Effects of Playing Surface Type on Physical and Soccer Match-Play Performance in Children; Randomized Crossover Trial

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This study investigated the effects of playing surface type on physical and soccer match-play performance in children. Methods Fourteen boys from a regional soccer club completed physical performance tests, including standing long jump, zigzag run test, 20-meter sprint, and dribble test on three surface types; natural grass (NG), artificial turf (AT), and dirt field (DF). Soccer match-play performance was assessed during 7 vs. 7 small-sided games with total distance, maximum speed, average speed, and agility measured using GPS technologies. Results Performance outcomes including standing long jump (p < 0.001) and zigzag run (p < 0.001) were significantly better on the NG and AT compared with DF. Sprint performance was the highest on NG (p < 0.001), while dribble performance showed no significant difference across surface types (p = 0.053). During match-play, total distance (p < 0.001) and average speed (p < 0.001) were also greater on NG and AT compared with DF. Conclusions NG and AT provide more favorable conditions for enhancing physical and match-play performance than DF. These findings suggest the importance for physical education teachers and coaches to consider surface type to optimize performance and improve the level of physical activities. Soccer Artificial Turf Natural Grass Dirt field Youth development Figures Figure 1 Figure 2 Figure 3 Introduction The playing surface is a crucial factor in optimizing performance and preventing injuries during training or competition [ 10 ]. One of its main purposes is to enhance athletes’ physical abilities and technical execution, which has been driven by continuous surface development and improvement [ 11 ]. Previous studies have shown that the type and quality of playing surfaces affect performance and match outcomes [ 4 , 19 , 26 ]. Variations in surface hardness, elasticity, and friction also influence movement efficiency. For example, it has been found that different surface types in stadiums are associated with differences in technical movements in soccer, running ability, and playing styles, ball rebound in tennis [ 4 , 19 , 26 ]. Moreover, the playing surface and environmental conditions significantly affect injury rates, underscoring a close association between the playing surface and sports results [ 9 , 38 ]. In soccer, most training and competition occur on natural grass (NG), artificial turf (AT), or dirt field (DF) with distinct physical and mechanical characteristics [ 37 ]. While NG has been recognized as the preferred surface for training and competition, the use of AT has increased steadily due to its durability, lower maintenance, and cost effectiveness [ 36 ]. In contrast, DF surfaces provide a less favorable environment for football players owing to their irregular texture and limited shock absorption. Despite this, the DF surface is predominant in the school environment, accounting for 72.1%, compared with only 9.7% for NG and 12.2% for AT in Korea [ 17 ]. Soccer is one of the widely practiced sports among children and adolescents, accounting for 19.7% of physical activity participation [ 28 ]. Beyond its recreational appeal, football plays an important developmental role by promoting physical growth, motor skill acquisition, and social interaction during childhood and adolescence [ 31 , 35 ]. The sport’s diverse and complex movements can further enhance fundamental motor skills and contribute positively to overall physical development [ 1 , 25 ]. However, the repetitive and high-impact nature of soccer characterized by repetitive running, jumping, and multidirectional changes also increases the risk of injury, which can be further influenced by the playing surface characteristics [ 27 , 30 ]. Despite the growing participation of children and adolescents, previous studies on surface effects have predominantly focused on adults [ 2 , 15 , 23 ]. We believe that sports environment such as a playing surface, is important for children who experience early in sports activities. Because sports activities during childhood can provide positive establishment of active lifestyle during adulthood. Therefore, this study examined how different surface types affect the physical and match-play performance in children who experience soccer. We hypothesized that performance outcomes would differ significantly across surface types. Materials and Methods Participants Sixteen boys (age: 9.4 ± 0.73 years, height: 1.4 ± 0.07 m, weight: 38.6 ± 11.19 kg) were initially recruited from the regional soccer clubs in Y-city through advertisement in the community centers. The inclusion criteria consisted of male children who participated in the regional soccer club for at least one year, and the exclusion criteria consisted of children who failed to complete all measures. G*Power software (v3.1.9.2, Kiel University, Düsseldorf, Germany) was used to obtain the sample size. Based on the match play performance in the previous study, the total number of subjects was set at 12, with a significance level of 0.05, a power level of 0.80, and a high effect level [ 6 ]. A total of 16 subjects were recruited, taking into account a 30% dropout rate. The initial number of participants was 16, but 2 players were unable to complete the match-play performance, resulting in 14 children completing the study. A informed consent form was obtained from all participants’ parent or guardians prior to the study, and this study was approved by the Institutional Review Board of the University (KHGIRB-24-033). Basic characteristics of participants are presented in Table 1 . Table 1 Basic characteristics of participants Participant characteristics Mean ± SD Age (yrs) 9.4 ± 0.73 Bone age (yrs) 9.8 ± 1.39 Height (m) 1.4 ± 0.07 Weight (kg) 38.6 ± 11.19 Seat height (cm) 75.4 ± 3.92 Fat mass (kg) 11.0 ± 6.60 Lean mass (kg) 25.1 ± 2.67 Body fat (%) 27.2 ± 7.94 Bone mineral contents (kg) 1.17 ± 0.15 Bone mineral density (g/cm2) 0.845 ± 0.05 Body mass index 19.2 ± 3.62 Note. SD: standard deviation ---------------------------------------------- ---------------------------------------------- Study Procedures This study was conducted with a randomized crossover design in which performance tests were randomly evaluated on three different playing surfaces; natural grass (NG), artificial turf (AT) and dirt field (DF), with a 7-day rest interval between sessions. Prior to the performance test, participants’ height, body mass, and skeletal maturity were measured (Fig. 1 ). The physical performance tests included standing long jump, zig-zag run, 20-meter sprint, and dribbling ability test. Sufficient recovery time was provided between each test to minimize fatigue. Match play performance was evaluated through a small-sided 7 vs. 7 soccer game, consisting of 15-minute halves separated by a 5-minute rest period. Performance variables, including total distance, maximum speed, and average speed, were recorded using a GPS tracking system (Soccerbee, Ubis Labs, Korea). ------------------------------------------------------------------------------------------- Physique, body composition and bone age Standing height and body mass were measured to the nearest 0.01 cm and 0.01kg using a stadiometer T.K.K. 11253 (Takei Scientific Ins Co., Japan) and a digital weight scale (CAS 150A, Korea), respectively. Body composition was assessed using DXA (Dual X-ray Absorptiometry: QDR-4500, Hologic, USA), which estimates fat mass (FM), lean mass (LM), and percent body fat (%BF) based on differences in tissue density. The coefficient of variation (CV) of the QDR-4500 (Hologic, USA) used for body composition analysis has been reported to range from 0.2% to 3.5% [ 33 ]. Skeletal maturity was evaluated using the CORUS system (Y.C. Growth Well Co., Korea) following the Tanner-Whitehouse III (TW3) method. Radiographs were obtained for the radius, ulna, and the first, third, and fifth metacarpals, including the metacarpal, proximal, middle, and distal segments. Two examiners independently evaluated the skeletal maturity score (radius–ulna–short bones, RUS score) and estimated bone age. If bone age differed from chronological age by more than six months, reassessment was performed. The correlation coefficient between bone age and chronological age for this method has been reported as R² = 0.88 [ 20 ]. Physical Performance Tests Physical performance tests were designed to assess power, speed, agility, and soccer-specific dribbling ability. Power was measured using the standing long jump test. Participants began from a standing position with knees bent and hands free to swing. Jump distance was measured with a tape measure, and the best value from two recorded trials was used for analysis after two familiarization attempts. Speed was measured using a 20-meter sprint test. Participants placed their lead foot between the first pair of electronic timing gates (Brower Timing System, Salt Lake City, UT, USA) and sprinted maximally to the finish line. The best time from the two trials was recorded after two practice runs. Agility was evaluated using the barrow zigzag run test. The first foot was placed between the first pair of electronic timing gates (Brower Timing System, Salt Lake City, UT, USA), and participants completed the zigzag course as quickly as possible. The fastest time from two trials was recorded after two practice runs. The soccer-specific dribbling ability was assessed using a modified version of the soccer performance test developed by the Finnish Football Association [ 39 ]. Participants dribbled a ball through a designated course equipped with electronic timing gates (Brower Timing System, Salt Lake City, UT, USA). The best time from two trials was recorded after two practice runs. Test-retest reliability for all performance measures was evaluated using the intraclass correlation coefficient (ICC) with two-way mixed effects model (Table 2 ). The range of reliability levels for each test was as follows (ICC < 0.50-low reliability, 0.50 ≤ ICC < 0.75-moderate reliability, 0.75 ≤ ICC < 0.90-high reliability, and ICC ≥ 0.90-very high reliability) [ 21 ]. Table 2 Intraclass correlation coefficient of physical performance test Measures ICC 95% CI % CV Standing Long Jump (cm) 0.84 0.73–0.91 10.56 Zigzag run test (s) 0.88 0.79–0.93 7.12 Sprint (s) 0.91 0.84–0.95 6.71 Dribble test (s) 0.84 0.71–0.91 13.74 Note. ICC: intraclass correlation coefficient; CI: confidence interval, CV: coefficient of variation (%) ---------------------------------------------- ---------------------------------------------- Match Play Performance Match play performance was measured using a GPS-based wearable tracking device (Soccerbee lite GPS tracker, Ubeeslab, Korea). Participants engaged in 7 vs. 7 small-sided soccer games while wearing the device, which was securely fitted to a chest harness of appropriate size. Each match consisted of two 15-minute halves separated by a 5-minute rest period. Performance variables included total distance covered, maximum speed, average speed, and agility. Data were recorded and processed using the Soccerbee analysis application, which automatically synchronized the start and end times of each match. Agility was defined as the percentage of time spent moving at speeds greater than 7.6 km/h with acceleration exceeding 1.71 m/s². Only participants who completed both halves were included in the final analysis. Statistical Analysis All data were analyzed using R software (version 4.1.3, RStudio, Boston, MA, USA). Descripted data was presented as mean and standard deviation. A one-way analysis of variance was conducted to compare the physical performance and match-play performance across three playing surface conditions. Bonferroni-adjusted pairwise comparisons were applied when significant differences were found. Generalized effect size (GES) was calculated, with the following thresholds: Small (η2G < 0.01), Medium (0.01 ≤ η2G < 0.06), Large (0.06 ≤ η2G < 0.14) (26). Statistical significance was set at p < 0.05. Results Physical Performance The results of physical performance are presented in Table 3 . There were significant differences between surface types for the Standing Long Jump (F = 14.25, p < 0.001), Zigzag Test (F = 32.69, p < 0.001), and Sprint (F = 15.55, p < 0.001). However, no significant difference was observed in the dribbled test (F = 3.31, p = 0.053). Post-hoc analysis revealed that both AT and NG had significantly higher performance in the Standing Long Jump (DF < AT, p = 0.007; DF < NG, p = 0.004) and Zigzag Test (AT < DF, p < 0.001; NG < DF, p AT, p DF, p = 0.01), with no significant difference between AT and DF (Fig. 2 ). Table 3 The differences of physical performance outcomes by surface types Artificial turf Natural grass Dirt field F-value ηG2 P-value SLJ (cm) 1.44 ± 0.14a 1.49 ± 0.13a 1.35 ± 0.15b 14.25 0.15 < 0.001 Zigzag test (s) 12.6 ± 0.68a 12.5 ± 0.68a 13.9 ± 0.78b 32.69 0.44 < 0.001 Sprint (s) 4.3 ± 0.28a 4.1 ± 0.25b 4.3 ± 0.29a 15.55 0.1 < 0.001 Dribble (s) 16.4 ± 2.15 17.7 ± 2.49 18.1 ± 2.36 3.31 0.09 0.053 Note. SLJ, standing long jump --------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- Match Play Performance The result of match play performance by surface types are presented in Table 4 . Significant differences between surface types were observed for Total Distance (F = 29.46, p < 0.001) and Average Speed (F = 20.95, p < 0.001). However, there were no significant differences in Top Speed (F = 0.13, p = 0.80) and Agility (F = 0.4, p = 0.60). Post-hoc analysis indicated that both AT and NG had significantly higher Total Distance covered compared to DF (DF < AT, P < 0.001; DF < NG, p < 0.001). For Average Speed, NG recorded the highest values, followed by AT and DF (DF < AT, p = 0.002, DF < NG, P < 0.001; AT < NG, p = 0.03) (Fig. 3 ). Table 4 The differences of match-play performance outcomes by surface types Artificial turf Natural grass Dirt field F-value ηG2 P-value Total distance (km) 1.6 ± 0.54a 1.7 ± 0.57a 1.3 ± 0.43b 29.46 0.12 < 0.001 Top speed (km/h) 16.4 ± 3.24 16.6 ± 2.25 16.8 ± 1.70 0.13 0 0.80 Averages peed (km/h) 3.1 ± 1.07a 3.5 ± 1.18c 2.8 ± 0.96b 20.95 0.07 < 0.001 Agility (%) 21.9 ± 2.87 21.6 ± 3.01 22.4 ± 4.14 0.4 0.01 0.60 ------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------- Discussion This study compared the physical and match-play performance of recreational soccer children across three different surface types: artificial turf (AT), natural grass (NG), and dirt field (DF). The results revealed that performance outcomes, including power, speed, agility, and overall match-play performance, were better on the NG, and AT surface compared with DF surface. Our findings indicate that the playing surface has a substantial influence on both physical and technical aspect of soccer performance. Physical Performance Standing long jump was significantly higher on NG and AT surfaces compared to DF surface, suggesting that surface type plays a significant role in jump performance. It is assumed that softer and more elastic surfaces such as NG and AT can enhance jump performance by facilitating greater energy return during the stretch-shortening cycle [ 29 ]. Our finding aligns with Karve and Tiwari that compliant surfaces improve jump performance while reducing joint stress [ 18 ]. In contrast, DF increases muscle fatigue and reduces force production efficiency, leading to lower propulsion and lower jump performance [ 24 ]. Hard surfaces like dirt fields increase impact forces and stress on joints and muscles, potentially reducing jump performance [ 18 ]. Although, some studies reported no significant differences between NG and AT, our results suggest that both surfaces are advantageous compared to DF, emphasizing the negative effect of hard on power-related movements [ 16 , 34 ]. Agility, assessed through the zigzag test was significantly faster on NG and AT compared to DF. Our findings underscore the importance of surface traction and stability for change-of-direction. Gains et al. reported better agility on AT while Hughes et al., observed faster movements on NG, indicating that both surfaces can support high-performance agility depending on context [ 12 , 16 ]. Brito et al. also demonstrated that NG provides uniformity conductive to consistent movement whereas DF instability and unevenness hinder performance [ 6 , 14 ]. Our results confirmed that improved traction and evenness of NG and AT facilitate quicker, more stable directional changes. Sprint performance was significantly faster on NG compared to AT and DF. Gains et al. found no significant difference in 40-yard dash performance between NG and AT [ 12 ]. Another meta-analysis study confirmed no significant difference in sprint performance between NG and AT. Several factors may influence these conflicting results. Specifically, the type of soccer shoes used on each surface could alter muscle activation and affect players' perceptions, impacting performance. Since our study subjects wore futsal shoes, accurately identifying significant differences between surfaces may be challenging. Additionally, our subjects were under 10 years old, whereas previous studies involved college soccer players or adults, suggesting that differences in surface familiarity or developmental cognitive factors could have influenced the results. Although, no significant differences were found in the dribbling test (F(13) = 3.31, p = 0.053, ηG2 = 0.09), the performance tended to be better on AT compared with NG and DF. Previous studies have shown similar trends; Garcia-Unanue et al., reported more successful passes on AT due to consistent ball behavior while Diaz-Cidoncha et al. highlighted the irregular bounce of DF surfaces, which complicated control [ 8 , 13 ]. NF offers stability but may vary depending on maintenance quality. Therefore, surface uniformity appears critical for precise ball handling suggesting AT provides the most consistent environment for dribbling performance. Match play Performance Our study showed significant differences in total distance and average speed across various playing surfaces. The total distance covered during match play was the greatest on NG, followed by AT and DF surfaces. These results indicate that NG surfaces enable players to maintain higher overall activity levels and movement efficiency. Another study support our result that NG surface covered higher total distances compared to other surfaces [ 23 ]. In contrast, Andersson et al. found no significant difference in total distance between NG and AT during competitive matches, suggesting that surface familiarity and playing conditions may also influence movement patterns. Average speeds were also highest on NG, followed by AT and DF surfaces [ 3 ]. The superior average speed on NG can be attributed to its optimal balance of compliance and traction, which promotes smoother movement and mitigates muscular fatigue. Brito et al. also noted that NG surface promotes higher average speeds due to better energy return and reduced impact fatigue [ 5 ]. Although AT provides stable condition, its greater hardness may accelerate fatigue over prolonged play. DF surface, characterized by irregularity and higher mechanical stress, limit running efficiency and result in slower movement. Kordi et al. also reported higher injury rates on DF, contributing to more cautious movement and reduced total distance [ 22 ]. Brito et al. reported greater variability in player movements on DF, indicating that surface instability affects tactical execution and positioning [ 6 ]. These findings highlight the importance of surface characteristics in influencing physical performance and tactical decisions in sports. Conclusion Our findings suggest that playing surface exerts a significant influence on physical and soccer match-play performance in children. Overall NG and AT provide more favorable condition for optimizing performance compared with DF. Future research should incorporate detailed technical and tactical analyses of match play to better understand how surface characteristics affect game dynamics. Coaches should consider the performance variations associated with surface types when planning training sessions, match strategies, and youth player development programs. Abbreviations AT Artificial turf DT Dirt field NG Natural grass FM Fat mass LM Lean mass %BF Percent body weight CV Coefficient of variation TW3 Tanner-Whitehouse III Declarations Ethics approval and informed consent to participate : This study was conducted following the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of the Kyung Hee University (KHGIRB-24-033). A informed consent form was obtained from all participants’ parent or guardians prior to the study. Consent for publication: not applicable Competing Interests: The authors declare no competing interests. Funding: This work was supported by Korea Sports Facilities Industry Association. Author Contribution Conceptualization: H. U. J., H. C. J.; methodology: C. Y. S., H. U. J., H. C. J.; formal analysis: H. U. J., H. C. J.; investigation: C. Y. S., H. U. J., H. S. K., H. R. P., H.C.J.; writing—original draft preparation: C. Y. S., H. U. J.; writing—review & editing: D. K. K., H. C. 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Heart rate responses and technical-tactical aspects of official 5-a-side youth soccer matches played on clay and artificial turf. J Strength Conditioning Res. 2012;26(1):106–12. https://DOI:10.1519/JSC.0b013e31821854f2 . Trewin J, Meylan C, Varley MC, Cronin J. The influence of situational and environmental factors on match-running in soccer: a systematic review. Sci Med Footb. 2017;1(2):183–94. https://doi.org/10.1080/24733938.2017.1329589 . Vänttinen T, Blomqvist M, Häkkinen K. (2010). Development of body composition, hormone profile, physical fitness, general perceptual motor skills, soccer skills and on-the-ball performance in soccer-specific laboratory test among adolescent soccer players. Journal of Sports Science & Medicine, 9(4), 547. PMCID: PMC3761817. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8466278","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583096297,"identity":"1af86c8e-3c54-48b3-b3b9-2961924acac6","order_by":0,"name":"Chae Young Sim","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Chae","middleName":"Young","lastName":"Sim","suffix":""},{"id":583096302,"identity":"7d007ed6-95f0-469c-8b31-c9f2835d2b9b","order_by":1,"name":"Hyung Ung Jung","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Hyung","middleName":"Ung","lastName":"Jung","suffix":""},{"id":583096305,"identity":"14246530-d409-4d95-a304-a8b53dfa44d4","order_by":2,"name":"Hyun Seo Ko","email":"","orcid":"","institution":"University of Illinois Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Hyun","middleName":"Seo","lastName":"Ko","suffix":""},{"id":583096306,"identity":"7c3fd752-8dd1-4579-836f-4b7ce7b8891e","order_by":3,"name":"Hee Ran Park","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Hee","middleName":"Ran","lastName":"Park","suffix":""},{"id":583096308,"identity":"a008ec78-8b75-4795-a01f-6e1f76929204","order_by":4,"name":"Do Kyun Kim","email":"","orcid":"","institution":"Kyung Hee University-, Yongin","correspondingAuthor":false,"prefix":"","firstName":"Do","middleName":"Kyun","lastName":"Kim","suffix":""},{"id":583096310,"identity":"d22024a6-5917-43a0-a21b-d064a8bcbb2b","order_by":5,"name":"Hyun Chul Jung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoklEQVRIiWNgGAWjYBACxhkMjA8bG8BsZqK1MBuSpoVBgoFNkjQtzLObn1XO3HGYgb/9ALNxBVEOm3PM7ObGM4cZJM4kMCeeIUrLjBy2mw/bDjMw3GBgPthArJZCkBZ5krQwbgRqMQBqSSRSS5qx5My2dB7DM4nNhkRpMZyR/PBjb5u1nNzxw4clidMCVcUDtJAoDQwM8sQpGwWjYBSMghENAGBSMoHvo5K+AAAAAElFTkSuQmCC","orcid":"","institution":"Kyung Hee University","correspondingAuthor":true,"prefix":"","firstName":"Hyun","middleName":"Chul","lastName":"Jung","suffix":""}],"badges":[],"createdAt":"2025-12-28 14:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8466278/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8466278/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101787987,"identity":"555c0130-d39e-4167-b47f-49e7084ee9bb","added_by":"auto","created_at":"2026-02-03 15:52:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129151,"visible":true,"origin":"","legend":"\u003cp\u003eStudy procedure\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8466278/v1/ac9661e3ef18329da9c9509d.png"},{"id":101787986,"identity":"108c968a-8169-48ec-a2b0-37d5ab755968","added_by":"auto","created_at":"2026-02-03 15:52:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103979,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences of physical performance outcomes by surface types\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8466278/v1/d9041f1f1513eba8124ac426.png"},{"id":101787989,"identity":"cb7f6c80-0cc4-4578-a04f-4884851d01fc","added_by":"auto","created_at":"2026-02-03 15:52:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101161,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences of match-play performance outcomes by surface types\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8466278/v1/c7f656760c633135fae896e3.png"},{"id":108082406,"identity":"6c045924-eaac-4dfe-b112-cbc11a75fe3a","added_by":"auto","created_at":"2026-04-29 07:56:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":544773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8466278/v1/24725f53-c58c-45c3-8a88-7eac44fbaaa6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Playing Surface Type on Physical and Soccer Match-Play Performance in Children; Randomized Crossover Trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe playing surface is a crucial factor in optimizing performance and preventing injuries during training or competition [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. One of its main purposes is to enhance athletes\u0026rsquo; physical abilities and technical execution, which has been driven by continuous surface development and improvement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous studies have shown that the type and quality of playing surfaces affect performance and match outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Variations in surface hardness, elasticity, and friction also influence movement efficiency. For example, it has been found that different surface types in stadiums are associated with differences in technical movements in soccer, running ability, and playing styles, ball rebound in tennis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, the playing surface and environmental conditions significantly affect injury rates, underscoring a close association between the playing surface and sports results [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn soccer, most training and competition occur on natural grass (NG), artificial turf (AT), or dirt field (DF) with distinct physical and mechanical characteristics [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. While NG has been recognized as the preferred surface for training and competition, the use of AT has increased steadily due to its durability, lower maintenance, and cost effectiveness [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In contrast, DF surfaces provide a less favorable environment for football players owing to their irregular texture and limited shock absorption. Despite this, the DF surface is predominant in the school environment, accounting for 72.1%, compared with only 9.7% for NG and 12.2% for AT in Korea [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSoccer is one of the widely practiced sports among children and adolescents, accounting for 19.7% of physical activity participation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Beyond its recreational appeal, football plays an important developmental role by promoting physical growth, motor skill acquisition, and social interaction during childhood and adolescence [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The sport\u0026rsquo;s diverse and complex movements can further enhance fundamental motor skills and contribute positively to overall physical development [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the repetitive and high-impact nature of soccer characterized by repetitive running, jumping, and multidirectional changes also increases the risk of injury, which can be further influenced by the playing surface characteristics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the growing participation of children and adolescents, previous studies on surface effects have predominantly focused on adults [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We believe that sports environment such as a playing surface, is important for children who experience early in sports activities. Because sports activities during childhood can provide positive establishment of active lifestyle during adulthood. Therefore, this study examined how different surface types affect the physical and match-play performance in children who experience soccer. We hypothesized that performance outcomes would differ significantly across surface types.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eSixteen boys (age: 9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 years, height: 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 m, weight: 38.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.19 kg) were initially recruited from the regional soccer clubs in Y-city through advertisement in the community centers. The inclusion criteria consisted of male children who participated in the regional soccer club for at least one year, and the exclusion criteria consisted of children who failed to complete all measures. G*Power software (v3.1.9.2, Kiel University, D\u0026uuml;sseldorf, Germany) was used to obtain the sample size. Based on the match play performance in the previous study, the total number of subjects was set at 12, with a significance level of 0.05, a power level of 0.80, and a high effect level [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A total of 16 subjects were recruited, taking into account a 30% dropout rate. The initial number of participants was 16, but 2 players were unable to complete the match-play performance, resulting in 14 children completing the study. A informed consent form was obtained from all participants\u0026rsquo; parent or guardians prior to the study, and this study was approved by the Institutional Review Board of the University (KHGIRB-24-033). Basic characteristics of participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eBasic characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone age (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e38.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeat height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e75.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLean mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone mineral contents (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone mineral density (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.845\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote. SD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n \u003cp\u003e \u003cem\u003e----------------------------------------------\u0026lt;Insert Table 1\u0026gt;\u003c/em\u003e----------------------------------------------\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Procedures\u003c/h2\u003e \u003cp\u003eThis study was conducted with a randomized crossover design in which performance tests were randomly evaluated on three different playing surfaces; natural grass (NG), artificial turf (AT) and dirt field (DF), with a 7-day rest interval between sessions. Prior to the performance test, participants\u0026rsquo; height, body mass, and skeletal maturity were measured (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The physical performance tests included standing long jump, zig-zag run, 20-meter sprint, and dribbling ability test. Sufficient recovery time was provided between each test to minimize fatigue. Match play performance was evaluated through a small-sided 7 vs. 7 soccer game, consisting of 15-minute halves separated by a 5-minute rest period. Performance variables, including total distance, maximum speed, and average speed, were recorded using a GPS tracking system (Soccerbee, Ubis Labs, Korea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e----------------------------------------------\u0026lt;Insert Fig. 1 \u0026gt;---------------------------------------------\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePhysique, body composition and bone age\u003c/h2\u003e \u003cp\u003eStanding height and body mass were measured to the nearest 0.01 cm and 0.01kg using a stadiometer T.K.K. 11253 (Takei Scientific Ins Co., Japan) and a digital weight scale (CAS 150A, Korea), respectively. Body composition was assessed using DXA (Dual X-ray Absorptiometry: QDR-4500, Hologic, USA), which estimates fat mass (FM), lean mass (LM), and percent body fat (%BF) based on differences in tissue density. The coefficient of variation (CV) of the QDR-4500 (Hologic, USA) used for body composition analysis has been reported to range from 0.2% to 3.5% [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Skeletal maturity was evaluated using the CORUS system (Y.C. Growth Well Co., Korea) following the Tanner-Whitehouse III (TW3) method. Radiographs were obtained for the radius, ulna, and the first, third, and fifth metacarpals, including the metacarpal, proximal, middle, and distal segments. Two examiners independently evaluated the skeletal maturity score (radius\u0026ndash;ulna\u0026ndash;short bones, RUS score) and estimated bone age. If bone age differed from chronological age by more than six months, reassessment was performed. The correlation coefficient between bone age and chronological age for this method has been reported as R\u0026sup2; = 0.88 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhysical Performance Tests\u003c/h2\u003e \u003cp\u003ePhysical performance tests were designed to assess power, speed, agility, and soccer-specific dribbling ability. Power was measured using the standing long jump test. Participants began from a standing position with knees bent and hands free to swing. Jump distance was measured with a tape measure, and the best value from two recorded trials was used for analysis after two familiarization attempts. Speed was measured using a 20-meter sprint test. Participants placed their lead foot between the first pair of electronic timing gates (Brower Timing System, Salt Lake City, UT, USA) and sprinted maximally to the finish line. The best time from the two trials was recorded after two practice runs. Agility was evaluated using the barrow zigzag run test. The first foot was placed between the first pair of electronic timing gates (Brower Timing System, Salt Lake City, UT, USA), and participants completed the zigzag course as quickly as possible. The fastest time from two trials was recorded after two practice runs. The soccer-specific dribbling ability was assessed using a modified version of the soccer performance test developed by the Finnish Football Association [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Participants dribbled a ball through a designated course equipped with electronic timing gates (Brower Timing System, Salt Lake City, UT, USA). The best time from two trials was recorded after two practice runs. Test-retest reliability for all performance measures was evaluated using the intraclass correlation coefficient (ICC) with two-way mixed effects model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The range of reliability levels for each test was as follows (ICC\u0026thinsp;\u0026lt;\u0026thinsp;0.50-low reliability, 0.50\u0026thinsp;\u0026le;\u0026thinsp;ICC\u0026thinsp;\u0026lt;\u0026thinsp;0.75-moderate reliability, 0.75\u0026thinsp;\u0026le;\u0026thinsp;ICC\u0026thinsp;\u0026lt;\u0026thinsp;0.90-high reliability, and ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.90-very high reliability) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntraclass correlation coefficient of physical performance test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICC\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\u003e% CV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStanding Long Jump (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZigzag run test (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprint (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDribble test (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026ndash;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote. ICC: intraclass correlation coefficient; CI: confidence interval, CV: coefficient of variation (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cp\u003e \u003cem\u003e----------------------------------------------\u0026lt;Insert Table 2\u0026gt;\u003c/em\u003e----------------------------------------------\u003c/p\u003e\n\u003ch3\u003eMatch Play Performance\u003c/h3\u003e\n\u003cp\u003eMatch play performance was measured using a GPS-based wearable tracking device (Soccerbee lite GPS tracker, Ubeeslab, Korea). Participants engaged in 7 vs. 7 small-sided soccer games while wearing the device, which was securely fitted to a chest harness of appropriate size. Each match consisted of two 15-minute halves separated by a 5-minute rest period. Performance variables included total distance covered, maximum speed, average speed, and agility. Data were recorded and processed using the Soccerbee analysis application, which automatically synchronized the start and end times of each match. Agility was defined as the percentage of time spent moving at speeds greater than 7.6 km/h with acceleration exceeding 1.71 m/s\u0026sup2;. Only participants who completed both halves were included in the final analysis.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data were analyzed using R software (version 4.1.3, RStudio, Boston, MA, USA). Descripted data was presented as mean and standard deviation. A one-way analysis of variance was conducted to compare the physical performance and match-play performance across three playing surface conditions. Bonferroni-adjusted pairwise comparisons were applied when significant differences were found. Generalized effect size (GES) was calculated, with the following thresholds: Small (η2G\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Medium (0.01\u0026thinsp;\u0026le;\u0026thinsp;η2G\u0026thinsp;\u0026lt;\u0026thinsp;0.06), Large (0.06\u0026thinsp;\u0026le;\u0026thinsp;η2G\u0026thinsp;\u0026lt;\u0026thinsp;0.14) (26). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePhysical Performance\u003c/h2\u003e \u003cp\u003eThe results of physical performance are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There were significant differences between surface types for the Standing Long Jump (F\u0026thinsp;=\u0026thinsp;14.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Zigzag Test (F\u0026thinsp;=\u0026thinsp;32.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Sprint (F\u0026thinsp;=\u0026thinsp;15.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no significant difference was observed in the dribbled test (F\u0026thinsp;=\u0026thinsp;3.31, p\u0026thinsp;=\u0026thinsp;0.053). Post-hoc analysis revealed that both AT and NG had significantly higher performance in the Standing Long Jump (DF\u0026thinsp;\u0026lt;\u0026thinsp;AT, p\u0026thinsp;=\u0026thinsp;0.007; DF\u0026thinsp;\u0026lt;\u0026thinsp;NG, p\u0026thinsp;=\u0026thinsp;0.004) and Zigzag Test (AT\u0026thinsp;\u0026lt;\u0026thinsp;DF, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; NG\u0026thinsp;\u0026lt;\u0026thinsp;DF, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to DF. In the Sprint test, participants showed the best performance on the NG surface compared with AT and DF surfaces (NG\u0026thinsp;\u0026gt;\u0026thinsp;AT, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; NG\u0026thinsp;\u0026gt;\u0026thinsp;DF, p\u0026thinsp;=\u0026thinsp;0.01), with no significant difference between AT and DF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe differences of physical performance outcomes by surface types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial turf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural grass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirt field\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eηG2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLJ (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\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\u003eZigzag test (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\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\u003eSprint (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\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\u003eDribble (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. SLJ, standing long jump\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e----------------------------------------------\u0026lt;Insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e\u0026gt;-----------------------------------------------\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e----------------------------------------------\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026gt;---------------------------------------------\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eMatch Play Performance\u003c/h2\u003e \u003cp\u003eThe result of match play performance by surface types are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Significant differences between surface types were observed for Total Distance (F\u0026thinsp;=\u0026thinsp;29.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Average Speed (F\u0026thinsp;=\u0026thinsp;20.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there were no significant differences in Top Speed (F\u0026thinsp;=\u0026thinsp;0.13, p\u0026thinsp;=\u0026thinsp;0.80) and Agility (F\u0026thinsp;=\u0026thinsp;0.4, p\u0026thinsp;=\u0026thinsp;0.60). Post-hoc analysis indicated that both AT and NG had significantly higher Total Distance covered compared to DF (DF\u0026thinsp;\u0026lt;\u0026thinsp;AT, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DF\u0026thinsp;\u0026lt;\u0026thinsp;NG, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For Average Speed, NG recorded the highest values, followed by AT and DF (DF\u0026thinsp;\u0026lt;\u0026thinsp;AT, p\u0026thinsp;=\u0026thinsp;0.002, DF\u0026thinsp;\u0026lt;\u0026thinsp;NG, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AT\u0026thinsp;\u0026lt;\u0026thinsp;NG, p\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe differences of match-play performance outcomes by surface types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial turf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural grass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirt field\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eηG2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal distance (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\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\u003eTop speed (km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverages peed (km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\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\u003eAgility (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e----------------------------------------------\u0026lt;Insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cem\u003e\u0026gt;--------------------------------------------\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e----------------------------------------------\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026gt;---------------------------------------------\u003c/h2\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study compared the physical and match-play performance of recreational soccer children across three different surface types: artificial turf (AT), natural grass (NG), and dirt field (DF). The results revealed that performance outcomes, including power, speed, agility, and overall match-play performance, were better on the NG, and AT surface compared with DF surface. Our findings indicate that the playing surface has a substantial influence on both physical and technical aspect of soccer performance.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePhysical Performance\u003c/h2\u003e \u003cp\u003eStanding long jump was significantly higher on NG and AT surfaces compared to DF surface, suggesting that surface type plays a significant role in jump performance. It is assumed that softer and more elastic surfaces such as NG and AT can enhance jump performance by facilitating greater energy return during the stretch-shortening cycle [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our finding aligns with Karve and Tiwari that compliant surfaces improve jump performance while reducing joint stress [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In contrast, DF increases muscle fatigue and reduces force production efficiency, leading to lower propulsion and lower jump performance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Hard surfaces like dirt fields increase impact forces and stress on joints and muscles, potentially reducing jump performance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although, some studies reported no significant differences between NG and AT, our results suggest that both surfaces are advantageous compared to DF, emphasizing the negative effect of hard on power-related movements [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Agility, assessed through the zigzag test was significantly faster on NG and AT compared to DF. Our findings underscore the importance of surface traction and stability for change-of-direction. Gains et al. reported better agility on AT while Hughes et al., observed faster movements on NG, indicating that both surfaces can support high-performance agility depending on context [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Brito et al. also demonstrated that NG provides uniformity conductive to consistent movement whereas DF instability and unevenness hinder performance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our results confirmed that improved traction and evenness of NG and AT facilitate quicker, more stable directional changes. Sprint performance was significantly faster on NG compared to AT and DF. Gains et al. found no significant difference in 40-yard dash performance between NG and AT [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Another meta-analysis study confirmed no significant difference in sprint performance between NG and AT. Several factors may influence these conflicting results. Specifically, the type of soccer shoes used on each surface could alter muscle activation and affect players' perceptions, impacting performance. Since our study subjects wore futsal shoes, accurately identifying significant differences between surfaces may be challenging. Additionally, our subjects were under 10 years old, whereas previous studies involved college soccer players or adults, suggesting that differences in surface familiarity or developmental cognitive factors could have influenced the results. Although, no significant differences were found in the dribbling test (F(13)\u0026thinsp;=\u0026thinsp;3.31, p\u0026thinsp;=\u0026thinsp;0.053, ηG2\u0026thinsp;=\u0026thinsp;0.09), the performance tended to be better on AT compared with NG and DF. Previous studies have shown similar trends; Garcia-Unanue et al., reported more successful passes on AT due to consistent ball behavior while Diaz-Cidoncha et al. highlighted the irregular bounce of DF surfaces, which complicated control [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. NF offers stability but may vary depending on maintenance quality. Therefore, surface uniformity appears critical for precise ball handling suggesting AT provides the most consistent environment for dribbling performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMatch play Performance\u003c/h2\u003e \u003cp\u003eOur study showed significant differences in total distance and average speed across various playing surfaces. The total distance covered during match play was the greatest on NG, followed by AT and DF surfaces. These results indicate that NG surfaces enable players to maintain higher overall activity levels and movement efficiency. Another study support our result that NG surface covered higher total distances compared to other surfaces [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, Andersson et al. found no significant difference in total distance between NG and AT during competitive matches, suggesting that surface familiarity and playing conditions may also influence movement patterns. Average speeds were also highest on NG, followed by AT and DF surfaces [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe superior average speed on NG can be attributed to its optimal balance of compliance and traction, which promotes smoother movement and mitigates muscular fatigue. Brito et al. also noted that NG surface promotes higher average speeds due to better energy return and reduced impact fatigue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although AT provides stable condition, its greater hardness may accelerate fatigue over prolonged play. DF surface, characterized by irregularity and higher mechanical stress, limit running efficiency and result in slower movement. Kordi et al. also reported higher injury rates on DF, contributing to more cautious movement and reduced total distance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Brito et al. reported greater variability in player movements on DF, indicating that surface instability affects tactical execution and positioning [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These findings highlight the importance of surface characteristics in influencing physical performance and tactical decisions in sports.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings suggest that playing surface exerts a significant influence on physical and soccer match-play performance in children. Overall NG and AT provide more favorable condition for optimizing performance compared with DF. Future research should incorporate detailed technical and tactical analyses of match play to better understand how surface characteristics affect game dynamics. Coaches should consider the performance variations associated with surface types when planning training sessions, match strategies, and youth player development programs.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eAT Artificial turf\u003c/p\u003e \u003cp\u003eDT Dirt field\u003c/p\u003e \u003cp\u003eNG Natural grass\u003c/p\u003e \u003cp\u003eFM Fat mass\u003c/p\u003e \u003cp\u003eLM Lean mass\u003c/p\u003e \u003cp\u003e%BF Percent body weight\u003c/p\u003e \u003cp\u003eCV Coefficient of variation\u003c/p\u003e \u003cp\u003eTW3 Tanner-Whitehouse III\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eEthics approval and informed consent to participate\u003c/b\u003e: This study was conducted following the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of the Kyung Hee University (KHGIRB-24-033). A informed consent form was obtained from all participants\u0026rsquo; parent or guardians prior to the study.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003enot applicable\u003c/p\u003e \u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by Korea Sports Facilities Industry Association.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: H. U. J., H. C. J.; methodology: C. Y. S., H. U. J., H. C. J.; formal analysis: H. U. J., H. C. J.; investigation: C. Y. S., H. U. J., H. S. K., H. R. P., H.C.J.; writing\u0026mdash;original draft preparation: C. Y. S., H. U. J.; writing\u0026mdash;review \u0026amp; editing: D. K. K., H. C. J.; supervision: H. C. J.; funding acquisition: D. K. K., H. C. J.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eAll authors would like to thank all participants in the present study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlesi M, Bianco A, Padulo J, Luppina G, Petrucci M, Paoli A, Pepi A. Motor and cognitive growth following a Football Training Program. 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PMCID: PMC3761817.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soccer, Artificial Turf, Natural Grass, Dirt field, Youth development","lastPublishedDoi":"10.21203/rs.3.rs-8466278/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8466278/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe playing surface is a critical factor in field sports, influencing both performance and injury risk. This study investigated the effects of playing surface type on physical and soccer match-play performance in children.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFourteen boys from a regional soccer club completed physical performance tests, including standing long jump, zigzag run test, 20-meter sprint, and dribble test on three surface types; natural grass (NG), artificial turf (AT), and dirt field (DF). Soccer match-play performance was assessed during 7 vs. 7 small-sided games with total distance, maximum speed, average speed, and agility measured using GPS technologies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePerformance outcomes including standing long jump (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and zigzag run (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly better on the NG and AT compared with DF. Sprint performance was the highest on NG (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while dribble performance showed no significant difference across surface types (p\u0026thinsp;=\u0026thinsp;0.053). During match-play, total distance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and average speed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also greater on NG and AT compared with DF.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eNG and AT provide more favorable conditions for enhancing physical and match-play performance than DF. These findings suggest the importance for physical education teachers and coaches to consider surface type to optimize performance and improve the level of physical activities.\u003c/p\u003e","manuscriptTitle":"Effects of Playing Surface Type on Physical and Soccer Match-Play Performance in Children; Randomized Crossover Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:52:05","doi":"10.21203/rs.3.rs-8466278/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90a6dd24-cff0-4e18-ba95-e84741c0d4e1","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T07:55:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:52:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8466278","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8466278","identity":"rs-8466278","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}