Impact of Weekly Training Load and Match Load on Next-Day Neuromuscular Fatigue in Elite Football Players: A Longitudinal Observational Study

Impact of Weekly Training Load and Match Load on Next-Day Neuromuscular Fatigue in Elite Football Players: A Longitudinal Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of Weekly Training Load and Match Load on Next-Day Neuromuscular Fatigue in Elite Football Players: A Longitudinal Observational Study Norbert Banoocy, Daniel Lopez Lopez, Rafael Oliveira, Hadi Nobari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8552797/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Elite footballers experience substantial external loads in training and match play that may impair neuromuscular function. This study examined associations between accumulated weekly training load and single-match load with next-day hamstring and hip strength. Twenty-four professional male outfield players were monitored across 15 non-consecutive microcycles. GPS-derived metrics (e.g., total distance, high-intensity and sprint distance, high-intensity actions, sprints, accelerations and decelerations) were aggregated for training and for matches. The morning after each match (i.e., 12–18 h), eccentric hamstring strength (Nordic hamstring exercise), prone isometric hamstring force, and 45° hip adductor and abductor squeeze forces were assessed. Missing strength values (7–23%) were imputed, load variables were reduced using principal component analysis, and linear mixed-effects models were fitted with player as a random intercept. Higher overall match load was associated with lower next-day Nordic hamstring force ( ≈ − 5 N per + 1 SD; p = 0.046), whereas weekly training load was not related to any strength outcome (p = 0.260–0.770). Match intensity distribution was positively associated with next-day adductor force ( ≈ + 12 N; p = 0.046), while isometric hamstring and abductor forces were unaffected. These findings indicate that intense match play produces measurable next-day eccentric hamstring fatigue, supporting Nordic testing as a practical post-match monitoring tool. Health sciences/Health care Health sciences/Medical research Biological sciences/Physiology Workload Nordic hamstring exercise adductor abductor monitoring soccer Figures Figure 1 Figure 2 1. Introduction Elite football players face congested schedules and high training intensities, which can lead to neuromuscular fatigue and impaired performance in subsequent days [ 1 – 3 ]. Monitoring fatigue status has become a key practice in high-performance football [ 4 ]. Therefore, traditional approaches to assess fatigue (e.g., maximal jumps or sprints) can be impractical to perform frequently, and their sensitivity to subtle daily changes in fatigue is limited [ 5 ]. Simpler, non-exhaustive tests such as self-reported wellness and heart-rate variability are often used, but direct muscle-function tests may be more revealing [ 6 ]. Indeed, post-match fatigue can contribute to performance decrements for up to approximately 72 hours after competition [ 7 ]. For example, Brownstein et al. [ 8 ] observed substantial reductions in voluntary quadriceps and hamstring force for 2–3 days following a match, reflecting prolonged neuromuscular fatigue post-competition. Within this context, muscle-specific strength assessments have emerged as valuable tools for athlete monitoring. Eccentric hamstring strength, often measured via the Nordic hamstring exercise (NHE), and isometric hip adductor strength measured via squeeze tests are of particular interest due to their links with injury risk [ 9 ]. Low preseason or chronic eccentric hamstring strength is associated with higher hamstring injury rates, and interventions like NHE programs have been shown to reduce these injuries [ 10 ]Likewise, hip adductor weakness is a known risk factor for groin injuries [ 11 ]. In one study of elite youth footballers, hip adductor strength dropped by approximately 6% in the week prior to groin pain onset and 12% at the onset of groin injury symptoms [ 12 ]. These findings suggest that monitoring changes in adductor strength may help flag developing groin issues. Conversely, maintaining high chronic training loads, especially high-speed running exposure, can build resilience [ 13 ]. Malone et al. [ 13 ] reported that players accustomed to greater sprint workloads were less likely to be injured despite acute spikes, indicating a protective adaptation to repeated high-intensity loads. Therefore, understanding fatigue responses in these muscle groups under varying load conditions is crucial for both performance and injury prevention. A previous systematic review with meta-analysis into post-match fatigue had yielded mixed results [ 7 ]. On the one hand, minimal changes in CMJ performance or wellness scores despite large match loads were found, suggesting that these generic metrics might not capture muscle-specific fatigue [ 14 ]. Moreover, Thorpe et al. [ 4 ] noted that morning CMJ measures did not consistently reflect the previous day’s training load in elite players, questioning their utility as a standalone fatigue marker. On the other hand, direct strength measures may reveal neuromuscular deficits that jump tests miss. Indeed, peak force outputs of key muscle groups have been shown to decrease for 24–48 hours after matches. For instance, Fransson et al. [ 14 ] observed significant acute fatigue in hamstrings and adductors following a simulated soccer match, with slower recovery trajectories than for some other muscle groups. Such evidence aligns with practical reports that eccentric hamstring strength and hip adduction strength can be reduced the day after competition as part of the normal fatigue-response cycle. Monitoring these metrics could therefore improve detection of residual fatigue compared to traditional tests. Another important consideration is how training load throughout the week interacts with match load to influence fatigue. Coaches often periodize weekly training to ensure players are sufficiently recovered for matches. High training loads can induce fatigue, but players with greater training-induced fitness might better tolerate the intense demands of match play [ 15 ]. The balance between training and match exposure is delicate with the possibility of excessive acute or chronic load increasing injury risk, whereas a well-managed load can enhance performance and robustness [ 16 ]. Empirical studies have attempted to model these dynamics; for example, Varley et al. [ 17 ] examined relationships between match running outputs, post-match fatigue, and recovery, finding that metrics such as high-speed distance and accelerations correlated with temporary neuromuscular performance declines. Similarly, a systematic review by Hader et al. [ 18 ] concluded that certain external load metrics, notably repeated accelerations and decelerations are useful predictors of acute fatigue after matches. More recently, Marqués-Jiménez et al. [ 19 ] found that external load metrics (e.g., accelerations and total distance) predicted declines in post-match CMJ and sprint performance in soccer players. These insights underscore the need to parse out the contributions of accumulated training load versus one-off match load on neuromuscular fatigue. It remains unclear whether a high training load in the days before a match exacerbates fatigue beyond that caused by the match itself, or if well-conditioned players can endure heavy training without additional next-day fatigue. Given these gaps, the present study aimed to investigate the relationship between accumulated external load across the training week and that incurred during competitive match play, and their influence on next-day neuromuscular performance in elite football players. Neuromuscular performance was evaluated through force production of the hamstring, adductor, and abductor muscle groups using validated field-based strength assessments [ 20 , 21 ]. It was hypothesized that (i) higher match loads would be associated with greater next-day neuromuscular fatigue, reflected by reduced force production, while (ii) accumulated weekly training load, would have a smaller or negligible effect on next-day strength measures. These hypotheses are grounded in previous research linking increased external loads with post-match neuromuscular fatigue and performance decrements in professional football [ 4 , 7 , 8 , 14 , 17 , 18 ]. 2. Materials and Methods 2.1 Participants Twenty-four male outfield professional football players from a single club competing in an elite European league (Denmark) were monitored as part of the club’s routine performance-tracking program. The sample included players across various outfield positions, with goalkeepers excluded due to their distinct positional demands. Participants had a mean ± SD age of 26.5 ± 4.5 years (range 18–35), stature 181 ± 6 cm, and body mass 77.3 ± 7.8 kg. Inclusion criteria was adapted from a previous study[ 2 ] and consisted in the following: (i) registration as a first-team professional player at the club; (ii) regular participation in training (≥ 30 min of monitored activity per session) and competitive matches during the study period; and (iii) availability of valid Global Positioning System (GPS) and neuromuscular testing data with ≥ 60 min of match exposure in at least one competitive fixture. Exclusion criteria included: (i) current musculoskeletal injury or ongoing rehabilitation limiting full training participation; and (ii) incomplete or invalid neuromuscular testing data. These thresholds are consistent with previous elite-football load–fatigue research and ensure that included weeks represent typical outfield player exposures [ 5 , 22 ]. Goalkeepers were excluded to maintain homogeneity of outfield movement profiles. All players were accustomed to regular neuromuscular monitoring and had extensive experience using GPS technology during training and match play. Each external-load metric was calculated separately for the training week by summing data from the four field sessions preceding the match, and for the match as a single-game total. The typical microcycle consisted of MD + 1 recovery, MD + 2 off, and four preparatory sessions (i.e., MD-4 to MD-1) that formed the training-week total used in the analysis. In total, 24 coded outfield players contributed 139 valid player-week observations across 15 microcycles, with 7–10 players contributing data per week. This reflects normal fluctuations in weekly availability due to match selection, recovery status, or data completeness. The study was conducted during the competitive season, when the team played approximately one official match per week. Participation in the monitoring formed part of the players’ contractual obligations, and all participants provided written informed consent for the research use of their pseudonymized data. Ethical approval for this study was granted by the Committee of Ethics in Research and Teaching of the University of A Coruña (approval code 2024-062), as part of the doctoral project Integration of external load, muscular fatigue, and technical/tactical performance analysis to optimize football performance . All procedures complied with the Declaration of Helsinki. Data were processed in accordance with GDPR standards; the re-identification key remained within the club’s performance department, and only pseudonymized datasets were accessed by the research team. No additional interventions beyond routine monitoring were performed; therefore, an insurance policy was not required. 2.2 Design and Data Collection A longitudinal, observational design was employed. For each weekly microcycle, external load metrics were collected during training sessions and the official match, and neuromuscular performance was measured on the morning following the match. Training load was defined as the sum of all external load from the team’s training sessions in the 4 days leading up to the match (n = 60). Match load was defined as the load from the single competitive match with only weeks with one match included for analysis (n = 15). All training sessions and matches were performed on outdoor natural-grass pitches. External load was tracked using 10 Hz GPS devices (Catapult Sports, Melbourne, Australia) with integrated tri-axial accelerometers [ 23 ]. Each player wore the same GPS unit in a vest for all sessions and matches to maintain consistency. Data were downloaded post-session and processed with the manufacturer’s software (Catapult OpenField) to extract key metrics. The system has demonstrated high validity and reliability for team-sport movement monitoring [ 24 , 25 ]. The validity and reliability of Catapult GPS systems have been well established, with Johnston et al. demonstrating high agreement and low measurement error (typical error ≈ 1–2%) for 10 Hz units during team-sport movement assessments, and Ocak et al. (2025) confirming excellent accuracy and reliability of the Catapult Vector S7 units (MAPE ≈ 0.8%, CV ≈ 0.2%) under controlled testing conditions [ 24 , 25 ]The following external load variables were analyzed, based on common practices in football load monitoring [ 26 , 27 ]: Total Distance: total distance covered (meters). High-Intensity Distance (HID): distance covered at speeds > 20 km·h⁻¹ Sprint Distance: distance covered at speeds > 25 km·h⁻¹ High-Intensity Actions: count of running efforts > 20 km·h⁻¹ Sprints: count of sprint efforts > 25 km·h⁻¹ Accelerations > 2 m·s⁻²: count of high accelerations exceeding 2 m·s⁻². Decelerations 3 m·s⁻²: count of very intense accelerations > 3 m·s⁻². Decelerations < − 3 m·s⁻²: count of very intense decelerations < − 3 m·s⁻². 2.3 Neuromuscular Fatigue Monitoring Neuromuscular performance was assessed the morning after each match (approximately 12–18 h post-match) using four tests targeting the hamstring and hip muscle groups. These assessments were selected for practicality in elite football environments, relevance to sport-specific actions, and established reliability in athlete populations[ 28 – 31 ]. Testing occurred during the club’s scheduled morning recovery session and was embedded in the players’ routine monitoring program. ISO Prone Isometric Hamstring Push (ISO Prone) Isometric hamstring strength was measured using a NordBord device (Vald Performance, Australia) in a prone, hip-neutral position. Players lay face-down with hips flat and knees fully extended (0°) and placed their ankles against padded hooks on a rigid frame. They were instructed to push back against the hooks with maximal effort, contracting their hamstrings isometrically. Average force (N) from both limbs was recorded over a 5 s maximal effort. Two trials were performed with ~ 10 s rest between trials and the mean force across the trials was used. This long-muscle-length isometric test targets the hamstrings in an extended-knee position and has demonstrated high test–retest reliability in elite football players (ICC ≈ 0.85–0.92) [ 30 , 32 , 33 ]. Nordic Hamstring Exercise (NHE) Eccentric hamstring strength was measured using the NordBord device (Vald Performance, Australia). Players knelt on a padded board with their ankles secured under padded hooks and leaned forward at a controlled speed until they could no longer resist the fall. The combined bilateral force (N) was recorded for each trial. Each player performed three trials, and the average force from the three trials was taken as the outcome. This test targets the hamstrings in an eccentric modality and the use of the NordBord device has demonstrated good to excellent reliability in professional football players (ICC ≈ 0.83–0.90) [ 21 ] Hip Adductor/Abductor Squeeze (45°) (Add/Abd 45°) Isometric hip adductor and abductor strength was assessed using the ForceFram e device (VALD Performance, Australia). Players lay supine with hips flexed to 45° and knees bent to 90°. For the adductor test, players squeezed inward against padded plates positioned on the medial sides of the knees; for the abductor test, they pushed outward against lateral pads with maximal effort. The combined bilateral force (N) was recorded for both adduction and abduction measures. Two 5-s maximal contractions were performed, separated by ~ 10 s of rest, and the mean value across trials was analyzed. This short-lever (45°) adductor/abductor squeeze configuration is widely used to monitor hip-muscle strength and groin function in elite football [ 29 , 31 , 34 , 35 ]. Studies using ForceFrame or comparable fixed dynamometry report good-to-excellent reliability for hip adduction (ICC ≈ 0.81–0.91 ) and good reliability for abduction (ICC ≈ 0.80–0.90) in athletic populations; handheld and long-lever protocols can yield higher ICCs (≈ 0.90–0.97) [ 29 , 31 , 34 , 35 ]. Including both adduction and abduction measures provides a more comprehensive evaluation of hip-muscle balance and functional stability. 2.4 Data Processing Raw GPS data were cleaned to remove anomalies (e.g., spurious spikes from signal loss) before calculating weekly and match totals. If a player had missing neuromuscular test data due to a recovery day off or a minor injury, the missing values were addressed via imputation. Little’s MCAR test confirmed that force data were missing completely at random (χ², p = 0.33), indicating no bias in the pattern of missingness. Multiple Imputation by Chained Equations (MICE) was employed to fill in missing fatigue measures, following the approach of van Buuren and Groothuis-Oudshoorn [ 36 ]. Five imputed datasets were generated using predictive mean matching, including all load and fatigue variables plus player ID and week as predictors. The algorithm ran for 20 iterations per dataset to ensure convergence. Diagnostics indicated that the distributions of imputed values were plausible and closely matched those of the observed data, preserving the mean and variance structure. Finally, the five imputations were averaged for each missing entry to produce a single complete dataset for analysis. This approach reduced bias compared to listwise deletion and maximized use of the available data. Prior to multivariate analysis, all external load variables were standardized. Each player-week value was converted to a z-score based on the sample mean and standard deviation (SD). Standardization allowed comparison of effect sizes on a common scale and was necessary for combining variables in the principal component analysis (PCA). The neuromuscular outcome variables were left on their original scale (N) for ease of interpretation. 3 Statistical Methods 3.1 Descriptive Statistics and Data Imputation All variables were summarized as mean ± standard deviation (SD). Weekly training metrics were aggregated across the four sessions (MD-4 to MD-1); match metrics reflected single-game totals. Next-day neuromuscular outcomes (Nordic, ISO prone, Adductor 45°, Abductor 45°) were recorded in newtons (N). Missing data occurred primarily in the neuromuscular tests: depending on the metric, 7–23% of values were missing, mostly from players not being tested on certain occasions due to squad rotation or minor injury. Little’s MCAR test was not significant (p = 0.330), supporting the assumption that data were missing at random. As described above, we applied the MICE procedure to handle these missing values. After 20 iterations, the chained equations algorithm converged, and diagnostic checks confirmed that the imputed values fell within plausible ranges for each player. Importantly, the imputation did not distort the overall means or SDs; for instance, the group mean Nordic hamstring force remained ~ 408 N). All subsequent analyses used the imputed, complete dataset. Additionally, all external load variables were standardized via z-scores prior to PCA and modeling to facilitate comparison of coefficients. The standardization does not affect statistical significance but allows effects to be interpreted per each variable’s SD. To facilitate interpretation and comparability, all external-load predictors were z-standardized (mean 0, SD 1) before dimensionality reduction and modeling. Standardization rescales coefficients (to units of N per 1 SD change in load) without altering hypothesis tests. 3.2 Multicollinearity and Principal Component Analysis (PCA) The 18 external load features, 9 training and 9 match variables were first examined for multicollinearity. As expected, many metrics within the training set or within the match set were strongly intercorrelated. For example, in the training data, players who performed more sprints in a week also accumulated more sprint distance (r ≈ 0.93), and those with higher acceleration counts tended to have higher total distance. Similarly, within matches, metrics of volume and intensity were interrelated (e.g., high-intensity distance vs. High-Intensity Actions, r ≈ 0.87; p < 0.001). In contrast, training load vs. match load for the same week showed little to no correlation; in fact, the relationship was slightly negative in some cases with players with a very high match load often had a reduced training load that week. Variance inflation analysis confirmed that including all 18 metrics in a single model would be problematic due to multicollinearity with many VIFs > 10, with a maximum around 36 for weekly accelerations > 2 m·s⁻². To address this and reduce dimensionality, PCA was performed separately on the training load variables and on the match load variables after standardization. Criterion of eigenvalues > 1 was used and visual inspection of scree plots determined the number of principal components (PCs) to retain. The PCA on weekly training load metrics (Table 4 ) yielded a dominant first component, explaining 77.2% of the variance. This component reflected an overall measure of weekly training volume and intensity. Loading values for each weekly training metric are presented in Table 4 . Subsequent components each contributed much less (PC2: 11.7%, PC3: 4.0%, others < 3%). The PCA on match load metrics (Table 5 ) showed a more gradual decline in variance: PC1 explained 52.1% and PC2 26.6%, with PC3 and PC4 adding about 7% and 6%, respectively. The scree plot (Fig. 1 ) illustrates the variance explained by each component for both the training and match load data. Based on the “elbow” of the curves, only the first component for training load was retained with one component sufficiently summarizing weekly training load and the first two components for match load capturing both overall volume and intensity distribution. 3.3 Linear Mixed-Effects Model (LMM) Linear mixed-effects models (LMMs) were used to examine the association between external load components and next-day neuromuscular outputs. Separate LMMs were constructed for each of the four neuromuscular outcome measures. In each model, the fixed effects were Training PC1, Match PC1, and Match PC2, representing weekly training load, overall match load, and match intensity distribution, respectively. Player identity was included as a random intercept to account for repeated measures on the same player to allow each player to have a different baseline strength level. Change-from-baseline was not computed because a single pre-season reference is not stable across the competitive period and change scores can inflate error and bias via regression-to-the-mean. Methodological guidance recommends analyzing the absolute outcome with appropriate covariate or random-effects adjustment (e.g., ANCOVA/LMM) rather than change scores in repeated-measures design [ 37 – 42 ]. Random slope terms for the load predictors were tested but did not significantly improve model fit (likelihood ratio tests, p > 0.2 for each) and led to convergence issues given the limited observations per player. Thus, the final models included random intercepts only. The LMMs were fitted using the lme4 package in R [ 43 ] with restricted maximum likelihood. Significance of fixed effects was evaluated with Satterthwaite-approximated degrees of freedom for t-tests via the lmerTest package, using α = 0.05. Effect sizes were interpreted via the magnitude of the fixed-effect coefficients in units of N change per 1 SD change in load with 95% confidence intervals, following recommended guidelines for practical significance in sports science [ 44 ]. All statistical analyses were conducted in R (R Foundation for Statistical Computing, Vienna, Austria) and Python (for data preprocessing and visualization). 3.4 Model Assumption and Diagnostics Diagnostic plots were inspected to ensure the assumption of the LMM were met. Figure 2 shows residuals vs. fitted values and the normal Q–Q plot for the model predicting Nordic hamstring force, which had the clearest significant effect. The residuals showed no obvious heteroscedasticity or non-linear pattern against fitted values; points were roughly symmetrically scattered around zero across the range of fitted values (Fig. 2 , left). A few cases with the highest fitted values (i.e., where the model predicted a relatively high next-day force) had moderately large negative residuals with actual force being lower than predicted. This corresponds to instances where players underperformed the model’s expectation likely in those extreme high load matches where fatigue was slightly greater than average. These could be considered high leverage points but given the sample size they did not unduly influence the fixed effects. The model was refitted excluding the top two residuals and negligible changes in coefficients were observed. The Normal Q–Q plot (Fig. 2 , right) showed residuals mostly following the reference line, with only slight deviations in the tails. A few residuals at the very low end were more negative than expected (left tail) and a few at the high end were slightly higher than expected, indicating minor heavy-tailed behavior. Nonetheless, the Shapiro–Wilk test (Table 1 ) for normality of residuals was non-significant for all models (p > 0.20) and given the relatively large residual degrees of freedom (~ 100+), slight departures from normality are not critically important. It was concluded that the LMM fit was adequate and that the results, particularly the significant effects for Nordic and adductor strength are robust. Other outcome models showed similar diagnostics, the ISO Prone and Abductor models had very symmetric residuals with no systematic effects present, and the Adductor model’s residuals also showed homoscedasticity (Table 2 ), though its Q–Q plot had a couple of mild outliers on the high end with some players producing much higher adductor force than predicted, possibly reflecting individual strength variability not captured by the random intercept alone. Table 1 Normality of Residuals (Shapiro–Wilk Tests) Neuromuscular Test W Statistic p-value Interpretation Nordic Hamstring 0.983 0.21 Normal ISO Prone Hamstring 0.992 0.73 Normal Adductor 45° 0.987 0.43 Normal Abductor 45° 0.989 0.51 Normal Table 2 Homoscedasticity Checks (Levene’s Tests) Neuromuscular Test F Statistic p-value Interpretation Nordic Hamstring 1.45 0.15 Homoscedastic ISO Prone Hamstring 0.89 0.62 Homoscedastic Adductor 45° 1.02 0.43 Homoscedastic Abductor 45° 1.11 0.31 Homoscedastic 4 Results 4.1 Descriptive Statistics Table 3 presents the descriptive statistics for the weekly external load metrics and the strength measures. On average, players covered about 17,900 meters per week in training (including ~ 1,100 m of high-intensity running > 20 km/h and ~ 170 m of sprinting > 25 km/h). In matches, the average distance covered was ~ 10,300 m, with ~ 712 m of high-intensity running and ~ 94 m of sprinting. The counts of intense efforts followed a similar pattern (i.e., approximately 60 high-intensity runs per week in training vs. 50 in a match). These values align with typical load profiles for professional football, where a single match often contributes a large portion of the week’s high-intensity distance [ 45 ]. The neuromuscular strength measures showed mean values around 400 N for hamstrings and 450 N for adductors, consistent with expected levels for elite male players [ 46 ]. Notably, there was considerable inter-individual variability (e.g., SD ≈ 48 N in Nordic hamstring force, corresponding to some players producing < 360 N while others exceeded 450 N). Table 3 Descriptive statistics for external load metrics and next-day neuromuscular strength measures (N = 139 player-weeks). Training metrics are weekly totals; match metrics are per game. “–” indicates not applicable for match (strength tests performed only post-match). Variable Training (Mean ± SD) Match (Mean ± SD) Total Distance (m) 17,921 ± 4,856 10,304 ± 2,124 High-Intensity Distance > 20 km·h⁻¹ (m) 1,087 ± 402 712 ± 183 Sprint Distance > 25 km·h⁻¹ (m) 168 ± 82 94 ± 47 High-Intensity Actions (> 20 km·h⁻¹) 60 ± 21 50 ± 14 Sprints (> 25 km·h⁻¹) 17 ± 8 13 ± 4 Accelerations > 2 m·s⁻² (counts) 257 ± 85 135 ± 30 Decelerations 3 m·s⁻² (counts) 55 ± 23 33 ± 10 Decelerations < –3 m·s⁻² (counts) 49 ± 18 31 ± 11 Nordic Hamstring Force (N) 407.5 ± 48.0 – ISO Prone Hamstring Force (N) 385.5 ± 47.6 – Adductor (45°) Force (N) 467.3 ± 66.5 – Abductor (45°) Force (N) 416.9 ± 45.2 – 4.2 Principal Component Analysis Training Load PC1 displayed mixed-sign loadings across the weekly with larger contributions from total distance and high-intensity effort counts. Component signs were oriented such that higher PC1 scores correspond to greater overall weekly load. Thus, Training PC1 effectively summarizes weekly training volume/intensity and was retained as the sole training-load component. The second training component explained only 11.7% of variance and mainly differentiated a subset of metrics slightly isolating sprint-related metrics but given its small contribution it was not utilized. For match load, the first two principal components together explained 78.7% of the variance (PC1: 52.1%, PC2: 26.6%). Match PC1 had positive loadings on all match metrics with especially large contributions from metrics such as, the number of high-intensity actions and high decelerations (e.g., Decelerations < − 3 m·s⁻²). This suggests Match PC1 represents the overall magnitude of match demands, with higher values indicating the player covered more distance and executed many high-intensity runs with aggressive accelerations/decelerations with essentially a “harder” match in terms of total work and intensity. Match PC2, on the other hand, showed a trade-off pattern, it loaded positively on high-speed running and sprint distance, but negatively on total distance and moderate accelerations This indicates Match PC2 captures the intensity distribution within a match (relatively more sprinting/high-speed running versus total volume). For example, a player with lower minutes who completes frequent sprints might have a high Match PC2 despite lower total distance, whereas a player who plays 90 minutes at a steady high pace could have a high Match PC1 but lower PC2. By retaining both components, the overall load and the intensity skew as separate factors was accounted for. After the PCA, multicollinearity was effectively resolved. The selected components (Training PC1, Match PC1, Match PC2) had low inter-correlations and all VIFs < 1.5. Table 4 PCA loadings for weekly training external load metrics. Values are loading coefficients of each original variable on the first three principal components (PC). The percentage of variance explained by each component is shown in parentheses. Training Load Metric PC1 (77.2%) PC2 (11.7%) PC3 (4.0%) Total Distance 0.450 –0.195 –0.277 High-Intensity Distance (> 20 km/h) –0.122 –0.573 –0.337 Sprint Distance (> 25 km/h) –0.255 –0.054 –0.069 High-Intensity Actions (> 20 km/h) 0.203 0.397 0.505 Sprints (> 25 km/h) 0.330 0.263 –0.305 Accelerations (> 2 m·s⁻²) –0.273 –0.117 0.326 Decelerations ( 3 m·s⁻²) 0.227 –0.261 0.449 Decelerations (<–3 m·s⁻²) –0.312 0.564 –0.370 Table 5 PCA loadings for match external load metrics (first 2 components). Values are coefficients (loading weights) of each original variable on the principal components. The percentage of variance explained by each component is shown in parentheses. Match Load Metric PC1 (52.1%) PC2 (26.6%) Total Distance 0.276 –0.358 High-Intensity Distance (> 20 km/h) 0.356 0.348 Sprint Distance (> 25 km/h) 0.269 0.471 High-Intensity Actions (> 20 km/h) 0.399 0.147 Sprints (> 25 km/h) 0.274 0.468 Accelerations > 2 m·s 2 0.324 –0.385 Decelerations 3 m·s 2 0.340 –0.185 Decelerations < –3 m·s 2 0.370 –0.100 4.3 Linear Mixed Models of Fatigue Outcomes Linear mixed-effects models were applied for each neuromuscular outcome to determine the effects of training and match loads on next-day performance with lower force indicating greater fatigue. Table 6 summarizes the fixed-effects estimates from these models, including coefficients (β), standard errors, 95% confidence intervals, and p-values for each predictor. A positive β indicates higher load was associated with higher next-day force (i.e., less fatigue), while a negative β indicates higher load was associated with lower force (i.e., greater fatigue). Nordic Hamstring Exercise Strength : There was a significant negative association between Match PC1 (overall match load) and next-day Nordic hamstring force. Specifically, for each + 1 SD increase in Match PC1, Nordic force was about 5.1 N lower the next morning (β = − 5.12 N, 95% CI: − 10.07 to − 0.17 N, p = 0.046). In practical terms, a very high match load (+ 2 SD above average) would predict ~ 10 N lower Nordic force the next day compared to a low-load match. Training PC1 had a small positive but non-significant coefficient (β = +1.42 N, CI: − 2.15 to + 5.00 N, p = 0.430), suggesting that weeks with higher training load did not lead to additional hamstring fatigue beyond match effects, contrary there was a slight trend toward higher Nordic force after higher training weeks, but this was not reliable. Match PC2 had β = +3.78 N (CI: − 1.74 to + 9.30 N, p = 0.180), indicating that matches with a greater high-speed component relative to total volume tended to show less Nordic force loss, a positive coefficient, though this did not reach significance. ISO Prone Hamstring Strength : No significant relationships were found for the prone isometric hamstring test. Both training and match components had small, non-significant coefficients (Training PC1: β = − 0.39 N, p = 0.770; Match PC1: β = − 2.10 N, p = 0.240; Match PC2: β = − 1.74 N, p = 0.250). The negative signs for match predictors hint at a possible reduction in isometric hamstring force with higher loads, but the effects were much smaller than for the Nordic test and did not achieve significance. This contrast between the Nordic and ISO tests suggests that the eccentric hamstring measure was more sensitive to fatigue from match play than the isometric measure. It may be that the Nordic exercise, which stresses the hamstrings under lengthening contractions, picks up fatigue-related weakness that a mid-range isometric test does not. Adductor (45°) Strength : Contrary to expectation, Match PC2 showed a significant positive association with next-day adductor force (β = +11.68 N per 1 SD, 95% CI: +0.19 to + 23.17 N, p = 0.046). This indicates that when a match involved a high proportion of sprinting/high-speed running, relative to total distance, players produced slightly higher adductor squeeze force the following morning. Neither Training PC1 nor Match PC1 significantly affected adductor strength (Training PC1: β = − 2.94 N, p = 0.380; Match PC1: β = − 1.76 N, p = 0.640). The Match PC2 effect, if taken at face value, implies that intense, sprint-heavy matches were associated with an ~ 12 N increase in adductor strength the next day, compared to more high-volume matches. This result was unexpected, as the hypotheses expected all load would either have no effect or a negative effect on strength. Potential explanations are considered in the discussion (e.g., a potentiation effect or an influence of playing time). Abductor (45°) Strength : No significant effects were observed for hip abductor strength. All coefficients were near zero (Training PC1: +1.63 N, p = 0.260; Match PC1: − 0.85 N, p = 0.630; Match PC2: +1.08 N, p = 0.600). This suggests that the hip abductors did not exhibit measurable fatigue or any consistent change related to training or match loads in this timeframe. It may be that a single match, and the typical weekly training, were not sufficient stimuli to cause detectable strength loss in the abductors, or that the abductor test was not sensitive to subtle changes. Across all models, the random intercept for player was significant, reflecting substantial baseline differences in strength between players (e.g., some players consistently had higher absolute Nordic forces than others). The standard deviation of the random intercept was on the order of 20–35 N across models, indicating the typical variation between players’ mean strength levels. No evidence was found that load effects varied markedly by player as random slopes were not needed, implying that the observed relationships or lack thereof were relatively consistent across the squad. Table 6 Linear mixed model fixed-effects estimates for the influence of weekly training load (Training PC1) and match load components (Match PC1, Match PC2) on next-day neuromuscular performance. β values (± SE) represent the change in force (N) associated with a + 1 SD change in the predictor. 95% confidence intervals [CI] and p-values are shown. Significant effects (p < 0.05)**. Outcome (Next-Day Force) β Training PC1 (SE) [95% CI] β Match PC1 (SE) [95% CI] β Match PC2 (SE) [95% CI] Nordic Hamstring (N) + 1.42 (1.78) [–2.15, + 5.00], p = 0.430 –5.12 (2.50) [–10.07, − 0.17] , p = 0.046** + 3.78 (2.82) [–1.74, + 9.30], p = 0.180 ISO Prone Hamstring (N) –0.39 (1.29) [–2.99, + 2.22], p = 0.770 –2.10 (1.67) [–5.38, + 1.19], p = 0.240 –1.74 (1.52) [–4.77, + 1.30], p = 0.250 Adductor 45° (N) –2.94 (3.30) [–9.56, + 3.68], p = 0.380 –1.76 (3.98) [–9.18, + 5.66], p = 0.640 + 11.68 (5.81) [+ 0.19, + 23.17], p = 0.046** Abductor 45° (N) + 1.63 (1.45) [–1.25, + 4.50], p = 0.260 –0.85 (1.64) [–4.12, + 2.42], p = 0.630 + 1.08 (2.05) [–2.96, + 5.12], p = 0.600 5. Discussion The purpose of this study was to examine how external training and match load correlates with objective neuromuscular fatigue markers in elite football players. Overall, the findings highlight that the strain of a competitive match is a key driver of short-term neuromuscular fatigue, particularly affecting eccentric hamstring strength, whereas the cumulative training load in the preceding week had minimal additional influence on next-day neuromuscular function. In practical terms, this suggests that elite players are generally well adapted to their routine training workloads, but a single match of high intensity can still induce measurable fatigue that persists into the next morning. The acute match external load had a more pronounced impact on next-day neuromuscular function than accumulated weekly training load in these elite footballers. Specifically, players who experienced a high overall match load covering more distance with more high-intensity efforts showed reduced eccentric hamstring strength the following morning, consistent with acute hamstring fatigue. This aligns with the concept that intense match play induces muscle damage or fatigue that temporarily impairs maximal force production [ 47 , 48 ]. By contrast, variations in training load during the week did not significantly predict next-day strength, suggesting that the team’s training periodization successfully managed fatigue or that any training-induced fatigue had dissipated by the post-match assessment as players often tapered before games. Highly sprint-intensive matches may transiently increase adductor strength due to potentiation via high neural drive. This could imply a form of residual potentiation or simply that those matches were less taxing overall despite the sprinting, so that players’ adductors were relatively fresh [ 49 ]. However, given the borderline significance of this finding and its isolated nature, it should be interpreted cautiously. There were no notable load effects on the isometric hamstring test or hip abductor strength, indicating those measures might be less sensitive to the range of loads observed or may recover more quickly. The significant negative effect of match load on Nordic hamstring force supports the notion that match play incurs neuromuscular fatigue in the hamstrings. This result is consistent with prior research demonstrating post-match declines in muscle force and performance. Brownstein et al. [ 8 ] reported marked reductions in voluntary quadriceps and hamstring force immediately after matches, with recovery taking up to 72 h. Although our study assessed fatigue the next day (~ 12–18 h post-match) rather than immediately, the detection of ~ 1–2% decreases in hamstring force per SD of match load aligns with an early fatigue phase. It also reinforces the idea that eccentric tests like the Nordic are sensitive to fatigue-related strength loss. Notably, Thorpe et al. [ 5 ] argued that standard performance tests such as CMJ often fail to reflect accumulated fatigue in elite soccer. The data from this study suggest that the Nordic eccentric strength test may succeed where the CMJ falls short, capturing deficits when players are fatigued from intense match activity. Eccentric muscle actions, like those in the Nordic, might be more affected by muscle damage or soreness from match play due to high-speed running and sudden decelerations that load the hamstrings during sprinting and cutting actions. This aligns with evidence that eccentric knee-flexor strength is acutely reduced after matches or heavy training. However, absolute Nordic strength outputs can also depend on a player’s body mass, so monitoring relative strength via force normalized to body weight could help account for individual differences in player size [ 46 ] From an applied perspective, the drop in Nordic strength after high-load matches and its absence after lower-load matches suggests that monitoring eccentric hamstring strength the day after a game can serve as a useful fatigue indicator. If a player’s NHE scores are substantially lower than their baseline following a match, it likely reflects significant neuromuscular fatigue or muscle damage. Practitioners could use this information to individualize recovery strategies or adjust subsequent training. For instance, a player showing a large Nordic strength deficit might benefit from reduced high-speed work or extra recovery modalities in the 1–2 days post-match. Over time, tracking this metric might also aid in injury prevention, since incomplete recovery of hamstring function could predispose players to muscle strains if they are overexposed. Our results dovetail with earlier findings that high-speed running in matches is a major contributor to fatigue and injury risk with Malone et al. [ 13 ] showing that acute spikes in sprint distance elevated injury risk unless players had developed a high chronic exposure. Therefore, identifying when a player is in a fatigued state could inform safer training loads until recovery is achieved. Interestingly, weekly training load did not have any significant impact on next-day neuromuscular performance. The interpretation is that the team’s training loads were managed such that players generally entered matches relatively fresh. The club’s practice of tapering before games and adjusting training for those who played big minutes likely prevented excessive fatigue accumulation from training alone. In fact, data showed a slight negative correlation between training and match loads; players with high match demands often had their training reduced. For example, starters might do less intense mid-week training, while non-starters trained harder. This adaptive approach is common in elite football periodization and aims to ensure that training augments fitness without causing residual fatigue that could harm match performance. Our findings suggest that this approach was effective; there was no evidence that a harder training week left players weaker or more fatigued the morning after the subsequent match. In other words, the match’s impact dominated any training effects. It’s also worth noting that the absolute range of training load in the sample, while considerable, might not have been extreme enough to elicit large differences in next-day fatigue. Elite players are highly trained and may recover quickly from normal training sessions with 24–48 h being usually sufficient for muscle recovery after typical football training [ 50 ]. By match day + 1, any fatigue from earlier in the week would likely have dissipated, leaving primarily the match to account for any deficits we observed. This is supported by research showing that markers like jump performance or isometric strength often return to baseline within 48–72 h after isolated training bouts [ 51 ]. However, overuse or excessive cumulative load can still manifest as fatigue or injury over longer periods. This study focused on acute effects rather than long-term trends, so it cannot be ruled out that consistently high training loads week after week might eventually lead to fatigue or underperformance. But within the scope of single-week variations, the players appeared to cope well. One surprising outcome was the positive association between Match PC2 and adductor strength. It was hypothesized that all muscle groups would either show no change or a decrease in strength after harder matches, yet the data indicated that matches with more high-speed running corresponded to slightly higher adductor squeeze force the next day. There are several potential explanations for this counterintuitive result. Firstly, it could be a statistical anomaly (Type I error). Multiple outcomes and predictors were tested, and while the modelling approach was targeted, the chance of one spurious significant finding exists. The confidence interval for the adductor Match PC2 effect was wide and only just excluded zero. Thus, replication is needed to confirm this relationship. Another possible interpretation is a post-activation performance enhancement (PAPE) effect. High-intensity, intermittent activity can transiently enhance subsequent force or explosive performance, depending on the balance between fatigue and potentiation mechanisms [ 49 , 52 ]. The adductors play a role in sprinting stabilizing the swing leg, contributing to hip flexion/extension synergy [ 53 ], but sprinting might not fatigue them as much as prolonged running or repeated cutting actions would. Sprint running provides a substantial neuromuscular stimulus to the hip musculature, and modern muscle-activity measurement approaches indicate meaningful involvement of the hip adductors during sprinting [ 54 ]. Conceptually, therefore, a sprint-heavy match profile could, in some contexts, be consistent with a short-term neuromuscular enhancement signal rather than a pure fatigue response. However, because potentiation effects are typically short-lived and highly dependent on individual responses and testing time-points, this explanation remains speculative and should be interpreted cautiously [ 49 , 52 ] Another factor could be selection bias in who had high-PC2 matches. Matches with high PC2 might often be those where a player was used for partial game. It’s plausible that playing fewer minutes resulted in less overall fatigue, so the player’s muscles, including adductors were relatively fresh the next morning. In the data, minutes played were not explicitly controlled, which could be a confounder. Match PC1 is naturally correlated with minutes, but Match PC2 might highlight cases of short-duration involvement. If players with less minutes generally showed higher adductor strength that could manifest as this positive PC2 effect. This explanation aligns with findings that cumulative fatigue is more evident after consecutive full matches or tournaments. For instance, Sánchez-Migallón et al. [ 55 ] found that hip adductor strength significantly dropped after two consecutive days of matches in female players, especially 48 h after the second match. It’s also possible that the adductor test itself has higher day-to-day variability. If a player isn’t truly fatigued, a small improvement could fall within normal variation, whereas if they were fatigued, a decline would be expected. Most of the data showed no significant average change in adductor strength post-match since Match PC1 had no effect, so the Match PC2 result should be interpreted cautiously. It may be highlighting a subset of observations with perhaps lower minutes of play or unique game scenarios rather than a general principle. Future research should investigate adductor strength changes with controlled variations in playing time and intensity to confirm this pattern. Meanwhile, hip abductor strength was largely unchanged by load. Hip abductors may not be stressed as heavily during typical match play compared to adductors, which work during cutting and kicking or hamstrings during sprinting. Another possibility is that the abductors recover quickly or that 45° isometric abduction test wasn’t sensitive to subtle changes. Some studies have noted decreases in abductor strength in very congested schedules, but in a single-match context our results suggest minimal acute fatigue in this muscle group. For practitioners, this means the adductor squeeze test might be more informative than an abductor test for post-match fatigue monitoring, as it has been linked to groin injury risk and appears to respond to certain match conditions. The findings resonate with the growing body of work highlighting the value of neuromuscular tests in monitoring training and competition load. For example, Marqués-Jiménez et al., [ 19 ] found that external load metrics like accelerations and decelerations predicted declines in post-match CMJ and sprint performance. Match load was similarly identified with heavily loaded on accelerations/decelerations as a predictor of reduced force in a direct muscle test. This underscores the connection between high mechanical load and fatigue. Interestingly, this was detected via a muscle-specific test rather than a general performance test, reinforcing arguments that a multi-metric approach is beneficial. It seems that monitoring both neuromuscular performance (e.g., jump tests) and muscle capacity (e.g., strength tests) can provide complementary insights. Whereas a CMJ might indicate general power-output readiness, a NHE strength test can pinpoint hamstring status specifically. Coaches may thus consider implementing a combination of metrics to get a fuller picture of player readiness. In support of this idea, a recent study by Collins et al. [ 56 ] reported associations between internal and external training load measures and neuromuscular performance in elite soccer players, illustrating the interplay of multiple load and performance indicators in practice. Another point of discussion is the lack of an observed effect of training load, which aligns with some studies but not others. Rowell et al. [ 57 ], tracking a pro team over a season, noted that heavy training and match schedules impacted players’ hormonal markers and perceived recovery, but effects on neuromuscular tests were less clear. Thorpe et al. [ 5 ] found that changes in acute training load on preceding days did not significantly alter next-morning fatigue measures in English Premier League players, which is consistent with our finding that weekly training load had no acute effect on strength. It seems that for well-trained athletes, a single week’s workload, if periodized properly, might not push them into a state of measurable neuromuscular deficit beyond what the main competitive event causes. This speaks to the high level of adaptation and fitness in elite players, they can handle a lot of work if it’s managed and not beyond their chronic load capacity. However, if training load were to spike abnormally or if recovery was insufficient, different results might be observed. Practical Implications The day after a match, objective strength tests, especially the Nordic hamstring test can reveal fatigue that might not be evident from subjective reports or jump testing. If a player demonstrates a significant drop in Nordic strength compared to the player’s own typical in-season range, this can present as a red flag indicating they are in a fatigued state and possibly at higher risk of hamstring strain if loaded again too soon. Interventions could include modified training or targeted recovery in the 1–2 days post-match. Conversely, if a player’s strength levels remain at baseline the next day, it suggests they have recovered well or were not excessively loaded in the match, and normal training can likely resume. Adductor squeeze tests should also be monitored; large drops might indicate groin fatigue or impending issues; though, as noted, a small increase could occur in some cases without negative implications. The findings indicate that typical training loads, when properly managed, do not unduly carry over fatigue into match recovery. Coaches can maintain intensive training during the week, provided appropriate tapering is applied before matches. The primary focus for recovery should be after matches, where the biggest fatigue impact is observed. It remains important to individualize recovery and training adjustments based on objective measures. Not all players respond identically to load, whereas ongoing monitoring allows identification of those who may need extra recovery versus those who are coping well. Integrating neuromuscular tests into regular monitoring can help optimize performance and reduce injury risk by informing evidence-based decisions on training load adjustments. Limitations and Future Directions Outcomes were not anchored to a fixed pre-season baseline; instead, absolute next-day force was modeled with player-level random intercepts to estimate within-player associations between external load and neuromuscular function. This approach avoids known drawbacks of change-from-baseline analyses (e.g., amplification of measurement error and regression-to-the-mean) and is recommended for repeated-measures designs; however, it limits direct statements about individual “percentage drop from a static baseline.” Weekly microcycles were non-consecutive, environmental conditions were not systematically recorded, and positional information and exact match-minutes were not included as covariates, which may contribute unexplained variance. Although missingness was addressed via MICE with satisfactory diagnostics, residual bias cannot be entirely excluded. Finally, the sample reflects a single professional squad, which may constrain generalizability to other competitions or sexes. Subsequent research should (i) record and incorporate pre-specified operational baselines (e.g., rolling 4–6-week typical values per player) to complement mixed-model inference; (ii) add position, match minutes, and environmental covariates; (iii) evaluate recovery time-courses beyond 12–18 h (e.g., 24–72 h) to map kinetics of neuromuscular restoration; and (iv) link acute fatigue markers to prospective injury outcomes. Conclusions This study demonstrated that in elite football players, the external load from a match has a quantifiable impact on next-day neuromuscular performance, whereas the typical range of training load in the days before a match does not produce additional immediate fatigue. Specifically, high match loads led to reduced eccentric hamstring strength, highlighting the hamstrings’ susceptibility to fatigue from intense competition. Meanwhile, isometric tests of hamstrings and hip muscles showed no decline, and an unexpected increase in adductor strength was observed after sprint-heavy matches; a finding that warrants further investigation but suggests that not all muscle groups respond identically to a given load. These results emphasize the value of incorporating muscle-specific strength assessments such as the Nordic hamstring test into post-match recovery monitoring. Doing so can aid in detecting residual fatigue that might not be apparent from subjective or general measures, enabling practitioners to tailor recovery and subsequent training. In a practical application, coaches and performance staff should pay particular attention to players with very high match loads, as they are likely to experience hamstring fatigue and may benefit from targeted recovery strategies or modified workloads in the short term. Conversely, the lack of detrimental effect from normal training load implies that coaches can continue to condition players during the week without fear of undermining next-day neuromuscular function, provided adequate tapering and recovery are in place before and after matches. Overall, effective load management via balancing training and match demands combined with regular fatigue monitoring is key to optimizing performance and minimizing injury risk in elite football. By understanding which loads matter most and how they manifest in neuromuscular fatigue, practitioners can make more informed decisions to keep players both fit and fresh throughout the competitive season. This aligns with the call for practical, evidence-based training-load monitoring solutions in elite team sports [ 58 ]. Declarations Competing interests NB, DLL, RO and HN declare that they have no competing interests. Ethics approval and consent to participate This study was approved by the Committee of Ethics in Research and Teaching of the University of A Coruña (approval code 2024-062) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent for the use of their pseudonymized data for research purposes. Consent for publication Not applicable. This manuscript does not contain any individual person’s identifiable data. Authors’ information Not applicable. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution NB: Conceptualization, data curation, formal analysis, writing the original draft. DLL: Methodology, supervision, writing, reviewing & editing. RO and HN: Conceptualization, interpretation, writing, reviewing & editing. All authors read and approved the final manuscript. 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H., de Jong, J. & Lemmink, K. A. P. M. Managing load to optimize well-being and recovery during short-term match congestion in elite basketball. Int. J. Sports Physiol. Perform. 16 , 45–50 (2021). Blazevich, A. J. & Babault, N. Post-activation Potentiation Versus Post-activation Performance Enhancement in Humans: Historical Perspective, Underlying Mechanisms, and Current Issues. Front. Physiol. 10 , 1359 (2019). Schache, A. G., Dorn, T. W., Williams, G. P., Brown, N. A. T. & Pandy, M. G. Lower-limb muscular strategies for increasing running speed. J. Orthop. Sports Phys. Ther. 44 , 813–824 (2014). Yoshimoto, T., Chiba, Y., Ohnuma, H., Yanaka, T. & Sugisaki, N. Measuring Muscle Activity in Sprinters Using T2-Weighted Magnetic Resonance Imaging. Int. J. Sports Physiol. Perform. 17 , 774–779 (2022). Sánchez-Migallón, V. et al. Effects of consecutive days of matchplay on maximal hip abductor and adductor strength in female field hockey players. BMC Sports Sci. Med. Rehabil . 14 , 3 (2022). Collins, J. J., Malone, S. & Collins, K. D. Associations between internal and external training load measures and neuromuscular performance in elite soccer players. Sport Sci. Health . 21 , 1575–1582 (2025). Rowell, A. E. et al. Effects of training and competition load on neuromuscular recovery, testosterone, cortisol, and match performance during a season of professional football. Front. Physiol. 9 , 668 (2018). Burgess, D. J. The research doesn’t always apply: Practical solutions to evidence-based training-load monitoring in elite team sports. International Journal of Sports Physiology and Performance vol. 12 136–141 Preprint at (2017). https://doi.org/10.1123/ijspp.2016-0608 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor invited by journal 16 Jan, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 08 Jan, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8552797","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602007913,"identity":"9092d1f7-d196-4e3f-a546-b8a132db1b76","order_by":0,"name":"Norbert Banoocy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3QMQ6CMBSA4WdM6oLOJSRwhRITibeBmOgCd2CCBXe5BQdwqGliFw7g4FCXuurG0EEgamKioJtD/+GlHb7ktQA63X+G2ombgw/Uhnp+Q8iTTH8kADSI+4g3Wktx3Sow01QKoY6rgi8EXNRnMs+4524kAcsoPRIkMipKSQZ58pmQwxJZBiVg4xDhIGZRcQhhOI57iGqIc5bYV2xFGqI6FmsJ1MTCMMM+Yn5L7j//npT7kZvRqWFm4QwHCXPz+i27dddbeIJERW0bcy7NSjFnwhcnUXUs9sh4udF+oNPpdLquboXpVQR0v5xVAAAAAElFTkSuQmCC","orcid":"","institution":"Universidade Da Coruña","correspondingAuthor":true,"prefix":"","firstName":"Norbert","middleName":"","lastName":"Banoocy","suffix":""},{"id":602007916,"identity":"832e2d85-6b93-4275-8ac4-708c40d51d35","order_by":1,"name":"Daniel Lopez Lopez","email":"","orcid":"","institution":"Universidade Da Coruña","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Lopez","lastName":"Lopez","suffix":""},{"id":602007919,"identity":"1b7fe923-0977-4a22-a65d-1ef4f22bfbaa","order_by":2,"name":"Rafael Oliveira","email":"","orcid":"","institution":"Santarém Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Oliveira","suffix":""},{"id":602007923,"identity":"4e90a4d1-05d1-4585-8042-54caeb74bd42","order_by":3,"name":"Hadi Nobari","email":"","orcid":"","institution":"Universidad Politécnica de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Hadi","middleName":"","lastName":"Nobari","suffix":""}],"badges":[],"createdAt":"2026-01-08 14:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8552797/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8552797/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104403739,"identity":"aaff9eaf-f2ce-4d8a-9835-510b675a444e","added_by":"auto","created_at":"2026-03-11 12:18:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91680,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot of principal component analysis for weekly training load (orange circles) and match load (red squares). Training load PC1 (77.2% of variance) captures most of the weekly load variance. Match load shows two notable components (PC1: 52.1%, PC2: 26.6%), reflecting overall load volume vs. high-intensity distribution.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8552797/v1/dc6342897c92ac79fda61739.jpeg"},{"id":104208318,"identity":"688ed824-5393-475b-aa4a-f383d3be917b","added_by":"auto","created_at":"2026-03-09 07:21:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125808,"visible":true,"origin":"","legend":"\u003cp\u003eResidual diagnostics for the LMM predicting next-day Nordic hamstring force. Left: Residuals vs. fitted values, showing no clear funnel shape or systematic pattern (horizontal red dashed line at zero residual). Each blue “×” represents an observation; a few points at high fitted values show larger negative residuals, indicating instances of greater-than-expected fatigue. Right: Normal Q–Q plot of residuals. Most points lie close to the diagonal reference line (red dashed), suggesting residuals are approximately normally distributed; slight deviations at the extremes indicate minor heavy-tailed residuals.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8552797/v1/2e5e564e9110d1da93f99522.png"},{"id":104409363,"identity":"337c84ae-72a8-4fc1-8574-27ab18b313c3","added_by":"auto","created_at":"2026-03-11 12:44:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1424156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8552797/v1/0a629bc3-9be8-441d-a1c2-d6bd1f7b1b1b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Weekly Training Load and Match Load on Next-Day Neuromuscular Fatigue in Elite Football Players: A Longitudinal Observational Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eElite football players face congested schedules and high training intensities, which can lead to neuromuscular fatigue and impaired performance in subsequent days [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Monitoring fatigue status has become a key practice in high-performance football [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, traditional approaches to assess fatigue (e.g., maximal jumps or sprints) can be impractical to perform frequently, and their sensitivity to subtle daily changes in fatigue is limited [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Simpler, non-exhaustive tests such as self-reported wellness and heart-rate variability are often used, but direct muscle-function tests may be more revealing [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndeed, post-match fatigue can contribute to performance decrements for up to approximately 72 hours after competition [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For example, Brownstein et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] observed substantial reductions in voluntary quadriceps and hamstring force for 2\u0026ndash;3 days following a match, reflecting prolonged neuromuscular fatigue post-competition. Within this context, muscle-specific strength assessments have emerged as valuable tools for athlete monitoring. Eccentric hamstring strength, often measured via the Nordic hamstring exercise (NHE), and isometric hip adductor strength measured via squeeze tests are of particular interest due to their links with injury risk [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Low preseason or chronic eccentric hamstring strength is associated with higher hamstring injury rates, and interventions like NHE programs have been shown to reduce these injuries [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]Likewise, hip adductor weakness is a known risk factor for groin injuries [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In one study of elite youth footballers, hip adductor strength dropped by approximately 6% in the week prior to groin pain onset and 12% at the onset of groin injury symptoms [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings suggest that monitoring changes in adductor strength may help flag developing groin issues. Conversely, maintaining high chronic training loads, especially high-speed running exposure, can build resilience [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Malone et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that players accustomed to greater sprint workloads were less likely to be injured despite acute spikes, indicating a protective adaptation to repeated high-intensity loads. Therefore, understanding fatigue responses in these muscle groups under varying load conditions is crucial for both performance and injury prevention.\u003c/p\u003e \u003cp\u003eA previous systematic review with meta-analysis into post-match fatigue had yielded mixed results [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. On the one hand, minimal changes in CMJ performance or wellness scores despite large match loads were found, suggesting that these generic metrics might not capture muscle-specific fatigue [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, Thorpe et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] noted that morning CMJ measures did not consistently reflect the previous day\u0026rsquo;s training load in elite players, questioning their utility as a standalone fatigue marker. On the other hand, direct strength measures may reveal neuromuscular deficits that jump tests miss. Indeed, peak force outputs of key muscle groups have been shown to decrease for 24\u0026ndash;48 hours after matches. For instance, Fransson et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] observed significant acute fatigue in hamstrings and adductors following a simulated soccer match, with slower recovery trajectories than for some other muscle groups. Such evidence aligns with practical reports that eccentric hamstring strength and hip adduction strength can be reduced the day after competition as part of the normal fatigue-response cycle. Monitoring these metrics could therefore improve detection of residual fatigue compared to traditional tests.\u003c/p\u003e \u003cp\u003eAnother important consideration is how training load throughout the week interacts with match load to influence fatigue. Coaches often periodize weekly training to ensure players are sufficiently recovered for matches. High training loads can induce fatigue, but players with greater training-induced fitness might better tolerate the intense demands of match play [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The balance between training and match exposure is delicate with the possibility of excessive acute or chronic load increasing injury risk, whereas a well-managed load can enhance performance and robustness [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Empirical studies have attempted to model these dynamics; for example, Varley et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] examined relationships between match running outputs, post-match fatigue, and recovery, finding that metrics such as high-speed distance and accelerations correlated with temporary neuromuscular performance declines. Similarly, a systematic review by Hader et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] concluded that certain external load metrics, notably repeated accelerations and decelerations are useful predictors of acute fatigue after matches. More recently, Marqu\u0026eacute;s-Jim\u0026eacute;nez et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] found that external load metrics (e.g., accelerations and total distance) predicted declines in post-match CMJ and sprint performance in soccer players. These insights underscore the need to parse out the contributions of accumulated training load versus one-off match load on neuromuscular fatigue. It remains unclear whether a high training load in the days before a match exacerbates fatigue beyond that caused by the match itself, or if well-conditioned players can endure heavy training without additional next-day fatigue.\u003c/p\u003e \u003cp\u003eGiven these gaps, the present study aimed to investigate the relationship between accumulated external load across the training week and that incurred during competitive match play, and their influence on next-day neuromuscular performance in elite football players. Neuromuscular performance was evaluated through force production of the hamstring, adductor, and abductor muscle groups using validated field-based strength assessments [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It was hypothesized that (i) higher match loads would be associated with greater next-day neuromuscular fatigue, reflected by reduced force production, while (ii) accumulated weekly training load, would have a smaller or negligible effect on next-day strength measures. These hypotheses are grounded in previous research linking increased external loads with post-match neuromuscular fatigue and performance decrements in professional football [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eTwenty-four male outfield professional football players from a single club competing in an elite European league (Denmark) were monitored as part of the club\u0026rsquo;s routine performance-tracking program. The sample included players across various outfield positions, with goalkeepers excluded due to their distinct positional demands. Participants had a mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age of 26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 years (range 18\u0026ndash;35), stature 181\u0026thinsp;\u0026plusmn;\u0026thinsp;6 cm, and body mass 77.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8 kg.\u003c/p\u003e \u003cp\u003eInclusion criteria was adapted from a previous study[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and consisted in the following: (i) registration as a first-team professional player at the club; (ii) regular participation in training (\u0026ge;\u0026thinsp;30 min of monitored activity per session) and competitive matches during the study period; and (iii) availability of valid Global Positioning System (GPS) and neuromuscular testing data with \u0026ge;\u0026thinsp;60 min of match exposure in at least one competitive fixture. Exclusion criteria included: (i) current musculoskeletal injury or ongoing rehabilitation limiting full training participation; and (ii) incomplete or invalid neuromuscular testing data. These thresholds are consistent with previous elite-football load\u0026ndash;fatigue research and ensure that included weeks represent typical outfield player exposures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Goalkeepers were excluded to maintain homogeneity of outfield movement profiles. All players were accustomed to regular neuromuscular monitoring and had extensive experience using GPS technology during training and match play. Each external-load metric was calculated separately for the training week by summing data from the four field sessions preceding the match, and for the match as a single-game total. The typical microcycle consisted of MD\u0026thinsp;+\u0026thinsp;1 recovery, MD\u0026thinsp;+\u0026thinsp;2 off, and four preparatory sessions (i.e., MD-4 to MD-1) that formed the training-week total used in the analysis.\u003c/p\u003e \u003cp\u003eIn total, 24 coded outfield players contributed 139 valid player-week observations across 15 microcycles, with 7\u0026ndash;10 players contributing data per week. This reflects normal fluctuations in weekly availability due to match selection, recovery status, or data completeness. The study was conducted during the competitive season, when the team played approximately one official match per week.\u003c/p\u003e \u003cp\u003eParticipation in the monitoring formed part of the players\u0026rsquo; contractual obligations, and all participants provided written informed consent for the research use of their pseudonymized data. Ethical approval for this study was granted by the Committee of Ethics in Research and Teaching of the University of A Coru\u0026ntilde;a (approval code 2024-062), as part of the doctoral project \u003cem\u003eIntegration of external load, muscular fatigue, and technical/tactical performance analysis to optimize football performance\u003c/em\u003e. All procedures complied with the Declaration of Helsinki. Data were processed in accordance with GDPR standards; the re-identification key remained within the club\u0026rsquo;s performance department, and only pseudonymized datasets were accessed by the research team. No additional interventions beyond routine monitoring were performed; therefore, an insurance policy was not required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Design and Data Collection\u003c/h2\u003e \u003cp\u003eA longitudinal, observational design was employed. For each weekly microcycle, external load metrics were collected during training sessions and the official match, and neuromuscular performance was measured on the morning following the match. Training load was defined as the sum of all external load from the team\u0026rsquo;s training sessions in the 4 days leading up to the match (n\u0026thinsp;=\u0026thinsp;60). Match load was defined as the load from the single competitive match with only weeks with one match included for analysis (n\u0026thinsp;=\u0026thinsp;15). All training sessions and matches were performed on outdoor natural-grass pitches.\u003c/p\u003e \u003cp\u003eExternal load was tracked using 10 Hz GPS devices (Catapult Sports, Melbourne, Australia) with integrated tri-axial accelerometers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Each player wore the same GPS unit in a vest for all sessions and matches to maintain consistency. Data were downloaded post-session and processed with the manufacturer\u0026rsquo;s software (Catapult OpenField) to extract key metrics. The system has demonstrated high validity and reliability for team-sport movement monitoring [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The validity and reliability of Catapult GPS systems have been well established, with Johnston et al. demonstrating high agreement and low measurement error (typical error\u0026thinsp;\u0026asymp;\u0026thinsp;1\u0026ndash;2%) for 10 Hz units during team-sport movement assessments, and Ocak et al. (2025) confirming excellent accuracy and reliability of the Catapult Vector S7 units (MAPE\u0026thinsp;\u0026asymp;\u0026thinsp;0.8%, CV\u0026thinsp;\u0026asymp;\u0026thinsp;0.2%) under controlled testing conditions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]The following external load variables were analyzed, based on common practices in football load monitoring [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTotal Distance: total distance covered (meters).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigh-Intensity Distance (HID): distance covered at speeds\u0026thinsp;\u0026gt;\u0026thinsp;20 km\u0026middot;h⁻\u0026sup1;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSprint Distance: distance covered at speeds\u0026thinsp;\u0026gt;\u0026thinsp;25 km\u0026middot;h⁻\u0026sup1;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigh-Intensity Actions: count of running efforts\u0026thinsp;\u0026gt;\u0026thinsp;20 km\u0026middot;h⁻\u0026sup1;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSprints: count of sprint efforts\u0026thinsp;\u0026gt;\u0026thinsp;25 km\u0026middot;h⁻\u0026sup1;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAccelerations\u0026thinsp;\u0026gt;\u0026thinsp;2 m\u0026middot;s⁻\u0026sup2;: count of high accelerations exceeding 2 m\u0026middot;s⁻\u0026sup2;.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDecelerations\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2 m\u0026middot;s⁻\u0026sup2;: count of high decelerations exceeding \u0026minus;\u0026thinsp;2 m\u0026middot;s⁻\u0026sup2;.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAccelerations\u0026thinsp;\u0026gt;\u0026thinsp;3 m\u0026middot;s⁻\u0026sup2;: count of very intense accelerations\u0026thinsp;\u0026gt;\u0026thinsp;3 m\u0026middot;s⁻\u0026sup2;.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDecelerations\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;3 m\u0026middot;s⁻\u0026sup2;: count of very intense decelerations\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;3 m\u0026middot;s⁻\u0026sup2;.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Neuromuscular Fatigue Monitoring\u003c/h2\u003e \u003cp\u003eNeuromuscular performance was assessed the morning after each match (approximately 12\u0026ndash;18 h post-match) using four tests targeting the hamstring and hip muscle groups. These assessments were selected for practicality in elite football environments, relevance to sport-specific actions, and established reliability in athlete populations[\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Testing occurred during the club\u0026rsquo;s scheduled morning recovery session and was embedded in the players\u0026rsquo; routine monitoring program.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eISO Prone Isometric Hamstring Push (ISO Prone)\u003c/strong\u003e \u003cp\u003eIsometric hamstring strength was measured using a NordBord device (Vald Performance, Australia) in a prone, hip-neutral position. Players lay face-down with hips flat and knees fully extended (0\u0026deg;) and placed their ankles against padded hooks on a rigid frame. They were instructed to push back against the hooks with maximal effort, contracting their hamstrings isometrically. Average force (N) from both limbs was recorded over a 5 s maximal effort. Two trials were performed with ~\u0026thinsp;10 s rest between trials and the mean force across the trials was used. This long-muscle-length isometric test targets the hamstrings in an extended-knee position and has demonstrated high test\u0026ndash;retest reliability in elite football players (ICC\u0026thinsp;\u0026asymp;\u0026thinsp;0.85\u0026ndash;0.92) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNordic Hamstring Exercise (NHE)\u003c/strong\u003e \u003cp\u003eEccentric hamstring strength was measured using the NordBord device (Vald Performance, Australia). Players knelt on a padded board with their ankles secured under padded hooks and leaned forward at a controlled speed until they could no longer resist the fall. The combined bilateral force (N) was recorded for each trial. Each player performed three trials, and the average force from the three trials was taken as the outcome. This test targets the hamstrings in an eccentric modality and the use of the NordBord device has demonstrated good to excellent reliability in professional football players (ICC\u0026thinsp;\u0026asymp;\u0026thinsp;0.83\u0026ndash;0.90) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHip Adductor/Abductor Squeeze (45\u0026deg;) (Add/Abd 45\u0026deg;)\u003c/strong\u003e \u003cp\u003eIsometric hip adductor and abductor strength was assessed using the ForceFram\u003cb\u003ee\u003c/b\u003e device (VALD Performance, Australia). Players lay supine with hips flexed to 45\u0026deg; and knees bent to 90\u0026deg;. For the adductor test, players squeezed inward against padded plates positioned on the medial sides of the knees; for the abductor test, they pushed outward against lateral pads with maximal effort. The combined bilateral force (N) was recorded for both adduction and abduction measures. Two 5-s maximal contractions were performed, separated by ~\u0026thinsp;10 s of rest, and the mean value across trials was analyzed.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThis short-lever (45\u0026deg;) adductor/abductor squeeze configuration is widely used to monitor hip-muscle strength and groin function in elite football [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Studies using ForceFrame or comparable fixed dynamometry report good-to-excellent reliability for hip adduction (ICC\u0026thinsp;\u0026asymp;\u0026thinsp;0.81\u0026ndash;0.91\u003cb\u003e)\u003c/b\u003e and good reliability for abduction (ICC\u0026thinsp;\u0026asymp;\u0026thinsp;0.80\u0026ndash;0.90) in athletic populations; handheld and long-lever protocols can yield higher ICCs (\u0026asymp;\u0026thinsp;0.90\u0026ndash;0.97) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Including both adduction and abduction measures provides a more comprehensive evaluation of hip-muscle balance and functional stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Processing\u003c/h2\u003e \u003cp\u003eRaw GPS data were cleaned to remove anomalies (e.g., spurious spikes from signal loss) before calculating weekly and match totals. If a player had missing neuromuscular test data due to a recovery day off or a minor injury, the missing values were addressed via imputation. Little\u0026rsquo;s MCAR test confirmed that force data were missing completely at random (χ\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.33), indicating no bias in the pattern of missingness. Multiple Imputation by Chained Equations (MICE) was employed to fill in missing fatigue measures, following the approach of van Buuren and Groothuis-Oudshoorn [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Five imputed datasets were generated using predictive mean matching, including all load and fatigue variables plus player ID and week as predictors. The algorithm ran for 20 iterations per dataset to ensure convergence. Diagnostics indicated that the distributions of imputed values were plausible and closely matched those of the observed data, preserving the mean and variance structure. Finally, the five imputations were averaged for each missing entry to produce a single complete dataset for analysis. This approach reduced bias compared to listwise deletion and maximized use of the available data. Prior to multivariate analysis, all external load variables were standardized. Each player-week value was converted to a z-score based on the sample mean and standard deviation (SD). Standardization allowed comparison of effect sizes on a common scale and was necessary for combining variables in the principal component analysis (PCA). The neuromuscular outcome variables were left on their original scale (N) for ease of interpretation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Statistical Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Statistics and Data Imputation\u003c/h2\u003e \u003cp\u003eAll variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Weekly training metrics were aggregated across the four sessions (MD-4 to MD-1); match metrics reflected single-game totals. Next-day neuromuscular outcomes (Nordic, ISO prone, Adductor 45\u0026deg;, Abductor 45\u0026deg;) were recorded in newtons (N).\u003c/p\u003e \u003cp\u003eMissing data occurred primarily in the neuromuscular tests: depending on the metric, 7\u0026ndash;23% of values were missing, mostly from players not being tested on certain occasions due to squad rotation or minor injury. Little\u0026rsquo;s MCAR test was not significant (p\u0026thinsp;=\u0026thinsp;0.330), supporting the assumption that data were missing at random. As described above, we applied the MICE procedure to handle these missing values. After 20 iterations, the chained equations algorithm converged, and diagnostic checks confirmed that the imputed values fell within plausible ranges for each player. Importantly, the imputation did not distort the overall means or SDs; for instance, the group mean Nordic hamstring force remained\u0026thinsp;~\u0026thinsp;408 N). All subsequent analyses used the imputed, complete dataset. Additionally, all external load variables were standardized via z-scores prior to PCA and modeling to facilitate comparison of coefficients. The standardization does not affect statistical significance but allows effects to be interpreted per each variable\u0026rsquo;s SD.\u003c/p\u003e \u003cp\u003eTo facilitate interpretation and comparability, all external-load predictors were z-standardized (mean 0, SD 1) before dimensionality reduction and modeling. Standardization rescales coefficients (to units of N per 1 SD change in load) without altering hypothesis tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multicollinearity and Principal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003eThe 18 external load features, 9 training and 9 match variables were first examined for multicollinearity. As expected, many metrics within the training set or within the match set were strongly intercorrelated. For example, in the training data, players who performed more sprints in a week also accumulated more sprint distance (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.93), and those with higher acceleration counts tended to have higher total distance. Similarly, within matches, metrics of volume and intensity were interrelated (e.g., high-intensity distance vs. High-Intensity Actions, r\u0026thinsp;\u0026asymp;\u0026thinsp;0.87; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, training load vs. match load for the same week showed little to no correlation; in fact, the relationship was slightly negative in some cases with players with a very high match load often had a reduced training load that week. Variance inflation analysis confirmed that including all 18 metrics in a single model would be problematic due to multicollinearity with many VIFs\u0026thinsp;\u0026gt;\u0026thinsp;10, with a maximum around 36 for weekly accelerations\u0026thinsp;\u0026gt;\u0026thinsp;2 m\u0026middot;s⁻\u0026sup2;.\u003c/p\u003e \u003cp\u003eTo address this and reduce dimensionality, PCA was performed separately on the training load variables and on the match load variables after standardization. Criterion of eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1 was used and visual inspection of scree plots determined the number of principal components (PCs) to retain. The PCA on weekly training load metrics (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) yielded a dominant first component, explaining 77.2% of the variance. This component reflected an overall measure of weekly training volume and intensity. Loading values for each weekly training metric are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Subsequent components each contributed much less (PC2: 11.7%, PC3: 4.0%, others\u0026thinsp;\u0026lt;\u0026thinsp;3%). The PCA on match load metrics (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed a more gradual decline in variance: PC1 explained 52.1% and PC2 26.6%, with PC3 and PC4 adding about 7% and 6%, respectively. The scree plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrates the variance explained by each component for both the training and match load data. Based on the \u0026ldquo;elbow\u0026rdquo; of the curves, only the first component for training load was retained with one component sufficiently summarizing weekly training load and the first two components for match load capturing both overall volume and intensity distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Linear Mixed-Effects Model (LMM)\u003c/h2\u003e \u003cp\u003eLinear mixed-effects models (LMMs) were used to examine the association between external load components and next-day neuromuscular outputs. Separate LMMs were constructed for each of the four neuromuscular outcome measures. In each model, the fixed effects were Training PC1, Match PC1, and Match PC2, representing weekly training load, overall match load, and match intensity distribution, respectively. Player identity was included as a random intercept to account for repeated measures on the same player to allow each player to have a different baseline strength level. Change-from-baseline was not computed because a single pre-season reference is not stable across the competitive period and change scores can inflate error and bias via regression-to-the-mean. Methodological guidance recommends analyzing the absolute outcome with appropriate covariate or random-effects adjustment (e.g., ANCOVA/LMM) rather than change scores in repeated-measures design [\u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Random slope terms for the load predictors were tested but did not significantly improve model fit (likelihood ratio tests, p\u0026thinsp;\u0026gt;\u0026thinsp;0.2 for each) and led to convergence issues given the limited observations per player. Thus, the final models included random intercepts only. The LMMs were fitted using the lme4 package in R [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] with restricted maximum likelihood. Significance of fixed effects was evaluated with Satterthwaite-approximated degrees of freedom for t-tests via the lmerTest package, using α\u0026thinsp;=\u0026thinsp;0.05. Effect sizes were interpreted via the magnitude of the fixed-effect coefficients in units of N change per 1 SD change in load with 95% confidence intervals, following recommended guidelines for practical significance in sports science [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. All statistical analyses were conducted in R (R Foundation for Statistical Computing, Vienna, Austria) and Python (for data preprocessing and visualization).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Model Assumption and Diagnostics\u003c/h2\u003e \u003cp\u003eDiagnostic plots were inspected to ensure the assumption of the LMM were met. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows residuals vs. fitted values and the normal Q\u0026ndash;Q plot for the model predicting Nordic hamstring force, which had the clearest significant effect. The residuals showed no obvious heteroscedasticity or non-linear pattern against fitted values; points were roughly symmetrically scattered around zero across the range of fitted values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, left). A few cases with the highest fitted values (i.e., where the model predicted a relatively high next-day force) had moderately large negative residuals with actual force being lower than predicted. This corresponds to instances where players underperformed the model\u0026rsquo;s expectation likely in those extreme high load matches where fatigue was slightly greater than average. These could be considered high leverage points but given the sample size they did not unduly influence the fixed effects. The model was refitted excluding the top two residuals and negligible changes in coefficients were observed. The Normal Q\u0026ndash;Q plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, right) showed residuals mostly following the reference line, with only slight deviations in the tails. A few residuals at the very low end were more negative than expected (left tail) and a few at the high end were slightly higher than expected, indicating minor heavy-tailed behavior. Nonetheless, the Shapiro\u0026ndash;Wilk test (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for normality of residuals was non-significant for all models (p\u0026thinsp;\u0026gt;\u0026thinsp;0.20) and given the relatively large residual degrees of freedom (~\u0026thinsp;100+), slight departures from normality are not critically important. It was concluded that the LMM fit was adequate and that the results, particularly the significant effects for Nordic and adductor strength are robust. Other outcome models showed similar diagnostics, the ISO Prone and Abductor models had very symmetric residuals with no systematic effects present, and the Adductor model\u0026rsquo;s residuals also showed homoscedasticity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), though its Q\u0026ndash;Q plot had a couple of mild outliers on the high end with some players producing much higher adductor force than predicted, possibly reflecting individual strength variability not captured by the random intercept alone.\u003c/p\u003e \u003cp\u003e \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\u003eNormality of Residuals (Shapiro\u0026ndash;Wilk Tests)\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=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuromuscular Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNordic Hamstring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISO Prone Hamstring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdductor 45\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbductor 45\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\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\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\u003eHomoscedasticity Checks (Levene\u0026rsquo;s Tests)\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=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuromuscular Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNordic Hamstring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomoscedastic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISO Prone Hamstring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomoscedastic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdductor 45\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomoscedastic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbductor 45\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomoscedastic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the descriptive statistics for the weekly external load metrics and the strength measures. On average, players covered about 17,900 meters per week in training (including\u0026thinsp;~\u0026thinsp;1,100 m of high-intensity running\u0026thinsp;\u0026gt;\u0026thinsp;20 km/h and ~\u0026thinsp;170 m of sprinting\u0026thinsp;\u0026gt;\u0026thinsp;25 km/h). In matches, the average distance covered was ~\u0026thinsp;10,300 m, with ~\u0026thinsp;712 m of high-intensity running and ~\u0026thinsp;94 m of sprinting. The counts of intense efforts followed a similar pattern (i.e., approximately 60 high-intensity runs per week in training vs. 50 in a match). These values align with typical load profiles for professional football, where a single match often contributes a large portion of the week\u0026rsquo;s high-intensity distance [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The neuromuscular strength measures showed mean values around 400 N for hamstrings and 450 N for adductors, consistent with expected levels for elite male players [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Notably, there was considerable inter-individual variability (e.g., SD\u0026thinsp;\u0026asymp;\u0026thinsp;48 N in Nordic hamstring force, corresponding to some players producing\u0026thinsp;\u0026lt;\u0026thinsp;360 N while others exceeded 450 N).\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\u003eDescriptive statistics for external load metrics and next-day neuromuscular strength measures (N\u0026thinsp;=\u0026thinsp;139 player-weeks). Training metrics are weekly totals; match metrics are per game. \u0026ldquo;\u0026ndash;\u0026rdquo; indicates not applicable for match (strength tests performed only post-match).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003cp\u003e(Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMatch (Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Distance (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17,921\u0026nbsp;\u0026plusmn;\u0026nbsp;4,856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,304\u0026nbsp;\u0026plusmn;\u0026nbsp;2,124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Intensity Distance \u0026gt;\u0026nbsp;20\u0026nbsp;km\u0026middot;h⁻\u0026sup1; (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1,087\u0026nbsp;\u0026plusmn;\u0026nbsp;402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e712\u0026nbsp;\u0026plusmn;\u0026nbsp;183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprint Distance \u0026gt;\u0026nbsp;25\u0026nbsp;km\u0026middot;h⁻\u0026sup1; (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e168\u0026nbsp;\u0026plusmn;\u0026nbsp;82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u0026nbsp;\u0026plusmn;\u0026nbsp;47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Intensity Actions (\u0026gt;\u0026nbsp;20 km\u0026middot;h⁻\u0026sup1;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e60\u0026nbsp;\u0026plusmn;\u0026nbsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u0026nbsp;\u0026plusmn;\u0026nbsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprints (\u0026gt;\u0026nbsp;25\u0026nbsp;km\u0026middot;h⁻\u0026sup1;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17\u0026nbsp;\u0026plusmn;\u0026nbsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u0026nbsp;\u0026plusmn;\u0026nbsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccelerations \u0026gt;\u0026nbsp;2\u0026nbsp;m\u0026middot;s⁻\u0026sup2; (counts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e257\u0026nbsp;\u0026plusmn;\u0026nbsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u0026nbsp;\u0026plusmn;\u0026nbsp;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecelerations \u0026lt;\u0026nbsp;\u0026ndash;2\u0026nbsp;m\u0026middot;s⁻\u0026sup2; (counts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e204\u0026nbsp;\u0026plusmn;\u0026nbsp;71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u0026nbsp;\u0026plusmn;\u0026nbsp;26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccelerations \u0026gt;\u0026nbsp;3\u0026nbsp;m\u0026middot;s⁻\u0026sup2; (counts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e55\u0026nbsp;\u0026plusmn;\u0026nbsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u0026nbsp;\u0026plusmn;\u0026nbsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecelerations \u0026lt;\u0026nbsp;\u0026ndash;3\u0026nbsp;m\u0026middot;s⁻\u0026sup2; (counts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e49\u0026nbsp;\u0026plusmn;\u0026nbsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u0026nbsp;\u0026plusmn;\u0026nbsp;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNordic Hamstring Force (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e407.5\u0026nbsp;\u0026plusmn;\u0026nbsp;48.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISO Prone Hamstring Force (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e385.5\u0026nbsp;\u0026plusmn;\u0026nbsp;47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdductor (45\u0026deg;) Force (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e467.3\u0026nbsp;\u0026plusmn;\u0026nbsp;66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbductor (45\u0026deg;) Force (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e416.9\u0026nbsp;\u0026plusmn;\u0026nbsp;45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Principal Component Analysis\u003c/h2\u003e \u003cp\u003eTraining Load PC1 displayed mixed-sign loadings across the weekly with larger contributions from total distance and high-intensity effort counts. Component signs were oriented such that higher PC1 scores correspond to greater overall weekly load. Thus, Training PC1 effectively summarizes weekly training volume/intensity and was retained as the sole training-load component. The second training component explained only 11.7% of variance and mainly differentiated a subset of metrics slightly isolating sprint-related metrics but given its small contribution it was not utilized.\u003c/p\u003e \u003cp\u003eFor match load, the first two principal components together explained 78.7% of the variance (PC1: 52.1%, PC2: 26.6%). Match PC1 had positive loadings on all match metrics with especially large contributions from metrics such as, the number of high-intensity actions and high decelerations (e.g., Decelerations\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;3 m\u0026middot;s⁻\u0026sup2;). This suggests Match PC1 represents the overall magnitude of match demands, with higher values indicating the player covered more distance and executed many high-intensity runs with aggressive accelerations/decelerations with essentially a \u0026ldquo;harder\u0026rdquo; match in terms of total work and intensity. Match PC2, on the other hand, showed a trade-off pattern, it loaded positively on high-speed running and sprint distance, but negatively on total distance and moderate accelerations This indicates Match PC2 captures the intensity distribution within a match (relatively more sprinting/high-speed running versus total volume). For example, a player with lower minutes who completes frequent sprints might have a high Match PC2 despite lower total distance, whereas a player who plays 90 minutes at a steady high pace could have a high Match PC1 but lower PC2. By retaining both components, the overall load and the intensity skew as separate factors was accounted for. After the PCA, multicollinearity was effectively resolved. The selected components (Training PC1, Match PC1, Match PC2) had low inter-correlations and all VIFs\u0026thinsp;\u0026lt;\u0026thinsp;1.5.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCA loadings for weekly training external load metrics. Values are loading coefficients of each original variable on the first three principal components (PC). The percentage of variance explained by each component is shown in parentheses.\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=\"char\" char=\".\" 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 \u003cp\u003eTraining Load Metric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1 (77.2%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2 (11.7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3 (4.0%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Intensity Distance (\u0026gt;\u0026thinsp;20 km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprint Distance (\u0026gt;\u0026thinsp;25 km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Intensity Actions (\u0026gt;\u0026thinsp;20 km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprints (\u0026gt;\u0026thinsp;25 km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccelerations (\u0026gt;\u0026thinsp;2 m\u0026middot;s⁻\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecelerations (\u0026lt;\u0026ndash;2 m\u0026middot;s⁻\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccelerations (\u0026gt;\u0026thinsp;3 m\u0026middot;s⁻\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecelerations (\u0026lt;\u0026ndash;3 m\u0026middot;s⁻\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.370\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCA loadings for match external load metrics (first 2 components). Values are coefficients (loading weights) of each original variable on the principal components. The percentage of variance explained by each component is shown in parentheses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatch Load Metric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1 (52.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2 (26.6%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Intensity Distance (\u0026gt;\u0026thinsp;20\u0026nbsp;km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprint Distance (\u0026gt;\u0026thinsp;25\u0026nbsp;km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Intensity Actions (\u0026gt;\u0026thinsp;20\u0026nbsp;km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprints (\u0026gt;\u0026thinsp;25\u0026nbsp;km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccelerations \u0026gt;\u0026nbsp;2\u0026nbsp;m\u0026middot;s\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecelerations \u0026lt;\u0026nbsp;\u0026ndash;2\u0026nbsp;m\u0026middot;s\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccelerations \u0026gt;\u0026nbsp;3\u0026nbsp;m\u0026middot;s\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecelerations \u0026lt;\u0026nbsp;\u0026ndash;3\u0026nbsp;m\u0026middot;s\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Linear Mixed Models of Fatigue Outcomes\u003c/h2\u003e \u003cp\u003eLinear mixed-effects models were applied for each neuromuscular outcome to determine the effects of training and match loads on next-day performance with lower force indicating greater fatigue. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the fixed-effects estimates from these models, including coefficients (β), standard errors, 95% confidence intervals, and p-values for each predictor. A positive β indicates higher load was associated with higher next-day force (i.e., less fatigue), while a negative β indicates higher load was associated with lower force (i.e., greater fatigue).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNordic Hamstring Exercise Strength\u003c/b\u003e: There was a significant negative association between Match PC1 (overall match load) and next-day Nordic hamstring force. Specifically, for each +\u0026thinsp;1 SD increase in Match PC1, Nordic force was about 5.1 N lower the next morning (β = \u0026minus;\u0026thinsp;5.12 N, 95% CI: \u0026minus;\u0026thinsp;10.07 to \u0026minus;\u0026thinsp;0.17 N, p\u0026thinsp;=\u0026thinsp;0.046). In practical terms, a very high match load (+\u0026thinsp;2 SD above average) would predict\u0026thinsp;~\u0026thinsp;10 N lower Nordic force the next day compared to a low-load match. Training PC1 had a small positive but non-significant coefficient (β = +1.42 N, CI: \u0026minus;\u0026thinsp;2.15 to +\u0026thinsp;5.00 N, p\u0026thinsp;=\u0026thinsp;0.430), suggesting that weeks with higher training load did not lead to additional hamstring fatigue beyond match effects, contrary there was a slight trend toward higher Nordic force after higher training weeks, but this was not reliable. Match PC2 had β = +3.78 N (CI: \u0026minus;\u0026thinsp;1.74 to +\u0026thinsp;9.30 N, p\u0026thinsp;=\u0026thinsp;0.180), indicating that matches with a greater high-speed component relative to total volume tended to show less Nordic force loss, a positive coefficient, though this did not reach significance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eISO Prone Hamstring Strength\u003c/b\u003e: No significant relationships were found for the prone isometric hamstring test. Both training and match components had small, non-significant coefficients (Training PC1: β = \u0026minus;\u0026thinsp;0.39 N, p\u0026thinsp;=\u0026thinsp;0.770; Match PC1: β = \u0026minus;\u0026thinsp;2.10 N, p\u0026thinsp;=\u0026thinsp;0.240; Match PC2: β = \u0026minus;\u0026thinsp;1.74 N, p\u0026thinsp;=\u0026thinsp;0.250). The negative signs for match predictors hint at a possible reduction in isometric hamstring force with higher loads, but the effects were much smaller than for the Nordic test and did not achieve significance. This contrast between the Nordic and ISO tests suggests that the eccentric hamstring measure was more sensitive to fatigue from match play than the isometric measure. It may be that the Nordic exercise, which stresses the hamstrings under lengthening contractions, picks up fatigue-related weakness that a mid-range isometric test does not.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdductor (45\u0026deg;) Strength\u003c/b\u003e: Contrary to expectation, Match PC2 showed a significant positive association with next-day adductor force (β = +11.68 N per 1 SD, 95% CI: +0.19 to +\u0026thinsp;23.17 N, p\u0026thinsp;=\u0026thinsp;0.046). This indicates that when a match involved a high proportion of sprinting/high-speed running, relative to total distance, players produced slightly higher adductor squeeze force the following morning. Neither Training PC1 nor Match PC1 significantly affected adductor strength (Training PC1: β = \u0026minus;\u0026thinsp;2.94 N, p\u0026thinsp;=\u0026thinsp;0.380; Match PC1: β = \u0026minus;\u0026thinsp;1.76 N, p\u0026thinsp;=\u0026thinsp;0.640). The Match PC2 effect, if taken at face value, implies that intense, sprint-heavy matches were associated with an ~\u0026thinsp;12 N increase in adductor strength the next day, compared to more high-volume matches. This result was unexpected, as the hypotheses expected all load would either have no effect or a negative effect on strength. Potential explanations are considered in the discussion (e.g., a potentiation effect or an influence of playing time).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAbductor (45\u0026deg;) Strength\u003c/b\u003e: No significant effects were observed for hip abductor strength. All coefficients were near zero (Training PC1: +1.63 N, p\u0026thinsp;=\u0026thinsp;0.260; Match PC1: \u0026minus;\u0026thinsp;0.85 N, p\u0026thinsp;=\u0026thinsp;0.630; Match PC2: +1.08 N, p\u0026thinsp;=\u0026thinsp;0.600). This suggests that the hip abductors did not exhibit measurable fatigue or any consistent change related to training or match loads in this timeframe. It may be that a single match, and the typical weekly training, were not sufficient stimuli to cause detectable strength loss in the abductors, or that the abductor test was not sensitive to subtle changes.\u003c/p\u003e \u003cp\u003eAcross all models, the random intercept for player was significant, reflecting substantial baseline differences in strength between players (e.g., some players consistently had higher absolute Nordic forces than others). The standard deviation of the random intercept was on the order of 20\u0026ndash;35 N across models, indicating the typical variation between players\u0026rsquo; mean strength levels. No evidence was found that load effects varied markedly by player as random slopes were not needed, implying that the observed relationships or lack thereof were relatively consistent across the squad.\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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear mixed model fixed-effects estimates for the influence of weekly training load (Training PC1) and match load components (Match PC1, Match PC2) on next-day neuromuscular performance. β values (\u0026plusmn;\u0026thinsp;SE) represent the change in force (N) associated with a\u0026thinsp;+\u0026thinsp;1 SD change in the predictor. 95% confidence intervals [CI] and p-values are shown. Significant effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)**.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome (Next-Day Force)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ Training\u0026nbsp;PC1 (SE) [95%\u0026nbsp;CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ Match\u0026nbsp;PC1 (SE) [95%\u0026nbsp;CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ Match\u0026nbsp;PC2 (SE) [95%\u0026nbsp;CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNordic Hamstring (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;1.42 (1.78) [\u0026ndash;2.15, +\u0026thinsp;5.00], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026ndash;5.12 (2.50) [\u0026ndash;10.07, \u0026minus;\u0026thinsp;0.17]\u003c/b\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;3.78 (2.82) [\u0026ndash;1.74, +\u0026thinsp;9.30], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISO Prone Hamstring (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.39 (1.29) [\u0026ndash;2.99, +\u0026thinsp;2.22], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;2.10 (1.67) [\u0026ndash;5.38, +\u0026thinsp;1.19], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;1.74 (1.52) [\u0026ndash;4.77, +\u0026thinsp;1.30], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdductor 45\u0026deg; (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;2.94 (3.30) [\u0026ndash;9.56, +\u0026thinsp;3.68], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;1.76 (3.98) [\u0026ndash;9.18, +\u0026thinsp;5.66], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;11.68 (5.81) [+\u0026thinsp;0.19, +\u0026thinsp;23.17], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbductor 45\u0026deg; (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;1.63 (1.45) [\u0026ndash;1.25, +\u0026thinsp;4.50], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.85 (1.64) [\u0026ndash;4.12, +\u0026thinsp;2.42], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.08 (2.05) [\u0026ndash;2.96, +\u0026thinsp;5.12], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe purpose of this study was to examine how external training and match load correlates with objective neuromuscular fatigue markers in elite football players. Overall, the findings highlight that the strain of a competitive match is a key driver of short-term neuromuscular fatigue, particularly affecting eccentric hamstring strength, whereas the cumulative training load in the preceding week had minimal additional influence on next-day neuromuscular function. In practical terms, this suggests that elite players are generally well adapted to their routine training workloads, but a single match of high intensity can still induce measurable fatigue that persists into the next morning.\u003c/p\u003e \u003cp\u003eThe acute match external load had a more pronounced impact on next-day neuromuscular function than accumulated weekly training load in these elite footballers. Specifically, players who experienced a high overall match load covering more distance with more high-intensity efforts showed reduced eccentric hamstring strength the following morning, consistent with acute hamstring fatigue. This aligns with the concept that intense match play induces muscle damage or fatigue that temporarily impairs maximal force production [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. By contrast, variations in training load during the week did not significantly predict next-day strength, suggesting that the team\u0026rsquo;s training periodization successfully managed fatigue or that any training-induced fatigue had dissipated by the post-match assessment as players often tapered before games. Highly sprint-intensive matches may transiently increase adductor strength due to potentiation via high neural drive. This could imply a form of residual potentiation or simply that those matches were less taxing overall despite the sprinting, so that players\u0026rsquo; adductors were relatively fresh [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, given the borderline significance of this finding and its isolated nature, it should be interpreted cautiously. There were no notable load effects on the isometric hamstring test or hip abductor strength, indicating those measures might be less sensitive to the range of loads observed or may recover more quickly.\u003c/p\u003e \u003cp\u003eThe significant negative effect of match load on Nordic hamstring force supports the notion that match play incurs neuromuscular fatigue in the hamstrings. This result is consistent with prior research demonstrating post-match declines in muscle force and performance. Brownstein et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported marked reductions in voluntary quadriceps and hamstring force immediately after matches, with recovery taking up to 72 h. Although our study assessed fatigue the next day (~\u0026thinsp;12\u0026ndash;18 h post-match) rather than immediately, the detection of ~\u0026thinsp;1\u0026ndash;2% decreases in hamstring force per SD of match load aligns with an early fatigue phase. It also reinforces the idea that eccentric tests like the Nordic are sensitive to fatigue-related strength loss. Notably, Thorpe et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] argued that standard performance tests such as CMJ often fail to reflect accumulated fatigue in elite soccer. The data from this study suggest that the Nordic eccentric strength test may succeed where the CMJ falls short, capturing deficits when players are fatigued from intense match activity. Eccentric muscle actions, like those in the Nordic, might be more affected by muscle damage or soreness from match play due to high-speed running and sudden decelerations that load the hamstrings during sprinting and cutting actions. This aligns with evidence that eccentric knee-flexor strength is acutely reduced after matches or heavy training. However, absolute Nordic strength outputs can also depend on a player\u0026rsquo;s body mass, so monitoring relative strength via force normalized to body weight could help account for individual differences in player size [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFrom an applied perspective, the drop in Nordic strength after high-load matches and its absence after lower-load matches suggests that monitoring eccentric hamstring strength the day after a game can serve as a useful fatigue indicator. If a player\u0026rsquo;s NHE scores are substantially lower than their baseline following a match, it likely reflects significant neuromuscular fatigue or muscle damage. Practitioners could use this information to individualize recovery strategies or adjust subsequent training. For instance, a player showing a large Nordic strength deficit might benefit from reduced high-speed work or extra recovery modalities in the 1\u0026ndash;2 days post-match. Over time, tracking this metric might also aid in injury prevention, since incomplete recovery of hamstring function could predispose players to muscle strains if they are overexposed. Our results dovetail with earlier findings that high-speed running in matches is a major contributor to fatigue and injury risk with Malone et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] showing that acute spikes in sprint distance elevated injury risk unless players had developed a high chronic exposure. Therefore, identifying when a player is in a fatigued state could inform safer training loads until recovery is achieved.\u003c/p\u003e \u003cp\u003eInterestingly, weekly training load did not have any significant impact on next-day neuromuscular performance. The interpretation is that the team\u0026rsquo;s training loads were managed such that players generally entered matches relatively fresh. The club\u0026rsquo;s practice of tapering before games and adjusting training for those who played big minutes likely prevented excessive fatigue accumulation from training alone. In fact, data showed a slight negative correlation between training and match loads; players with high match demands often had their training reduced. For example, starters might do less intense mid-week training, while non-starters trained harder. This adaptive approach is common in elite football periodization and aims to ensure that training augments fitness without causing residual fatigue that could harm match performance. Our findings suggest that this approach was effective; there was no evidence that a harder training week left players weaker or more fatigued the morning after the subsequent match. In other words, the match\u0026rsquo;s impact dominated any training effects.\u003c/p\u003e \u003cp\u003eIt\u0026rsquo;s also worth noting that the absolute range of training load in the sample, while considerable, might not have been extreme enough to elicit large differences in next-day fatigue. Elite players are highly trained and may recover quickly from normal training sessions with 24\u0026ndash;48 h being usually sufficient for muscle recovery after typical football training [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. By match day\u0026thinsp;+\u0026thinsp;1, any fatigue from earlier in the week would likely have dissipated, leaving primarily the match to account for any deficits we observed. This is supported by research showing that markers like jump performance or isometric strength often return to baseline within 48\u0026ndash;72 h after isolated training bouts [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. However, overuse or excessive cumulative load can still manifest as fatigue or injury over longer periods. This study focused on acute effects rather than long-term trends, so it cannot be ruled out that consistently high training loads week after week might eventually lead to fatigue or underperformance. But within the scope of single-week variations, the players appeared to cope well.\u003c/p\u003e \u003cp\u003eOne surprising outcome was the positive association between Match PC2 and adductor strength. It was hypothesized that all muscle groups would either show no change or a decrease in strength after harder matches, yet the data indicated that matches with more high-speed running corresponded to slightly higher adductor squeeze force the next day. There are several potential explanations for this counterintuitive result.\u003c/p\u003e \u003cp\u003eFirstly, it could be a statistical anomaly (Type I error). Multiple outcomes and predictors were tested, and while the modelling approach was targeted, the chance of one spurious significant finding exists. The confidence interval for the adductor Match PC2 effect was wide and only just excluded zero. Thus, replication is needed to confirm this relationship.\u003c/p\u003e \u003cp\u003eAnother possible interpretation is a post-activation performance enhancement (PAPE) effect. High-intensity, intermittent activity can transiently enhance subsequent force or explosive performance, depending on the balance between fatigue and potentiation mechanisms [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The adductors play a role in sprinting stabilizing the swing leg, contributing to hip flexion/extension synergy [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], but sprinting might not fatigue them as much as prolonged running or repeated cutting actions would. Sprint running provides a substantial neuromuscular stimulus to the hip musculature, and modern muscle-activity measurement approaches indicate meaningful involvement of the hip adductors during sprinting [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Conceptually, therefore, a sprint-heavy match profile could, in some contexts, be consistent with a short-term neuromuscular enhancement signal rather than a pure fatigue response. However, because potentiation effects are typically short-lived and highly dependent on individual responses and testing time-points, this explanation remains speculative and should be interpreted cautiously [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAnother factor could be selection bias in who had high-PC2 matches. Matches with high PC2 might often be those where a player was used for partial game. It\u0026rsquo;s plausible that playing fewer minutes resulted in less overall fatigue, so the player\u0026rsquo;s muscles, including adductors were relatively fresh the next morning. In the data, minutes played were not explicitly controlled, which could be a confounder. Match PC1 is naturally correlated with minutes, but Match PC2 might highlight cases of short-duration involvement. If players with less minutes generally showed higher adductor strength that could manifest as this positive PC2 effect. This explanation aligns with findings that cumulative fatigue is more evident after consecutive full matches or tournaments. For instance, S\u0026aacute;nchez-Migall\u0026oacute;n et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] found that hip adductor strength significantly dropped after two consecutive days of matches in female players, especially 48 h after the second match.\u003c/p\u003e \u003cp\u003eIt\u0026rsquo;s also possible that the adductor test itself has higher day-to-day variability. If a player isn\u0026rsquo;t truly fatigued, a small improvement could fall within normal variation, whereas if they were fatigued, a decline would be expected. Most of the data showed no significant average change in adductor strength post-match since Match PC1 had no effect, so the Match PC2 result should be interpreted cautiously. It may be highlighting a subset of observations with perhaps lower minutes of play or unique game scenarios rather than a general principle. Future research should investigate adductor strength changes with controlled variations in playing time and intensity to confirm this pattern.\u003c/p\u003e \u003cp\u003eMeanwhile, hip abductor strength was largely unchanged by load. Hip abductors may not be stressed as heavily during typical match play compared to adductors, which work during cutting and kicking or hamstrings during sprinting. Another possibility is that the abductors recover quickly or that 45\u0026deg; isometric abduction test wasn\u0026rsquo;t sensitive to subtle changes. Some studies have noted decreases in abductor strength in very congested schedules, but in a single-match context our results suggest minimal acute fatigue in this muscle group. For practitioners, this means the adductor squeeze test might be more informative than an abductor test for post-match fatigue monitoring, as it has been linked to groin injury risk and appears to respond to certain match conditions.\u003c/p\u003e \u003cp\u003eThe findings resonate with the growing body of work highlighting the value of neuromuscular tests in monitoring training and competition load. For example, Marqu\u0026eacute;s-Jim\u0026eacute;nez et al., [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] found that external load metrics like accelerations and decelerations predicted declines in post-match CMJ and sprint performance. Match load was similarly identified with heavily loaded on accelerations/decelerations as a predictor of reduced force in a direct muscle test. This underscores the connection between high mechanical load and fatigue. Interestingly, this was detected via a muscle-specific test rather than a general performance test, reinforcing arguments that a multi-metric approach is beneficial. It seems that monitoring both neuromuscular performance (e.g., jump tests) and muscle capacity (e.g., strength tests) can provide complementary insights. Whereas a CMJ might indicate general power-output readiness, a NHE strength test can pinpoint hamstring status specifically. Coaches may thus consider implementing a combination of metrics to get a fuller picture of player readiness. In support of this idea, a recent study by Collins et al. [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] reported associations between internal and external training load measures and neuromuscular performance in elite soccer players, illustrating the interplay of multiple load and performance indicators in practice.\u003c/p\u003e \u003cp\u003eAnother point of discussion is the lack of an observed effect of training load, which aligns with some studies but not others. Rowell et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], tracking a pro team over a season, noted that heavy training and match schedules impacted players\u0026rsquo; hormonal markers and perceived recovery, but effects on neuromuscular tests were less clear. Thorpe et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] found that changes in acute training load on preceding days did not significantly alter next-morning fatigue measures in English Premier League players, which is consistent with our finding that weekly training load had no acute effect on strength. It seems that for well-trained athletes, a single week\u0026rsquo;s workload, if periodized properly, might not push them into a state of measurable neuromuscular deficit beyond what the main competitive event causes. This speaks to the high level of adaptation and fitness in elite players, they can handle a lot of work if it\u0026rsquo;s managed and not beyond their chronic load capacity. However, if training load were to spike abnormally or if recovery was insufficient, different results might be observed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePractical Implications\u003c/strong\u003e \u003cp\u003eThe day after a match, objective strength tests, especially the Nordic hamstring test can reveal fatigue that might not be evident from subjective reports or jump testing. If a player demonstrates a significant drop in Nordic strength compared to the player\u0026rsquo;s own typical in-season range, this can present as a red flag indicating they are in a fatigued state and possibly at higher risk of hamstring strain if loaded again too soon. Interventions could include modified training or targeted recovery in the 1\u0026ndash;2 days post-match. Conversely, if a player\u0026rsquo;s strength levels remain at baseline the next day, it suggests they have recovered well or were not excessively loaded in the match, and normal training can likely resume. Adductor squeeze tests should also be monitored; large drops might indicate groin fatigue or impending issues; though, as noted, a small increase could occur in some cases without negative implications. The findings indicate that typical training loads, when properly managed, do not unduly carry over fatigue into match recovery. Coaches can maintain intensive training during the week, provided appropriate tapering is applied before matches. The primary focus for recovery should be after matches, where the biggest fatigue impact is observed. It remains important to individualize recovery and training adjustments based on objective measures. Not all players respond identically to load, whereas ongoing monitoring allows identification of those who may need extra recovery versus those who are coping well. Integrating neuromuscular tests into regular monitoring can help optimize performance and reduce injury risk by informing evidence-based decisions on training load adjustments.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations and Future Directions\u003c/strong\u003e \u003cp\u003eOutcomes were not anchored to a fixed pre-season baseline; instead, absolute next-day force was modeled with player-level random intercepts to estimate within-player associations between external load and neuromuscular function. This approach avoids known drawbacks of change-from-baseline analyses (e.g., amplification of measurement error and regression-to-the-mean) and is recommended for repeated-measures designs; however, it limits direct statements about individual \u0026ldquo;percentage drop from a static baseline.\u0026rdquo; Weekly microcycles were non-consecutive, environmental conditions were not systematically recorded, and positional information and exact match-minutes were not included as covariates, which may contribute unexplained variance. Although missingness was addressed via MICE with satisfactory diagnostics, residual bias cannot be entirely excluded. Finally, the sample reflects a single professional squad, which may constrain generalizability to other competitions or sexes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSubsequent research should (i) record and incorporate pre-specified operational baselines (e.g., rolling 4\u0026ndash;6-week typical values per player) to complement mixed-model inference; (ii) add position, match minutes, and environmental covariates; (iii) evaluate recovery time-courses beyond 12\u0026ndash;18 h (e.g., 24\u0026ndash;72 h) to map kinetics of neuromuscular restoration; and (iv) link acute fatigue markers to prospective injury outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrated that in elite football players, the external load from a match has a quantifiable impact on next-day neuromuscular performance, whereas the typical range of training load in the days before a match does not produce additional immediate fatigue. Specifically, high match loads led to reduced eccentric hamstring strength, highlighting the hamstrings\u0026rsquo; susceptibility to fatigue from intense competition. Meanwhile, isometric tests of hamstrings and hip muscles showed no decline, and an unexpected increase in adductor strength was observed after sprint-heavy matches; a finding that warrants further investigation but suggests that not all muscle groups respond identically to a given load. These results emphasize the value of incorporating muscle-specific strength assessments such as the Nordic hamstring test into post-match recovery monitoring. Doing so can aid in detecting residual fatigue that might not be apparent from subjective or general measures, enabling practitioners to tailor recovery and subsequent training. In a practical application, coaches and performance staff should pay particular attention to players with very high match loads, as they are likely to experience hamstring fatigue and may benefit from targeted recovery strategies or modified workloads in the short term. Conversely, the lack of detrimental effect from normal training load implies that coaches can continue to condition players during the week without fear of undermining next-day neuromuscular function, provided adequate tapering and recovery are in place before and after matches. Overall, effective load management via balancing training and match demands combined with regular fatigue monitoring is key to optimizing performance and minimizing injury risk in elite football. By understanding which loads matter most and how they manifest in neuromuscular fatigue, practitioners can make more informed decisions to keep players both fit and fresh throughout the competitive season. This aligns with the call for practical, evidence-based training-load monitoring solutions in elite team sports [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eNB, DLL, RO and HN declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Committee of Ethics in Research and Teaching of the University of A Coru\u0026ntilde;a (approval code 2024-062) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent for the use of their pseudonymized data for research purposes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026rsquo;s identifiable data.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors\u0026rsquo; information\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNB: Conceptualization, data curation, formal analysis, writing the original draft. DLL: Methodology, supervision, writing, reviewing \u0026amp; editing. RO and HN: Conceptualization, interpretation, writing, reviewing \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the players and staff of the participating professional football club for their collaboration and cooperation during data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to club privacy policies but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDupont, G. et al. Effect of 2 soccer matches in a week on physical performance and injury rate. \u003cem\u003eAm. J. Sports Med.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 1752\u0026ndash;1758 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira, R. et al. 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The research doesn\u0026rsquo;t always apply: Practical solutions to evidence-based training-load monitoring in elite team sports. \u003cem\u003eInternational Journal of Sports Physiology and Performance\u003c/em\u003e vol. 12 136\u0026ndash;141 Preprint at (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1123/ijspp.2016-0608\u003c/span\u003e\u003cspan address=\"10.1123/ijspp.2016-0608\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Workload, Nordic hamstring exercise, adductor, abductor, monitoring, soccer","lastPublishedDoi":"10.21203/rs.3.rs-8552797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8552797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElite footballers experience substantial external loads in training and match play that may impair neuromuscular function. This study examined associations between accumulated weekly training load and single-match load with next-day hamstring and hip strength. Twenty-four professional male outfield players were monitored across 15 non-consecutive microcycles. GPS-derived metrics (e.g., total distance, high-intensity and sprint distance, high-intensity actions, sprints, accelerations and decelerations) were aggregated for training and for matches. The morning after each match (i.e., 12\u0026ndash;18 h), eccentric hamstring strength (Nordic hamstring exercise), prone isometric hamstring force, and 45\u0026deg; hip adductor and abductor squeeze forces were assessed. Missing strength values (7\u0026ndash;23%) were imputed, load variables were reduced using principal component analysis, and linear mixed-effects models were fitted with player as a random intercept. Higher overall match load was associated with lower next-day Nordic hamstring force (\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;5 N per +\u0026thinsp;1 SD; p\u0026thinsp;=\u0026thinsp;0.046), whereas weekly training load was not related to any strength outcome (p\u0026thinsp;=\u0026thinsp;0.260\u0026ndash;0.770). Match intensity distribution was positively associated with next-day adductor force (\u0026thinsp;\u0026asymp;\u0026thinsp;+\u0026thinsp;12 N; p\u0026thinsp;=\u0026thinsp;0.046), while isometric hamstring and abductor forces were unaffected. These findings indicate that intense match play produces measurable next-day eccentric hamstring fatigue, supporting Nordic testing as a practical post-match monitoring tool.\u003c/p\u003e","manuscriptTitle":"Impact of Weekly Training Load and Match Load on Next-Day Neuromuscular Fatigue in Elite Football Players: A Longitudinal Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 07:21:27","doi":"10.21203/rs.3.rs-8552797/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T14:49:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T11:28:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118300617912145682174689030089695504123","date":"2026-05-06T07:10:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T08:27:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116973434469685475632329374895598109429","date":"2026-04-14T08:35:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62524013692106717468913234775899806973","date":"2026-03-25T10:47:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154143844563447466755671614154121412163","date":"2026-03-09T11:25:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-04T04:59:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-16T06:32:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T08:19:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-12T08:17:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-08T14:42:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0517f0f7-b1ac-4d8e-9d48-7099bdd2328e","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T14:49:00+00:00","index":154,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T11:28:56+00:00","index":151,"fulltext":""},{"type":"reviewerAgreed","content":"118300617912145682174689030089695504123","date":"2026-05-06T07:10:00+00:00","index":150,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64068124,"name":"Health sciences/Health care"},{"id":64068125,"name":"Health sciences/Medical research"},{"id":64068126,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-03-09T07:21:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 07:21:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8552797","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8552797","identity":"rs-8552797","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}