\documentclass[fleqn,10pt]{wlscirep}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage[export]{adjustbox}
\usepackage[utf8]{inputenc}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{threeparttable}
\usepackage{graphicx}
\usepackage[sort, numbers]{natbib}
\usepackage{xpatch}

\title{Differences in the Profiles of Injured and Non-Injured Professional Football Players}

\author[1,2,3]{Francisco Martins}
\author[4]{Krzysztof Przednowek}
\author[1]{Hugo Sarmento}
\author[5,6,7]{Filipe Clemente}
\author[1,3]{Diogo Martinho}
\author[2,3,8*]{Élvio Rúbio Gouveia}

\affil[1]{University of Coimbra, Research Unit for Sport and Physical Activity (CIDAF), Faculty of Sport Sciences and Physical Education, 2004-504 Coimbra, Portugal}
\affil[2]{Department of Physical Education and Sport, University of Madeira, 9020-105 Funchal, Portugal}
\affil[3]{LARSYS, Interactive Technologies Institute, 9020-105 Funchal, Portugal}
\affil[4]{Institute of Physical Culture Sciences, Medical College, University of Rzeszów, 35-959 Rzeszów, Poland}
\affil[5]{Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, Viana do Castelo, Portugal}
\affil[6]{Sport Physical Activity and Health Research \& Innovation Center, Viana do Castelo, Portugal}
\affil[7]{Gdansk University of Physical Education and Sport, Gdansk, Poland}
\affil[8] {Swiss Center of Expertise in Life Course Research LIVES, 1227 Carouge, Switzerland}

\affil[*]{erubiog@staff.uma.pt}

\begin{abstract}
Football is distinguished by its intense sprinting, continuous vertical jumping, and abrupt direction changes, among other high-intensity motions. These typical football actions are primarily associated with the occurrence of sports injuries. This study aimed to (i) describe the injury profile over four seasons (i.e., number of injured players, injury types, and locations) and (ii) analyze differences between injured and non-injured professional football players in body composition and fitness tests. The sample included 121 professional male football players (26.1 ± 4.2 years). Injuries were recorded daily. Body composition, lower-body strength, flexibility, static strength, speed, agility, and endurance were assessed during four preseasons. Mann-Whitney U test assessed group differences. The results showed a total of one hundred and fourteen sports injuries. Older players (p = 0.02, r = 0.28) with more senior experience (p = 0.03, r = 0.26) and higher results in lower-limb explosive strength (p = 0.02, r = 0.48), were the ones significantly affected by injuries. The results support that possibly football players who have a higher capability for lower-limb explosiveness and lower values of total body water, fat-free mass, and skeletal muscle mass can benefit from additional preventive training to avoid injuries. Still, longitudinal assessments were verified as more capable of drawing detailed trends that characterize and differentiate the injured and non-injured groups, compared to preseason cross-sectional assessments. Longitudinal studies with other sporting samples can be beneficial to enhance sports science knowledge.

{\bf{Keywords:}} Soccer, Musculoskeletal, Explosive Strength, Body Composition, Injury Prevention

\end{abstract}

\begin{document}


\flushbottom
\maketitle
% * <john.hammersley@gmail.com> 2015-02-09T12:07:31.197Z:
%
%  Click the title above to edit the author information and abstract
%
\thispagestyle{empty}

\section*{Introduction}

Football is a high-impact sport that can be recognized by its high-intensity movements, such as intense sprinting actions, accelerations, decelerations, and sudden direction changes, all of which contribute significantly to the risk of injury \cite{medica2019modeling, silva2018acute, sarmento2024influence, ju2023contextualised}. Epidemiological studies consistently show that professional football players are at high risk of acute and overuse injuries, particularly involving the lower limbs \cite{mohammadi2024evaluation, gurau2023epidemiology, lopez2020epidemiology}. Acute injuries, such as muscle strains and ligament sprains, usually result from a single traumatic event or excessive force \cite{edouard2023traumatic}. In addition, overuse injuries develop gradually over time due to repetitive microtrauma to tissues without adequate recovery \cite{trojner2023growing}. Recent investigations have reported that hamstring strains and ankle sprains are common acute injuries, while patellar tendinopathy and adductor-related groin pain are frequently associated with overuse \cite{kolokotsios2021ankle, dinis2024athletes}. The mechanisms behind these injuries often involve muscle fatigue, imbalances in strength or flexibility, and repetitive load, especially during eccentric contractions or rapid decelerations \cite{rudisill2023evidence, afonso2023prevention}. These actions place substantial stress on the musculoskeletal structures, predisposing football players to injury.

Musculoskeletal injuries can cause pain and inflammation after hamstring and quadriceps strains and consequently restrict the participation of players in sports \cite{krustrup2018football}. 
Epidemiological investigations have shown that hamstring strains, one of the most common muscle injuries in football, usually result in an average player absence of two to three weeks \cite{ekstrand2023hamstring, gurau2023epidemiology}. Quadriceps strains, while less frequent, also cause significant absences, with an average recovery period of approximately two weeks \cite{pietsch2022risk}. More severe injuries, such as ruptures of the anterior cruciate ligaments, can lead to much longer periods of absence, never less than 6 months \cite{mardani2020return}. Approximately two out of three of all injuries in football that are the cause of missing two to three weeks of training sessions and official matches are related to injuries that affect muscle structures, often in the lower extremities and mainly in the quadriceps, hamstrings, and adductors of the thighs \cite{yanez2021lesiones, torrontegui2020incidence, martins2022sports}. Therefore, musculoskeletal injuries have a huge financial impact because they often result in large expenses for clubs in terms of medical care, rehabilitation, and salaries from non-active professional players \cite{nieto2024economic}. 

Neuromuscular functions are crucial for the effective performance of balance, strength, and power abilities, as well as to reduce compensatory lower limb movements \cite{sh2013neuromuscular}. Recent investigations indicate that deficiencies in these areas can lead to compensatory tendencies in lower extremity mechanics, increasing the risk of injury for football players \cite{francca2023bilateral, kirkendall2010effective}. The importance of physical assessments cannot be overestimated, as they are essential tools for pinpointing areas where players’ physical capacities need improvement. Frequent assessments of physical fitness metrics, such as speed and agility, help to customize training regimens and provide valuable information on the performance profiles of players \cite{martinho2024quantification, clemente2019quantifying}. Injury prevention programs focusing on improving neuromuscular control and strength have been shown to significantly reduce the frequency of lower limb injuries among football players. Specialized training programs, including plyometric and strength training, can lead to improved neuromuscular function and consequently to lower injury rates \cite{schlick2024effect, stevenson2015assessing}. In addition to improving performance, these programs contribute to long-term health and longevity of players, reflecting a paradigm shift in the approach of sports medicine to injury prevention over the past decade \cite{gouveia2023characterization, ugalde2015single}. Proactive methods are now more critical than ever to prevent injuries. 

As these training regimens emphasize speed and agility, they seem to play a vital role in enhancing players' overall performance. Speed and agility are abilities that consistently differentiate professional football players and are closely linked to lower body explosive strength, which is often associated with actions that occur before clear goal scenarios in elite contexts \cite{haugen2014role, faude2012straight}. The literature has increasingly studied the relationship between lower body strength, speed, agility, and the ability to change directions, identifying these elements as potential correlated actions \cite{andersen2018relationship, shalaj2020potential}. 

Numerous studies have examined variations in body composition parameters between injured and non-injured professional football players, highlighting their significance as risk factors for injury occurrence at this elite level \cite{binkley2015changes, dvorak2000risk, martins2022body}. Research has shown that excess body fat negatively affects players' ability to produce effective sprints and agility actions \cite{mohammadi2024evaluation, martins2022predictive}. This detrimental influence can be attributed to the additional weight that fat mass adds to the body, which can compromise agility and speed, leading to increased fatigue and increased stress on the musculoskeletal system during dynamic movements. This stress can increase the risk of musculoskeletal injuries, particularly in the lower extremities, as players often need to change direction rapidly and maintain high-intensity efforts throughout a match \cite{arslan2021effect,francca2022speed}. In contrast, fat-free mass has been reported as a beneficial factor that improves player performance and helps prevent injuries \cite{hughes2020value, hughes2023can}. This component of body composition, which includes muscle mass, is crucial for producing the strength and power necessary for explosive movements in football. Increased fat-free mass can improve an athlete's ability to absorb impact forces and maintain stability during high-intensity actions, thereby reducing the likelihood of injury \cite{martins2022body, nikolaidis2014age}. Furthermore, a higher proportion of lean muscle can contribute to better neuromuscular control, which is essential for balance and coordination during play.

Consequently, to implement injury prevention programs and identify athletes who are prone to injury, the assessment of body composition and physical fitness of players at the beginning of each sports season has usually been carried out in a professional context \cite{mohammadi2024evaluation,martins2022predictive}. In fact, performing this kind of assessment in the preseason can play a significant role in preventing injuries to players throughout the season \cite{hughes2020value,hughes2023can}.

Although all the studies mentioned have contributed to the topic in question, in methodological terms, most of them focus on evaluations from just one season and do not combine longitudinal and cross-sectional evaluations of a sample composed of professional football players. Still, the literature seems to have limitations in terms of analyzing possible differences (modifiable and non-modifiable factors) between injured and non-injured professional football players in longitudinal time spans of more than a single sports season. Therefore, our research objectives are twofold, (i) to describe the team's injury profile over four sports seasons, according to the number of injured players, total injuries, and specific area, type, and location, and (ii) to analyze the differences between injured and non-injured professional football players in body composition indicators and selected physical fitness tests during the study period. It is believed that a longitudinal analysis with this time frame will allow the results to be more scientifically accurate and valuable. 

\section*{Methods}
\subsection*{Study design}
This four-year dynamic cohort study was conducted in a professional male football team competing in the Portuguese Professional Football Leagues during four consecutive sporting seasons (from 2020/2021 to 2023/2024). All applied procedures were approved by the Ethics Committee of the Faculty of Human Kinetics (CEIFMH No. 34/2021). The research was carried out according to the principles of the Declaration of Helsinki and all participants signed an informed consent for participation in this study. In addition, all participants were treated ethically according to the American Psychological Association code of ethics. The participation of players in activities such as preseason and in-season assessments is a mandatory process outlined in the contractual agreements between the club and its male professional football players. Preseason evaluations were conducted at the Club's Training Center and in the University of Madeira Sports Science Laboratory, year after year, the week before the start of the corresponding season (that is, typically in July). In-season evaluations of body composition and lower-body explosive strength followed the same regular intervals across the four sporting seasons (every two weeks from July to May) at the Club's Training Center, with evaluations scheduled for the morning of the second day before match day. This schedule ensures consistent monitoring of players' physical performance and condition throughout the study period. Data collection included daily injury reports and assessments that spanned from the start of the preseason through the end of each competitive season, allowing for comprehensive follow-up of players' performance and recovery. In order to detect and highlight significant differences between injured and non-injured players over the study period, we present nine evaluation points strategically distributed over the competitive period (Figure 1). These points correspond to (i) three evaluations at the beginning of the competitive period, (ii) three evaluations in the mid-competitive period, and (iii) three evaluations in the final phase of the season. 


\subsection*{Participants}
The G*Power 3.1 software \cite{kang2021sample} was used to estimate the optimal sample size. According to the a priori Mann–Whitney test, a total of 74 (37 in each group) patterns were required to detect an interaction effect size of 0.80 at the 0.05 significance level and a power of 0.95. In total, 121 professional football players (26.1 ± 4.2 years old)  who represented this team were included in the study sample. If a player was part of the team in consecutive seasons, their data from each season were considered independently. As a result, the study sample includes multiple entries for players who participated in more than one season, allowing us to analyze their performance and injury patterns season by season. In the study sample, 13 were goalkeepers (11\%), 37 (30. 5\%) defenders, 37 (30. 5\%) midfielders and 34 (28\%) forwards. In addition, 27 had the left lower limb as the preferred limb to kick the ball (22.3\%) and 94 had the right lower limb and the preferred (77.7\%). The experience of the players was also a factor to be taken into account, measured by the number of years they had played in a senior squad. Injured players at the end of a season were followed until the end of their recovery period. The eligibility criteria for inclusion in the analysis were as follows: The players had to complete full preseason evaluations and complete in-season evaluations of body composition and lower body explosive strength, regardless of injury status. The injured players were followed until the end of their recovery period, with musculoskeletal injuries requiring a minimum absence of a full training session or official match to be considered for analysis. This approach ensured that data from injured players were representative of their recovery and performance during study period.


\subsection*{Injury report}
This study followed the recommendations of the Union of European Football Associations (UEFA) for epidemiological investigations. An injury was defined as an event during a scheduled training session or match that results in an absence from the next training session or match \cite{hagglund2005methods}. The medical department maintained daily injury records during the seasons (preseason and competitive period), including all training and official matches. The type, zone, and specific location were retained for analysis. Those are complementary variables that identify the part of the body that suffered structural and/or functional changes. Still, during the analyzed seasons this professional football team always presented the same physical trainer responsible for the area of injury prevention and load control training. This professional in the sports area was responsible for exposing this team to a preventive injury workload based mainly on mobility, myofascial, proprioceptive, plyometric, eccentric strength, bilateral and unilateral strength, isometric and dynamic core exercises. During the study period, the athletes performed such preventive exercises, on average, 2 sets with 4 to 8 repetitions, with durations between 15 and 25 min.

\subsection*{Body composition}
Body composition variables were evaluated using hand-to-foot bioelectrical impedance analysis (InBody 770, Cerritos, CA). Height was measured to the nearest 0.01 cm using a stadiometer (SECA 213, Hamburg, Germany). The measurement was carried out in the early morning, and the participants fasted and used only their underwear. Body composition evaluations were performed every two weeks during each of the four seasons. Total body water (in liters), body fat mass (in kilograms), fat free mass (in kilograms), skeletal muscle mass (in kilograms), body mass index (in kilograms per square meter) and percentage of body fat (in percentage) were the variables retained for analysis.

\subsection*{Physical fitness}
\subsubsection*{Lower-body explosive strength}
Lower body explosive strength was evaluated using the countermovement jump (CMJ) and the squat jump (SJ) \cite{bosco1983simple}. Each protocol included four data collection trials. The Optojump Next (Microgate, Bolzano, Italy) system was used for the measurement. Participants were incentivized to jump for maximum height in both tests, following three experimental trials to ensure proper execution. The participants returned to the starting position after each jump execution. Throughout the four seasons, the explosive strength of the lower body was evaluated by these two jumping tests every two weeks. Due to time constraints in our study, particularly in the professional team, we took into consideration a 30 second rest break between each jump performance. Shorter recovery durations, such as 20 and 30 seconds between each repetition of the CMJ, have also been reported in several investigations \cite{wu2019predicting, ishak2022influence}. Any deviation, such as hand movement or excessive knee flexion during countermovement, led to trial repetition. In each protocol, the maximum jumping height (in centimeters) reached was retained for analysis.

\subsubsection*{Flexibility}
Flexibility measurements were evaluated using two sit and reach tests \cite{plowman2013fitnessgram}. A trunk flexibility box (32.4 cm high and 53.3 cm long) with a 23 cm heel line mark was used. The protocol was initiated with participants sitting barefoot in front of the box for the bilateral evaluation, with the knee fully extended and both heels placed against the box. Participants were asked to achieve the maximum forward position and hold that position for about 3 s. The rest interval between each trial was set at 30 s. The best score (in centimeters) was retained for analysis.

\subsubsection*{Static strength}
The hand grip strength protocol consisted of three alternate data collection trials for each arm, performed using a hand dynamometer (Jamar Plus+, Chicago, IL, USA). The rest interval between each trial was set at 30 s. Participants held the dynamometer in one hand with the elbow at a 90° position and laterally to their trunk \cite{gerodimos2012reliability}. From this position, participants were asked to squeeze as hard as possible for about two seconds. If the participant’s arm was extended or if the dynamometer touched the body during execution, the test was repeated. The mean of the best performances of each upper limb (in kilograms) was retained for analysis.

\subsubsection*{Speed and agility}
Speed was evaluated using linear sprints at 5, 10, and 35 m, starting from a stationary position. Agility was evaluated using the t test. This is a 4-directional agility and body control test that assesses the ability to rapidly change direction while maintaining balance and without losing speed \cite{semenick1990tests}. For all tests, two test trials were performed separated by a 2-minute rest period. Both tests were recorded using the Witty-Gate photocell system (Microgate, Bolzano, Italy), and the best score of each (in seconds) was retained for analysis.

\subsubsection*{Cardio-respiratory endurance}

To estimate cardio-respiratory endurance, a continuous exercise of running to exhaustion was used. The protocol called 'Soccer test' consists of a physical exercise test with maximum incremental loads (speed and incline) on a motorized treadmill. The K5 Wearable Metabolic Technology (K5WMT) device was used to carry out the test. Three calibrations were performed prior to each assessment: (a) calibration of the volume disturbance; (b) calibration of the sample line (using the scrubber); and (c) calibration of the reference gases. All calibrations followed the order mentioned above. The examiner must also prepare in advance other equipment that will be used in the course of the assessment, such as the torso harness where the K5WMT will be placed and the head harness for the allocation of the face mask. In addition, a heart rate monitor was placed in the subject, roughly in the area of the diaphragm, to monitor the heart rate. The nature of the protocol is similar to other protocols described in the literature \cite{kaminsky1998evaluation, roecker1998predicting}. The assessment started at a speed of 9 km/h and stabilized at 12 km/h. The incline of the treadmill increased by 2\% every 3 minutes. The maximum duration of the test was 30 minutes. The maximum oxygen volume (in liters per kilogram per minute), the maximum heart rate (in beats per minute), and the maximum heart rate in the lactate phase (in beats per minute) were used for the analysis.

\subsubsection*{Addressing Potential Risk of Bias}
To address potential sources of bias, some strategies were followed. By selecting participants who were representative of a homogeneous sample of professional players from the same team, factors such as training and environment heterogeneity were minimized. All participants who met eligibility requirements were included, guaranteeing a representative and complete sample. The medical staff meticulously followed the injuries according to the UEFA regulations. This involved a thorough record of all injuries, their characteristics and the recovery times needed. Using daily injury records reduced the potential for under-reporting, which is a common cause of bias in epidemiological research. Each physical fitness and body composition assessment was completed by qualified experts following standardized methods, in a controlled environment and always following the same patterns of assessment moments and rest periods. This provided uniformity in the measures and reduced the bias associated with the evaluation procedures. During performance testing, players were urged to give it their all and measurements were made with gold-standard calibrated instruments. By doing this, measurement variability was reduced and it was guaranteed that the outcomes correctly represented players' actual performance levels. In terms of statistical analysis, due to the lack of normal distribution conformity, non-parametric tests of significance of intergroup differences were applied using the Mann-Whitney U test. This guaranteed that the conclusions reached were solid and that the data were appropriately interpreted. Giving each of these subjects careful thought enhances the overall quality of the study and preserves its scientific validity.

\subsubsection*{Statistical analysis}
Descriptive statistics were used to summarize the data collected. The absolute values present the number of football players, the total number of injuries and the zone, type, and specific location affected by the injuries. Two groups were built according to the status of muscle injury occurrence: injured and non-injured. Each player was observed as an individual pattern season after season. Therefore, if in a sporting season a player contracted a muscle injury, his preseason body composition and physical fitness performance were added to the group of injured players. Still, if the same player did not suffer a muscle injury in one of the other analyzed seasons, his preseason body composition and physical fitness performance of that specific season are counted in the non-injured group. The longitudinal evaluations are represented by the means and standard deviation of three moments of evaluation of the structural parts of the four sports seasons analyzed (i.e., start of the season, mid-season, and end of the season), according to the injured and non-injured groups. The Shapiro-Wilk test was applied to verify the conformity of the normal distribution. Due to the lack of conformity of normal distribution, non-parametric tests of significance of inter-group differences were applied through the Mann-Whitney U test. All analyses were performed with Jamovi 2.5.3 software (Sydney, AU). The significance level was set at $\textit{p} \leq 0.05$. The charts were prepared with R using the ggplot2 package \cite{Rggplot}.

\section*{Results}




\subsection*{Injury characterization}


\begin{table}[]
\centering
\caption{Four-year players injury report}
\label{tab:Table_1}
\begin{tabular}{lccccc}
\multicolumn{6}{l}
{\textbf{}} \\ \hline
Injury Report                          & 20/21  & 21/22  & 22/23  & 23/24  & Total  \\ \hline
Team (Nº)                              & 27     & 31     & 34     & 29     & 121    \\
Injured Players                        & 14     & 15     & 21     & 14      & 64     \\
Total number of   injuries             & 25     & 21     & 48     & 20     & 114    \\
Average number of injuries per player  & 0.9    & 0.7    & 1.4    & 0.7    & 0.9    \\
Players with 2 or more injuries (Nº)   & 4      & 6      & 14     & 3      & 27     \\ \hline
\end{tabular}
\end{table}



\begin{table}[!ht]
    \centering
    \caption{Injury typology based on the UEFA model proposed by Hägglund et al. (2005)}

    \label{tab:Table_2}
    \begin{tabular}{l c c c c c c} \hline
        ~ & \multicolumn{1}{c}{20/21} & \multicolumn{1}{c}{21/22} & \multicolumn{1}{c}{22/23} & \multicolumn{1}{c}{23/24} & \multicolumn{1}{c}{Total}  & ~ \\ \hline
        Zone & ~ & ~ & ~ & ~ &   & ~ \\ 
        Lower Limbs & 24 & 17 & 46 & 16 & 103  & ~ \\ 
        Upper Limbs & 2 & 2 & 1 & 1 & 6  & ~ \\ 
        Trunk & 1 & 1 & 0 & 3 & 5  & ~ \\ \hline
        Type & ~ & ~ & ~ & ~ &   & ~ \\ 
        Sprain & 11 & 5 & 13 & 3 & 32  & ~ \\ 
        Muscular & 15 & 11 & 34 & 16 & 76  & ~ \\ 
        Fracture & 0 & 2 & 0 & 0 & 2  & ~ \\
        Other & 0 & 3 & 0 & 1 & 4  & ~ \\ \hline
        Specific location & ~ & ~ & ~ & ~ &   & ~ \\ 
        Shoulder & 3 & 2 & 0 & 1 & 6  & ~ \\ 
        Forearm & 0 & 0 & 1 & 0 & 1  & ~ \\ 
        Abdomen & 0 & 1 & 0 & 1 & 2  & ~ \\ 
        Pelvis & 0 & 0 & 1 & 2 & 3  & ~ \\ 
        Calf & 0 & 2 & 1 & 4 & 7  & ~ \\ 
        Adductor & 3 & 2 & 6 & 1 & 12  & ~ \\ 
        Quadricept & 4 & 2 & 13 & 6 & 25  & ~ \\ 
        Hamstring & 3 & 3 & 14 & 2 & 22  & ~ \\ 
        Knee & 5 & 5 & 6 & 2 & 18  & ~ \\ 
        Tibiotarsal & 8 & 4 & 5 & 1 & 18  & ~ \\ \hline
       
    \end{tabular}
\end{table}

Table 1 shows the characterization of the injury report for the four sportive seasons analyzed. The report demonstrates that 64 of the 121 professional male football players suffered a sportive injury in four seasons, with a total of 114 injuries in that period. The season 2022/2023 was the one with more injured players (that is, 21) and also the sportive season where the number of injuries was higher (that is, 48). It is worth noting that in the first analyzed season, the results showed that we had almost had an identical value between the number of players and the number of injuries that occurred. However, the season with a more representative average number of injuries per player was also the 2022/2023 sports season (i.e., 1.4). Finally, 27 players had two or more sportive injuries during the study period, with season 2022/2023 showing the highest results, as this season had 14 players with injury recurrence, regardless of the type of injury.

Table 2 represents the characterization of all the injuries that occurred in the four seasons analyzed according to their zone, type, and specific location affected. Approximately 90\% of the injuries affected the lower limbs, with a total of 103 injuries affecting that body zone. In addition, almost 67\% of the total injuries affected the muscular regions, especially the quadriceps (that is, 25) and the hamstrings (that is, 22). In addition to that, sprains were also very common (that is, 36), mainly affecting the ankles and knee joints.

\subsection*{Differences in the profiles of injured and non-injured players according to preseason assessments}

\begin{table}[!htbp]
\caption{Descriptive statistics of preseason assessments to differentiate injured and non-injured player profiles}
\centering
\label{tab:Table_3}
\begin{threeparttable}
\begin{adjustbox}{max size={\columnwidth}{\textheight}}
\centering
\begin{tabular}{llcccccccccc}
\toprule
Variable        & Group             &   M   &    SD &   Min &   Me &   Max & $M_{dif}$ & p & r & $r_{mag}$\\
\midrule
Age (y)             & I                &  27.60 &  4.61  &  19.00 & 28.00  & 37.00  & 1.98 & 0.021* & 0.28 & Small \\
~               & NI               &  25.62 &  3.95   & 19.00 & 25.00  &  36.00 & ~ & ~ & ~ & ~ \\
Experience (y)      & I                &   9.47 &  4.63    & 1.00 &  9.50  & 19.00  & 1.88 & 0.031* & 0.26 & Small \\
~               & NI               &   7.59 &  3.90   &  1.00 &  7.00  & 18.00  & ~ & ~ & ~ & ~ \\
TBW (L)             & I                &  49.94 &  4.53  &  42.00 & 48.91  & 59.70  & -0.72 & 0.503 & -0.14 & Small \\
~               & NI               &  50.66 &  5.23  & 38.20  & 50.44  & 62.40  & ~ & ~ & ~ & ~ \\
BFM (kg)             & I                &   8.90 &  2.44  & 4.30  &  8.61  & 15.50  & 0.02 & 0.988  & 0.00 & None \\
~               & NI               &   8.88 &  2.37  & 3.80  &  8.43  & 17.70  &  ~ & ~ & ~ & ~ \\
FFM (kg)             & I                &  68.50 &  6.21  & 57.20  &  67.04 & 81.80  & -0.98 & 0.501 & -0.14 & Small \\
~               & NI               &  69.48 &  7.17  & 52.20  &  69.50  & 86.10 &  ~ & ~ & ~ & ~ \\
SMM (kg)             & I                &  39.30 &  3.66  &  32.30 & 38.44  & 46.90  & -0.60 & 0.486  & -0.15 & Small \\
~               & NI               &  39.90 &  4.24  & 29.30  & 39.95  & 49.80  &  ~ & ~ & ~ & ~ \\
BMI (kg/m\textsuperscript{2})             & I                &  23.73 &  1.24 &  21.30 &  23.80 & 25.80  & 0.05 & 0.861 & 0.04 & None \\
~               & NI               &  23.68 &  1.37 &  21.00 &  23.44 &  27.70 &  ~ & ~ & ~ & ~ \\
PBF (\%)             & I                &  11.51 &  2.99 &  5.80 &  11.02 & 19.50  & 0.18 & 0.902 &  0.02 & None \\
~               & NI               &  11.33 &  2.71 &  4.60 & 11.10  & 20.90  &  ~ & ~ & ~ & ~ \\
CMJ (cm)             & I                &  42.19 &  4.67 & 33.50  &  41.69 & 49.70 & 2.23 & 0.023* & 0.48 & Medium \\
~               & NI               &  39.96 &  4.57 &  26.00 &  39.55 & 52.80  &  ~ & ~ & ~ & ~ \\
SJ (cm)              & I                &  40.61 &  5.23 & 29.20  &  39.34 &  46.50 & -3.98 & 0.101 & 0.20 & Small \\
~               & NI               &  44.59 &  5.01 & 26.80  & 38.20  & 52.80  &  ~ & ~ & ~ & ~ \\
Sit\_And\_Reach (cm) & I                &  36.92 &  6.62 &  17.00 &  36.50 &  50.70 & 1.81 & 0.215 &  0.26 & Small \\
~               & NI               &  35.11 &  6.94 & 19.00  & 35.40 &  55.60  & ~ & ~ & ~ & ~ \\
Handgrip (kg)        & I                &  49.55 &  6.20 &  38.15 &  49.20 & 65.37  & 1.29 & 0.222 &  0.15 & Small \\
~               & NI               &  48.26 &  6.32 & 32.90  & 47.37 & 72.00   & ~ & ~ & ~ & ~ \\
V5m (s)          & I                &   1.06 &  0.08 & 0.87  &  1.05 & 1.24   & -0.01 & 0.394  &  0.10 & Small \\
~               & NI               &   1.07 &  0.11 &  0.74  &  1.05  & 1.44   & ~ & ~ & ~ & ~ \\
V10m (s)         & I                &   1.76 &  0.09 &  1.50  &   1.77 &  1.94  & -0.03 & 0.281 &  0.13 & Small \\
~               & NI               &   1.79 &  0.11 &   1.56  &  1.77  &  2.13  & ~ & ~ & ~ & ~ \\
V35m (s)         & I                &   4.75 &  0.22 &  4.27   &  4.75  & 5.23   & -0.05 & 0.333 & 0.12 & Small \\
~               & NI               &   4.80 &  0.22 &   4.35  &  4.75 & 5.37  & ~ & ~ & ~ & ~ \\
T\_test (s)      & I                &   9.18 &  0.32 &  8.53  &  9.13  & 10.09   & 0.09 & 0.158 &  0.17 & Small \\
~               & NI               &   9.09 &  0.34 &   8.10  &  9.11  &  10.00  & ~ & ~ & ~ & ~ \\
VO2Max (L/kg/min)          & I                &  53.87 &  5.37 &  39.00  & 53.33  & 68.20  &  0.72 & 0.780 &  0.14 & Small \\
~               & NI               &  53.15 &  5.33 &  39.60  &  53.30 & 71.00  & ~ & ~ & ~ & ~ \\
HRmax (bpm)          & I                &  191.12 &  9.00 & 175.00  &  189.08 & 209.00  &  2.39 & 0.181 &  0.14 & Small \\
~               & NI               &  188.73 &  7.61 &  166.00 &  189.06 & 207.00  &  ~ & ~ & ~ & ~ \\
HRmax\_LA (bpm)       & I                &  181.22 &  7.89 &  163.00 &  179.88 & 200.00  &  2.04 & 0.247 &  0.14 & Small \\
~               & NI               &  179.18 &  7.99 & 162.00  &  179.65 & 201.00  &  ~ & ~ & ~ & ~ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\begin{tablenotes}
\footnotesize
\item I - injury group; NI - non-injury group; M - mean; SD - standard deviation; Min - minimum value; Me - median; $M_{dif}$ - mean difference; Max - maximum value; p - probability of testing; r - effect size; $r_{mag}$ - magnitude of effect size; TBW - total body water; BFM - body fat mass; FFM - fat-free mass; SMM - skeletal muscle mass; BMI - body mass index; PBF - body fat percentage; CMJ - countermovement jump; SJ - squat jump; V5m - five meter velocity; V10m - ten meters velocity; V35m - thirty-five meters velocity; VO2Max - maximum oxygen uptake; HRmax - maximum heart rate; HRmax\_LA - maximum heart rate in lactate phase; y - years; L - liters; kg - kilograms; cm - centimeters; s - seconds; min - minutes; bpm - beats per minute.
\end{tablenotes}
\end{threeparttable}
\end{table}

The differential analysis between injured and non-injured professional football players in terms of age, experience, body composition, and physical fitness performance is reported in Table 3. The results show that age (\textit{p} = 0.02, \textit{r} = 0.28, small effect size), experience (\textit{p} = 0.03, \textit{r} = 0.26, small effect size), and countermovement jump (\textit{p} = 0.02, \textit{r} = 0.48, medium effect size) are the study variables in which the values are significantly different between players who have had muscle injuries over the period studied and those who have not had this type of injury. Thus, it was observed that the players who were older, more experienced, and had better countermovement jump performances were, on average, the players who suffered muscle injuries during the four seasons under study. Although the other variables analyzed also show differences between groups, no statistically significant differences were detected.

\subsection*{Longitudinal trends in the profiles of injured and non-injured players}

\begin{figure}[!htbp]
\centering
\includegraphics[width=0.9\textwidth]{graphs (1).eps}
\caption{Four-year longitudinal trends
of injured and non-injured players according to three moments in the beginning, middle,
and final phases of each competitive season}
\label{fig1}
\end{figure}

The longitudinal characterization and differentiation between injured and non-injured players in terms of body composition variables and vertical jumps can be seen in Figure 1. It presents data from nine evaluation points strategically distributed throughout each competitive season: (i) three evaluations at the beginning of the competitive period, (ii) three evaluations in the mid-competitive period, and (iii) three evaluations in the final phase of the season. These specific time points were selected to illustrate key trends in players' physical condition and readiness throughout the seasons' stages. The differences in body composition indicators were significant at the end of the sports seasons, with the injured player group showing lower values throughout the four seasons in terms of TBW (W30, $p = 0.03$; W32, $p = 0.02$), FFM (W28, $p = 0.04$; W30, $p = 0.03$; W32, $p = 0.03$) and SMM (W30, $p = 0.03$). In terms of BFM and PBF, there was a notable balance between the groups. Although the non-injured group had significantly higher BFM (W28, $p = 0.02$; W30, $p = 02$) and PBF (W30, $p = 0.04$) values at the end of the seasons. On the other hand, performance in vertical jumps showed statistically significant differences throughout the longitudinal period of the seasons analyzed, where the group of injured players consistently performed better in CMJ vertical jumps (W1, $p = 0. 02$; W2, $p = 0.02$; W4, $p = 0.03$; W16, $p = 0.04$; W28, $p \leq 0.05$; W30, $p \leq 0.01$; W32, $p \leq 0.05$) and SJ (W1, $p = 0.05$; W2, $p = 0.02$; W4, $p = 0.04$; W16, $p = 0.04$; W28, $p \leq 0.02$; W30, $p \leq 0.01$; W32, $p \leq 0.05$).

\section*{Discussion}
The main objective of this investigation was to analyze the differences between injured and non-injured professional football players in body composition indicators (that is, TBW, BFM, FFM, SMM, BMI and PBF) and selected physical fitness abilities (that is, explosive strength of the lower extremities, flexibility, static strength, speed, agility and cardio-respiratory endurance) during the study period. The results showed that older players, with more experience in the senior role, with lower values of TBW, FFM, and SMM, and better performance in explosive strength of the lower extremities during the study period, were the ones significantly affected by injuries during the four sports seasons. The importance of longitudinal assessments in monitoring body composition and physical fitness over the course of the season was also evidenced by the results of this study. Reinforcing this vision, a study conducted with 26 football players demonstrates that a relatively short period of inactivity can cause significant changes in the profiles of lower limb strength and body composition \cite{sannicandro2021soccer}. Thus, regular assessments are essential to detect early changes in players' physical profiles and implement appropriate preventative strategies.

According to the injury report, if we take into account only muscle injuries, the type of injury that occurs with greater frequency, the average number of injuries per player was 0.6 muscle injuries. In fact, numerous investigations have verified that muscle injuries are the most common in the male professional football context \cite{yanez2021lesiones, ekstrand2011epidemiology, hoffman2019australian}. Also, comparing the average number of muscle injuries per player, there is no consensus in the literature on an expected value. A longitudinal study conducted with data from nine sports seasons with a sample of more than 2000 professional football players reached exactly the same ratio (that is, 0.6) of muscle injuries per number of players \cite{ekstrand2011epidemiology}. Another study carried out over three seasons in youth football reported an average value close to 0.90 muscle injuries per player \cite{raya2020longitudinal}. This multitude of results are expected regarding the frequency of muscle injuries in professional football, since different players will need different training methodologies and workloads to be able to optimize their sporting performance to distinct competitive demands without coming close to sustaining a muscle injury. In fact, this type of insight is still urgent to strengthen the sense of need to diagnose and assess players regularly throughout a sporting season \cite{martins2022body}.

Clinically, analyzing the occurrence of injuries in the four seasons, it was noticed that around nine out of ten injuries affected the structures of the lower limbs. In fact, in recent years, there has been much research done in football and the incidence of lower limb injuries is clearly the most common rate among different samples in a professional context, with a rate of around 90\% \cite{shalaj2016injuries, lakshakar2022common, pfirrmann2016analysis}. In addition, most types of injury occur in muscle structures, mainly affecting the two main areas of the thigh, more precisely the quadriceps and hamstrings. Also, investigation in this clinical field verified that muscle injuries affecting the four main muscle groups in the lower extremities are the most recurrent, including the quadriceps and hamstrings \cite{ekstrand2011epidemiology}.  

Regarding the variables that significantly differentiated injured players from non-injured players, several studies have already found that older players, and consequently more experienced players, are at greater risk of suffering injuries, especially in muscular structures, over the course of a season \cite{arnason2004risk, ekstrand2011epidemiology}. In fact, age is usually associated with significant alterations in physiological and biological components and recurrent injury events related to professional football performance \cite{martins2022body}. This result is quite to be expected, since older players usually have higher rates of accumulated workload, both in training and in competition, than their younger counterparts, making age possibly a relevant demographic factor in differentiating players' injury profiles.

In addition, CMJ showed significant differences between injured and non-injured players, with the group of injured players showing higher average values throughout the four sportive seasons in this explosive strength indicator. Furthermore, if we add a longitudinal analysis, we verify that in both vertical jumps (that is, CMJ and SJ), the non-injured group consistently showed higher jumping ability throughout the study period. Vertical jump performance, designed to assess the explosive strength of the lower extremities, has been widely evaluated as a risk factor for muscle injuries in professional football \cite{fort2022higher, de2019systematic}. A research involving 36 professional football players conducted in a single season verified that the chance of getting hurt increases for each centimeter of height performed during the SJ evaluation \cite{henderson2010factors}. However, according to a recent systematic review on this topic, the literature has yet to reach consensus on the impact of strength variables on increasing or decreasing the risk of injury \cite{de2019systematic}. Regardless, players identified as having more explosive strength in their lower limbs over the course of the study period possibly deserve an extra caution in injury prevention training across an entire sporting season. In fact, one of the main concerns of training programs is the balance between injury prevention and fatigue symptoms and the need to increase training demands to those of competition, since lower body explosive strength has often emerged as significant for professional players to perform high-intensity actions, including sprints and change of direction \cite{andersen2018relationship, penailillo2016muscle}. This concern has been amplified by the suspension of all professional competitions in the face of the COVID-19 pandemic. A study aimed at investigating the impact of this inactivity on soccer players found a significant reduction in explosive strength and an increase in asymmetry between the lower extremities after a period of inactivity \cite{sannicandro2021soccer}. Thus, it could be suggested that the absence of adequate stimuli or an abrupt return to competitive load could be another aspect that negatively affects the ability to generate strength in a balanced way, increasing the predisposition to injury.

Although no significant differences were found in any of the body composition variables in the preseason assessments, the impact that these variables can have on players' performance at a longitudinal level should not be overlooked. However, in clinical terms, differences in the longitudinal trajectories of the body composition variables were noticeable throughout the study period. The group of non-injured players showed consistently higher values in the TBW, FFM, and SMM variables. On the other hand, in the BFM, BMI and PBF variables, there were oscillations between the two groups. In fact, some investigations have provided the literature with results showing that higher percentage of body fat and body fat indexes during the season can be associated with differentiating the profiles of professional football players, with higher values observed in the injury group \cite{martins2022body}. In addition, body composition variables such as BMI and FFM have been emphasized in the literature as essential to improve players' athletic skills, including jumping, sprinting, and direction changes \cite{binkley2015changes,malina2004growth}. In line with this evidence, the study by \cite{sannicandro2021soccer} showed that after a period of forced inactivity, there was a reduction in body weight and changes in BMI in football players. These seem to negatively impact players' ability to sustain training and competition loads, contributing to an increased vulnerability to muscle injuries. Therefore, these results reinforce the relevance of conducting regular body composition assessments over the course of a sporting season, which can be a significant complement to detecting player injury profiles.

This study has some limitations that should be considered when interpreting and contextualizing the results and their findings. The fact that this study was carried out with a single football club may limit the generalizability of the results. Also, the history of injuries was not taken into account, which could have an influence on the players' current abilities and could also affect the recurrence of injuries. However, the main strengths of the study are the development of this longitudinal investigation over four sports seasons and the conduct of frequent assessments with instruments described in the literature as the gold standard.

\section*{Conclusions}
In our investigation, preseason evaluations showed that age, experience, and explosive strength of the lower extremities, characterized by vertical jump amplitude, were the most significant variables in differentiating players who contracted muscle injuries and those who did not during the study period. In addition, the longitudinal evaluations (that is, every two weeks) reinforced the fact that the players with the greatest amplitude of vertical jumps were the ones who contracted muscle injuries. The two-weekly assessments also showed that players with lower values of total body water, fat-free mass, and skeletal muscle mass throughout the season were the most affected by muscle injuries over the four seasons analyzed. Therefore, longitudinal assessments were verified as more capable of drawing detailed trends that characterize and differentiate the injured and non-injured groups, compared to preseason cross-sectional assessments. Even so, the homogeneity of the group in terms of workloads and all the normal daily habits of belonging to a professional team may lead to the fact that there are no significantly different results in more study indicators. Sports science will benefit if longitudinal studies of this nature are developed in other teams, leagues, and other competitive contexts (i.e. semiprofessional, amateur, and youth).

\bibliography{bib}

\section*{Author Contributions}

Conceptualization, F.M., K.P., H.S. and E.R.G.; Methodology, F.M., D.M. and E.R.G.; Validation, K.P., H.S., F.C. and E.R.G.; Formal analysis, F.M., D.M. and K.P.; Investigation, F.M. and E.R.G.; Resources, H.S., F.C. and E.R.G.; Writing—original draft preparation, F.M. and K.P.; Writing—review and editing, D.M., F.C., E.R.G. and H.S.; Visualization, F.M., F.C., E.R.G. and H.S.; Project administration, E.R.G, K.P. and H.S.; Funding acquisition, E.R.G. and H.S. All authors have read and agreed to the published version of the manuscript.

\section*{Funding}
F.M. acknowledge support from Foundation for Science and Technology under a doctoral scholarship 2023-2027 (2023.01187.BD). F.M. and E.R.G. acknowledge support from LARSyS—Portuguese national funding agency for science, research, and technology (FCT) pluriannual funding 2020–2023 (Reference: UIDB/50009/2020). This research was funded by the Portuguese Recovery and Resilience Program (PRR), IAPMEI/ANI/FCT under Agenda C645022399-00000057 (eGamesLab).

\section*{Institutional Review Board Statement}
This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Human Kinetics, (CEIFMH Nº34/2021), and followed the ethical standards of the Declaration of Helsinki for Medical Research in Humans (2013) and the Oviedo Convention (1997). 

\section*{Informed Consent Statement}
Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from all players to publish this paper.

\section*{Data Availability Statement}
The data presented in this study are available upon request from the corresponding author.

\section*{Acknowledgments}
The authors would like to thank all players for participating in this study.

\section*{Competing Interests Statement}
The authors declare no conflict of interest.

\end{document}