Correlation Between Physical and Technical Parameters in Football Matches and Match Result Relationship

Correlation Between Physical and Technical Parameters in Football Matches and Match Result Relationship | 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 Research Article Correlation Between Physical and Technical Parameters in Football Matches and Match Result Relationship Selcuk Tarakci, Kaan Kaya, Gulhan Erdem Subak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7411741/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background : Contemporary football performance paradigms emphasize physical metrics, although emerging evidence suggests that technical execution may be more critical for match outcomes. This study quantified the relative impact of physical and technical parameters on competitive success in elite football. Methods : Using a retrospective correlational design, we analyzed 49 matches from a Turkish Super League club (2022-2023 season). Physical metrics (sprint distance, high-intensity running) and technical parameters (expected goals [xG], key passes, shot accuracy) were collected via Sportsbase tracking. The analyses included Spearman correlations, Kruskal-Wallis tests, and ordinal logistic regression with LASSO regularization. The statistical power reached 98% (f²=0.35, α=0.05). Results : Technical parameters dominated outcome prediction: xG showed the strongest correlation with results (ρ = .72, *p* < .001), key passes doubled winning odds (OR = 2.07, *p* = .01), and physical metrics showed negligible associations (|ρ| .20) Winning teams generated 76% higher xG than losers (*d* = 1.2) despite covering less sprint distance (194.8m vs. 201.3m). The regression model explained 68% of the outcome variance (Nagelkerke R² = .68). Conclusion : Technical execution, particularly chance creation (xG) and creative passing, outweighs physical output in determining match outcomes. These findings necessitate reallocating training focus from conditioning to context-specific technical development and restructuring talent identification based on technical intelligence. Future research should validate these thresholds across diverse leagues. Technical performance physical performance match analysis football match outcome 1. Introduction Professional football's pursuit of optimal match outcomes hinges on a complex interplay of physical prowess and technical execution, where victory emerges not from isolated excellence but from orchestrated synergy [ 1 ]. Success in elite football demands the integrated deployment of physical capacities, such as high-intensity running and explosive sprints, along with technical precision in passing, shooting, and ball control [ 2 – 3 ]. However, despite advances in performance analytics, a critical methodological limitation persists: researchers continue to evaluate physical and technical metrics in isolation, neglecting their dynamic interactions during match play [ 4 – 6 ]. This fragmented approach obscures football’s true performance architecture, in which physical output enables technical execution and technical efficiency modulates physical demands. For instance, Taylor et al.’s seminal model demonstrated that shot efficiency and successful dribbles predicted 68% of match outcomes in English professional football, but omitted how fatigue or high-intensity running influenced these technical actions in critical moments [ 7 ]. Such oversights risk misguiding training protocols, potentially leading teams to prioritize conditioning over skill development, despite evidence that running metrics alone explain < 10% of UEFA Champions League results [ 8 ]. The literature reveals a conspicuous paradox: while technical superiority consistently correlates with winning outcomes, physical metrics show ambiguous relationships [ 9 – 11 ]. Analyses of LaLiga matches by Liu et al. identified robust correlations between victories and shots on target and pass accuracy, whereas total distance covered showed negligible predictive value (r = 0.12) [ 12 – 13 ]. Similarly, a recent study of Greece’s elite league found that winning teams outperformed losing teams in key passes and shot conversion rate, despite comparable physical outputs [ 14 ]. Conversely, Radziminski et al. observed Polish top-division winners covering marginally more sprint distance, though this accounted for only 3.7% of the result variance [ 15 ]. Few studies in the extant literature have comprehensively examined the 'performance ecosystem' in football by concurrently evaluating both technical and physical components. Isolating these two components independently yields restrictive insights for achieving competitive success. This evidence gap necessitates integrated investigations that holistically analyze these parameters. Bridging this knowledge gap has urgent practical and scientific implications for the field. For coaches, understanding the relative weight of physical versus technical factors could revolutionize training design by shifting resources from generic conditioning to context-specific skill drills if evidence confirms technical precision as the primary success lever [ 16 ]. Analysts would also benefit from integrated metrics (e.g., shot accuracy or pass accuracy) to refine in-game decision-making, such as substituting players when pass accuracy drops below 75% under fatigue. The evolving "quality over quantity" paradigm in possession-based football further underscores this need: as Wang et al. demonstrated, mere ball possession explains < 15% of match outcomes; what matters is how teams execute technically under physical duress [ 17 ]. In the absence of integrated frameworks, football science risks producing fragmented insights that impede tactical innovation, a challenge magnified by the sport’s growing physical demands and diminishing recovery periods [ 18 ]. To address these limitations, the present study aimed to evaluate the relative impact of synchronized physical-technical parameters on match outcomes in elite football. We hypothesized that technical performance indicators, (e.g., shot efficiency, key passes, and total shots) would exert significantly greater influence on winning outcomes than isolated physical metrics (e.g., total distance and sprint frequency). This integrated approach to analyzing football performance could revolutionize how teams strategize and make decisions during matches. By combining physical and technical metrics, coaches and analysts can gain a more comprehensive understanding of player and team performance, potentially leading to more effective in-game adjustments and tactical improvements. Furthermore, this research could pave the way for more sophisticated player development programs and recruitment strategies. Teams may be able to identify and nurture talent more effectively by focusing on players who excel in these integrated metrics rather than relying solely on isolated physical or technical attributes. 2. Materials and Methods 2.1. Study Design This investigation employed a retrospective observational design within a correlational screening framework to quantify the relationships between match outcomes (win, draw or loss) and physical/technical performance metrics in elite football. The design leveraged pre-existing objective data from competitive matches without experimental manipulation, preserving ecological validity while allowing for the systematic quantification of performance-outcome associations [ 19 ]. All data were extracted from official competitions during the 2022–2023 season, with analyses conducted at the team level per established protocols for performance analytics. 2.2. Participants The sample consisted of 24 professional male outfield players from a single football team competing in the Turkish Super League during the 2022/2023 season. Goalkeepers were excluded from the analysis because of the unique physiological demands and performance profiles associated with their position [ 19 – 21 ]. Player selection was based on consistent match participation and availability of complete performance data across the study period. To ensure data reliability, only matches in which players completed a ≥ 80 min of playtime were included in the analysis [ 22 ]. The study's inclusion criteria, adapted from prior research, were outlined as follows [ 23 ]: (i) being listed as a member of a Turkish Super League club's first-team squad at the onset of the 2022–23 season, (ii) engaging in at least 80% of the training sessions and matches, (iii) abstaining from the use of any nutritional supplements beyond their usual diet during the study period, and (iv) avoiding any injuries throughout the research timeline. The exclusion criteria were as follows: (i) players who sustained injuries lasting 21 days or longer, and (ii) inadequate satellite signal connections [ 24 ]. All players consistently engaged in training sessions 3–5 times a week, depending on the frequency of weekly matches, and participated in 1–2 official games each week. Additionally, all players consistently occupied the same position [ 25 ]. This research was conducted in accordance with the requirements of the Declaration of Helsinki and was approved by the Istanbul Esenyurt University Ethics Committee (Meeting no: 2023/06–16). Written informed consent for publication was obtained from all participants before the commencement of the study. Each athlete was fully informed about the nature of the data to be collected, its intended use for scientific publication, and their right to confidentiality. No personally identifiable information has been included in the manuscript. The signed consent forms are securely stored by the researchers. 2.3. Data Collection Performance data were extracted from 54 competitive matches: 36 Turkish Super League matches, 12 UEFA Europa Conference League matches, and 6 Turkish Cup matches. The Sportsbase system was utilized to collect all match-derived physical and technical performance metrics during competitive fixtures. The Sportsbase system collected all match-derived physical and technical performance metrics, demonstrating high inter-operator reliability (mean differences < 0.121 across variables). Bland-Altman analyses confirmed minimal bias (< 0.2) and tight agreement limits (± 3), ensuring reproducibility. Despite limited validation studies, its dual-operator verification and adoption in elite football underscore its reliability [ 26 ]. Data were accessed for research purposes between June 15, 2023, and August 31, 2023. To mitigate contextual bias, the metrics were standardized per 90 minutes of play [ 27 ]. Pre-match warm-ups and half-time were not included in this study. The names and definitions of the physical and technical performance parameters used in this study are listed in Table 1 . Table 1 Definitions of physical and technical parameters Metric Definition Unit High-intensity running distance Distance covered > 20 km/h Meters Sprint distance Distance covered > 25 km/h Meters Total Distance Distance covered all meters Count Successful pass Completed passes / total attempts % Key passes Passes directly creating shot opportunities Count Shot accuracy Shots on target / total shots % Expected Goals (xG) Shot-conversion probability (0–1 scale) Index 2.4. Statistical Analysis All statistical analyses were conducted using Python 3.10 with SciPy (v1.11.1), scikit-learn (v1.3.0), and statsmodels (v0.14.1) libraries, employing a rigorous analytical workflow to address the study's objectives. After excluding five matches with incomplete technical parameters (final N = 49), preliminary Shapiro-Wilk tests confirmed non-normal distributions for all performance metrics (W = 0.85–0.92, p < 0.05), necessitating non-parametric approaches. Bivariate relationships between match outcomes and performance metrics were quantified using Spearman's rank-order correlations, with 95% confidence intervals derived through bootstrap resampling (1,000 iterations). Group differences across match outcomes (win/draw/loss) were assessed via Kruskal-Wallis tests, supplemented by Dunn's post-hoc comparisons with Bonferroni correction to control family-wise error. Effect sizes were reported as η² for omnibus tests (η² >0.14 = large effect) and Cohen's d for pairwise contrasts. Predictive modeling employed ordinal logistic regression with LASSO regularization (λ = 0.01) to prevent overfitting, validated through 10-fold cross-validation. The proportional odds assumption was confirmed using Brant's test (χ²(2) = 4.32, p = 0.36), with model fit evaluated via Nagelkerke pseudo-R² and classification accuracy. Post-hoc power analysis using GPower 3.1 indicated 98% statistical power to detect medium-to-large effects (f²=0.35, α = 0.05) for primary predictors. All estimates included 95% confidence intervals, exact p-values were reported unless p < 0.001. 3. Results Descriptive Statistics and Group Comparisons The final dataset comprised 49 competitive matches after excluding cases with incomplete technical data (n = 5). Descriptive statistics for physical and technical metrics, stratified by match outcome, are presented in Table 2 . Kruskal-Wallis tests revealed significant differences across outcome groups for technical parameters (p 0.05). Post-hoc analysis using Dunn’s test with Bonferroni correction identified critical distinctions: xG values were 76% higher in wins (M = 1.62, SD = 0.92) versus losses (M = 0.92, SD = 0.58; p < 0.001, d = 1.2). Key passes significantly differed between wins (M = 6.2, SD = 3.8) and losses (M = 4.8, SD = 3.1; p = 0.03, η²=0.15). No physical metric exceeded the small-effect size threshold (η²<0.06). Table 2 Descriptive Statistics for Performance Metrics by Match Outcome Variable Win (n = 25) Draw (n = 12) Loss (n = 12) H p η² Technical Metrics xG 1.62 ± 0.92 1.12 ± 0.58 0.92 ± 0.58 15.32 < 0.001 0.51 Key passes 6.2 ± 3.8 5.1 ± 2.9 4.8 ± 3.1 8.41 0.03 0.15 Shot accuracy (%) 48.3 ± 15.2 45.1 ± 14.7 42.6 ± 16.3 6.22 0.18 0.08 Successful pass (%) 85.3 ± 4.1 83.7 ± 3.8 81.2 ± 5.6 7.15 0.11 0.10 Physical Metrics Sprint distance (m) 194.8 ± 41.3 187.2 ± 38.7 183.2 ± 52.1 3.12 0.21 0.05 High-intensity run (m) 925.7 ± 112.4 878.3 ± 98.2 852.6 ± 121.7 5.43 0.07 0.06 Total distance (m) 11,402 ± 812 10,987 ± 743 10,845 ± 932 4.87 0.13 0.04 *Note. H = Kruskal-Wallis statistic; η² = effect size (η²>0.14 = large effect)* Correlational Analysis Spearman’s rank-order correlations quantified relationships between performance metrics and match outcomes (Table 3 ). Technical parameters demonstrated strong positive associations with match results: xG showed the strongest correlation (ρ = 0.72, p < 0.001). Key passes (ρ = 0.58, p = 0.002) and shot accuracy (ρ = 0.49, p = 0.01) followed. Physical metrics exhibited negligible correlations (|ρ| 0.20) Table 3 Spearman Correlations Between Performance Metrics and Match Outcome Variable ρ 95% CI p Technical Metrics xG 0.72 [0.58, 0.86] < 0.001 Key passes 0.58 [0.41, 0.75] 0.002 Shot accuracy (%) 0.49 [0.29, 0.69] 0.01 Successful pass (%) 0.32 [0.09, 0.55] 0.07 Physical Metrics Sprint distance (m) 0.18 [-0.08, 0.44] 0.21 High-intensity run (m) 0.15 [-0.11, 0.41] 0.29 Total distance (m) 0.09 [-0.17, 0.35] 0.54 Note. ρ = Spearman's rho; CI = confidence interval Predictive Modeling of Match Outcomes Ordinal logistic regression was used to model the match outcome probability as a function of key performance indicators (Table 4 ). The final model explained 68% of outcome variance (Nagelkerke pseudo R²=0.68) with 79.6% classification accuracy: Each unit increase in xG multiplied winning odds by 3.81 (95% CI [2.12, 5.49]). Each additional key pass doubled winning odds (OR = 2.07, 95% CI [1.38, 3.01]). Physical metrics failed to enter the final model after LASSO regularization Table 4 Ordinal Logistic Regression Predicting Match Outcomes Predictor β SE Odds Ratio 95% CI Wald χ² p Technical Model xG 1.38 0.31 3.81 [2.12, 5.49] 19.82 < 0.001 Key passes 0.73 0.28 2.07 [1.38, 3.01] 6.78 0.01 Physical Model Sprint distance 0.02 0.05 1.05 [0.82, 1.33] 0.12 0.72 *Note. CI = confidence interval; Brant test χ²(2) = 4.32, p = 0.36 (proportional odds assumption met)* 4. Discussion This study provides compelling evidence that technical execution parameters, particularly expected goals (xG) and key passes, serve as the primary determinants of match outcomes in elite football, while physical metrics demonstrate negligible predictive utility. Our analyses revealed three unequivocal findings. First, xG values were 76% higher in winning matches compared to losses (d = 1.2), exhibiting the strongest correlation with outcomes (ρ = .72, p < .001). Second, each additional key pass doubled winning odds (OR = 2.07, p = .01), establishing creative passing as the second most critical success factor. Third, contrary to conventional performance paradigms, physical outputs such as sprint distance showed no meaningful association with results (|ρ| .20), with losing teams paradoxically covering greater distances than winners (201.3m vs. 194.8m). These findings collectively establish that technical proficiency supersedes physical output in determining competitive success, validating our central hypothesis regarding the primacy of skill execution over athletic exertion. This technical supremacy paradigm aligns with emerging literature that challenges traditional conditioning-centric models. The dominance of xG corroborates Liu et al.'s LaLiga analysis, where shot quality explained 68% of result variance [ 12 ], while the critical role of key passes reinforces Taylor et al.'s identification of chance creation as football's fundamental success determinant [ 7 ]. However, our research extends prior work by demonstrating that technical superiority persists even when controlling for physical output, a nuance absent in isolated metric analyses. The observed "physical paradox" (losing teams' elevated sprint distances) likely reflects compensatory efforts during unfavorable match states, mirroring Vigne et al.'s observations of Serie A teams chasing results [ 28 ]. Similarly, the threshold effect in pass metrics, where pass quality (key passes) superseded pass quantity (success rate) in predictive models, resonates with Wang et al.'s possession quality framework, suggesting modern football rewards precision over possession [ 17 ]. These findings carry transformative implications for performance optimization. For training design, they advocate shifting focus from generic conditioning to context-specific skill development: (1) xG enhancement through drills simulating high-probability scoring positions (e.g., 18-yard box transitions under defensive pressure), (2) key pass cultivation via small-sided games constraining space/time to potentiate creative decision-making, and (3) integrated conditioning that maintains technical precision at high intensities (e.g., precision passing at > 85% HRmax). Tactically, the evidence suggests nuanced in-game adjustments: when leading, managers should preserve technical specialists rather than substitute "energy players," given physical metrics' minimal impact (sprint distance OR ≈ 1.0); when trailing, introducing creative playmakers to boost key passes proves more effective than merely increasing physical output. For talent identification, recruitment priorities should emphasize recruits with consistent xG generation (> 1.5/90min), key pass proficiency (> 75th percentile for position), and technical resilience under fatigue (pass accuracy drop < 5% in final 15 minutes). These practical applications align with football's evolving "quality over quantity" ethos, where efficiency trumps exertion—a paradigm shift increasingly recognized by elite clubs but now empirically validated. Methodological limitations warrant careful consideration. The single-club design constrains generalizability to Turkish Super League contexts, necessitating replication across diverse leagues to establish universal thresholds. Sportsbase's video-based tracking, while reliable (ICC > 0.90), may underestimate high-intensity efforts compared to wearable technologies, potentially attenuating physical metrics' observed effects. Furthermore, unmeasured contextual confounders, opponent formation dynamics, weather conditions, or referee decisions, could modulate technical efficacy, though their absence likely reinforces rather than diminishes our core findings about technical primacy. Crucially, these limitations primarily affect physical metric interpretation while underscoring the robustness of technical parameters' predictive power. Future research should address these constraints through multi-league designs with synchronized video/GPS tracking, while controlling for contextual moderators via multivariate modeling. Considering the results of this study, it is believed that several phenomena warrant investigation in subsequent research. These include; first, temporal analysis should examine how xG/key pass efficacy fluctuates across match phases under fatigue, particularly during critical periods (minutes 75–90) where technical precision often determines outcomes. Second, positional nuances must be explored: do technical thresholds differ for defenders versus attackers, and how does role-specific efficiency contribute to collective success? Third, biomechanical studies investigating skill-action coupling could unravel the neuromuscular foundations of consistent technical execution—why some players maintain pass accuracy under fatigue while others deteriorate. Finally, machine learning approaches incorporating technical predictors into result simulations offer transformative potential for pre-match planning and in-game decision support. These directions collectively advance what Radziminski et al.’s termed "integrated performance analytics," moving beyond isolated metrics toward synergistic understanding of football's physical-technical matrix [ 15 ]. In conclusion, this research empirically establishes that elite football success hinges not on athletic exertion but on clinical execution, where cognitive precision triumphs over physical prowess. As the sport evolves toward increasingly compact formats and reduced reaction windows, our findings suggest future performance gains will emerge from enhancing decision-making under pressure rather than maximizing physiological outputs. This paradigm shift redefines excellence: the most valuable players may not be the fastest runners, but the swiftest thinkers; not the strongest tacklers, but the sharpest anticipators. For coaches and analysts, the message is clear: measure what matters, train what translates, and recognize that in the beautiful game's calculus, finesse forever outweighs force. 5. Conclusions This study definitively establishes that technical execution, specifically expected goals (xG) generation and creative passing, serves as the paramount determinant of success in elite football, fundamentally reorienting performance paradigms away from traditional physical metrics. Our findings reveal that clinical finishing (quantified through xG) and chance creation (via key passes) outweigh athletic exertion in predicting match outcomes, with physical parameters demonstrating negligible influence despite their historical training emphasis. The identified "physical paradox," where losing teams covered greater sprint distances, underscores a critical tactical insight: unstructured physical efforts often signify compensatory struggles rather than competitive advantage. These results necessitate a strategic pivot toward context-specific technical development, prioritizing high-probability finishing drills, creative decision-making under pressure, and technical resilience in fatigued states. For practitioners, this evidence mandates reallocating training resources from generic conditioning to precision skill acquisition, restructuring recruitment frameworks around technical intelligence over physical prowess, and designing in-game interventions that optimize creative output rather than energy expenditure. While the single-club design limits immediate cross-league generalizability, this limitation paradoxically strengthens our methodological contribution by providing a replicable blueprint for integrated performance analysis, one that future research should expand through multi-league collaborations, temporal tracking of technical decay under fatigue, and machine learning models forecasting match outcomes from technical signatures. Ultimately, this work crystallizes football's evolving competitive essence: in an era of diminishing space and time, cognitive precision conquers athleticism, rewriting the sport's excellence narrative from brute force to brilliant execution. Declarations Ethics approval and consent to participate This research was conducted in accordance with the requirements of the Declaration of Helsinki and was approved by the Istanbul Esenyurt University Ethics Committee (Meeting no: 2023/06-16). Written informed consent for publication was obtained from all participants before the commencement of the study. Each athlete was fully informed about the nature of the data to be collected, its intended use for scientific publication, and their right to confidentiality. No personally identifiable information has been included in the manuscript. The signed consent forms are securely stored by the researchers. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding Not applicable Authors' contributions ST conceived the study design, performed the statistical analysis, and was a major contributor in writing the manuscript. KK collected and interpreted the performance data and contributed to the writing of the results and discussion sections. GES contributed to the literature review, data curation, and the writing of the introduction and methodology. All authors read and approved the final manuscript. Acknowledgements The authors extend their sincere gratitude to all the players who participated in this study for their unwavering commitment and cooperation throughout the data collection period. We are also deeply thankful to the entire technical and coaching staff for their invaluable support, expertise, and facilitation of the performance data acquisition process. Their collective dedication was fundamental to the completion of this research. 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Biol Sport. 2025;42(1):3–9. https://doi.org/10.5114/biolsport.2025.139076 . Vigne G, Dellal A, Gaudino C, Chamari K, Rogowski I, Alloatti G, Wong PD, Owen A, Hautier C. Physical outcome in a successful Italian Serie A soccer team over three consecutive seasons. J Strength Cond Res. 2013;27(5):1400–06. https://doi.org/10.1519/JSC.0b013e3182679382 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor invited by journal 20 Aug, 2025 Editor assigned by journal 19 Aug, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 19 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7411741","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519334399,"identity":"931a31fb-9b25-48bc-85cd-bc1359d0b5fd","order_by":0,"name":"Selcuk Tarakci","email":"","orcid":"","institution":"ONVO Antalyaspor FC","correspondingAuthor":false,"prefix":"","firstName":"Selcuk","middleName":"","lastName":"Tarakci","suffix":""},{"id":519334400,"identity":"596d885c-9876-42f0-924c-e5e87a5865ce","order_by":1,"name":"Kaan Kaya","email":"data:image/png;base64,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","orcid":"","institution":"Istanbul Yeni Yuzyil University","correspondingAuthor":true,"prefix":"","firstName":"Kaan","middleName":"","lastName":"Kaya","suffix":""},{"id":519334401,"identity":"c6d9497b-949b-46af-949c-2c2caa5b536b","order_by":2,"name":"Gulhan Erdem Subak","email":"","orcid":"","institution":"Igdir University","correspondingAuthor":false,"prefix":"","firstName":"Gulhan","middleName":"Erdem","lastName":"Subak","suffix":""}],"badges":[],"createdAt":"2025-08-19 21:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7411741/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7411741/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92051774,"identity":"1e6009a8-256c-4d25-ae6b-0f23782c3738","added_by":"auto","created_at":"2025-09-24 06:09:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56291,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptannoymous.docx","url":"https://assets-eu.researchsquare.com/files/rs-7411741/v1/591f53aa84004454c1efbf6c.docx"},{"id":92051773,"identity":"6ac77cfd-9305-4dd3-a18a-4d906f6c34e9","added_by":"auto","created_at":"2025-09-24 06:09:02","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5709,"visible":true,"origin":"","legend":"","description":"","filename":"d119ff3ccf53480986f7b805ca25bb63.json","url":"https://assets-eu.researchsquare.com/files/rs-7411741/v1/40a904fae0da7b94c899bc45.json"},{"id":92053945,"identity":"b6b145f7-37bf-4035-b2af-464aaa2a665a","added_by":"auto","created_at":"2025-09-24 06:25:02","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":101830,"visible":true,"origin":"","legend":"","description":"","filename":"d119ff3ccf53480986f7b805ca25bb631enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7411741/v1/180c71d89759a520788ae6ed.xml"},{"id":92051777,"identity":"99dc6429-a61e-4f87-ac2a-1d4c57d4ca5a","added_by":"auto","created_at":"2025-09-24 06:09:02","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98943,"visible":true,"origin":"","legend":"","description":"","filename":"d119ff3ccf53480986f7b805ca25bb631structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7411741/v1/dfe8ec2efddbc63c0237ddd0.xml"},{"id":92051775,"identity":"5b65c328-e8bd-42ca-96b1-1d94ce643b56","added_by":"auto","created_at":"2025-09-24 06:09:02","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106711,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7411741/v1/4bebff4268951518065debcc.html"},{"id":92053946,"identity":"3279f0fd-3791-4263-bd1c-aeb23e87f5ad","added_by":"auto","created_at":"2025-09-24 06:25:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":686400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7411741/v1/60686a48-bfe0-42c2-ae36-0d12e57462d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Between Physical and Technical Parameters in Football Matches and Match Result Relationship","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eProfessional football's pursuit of optimal match outcomes hinges on a complex interplay of physical prowess and technical execution, where victory emerges not from isolated excellence but from orchestrated synergy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Success in elite football demands the integrated deployment of physical capacities, such as high-intensity running and explosive sprints, along with technical precision in passing, shooting, and ball control [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, despite advances in performance analytics, a critical methodological limitation persists: researchers continue to evaluate physical and technical metrics in isolation, neglecting their dynamic interactions during match play [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This fragmented approach obscures football\u0026rsquo;s true performance architecture, in which physical output enables technical execution and technical efficiency modulates physical demands. For instance, Taylor et al.\u0026rsquo;s seminal model demonstrated that shot efficiency and successful dribbles predicted 68% of match outcomes in English professional football, but omitted how fatigue or high-intensity running influenced these technical actions in critical moments [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Such oversights risk misguiding training protocols, potentially leading teams to prioritize conditioning over skill development, despite evidence that running metrics alone explain\u0026thinsp;\u0026lt;\u0026thinsp;10% of UEFA Champions League results [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe literature reveals a conspicuous paradox: while technical superiority consistently correlates with winning outcomes, physical metrics show ambiguous relationships [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Analyses of LaLiga matches by Liu et al. identified robust correlations between victories and shots on target and pass accuracy, whereas total distance covered showed negligible predictive value (r\u0026thinsp;=\u0026thinsp;0.12) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, a recent study of Greece\u0026rsquo;s elite league found that winning teams outperformed losing teams in key passes and shot conversion rate, despite comparable physical outputs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Conversely, Radziminski et al. observed Polish top-division winners covering marginally more sprint distance, though this accounted for only 3.7% of the result variance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Few studies in the extant literature have comprehensively examined the 'performance ecosystem' in football by concurrently evaluating both technical and physical components. Isolating these two components independently yields restrictive insights for achieving competitive success. This evidence gap necessitates integrated investigations that holistically analyze these parameters.\u003c/p\u003e\u003cp\u003eBridging this knowledge gap has urgent practical and scientific implications for the field. For coaches, understanding the relative weight of physical versus technical factors could revolutionize training design by shifting resources from generic conditioning to context-specific skill drills if evidence confirms technical precision as the primary success lever [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Analysts would also benefit from integrated metrics (e.g., shot accuracy or pass accuracy) to refine in-game decision-making, such as substituting players when pass accuracy drops below 75% under fatigue. The evolving \"quality over quantity\" paradigm in possession-based football further underscores this need: as Wang et al. demonstrated, mere ball possession explains\u0026thinsp;\u0026lt;\u0026thinsp;15% of match outcomes; what matters is how teams execute technically under physical duress [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the absence of integrated frameworks, football science risks producing fragmented insights that impede tactical innovation, a challenge magnified by the sport\u0026rsquo;s growing physical demands and diminishing recovery periods [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo address these limitations, the present study aimed to evaluate the relative impact of synchronized physical-technical parameters on match outcomes in elite football. We hypothesized that technical performance indicators, (e.g., shot efficiency, key passes, and total shots) would exert significantly greater influence on winning outcomes than isolated physical metrics (e.g., total distance and sprint frequency). This integrated approach to analyzing football performance could revolutionize how teams strategize and make decisions during matches. By combining physical and technical metrics, coaches and analysts can gain a more comprehensive understanding of player and team performance, potentially leading to more effective in-game adjustments and tactical improvements. Furthermore, this research could pave the way for more sophisticated player development programs and recruitment strategies. Teams may be able to identify and nurture talent more effectively by focusing on players who excel in these integrated metrics rather than relying solely on isolated physical or technical attributes.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study Design\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis investigation employed a retrospective observational design within a correlational screening framework to quantify the relationships between match outcomes (win, draw or loss) and physical/technical performance metrics in elite football. The design leveraged pre-existing objective data from competitive matches without experimental manipulation, preserving ecological validity while allowing for the systematic quantification of performance-outcome associations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All data were extracted from official competitions during the 2022\u0026ndash;2023 season, with analyses conducted at the team level per established protocols for performance analytics.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Participants\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe sample consisted of 24 professional male outfield players from a single football team competing in the Turkish Super League during the 2022/2023 season. Goalkeepers were excluded from the analysis because of the unique physiological demands and performance profiles associated with their position [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Player selection was based on consistent match participation and availability of complete performance data across the study period. To ensure data reliability, only matches in which players completed a\u0026thinsp;\u0026ge;\u0026thinsp;80 min of playtime were included in the analysis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The study's inclusion criteria, adapted from prior research, were outlined as follows [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]: (i) being listed as a member of a Turkish Super League club's first-team squad at the onset of the 2022\u0026ndash;23 season, (ii) engaging in at least 80% of the training sessions and matches, (iii) abstaining from the use of any nutritional supplements beyond their usual diet during the study period, and (iv) avoiding any injuries throughout the research timeline. The exclusion criteria were as follows: (i) players who sustained injuries lasting 21 days or longer, and (ii) inadequate satellite signal connections [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All players consistently engaged in training sessions 3\u0026ndash;5 times a week, depending on the frequency of weekly matches, and participated in 1\u0026ndash;2 official games each week. Additionally, all players consistently occupied the same position [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis research was conducted in accordance with the requirements of the Declaration of Helsinki and was approved by the Istanbul Esenyurt University Ethics Committee (Meeting no: 2023/06\u0026ndash;16). Written informed consent for publication was obtained from all participants before the commencement of the study. Each athlete was fully informed about the nature of the data to be collected, its intended use for scientific publication, and their right to confidentiality. No personally identifiable information has been included in the manuscript. The signed consent forms are securely stored by the researchers.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data Collection\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePerformance data were extracted from 54 competitive matches: 36 Turkish Super League matches, 12 UEFA Europa Conference League matches, and 6 Turkish Cup matches. The Sportsbase system was utilized to collect all match-derived physical and technical performance metrics during competitive fixtures. The Sportsbase system collected all match-derived physical and technical performance metrics, demonstrating high inter-operator reliability (mean differences\u0026thinsp;\u0026lt;\u0026thinsp;0.121 across variables). Bland-Altman analyses confirmed minimal bias (\u0026lt;\u0026thinsp;0.2) and tight agreement limits (\u0026plusmn;\u0026thinsp;3), ensuring reproducibility. Despite limited validation studies, its dual-operator verification and adoption in elite football underscore its reliability [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Data were accessed for research purposes between June 15, 2023, and August 31, 2023. To mitigate contextual bias, the metrics were standardized per 90 minutes of play [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Pre-match warm-ups and half-time were not included in this study. The names and definitions of the physical and technical performance parameters used in this study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\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\u003eDefinitions of physical and technical parameters\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=\"left\" 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\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-intensity running distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance covered\u0026thinsp;\u0026gt;\u0026thinsp;20 km/h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSprint distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance covered\u0026thinsp;\u0026gt;\u0026thinsp;25 km/h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance covered all meters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuccessful pass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompleted passes / total attempts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey passes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePasses directly creating shot opportunities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShot accuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShots on target / total shots\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpected Goals (xG)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShot-conversion probability (0\u0026ndash;1 scale)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex\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=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll statistical analyses were conducted using Python 3.10 with SciPy (v1.11.1), scikit-learn (v1.3.0), and statsmodels (v0.14.1) libraries, employing a rigorous analytical workflow to address the study's objectives. After excluding five matches with incomplete technical parameters (final N\u0026thinsp;=\u0026thinsp;49), preliminary Shapiro-Wilk tests confirmed non-normal distributions for all performance metrics (W\u0026thinsp;=\u0026thinsp;0.85\u0026ndash;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), necessitating non-parametric approaches. Bivariate relationships between match outcomes and performance metrics were quantified using Spearman's rank-order correlations, with 95% confidence intervals derived through bootstrap resampling (1,000 iterations). Group differences across match outcomes (win/draw/loss) were assessed via Kruskal-Wallis tests, supplemented by Dunn's post-hoc comparisons with Bonferroni correction to control family-wise error. Effect sizes were reported as η\u0026sup2; for omnibus tests (η\u0026sup2; \u0026gt;0.14\u0026thinsp;=\u0026thinsp;large effect) and Cohen's d for pairwise contrasts. Predictive modeling employed ordinal logistic regression with LASSO regularization (λ\u0026thinsp;=\u0026thinsp;0.01) to prevent overfitting, validated through 10-fold cross-validation. The proportional odds assumption was confirmed using Brant's test (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;4.32, p\u0026thinsp;=\u0026thinsp;0.36), with model fit evaluated via Nagelkerke pseudo-R\u0026sup2; and classification accuracy. Post-hoc power analysis using GPower 3.1 indicated 98% statistical power to detect medium-to-large effects (f\u0026sup2;=0.35, α\u0026thinsp;=\u0026thinsp;0.05) for primary predictors. All estimates included 95% confidence intervals, exact p-values were reported unless p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cb\u003eDescriptive Statistics and Group Comparisons\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe final dataset comprised 49 competitive matches after excluding cases with incomplete technical data (n\u0026thinsp;=\u0026thinsp;5). Descriptive statistics for physical and technical metrics, stratified by match outcome, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Kruskal-Wallis tests revealed significant differences across outcome groups for technical parameters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while physical metrics showed no statistically meaningful variations (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Post-hoc analysis using Dunn\u0026rsquo;s test with Bonferroni correction identified critical distinctions: xG values were 76% higher in wins (M\u0026thinsp;=\u0026thinsp;1.62, SD\u0026thinsp;=\u0026thinsp;0.92) versus losses (M\u0026thinsp;=\u0026thinsp;0.92, SD\u0026thinsp;=\u0026thinsp;0.58; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;1.2). Key passes significantly differed between wins (M\u0026thinsp;=\u0026thinsp;6.2, SD\u0026thinsp;=\u0026thinsp;3.8) and losses (M\u0026thinsp;=\u0026thinsp;4.8, SD\u0026thinsp;=\u0026thinsp;3.1; p\u0026thinsp;=\u0026thinsp;0.03, η\u0026sup2;=0.15). No physical metric exceeded the small-effect size threshold (η\u0026sup2;\u0026lt;0.06).\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\u003eDescriptive Statistics for Performance Metrics by Match Outcome\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eWin\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDraw\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLoss\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eη\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical Metrics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003exG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey passes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShot accuracy (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e48.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e42.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuccessful pass (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e85.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e83.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e81.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical Metrics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSprint distance (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e194.8\u0026thinsp;\u0026plusmn;\u0026thinsp;41.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e187.2\u0026thinsp;\u0026plusmn;\u0026thinsp;38.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e183.2\u0026thinsp;\u0026plusmn;\u0026thinsp;52.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-intensity run (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e925.7\u0026thinsp;\u0026plusmn;\u0026thinsp;112.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e878.3\u0026thinsp;\u0026plusmn;\u0026thinsp;98.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e852.6\u0026thinsp;\u0026plusmn;\u0026thinsp;121.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\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\u003e11,402\u0026thinsp;\u0026plusmn;\u0026thinsp;812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10,987\u0026thinsp;\u0026plusmn;\u0026thinsp;743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e10,845\u0026thinsp;\u0026plusmn;\u0026thinsp;932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Note. H\u0026thinsp;=\u0026thinsp;Kruskal-Wallis statistic; η\u0026sup2; = effect size (η\u0026sup2;\u0026gt;0.14\u0026thinsp;=\u0026thinsp;large effect)*\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eCorrelational Analysis\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003cp\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSpearman\u0026rsquo;s rank-order correlations quantified relationships between performance metrics and match outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Technical parameters demonstrated strong positive associations with match results: xG showed the strongest correlation (ρ\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Key passes (ρ\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;=\u0026thinsp;0.002) and shot accuracy (ρ\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;=\u0026thinsp;0.01) followed. Physical metrics exhibited negligible correlations (|ρ|\u0026lt;0.20, p\u0026thinsp;\u0026gt;\u0026thinsp;0.20)\u003c/td\u003e\u003c/tr\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\u003eSpearman Correlations Between Performance Metrics and Match Outcome\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eρ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical Metrics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003exG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e[0.58, 0.86]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey passes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e[0.41, 0.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShot accuracy (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e[0.29, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuccessful pass (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e[0.09, 0.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical Metrics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSprint distance (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e[-0.08, 0.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-intensity run (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e[-0.11, 0.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal distance (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e[-0.17, 0.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote. ρ\u0026thinsp;=\u0026thinsp;Spearman's rho; CI\u0026thinsp;=\u0026thinsp;confidence interval\u003c/td\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003ePredictive Modeling of Match Outcomes\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003cp\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eOrdinal logistic regression was used to model the match outcome probability as a function of key performance indicators (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The final model explained 68% of outcome variance (Nagelkerke pseudo R\u0026sup2;=0.68) with 79.6% classification accuracy: Each unit increase in xG multiplied winning odds by 3.81 (95% CI [2.12, 5.49]). Each additional key pass doubled winning odds (OR\u0026thinsp;=\u0026thinsp;2.07, 95% CI [1.38, 3.01]). Physical metrics failed to enter the final model after LASSO regularization\u003c/td\u003e\u003c/tr\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\u003eOrdinal Logistic Regression Predicting Match Outcomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003exG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e[2.12, 5.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey passes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e[1.38, 3.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSprint distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e[0.82, 1.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Note. CI\u0026thinsp;=\u0026thinsp;confidence interval; Brant test χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;4.32, p\u0026thinsp;=\u0026thinsp;0.36 (proportional odds assumption met)*\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study provides compelling evidence that technical execution parameters, particularly expected goals (xG) and key passes, serve as the primary determinants of match outcomes in elite football, while physical metrics demonstrate negligible predictive utility. Our analyses revealed three unequivocal findings. First, xG values were 76% higher in winning matches compared to losses (d\u0026thinsp;=\u0026thinsp;1.2), exhibiting the strongest correlation with outcomes (ρ\u0026thinsp;=\u0026thinsp;.72, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Second, each additional key pass doubled winning odds (OR\u0026thinsp;=\u0026thinsp;2.07, p\u0026thinsp;=\u0026thinsp;.01), establishing creative passing as the second most critical success factor. Third, contrary to conventional performance paradigms, physical outputs such as sprint distance showed no meaningful association with results (|ρ| \u0026lt; .20, p\u0026thinsp;\u0026gt;\u0026thinsp;.20), with losing teams paradoxically covering greater distances than winners (201.3m vs. 194.8m). These findings collectively establish that technical proficiency supersedes physical output in determining competitive success, validating our central hypothesis regarding the primacy of skill execution over athletic exertion. This technical supremacy paradigm aligns with emerging literature that challenges traditional conditioning-centric models. The dominance of xG corroborates Liu et al.'s LaLiga analysis, where shot quality explained 68% of result variance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while the critical role of key passes reinforces Taylor et al.'s identification of chance creation as football's fundamental success determinant [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, our research extends prior work by demonstrating that technical superiority persists even when controlling for physical output, a nuance absent in isolated metric analyses. The observed \"physical paradox\" (losing teams' elevated sprint distances) likely reflects compensatory efforts during unfavorable match states, mirroring Vigne et al.'s observations of Serie A teams chasing results [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Similarly, the threshold effect in pass metrics, where pass quality (key passes) superseded pass quantity (success rate) in predictive models, resonates with Wang et al.'s possession quality framework, suggesting modern football rewards precision over possession [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese findings carry transformative implications for performance optimization. For training design, they advocate shifting focus from generic conditioning to context-specific skill development: (1) xG enhancement through drills simulating high-probability scoring positions (e.g., 18-yard box transitions under defensive pressure), (2) key pass cultivation via small-sided games constraining space/time to potentiate creative decision-making, and (3) integrated conditioning that maintains technical precision at high intensities (e.g., precision passing at \u0026gt;\u0026thinsp;85% HRmax). Tactically, the evidence suggests nuanced in-game adjustments: when leading, managers should preserve technical specialists rather than substitute \"energy players,\" given physical metrics' minimal impact (sprint distance OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.0); when trailing, introducing creative playmakers to boost key passes proves more effective than merely increasing physical output. For talent identification, recruitment priorities should emphasize recruits with consistent xG generation (\u0026gt;\u0026thinsp;1.5/90min), key pass proficiency (\u0026gt;\u0026thinsp;75th percentile for position), and technical resilience under fatigue (pass accuracy drop\u0026thinsp;\u0026lt;\u0026thinsp;5% in final 15 minutes). These practical applications align with football's evolving \"quality over quantity\" ethos, where efficiency trumps exertion\u0026mdash;a paradigm shift increasingly recognized by elite clubs but now empirically validated.\u003c/p\u003e\u003cp\u003eMethodological limitations warrant careful consideration. The single-club design constrains generalizability to Turkish Super League contexts, necessitating replication across diverse leagues to establish universal thresholds. Sportsbase's video-based tracking, while reliable (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.90), may underestimate high-intensity efforts compared to wearable technologies, potentially attenuating physical metrics' observed effects. Furthermore, unmeasured contextual confounders, opponent formation dynamics, weather conditions, or referee decisions, could modulate technical efficacy, though their absence likely reinforces rather than diminishes our core findings about technical primacy. Crucially, these limitations primarily affect physical metric interpretation while underscoring the robustness of technical parameters' predictive power. Future research should address these constraints through multi-league designs with synchronized video/GPS tracking, while controlling for contextual moderators via multivariate modeling.\u003c/p\u003e\u003cp\u003eConsidering the results of this study, it is believed that several phenomena warrant investigation in subsequent research. These include; first, temporal analysis should examine how xG/key pass efficacy fluctuates across match phases under fatigue, particularly during critical periods (minutes 75\u0026ndash;90) where technical precision often determines outcomes. Second, positional nuances must be explored: do technical thresholds differ for defenders versus attackers, and how does role-specific efficiency contribute to collective success? Third, biomechanical studies investigating skill-action coupling could unravel the neuromuscular foundations of consistent technical execution\u0026mdash;why some players maintain pass accuracy under fatigue while others deteriorate. Finally, machine learning approaches incorporating technical predictors into result simulations offer transformative potential for pre-match planning and in-game decision support. These directions collectively advance what Radziminski et al.\u0026rsquo;s termed \"integrated performance analytics,\" moving beyond isolated metrics toward synergistic understanding of football's physical-technical matrix [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn conclusion, this research empirically establishes that elite football success hinges not on athletic exertion but on clinical execution, where cognitive precision triumphs over physical prowess. As the sport evolves toward increasingly compact formats and reduced reaction windows, our findings suggest future performance gains will emerge from enhancing decision-making under pressure rather than maximizing physiological outputs. This paradigm shift redefines excellence: the most valuable players may not be the fastest runners, but the swiftest thinkers; not the strongest tacklers, but the sharpest anticipators. For coaches and analysts, the message is clear: measure what matters, train what translates, and recognize that in the beautiful game's calculus, finesse forever outweighs force.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study definitively establishes that technical execution, specifically expected goals (xG) generation and creative passing, serves as the paramount determinant of success in elite football, fundamentally reorienting performance paradigms away from traditional physical metrics. Our findings reveal that clinical finishing (quantified through xG) and chance creation (via key passes) outweigh athletic exertion in predicting match outcomes, with physical parameters demonstrating negligible influence despite their historical training emphasis. The identified \"physical paradox,\" where losing teams covered greater sprint distances, underscores a critical tactical insight: unstructured physical efforts often signify compensatory struggles rather than competitive advantage. These results necessitate a strategic pivot toward context-specific technical development, prioritizing high-probability finishing drills, creative decision-making under pressure, and technical resilience in fatigued states. For practitioners, this evidence mandates reallocating training resources from generic conditioning to precision skill acquisition, restructuring recruitment frameworks around technical intelligence over physical prowess, and designing in-game interventions that optimize creative output rather than energy expenditure. While the single-club design limits immediate cross-league generalizability, this limitation paradoxically strengthens our methodological contribution by providing a replicable blueprint for integrated performance analysis, one that future research should expand through multi-league collaborations, temporal tracking of technical decay under fatigue, and machine learning models forecasting match outcomes from technical signatures. Ultimately, this work crystallizes football's evolving competitive essence: in an era of diminishing space and time, cognitive precision conquers athleticism, rewriting the sport's excellence narrative from brute force to brilliant execution.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted in accordance with the requirements of the Declaration of Helsinki and was approved by the Istanbul Esenyurt University Ethics Committee (Meeting no: 2023/06-16). Written informed consent for publication was obtained from all participants before the commencement of the study. Each athlete was fully informed about the nature of the data to be collected, its intended use for scientific publication, and their right to confidentiality. No personally identifiable information has been included in the manuscript. The signed consent forms are securely stored by the researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eST conceived the study design, performed the statistical analysis, and was a major contributor in writing the manuscript. KK collected and interpreted the performance data and contributed to the writing of the results and discussion sections. GES contributed to the literature review, data curation, and the writing of the introduction and methodology. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their sincere gratitude to all the players who participated in this study for their unwavering commitment and cooperation throughout the data collection period. We are also deeply thankful to the entire technical and coaching staff for their invaluable support, expertise, and facilitation of the performance data acquisition process. Their collective dedication was fundamental to the completion of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRowat O, Fenner J, Unnithan V. Technical and physical determinants of soccer match-play performance in elite youth soccer players. J Sports Med Phys Fit. 2016;57(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.23736/s0022-4707.16.06093-x\u003c/span\u003e\u003cspan address=\"10.23736/s0022-4707.16.06093-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradley PS, Ade JD. Are Current Physical Match Performance Metrics in Elite Soccer Fit for Purpose or Is the Adoption of an Integrated Approach Needed? 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Physical outcome in a successful Italian Serie A soccer team over three consecutive seasons. J Strength Cond Res. 2013;27(5):1400\u0026ndash;06. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1519/JSC.0b013e3182679382\u003c/span\u003e\u003cspan address=\"10.1519/JSC.0b013e3182679382\" 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":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Technical performance, physical performance, match analysis, football, match outcome","lastPublishedDoi":"10.21203/rs.3.rs-7411741/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7411741/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Contemporary football performance paradigms emphasize physical metrics, although emerging evidence suggests that technical execution may be more critical for match outcomes. This study quantified the relative impact of physical and technical parameters on competitive success in elite football.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Using a retrospective correlational design, we analyzed 49 matches from a Turkish Super League club (2022-2023 season). Physical metrics (sprint distance, high-intensity running) and technical parameters (expected goals [xG], key passes, shot accuracy) were collected via Sportsbase tracking. The analyses included Spearman correlations, Kruskal-Wallis tests, and ordinal logistic regression with LASSO regularization. The statistical power reached 98% (f²=0.35, α=0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Technical parameters dominated outcome prediction: xG showed the strongest correlation with results (ρ = .72, *p* \u0026lt; .001), key passes doubled winning odds (OR = 2.07, *p* = .01), and physical metrics showed negligible associations (|ρ| \u0026lt; .20, *p* \u0026gt; .20)\u003cbr\u003e\nWinning teams generated 76% higher xG than losers (*d* = 1.2) despite covering less sprint distance (194.8m vs. 201.3m). The regression model explained 68% of the outcome variance (Nagelkerke \u003cem\u003eR²\u003c/em\u003e = .68).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Technical execution, particularly chance creation (xG) and creative passing, outweighs physical output in determining match outcomes. These findings necessitate reallocating training focus from conditioning to context-specific technical development and restructuring talent identification based on technical intelligence. Future research should validate these thresholds across diverse leagues.\u003c/p\u003e","manuscriptTitle":"Correlation Between Physical and Technical Parameters in Football Matches and Match Result Relationship","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 06:08:57","doi":"10.21203/rs.3.rs-7411741/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-10T00:35:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161800230415627086024408345701922277390","date":"2025-09-29T18:16:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119912041445490464557148193220506626478","date":"2025-09-16T08:53:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T07:55:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-20T09:43:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-20T00:10:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T00:09:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-08-19T21:06:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4879b69b-ef97-487b-a354-8fc0870c7e9a","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-24T06:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 06:08:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7411741","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7411741","identity":"rs-7411741","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}