed by ACL and chondromalacia. Adductor muscle and ankle injuries. Less common injuries included abdominal muscle tears in two players, tennis leg in one player, and shin splints in another, representing the least frequent injuries observed (Fig. 4 ). The distribution of injuries by anatomical region and their respective percentages across the five seasons were as follows: Spinal region: 8.48%, Abdomen: 0.89%, Pelvis: 2.68%, Osteitis pubis: 1.34%, Anterior thigh (Quadriceps): 8.93%, Posterior thigh (Hamstring): 26.34%, Groin: 13.39%, Lateral thigh (IT band): 0.89%, Knee: 14.29%, Tibia (shin splint): 0.89%, Calf: 9.82%, Achilles tendon: 0.89%, Foot and ankle: 2.23%, Upper extremity: 8.93%, Injuries causing more than 28 days of missed training were most common in the hamstring region, with nearly half of all knee injuries categorized as severe Overall, 88.56% of missed training due to injury originated from lower extremity injuries, underscoring the significant impact of lower body injuries on training availability. In addition, hamstring and knee injuries were more frequently associated with severe injuries, leading to more than 28 days of missed training. This indicates a higher propensity for prolonged recovery periods in these areas, highlighting them as critical zones for severe injury risk and extended time away from training (Fig. 4 ). In addition, the injury rates per 1,000 hours of total match and training exposure were recorded across different injury types. The highest incidence was observed in hamstring injuries, with 2.36 injuries per 1,000 hours (95% CI: 1.76–2.96). Knee injuries followed with 1.28 injuries per 1,000 hours (95% CI: 0.84–1.72), while groin injuries were reported at 1.20 injuries per 1,000 hours (95% CI: 0.77–1.63). The calf injury rate was 0.88 injuries per 1,000 hours (95% CI: 0.51–1.25). Additionally, both ankle and quadriceps injuries were recorded at 0.80 injuries per 1,000 hours (95% CI: 0.45–1.15). Of 229 injuries, 104 lasted less than 8 days, representing approximately 45.41% of all injuries and classified as minor injuries. Injuries lasting between 8 and 28 days: 61 occurrences, representing approximately 26.64% of all injuries, were classified as moderate injuries. Injuries lasting more than 28 days: 61 occurrences, making up approximately 26.64% of all injuries, classified as severe injuries. The 2018–2019 season saw an increase in minor injuries. Meanwhile, players experienced the highest incidence of severe injuries (those causing more than 28 days of missed training) during the 2020–2021 season. Minor injuries peaked in the 2018–2019 season, while the 2017–2018 season displayed a more balanced or homogeneous distribution across minor, moderate, and severe injuries. This trend underscores the variations in injury severity across seasons, with certain seasons showing distinct peaks in either minor or severe injury occurrences (Fig. 5 ). Overall, over the five seasons, players experienced: 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), 8.9 injuries per 1,000 combined training and match hours (95% CI: 8.72- 9.0). The highest injury rate per 1,000 hours occurred in the 2018–2019 season, with: 23.6 injuries per 1,000 match hours and 10.8 injuries per 1,000 training hours. Conversely, the lowest injury rate was recorded in the 2017–2018 season, with: 9.6 injuries per 1,000 match hours and 5.5 injuries per 1,000 training hours. This distribution highlights the variability in injury rates by season, particularly with increased risk during competitive matches compared to training (Fig. 6 ). Table 5 Distribution of injury types by player position Injury Type Goalkeeper (n = 17) Defenders (n = 51) Midfielders (n = 67) Forwards (n = 34) Columna Vertebralis 9 (31.03%) 3 (3.95%) 4 (4.26%) 3 (7.50%) Abdomen 0 (0.00%) 1 (1.32%) 1 (1.06%) 0 (0.00%) Pelvis 1 (3.45%) 2 (2.63%) 2 (2.13%) 1 (2.50%) Ost. Pupis 0 (0.00%) 1 (1.32%) 0 (0.00%) 2 (5.00%) Tight (Quadriceps) 1 (3.45%) 5 (6.58%) 10 (10.64%) 4 (10.00%) Tight (Hamstring) 5 (17.24%) 20 (26.32%) 24 (25.53%) 10 (25.00%) Groin 0 (0.00%) 10 (13.16%) 16 (17.02%) 4 (10.00%) Lateral Tight 0 (0.00%) 1 (1.32%) 1 (1.06%) 0 (0.00%) Knee 5 (17.24%) 13 (17.11%) 11 (11.70%) 6 (15.00%) Tibia 0 (0.00%) 0 (0.00%) 2 (2.13%) 0 (0.00%) Calf Muscle 2 (6.90%) 9 (11.84%) 8 (8.51%) 3 (7.50%) Achilles 2 (6.90%) 2 (2.63%) 1 (1.06%) 0 (0.00%) Foot-Ankle 0 (0.00%) 6 (7.89%) 8 (8.51%) 6 (15.00%) Upper Extremity 1 (3.45%) 0 (0.00%) 0 (0.00%) 1 (2.50%) Trauma 1 (3.45%) 3 (3.95%) 0 (0.00%) 0 (0.00%) Total 27 (100%) 76 (100%) 88(100%) 40 (100%) Among goalkeepers, 31.03% of all injuries were vertebral issues, indicating a high prevalence of spinal problems in this position. For players in non-goalkeeper positions (defenders, midfielders, and forwards ) , hamstring injuries accounted for approximately one-quarter (25%) of all injuries, highlighting a common vulnerability in the hamstring area for these roles. This distribution underscores the position-specific injury patterns, with goalkeepers being more prone to back-related injuries, whereas outfield players are more susceptible to hamstring strains (Table 5 ). The effect of players' positions on injury status was found to be p = 0.441, while their effect on FMS scores was p = 0.382. Since these values do not meet the p < 0.05 significance threshold, it was determined that playing position does not have a significant impact on injury status or FMS scores. Table 6 Variance inflation factor (VIF) VIF Tolerance FMS Score 1.00 0.997 Assymetry 1.00 0.997 Injury Status Age 1.20 0.835 Heigh 1.46 0.686 Weight BMI 1.68 1.38 0.597 0.723 FMS Score 1.02 0.983 Asymmetry 1.02 0.983 Missing Training Status Age 1.18 0.848 Height 1.44 0.692 Weight BMI 1.64 1.32 0.609 0.738 In the logistic regression analysis of injury status and training availability, the Variance Inflation Factor (VIF) values for the variables FMS score, asymmetry, age, height, and weight were all VIF < 10. This indicates that these variables do not exhibit multicollinearity, meaning that there is no significant linear relationship among them that would affect the reliability of the model. This lack of multicollinearity supports the robustness of the variables in the logistic regression model (Table 6 ). Table 7 The logistic regression analysis of injury status and missing training status Estimate SE Z P Intercept 0.6120 1.2567 0.487 0.626 Fms Score 0.0479 0.0781 0.614 0.539 Asymmetry 0.2112 0.3401 0.621 0.534 Injury Status Intercept -5.5252 6.4231 -0.860 0.390 Age 0.1143 0.0525 2.177 0.029 Height 0.6551 4.2604 0.154 0.878 Weight BMI 0.0351 0.4251 0.0584 3.1020 0.600 0.137 0.549 0.891 Intercept 2.2901 1.687 1.358 0.175 Fms Skor -0.0707 0.103 -0.685 0.494 Asymmetry 0.2711 0.333 0.814 0.415 Missing training status Intercept -6.3495 6.1603 -1.031 0.303 Age 0.1432 0.0510 2.810 0.005 Height 1.3930 4.0670 0.343 0.732 Weight BMI 0.0160 0.430 0.0555 2.9844 0.288 0.144 0.773 0.886 In the Logistic Regression Analysis examining injury status, FMS score and asymmetry were not found to be significant predictors of injury: FMS score (p = 0.539) and asymmetry (p = 0.534) were identified. The model showed an R² of 0.004 and an overall cut-off p-value of 0.702, indicating that there was no significant relationship between these variables and injury status, as the p-values ​​for both predictors were above the significance threshold of 0.05. In the Logistic Regression Analysis of demographic data; Age (p = 0.029) (p < 0.05). This suggests that age has a significant effect on the probability of injury and that older players are probably at a higher risk. Weight, height and body mass index were not significant predictors: Weight (p = 0.548), Height (p = 0.877), Body mass index (p = 0.89). Model R² = 0.051 for age, weight and height and an overall cut-off p-value of 0.037 were detected. Age was a statistically significant predictor of injury status (Table 7 ). In the Logistic Regression Analysis examining training missed status, FMS score and asymmetry were not found to be significant predictors of injury: FMS score (p = 0.494) and asymmetry (p = 0.415) were identified. The model showed an R² of 0.007 and an overall cut-off p-value of 0.494, indicating that there was no significant association between these variables and training missed status, as the p-values ​​for both predictors were above the significance threshold of 0.05. Demographic data included Age (p = 0.005), Weight (p = 0.773), Height (p = 0.732), Body mass index (p = 0.886). For age, weight, height and body mass index, the model showed R² = 0.067 and an overall cut-off p-value of 0.017. (Table 7 ). Discussion This study aimed to establish an injury profile for players in a professional football club over five seasons and to determine whether injuries sustained by players across consecutive seasons could be predicted using the Functional Movement Screen (FMS). Over the five seasons, it was found that FMS was not effective in predicting injuries, either within individual seasons or across the entire observation period. This suggests that the FMS may not serve as a reliable tool for forecasting injury risk in a high-performance football environment. In our study, while FMS and asymmetry scores were not found to be effective in determining injury risk, an increase in age had a slight impact on injury occurrence. This suggests that age explains only a small portion of injury risk, and other unmeasured factors may also play a significant role. Additionally, players experienced variations in the number, type, and severity of injuries across seasons, highlighting the dynamic nature of injury patterns among professional football players. Injury types also vary by player position, with certain injuries appearing to be position-specific. Long-term monitoring of a football team provided unique insights into how variables such as match schedules, UEFA participation, national cup victories, league standings, fluctuating match counts, and the COVID-19 pandemic influenced in-season injury rates. These observations underscore the complexity of injury risk factors in football, suggesting that factors beyond physical screening, including external and contextual elements, play a significant role in the occurrence of injury. Injury Incidence The incidence of injuries per 1,000 hours of training and matches was calculated as 8.49 injuries, with 7.76 injuries per 1,000 training hours and 15.47 injuries per 1,000 match hours. The highest injury incidence was observed during the 2018–2019 season, with 12.6 injuries per 1,000 combined hours of training and matches, 10.8 injuries per 1,000 training hours, and 23.6 injuries per 1,000 match hours. This increase in injury rates was likely influenced by disruptions due to the onset of the COVID-19 pandemic, which led to interruptions in training, subsequent reconditioning periods, restrictions on heavy training, and reduced training times, all of which may have contributed to the higher injury incidence. In the 2018–2019 season, there was a sudden interruption in the league, preventing athletes from training when they were originally scheduled to compete. After this prolonged break, training sessions resumed, which may have contributed to an increase in minor injuries during that season. In the literature, general injury incidence for professional male football players is reported as 8.1 injuries per 1,000 hours of exposure. Studies examining various leagues worldwide report injury rates ranging from 12 to 35.5 injuries per 1,000 match hours and 1.5 to 7.6 injuries per 1,000 training hours [ 37 ]. Our findings align with these global averages, suggesting that the observed injury rates are consistent with the broader trends reported in the literature. Injury Distribution by Position The study also examined the distribution of injuries by playing position over the five seasons. Goalkeepers are most frequently affected by lumbar spine injuries, with a high incidence of lumbar disc herniation. Spinal injuries are common in elite football due to repetitive high-impact and rotational movements, such as headers, kicks, hyperextension, hyperflexion, and rotational actions, which place players at risk of both overuse and acute spinal injuries [ 38 ]. Research indicates that goalkeepers experience fewer injuries overall than players in other positions [ 39 ], highlighting the importance of targeted injury prevention strategies for goalkeepers to address their unique movement demands. When examining injuries by position, midfielders and forwards were found to sustain the most muscle injuries, likely because of their high physical demands and frequent involvement in dynamic play. Conversely, goalkeepers have the lowest injury rates, which may be attributed to their reduced physical contact and lower physical demands during play [ 39 , 40 , 41 ]. This positional insight is valuable for designing injury prevention programs, especially for goalkeepers who may benefit from specific spine-strengthening exercises and load management throughout the season. Injury Location The analysis of injury locations and types can guide the development of targeted prevention programs and resource allocation [ 42 ]. In this study, 88% of injuries were identified as lower extremity injuries, which aligns with previous studies reporting lower extremity injury rates between 81% and 92% among football players[ 30 , 43 ]. This high rate of lower extremity injuries is consistent with findings in the literature. Over the five seasons, players sustained 224 injuries, with the thigh region being the most frequently injured area. The hamstring muscles were the most commonly affected anatomical structures, followed by knee issues (primarily meniscal injuries, ACL injuries, and chondromalacia), adductor muscle strains, and ankle injuries. Rare injuries included abdominal muscle tears in two players, tennis leg in one player, and shin splints in another, with two players experiencing rectus abdominis tears in the 2019–2020 and 2020–2021 seasons. Muscle injury analysis for a typical professional football team over a season often identifies the quadriceps, hamstrings, and adductors as the most affected regions [ 43 ]. In line with this, our study found that 26% of all injuries occurred in the hamstring muscle group, with injury rates of 8.93% in the quadriceps and 13.39% in the adductor region. Hamstring injuries are known to be common in football [ 4 , 44 ], with a four-season study of a Spanish football team reporting the hamstring as the most frequently injured area (36%) [45) In addition, the injury rates per 1,000 hours of total match and training exposure were recorded across different injury types. The highest incidence was observed in hamstring injuries, with 2.36 injuries per 1,000 hours (95% CI: 1.76–2.96). In a study conducted on La Liga football players, the incidence rate of hamstring injuries was reported as 3.34 injuries per 1,000 hours [ 46 ]. Similarly, in a study involving 143 professional football players in Kosovo, the total hamstring injury incidence was found to be 1.17 injuries per 1,000 hours (95% CI: 0.84–1.57) 47]. Although the Kosovo study reported a lower hamstring injury rate, most of these injuries occurred during matches, which the authors attributed to the lack of adequate football infrastructure. In contrast, in La Liga, hamstring injuries were associated with high-speed actions, highlighting the impact of different playing styles and league dynamics on injury rates. These findings emphasize the variability in hamstring injury incidence across national leagues, demonstrating how different football systems and playing intensities can influence injury trends. Similarly, the findings from our study contribute valuable data by illustrating hamstring injury incidence in a different professional football league, further supporting the need for league-specific injury prevention strategies. Contact vs. Non-Contact Injury Status Over the five seasons, players missed a total of 4,573 training days due to injuries and illnesses, with 3,907 days (85%) resulting from non-contact injuries. This high proportion aligns with findings from previous studies, such as [ 48 ], where the non-contact injury rate among young football players was 66%. Another study observed that all injuries sustained by injured players were non-contact, affecting 71 of 122 footballers [ 49 ]. In general, noncontact injuries are more prevalent in football [ 50 ], which is consistent with our study findings. In our study, 61 of the 121 non-contact injuries (50.4%) occurred during matches, while 60 (49.6%) occurred during training [ 49 ]. Lower extremity injuries, which are common in football, accounted for the majority of these missed days, aligning with the literature on football injury patterns. This trend may be partly because upper extremity injuries are less likely to prevent training. However, the high rate of lower extremity injuries emphasizes the significant impact of these injuries on training availability in football, highlighting the need for targeted prevention efforts focused on lower body resilience and injury prevention. FMS and Injury Prediction The effectiveness of the Functional Movement Screen (FMS) in predicting injuries has yielded mixed results [ 26 ]. Many studies have attempted to control for the effect of a previous injury history, as it is widely recognized as a strong risk factor for future injuries [ 22 ]. A meta-analysis of 24 studies concluded that FMS scores are insufficient as a standalone test battery for injury prediction [ 26 ]. For instance, a study of 84 youth academy football players in the English Premier League assessed players using the FMS pre-season, tracking non-contact injuries throughout the season, and found no significant predictive capability for non-contact injuries [ 51 ]. Similarly, a study of 573 Australian football players determined that FMS scores were ineffective in predicting non-contact injuries [ 48 ]. A separate study involving 439 young Australian football players also concluded that FMS scores were not adequate for predicting noncontact injuries [ 52 ]. In a study of 124 high school football players, FMS was again deemed insufficient as a sole predictor of injuries [ 53 ]. Furthermore, [ 29 ] found that even when the FMS composite cutoff score was lowered to 14, 13, or 12, it failed to reliably predict injuries. A machine learning study tracking FMS scores strongly indicated that the FMS is ineffective at predicting injuries in average adolescent populations. Additionally, FMS scores could not differentiate between injured and non-injured subjects in both athletic and non-athletic youths [ 54 ]. In Japanese collegiate football, a study following 75 players for one season also concluded that the FMS lacks sufficient sensitivity and specificity to predict injuries [ 55 ]. These findings suggest that while FMS may provide valuable information on movement quality, it is not a reliable tool for injury prediction, especially for non-contact injuries in football. Our results are consistent with previous studies indicating that FMS is not a reliable predictor of injury risk, aligning with existing research that questions its effectiveness in forecasting injuries. On the other hand, several studies in the literature support the effectiveness of FMS in predicting injuries. For example, a study involving 65 young football players aged 12–13 found that the FMS test battery was effective in predicting injury risk in youth athletes [ 56 ]. This study differs from ours due to its focus on young athletes and the inclusion of players with a prior injury history. Similarly, FMS has been shown to be an effective predictor of injuries in female combat sports athletes [ 57 ]. A study of 131 young volleyball players also found that FMS had the necessary sensitivity to predict injury risk [ 58 ]. The relationship between the asymmetries identified in the FMS tests and injury prediction is also debated. For instance, a study of 237 elite young Australian football players found that while FMS scores alone could not reliably predict injuries, players with two or more asymmetrical findings had a lower-level predictive capacity for injuries [ 28 ]. [ 59 ] conducted a systematic review and concluded that there is moderate-to-low evidence that asymmetry is a risk factor for sports injuries. Other studies have found more robust links between FMS scores and injury risk. For example, research involving 527 male athletes indicated that the FMS is an effective tool for assessing injury susceptibility [ 60 ]. Similarly, in a study of 68 rugby players over a season, FMS was found to predict non-contact muscle injuries [ 61 ]. These studies suggest that the FMS may have predictive utility in certain athlete populations and sports contexts, although its effectiveness appears to vary based on factors such as age, sport type, and the presence of prior injuries. Given the conflicting results regarding FMS, it appears that outcomes vary across different sports, potentially due to differences in injury profiles and biomechanical stresses unique to each sport. For instance, in football, players face varied injury risks and biomechanical demands based on their playing positions [ 62 , 63 ]. This variability calls into question the utility of universal injury prediction tools in all sports. These studies in the literature support the notion that FMS can predict injury risk. However, the differences observed in our study may stem from the fact that studies supporting FMS as a predictive tool have been conducted on younger athletes and in different sports disciplines. This suggests that football, as a distinct sport discipline, may require different injury risk assessment tools tailored to its unique demands and injury mechanisms. Research has shown that FMS results differ significantly across sports [ 64 , 65 ]. Football, characterized by frequent sprinting, jumping, and change-of-direction activities [ 66 , 67 ], may not ideally be assessed through a static test such as the FMS. Our study’s findings, which suggest that FMS composite scores and asymmetry findings do not predict injuries in football, may reflect the dynamic and high-intensity nature of the sport, where injury risk is influenced by multiple factors beyond basic functional movements, such as tactical contact situations, player fatigue, and playing surface conditions.Alternative assessments, such as the Lower Extremity Functional Test (LEFT), which evaluates athletic conditioning, fatigue resistance, and speed through a series of dynamic maneuvers (e.g., forward and backward sprints, lateral shuffling, and 45° and 90° direction changes), have shown predictive accuracy for football-related injuries [ 68 , 69 ]. Additionally, factors such as foot posture have been shown to elevate injury risk, and FMS does not account for such biomechanical elements [ 70 , 71 ]. This limitation of the FMS might contribute to its reduced predictive power for lower-extremity injuries in football, a sport with high lower-limb injury rates. A similar pattern was observed in dance, where FMS was found to be inadequate for injury prediction. In that study, the FMS was noted for assessing proper movement but was not sensitive to the movement patterns critical in dance [ 72 ]. This insight may be extended to football, a sport that also involves diverse movement patterns, suggesting that sport-specific assessments could better capture injury risk factors in dynamic environments. In addition to mapping the injury profile in football, this study used FMS assessments at the start of each season to predict injuries, with players tracked over time to test the consistency of these predictions. Unlike many studies focusing on the retrospective identification of past injuries via FMS, this study’s prospective approach strengthens its findings. Another strength of this study was the detailed injury documentation provided by the club’s medical team. However, the limitations include focusing on a single team and the lack of female participants. Despite the lack of significant predictive value of FMS scores, this finding aligns with recent literature questioning the utility of FMS in professional football settings. Future research could explore alternative screening tools tailored to football's dynamic demands. While our study adds to the growing body of evidence questioning the utility of FMS as a standalone tool for injury prediction in football, it does not diminish the value of FMS as part of a comprehensive athlete assessment toolkit. It underscores the necessity for multifactorial assessment approaches that incorporate dynamic and sport-specific movement analyses. Limitations While our study benefits from standardized training loads and medical staff within a single team, this approach may limit the generalizability of our findings. Future studies should consider including multiple teams from different leagues to explore the potential variability in injury profiles and FMS predictive value across different playing styles and training regimens. The exclusive use of FMS in our study limits the breadth of our injury risk assessment. The inclusion of additional functional tests, such as the Y Balance Test and the Lower Extremity Functional Test, could offer a more holistic view of an athlete's injury risk factors. Future research should consider incorporating a multi-test approach to injury prediction to potentially uncover additional or more nuanced risk factors not captured by FMS alone. The study's focus on a single team limits the generalizability of the findings. Future studies should consider a multi-team approach to account for variations in training, play style, and medical support across different clubs. Our study focused solely on male professional football players, which limits the applicability of our findings to female athletes. Given the physiological and biomechanical differences between genders, further research is needed to explore injury profiles and the predictive value of FMS in women's football. Conclusion The results indicate an injury frequency of 8.9 injuries per 1,000 hours of exposure in a professional men’s football team, with approximately 26% of injuries classified as major/severe, leading to over 28 days of absence from training or matches. The significant number of severe injuries leading to over 28 days of missed training highlights the need for enhanced rehabilitation and prevention strategies to mitigate long-term absences. Both injury frequency and severity were high in this cohort. Our study aimed to explore injury profiles over five seasons and assess the FMS's predictive value in a professional football setting. Despite a thorough investigation, FMS scores did not predict injuries, suggesting the need for a multifaceted approach to injury risk assessment beyond the scope of FMS alone.Throughout the study, hamstring injuries were the most frequent, whereas goalkeepers primarily experienced back problems. Additionally, our injury profile highlights the need for targeted prevention efforts focused on the most commonly injured areas, such as the hamstring. Furthermore, in goalkeepers, the findings suggest that injury prevention programs should be developed specifically for the lower back region to mitigate injury risk. Evidence also suggests that parameters such as age, height, and body mass may influence the risk of injury. Findings on injury types, time lost from training due to injury, and injury-specific profiles could inform the development of targeted injury prevention programs, team preseason assessments, and strategic roster planning. Additionally, these insights could help technical staff anticipate training absences and injury patterns based on position, thereby enhancing player management and seasonal planning. Abbreviations FMS Functional Movement Screeen VIF Variance Inflation Factor Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Ethics Committee of Selçuk University, Faculty of Sports Sciences (decision dated 28.11.2024 and numbered 885482). Permission to use the study for scientific publication was also granted by the relevant club. Informed consent forms were obtained from all players who participated in the study. This study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials Data supporting the findings of this study are provided in the manuscript and supplementary information files. Competing interests The authors declare that they have no conflicts of interest. Funding No funding was received for this study. Authors' contributions All stages of this study, including conceptualization, methodology, data collection, analysis, and manuscript preparation, were performed solely by AB. Acknowledgements The authors would like to express their heartfelt gratitude to the Konyaspor Football Club for their invaluable support throughout the 5-year study period. The cooperation and commitment of the club made this research possible, and we sincerely appreciate their assistance and collaboration. References Ekstrand J, Roos H, Tropp H. Normal course of events amongst Swedish soccer players: an 8-year follow-up study. 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Analysis of football injuries by position group in Division I college football: a 5-year program review. Clinical Journal of Sport Medicine. 2020;30:216-223. https://doi.org/10.1097/jsm.0000000000000574 Hall EC, Larruskain J, Gil SM, Lekue JA, Baumert P, Rienzi E, et al. Playing position and the injury incidence rate in male academy soccer players. Journal of Athletic Training. 2022;57: 696-703. https://doi.org/10.4085/1062-6050-0346.21. Campa F, Piras A, Raffi M, Toselli S. Functional movement patterns and body composition of high-level volleyball, soccer, and rugby players. Journal of sport Rehabilitation. 2019; 28: 740-745. https://doi.org/10.1123/jsr.2018-0087 Moore E, Chalmers S, Milanese S, Fuller JT. Factors influencing the relationship between the functional movement screen and injury risk in sporting populations: a systematic review and meta-analysis. Sports Medicine. 2019,49:1449-1463. https://doi.org/10.1007/s40279-019-01126-5 Konefal M, Chmura P, Kowalczuk E, Figueiredo AJ, Sarmento H, Rokita A, et al. Modeling of relationships between physical and technical activities and match outcome in elite German soccer players. The Journal of Sports Medicine and Physical Fitness. 2018;59:752-759. https://doi.org/10.23736/S0022-4707.18.08506-7 Martins F, França C, Henriques R, Ihle A, Przednowek K, Marques A, et al. Body composition variations between injured and non-injured professional soccer players. Scientific Reports. 2022;12: 20779. https://doi.org/10.1038/s41598-022-24609-4 Shi S, Shi X, Yang Z, Chen Z, Witchalls J, Adams R. Use of the lower extremity functional test to predict injury risk in active athletes. The Journal of Sports Medicine and Physical Fitness. 2020;61:592-599. https://doi.org/10.23736/s0022-4707.20.11311-2 Mohammadi H, Ghaffari R, Kazemi A, Behm DG, Hosseinzadeh M. Evaluation of the lower extremity functional test to predict lower limb injuries in professional male footballers. Scientific Reports. 2024;14:2596. https://doi.org/10.1038/s41598-024-53223-9 Tong JWK, Kong PW. Association Between Foot Type and Lower Extremity Injuries: Systematic Literature Review With Meta-analysis. Journal of Orthopaedic & Sports Physical Therapy. 2013;43:700–714. https://doi.org/10.2519/jospt.2013.4225 Anam K, Setiowati A, Indardi N, Irawan FA, Pavlović R, Susanto N, et al. Functional movement screen score to predict injury risk of sports students: a review of foot shape and body mass index. Pedagogy of Physical Culture and Sports. 2024;28:124-131. https://doi.org/10.15561/26649837.2024.0206 Coogan SM, Schock CS, Hansen‐Honeycutt J, Caswell S, Cortes N, Ambegaonkar JP. Functional Movement Screen™(FMS™) scores do not predict overall or lower extremity injury risk in collegiate dancers. International Journal of Sports Physical Therapy. 2020; 15:1029. https://doi.org/10.26603/ijspt20201029 Additional Declarations No competing interests reported. Supplementary Files DatasupportingthefindingsofthisstudyinjuryprofileandFMS.xlsx Cite Share Download PDF Status: Published Journal Publication published 28 Jul, 2025 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 22 May, 2025 Reviews received at journal 10 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 27 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 14 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Bayrak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBAC9gYGBmYQgx9EJBQQoYXnADNYi4RkA0iLASlaDA6AuERp4T9/8HNhjk2d8fnViR8eGDDI84sdIKCF4TCz9MxtaRJmN95ulgA6zHDm7AT8WuwZmxmkebcdBmo5uwGkJcHgNgEtPMzMzL95t/2XMJ5xdvMP4rSwMbMBbTkgYcDfu41IW3iYzax5tyVLzrjBu80iwUCCsF94+A8+vs27zY6fv//s5ps/Kmzk+aUJaEEACbBKCWKVgwD/AVJUj4JRMApGwUgCAFxrPWOSjI/0AAAAAElFTkSuQmCC","orcid":"","institution":"Selcuk University","correspondingAuthor":true,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"Bayrak","suffix":""}],"badges":[],"createdAt":"2025-01-06 15:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5775124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5775124/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13102-025-01262-8","type":"published","date":"2025-07-28T16:05:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81797347,"identity":"d34a2a03-edbd-486a-90e8-f057f8fd9b07","added_by":"auto","created_at":"2025-05-02 04:09:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248463,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants diagram flow\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5775124/v1/daca6a4d07dc7abd8e44e427.png"},{"id":81796967,"identity":"06ae8fec-6e6c-4e4b-b14d-6df2c2d6bbac","added_by":"auto","created_at":"2025-05-02 04:01:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30467,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of injuries and matches over five seasons.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5775124/v1/8b00d971425681694f8fc017.png"},{"id":81797348,"identity":"d870afe2-8b8a-4ba3-b0aa-462ddf30b718","added_by":"auto","created_at":"2025-05-02 04:09:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45065,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of missed training days due to injuries over five seasons\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5775124/v1/a07bb0a92af2c9005c6aa129.png"},{"id":81797486,"identity":"85b257f9-38c7-4752-bb6b-fbdd7f982e34","added_by":"auto","created_at":"2025-05-02 04:17:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68057,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the distribution of injuries by anatomical region, highlighting the predominance of lower extremity injuries, particularly to the hamstring muscles.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5775124/v1/383e79a7238612b61587651e.png"},{"id":81796990,"identity":"c070f09e-fee3-4814-8ee7-9d165e246f78","added_by":"auto","created_at":"2025-05-02 04:01:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31383,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of injury severity over five seasons, highlights the peak of minor injuries in the 2018-2019 season.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5775124/v1/40194f066f49ea7482a34f67.png"},{"id":81796987,"identity":"9583adfb-739c-4ee6-9f62-050ae2b4787d","added_by":"auto","created_at":"2025-05-02 04:01:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":37759,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates injury rates per 1000 hours over five seasons and highlights that in the 2018-2019 season the highest number of injuries per 1000 hours were sustained in competitions.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5775124/v1/b39b169a0ddea75bdbea278a.png"},{"id":88268172,"identity":"2435c784-f009-4954-beb2-39005d0b4362","added_by":"auto","created_at":"2025-08-04 16:49:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1281632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5775124/v1/476ea9b4-1b05-4669-b4c1-30f53c7808cc.pdf"},{"id":81796971,"identity":"c2d78eb1-0e7a-4525-90e3-4d05d8d9bbc5","added_by":"auto","created_at":"2025-05-02 04:01:11","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19143,"visible":true,"origin":"","legend":"","description":"","filename":"DatasupportingthefindingsofthisstudyinjuryprofileandFMS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5775124/v1/8c84381b7703a2df4eca9d1f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Injury Profiles in Professional Football: Evidence from a Five-Year Study and the Role of the Functional Movement Screen","fulltext":[{"header":"Introductıon","content":"\u003cp\u003eSports injuries can diminish an individual's participation in sports activities and reduce their performance level, potentially ending a sports career [1]. Complex contact sports such as football, which demand high physical, technical, and tactical skills [2], significantly increase the risk of injury [3,4]. Some injuries in football are devastating for players and can jeopardize their sports careers. Moreover, injuries entail substantial financial costs. It is estimated that the average cost of a top-level professional team player sidelined for one month is approximately €500,000. Therefore, quantifying injury cases in professional football is critical [5,6]. In professional football, the injury incidence is reported to vary from 4.8 to 14.4 injuries per 1000 hours of football play, with injuries during matches ranging from 22.7 to 43.5 per 1000 hours and during training sessions from 2.8 to 11.2 per 1000 hours [7]. Because lower numbers of injuries are associated with team success, minimizing injuries and reducing lost playtime are crucial [4,8]. Determining the level of injury risk is of great importance in football, a sport with such a high injury rate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSports injuries are associated with both intrinsic and extrinsic factors. In football, although it is difficult to control some extrinsic factors, such as contact injuries, specific predictable intrinsic factors that lead to non-contact injuries can be identified [9,10]. Coaches, sports physicians, and physiotherapists must implement injury prevention programs to support the health and continuous development of athlete [11]. To create injury prevention programs, it is necessary to accurately determine injury profiles specific to a sport. However, interventions related to the prevention of non-contact muscular injuries have been found to be inadequate [12]. There is a pressing need for further research to establish injury profiles in various sports. Identifying the most common and severe injuries, as well as understanding where (anatomically) and when (during matches or training sessions) they typically occur, will enable coaches, physiotherapists, and physicians to prioritize specific measures to prevent or reduce the risk of such injuries [6].\u003c/p\u003e\n\u003cp\u003eDespite the complexity and multifactorial nature of injury mechanisms, there are various intrinsic risk factors that may potentially increase the risk of injury (e.g., previous injury history, joint instability, muscle strength imbalances, and anatomical asymmetries, etc.) [13,14]. Musculoskeletal screening tests are designed to identify these risk factors so that medical professionals can implement appropriate training strategies (prehabilitation) aimed at reducing injury cases. One of the most well-known injury prediction tools, the Functional Movement Screen (FMS), was developed by Cook et al. in 1997[15]. The FMS is a battery of seven tests that evaluate fundamental movement patterns to identify dysfunctional, asymmetric, and painful movements that may contribute to future injuries [16,17,18]. During the FMS test, the presence or absence of pain and the number of asymmetries observed can be determined, with a composite score of ≤14 indicating an increased risk of injury [19]. \u0026nbsp;In the literature, FMS tests are conducted at the beginning of the season and used as a method for injury risk analysis [20,21]. However, discussions continue regarding the accuracy of the FMS in predicting injuries. While some studies support a relationship between FMS scores and injury [22-25], others suggest that it cannot be used as a method of injury risk analysis [26- 29].\u0026nbsp;There is a gap in the literature regarding such a longitudinal study, as the debate on the injury predictive value of the Functional Movement Screen (FMS) continues, while existing studies lack comprehensive data on injury type, location, and severity. Thus, this study aims to address these gaps and better evaluate whether FMS can predict injuries by providing a more detailed and long-term analysis of these factors in professional football. Despite extensive research into sports injuries, there remains a lack of comprehensive, long-term studies that examine both injury profiles and the predictive efficacy of FMS across multiple seasons in professional football.Additionally, studies are needed to shed light on topics such as changes in FMS scores along with the evolving sports season and the impact of age. This study aimed to reveal the injury profiles of football players over a 5-year period by examining injury types, severities, and the number of training sessions missed, and to lay the groundwork for future injury prevention studies. Furthermore, injury risk predictions will be generated using pre-season FMS tests, and the predictive value of the FMS in assessing injury risk will be evaluated through long-term injury monitoring. Today, critical perspectives on the FMS test have expanded, and it is believed that this study will contribute to the literature on this subject.\u003c/p\u003e\n\u003cp\u003eThis research aims to achieve the following objectives:\u003c/p\u003e\n\u003cp\u003e-To examine injury profiles in a professional football team over five seasons, analyzing factors such as injury incidence, severity, missed training days, and injury location.\u003c/p\u003e\n\u003cp\u003e-To evaluate the long-term predictive value of the Functional Movement Screen (FMS) and determine whether FMS is a reliable tool for injury prediction in professional football players.\u003c/p\u003e\n\u003cp\u003e-To address contextual factors influencing injury risk (match schedule, UEFA competition participation, training load variations) and contribute to injury management strategies.\u003c/p\u003e\n\u003cp\u003e-To fill existing gaps in the literature and provide new findings for the development of long-term injury profiling and prediction methods.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eOur study is based on data collected from professional football players from a football team in the Turkish Super League during the 2016\u0026ndash;2017, 2017\u0026ndash;2018, 2018\u0026ndash;2019, 2019\u0026ndash;2020, and 2020\u0026ndash;2021 seasons. This was a prospective cohort study in which the players' injury status and functional movement analysis results were tracked to assess injury prediction. Injury risks were determined using pre-season FMS tests, and the accuracy of the injury analyses was evaluated at the end of each season. Players who remained with the team for the full season were included in the study, whereas those who left the team mid-season were excluded.\u003c/p\u003e \u003cp\u003eA total of 169 players were included in the study, with 33 players in the 2016\u0026ndash;2017 season, 33 players in the 2017\u0026ndash;2018 season, 31 players in the 2018\u0026ndash;2019 season, 35 players in the 2019\u0026ndash;2020 season, and 37 players in the 2020\u0026ndash;2021 season. However, players who did not complete an entire season were excluded: 2 players in the 2016\u0026ndash;2017 season, 2 players in the 2017\u0026ndash;2018 season, 10 players in the 2018\u0026ndash;2019 season, 8 players in the 2019\u0026ndash;2020 season, and 6 players in the 2020\u0026ndash;2021 season (In this study, the excluded football players participated in the FMS test at the beginning of the season but were transferred to other teams shortly thereafter. They were not included in the study due to their exposure to different training loads in their new teams and, more importantly, the inability to track their injury status) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In order to ensure standardization in study outcomes and the integrity of the collected data, football players who did not remain with the club for a full season and those who joined during the mid-season were not included in the study. Consent forms were obtained from all football players for the tests to be conducted at the beginning of the season and for the recording of injury data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInjury Identification\u003c/h3\u003e\n\u003cp\u003eThroughout the season, all injuries sustained by the players were recorded. These injuries were diagnosed by the club's sports physicians and/or relevant medical doctors from the associated hospitals. Only injuries that prevented players from participating in training/match for over 72 hours (injuries resulting in 3 days or less of missed training/match were not considered) were recorded [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Injuries were categorized as minimal (1\u0026ndash;3 days of absence), minor (4\u0026ndash;7 days of absence), moderate (8\u0026ndash;28 days of absence), or severe/major (\u0026gt;\u0026thinsp;28 days of absence) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Diagnoses made by the sports physician or a relevant specialist on the medical team were further documented by a sports physiotherapist, who was also part of the health team. Each injury was recorded immediately after the incident using a standard injury form, which included the type of injury, injury location, duration of absence from training due to the injury, and timing of the injury. Injury data in the sports club were recorded daily using a standardized form, and an Excel database was created. These records were archived at the end of each season.\u003c/p\u003e \u003cp\u003eIn this study, the definition of injury severity was based on the recommendations of the International Consensus Group on Injury, defined as the number of days from the date of injury to the player\u0026rsquo;s full return to team training and readiness for match selection [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eInjury Follow-Up Period\u003c/h3\u003e\n\u003cp\u003eThe risk and type of injury may vary throughout the football season [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; therefore, the study period should encompass the entire season or multiple seasons, including both the pre-season and competitive season [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, players were followed over five full seasons (2016\u0026ndash;2021). Of the 169 players observed during the 2016\u0026ndash;2017, 2017\u0026ndash;2018, 2018\u0026ndash;2019, 2019\u0026ndash;2020, and 2020\u0026ndash;2021 seasons, 133 players missed training due to injuries.\u003c/p\u003e\n\u003ch3\u003eCalculation of Injury Incidence\u003c/h3\u003e\n\u003cp\u003eThe incidence of injuries per 1,000 training and match hours were calculated using the following formula: 1000 Incidence = (Total hours of exposure to training and matches Number of injuries​) \u0026times;1000 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eInjury Risk Assessment / Functional Movement Screen\u003c/h3\u003e\n\u003cp\u003eFMS tests were administered at the start of the season following the players' holiday period. During the FMS testing, each player was recorded from two different angles. All assessments were conducted at the facilities of the respective sports clubs. Before testing, the players completed a standardized warm-up led by the club\u0026rsquo;s fitness trainer. The FMS consists of seven subtests: deep squat, hurdle step, in-line lunge, shoulder mobility, trunk stability push-up, active straight leg raise, and rotary stability [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Five of the subtests (hurdle step, in-line lunge, shoulder mobility, active straight leg raise, and rotary stability) were performed on both sides of the body, and any asymmetries observed between sides were recorded as asymmetric findings.\u003c/p\u003e \u003cp\u003eTo calculate the FMS composite score, each of the seven tests was scored on a scale of 0 to 3. A score of 0 indicates the presence of pain during movement; a score of 1 is given if the individual is unable to perform the movement; a score of 2 is assigned if the individual completes the movement but with some form of compensation; and a score of 3 is awarded if the movement is performed correctly [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. For tests completed on both sides of the body, the lowest score was recorded as the final score. Scores from all seven subtests were combined to yield a composite score of 21. FMS was conducted using standard FMS test kits (Functional Movement Systems Inc., Virginia, USA). FMS tests were administered by a certified physiotherapist who had received FMS training. Prior to the tests, the club informed the players about the testing process. To eliminate the effects of fatigue, players did not participate in training the day before the tests, which were conducted during the pre-season. The players\u0026rsquo; instances of missed training and the reasons for their absence were documented. Instances of illness or contact-related injuries, which could cause missed training, were recorded but excluded from the FMS comparison. Only non-contact injuries were considered in the comparison with the FMS evaluations.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData are presented as mean and standard deviation. Skewness-Kurtosis and Shapiro-Wilk tests were used to assess whether the groups showed normal distribution. Independent Samples T-test was used to analyze the differences between demographic data and injury, while Mann-Whitney U test was used for FMS and asymmetry. while logistic regression was applied to evaluate the FMS\u0026rsquo;s predictive validity regarding injury. Statistical analyses were performed using IBM SPSS Statistics 25 (IBM Corporation), Jamovi (The Jamovi project, 2024. Jamovi (Version 2.5) Computer Software. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jamovi.org\u003c/span\u003e\u003cspan address=\"https://www.jamovi.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The relationship between FMS scores, asymmetry, injury occurrence, and missed training days was analyzed using the Mann-Whitney U test. A significance level of 0.05 was set for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe demographic characteristics of the football players included in this study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The age, body mass, height and body mass index of players who sustained injuries were similar to those of players who did not (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic information, fms score and asymmetry data normality test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShapiro-Wilk\u003c/p\u003e \u003cp\u003e(p değeri)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,0,268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (mt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass İndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFms Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt was determined that the Skewness-Kurtosis (+\u0026thinsp;1.50 and \u0026minus;\u0026thinsp;1.50) was between and Shapiro-Wilk (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) test showed that the players' age, weight, height and body mass index showed normal distribution, but the players' fms score and asymmetry values ​​did not show normal distribution. (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eInjury Status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInjuried (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Injuried (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u0026thinsp;\u0026plusmn;\u0026thinsp;4,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;4,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79,3\u0026thinsp;\u0026plusmn;\u0026thinsp;3,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76,6\u0026thinsp;\u0026plusmn;\u0026thinsp;4,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (mt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,83\u0026thinsp;\u0026plusmn;\u0026thinsp;0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,81\u0026thinsp;\u0026plusmn;\u0026thinsp;0,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass İndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,40\u0026thinsp;\u0026plusmn;\u0026thinsp;1,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,23\u0026thinsp;\u0026plusmn;\u0026thinsp;1,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTraining Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Training (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u0026thinsp;\u0026plusmn;\u0026thinsp;3,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,2\u0026thinsp;\u0026plusmn;\u0026thinsp;4,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78,2\u0026thinsp;\u0026plusmn;\u0026thinsp;3,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76,7\u0026thinsp;\u0026plusmn;\u0026thinsp;4,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (mt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,83\u0026thinsp;\u0026plusmn;\u0026thinsp;0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,81\u0026thinsp;\u0026plusmn;\u0026thinsp;0,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass İndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,40\u0026thinsp;\u0026plusmn;\u0026thinsp;1,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,33\u0026thinsp;\u0026plusmn;\u0026thinsp;1,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIndependent t-test was performed for normally distributed demographic data. In the injury cases, comparisons between injured and uninjured players based on preseason FMS scores revealed the following: No significant differences were found for height (p\u0026thinsp;=\u0026thinsp;0.249), body mass (p\u0026thinsp;=\u0026thinsp;0.042), body mass index (p\u0026thinsp;=\u0026thinsp;0.553). However, a significant difference was found in age (p\u0026thinsp;=\u0026thinsp;0.005), injured players differed in age from uninjured players (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of missed training days, comparisons between players who missed and did not miss training based on preseason FMS scores revealed the following: No significant differences were found for height (p\u0026thinsp;=\u0026thinsp;0.135), body mass (p\u0026thinsp;=\u0026thinsp;0.064), body mass index (p\u0026thinsp;=\u0026thinsp;0.856), However, a significant difference was found in age (p\u0026thinsp;=\u0026thinsp;0.002), players who missed training differed in age from players who did not miss training (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInjury status analysis of fms score and asymmetry data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eInjury Status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFms Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2118,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eTraining Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFms Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2341,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2167,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMann-Whitney U test was performed for fms scores and asymmetry data that did not show normal distribution. In injury cases, it was found that Fms scores had no significant effect on injury status. (U\u0026thinsp;=\u0026thinsp;2118.5, z=-0.304, p\u0026thinsp;=\u0026thinsp;0.761, R=-0.025). It was also found that asymmetry had no significant effect on injury cases. (U\u0026thinsp;=\u0026thinsp;2012.5, z=-0.848, p\u0026thinsp;=\u0026thinsp;0.361, R=-0.072). Since FMS and Asymmetry P values ​​were greater than 0.05, it was determined that fms score and asymmetry had no effect on injury. It was found that Fms scores had no significant effect on missed training days. (U\u0026thinsp;=\u0026thinsp;2341.0, z=-0.210, p\u0026thinsp;=\u0026thinsp;0.834, R=-0.016). It was also found that asymmetry had no significant effect on missed training days. (U\u0026thinsp;=\u0026thinsp;2167.5, z=-1.024, p\u0026thinsp;=\u0026thinsp;0.306, R=-0.078). When injury status and missed training days were analyzed together, FMS score, asymmetry, body mass, body mass index, and height did not differ significantly between injured and uninjured players. However, there was a significant age difference between injured and uninjured athletes in both injury and missed training days scenarios (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of five years of data indicated that FMS scores and asymmetry findings did not significantly differ between injured and non-injured players, nor in the number of missed training days (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Across the seasons from to 2016\u0026ndash;2017 to 2020\u0026ndash;2021, 137 of the 169 players experienced injuries (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe highest injury count coincided with a period of fewer matches. Specifically, during the 2018\u0026ndash;2019 season, the highest number of injuries occurred, despite fewer matches being played compared to other seasons. Similarly, in the 2019\u0026ndash;2020 season, there was a notably low number of matches, yet injury counts remained high relative to other seasons. This suggests a possible disconnect between match frequency and injury incidence, highlighting that factors other than the number of matches may contribute significantly to injury risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOver the five seasons, players missed a total of 4,573 training sessions due to injuries and illnesses (training losses lasting less than 72 h were excluded). The breakdown of missed training sessions is as follows: 116 days missed due to illness, 550 days missed due to contact injuries, and 3,907 days missed due to non-contact injuries. As seen in the table, approximately 85% of the total missed training sessions were due to non-contact injuries. Additionally, it is noteworthy that missed training sessions due to illness became apparent only after the 2018\u0026ndash;2019 season, indicating a possible change in health-related absences in the latter seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the five seasons, the players experienced 224 injuries, with the most frequent injuries occurring in the thigh region, particularly affecting the hamstring muscles. The most common injury type recorded was hamstring muscle injury, accounting for 26% of all injuries. Knee issues: predominantly meniscal injuries, followed by ACL and chondromalacia. Adductor muscle and ankle injuries. Less common injuries included abdominal muscle tears in two players, tennis leg in one player, and shin splints in another, representing the least frequent injuries observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe distribution of injuries by anatomical region and their respective percentages across the five seasons were as follows: Spinal region: 8.48%, Abdomen: 0.89%, Pelvis: 2.68%, Osteitis pubis: 1.34%, Anterior thigh (Quadriceps): 8.93%, Posterior thigh (Hamstring): 26.34%, Groin: 13.39%, Lateral thigh (IT band): 0.89%, Knee: 14.29%, Tibia (shin splint): 0.89%, Calf: 9.82%, Achilles tendon: 0.89%, Foot and ankle: 2.23%, Upper extremity: 8.93%, Injuries causing more than 28 days of missed training were most common in the hamstring region, with nearly half of all knee injuries categorized as severe Overall, 88.56% of missed training due to injury originated from lower extremity injuries, underscoring the significant impact of lower body injuries on training availability. In addition, hamstring and knee injuries were more frequently associated with severe injuries, leading to more than 28 days of missed training. This indicates a higher propensity for prolonged recovery periods in these areas, highlighting them as critical zones for severe injury risk and extended time away from training (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, the injury rates per 1,000 hours of total match and training exposure were recorded across different injury types. The highest incidence was observed in hamstring injuries, with 2.36 injuries per 1,000 hours (95% CI: 1.76\u0026ndash;2.96). Knee injuries followed with 1.28 injuries per 1,000 hours (95% CI: 0.84\u0026ndash;1.72), while groin injuries were reported at 1.20 injuries per 1,000 hours (95% CI: 0.77\u0026ndash;1.63). The calf injury rate was 0.88 injuries per 1,000 hours (95% CI: 0.51\u0026ndash;1.25). Additionally, both ankle and quadriceps injuries were recorded at 0.80 injuries per 1,000 hours (95% CI: 0.45\u0026ndash;1.15).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOf 229 injuries, 104 lasted less than 8 days, representing approximately 45.41% of all injuries and classified as minor injuries. Injuries lasting between 8 and 28 days: 61 occurrences, representing approximately 26.64% of all injuries, were classified as moderate injuries. Injuries lasting more than 28 days: 61 occurrences, making up approximately 26.64% of all injuries, classified as severe injuries. The 2018\u0026ndash;2019 season saw an increase in minor injuries. Meanwhile, players experienced the highest incidence of severe injuries (those causing more than 28 days of missed training) during the 2020\u0026ndash;2021 season. Minor injuries peaked in the 2018\u0026ndash;2019 season, while the 2017\u0026ndash;2018 season displayed a more balanced or homogeneous distribution across minor, moderate, and severe injuries. This trend underscores the variations in injury severity across seasons, with certain seasons showing distinct peaks in either minor or severe injury occurrences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, over the five seasons, players experienced: 7.76 injuries per 1,000 training hours (95% CI: 7.59\u0026ndash;7.93), 15.47 injuries per 1,000 match hours (95% CI: 15.23\u0026ndash;15.71), 8.9 injuries per 1,000 combined training and match hours (95% CI: 8.72- 9.0). The highest injury rate per 1,000 hours occurred in the 2018\u0026ndash;2019 season, with: 23.6 injuries per 1,000 match hours and 10.8 injuries per 1,000 training hours. Conversely, the lowest injury rate was recorded in the 2017\u0026ndash;2018 season, with: 9.6 injuries per 1,000 match hours and 5.5 injuries per 1,000 training hours. This distribution highlights the variability in injury rates by season, particularly with increased risk during competitive matches compared to training (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of injury types by player position\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjury Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoalkeeper (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefenders (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMidfielders (n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eForwards (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColumna Vertebralis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (31.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (4.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (7.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOst. Pupis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (5.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTight (Quadriceps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (6.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (10.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTight (Hamstring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (17.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (26.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (25.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (25.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (13.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (17.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (10.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLateral Tight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (17.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (17.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (11.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (15.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalf Muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (6.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (11.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (8.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (7.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAchilles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (6.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoot-Ankle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (8.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (15.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Extremity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong goalkeepers, 31.03% of all injuries were vertebral issues, indicating a high prevalence of spinal problems in this position. For players in non-goalkeeper positions (defenders, midfielders, and forwards\u003cb\u003e)\u003c/b\u003e, hamstring injuries accounted for approximately one-quarter (25%) of all injuries, highlighting a common vulnerability in the hamstring area for these roles. This distribution underscores the position-specific injury patterns, with goalkeepers being more prone to back-related injuries, whereas outfield players are more susceptible to hamstring strains (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe effect of players' positions on injury status was found to be p\u0026thinsp;=\u0026thinsp;0.441, while their effect on FMS scores was p\u0026thinsp;=\u0026thinsp;0.382. Since these values do not meet the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance threshold, it was determined that playing position does not have a significant impact on injury status or FMS scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance inflation factor (VIF)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFMS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssymetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjury Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFMS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing Training Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the logistic regression analysis of injury status and training availability, the Variance Inflation Factor (VIF) values for the variables FMS score, asymmetry, age, height, and weight were all VIF\u0026thinsp;\u0026lt;\u0026thinsp;10. This indicates that these variables do not exhibit multicollinearity, meaning that there is no significant linear relationship among them that would affect the reliability of the model. This lack of multicollinearity supports the robustness of the variables in the logistic regression model (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe logistic regression analysis of injury status and missing training status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFms Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjury Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.5252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0351\u003c/p\u003e \u003cp\u003e0.4251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0584\u003c/p\u003e \u003cp\u003e3.1020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFms Skor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing training status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.3495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.1603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.0670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0160\u003c/p\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0555\u003c/p\u003e \u003cp\u003e2.9844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the Logistic Regression Analysis examining injury status, FMS score and asymmetry were not found to be significant predictors of injury: FMS score (p\u0026thinsp;=\u0026thinsp;0.539) and asymmetry (p\u0026thinsp;=\u0026thinsp;0.534) were identified. The model showed an R\u0026sup2; of 0.004 and an overall cut-off p-value of 0.702, indicating that there was no significant relationship between these variables and injury status, as the p-values ​​for both predictors were above the significance threshold of 0.05. In the Logistic Regression Analysis of demographic data; Age (p\u0026thinsp;=\u0026thinsp;0.029) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This suggests that age has a significant effect on the probability of injury and that older players are probably at a higher risk. Weight, height and body mass index were not significant predictors: Weight (p\u0026thinsp;=\u0026thinsp;0.548), Height (p\u0026thinsp;=\u0026thinsp;0.877), Body mass index (p\u0026thinsp;=\u0026thinsp;0.89). Model R\u0026sup2; = 0.051 for age, weight and height and an overall cut-off p-value of 0.037 were detected. Age was a statistically significant predictor of injury status (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Logistic Regression Analysis examining training missed status, FMS score and asymmetry were not found to be significant predictors of injury: FMS score (p\u0026thinsp;=\u0026thinsp;0.494) and asymmetry (p\u0026thinsp;=\u0026thinsp;0.415) were identified. The model showed an R\u0026sup2; of 0.007 and an overall cut-off p-value of 0.494, indicating that there was no significant association between these variables and training missed status, as the p-values ​​for both predictors were above the significance threshold of 0.05. Demographic data included Age (p\u0026thinsp;=\u0026thinsp;0.005), Weight (p\u0026thinsp;=\u0026thinsp;0.773), Height (p\u0026thinsp;=\u0026thinsp;0.732), Body mass index (p\u0026thinsp;=\u0026thinsp;0.886). For age, weight, height and body mass index, the model showed R\u0026sup2; = 0.067 and an overall cut-off p-value of 0.017. (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to establish an injury profile for players in a professional football club over five seasons and to determine whether injuries sustained by players across consecutive seasons could be predicted using the Functional Movement Screen (FMS). Over the five seasons, it was found that FMS was not effective in predicting injuries, either within individual seasons or across the entire observation period. This suggests that the FMS may not serve as a reliable tool for forecasting injury risk in a high-performance football environment. In our study, while FMS and asymmetry scores were not found to be effective in determining injury risk, an increase in age had a slight impact on injury occurrence. This suggests that age explains only a small portion of injury risk, and other unmeasured factors may also play a significant role.\u003c/p\u003e \u003cp\u003eAdditionally, players experienced variations in the number, type, and severity of injuries across seasons, highlighting the dynamic nature of injury patterns among professional football players. Injury types also vary by player position, with certain injuries appearing to be position-specific. Long-term monitoring of a football team provided unique insights into how variables such as match schedules, UEFA participation, national cup victories, league standings, fluctuating match counts, and the COVID-19 pandemic influenced in-season injury rates.\u003c/p\u003e \u003cp\u003eThese observations underscore the complexity of injury risk factors in football, suggesting that factors beyond physical screening, including external and contextual elements, play a significant role in the occurrence of injury.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInjury Incidence\u003c/h2\u003e \u003cp\u003eThe incidence of injuries per 1,000 hours of training and matches was calculated as 8.49 injuries, with 7.76 injuries per 1,000 training hours and 15.47 injuries per 1,000 match hours. The highest injury incidence was observed during the 2018\u0026ndash;2019 season, with 12.6 injuries per 1,000 combined hours of training and matches, 10.8 injuries per 1,000 training hours, and 23.6 injuries per 1,000 match hours. This increase in injury rates was likely influenced by disruptions due to the onset of the COVID-19 pandemic, which led to interruptions in training, subsequent reconditioning periods, restrictions on heavy training, and reduced training times, all of which may have contributed to the higher injury incidence. In the 2018\u0026ndash;2019 season, there was a sudden interruption in the league, preventing athletes from training when they were originally scheduled to compete. After this prolonged break, training sessions resumed, which may have contributed to an increase in minor injuries during that season.\u003c/p\u003e \u003cp\u003eIn the literature, general injury incidence for professional male football players is reported as 8.1 injuries per 1,000 hours of exposure. Studies examining various leagues worldwide report injury rates ranging from 12 to 35.5 injuries per 1,000 match hours and 1.5 to 7.6 injuries per 1,000 training hours [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our findings align with these global averages, suggesting that the observed injury rates are consistent with the broader trends reported in the literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInjury Distribution by Position\u003c/h2\u003e \u003cp\u003eThe study also examined the distribution of injuries by playing position over the five seasons. Goalkeepers are most frequently affected by lumbar spine injuries, with a high incidence of lumbar disc herniation. Spinal injuries are common in elite football due to repetitive high-impact and rotational movements, such as headers, kicks, hyperextension, hyperflexion, and rotational actions, which place players at risk of both overuse and acute spinal injuries [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Research indicates that goalkeepers experience fewer injuries overall than players in other positions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], highlighting the importance of targeted injury prevention strategies for goalkeepers to address their unique movement demands.\u003c/p\u003e \u003cp\u003eWhen examining injuries by position, midfielders and forwards were found to sustain the most muscle injuries, likely because of their high physical demands and frequent involvement in dynamic play. Conversely, goalkeepers have the lowest injury rates, which may be attributed to their reduced physical contact and lower physical demands during play [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This positional insight is valuable for designing injury prevention programs, especially for goalkeepers who may benefit from specific spine-strengthening exercises and load management throughout the season.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInjury Location\u003c/h2\u003e \u003cp\u003eThe analysis of injury locations and types can guide the development of targeted prevention programs and resource allocation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this study, 88% of injuries were identified as lower extremity injuries, which aligns with previous studies reporting lower extremity injury rates between 81% and 92% among football players[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This high rate of lower extremity injuries is consistent with findings in the literature.\u003c/p\u003e \u003cp\u003eOver the five seasons, players sustained 224 injuries, with the thigh region being the most frequently injured area. The hamstring muscles were the most commonly affected anatomical structures, followed by knee issues (primarily meniscal injuries, ACL injuries, and chondromalacia), adductor muscle strains, and ankle injuries. Rare injuries included abdominal muscle tears in two players, tennis leg in one player, and shin splints in another, with two players experiencing rectus abdominis tears in the 2019\u0026ndash;2020 and 2020\u0026ndash;2021 seasons.\u003c/p\u003e \u003cp\u003eMuscle injury analysis for a typical professional football team over a season often identifies the quadriceps, hamstrings, and adductors as the most affected regions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In line with this, our study found that 26% of all injuries occurred in the hamstring muscle group, with injury rates of 8.93% in the quadriceps and 13.39% in the adductor region. Hamstring injuries are known to be common in football [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], with a four-season study of a Spanish football team reporting the hamstring as the most frequently injured area (36%) [45) In addition, the injury rates per 1,000 hours of total match and training exposure were recorded across different injury types. The highest incidence was observed in hamstring injuries, with 2.36 injuries per 1,000 hours (95% CI: 1.76\u0026ndash;2.96). In a study conducted on La Liga football players, the incidence rate of hamstring injuries was reported as 3.34 injuries per 1,000 hours [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Similarly, in a study involving 143 professional football players in Kosovo, the total hamstring injury incidence was found to be 1.17 injuries per 1,000 hours (95% CI: 0.84\u0026ndash;1.57) 47].\u003c/p\u003e \u003cp\u003eAlthough the Kosovo study reported a lower hamstring injury rate, most of these injuries occurred during matches, which the authors attributed to the lack of adequate football infrastructure. In contrast, in La Liga, hamstring injuries were associated with high-speed actions, highlighting the impact of different playing styles and league dynamics on injury rates. These findings emphasize the variability in hamstring injury incidence across national leagues, demonstrating how different football systems and playing intensities can influence injury trends. Similarly, the findings from our study contribute valuable data by illustrating hamstring injury incidence in a different professional football league, further supporting the need for league-specific injury prevention strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eContact vs. Non-Contact Injury Status\u003c/h2\u003e \u003cp\u003eOver the five seasons, players missed a total of 4,573 training days due to injuries and illnesses, with 3,907 days (85%) resulting from non-contact injuries. This high proportion aligns with findings from previous studies, such as [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], where the non-contact injury rate among young football players was 66%. Another study observed that all injuries sustained by injured players were non-contact, affecting 71 of 122 footballers [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In general, noncontact injuries are more prevalent in football [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], which is consistent with our study findings.\u003c/p\u003e \u003cp\u003eIn our study, 61 of the 121 non-contact injuries (50.4%) occurred during matches, while 60 (49.6%) occurred during training [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Lower extremity injuries, which are common in football, accounted for the majority of these missed days, aligning with the literature on football injury patterns. This trend may be partly because upper extremity injuries are less likely to prevent training. However, the high rate of lower extremity injuries emphasizes the significant impact of these injuries on training availability in football, highlighting the need for targeted prevention efforts focused on lower body resilience and injury prevention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFMS and Injury Prediction\u003c/h2\u003e \u003cp\u003eThe effectiveness of the Functional Movement Screen (FMS) in predicting injuries has yielded mixed results [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Many studies have attempted to control for the effect of a previous injury history, as it is widely recognized as a strong risk factor for future injuries [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A meta-analysis of 24 studies concluded that FMS scores are insufficient as a standalone test battery for injury prediction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor instance, a study of 84 youth academy football players in the English Premier League assessed players using the FMS pre-season, tracking non-contact injuries throughout the season, and found no significant predictive capability for non-contact injuries [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Similarly, a study of 573 Australian football players determined that FMS scores were ineffective in predicting non-contact injuries [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. A separate study involving 439 young Australian football players also concluded that FMS scores were not adequate for predicting noncontact injuries [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a study of 124 high school football players, FMS was again deemed insufficient as a sole predictor of injuries [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Furthermore, [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] found that even when the FMS composite cutoff score was lowered to 14, 13, or 12, it failed to reliably predict injuries. A machine learning study tracking FMS scores strongly indicated that the FMS is ineffective at predicting injuries in average adolescent populations. Additionally, FMS scores could not differentiate between injured and non-injured subjects in both athletic and non-athletic youths [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In Japanese collegiate football, a study following 75 players for one season also concluded that the FMS lacks sufficient sensitivity and specificity to predict injuries [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. These findings suggest that while FMS may provide valuable information on movement quality, it is not a reliable tool for injury prediction, especially for non-contact injuries in football. Our results are consistent with previous studies indicating that FMS is not a reliable predictor of injury risk, aligning with existing research that questions its effectiveness in forecasting injuries.\u003c/p\u003e \u003cp\u003eOn the other hand, several studies in the literature support the effectiveness of FMS in predicting injuries. For example, a study involving 65 young football players aged 12\u0026ndash;13 found that the FMS test battery was effective in predicting injury risk in youth athletes [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This study differs from ours due to its focus on young athletes and the inclusion of players with a prior injury history. Similarly, FMS has been shown to be an effective predictor of injuries in female combat sports athletes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. A study of 131 young volleyball players also found that FMS had the necessary sensitivity to predict injury risk [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between the asymmetries identified in the FMS tests and injury prediction is also debated. For instance, a study of 237 elite young Australian football players found that while FMS scores alone could not reliably predict injuries, players with two or more asymmetrical findings had a lower-level predictive capacity for injuries [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] conducted a systematic review and concluded that there is moderate-to-low evidence that asymmetry is a risk factor for sports injuries.\u003c/p\u003e \u003cp\u003eOther studies have found more robust links between FMS scores and injury risk. For example, research involving 527 male athletes indicated that the FMS is an effective tool for assessing injury susceptibility [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Similarly, in a study of 68 rugby players over a season, FMS was found to predict non-contact muscle injuries [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These studies suggest that the FMS may have predictive utility in certain athlete populations and sports contexts, although its effectiveness appears to vary based on factors such as age, sport type, and the presence of prior injuries.\u003c/p\u003e \u003cp\u003eGiven the conflicting results regarding FMS, it appears that outcomes vary across different sports, potentially due to differences in injury profiles and biomechanical stresses unique to each sport. For instance, in football, players face varied injury risks and biomechanical demands based on their playing positions [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This variability calls into question the utility of universal injury prediction tools in all sports. These studies in the literature support the notion that FMS can predict injury risk. However, the differences observed in our study may stem from the fact that studies supporting FMS as a predictive tool have been conducted on younger athletes and in different sports disciplines. This suggests that football, as a distinct sport discipline, may require different injury risk assessment tools tailored to its unique demands and injury mechanisms.\u003c/p\u003e \u003cp\u003eResearch has shown that FMS results differ significantly across sports [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Football, characterized by frequent sprinting, jumping, and change-of-direction activities [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], may not ideally be assessed through a static test such as the FMS. Our study\u0026rsquo;s findings, which suggest that FMS composite scores and asymmetry findings do not predict injuries in football, may reflect the dynamic and high-intensity nature of the sport, where injury risk is influenced by multiple factors beyond basic functional movements, such as tactical contact situations, player fatigue, and playing surface conditions.Alternative assessments, such as the Lower Extremity Functional Test (LEFT), which evaluates athletic conditioning, fatigue resistance, and speed through a series of dynamic maneuvers (e.g., forward and backward sprints, lateral shuffling, and 45\u0026deg; and 90\u0026deg; direction changes), have shown predictive accuracy for football-related injuries [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Additionally, factors such as foot posture have been shown to elevate injury risk, and FMS does not account for such biomechanical elements [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. This limitation of the FMS might contribute to its reduced predictive power for lower-extremity injuries in football, a sport with high lower-limb injury rates.\u003c/p\u003e \u003cp\u003eA similar pattern was observed in dance, where FMS was found to be inadequate for injury prediction. In that study, the FMS was noted for assessing proper movement but was not sensitive to the movement patterns critical in dance [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. This insight may be extended to football, a sport that also involves diverse movement patterns, suggesting that sport-specific assessments could better capture injury risk factors in dynamic environments.\u003c/p\u003e \u003cp\u003eIn addition to mapping the injury profile in football, this study used FMS assessments at the start of each season to predict injuries, with players tracked over time to test the consistency of these predictions. Unlike many studies focusing on the retrospective identification of past injuries via FMS, this study\u0026rsquo;s prospective approach strengthens its findings. Another strength of this study was the detailed injury documentation provided by the club\u0026rsquo;s medical team. However, the limitations include focusing on a single team and the lack of female participants. Despite the lack of significant predictive value of FMS scores, this finding aligns with recent literature questioning the utility of FMS in professional football settings. Future research could explore alternative screening tools tailored to football's dynamic demands. While our study adds to the growing body of evidence questioning the utility of FMS as a standalone tool for injury prediction in football, it does not diminish the value of FMS as part of a comprehensive athlete assessment toolkit. It underscores the necessity for multifactorial assessment approaches that incorporate dynamic and sport-specific movement analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile our study benefits from standardized training loads and medical staff within a single team, this approach may limit the generalizability of our findings. Future studies should consider including multiple teams from different leagues to explore the potential variability in injury profiles and FMS predictive value across different playing styles and training regimens. The exclusive use of FMS in our study limits the breadth of our injury risk assessment. The inclusion of additional functional tests, such as the Y Balance Test and the Lower Extremity Functional Test, could offer a more holistic view of an athlete's injury risk factors. Future research should consider incorporating a multi-test approach to injury prediction to potentially uncover additional or more nuanced risk factors not captured by FMS alone. The study's focus on a single team limits the generalizability of the findings. Future studies should consider a multi-team approach to account for variations in training, play style, and medical support across different clubs. Our study focused solely on male professional football players, which limits the applicability of our findings to female athletes. Given the physiological and biomechanical differences between genders, further research is needed to explore injury profiles and the predictive value of FMS in women's football.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results indicate an injury frequency of 8.9 injuries per 1,000 hours of exposure in a professional men\u0026rsquo;s football team, with approximately 26% of injuries classified as major/severe, leading to over 28 days of absence from training or matches. The significant number of severe injuries leading to over 28 days of missed training highlights the need for enhanced rehabilitation and prevention strategies to mitigate long-term absences. Both injury frequency and severity were high in this cohort. Our study aimed to explore injury profiles over five seasons and assess the FMS's predictive value in a professional football setting. Despite a thorough investigation, FMS scores did not predict injuries, suggesting the need for a multifaceted approach to injury risk assessment beyond the scope of FMS alone.Throughout the study, hamstring injuries were the most frequent, whereas goalkeepers primarily experienced back problems. Additionally, our injury profile highlights the need for targeted prevention efforts focused on the most commonly injured areas, such as the hamstring. Furthermore, in goalkeepers, the findings suggest that injury prevention programs should be developed specifically for the lower back region to mitigate injury risk. Evidence also suggests that parameters such as age, height, and body mass may influence the risk of injury. Findings on injury types, time lost from training due to injury, and injury-specific profiles could inform the development of targeted injury prevention programs, team preseason assessments, and strategic roster planning. Additionally, these insights could help technical staff anticipate training absences and injury patterns based on position, thereby enhancing player management and seasonal planning.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFunctional Movement Screeen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariance Inflation Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Ethics Committee of Selçuk University, Faculty of Sports Sciences (decision dated 28.11.2024 and numbered 885482). Permission to use the study for scientific publication was also granted by the relevant club. Informed consent forms were obtained from all players who participated in the study. This study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are provided in the manuscript and supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll stages of this study, including conceptualization, methodology, data collection, analysis, and manuscript preparation, were performed solely by AB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their heartfelt gratitude to the Konyaspor Football Club for their invaluable support throughout the 5-year study period. The cooperation and commitment of the club made this research possible, and we sincerely appreciate their assistance and collaboration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eEkstrand J, Roos H, Tropp H. Normal course of events amongst Swedish soccer players: an 8-year follow-up study. British Journal of Sports Medicine. 1990;24: 117\u0026ndash;119. https://doi.org/10.1136/bjsm.24.2.117.\u003c/li\u003e\n \u003cli\u003eFransson D, Vigh-Larsen JF, Fatouros IG. Krustrup P, Mohr M. Fatigue responses in various muscle groups in well-trained competitive male players after a simulated soccer game. Journal of Human Kinetics. 2018;61:85-97. https://doi.org/10.1515/hukin-2017-0129\u003c/li\u003e\n \u003cli\u003eEkstrand J, Lundqvist D, Davison M, D\u0026apos;Hooghe M, Pensgaard AM. (2019). Communication quality between the medical team and the head coach/manager is associated with injury burden and player availability in elite football clubs. 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MedNEXT Journal of Medical and Health Sciences. 2020;1:29-34. http://dx.doi.org/10.34256/mdnt2016\u003c/li\u003e\n \u003cli\u003eNewton F, McCall A, Ryan D, Blackburne C, aus der F\u0026uuml;nten K, Meyer T, et al. Functional Movement Screen (FMS\u0026trade;) score does not predict injury in English Premier League youth academy football players. Science and Medicine in Football. 2017;1:102-106. http://dx.doi.org/10.1080/24733938.2017.1283436\u003c/li\u003e\n \u003cli\u003eFuller JT, Lynagh M, Tarca B, Zacharia A, Townsley A, Gleeson C, et al. Functional Movement Screen Pain Location and Impact on Scoring Have Limited Value for Injury Risk Estimation in Junior Australian Football Players. Journal of Orthopaedic \u0026amp; Sports Physical Therapy. 2020;50: 75-82. https://doi.org/10.2519/jospt.2020.9168\u003c/li\u003e\n \u003cli\u003eLisman P, Hildebrand E, Nadelen M, Leppert K. Association of functional movement screen and Y-balance test scores with injury in high school athletes. The Journal of Strength \u0026amp; Conditioning Research. 2021;35:1930-1938. https://doi.org/10.1519/jsc.0000000000003082\u003c/li\u003e\n \u003cli\u003eKaruc J, Mi\u0026scaron;igoj-Duraković M, \u0026Scaron;arlija M, Marković G, Hadžić V, Tro\u0026scaron;t-Bobić T, et al. Can injuries be predicted by functional movement screen in adolescents? The application of machine learning. The Journal of Strength \u0026amp; Conditioning Research. 2021; 35:910-919. https://doi.org/10.1519/jsc.0000000000003982\u003c/li\u003e\n \u003cli\u003eMiyamori T, Nagao M, Shimasaki Y, Okazaki T, Akiyoshi N, Nishio H, et al. Reliability assessment of the functional movement screen for predicting injury risk in Japanese college soccer players. Journal of Physical Therapy Science. 2020;32:850-855. https://doi.org/10.1589/jpts.32.850\u003c/li\u003e\n \u003cli\u003eŁyp M, Rosiński M, Chmielewski J, Czarny-Działak MA, Osuch M, Urbańska D, et al. Effectiveness of the Functional Movement Screen for assessment of injury risk occurrence in football players. Biology of Sport. 2022;39:889-894. https://doi.org/10.5114/biolsport.2022.107482\u003c/li\u003e\n \u003cli\u003eYacine Z, Othmane B, Adel B, Mohamed S, Aabdelkader B, Lalia C. Functional movement screening as a predictor of injury in highly trained female\u0026rsquo;s martial arts athletes. Polish Hyperbaric Research. 2020;71:67-74. http://dx.doi.org/10.2478/phr-2020-0012\u003c/li\u003e\n \u003cli\u003eZarei M, Soltanirad S, Kazemi A, Hoogenboom BJ, Hosseinzadeh M. Composite functional movement screen score predicts injuries in youth volleyball players: a prospective cohort study. Scientific Reports. 2022;12:20207. https://doi.org/10.1038/s41598-022-24508-8\u003c/li\u003e\n \u003cli\u003eHelme M, Tee J, Emmond S, Low C. Does lower-limb asymmetry increase injury risk in sport? A systematic review. 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Pedagogy of Physical Culture and Sports. 2024;28:124-131. https://doi.org/10.15561/26649837.2024.0206\u003c/li\u003e\n \u003cli\u003eCoogan SM, Schock CS, Hansen‐Honeycutt J, Caswell S, Cortes N, Ambegaonkar JP. Functional Movement Screen\u0026trade;(FMS\u0026trade;) scores do not predict overall or lower extremity injury risk in collegiate dancers. International Journal of Sports Physical Therapy. 2020; 15:1029. https://doi.org/10.26603/ijspt20201029\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Football Injury, Functional movement screen, Sports Injury profile, Injury prediction","lastPublishedDoi":"10.21203/rs.3.rs-5775124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5775124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis 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.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInjury data for 169 players between the 2016\u0026ndash;2017 and 2020\u0026ndash;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.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOver the five seasons, the injury incidence was 7.76 injuries per 1,000 training hours (95% CI: 7.59\u0026ndash;7.93), 15.47 injuries per 1,000 match hours (95% CI: 15.23\u0026ndash;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.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe injury frequency was 8.9 per 1,000 hours of exposure, with 26% of injuries classified as severe, leading to over 28 missed training days per injury. FMS scores and asymmetry indicators did not reliably predict injuries. 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.\u003c/p\u003e","manuscriptTitle":"Exploring Injury Profiles in Professional Football: Evidence from a Five-Year Study and the Role of the Functional Movement Screen","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 04:01:06","doi":"10.21203/rs.3.rs-5775124/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-22T09:35:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-10T15:51:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135926809284474079878486367449281959691","date":"2025-05-07T18:10:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T05:01:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33210510987420512585625918934108662669","date":"2025-05-06T17:06:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T14:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239524983612063578221670382940125999687","date":"2025-04-25T01:21:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4005245786268628189098210084082269147","date":"2025-04-22T18:40:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T14:54:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T03:41:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-03-14T21:55:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"433f770e-80ac-4e30-85d9-2f6993256595","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:40:13+00:00","versionOfRecord":{"articleIdentity":"rs-5775124","link":"https://doi.org/10.1186/s13102-025-01262-8","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2025-07-28 16:05:03","publishedOnDateReadable":"July 28th, 2025"},"versionCreatedAt":"2025-05-02 04:01:06","video":"","vorDoi":"10.1186/s13102-025-01262-8","vorDoiUrl":"https://doi.org/10.1186/s13102-025-01262-8","workflowStages":[]},"version":"v1","identity":"rs-5775124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5775124","identity":"rs-5775124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}