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

SelcukTarakci1Emailtarakciselcuk@gmail.com

KaanKaya2,4✉Phone+905468760049Emailkaan.kaya@yeniyuzyil.edu.tr

GulhanErdemSubak3Emailgerdem.subak@igdir.edu.tr

1Department of Athletic PerformanceONVO Antalyaspor FCAntalyaTurkey

2Department of Coaching Education, Faculty of Sports SciencesIstanbul Yeni Yuzyil UniversityCevizlibag, IstanbulTurkey

3Department of Physical Education and Sports, Faculty of Sports SciencesIgdir UniversityIgdirTurkey

Istanbul Yeni Yuzyil University

Selcuk Tarakci¹, Kaan Kaya^2,*, Gulhan Erdem Subak³

¹ Department of Athletic Performance, ONVO Antalyaspor FC, Antalya, Turkey; tarakciselcuk@gmail.com, Orcid: 0000-0003-3841-9571

² Department of Coaching Education, Faculty of Sports Sciences, Istanbul Yeni Yuzyil University, Cevizlibag, Istanbul, Turkey; kaan.kaya@yeniyuzyil.edu.tr, Orcid: 0000-0003-0892-645X

³ Department of Physical Education and Sports, Faculty of Sports Sciences, Igdir University, Igdir, Turkey; gerdem.subak@igdir.edu.tr, Orcid: 0000-0003-1698-262Xv

* Corresponding Author: Kaan KAYA, Istanbul Yeni Yuzyil University, kaan.kaya@yeniyuzyil.edu.tr, + 905468760049

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, *p* >.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.

Keywords:

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 < 0.01), while physical metrics showed no statistically meaningful variations (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, p > 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, p > .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

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

Author Contribution

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.

Acknowledgement

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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Yes

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, *p* >.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.